2 x 4-unit minors
1 x 4-unit minor 4 x electives (depending on
number of SEED-electives completed)
8 | | |
Courses to be studied | Units |
Core courses | 4 |
1 x 12-unit extended major | 12 |
1 x 8-unit major 2 x 4-unit minors 1 x 4-unit minor 4 x SEED-electives or other elective courses | 8 |
| |
Refer to the UniSQ Handbook for courses to be studied and recommended enrolment patterns.
Other majors
- Anthropology
- Archaeology
- Archaeology (Extended)
- Artificial Intelligence & Data Science
- Criminology & Criminal Justice
- Criminology & Criminal Justice (Extended)
- English Literature
- Environment & Sustainability
- First Nations Australia
- International Relations
- Legal Studies
- Physiology & Human Movement
- Social Justice & Entrepreneurship
- Visual Arts Curation
Double degrees
- Bachelor of Arts and Bachelor of Business
- Bachelor of Arts and Bachelor of Laws
- Bachelor of Arts and Bachelor of Science
Your actual fees may vary depending on the courses you select. We review our fees annually so these may be subject to change.
Approximate annual costs 1 for your first year of study (8 units) in this degree are:
Study Mode | Cost |
Commonwealth supported The Federal Government contributes to the cost of your course and you need to pay a student contribution. | AUD 16363 |
Domestic full fee paying You are responsible for the full cost of your course and are charged a tuition fee. | AUD 25040 |
1 These are indicative annual fees for 2024.
Student services and amenities fees
You may need to pay student services and amenities (SA) fees as part of your course costs.
Loan to help you pay your fees
You may not have to pay your course (subject) fees upfront. HECS-HELP and FEE-HELP allows you to defer your fees until your income reaches a certain level. Find out if you are eligible for HECS-HELP or FEE-HELP .
You may also be eligible to receive financial support from Centrelink .
Scholarships
At UniSQ, we offer a range of scholarships to support your success at university. Find a scholarship that works for you.
How to apply
Apply via qtac.
Apply via QTAC (Queensland Tertiary Admissions Centre) if you:
- have never studied with UniSQ before, or
- are a Year 12 student, or
- want to transfer to UniSQ from another university.
Mode | QTAC Codes |
Online | 909405 |
Springfield | 929441 |
Toowoomba | 909441 |
Copy your QTAC code and continue through to the QTAC website to complete your application.
If you are applying via QTAC, please check our application closing dates .
Apply directly to UniSQ
You may be able to apply directly to UniSQ if you:
- are a current or previous UniSQ student returning to study with complete or incomplete qualifications (excluding Head Start students), or
- have recently graduated from TAFE.
You can select to start your study in Trimester 1, 2 or 3.
Study period | Application closing date | Orientation | Study period start date | Finish date |
Trimester 1, 2024 | Monday 22 January | Monday 15 January | Monday 22 January | Friday 26 April |
Trimester 2, 2024 | Monday 20 May | Monday 6 May | Monday 13 May | Friday 16 August |
Trimester 3, 2024 | Monday 2 September | Monday 26 August | Monday 2 September | Friday 6 December |
Study period | Application closing date | Orientation | Study period start date | Finish date |
Trimester 1, 2025 | Monday 10 February | Monday 3 February | Monday 10 February | Friday 9 May |
Trimester 2, 2025 | Monday 26 May | Monday 19 May | Monday 26 May | Friday 22 August |
Trimester 3, 2025 | Monday 8 September | Monday 1 September | Monday 8 September | Friday 5 December |
More information on applying key dates is available.
Further information
Discover further information on applying to study at UniSQ that is relevant to you.
![creative writing brisbane Two men collaborating at a computer workstation in an office environment.](https://www.unisq.edu.au/-/media/usq/program-guide/cta/got-questions-degrees-cta.ashx?rev=9b6730ff8d304e068aecb26dd95d2ea6)
Got questions?
Begin your study journey today with the university rated 5/5 stars for graduates starting salaries* and start the degree that’s right for you.
*Good Universities Guide 2024
Writing a Novel
For emerging novelists ready to dive in and make the commitment to complete a full manuscript, this rigorous three-stage course will guide you through the process of planning and beginning your novel, developing essential skills in writing and composition, as well as establishing a dedicated writing practice.
7 March – 19 September 2023
Avid Reader 193 Boundary St West End QLD 4101
![creative writing brisbane creative writing brisbane](https://faberwritingacademy.com.au/wp-content/uploads/2020/06/avid-reader-300x300.jpg)
$6,500/$5.525 alumni
![creative writing brisbane creative writing brisbane](https://faberwritingacademy.com.au/wp-content/uploads/2021/09/FABER-BOOKED-OUT-NEW-1-750x750.png)
Presented by Australia’s leading independent publishing house Allen & Unwin, in partnership with the prestigious UK Publishers Faber & Faber, Writing a Novel is a highly practical, craft-focused program designed to help you find the focus you need to stay the distance and finish your manuscript draft.
In the first three-month stage of this course (March – May) , you will be given a proven combination of targeted writing exercises, group discussions and carefully selected readings. These introductory lessons will give you an important foundation for writing your novel. You will be taken through essential concepts such as narrative structure, character development, narration, voice, plotting, scene setting and more.
In the second half of the program (July – September) you will continue to develop the craft and technical skills required to turn a rough draft into a work of art. You will finish the course with a deep understanding of the novelist’s craft and how it applies to your own novel. These are lessons you can carry with you throughout your writing life.
After these two stages are complete, Writing a Novel students will be asked to submit work to our highly sought-after Faber Writing Anthology , a professionally edited and printed showcase of student work, sent to leading literary agents and publishers across both Australia and the United Kingdom.
This third stage will take place after your course work is finished, giving you the time and space to work towards completing a submittable draft of your manuscript in time for the anthology to be sent out to agents and publishers. This final stage will include a meeting with Faber’s Editor in Residence, a copy-editing process, and a meeting with the Faber UK team.
Guest speakers include Trent Dalton ( Boy Swallows Universe ), Mirandi Riwoe ( Stone Sky Gold Mountain ), Laura Elvery ( Ordinary Matter ), and Emily Bitto ( Wild Abandon ).
During this course, you will be provided with:
- Regular classes covering everything from the first conception of an idea through to getting words on a page, narrative structure and style.
- A personalised individual consultation on your project from your Course Director.
- The ability to connect with fellow committed novelists, joining a selective class curated by your Course Director.
- The opportunity to make valuable industry connections with key insiders from the Australian publishing industry.
- The chance to be showcased in the much-anticipated Faber Writing Anthology, sent to publishers and literary agents across Australia and the United Kingdom.
![creative writing brisbane creative writing brisbane](https://faberwritingacademy.com.au/wp-content/uploads/2021/11/Emily-OGrady-photo-1-scaled-300x300.jpg)
Emily O’Grady
Emily O’Grady is a writer from Brisbane. Her debut novel, The Yellow House won the 2018 Australian/Vogel’s Literary Award and was shortlisted for the 2019 Ned Kelly Award for Best First Fiction. Her work has been published in Meanjin, Kill Your Darlings, The Big Issue fiction edition, and New Australian Fiction 2021. She holds a PhD in Creative Writing from Queensland University of Technology, where…
This course consists of twenty-two two-hour evening classes, six full-day sessions and two additional evening sessions dedicated to the Faber Writing Anthology.
Session 1: Tuesday 7 March Introductions
Session 2: Tuesday 14 March Types of prose, showing and telling
Session 3: Saturday 18 March Sentences and tense
Session 4: Tuesday 21 March Characters major and minor
Session 5: Tuesday 28 March Point of View
Session 6: Saturday 1 April Guest Speaker
Session 7: Tuesday 4 April Exercises and thoughts on structure
COURSE BREAK
Session 8: Tuesday 2 May Dialogue
Session 9: Saturday 6 May Voice
Session 10: Tuesday 9 May Rhythm and pace
Session 11: Tuesday 16 May Guest speaker and more on character
Session 12: Tuesday 23 May Narrative drive
Session 13: Tuesday 30 May Guest Speaker
Session 14: Tuesday 6 June Place & Setting
Session 15: Tuesday 13 June The creative mind
Session 16: Tuesday 18 July Outlining
Session 17: Tuesday 25 July Object and detail: Language, description, metaphor. How to use ornament, from the pared back to the baroque.
Session 18: Tuesday 1 August Plot and structure: What happens next? How to build a compelling narrative.
Session 19: Saturday 5 August Beginnings: Your first pages are an invitation that can’t be ignored
Session 20: Tuesday 8 August Genre and anti-genre : What is ‘genre’? And which one are you working in? The good, the bad and the ugly of different literary forms.
Session 21: Tuesday 15 August Themes and symbolism: The thing you’ve come to say, and how to say it.
Session 22: Saturday 19 August Re-drafting bootcamp: Writing, writing, writing. And feedback.
Session 23: Tuesday 22 August Research and believability: How to incorporate research into authentic narrative.
Session 24: Tuesday 29 August Synopsis and pitch
Session 25: Saturday 2 September Acting coach
Session 26: Tuesday 5 September Publisher
Session 27: Tuesday 12 September Agent
Session 28: Tuesday 19 September Celebratory dinner class
Faber Writing Anthology sessions:
- Session 29: Meet the Editor in Residence
- Session 30: Meet the Faber UK Team
All evening sessions will take place on Tuesdays, 6.00 – 8.00pm. Full-day sessions will take place on Saturdays from 10.00am –4.00pm and are fully catered. All classes take place at Avid Reader bookstore. Anthology sessions take place on Zoom.
The exact course content could be adjusted according to the experience and concerns of the group and availability of guest writers. The detail of the course is at the discretion of the Course Director and Faber Writing Academy at Allen & Unwin.
‘The workshops were fantastic – particularly for gaining feedback from Emily.’ 2022
‘I found Emily’s insight most helpful.’ 2022
‘It’s a great way to show your work when you have no idea if it’s even worth reading, Kris has created a wonderfully supportive and caring environment that helps those with L plates gain some confidence.’ 2021
How to Apply
Meet our new Blacktown Campus students
We spoke to some of our new students about how they feel studying at ACU Blacktown.
![creative writing brisbane Street view of the entrance to the Blacktown Campus with students walking in front](https://www.acu.edu.au/-/media/feature/banner/_acu-light-1380px-wide-banner-images/blacktown-opening-1380x480.jpg?h=480&w=1380&la=en&hash=D345FA6B4D15073740BE5A480EF2FFB1)
A day in the life of an arts student
Meet Helena. She's kept busy during the day with community engagement and literacy classes, and then is on her feet working in a restaurant at night. Here's how she makes it all work.
![creative writing brisbane A day in the life of an arts student](https://www.acu.edu.au/-/media/feature/banner/cs0378790-hero-banner_dayinthelife_arts-student-copy.jpg?h=480&w=1255&la=en&hash=E57BA09805786C9D5EE18E13B6376A74)
Join us on campus to find out everything you need to know about studying at ACU, from how to get into your dream course to where it could take you.
![creative writing brisbane](https://www.acu.edu.au/-/media/feature/banner/2024/od_home-page-banner_1380x480px.jpg?h=480&w=1380&la=en&hash=F4CEC20A447E9DB3C9493F1807A668F6)
Creative writing
- Undergraduate
- Postgraduate
Course type
0">Showing {{filterResult.length}} of {{filterResult.length}} results
No results found
- AVAILABLE ONLINE
- COMMONWEALTH SUPPORTED COURSE
{{course.CourseName}}
{{course.CourseDescription}}
International
Register now
Career outcomes, our graduates have pursued careers as:.
- copy writers
- journalists
- policy writers
- communications coordinators
- social media writers
- media communications
About creative writing
![creative writing brisbane ACU creative writing student chatting to a fellow student](https://www.acu.edu.au/-/media/feature/pagecontent/imagewithrichtext/creative-writing_1up_600x650.jpg?h=650&w=600&la=en&hash=CB8D1A2956ADBD13F9F39488419E18DE)
Unlock your creativity through exposure to multiple forms of writing, including short stories, poetry, script writing and creative non-fiction. Make creative writing the focus of your degree and you’ll explore styles, methods and techniques and get a greater understanding of genre and writing for a range of audiences.
Develop your voice
Make the most of a supportive and encouraging environment to hone your craft. Throughout your degree you’ll be encouraged to experiment in your writing as you learn to identify and cultivate your particular voice. You’ll be taught to critically assess and edit your work and the work of others. And when you graduate you’ll have a portfolio of polished and marketable writing.
You might also like
Apply now to acu.
Ready to take the next step? Here’s how to apply as an undergraduate, postgraduate, domestic, international or research student.
Why study at ACU
At ACU, you’ll get more than an excellent education. You’ll get the opportunity to help others, inspire change, and make a real difference.
Study uni overseas
Learn more about your overseas study opportunities with our international partners, including internships, short courses and semesters abroad.
Fees and scholarships
Discover how much university costs and learn about how you can fund your study through fee assistance, government loans and ACU scholarships.
Have a question?
We're available 9am–5pm AEDT, Monday to Friday If you’ve got a question, our AskACU team has you covered. You can search FAQs, text us, email, live chat, call – whatever works for you.
Live chat with us now
Chat to our team for real-time answers to your questions.
Visit our FAQs page
Find answers to some commonly asked questions.
- Business and management
- Electrotechnology
- Allied health
- Horticulture
- Community services
😊 Personalise my view
- a school leaver
- a career enhancer
- a TAFE student
- an international student
- an employer
- a guidance counsellor
- a high school student
interested in studying
- Business and IT
- Creative industries
- Education and community
- Environment and animal services
- Health and science
- Infrastructure and transport
- Service industries
currently studying
Interested in.
- Greater Brisbane
- Sunshine Coast
- Darling Downs & South West
- Wide Bay Burnett
- North Queensland
- Acacia Ridge
- Alexandra Hills
- Bracken Ridge
- Springfield
- Coolangatta
- Coomera Marine
- Sunshine Coast Health Institute
- Charleville
- Lockyer Valley - Gatton
- Maryborough
- Charters Towers
- Palm Island
- Townsville (Trade Training Centre Bohle)
- The Whitsundays
- Townsville (Pimlico)
- Thursday Island
- Northern Peninsula Area
- Great Barrier International Marine College
We use cookies, including those from third-party providers, to enhance your online experience and deliver personalised advertisements. By using our website, you consent to our use of cookies and our privacy policy .
Creative Writing - Advanced
Non-accredited short course.
Expand your creative writing skills!
This is the total course fee. Read more about student loans and payment plans.
Course overview
- Ways to study
- Payment options
Important information
Ask a question.
This program is designed as a follow-up to ‘Creative Writing’ and was developed in response to demand from those who had successfully completed that program and wanted to learn more. It offers participants the opportunity to further expand their knowledge and skills about the creative writing process like Richard uses.
Topics include innovative ways to create atmosphere and excitement in your writing, how to choose and use different tenses, how to write effective dialogue, how to use emotive language, how to create memorable characters and atmospheric settings, how to make objects come to life in words, how to maintain control and form in a long work, and how to develop a personal writing style.
Testimonials:
“The course and Richard were above and beyond all expectations” - Kathy
“Enjoyed the comradery between participants” - Karen
“Really useful course, especially how the publishing process works” - Joanna
This short course is non-accredited therefore is not registered with the National Training Register
Course outcome:
Statement of Participation
You'll learn face to face with a teacher in a classroom or simulated workplace environment. You may access learning materials through our online learning management system Connect. Learn more
CAMPUS | STUDY MODE | START DATE | DURATION | WORKLOAD | WHAT YOU'LL PAY |
| | | | Saturday | about student loans and payment plans. |
On Campus Saturday about student loans and payment plans. |
Studying on campus is a great way to enjoy the benefits of face-to-face education in a way that is far removed from a typical school. No matter which campus you choose, you know that you will be learning in an adult environment where you will be respected and nurtured. Attend a campus to learn face-to-face with a teacher in one of our learning spaces; such as a classroom, workshop, laboratory or simulated environments, or interact with teachers using real-time video or web conference tools. Learn more
Courses delivered on campus may have a combination of online and classroom based learning. Students are expected to undertake additional personal study to complete the course requirements.
![](//sektorel.online/777/templates/cheerup/res/banner1.gif)
Download Brochure
Payment Options
How can i pay for this course.
No matter what your circumstances, TAFE Queensland has a payment option to suit you. If you are unsure of what’s right for you, get in touch. We’re here to help.
Payment plan
If you have difficulty paying for the full cost of a course upfront, you may be eligible for a payment plan.
Upfront payment
This may be the full fee for the course, or the student contribution amount if you are eligible for a subsidy or concession.
Entry requirements
There are no formal entry requirements for this course.
Selection criteria
You will need to have completed the 'Creative Writing' course.
Resources required
- Please bring your laptop (fully charged) to type on, or paper, pencils and an eraser.
- Facilities will be available for participants to make tea or coffee, but please bring your own cup, tea or coffee, milk and sugar.
- Students need to present their Confirmation of Enrolment to the car park attendant each week to be eligible for discounted parking.
Course commencement is subject to minimum enrolment numbers.
University pathways
If you want to go on to further study at university, TAFE Queensland has a pathway option to get you there. In many cases, completing one of our diplomas may give you a full year of credit towards a degree with one of our partner universities.
Learn more about University Pathways
![creative writing brisbane James Cook University](https://tafeqld.edu.au/content/dam/tafe/en/course-images/logos/logo-james-cook-university.png)
How to apply
Direct application.
If you're ready to get started, simply hit the Apply Now button on your preferred course information page and get your application underway online.
Course disclaimer
All information was accurate at time of publication, however TAFE policies, tuition fees and course content is subject to change without notice. Course commencement is dependent on sufficient enrolment numbers. Timetable information is subject to change prior to commencement and/or during the duration of your course.
Related courses
Creative writing.
Kick start your writing career!
![creative writing brisbane](https://writingquarter.com.au/wp-content/uploads/2022/06/Image-6.png)
- Creative Writing Course for Teenagers
Jessica Miller is a children’s writer from Brisbane. Her first novel, Elizabeth and Zenobia, was shortlisted for the Text Prize and the Readings Prize and was a CBCA Notable Book, as was her second book, The Republic of Birds. Her third novel, The Hotel Witch will be released in February 2023. She has taught creative writing for adults and children in Brisbane, Berlin, and London.
Start Date: 3rd September, 2024 Duration: 6 Weeks Price: $299 3 places remaining
Unlock Your Storytelling Talent: Creative Writing Course for Teens
Are you ready for an extraordinary literary adventure? Our 6-week online Creative Writing Course for Teens is tailor-made to ignite your imagination, unleash your inner storyteller, and elevate your writing skills. This immersive course offers expert guidance, interactive learning, and the flexibility to nurture your creative spirit.
As a young writer, you’ll receive personalized feedback from our experienced instructors, ensuring the development of your unique voice and writing style. Our carefully curated curriculum covers a diverse range of topics, from crafting unforgettable characters to mastering the art of description, empowering you to tap into your limitless potential.
Enjoy the freedom to learn at your own pace with no set class times, seamlessly fitting the course into your busy day. Our instructors, based in Australia, bring their wealth of knowledge and experience to students from around the globe.
Upon course completion, you’ll not only have a treasure trove of new writing tools and techniques at your disposal, but you’ll also have lifetime access to the course materials, allowing you to revisit lessons and refresh your knowledge whenever inspiration strikes.
What to Expect in this Course:
Each week, students will complete a written assignment and receive personalized feedback from their dedicated online tutor. We recommend dedicating approximately 2-3 hours per week to the course, with new modules released weekly as text lessons. No set class times mean you can work on assignments whenever it suits you. The first module will be emailed to students on the course start date (please check your SPAM folder).
With high demand for our courses, early registration is advisable to secure your spot.
Course Modules:
Week one – start where.
Ask any writer, and they’ll tell you that getting started is often the most difficult part. The greatest idea of all-time pops into your head, only for a blank page to glare back. For week one of the beginners course, we will ease some of the pressure by setting ourselves meaningful writing goals. By lessening that fear of putting pen to paper, we will make writing feel not only possible, but successful.
Week Two – Inspiration Is Everywhere
A cat sniffing your bowl of cereal. An unexpected text message from an old friend. In module two we will take inspiration from our surroundings to help generate and develop our writing ideas. You will begin to understand how anything can find its way into a story.
Week Three – Characters That Come Alive on The Page
Week three of our online writing course will look into more than just interesting description and dialogue. We will delve into the idea of complex characters, narrators and speakers of stories. By using a variety of tips and tricks, you will create a character that both you and your reader can truly invest in.
Week Four – If in Doubt, Describe
Module four will present how description can both set a scene and turn it on its head. Think: carefully placed sensory details and zooming in and out of the action unfolding. We will explore a variety of these techniques to allow your writing to truly leap off the page.
Week Five – Structure, Structure, Structure
This week we lay out what is happening in your story, when, and where. We will explore how learning the rules, only to break them, can work powerfully when it comes to the structure of a story, and how unexpected twists and turns can surprise both writer and reader.
Week Six – What We Did, What We Will Do Next
For the final module of the beginners creative writing course, we will reflect on all that we have achieved (no matter how seemingly small). We will then look ahead to the steps that we might take to continue writing. These might include a promise to continue revising a current story, or simply carving out space in your day-to-day life to keep going, and growing, as a writer.
Embark on Your Creative Journey:
Are you ready to embark on a thrilling journey of self-discovery, creative exploration, and literary mastery? Don’t miss this incredible opportunity to invest in your passion, unlock your creative potential, and join the ranks of the next generation of great storytellers. Register now, and let the adventure begin!
![creative writing brisbane Museum of Brisbane | MoB](https://www.museumofbrisbane.com.au/wp-content/uploads/2020/11/MoB-BCC-Logo.png)
- Become a member
- View favourites
Suggestions
What’s On
![creative writing brisbane creative writing brisbane](https://www.museumofbrisbane.com.au/wp-content/uploads/2024/04/Thumbnail1-New-Light.jpg)
- New Light: Photography Now + Then
- Rearranged: Art of the Flower
- Artist in Residence
- All Exhibitions
- Clock Tower Tour
Brisbane City Hall Tour
- Language Other Than English
- Write your heart out pop-up
Ekphrastic Writing Club
- Quiet Afternoons
- Spring School Holidays
- Bilingual Storytime
- BYO Baby Tour
- ALL MoB KIDS
![creative writing brisbane creative writing brisbane](https://www.museumofbrisbane.com.au/wp-content/uploads/2024/04/2024-first-look-thumbnail1.jpg)
2024 First Look
- Clay: Collected Ceramics
- Rearranged: Get to Know the Artists
- The Easton Pearson Archive
- MoB Sunday Stories
- Site Listening : Brisbane
- Where I Belong Podcast
- The Designers’ Guide: Easton Pearson Archive
- All Watch & Listen
- Primary School
- Secondary School
- Learn Assist
- Make a booking enquiry
![creative writing brisbane creative writing brisbane](https://www.museumofbrisbane.com.au/wp-content/uploads/2020/09/Clock-Tower-Tour-2.jpg)
- Our Partners
- Work with us
- Opportunities for Artists
- Museum of Brisbane Collection
- Brisbane Art Design
- Become a MoB Member
- Contact MoB
Museum of Brisbane
- Level 3, Brisbane City Hall King George Square, Brisbane 4000
- info@museumofbrisbane.com.au
- (07) 3339 0800
- Transport and Parking
- MoB Social Story
- Social Story: Clock Tower Tour
- MoB Sensory Map
- Group Bookings
- Teachers and Students
- School Bookings
- About City Hall
![creative writing brisbane creative writing brisbane](https://www.museumofbrisbane.com.au/wp-content/uploads/2020/11/Dontate-MoB-Thumbnail-868x654-@1.jpg)
- Donate to MoB
- 2024 Learn Assist Appeal
- Easton Pearson Archive
- MoB Member Terms and Conditions
- Add to Our Collection
- Partner with us
Grab a cup of tea and join us in the Museum for an opportunity to explore the generative art of Ekphrastic writing.
Ekphrasis has long been a vital craft tool used by poets, fiction writers, dramatists and essayists as a springboard for creative imaginings. It is the process of employing a slow and meditative response to a piece of art. Whether you’re a seasoned writer or looking to practice your writing skills in a supportive, peaceful environment, join our growing community of creatives on the first Saturday of every month for MoB’s Ekphrastic Club.
Tea, coffee and materials provided.
Date & Time
You might also like.
![creative writing brisbane creative writing brisbane](https://www.museumofbrisbane.com.au/wp-content/uploads/2024/06/Write-your-heart-out-Thumbnail1.jpg)
Write your heart out
![creative writing brisbane creative writing brisbane](https://www.museumofbrisbane.com.au/wp-content/uploads/2020/09/Museum-Highlights-Tour-_-Thumbnail1.jpg)
Museum Highlights Tour
![creative writing brisbane creative writing brisbane](https://www.museumofbrisbane.com.au/wp-content/uploads/2020/10/Thumbnail-City-Hall-Tour.jpg)
- info@museumofbrisbane.com.au
Connect with us
Opening hours.
Mon | 10:00am - 5:00pm |
Tue | 10:00am - 5:00pm |
Wed | 10:00am - 5:00pm |
Thu | 10:00am - 5:00pm |
Fri | 10:00am - 5:00pm |
Sat | 10:00am - 5:00pm |
Sun | 10:00am - 5:00pm |
Sign-up to stay up to date with Museum of Brisbane's news, events, exhibitions and tours.
- First Name *
- Last Name *
- Your Birthday Day Month Year
- State State QLD NSW ACT VIC SA WA NT INTERNATIONAL
- Brisbane History
- Aboriginal and Torres Strait Islander Arts and Cultures
- Design and Fashion
- Children and Families
- MoB Membership
- Terms & Conditions
- What’s On @ MoB
- MoB Kids Events
- Plan your visit
- Group Visits
- Watch & Listen
- Plan your excursion
- Programs & Initiatives
- Partnerships
- info@museumofbrisbane.com.au
- United Kingdom
- New Zealand
- Philippines
- United States
Are you a jobseeker or an employer?
Jobseeker log in
Don’t have a jora account register with:.
![creative writing brisbane Link to App Store](https://au.jora.com/assets/app-store-logos/app-store-en-db23f6109a00084902aab7eff6f70547b4fcb59525a7fc5bd56053369b5753cf.png)
- Any job type
- Casual/Temporary
- Last 24 hours
- Last 7 days
- Last 14 days
- Last 30 days
- Exact location
- Within 10km
- Within 25km
- Within 50km
- Within 100km
Popular filters
Listed date, salary estimate, 191 jobs – creative writing in brisbane qld, content writer content writer.
- Create engaging cryptocurrency content for various Swyftx platforms
- Collaborate with marketing team to optimize content for target audiences
- 2+ years content writing experience, SEO knowledge, cryptocurrency understanding
Copywriter - 3 Month Contract Copywriter - 3 Month Contract
- Craft engaging product descriptions and job ads for marketing team
- Maintain brand voice, apply SEO best practices for content visibility
- Shape language across eCommerce products, attract top-tier talent
Publishing Coordinator/Academic Content Creator Publishing Coordinator/Academic Content Creator
- Produce and manage academic content for secondary students
- Collaborate with stakeholders to ensure content meets education standards
- Represent Academic Group in Queensland, engaging with educators and retailers
Senior Editor, Queensland Curriculum and Assessment Authority Senior Editor, Queensland Curriculum and Assessment Authority
- Provide publishing advice for educational resources in various formats
- Maintain style guide, manage projects, and build partnerships
- Contribute to workflow development and performance management culture
Don't miss out!
We will notify you when new Creative Writing jobs in Brisbane QLD are posted.
Editor Editor
- Provide high-quality editing and proofreading for professional content
- Collaborate with design team to ensure materials align with guidelines
- Manage stakeholder relationships, report progress, gather feedback
Technical Writer Technical Writer
- Create engaging learning content for world-class SaaS product
- Translate technical aspects into user-friendly, engaging content
- Ensure users have exceptional learning experience with product knowledge
Cost Estimator Cost Estimator
- Craft compelling proposals to secure commercial cleaning contracts
- Research clients, collaborate with teams, and manage bid process
- Proven bid writing experience, strong communication and analytical skills
Bid Writer Bid Writer
- Work from home, flexible hours for stay-at-home parents
- Process fee requests, analyze client needs, and formulate proposals
- No formal qualifications required, proficiency in English essential
Senior Content Editor Senior Content Editor
- Lead development of communication style and editorial direction
- Mentor early-career writers and ensure accuracy of customer communications
- Journalism/Communications degree, financial services experience preferred
Tech Writers Tech Writers
- Baseline clearance or above required
- Converting documents related to aerospace/AV program
- Brisbane preferred, remote locations may be possible
Marketing Assistant Marketing Assistant
- Assist in executing marketing plans and campaigns
- Create engaging content for social media and company website
- Support event planning, bid creation, and marketing initiatives
Communications Manager, ANZ Communications Manager, ANZ
- Craft internal and external communication strategies for Domino's brand
- Lead national campaigns, manage media inquiries, and create compelling content
- Media-savvy guru with 5+ years experience in communications/journalism
Principal | Senior Curator - Middle East Principal | Senior Curator - Middle East
- Lead global art projects in the Middle East region
- Extensive knowledge of contemporary artists and public art practices
- Strong leadership, communication, and organizational skills required
Public Relations / Social Media Manager Public Relations / Social Media Manager
- Develop and execute PR and social media strategies
- Manage influencer campaigns and write press releases
- Stay updated on industry trends, engage with stakeholders, analyse campaigns
![creative writing brisbane creative writing brisbane](https://au.jora.com/assets/job-description-error-d70bb2d065b1538d289518fdb588c149f2607ce6ad444df32e3075ede7a31fde.png)
People also searched
Discover new jobs for this search.
Stay up to date with new jobs that match what you are looking for.
Accredited content creation solution
Are you weary of fading into the crowd of sameness? Do you envision propelling your brand to prominence, captivating your audience, and achieving unmatched triumph? Your quest ends here, as Brisbanecopywriting emerges as your guiding light toward metamorphosis. The moment has arrived to transcend the commonplace and welcome the remarkable potency of language.
![creative writing brisbane](https://brisbanecopywriting.com/wp-content/uploads/2023/08/21.jpg)
Established Provider of Writing Services
Envision your website visitors captivated by each sentence, your emails sparking enthusiasm, and your social media posts fueling engaging discussions. Picture your brand evolving into a storytelling phenomenon, with content that etches an enduring impression on both hearts and minds. This isn’t mere imagination; it’s the tangible reality we’re here to assist you in crafting.
Content Writing Services
Our team of dedicated copywriting maestros is standing by, ready to dive into your brand’s essence and craft narratives that resonate. We’re not here to deliver mere words; we’re here to deliver results. We’re here to give your ideas a voice, your offerings a personality, and your brand a soul.
Unleash the Potential of Your Online Existence through Captivating Website Content
Metamorphose Everyday Products into Captivating Narratives with Compelling Descriptions
Enhance Your Exposure and Expand Your Reach through Content Optimized for SEO
Spark Discussions and Cultivate Interaction via Vibrant Social Media Content
Well organize content
The more information you provide, the more accurate our quote will be. We’ll review your requirements and provide you with a customized quote tailored to your project’s scope and needs.
Copywriting involves crafting written content that captivates and persuades readers to take specific actions. It's a skillful blend of creativity, language proficiency, and strategic messaging to engage audiences effectively.
A professional copywriting agency brings expertise in crafting compelling content that resonates with your target audience. Their experience in various industries ensures your message is clear, impactful, and aligned with your business goals.
If you're looking to enhance your brand's messaging, improve conversions, or communicate complex ideas clearly, copywriting services can be immensely beneficial. Whether you need website content, marketing materials, or product descriptions, a skilled copywriter can make a significant difference.
We handle a wide range of projects, including website copy, blog posts, social media content, email campaigns, product descriptions, press releases, and more. Our versatility allows us to cater to various industries and communication channels.
Upon receiving your project details, we start with a thorough understanding of your brand, target audience, and objectives. Our team then crafts drafts, refines them based on your feedback, and delivers polished content that aligns with your vision.
Have a project in mind?
Transform your visions into compelling narratives that yield outcomes. Reach out to initiate your journey with Brisbanecopywriting today. Your achievements are just a few clicks away!
Request a Quote
You can do this through our user-friendly website contact form, email, or phone.
Select Services
The more details you provide, the better we can tailor our services to match your needs.
Receive Your Order
Throughout the process, we maintain open lines of communication, providing updates.
Happy Clients
It brings us pride to have contributed to their tales of success, and we eagerly anticipate crafting numerous more captivating narratives in the times ahead.
![creative writing brisbane](https://brisbanecopywriting.com/wp-content/uploads/2023/08/18.jpg)
Brisbanecopywriting truly transformed our brand's communication strategy. Their ability to capture our essence and craft engaging copy has been exceptional.
![creative writing brisbane](https://brisbanecopywriting.com/wp-content/uploads/2023/08/24.jpg)
At BrisbaneCopywriting.com, our mission centers around producing compelling and impactful content tailored for businesses and individuals alike.
Headquarter
ul. Lugansk 59, Pernik, Bulgaria, 2300
- What's on
- Catalogue Information collections Queensland Family history First Nations Art and design Caring for our collections Research
- Using the library Membership Ask a librarian Borrow and request Computers and internet Print, copy and scan Book spaces and equipment Justice of the Peace Venue hire GRAIL
- Getting here Opening hours Spaces Maps by level Food and facilities Access and inclusion
- Blog Children and families Digital and You Exhibitions First Nations cultures Makers and designers Queensland stories Readers and writers Teachers and students
- Awards and fellowships Caring for your collections Contribute to collections Donate Volunteer
- Contact us Corporate information Interim Truth and Treaty Body Jobs and employment News and media Partnerships and collaborations Pay an invoice
Dream, write, win: enter the 2024 Young Writers Award
By BValentino | 30 April 2024
Wordsmiths and dreamers, we want to hear from you! Ever felt that surge of excitement when your pen meets paper, your fingers dance across the keyboard, and a story begins to unfold? State Library of Queensland's Young Writers Award (YWA) is open for 2024 and we’re excited to find and celebrate short stories and a diversity of young and exciting new Queensland voices. Wherever you are in our state – Mackay, Toowoomba, Hervey Bay, Gold Coast, Bundaberg, Cairns, Goondiwindi, Brisbane, or somewhere else – you can enter .
Winners will be given the chance to have their work published by literary journal, Kill Your Darlings , which can boost your writing career and expose your work to a wider audience. With more eyes on your work, it could also open the door for future opportunities!
Author and poet Shastra Deo , whose story ‘ The Minutes Turn to Ours ’ was named runner-up in 2012, shared with us that participating in the award marked the first time she had shared her writing with a public audience under her own name. Prior to this, she had mainly engaged in writing fan-fiction, which provided a supportive community but little public recognition. Being a YWA runner-up represented a significant step in her journey as a writer.
![creative writing brisbane Shastra Deo wears a denim jacket and stands against a dark background, smiling](https://www.slq.qld.gov.au/sites/default/files/styles/slq_standard/public/Shastra%20Deo.jpeg?itok=HRwYbgk7)
Shastra's latest collection of poems is The Exclusion Zone , published by UQP in 2023.
The cash prizes offered, including $2,000 for first prize and $500 for up to three runners-up, can provide important financial support and cover expenses related to furthering your writing career, such as attending workshops, or purchasing books or a new laptop.
In 2015, Grace McCarter won with her story ‘ Surface ’ and the prize money was more than she had ever received at once. This financial boost enabled her to overcome hurdles in her life, such as moving out of home and getting closer to opportunities aligned with her future aspirations. It opened up opportunities for her writing career. Publishers approached her with informal mentorship offers and requests for samples of longer-form work. Her story was featured in Award Winning Australian Writing the following year, and another story of hers was shortlisted for the YWA in 2017. She was also invited to speak on a panel at the inaugural Young Writers Conference and conduct workshops through programs like Express Media Toolkits .
![creative writing brisbane Grace McCarter wears a black top and looks away from the camera, against a red background](https://www.slq.qld.gov.au/sites/default/files/styles/slq_standard/public/Grace%20McCarter.jpeg?itok=i9gU6Hya)
Grace's story , 'Surface', is set on a farm in Nanango . The narrator remembers: "As soon as I hit the water, I closed my eyes and mouth, and pinched my nose shut with both hands. I stayed that way until I felt calm, and opened my eyes."
Preparing and submitting a story for the Young Writers Award can be a valuable learning experience. It challenges you to refine your writing skills, hone your craft, and gain a deeper understanding of submission and selection processes within the literary world.
Writer and broadcaster Benjamin Law was a runner-up in 2006 for his story ‘ Simon Says ’. Entering was valuable, he said, also telling us that his story for the YWA contained elements that foreshadowed themes and ideas that would later feature prominently in his best-selling memoir, The Family Law . He recalls being awed and a little intimidated by the prospect of entering. Despite Ben's initial apprehension, being recognised as a runner-up in the YWA was a massive morale boost. It provided validation and encouraged him to continue pursuing his passion.
![creative writing brisbane Writer Benjamin Law in a white t-shirt and jeans is seated smiling](https://www.slq.qld.gov.au/sites/default/files/styles/slq_standard/public/BLawCredit-Daniel%20Francisco%20Robles_0.jpg?itok=zqdsvR2i)
Ben is the author or editor of 4 books of non-fiction plus co-executive producer, co-creator and co-writer of Wellmania (Netflix, 2023).
You could be the next Benjamin, Shastra or Grace – don't let doubt hold you back. Seize this opportunity to share your voice with the world. Submit your entry to the Young Writers Award today and pave the way for your own literary journey.
The Young Writers Award is free to enter! Entries open now and close Monday 13 May at 5 pm AEST.
Learn more about past winners and enter here . We can't wait to read your work!
Your email address will not be published.
We welcome relevant, respectful comments.
Information
Initiatives
You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.
Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
Original Submission Date Received: .
- Active Journals
- Find a Journal
- Proceedings Series
- For Authors
- For Reviewers
- For Editors
- For Librarians
- For Publishers
- For Societies
- For Conference Organizers
- Open Access Policy
- Institutional Open Access Program
- Special Issues Guidelines
- Editorial Process
- Research and Publication Ethics
- Article Processing Charges
- Testimonials
- Preprints.org
- SciProfiles
- Encyclopedia
![Information information-logo](https://pub.mdpi-res.com/img/journals/information-logo.png?8600e93ff98dbf14)
Article Menu
![creative writing brisbane creative writing brisbane](https://www.mdpi.com/profiles/859760/thumb/María_N._Moreno_García.jpg)
- Subscribe SciFeed
- Recommended Articles
- Google Scholar
- on Google Scholar
- Table of Contents
Find support for a specific problem in the support section of our website.
Please let us know what you think of our products and services.
Visit our dedicated information section to learn more about MDPI.
JSmol Viewer
Recurrent neural networks: a comprehensive review of architectures, variants, and applications.
![creative writing brisbane creative writing brisbane](https://www.mdpi.com/bundles/mdpisciprofileslink/img/unknown-user.png)
1. Introduction
2. related works, 3. fundamentals of rnns, 3.1. basic architecture and working principle of standard rnns, 3.2. activation functions, 3.3. the vanishing and exploding gradient problems, 3.4. bidirectional rnns, 3.5. deep rnns, 4. advanced variants of rnns, 4.1. long short-term memory networks, 4.2. bidirectional lstm, stacked lstm, 4.3. gated recurrent units, comparison with lstm, 4.4. other notable variants, 4.4.1. peephole lstm, 4.4.2. echo state networks.
- Deep Echo-State Networks: Recent research has extended the ESN architecture to deeper variants, known as deep echo-state networks (DeepESNs). In DeepESNs, multiple reservoir layers are stacked, allowing the network to capture hierarchical temporal features across different timescales [ 87 ]. Each layer in a DeepESN processes the output from the previous layer’s reservoir, enabling the model to learn more abstract and complex representations of the input data. The state update for a DeepESN can be generalized as follows: h t l = tanh ( W i n l h t l − 1 + W r e s l h t − 1 l ) , (31) where l denotes the layer number, h t l is the hidden state at layer l , W i n l is the input weight matrix for layer l , and h t l − 1 is the hidden state from the previous layer. DeepESNs have demonstrated improved performance in tasks requiring the modeling of complex temporal patterns, such as speech recognition and financial time series forecasting [ 88 ].
- Ensemble Deep ESNs: In ensemble deep ESNs, multiple DeepESNs are trained independently, and their outputs are combined to form the final prediction [ 89 ]. This ensemble approach leverages the diversity of the reservoirs and the deep architecture to improve robustness and accuracy, particularly in time series forecasting applications. For instance, Gao et al. [ 90 ] demonstrated the effectiveness of Deep ESN ensembles in predicting significant wave heights, where the ensemble approach helped mitigate the impact of reservoir initialization variability and improved the model’s generalization ability.
- Input Processing with Signal Decomposition: Another critical aspect of effectively utilizing RNNs and ESNs is the preprocessing of input signals. Given the complex and often noisy nature of real-world time series data, signal decomposition techniques such as the empirical wavelet transform (EWT) have been employed to enhance the input to ESNs [ 91 ]. The EWT decomposes the input signal into different frequency components, allowing the ESN to process each component separately and improve the model’s ability to capture underlying patterns. The combination of the EWT with ESNs has shown promising results in various applications, including time series forecasting, where it helps reduce noise and enhance the predictive performance of the model.
4.4.3. Independently Recurrent Neural Network
5. innovations in rnn architectures and training methodologies, 5.1. hybrid architectures, 5.2. neural architecture search, 5.3. advanced optimization techniques, 5.4. rnns with attention mechanisms, 5.5. rnns integrated with transformer models, 6. public datasets for rnn research, 7. applications of rnns in peer-reviewed literature, 7.1. natural language processing, 7.1.1. text generation, 7.1.2. sentiment analysis, 7.1.3. machine translation, 7.2. speech recognition, 7.3. time series forecasting, 7.4. signal processing, 7.5. bioinformatics, 7.6. autonomous vehicles, 7.7. anomaly detection, 8. challenges and future research directions, 8.1. scalability and efficiency, 8.2. interpretability and explainability, 8.3. bias and fairness, 8.4. data dependency and quality, 8.5. overfitting and generalization, 9. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.
AI | Artificial intelligence |
ANN | Artificial neural network |
BiLSTM | Bidirectional long short-term memory |
CNN | Convolutional neural network |
DL | Deep learning |
GRU | Gated recurrent unit |
LSTM | Long short-term memory |
ML | Machine learning |
NAS | Neural architecture search |
NLP | Natural language processing |
RNN | Recurrent neural network |
RL | Reinforcement learning |
SHAPs | Shapley Additive Explanations |
TPU | Tensor processing unit |
VAE | Variational autoencoder |
- O’Halloran, T.; Obaido, G.; Otegbade, B.; Mienye, I.D. A deep learning approach for Maize Lethal Necrosis and Maize Streak Virus disease detection. Mach. Learn. Appl. 2024 , 16 , 100556. [ Google Scholar ] [ CrossRef ]
- Peng, Y.; He, L.; Hu, D.; Liu, Y.; Yang, L.; Shang, S. Decoupling Deep Learning for Enhanced Image Recognition Interpretability. ACM Trans. Multimed. Comput. Commun. Appl. 2024 . [ Google Scholar ] [ CrossRef ]
- Khan, W.; Daud, A.; Khan, K.; Muhammad, S.; Haq, R. Exploring the frontiers of deep learning and natural language processing: A comprehensive overview of key challenges and emerging trends. Nat. Lang. Process. J. 2023 , 4 , 100026. [ Google Scholar ] [ CrossRef ]
- Obaido, G.; Achilonu, O.; Ogbuokiri, B.; Amadi, C.S.; Habeebullahi, L.; Ohalloran, T.; Chukwu, C.W.; Mienye, E.; Aliyu, M.; Fasawe, O.; et al. An Improved Framework for Detecting Thyroid Disease Using Filter-Based Feature Selection and Stacking Ensemble. IEEE Access 2024 , 12 , 89098–89112. [ Google Scholar ] [ CrossRef ]
- Mienye, I.D.; Obaido, G.; Aruleba, K.; Dada, O.A. Enhanced Prediction of Chronic Kidney Disease using Feature Selection and Boosted Classifiers. In Proceedings of the International Conference on Intelligent Systems Design and Applications, Virtual, 13–15 December 2021; pp. 527–537. [ Google Scholar ]
- Al-Jumaili, A.H.A.; Muniyandi, R.C.; Hasan, M.K.; Paw, J.K.S.; Singh, M.J. Big data analytics using cloud computing based frameworks for power management systems: Status, constraints, and future recommendations. Sensors 2023 , 23 , 2952. [ Google Scholar ] [ CrossRef ]
- Gill, S.S.; Wu, H.; Patros, P.; Ottaviani, C.; Arora, P.; Pujol, V.C.; Haunschild, D.; Parlikad, A.K.; Cetinkaya, O.; Lutfiyya, H.; et al. Modern computing: Vision and challenges. Telemat. Inform. Rep. 2024 , 13 , 100116. [ Google Scholar ] [ CrossRef ]
- Mienye, I.D.; Jere, N. A Survey of Decision Trees: Concepts, Algorithms, and Applications. IEEE Access 2024 , 12 , 86716–86727. [ Google Scholar ] [ CrossRef ]
- Aruleba, R.T.; Adekiya, T.A.; Ayawei, N.; Obaido, G.; Aruleba, K.; Mienye, I.D.; Aruleba, I.; Ogbuokiri, B. COVID-19 diagnosis: A review of rapid antigen, RT-PCR and artificial intelligence methods. Bioengineering 2022 , 9 , 153. [ Google Scholar ] [ CrossRef ]
- Alhajeri, M.S.; Ren, Y.M.; Ou, F.; Abdullah, F.; Christofides, P.D. Model predictive control of nonlinear processes using transfer learning-based recurrent neural networks. Chem. Eng. Res. Des. 2024 , 205 , 1–12. [ Google Scholar ] [ CrossRef ]
- Shahinzadeh, H.; Mahmoudi, A.; Asilian, A.; Sadrarhami, H.; Hemmati, M.; Saberi, Y. Deep Learning: A Overview of Theory and Architectures. In Proceedings of the 2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), Babol, Iran, 21–22 February 2024; pp. 1–11. [ Google Scholar ]
- Baruah, R.D.; Organero, M.M. Explicit Context Integrated Recurrent Neural Network for applications in smart environments. Expert Syst. Appl. 2024 , 255 , 124752. [ Google Scholar ] [ CrossRef ]
- Werbos, P. Backpropagation through time: What it does and how to do it. Proc. IEEE 1990 , 78 , 1550–1560. [ Google Scholar ] [ CrossRef ]
- Lalapura, V.S.; Amudha, J.; Satheesh, H.S. Recurrent neural networks for edge intelligence: A survey. ACM Comput. Surv. (CSUR) 2021 , 54 , 1–38. [ Google Scholar ] [ CrossRef ]
- Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997 , 9 , 1735–1780. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Cho, K.; Van Merriënboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; Bengio, Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv 2014 , arXiv:1406.1078. [ Google Scholar ]
- Liu, F.; Li, J.; Wang, L. PI-LSTM: Physics-informed long short-term memory network for structural response modeling. Eng. Struct. 2023 , 292 , 116500. [ Google Scholar ] [ CrossRef ]
- Ni, Q.; Ji, J.; Feng, K.; Zhang, Y.; Lin, D.; Zheng, J. Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit. Reliab. Eng. Syst. Saf. 2024 , 242 , 109753. [ Google Scholar ] [ CrossRef ]
- Niu, Z.; Zhong, G.; Yue, G.; Wang, L.N.; Yu, H.; Ling, X.; Dong, J. Recurrent attention unit: A new gated recurrent unit for long-term memory of important parts in sequential data. Neurocomputing 2023 , 517 , 1–9. [ Google Scholar ] [ CrossRef ]
- Lipton, Z.C.; Berkowitz, J.; Elkan, C. A critical review of recurrent neural networks for sequence learning. arXiv 2015 , arXiv:1506.00019. [ Google Scholar ]
- Yu, Y.; Si, X.; Hu, C.; Zhang, J. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 2019 , 31 , 1235–1270. [ Google Scholar ] [ CrossRef ]
- Tarwani, K.M.; Edem, S. Survey on recurrent neural network in natural language processing. Int. J. Eng. Trends Technol. 2017 , 48 , 301–304. [ Google Scholar ] [ CrossRef ]
- Tsoi, A.C.; Back, A.D. Locally recurrent globally feedforward networks: A critical review of architectures. IEEE Trans. Neural Netw. 1994 , 5 , 229–239. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Mastorocostas, P.A.; Theocharis, J.B. A stable learning algorithm for block-diagonal recurrent neural networks: Application to the analysis of lung sounds. IEEE Trans. Syst. Man. Cybern. Part B (Cybern.) 2006 , 36 , 242–254. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Dutta, K.K.; Poornima, S.; Sharma, R.; Nair, D.; Ploeger, P.G. Applications of Recurrent Neural Network: Overview and Case Studies. In Recurrent Neural Networks ; CRC Press: Boca Raton, FL, USA, 2022; pp. 23–41. [ Google Scholar ]
- Quradaa, F.H.; Shahzad, S.; Almoqbily, R.S. A systematic literature review on the applications of recurrent neural networks in code clone research. PLoS ONE 2024 , 19 , e0296858. [ Google Scholar ] [ CrossRef ]
- Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning ; MIT Press: Cambridge, MA, USA, 2016. [ Google Scholar ]
- Greff, K.; Srivastava, R.K.; Koutník, J.; Steunebrink, B.R.; Schmidhuber, J. LSTM: A search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 2016 , 28 , 2222–2232. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Al-Selwi, S.M.; Hassan, M.F.; Abdulkadir, S.J.; Muneer, A.; Sumiea, E.H.; Alqushaibi, A.; Ragab, M.G. RNN-LSTM: From applications to modeling techniques and beyond—Systematic review. J. King Saud-Univ.-Comput. Inf. Sci. 2024 , 36 , 102068. [ Google Scholar ] [ CrossRef ]
- Zaremba, W.; Sutskever, I.; Vinyals, O. Recurrent neural network regularization. arXiv 2014 , arXiv:1409.2329. [ Google Scholar ]
- Bai, S.; Kolter, J.Z.; Koltun, V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv 2018 , arXiv:1803.01271. [ Google Scholar ]
- Che, Z.; Purushotham, S.; Cho, K.; Sontag, D.; Liu, Y. Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 2018 , 8 , 6085. [ Google Scholar ] [ CrossRef ]
- Chung, J.; Gulcehre, C.; Cho, K.; Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv 2014 , arXiv:1412.3555. [ Google Scholar ]
- Badawy, M.; Ramadan, N.; Hefny, H.A. Healthcare predictive analytics using machine learning and deep learning techniques: A survey. J. Electr. Syst. Inf. Technol. 2023 , 10 , 40. [ Google Scholar ] [ CrossRef ]
- Ismaeel, A.G.; Janardhanan, K.; Sankar, M.; Natarajan, Y.; Mahmood, S.N.; Alani, S.; Shather, A.H. Traffic pattern classification in smart cities using deep recurrent neural network. Sustainability 2023 , 15 , 14522. [ Google Scholar ] [ CrossRef ]
- Mers, M.; Yang, Z.; Hsieh, Y.A.; Tsai, Y. Recurrent neural networks for pavement performance forecasting: Review and model performance comparison. Transp. Res. Rec. 2023 , 2677 , 610–624. [ Google Scholar ] [ CrossRef ]
- Chen, Y.; Cheng, Q.; Cheng, Y.; Yang, H.; Yu, H. Applications of recurrent neural networks in environmental factor forecasting: A review. Neural Comput. 2018 , 30 , 2855–2881. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Linardos, V.; Drakaki, M.; Tzionas, P.; Karnavas, Y.L. Machine learning in disaster management: Recent developments in methods and applications. Mach. Learn. Knowl. Extr. 2022 , 4 , 446–473. [ Google Scholar ] [ CrossRef ]
- Zhang, J.; Liu, H.; Chang, Q.; Wang, L.; Gao, R.X. Recurrent neural network for motion trajectory prediction in human-robot collaborative assembly. CIRP Ann. 2020 , 69 , 9–12. [ Google Scholar ] [ CrossRef ]
- Tsantekidis, A.; Passalis, N.; Tefas, A. Recurrent Neural Networks. In Deep Learning for Robot Perception and Cognition ; Elsevier: Amsterdam, The Netherlands, 2022; pp. 101–115. [ Google Scholar ]
- Mienye, I.D.; Jere, N. Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions. IEEE Access 2024 , 12 , 96893–96910. [ Google Scholar ] [ CrossRef ]
- Mienye, I.D.; Sun, Y. A machine learning method with hybrid feature selection for improved credit card fraud detection. Appl. Sci. 2023 , 13 , 7254. [ Google Scholar ] [ CrossRef ]
- Rezk, N.M.; Purnaprajna, M.; Nordström, T.; Ul-Abdin, Z. Recurrent neural networks: An embedded computing perspective. IEEE Access 2020 , 8 , 57967–57996. [ Google Scholar ] [ CrossRef ]
- Yu, Y.; Adu, K.; Tashi, N.; Anokye, P.; Wang, X.; Ayidzoe, M.A. Rmaf: Relu-memristor-like activation function for deep learning. IEEE Access 2020 , 8 , 72727–72741. [ Google Scholar ] [ CrossRef ]
- Mienye, I.D.; Ainah, P.K.; Emmanuel, I.D.; Esenogho, E. Sparse Noise Minimization in Image Classification using Genetic Algorithm and DenseNet. In Proceedings of the 2021 Conference on Information Communications Technology and Society (ICTAS), Durban, South Africa, 10–11 March 2021; pp. 103–108. [ Google Scholar ]
- Ciaburro, G.; Venkateswaran, B. Neural Networks with R: SMART Models Using CNN, RNN, Deep Learning, and Artificial Intelligence Principles ; Packt Publishing Ltd.: Birmingham, UK, 2017. [ Google Scholar ]
- Nwankpa, C.; Ijomah, W.; Gachagan, A.; Marshall, S. Activation functions: Comparison of trends in practice and research for deep learning. arXiv 2018 , arXiv:1811.03378. [ Google Scholar ]
- Szandała, T. Review and comparison of commonly used activation functions for deep neural networks. Bio-Inspired Neurocomp. 2021 , 203–224. [ Google Scholar ]
- Clevert, D.A.; Unterthiner, T.; Hochreiter, S. Fast and accurate deep network learning by exponential linear units (elus). arXiv 2015 , arXiv:1511.07289. [ Google Scholar ]
- Dubey, S.R.; Singh, S.K.; Chaudhuri, B.B. Activation functions in deep learning: A comprehensive survey and benchmark. Neurocomputing 2022 , 503 , 92–108. [ Google Scholar ] [ CrossRef ]
- Obaido, G.; Mienye, I.D.; Egbelowo, O.F.; Emmanuel, I.D.; Ogunleye, A.; Ogbuokiri, B.; Mienye, P.; Aruleba, K. Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects. Mach. Learn. Appl. 2024 , 17 , 100576. [ Google Scholar ] [ CrossRef ]
- Mienye, I.D.; Sun, Y. Effective Feature Selection for Improved Prediction of Heart Disease. In Proceedings of the Pan-African Artificial Intelligence and Smart Systems Conference, Durban, South Africa, 4–6 December 2021; pp. 94–107. [ Google Scholar ]
- Martins, A.; Astudillo, R. From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification. In Proceedings of the International Conference on Machine Learning, New York, NY, USA, 20–22 June 2016; pp. 1614–1623. [ Google Scholar ]
- Bianchi, F.M.; Maiorino, E.; Kampffmeyer, M.C.; Rizzi, A.; Jenssen, R.; Bianchi, F.M.; Maiorino, E.; Kampffmeyer, M.C.; Rizzi, A.; Jenssen, R. Properties and Training in Recurrent Neural Networks. In Recurrent Neural Networks for Short-Term Load Forecasting: An Overview and Comparative Analysis ; Springer: Berlin/Heidelberg, Germany, 2017; pp. 9–21. [ Google Scholar ]
- Mohajerin, N.; Waslander, S.L. State Initialization for Recurrent Neural Network Modeling of Time-Series Data. In Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 14–19 May 2017; pp. 2330–2337. [ Google Scholar ]
- Forgione, M.; Muni, A.; Piga, D.; Gallieri, M. On the adaptation of recurrent neural networks for system identification. Automatica 2023 , 155 , 111092. [ Google Scholar ] [ CrossRef ]
- Zhang, J.; He, T.; Sra, S.; Jadbabaie, A. Why gradient clipping accelerates training: A theoretical justification for adaptivity. arXiv 2019 , arXiv:1905.11881. [ Google Scholar ]
- Qian, J.; Wu, Y.; Zhuang, B.; Wang, S.; Xiao, J. Understanding Gradient Clipping in Incremental Gradient Methods. In Proceedings of the International Conference on Artificial Intelligence and Statistics, Virtual, 13–15 April 2021; pp. 1504–1512. [ Google Scholar ]
- Fei, H.; Tan, F. Bidirectional grid long short-term memory (bigridlstm): A method to address context-sensitivity and vanishing gradient. Algorithms 2018 , 11 , 172. [ Google Scholar ] [ CrossRef ]
- Dong, X.; Chowdhury, S.; Qian, L.; Li, X.; Guan, Y.; Yang, J.; Yu, Q. Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN. PLoS ONE 2019 , 14 , e0216046. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Chorowski, J.K.; Bahdanau, D.; Serdyuk, D.; Cho, K.; Bengio, Y. Attention-based models for speech recognition. Adv. Neural Inf. Process. Syst. 2015 , 28 . [ Google Scholar ]
- Zhou, M.; Duan, N.; Liu, S.; Shum, H.Y. Progress in neural NLP: Modeling, learning, and reasoning. Engineering 2020 , 6 , 275–290. [ Google Scholar ] [ CrossRef ]
- Naseem, U.; Razzak, I.; Khan, S.K.; Prasad, M. A comprehensive survey on word representation models: From classical to state-of-the-art word representation language models. Trans. Asian Low-Resour. Lang. Inf. Process. 2021 , 20 , 1–35. [ Google Scholar ] [ CrossRef ]
- Adil, M.; Wu, J.Z.; Chakrabortty, R.K.; Alahmadi, A.; Ansari, M.F.; Ryan, M.J. Attention-based STL-BiLSTM network to forecast tourist arrival. Processes 2021 , 9 , 1759. [ Google Scholar ] [ CrossRef ]
- Min, S.; Park, S.; Kim, S.; Choi, H.S.; Lee, B.; Yoon, S. Pre-training of deep bidirectional protein sequence representations with structural information. IEEE Access 2021 , 9 , 123912–123926. [ Google Scholar ] [ CrossRef ]
- Jain, A.; Zamir, A.R.; Savarese, S.; Saxena, A. Structural-rnn: Deep Learning on Spatio-Temporal Graphs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 5308–5317. [ Google Scholar ]
- Pascanu, R.; Gulcehre, C.; Cho, K.; Bengio, Y. How to construct deep recurrent neural networks. arXiv 2013 , arXiv:1312.6026. [ Google Scholar ]
- Shi, H.; Xu, M.; Li, R. Deep learning for household load forecasting—A novel pooling deep RNN. IEEE Trans. Smart Grid 2017 , 9 , 5271–5280. [ Google Scholar ] [ CrossRef ]
- Gal, Y.; Ghahramani, Z. A theoretically grounded application of dropout in recurrent neural networks. Adv. Neural Inf. Process. Syst. 2016 , 29 . [ Google Scholar ]
- Moradi, R.; Berangi, R.; Minaei, B. A survey of regularization strategies for deep models. Artif. Intell. Rev. 2020 , 53 , 3947–3986. [ Google Scholar ] [ CrossRef ]
- Salehin, I.; Kang, D.K. A review on dropout regularization approaches for deep neural networks within the scholarly domain. Electronics 2023 , 12 , 3106. [ Google Scholar ] [ CrossRef ]
- Cai, S.; Shu, Y.; Chen, G.; Ooi, B.C.; Wang, W.; Zhang, M. Effective and efficient dropout for deep convolutional neural networks. arXiv 2019 , arXiv:1904.03392. [ Google Scholar ]
- Garbin, C.; Zhu, X.; Marques, O. Dropout vs. batch normalization: An empirical study of their impact to deep learning. Multimed. Tools Appl. 2020 , 79 , 12777–12815. [ Google Scholar ] [ CrossRef ]
- Borawar, L.; Kaur, R. ResNet: Solving Vanishing Gradient in Deep Networks. In Proceedings of the International Conference on Recent Trends in Computing: ICRTC 2022, Delhi, India, 3–4 June 2022; Springer: Berlin/Heidelberg, Germany, 2023; pp. 235–247. [ Google Scholar ]
- Mienye, I.D.; Sun, Y. A deep learning ensemble with data resampling for credit card fraud detection. IEEE Access 2023 , 11 , 30628–30638. [ Google Scholar ] [ CrossRef ]
- Kiperwasser, E.; Goldberg, Y. Simple and accurate dependency parsing using bidirectional LSTM feature representations. Trans. Assoc. Comput. Linguist. 2016 , 4 , 313–327. [ Google Scholar ] [ CrossRef ]
- Zhang, W.; Li, H.; Tang, L.; Gu, X.; Wang, L.; Wang, L. Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks. Acta Geotech. 2022 , 17 , 1367–1382. [ Google Scholar ] [ CrossRef ]
- Cahuantzi, R.; Chen, X.; Güttel, S. A Comparison of LSTM and GRU Networks for Learning Symbolic Sequences. In Proceedings of the Science and Information Conference, Nanchang, China, 2–4 June 2023; Springer: Berlin/Heidelberg, Germany, 2023; pp. 771–785. [ Google Scholar ]
- Shewalkar, A.; Nyavanandi, D.; Ludwig, S.A. Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU. J. Artif. Intell. Soft Comput. Res. 2019 , 9 , 235–245. [ Google Scholar ] [ CrossRef ]
- Vatanchi, S.M.; Etemadfard, H.; Maghrebi, M.F.; Shad, R. A comparative study on forecasting of long-term daily streamflow using ANN, ANFIS, BiLSTM and CNN-GRU-LSTM. Water Resour. Manag. 2023 , 37 , 4769–4785. [ Google Scholar ] [ CrossRef ]
- Mateus, B.C.; Mendes, M.; Farinha, J.T.; Assis, R.; Cardoso, A.M. Comparing LSTM and GRU models to predict the condition of a pulp paper press. Energies 2021 , 14 , 6958. [ Google Scholar ] [ CrossRef ]
- Gers, F.A.; Schmidhuber, J. Recurrent Nets That Time and Count. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, Neural Computing: New Challenges and Perspectives for the New Millennium, Como, Italy, 24–27 July 2000; Volume 3, pp. 189–194. [ Google Scholar ]
- Gers, F.A.; Schraudolph, N.N.; Schmidhuber, J. Learning precise timing with LSTM recurrent networks. J. Mach. Learn. Res. 2002 , 3 , 115–143. [ Google Scholar ]
- Jaeger, H. Adaptive nonlinear system identification with echo state networks. Adv. Neural Inf. Process. Syst. 2002 , 15 , 593–600. [ Google Scholar ]
- Ishaq, M.; Kwon, S. A CNN-Assisted deep echo state network using multiple Time-Scale dynamic learning reservoirs for generating Short-Term solar energy forecasting. Sustain. Energy Technol. Assessments 2022 , 52 , 102275. [ Google Scholar ]
- Sun, C.; Song, M.; Cai, D.; Zhang, B.; Hong, S.; Li, H. A systematic review of echo state networks from design to application. IEEE Trans. Artif. Intell. 2022 , 5 , 23–37. [ Google Scholar ] [ CrossRef ]
- Gallicchio, C.; Micheli, A. Deep echo state network (deepesn): A brief survey. arXiv 2017 , arXiv:1712.04323. [ Google Scholar ]
- Gallicchio, C.; Micheli, A. Richness of Deep Echo State Network Dynamics. In Proceedings of the Advances in Computational Intelligence: 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Gran Canaria, Spain, 12–14 June 2019, Proceedings, Part I 15 ; Springer: Berlin/Heidelberg, Germany, 2019; pp. 480–491. [ Google Scholar ]
- Hu, R.; Tang, Z.R.; Song, X.; Luo, J.; Wu, E.Q.; Chang, S. Ensemble echo network with deep architecture for time-series modeling. Neural Comput. Appl. 2021 , 33 , 4997–5010. [ Google Scholar ] [ CrossRef ]
- Gao, R.; Li, R.; Hu, M.; Suganthan, P.N.; Yuen, K.F. Dynamic ensemble deep echo state network for significant wave height forecasting. Appl. Energy 2023 , 329 , 120261. [ Google Scholar ] [ CrossRef ]
- Gao, R.; Du, L.; Duru, O.; Yuen, K.F. Time series forecasting based on echo state network and empirical wavelet transformation. Appl. Soft Comput. 2021 , 102 , 107111. [ Google Scholar ] [ CrossRef ]
- Li, S.; Li, W.; Cook, C.; Zhu, C.; Gao, Y. Independently Recurrent Neural Network (indrnn): Building a Longer and Deeper rnn. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 5457–5466. [ Google Scholar ]
- Yang, J.; Qu, J.; Mi, Q.; Li, Q. A CNN-LSTM model for tailings dam risk prediction. IEEE Access 2020 , 8 , 206491–206502. [ Google Scholar ] [ CrossRef ]
- Ren, P.; Xiao, Y.; Chang, X.; Huang, P.Y.; Li, Z.; Chen, X.; Wang, X. A comprehensive survey of neural architecture search: Challenges and solutions. ACM Comput. Surv. (CSUR) 2021 , 54 , 1–34. [ Google Scholar ] [ CrossRef ]
- Mellor, J.; Turner, J.; Storkey, A.; Crowley, E.J. Neural Architecture Search without Training. In Proceedings of the International Conference on Machine Learning, Virtual, 18–24 July 2021; pp. 7588–7598. [ Google Scholar ]
- Zoph, B.; Le, Q.V. Neural architecture search with reinforcement learning. arXiv 2016 , arXiv:1611.01578. [ Google Scholar ]
- Chen, X.; Wu, S.Z.; Hong, M. Understanding gradient clipping in private sgd: A geometric perspective. Adv. Neural Inf. Process. Syst. 2020 , 33 , 13773–13782. [ Google Scholar ]
- Zhang, Z. Improved Adam Optimizer for Deep Neural Networks. In Proceedings of the 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), Banff, AB, Canada, 4–6 June 2018; pp. 1–2. [ Google Scholar ]
- De Santana Correia, A.; Colombini, E.L. Attention, please! A survey of neural attention models in deep learning. Artif. Intell. Rev. 2022 , 55 , 6037–6124. [ Google Scholar ] [ CrossRef ]
- Lin, J.; Ma, J.; Zhu, J.; Cui, Y. Short-term load forecasting based on LSTM networks considering attention mechanism. Int. J. Electr. Power Energy Syst. 2022 , 137 , 107818. [ Google Scholar ] [ CrossRef ]
- Chaudhari, S.; Mithal, V.; Polatkan, G.; Ramanath, R. An attentive survey of attention models. ACM Trans. Intell. Syst. Technol. (TIST) 2021 , 12 , 1–32. [ Google Scholar ] [ CrossRef ]
- Bahdanau, D.; Cho, K.; Bengio, Y. Neural machine translation by jointly learning to align and translate. arXiv 2014 , arXiv:1409.0473. [ Google Scholar ]
- Luong, M.T.; Pham, H.; Manning, C.D. Effective approaches to attention-based neural machine translation. arXiv 2015 , arXiv:1508.04025. [ Google Scholar ]
- Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, Ł.; Polosukhin, I. Attention is all you need. Adv. Neural Inf. Process. Syst. 2017 , 30 . [ Google Scholar ]
- Marcus, M.P.; Marcinkiewicz, M.A.; Santorini, B. Building a large annotated corpus of English: The Penn Treebank. Comput. Linguist. 1993 , 19 , 313–330. [ Google Scholar ]
- Maas, A.L.; Daly, R.E.; Pham, P.T.; Huang, D.; Ng, A.Y.; Potts, C. Learning Word Vectors for Sentiment Analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, USA, 19–24 June 2011; pp. 142–150. [ Google Scholar ]
- LeCun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 1998 , 86 , 2278–2324. [ Google Scholar ] [ CrossRef ]
- Garofolo, J.S.; Lamel, L.F.; Fisher, W.M.; Fiscus, J.G.; Pallett, D.S. TIMIT acoustic-phonetic continuous speech corpus. Linguist. Data Consort. 1993 , 93 , 27403. [ Google Scholar ]
- Lewis, D. Reuters-21578 Text Categorization Test Collection ; Distribution 1.0; AT&T Labs-Research: Atlanta, GA, USA, 1997. [ Google Scholar ]
- Dua, D.; Graff, C. UCI Machine Learning Repository ; School of Information and Computer Science, University of California: Irvine, CA, USA, 2017. [ Google Scholar ]
- Lomonaco, V.; Maltoni, D. Core50: A New Dataset and Benchmark for Continuous Object Recognition. In Proceedings of the Conference on Robot Learning. PMLR, Mountain View, CA, USA, 13–15 November 2017; pp. 17–26. [ Google Scholar ]
- Souri, A.; El Maazouzi, Z.; Al Achhab, M.; El Mohajir, B.E. Arabic Text Generation using Recurrent Neural Networks. In Proceedings of the Big Data, Cloud and Applications: Third International Conference, BDCA 2018, Kenitra, Morocco, 4–5 April 2018 ; Revised Selected Papers 3; Springer: Berlin/Heidelberg, Germany, 2018; pp. 523–533. [ Google Scholar ]
- Islam, M.S.; Mousumi, S.S.S.; Abujar, S.; Hossain, S.A. Sequence-to-sequence Bangla sentence generation with LSTM recurrent neural networks. Procedia Comput. Sci. 2019 , 152 , 51–58. [ Google Scholar ] [ CrossRef ]
- Gajendran, S.; Manjula, D.; Sugumaran, V. Character level and word level embedding with bidirectional LSTM–Dynamic recurrent neural network for biomedical named entity recognition from literature. J. Biomed. Inform. 2020 , 112 , 103609. [ Google Scholar ] [ CrossRef ]
- Hu, H.; Liao, M.; Mao, W.; Liu, W.; Zhang, C.; Jing, Y. Variational Auto-Encoder for Text Generation. In Proceedings of the 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 12–14 June 2020; pp. 595–598. [ Google Scholar ]
- Holtzman, A.; Buys, J.; Du, L.; Forbes, M.; Choi, Y. The curious case of neural text degeneration. arXiv 2019 , arXiv:1904.09751. [ Google Scholar ]
- Yin, W.; Schütze, H. Attentive convolution: Equipping cnns with rnn-style attention mechanisms. Trans. Assoc. Comput. Linguist. 2018 , 6 , 687–702. [ Google Scholar ] [ CrossRef ]
- Hussein, M.A.H.; Savaş, S. LSTM-Based Text Generation: A Study on Historical Datasets. arXiv 2024 , arXiv:2403.07087. [ Google Scholar ]
- Baskaran, S.; Alagarsamy, S.; S, S.; Shivam, S. Text Generation using Long Short-Term Memory. In Proceedings of the 2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Krishnankoil, India, 14–16 March 2024; pp. 1–6. [ Google Scholar ] [ CrossRef ]
- Keskar, N.S.; McCann, B.; Varshney, L.R.; Xiong, C.; Socher, R. Ctrl: A conditional transformer language model for controllable generation. arXiv 2019 , arXiv:1909.05858. [ Google Scholar ]
- Guo, H. Generating text with deep reinforcement learning. arXiv 2015 , arXiv:1510.09202. [ Google Scholar ]
- Yadav, V.; Verma, P.; Katiyar, V. Long short term memory (LSTM) model for sentiment analysis in social data for e-commerce products reviews in Hindi languages. Int. J. Inf. Technol. 2023 , 15 , 759–772. [ Google Scholar ] [ CrossRef ]
- Abimbola, B.; de La Cal Marin, E.; Tan, Q. Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model. Mach. Learn. Knowl. Extr. 2024 , 6 , 877–897. [ Google Scholar ] [ CrossRef ]
- Zulqarnain, M.; Ghazali, R.; Aamir, M.; Hassim, Y.M.M. An efficient two-state GRU based on feature attention mechanism for sentiment analysis. Multimed. Tools Appl. 2024 , 83 , 3085–3110. [ Google Scholar ] [ CrossRef ]
- Pujari, P.; Padalia, A.; Shah, T.; Devadkar, K. Hybrid CNN and RNN for Twitter Sentiment Analysis. In Proceedings of the International Conference on Smart Computing and Communication ; Springer: Berlin/Heidelberg, Germany, 2024; pp. 297–310. [ Google Scholar ]
- Wankhade, M.; Annavarapu, C.S.R.; Abraham, A. CBMAFM: CNN-BiLSTM multi-attention fusion mechanism for sentiment classification. Multimed. Tools Appl. 2024 , 83 , 51755–51786. [ Google Scholar ] [ CrossRef ]
- Sangeetha, J.; Kumaran, U. A hybrid optimization algorithm using BiLSTM structure for sentiment analysis. Meas. Sensors 2023 , 25 , 100619. [ Google Scholar ] [ CrossRef ]
- He, R.; McAuley, J. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In Proceedings of the 25th International Conference on World Wide Web, Montreal, QC, Canada, 11–15 April 2016; pp. 507–517. [ Google Scholar ]
- Samir, A.; Elkaffas, S.M.; Madbouly, M.M. Twitter Sentiment Analysis using BERT. In Proceedings of the 2021 31st International Conference on Computer Theory and Applications (ICCTA), Kochi, Kerala, India, 17–19 August 2021; pp. 182–186. [ Google Scholar ]
- Prottasha, N.J.; Sami, A.A.; Kowsher, M.; Murad, S.A.; Bairagi, A.K.; Masud, M.; Baz, M. Transfer learning for sentiment analysis using BERT based supervised fine-tuning. Sensors 2022 , 22 , 4157. [ Google Scholar ] [ CrossRef ]
- Mujahid, M.; Rustam, F.; Shafique, R.; Chunduri, V.; Villar, M.G.; Ballester, J.B.; Diez, I.d.l.T.; Ashraf, I. Analyzing sentiments regarding ChatGPT using novel BERT: A machine learning approach. Information 2023 , 14 , 474. [ Google Scholar ] [ CrossRef ]
- Wu, Y.; Schuster, M.; Chen, Z.; Le, Q.V.; Norouzi, M.; Macherey, W.; Krikun, M.; Cao, Y.; Gao, Q.; Macherey, K.; et al. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv 2016 , arXiv:1609.08144. [ Google Scholar ]
- Sennrich, R.; Haddow, B.; Birch, A. Neural machine translation of rare words with subword units. arXiv 2015 , arXiv:1508.07909. [ Google Scholar ]
- Kang, L.; He, S.; Wang, M.; Long, F.; Su, J. Bilingual attention based neural machine translation. Appl. Intell. 2023 , 53 , 4302–4315. [ Google Scholar ] [ CrossRef ]
- Yang, Z.; Dai, Z.; Salakhutdinov, R.; Cohen, W.W. Breaking the softmax bottleneck: A high-rank RNN language model. arXiv 2017 , arXiv:1711.03953. [ Google Scholar ]
- Song, K.; Tan, X.; Qin, T.; Lu, J.; Liu, T.Y. Mass: Masked sequence to sequence pre-training for language generation. arXiv 2019 , arXiv:1905.02450. [ Google Scholar ]
- Hinton, G.; Deng, L.; Yu, D.; Dahl, G.E.; Mohamed, A.r.; Jaitly, N.; Senior, A.; Vanhoucke, V.; Nguyen, P.; Sainath, T.N.; et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process. Mag. 2012 , 29 , 82–97. [ Google Scholar ] [ CrossRef ]
- Hannun, A.; Case, C.; Casper, J.; Catanzaro, B.; Diamos, G.; Elsen, E.; Prenger, R.; Satheesh, S.; Sengupta, S.; Coates, A.; et al. Deep speech: Scaling up end-to-end speech recognition. arXiv 2014 , arXiv:1412.5567. [ Google Scholar ]
- Amodei, D.; Ananthanarayanan, S.; Anubhai, R.; Bai, J.; Battenberg, E.; Case, C.; Casper, J.; Catanzaro, B.; Cheng, Q.; Chen, G.; et al. Deep Speech 2: End-to-End Speech Recognition in English and Mandarin. In Proceedings of the International Conference on Machine Learning, New York, NY, USA, 20–22 June 2016; pp. 173–182. [ Google Scholar ]
- Chiu, C.C.; Sainath, T.N.; Wu, Y.; Prabhavalkar, R.; Nguyen, P.; Chen, Z.; Kannan, A.; Weiss, R.J.; Rao, K.; Gonina, E.; et al. State-of-the-Art Speech Recognition with Sequence-to-Sequence Models. In Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 15–20 April 2018; pp. 4774–4778. [ Google Scholar ]
- Zhang, Y.; Chan, W.; Jaitly, N. Very Deep Convolutional Networks for End-to-End Speech Recognition. In Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 5–9 March 2017; pp. 4845–4849. [ Google Scholar ]
- Dong, L.; Xu, S.; Xu, B. Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition. In Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 15–20 April 2018; pp. 5884–5888. [ Google Scholar ]
- Bhaskar, S.; Thasleema, T. LSTM model for visual speech recognition through facial expressions. Multimed. Tools Appl. 2023 , 82 , 5455–5472. [ Google Scholar ] [ CrossRef ]
- Daouad, M.; Allah, F.A.; Dadi, E.W. An automatic speech recognition system for isolated Amazigh word using 1D & 2D CNN-LSTM architecture. Int. J. Speech Technol. 2023 , 26 , 775–787. [ Google Scholar ]
- Dhanjal, A.S.; Singh, W. A comprehensive survey on automatic speech recognition using neural networks. Multimed. Tools Appl. 2024 , 83 , 23367–23412. [ Google Scholar ] [ CrossRef ]
- Nasr, S.; Duwairi, R.; Quwaider, M. End-to-end speech recognition for arabic dialects. Arab. J. Sci. Eng. 2023 , 48 , 10617–10633. [ Google Scholar ] [ CrossRef ]
- Kumar, D.; Aziz, S. Performance Evaluation of Recurrent Neural Networks-LSTM and GRU for Automatic Speech Recognition. In Proceedings of the 2023 International Conference on Computer, Electronics & Electrical Engineering & Their Applications (IC2E3), Srinagar Garhwal, India, 8–9 June 2023; pp. 1–6. [ Google Scholar ]
- Fischer, T.; Krauss, C. Deep learning with long short-term memory networks for financial market predictions. Eur. J. Oper. Res. 2018 , 270 , 654–669. [ Google Scholar ] [ CrossRef ]
- Nelson, D.M.; Pereira, A.C.; De Oliveira, R.A. Stock Market’s Price Movement Prediction with LSTM Neural Networks. In Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 14–19 May 2017; pp. 1419–1426. [ Google Scholar ]
- Luo, A.; Zhong, L.; Wang, J.; Wang, Y.; Li, S.; Tai, W. Short-term stock correlation forecasting based on CNN-BiLSTM enhanced by attention mechanism. IEEE Access 2024 , 12 , 29617–29632. [ Google Scholar ] [ CrossRef ]
- Bao, W.; Yue, J.; Rao, Y. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE 2017 , 12 , e0180944. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Feng, F.; Chen, H.; He, X.; Ding, J.; Sun, M.; Chua, T.S. Enhancing Stock Movement Prediction with Adversarial Training. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), Macao, China, 10–16 August 2019; Volume 19, pp. 5843–5849. [ Google Scholar ]
- Rundo, F. Deep LSTM with reinforcement learning layer for financial trend prediction in FX high frequency trading systems. Appl. Sci. 2019 , 9 , 4460. [ Google Scholar ] [ CrossRef ]
- Devi, T.; Deepa, N.; Gayathri, N.; Rakesh Kumar, S. AI-Based Weather Forecasting System for Smart Agriculture System Using a Recurrent Neural Networks (RNN) Algorithm. Sustain. Manag. Electron. Waste 2024 , 97–112. [ Google Scholar ]
- Anshuka, A.; Chandra, R.; Buzacott, A.J.; Sanderson, D.; van Ogtrop, F.F. Spatio temporal hydrological extreme forecasting framework using LSTM deep learning model. Stoch. Environ. Res. Risk Assess. 2022 , 36 , 3467–3485. [ Google Scholar ] [ CrossRef ]
- Marulanda, G.; Cifuentes, J.; Bello, A.; Reneses, J. A hybrid model based on LSTM neural networks with attention mechanism for short-term wind power forecasting. Wind. Eng. 2023 , 0309524X231191163. [ Google Scholar ] [ CrossRef ]
- Chen, W.; An, N.; Jiang, M.; Jia, L. An improved deep temporal convolutional network for new energy stock index prediction. Inf. Sci. 2024 , 682 , 121244. [ Google Scholar ] [ CrossRef ]
- Hasanat, S.M.; Younis, R.; Alahmari, S.; Ejaz, M.T.; Haris, M.; Yousaf, H.; Watara, S.; Ullah, K.; Ullah, Z. Enhancing Load Forecasting Accuracy in Smart Grids: A Novel Parallel Multichannel Network Approach Using 1D CNN and Bi-LSTM Models. Int. J. Energy Res. 2024 , 2024 , 2403847. [ Google Scholar ] [ CrossRef ]
- Asiri, M.M.; Aldehim, G.; Alotaibi, F.; Alnfiai, M.M.; Assiri, M.; Mahmud, A. Short-term load forecasting in smart grids using hybrid deep learning. IEEE Access 2024 , 12 , 23504–23513. [ Google Scholar ] [ CrossRef ]
- Yıldız Doğan, G.; Aksoy, A.; Öztürk, N. A Hybrid Deep Learning Model to Estimate the Future Electricity Demand of Sustainable Cities. Sustainability 2024 , 16 , 6503. [ Google Scholar ] [ CrossRef ]
- Bhambu, A.; Gao, R.; Suganthan, P.N. Recurrent ensemble random vector functional link neural network for financial time series forecasting. Appl. Soft Comput. 2024 , 161 , 111759. [ Google Scholar ] [ CrossRef ]
- Mienye, E.; Jere, N.; Obaido, G.; Mienye, I.D.; Aruleba, K. Deep Learning in Finance: A Survey of Applications and Techniques. Preprints 2024 . [ Google Scholar ] [ CrossRef ]
- Mastoi, Q.U.A.; Wah, T.Y.; Gopal Raj, R. Reservoir computing based echo state networks for ventricular heart beat classification. Appl. Sci. 2019 , 9 , 702. [ Google Scholar ] [ CrossRef ]
- Valin, J.M.; Tenneti, S.; Helwani, K.; Isik, U.; Krishnaswamy, A. Low-Complexity, Real-Time Joint Neural Echo Control and Speech Enhancement Based on Percepnet. In Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, 6–11 June 2021; pp. 7133–7137. [ Google Scholar ]
- Li, Y.; Huang, C.; Ding, L.; Li, Z.; Pan, Y.; Gao, X. Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods 2019 , 166 , 4–21. [ Google Scholar ] [ CrossRef ]
- Zhang, Y.; Qiao, S.; Ji, S.; Li, Y. DeepSite: Bidirectional LSTM and CNN models for predicting DNA–protein binding. Int. J. Mach. Learn. Cybern. 2020 , 11 , 841–851. [ Google Scholar ] [ CrossRef ]
- Xu, J.; Mcpartlon, M.; Li, J. Improved protein structure prediction by deep learning irrespective of co-evolution information. Nat. Mach. Intell. 2021 , 3 , 601–609. [ Google Scholar ] [ CrossRef ]
- Yadav, S.; Ekbal, A.; Saha, S.; Kumar, A.; Bhattacharyya, P. Feature assisted stacked attentive shortest dependency path based Bi-LSTM model for protein–protein interaction. Knowl.-Based Syst. 2019 , 166 , 18–29. [ Google Scholar ] [ CrossRef ]
- Aybey, E.; Gümüş, Ö. SENSDeep: An ensemble deep learning method for protein–protein interaction sites prediction. Interdiscip. Sci. Comput. Life Sci. 2023 , 15 , 55–87. [ Google Scholar ] [ CrossRef ] [ PubMed ]
- Li, Z.; Du, X.; Cao, Y. DAT-RNN: Trajectory Prediction with Diverse Attention. In Proceedings of the 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 14–17 December 2020; pp. 1512–1518. [ Google Scholar ]
- Lee, M.j.; Ha, Y.g. Autonomous Driving Control Using End-to-End Deep Learning. In Proceedings of the 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), Busan, Republic of Korea, 19–22 February 2020; pp. 470–473. [ Google Scholar ] [ CrossRef ]
- Codevilla, F.; Müller, M.; López, A.; Koltun, V.; Dosovitskiy, A. End-to-End Driving via Conditional Imitation Learning. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 21–25 May 2018; pp. 4693–4700. [ Google Scholar ]
- Altché, F.; de La Fortelle, A. An LSTM Network for Highway Trajectory Prediction. In Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Abu Dhabi, United Arab Emirates, 25–28 October 2017; pp. 353–359. [ Google Scholar ]
- Li, P.; Zhang, Y.; Yuan, L.; Xiao, H.; Lin, B.; Xu, X. Efficient long-short temporal attention network for unsupervised video object segmentation. Pattern Recognit. 2024 , 146 , 110078. [ Google Scholar ] [ CrossRef ]
- Li, R.; Shu, X.; Li, C. Driving Behavior Prediction Based on Combined Neural Network Model. IEEE Trans. Comput. Soc. Syst. 2024 , 11 , 4488–4496. [ Google Scholar ] [ CrossRef ]
- Liu, Y.; Diao, S. An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning. PLoS ONE 2024 , 19 , e0297192. [ Google Scholar ] [ CrossRef ]
- Altindal, M.C.; Nivlet, P.; Tabib, M.; Rasheed, A.; Kristiansen, T.G.; Khosravanian, R. Anomaly detection in multivariate time series of drilling data. Geoenergy Sci. Eng. 2024 , 237 , 212778. [ Google Scholar ] [ CrossRef ]
- Matar, M.; Xia, T.; Huguenard, K.; Huston, D.; Wshah, S. Multi-Head Attention Based bi-lstm for Anomaly Detection in Multivariate Time-Series of wsn. In Proceedings of the 2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS), Hangzhou, China, 11–13 June 2023; pp. 1–5. [ Google Scholar ]
- Kumaresan, S.J.; Senthilkumar, C.; Kongkham, D.; Beenarani, B.; Nirmala, P. Investigating the Effectiveness of Recurrent Neural Networks for Network Anomaly Detection. In Proceedings of the 2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), Bangalore, India, 24–25 January 2024; pp. 1–5. [ Google Scholar ]
- Li, E.; Bedi, S.; Melek, W. Anomaly detection in three-axis CNC machines using LSTM networks and transfer learning. Int. J. Adv. Manuf. Technol. 2023 , 127 , 5185–5198. [ Google Scholar ] [ CrossRef ]
- Minic, A.; Jovanovic, L.; Bacanin, N.; Stoean, C.; Zivkovic, M.; Spalevic, P.; Petrovic, A.; Dobrojevic, M.; Stoean, R. Applying recurrent neural networks for anomaly detection in electrocardiogram sensor data. Sensors 2023 , 23 , 9878. [ Google Scholar ] [ CrossRef ]
- Zhou, C.; Paffenroth, R.C. Anomaly Detection with Robust Deep Autoencoders. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, 13–17 August 2017; pp. 665–674. [ Google Scholar ]
- Ren, H.; Xu, B.; Wang, Y.; Yi, C.; Huang, C.; Kou, X.; Xing, T.; Yang, M.; Tong, J.; Zhang, Q. Time-Series Anomaly Detection Service at Microsoft. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 4–8 August 2019; pp. 3009–3017. [ Google Scholar ]
- Munir, M.; Siddiqui, S.A.; Dengel, A.; Ahmed, S. DeepAnT: A deep learning approach for unsupervised anomaly detection in time series. IEEE Access 2018 , 7 , 1991–2005. [ Google Scholar ] [ CrossRef ]
- Hewamalage, H.; Bergmeir, C.; Bandara, K. Recurrent neural networks for time series forecasting: Current status and future directions. Int. J. Forecast. 2021 , 37 , 388–427. [ Google Scholar ] [ CrossRef ]
- Ahmed, S.F.; Alam, M.S.B.; Hassan, M.; Rozbu, M.R.; Ishtiak, T.; Rafa, N.; Mofijur, M.; Shawkat Ali, A.; Gandomi, A.H. Deep learning modelling techniques: Current progress, applications, advantages, and challenges. Artif. Intell. Rev. 2023 , 56 , 13521–13617. [ Google Scholar ] [ CrossRef ]
- Li, X.; Qin, T.; Yang, J.; Liu, T.Y. LightRNN: Memory and computation-efficient recurrent neural networks. Adv. Neural Inf. Process. Syst. 2016 , 29 . [ Google Scholar ]
- Katharopoulos, A.; Vyas, A.; Pappas, N.; Fleuret, F. Transformers Are rnns: Fast Autoregressive Transformers with Linear Attention. In Proceedings of the International Conference on Machine Learning, Virtual, 12–18 July 2020; pp. 5156–5165. [ Google Scholar ]
- Shao, W.; Li, B.; Yu, W.; Xu, J.; Wang, H. When Is It Likely to Fail? Performance Monitor for Black-Box Trajectory Prediction Model. IEEE Trans. Autom. Sci. Eng. 2024 , 4 , 765–772. [ Google Scholar ] [ CrossRef ]
- Jacobs, W.R.; Kadirkamanathan, V.; Anderson, S.R. Interpretable deep learning for nonlinear system identification using frequency response functions with ensemble uncertainty quantification. IEEE Access 2024 , 12 , 11052–11065. [ Google Scholar ] [ CrossRef ]
- Mamalakis, M.; Mamalakis, A.; Agartz, I.; Mørch-Johnsen, L.E.; Murray, G.; Suckling, J.; Lio, P. Solving the enigma: Deriving optimal explanations of deep networks. arXiv 2024 , arXiv:2405.10008. [ Google Scholar ]
- Shah, M.; Sureja, N. A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions. Arch. Comput. Methods Eng. 2024 , 1–13. [ Google Scholar ] [ CrossRef ]
- Goethals, S.; Calders, T.; Martens, D. Beyond Accuracy-Fairness: Stop evaluating bias mitigation methods solely on between-group metrics. arXiv 2024 , arXiv:2401.13391. [ Google Scholar ]
- Weerts, H.; Pfisterer, F.; Feurer, M.; Eggensperger, K.; Bergman, E.; Awad, N.; Vanschoren, J.; Pechenizkiy, M.; Bischl, B.; Hutter, F. Can fairness be automated? Guidelines and opportunities for fairness-aware AutoML. J. Artif. Intell. Res. 2024 , 79 , 639–677. [ Google Scholar ] [ CrossRef ]
- Bai, Y.; Geng, X.; Mangalam, K.; Bar, A.; Yuille, A.L.; Darrell, T.; Malik, J.; Efros, A.A. Sequential Modeling Enables Scalable Learning for Large Vision Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle WA, USA, 17–21 June 2024; pp. 22861–22872. [ Google Scholar ]
- Taye, M.M. Understanding of machine learning with deep learning: Architectures, workflow, applications and future directions. Computers 2023 , 12 , 91. [ Google Scholar ] [ CrossRef ]
Click here to enlarge figure
Reference | Year | Description |
---|
Zaremba et al. [ ] | 2014 | Insights into RNNs in language modeling |
Chung et al. [ ] | 2014 | Survey of advancements in RNN training, optimization, and architectures |
Goodfellow et al. [ ] | 2016 | Review on deep learning, including RNNs |
Greff et al. [ ] | 2016 | Extensive comparison of LSTM variants |
Tarwani et al. [ ] | 2017 | In-depth analysis of RNNs in NLP |
Chen et al. [ ] | 2018 | Effectiveness of RNNs in environmental monitoring and climate modeling |
Bai et al. [ ] | 2018 | Comparison of RNNs with other sequence modeling techniques like CNNs and attention mechanisms |
Che et al. [ ] | 2018 | Potential of RNNs in medical applications |
Zhang et al. [ ] | 2020 | RNN applications in robotics, including path planning, motion control, and human–robot interaction |
Dutta et al. [ ] | 2022 | Overview of RNNs, challenges in training, and advancements in LSTM and GRU for sequence learning |
Linardos et al. [ ] | 2022 | RNNs for early warning systems, disaster response, and recovery planning in natural disaster prediction |
Badawy et al. [ ] | 2023 | Integration of RNNs with other ML techniques for predictive analytics and patient monitoring in healthcare |
Ismaeel et al. [ ] | 2023 | Application of RNNs in smart city technologies, including traffic prediction, energy management, and urban planning |
Mers et al. [ ] | 2023 | Performance comparison of various RNN models in pavement performance forecasting |
Quradaa et al. [ ] | 2024 | Start-of-the-art review of RNNs, covering core architectures with a focus on applications in code clones |
Al-Selwi et al. [ ] | 2024 | Review of LSTM applications from 2018 to 2023 |
RNN Type | Key Features | Gradient Stability | Typical Applications |
---|
Basic RNN | Simple structure with short-term memory | High risk of vanishing gradients | Simple sequence tasks like text generation |
LSTM | Long-term memory with input, forget, and output gates | Stable, handles vanishing gradients well | Language translation, speech recognition |
GRU | Simplified LSTM with fewer gates | Stable, handles vanishing gradients effectively | Tasks requiring faster training than LSTM |
Bidirectional RNN | Processes data in both forward and backward directions for better context understanding | Medium stability, depends on depth | Speech recognition and sentiment analysis |
Deep RNN | Multiple RNN layers are stacked to learn hierarchical features | Variable, and the risk of vanishing gradients increases with depth | Complex sequence modeling like video processing |
ESN | Fixed hidden layer weights, trained only at the output | Not applicable as training bypasses typical gradient issues | Time series prediction and system control |
Peephole LSTM | Adds peephole connections to LSTM gates | Stable and similar to LSTM | Recognition of complex temporal patterns like musical notation |
IndRNN | Allows training of deeper networks by maintaining independence between time steps | Reduces risk of vanishing and exploding gradients | Very long sequences, such as in video processing or long text generation |
Dataset Name | Application | Description |
---|
Penn Treebank [ ] | Natural language processing | A corpus of English sentences annotated for part-of-speech tagging, parsing, and named entity recognition; widely used for language modeling with RNNs |
IMDB Reviews [ ] | Sentiment analysis | A dataset of movie reviews used for binary sentiment classification; suitable for studying the effectiveness of RNNs in text sentiment classification tasks |
MNIST Sequential [ ] | Image recognition | A version of the MNIST dataset formatted as sequences for studying sequence-to-sequence learning with RNNs |
TIMIT Speech Corpus [ ] | Speech recognition | An annotated speech database used for automatic speech recognition systems |
Reuters-21578 Text Categorization Collection [ ] | Text categorization | A collection of newswire articles that is a common benchmark for text categorization and NLP tasks with RNNs |
UCI ML Repository: Time Series Data [ ] | Time series analysis | Contains various time series datasets, including stock prices and weather data, ideal for forecasting with RNNs. |
CORe50 Dataset [ ] | Object Recognition | Used for continuous object recognition, ideal for RNN models dealing with video input sequences where object persistence and temporal context are important |
Application Domain | Reference | Year | Methods and Application |
---|
Text generation | Souri et al. [ ] | 2018 | RNNs for generating coherent and contextually relevant Arabic text |
| Holtzman et al. [ ] | 2019 | Controlled text generation using RNNs for style and content control |
| Hu et al. [ ] | 2020 | VAEs combined with RNNs to enhance creativity in text generation |
| Gajendran et al. [ ] | 2020 | Character-level text generation using BiLSTM for various tasks |
| Hussein and Savas [ ] | 2024 | LSTM for text generation |
| Baskaran et al. [ ] | 2024 | LSTM for text generation, achieving excellent performance |
| Islam [ ] | 2019 | Sequence-to-sequence framework using LSTM for improved text generation quality |
| Yin et al. [ ] | 2018 | Attention mechanisms with RNNs for improved text generation quality |
| Guo [ ] | 2015 | Integration of reinforcement learning with RNNs for text generation |
| Keskar et al. [ ] | 2019 | Conditional Transformer Language (CTRL) for generating text in various styles |
Sentiment analysis | He and McAuley [ ] | 2016 | Adversarial training framework for robustness in sentiment analysis |
| Pujari et al. [ ] | 2024 | Hybrid CNN-RNN model for sentiment classification |
| Wankhade et al. [ ] | 2024 | Fusion of CNN and BiLSTM with attention mechanism for sentiment classification |
| Sangeetha and Kumaran [ ] | 2023 | BiLSTM for sentiment analysis by processing text in both directions |
| Yadav et al. [ ] | 2023 | LSTM-based models for sentiment analysis in customer reviews and social media posts |
| Zulqarnain et al. [ ] | 2024 | Attention mechanisms and GRU for enhanced sentiment analysis |
| Samir et al. [ ] | 2021 | Use of pre-trained models like BERT for sentiment analysis |
| Prottasha et al. [ ] | 2022 | Transfer learning with BERT and GPT for sentiment analysis |
| Abimbola et al. [ ] | 2024 | Hybrid LSTM-CNN model for document-level sentiment classification |
| Mujahid et al. [ ] | 2023 | Analyzing sentiment with pre-trained models fine-tuned for specific tasks |
Machine Translation | Sennrich et al. [ ] | 2015 | Byte-Pair Encoding for handling rare words in translation models |
| Wu et al. [ ] | 2016 | Google Neural Machine Translation with deep RNNs for improved accuracy |
| Vaswani et al. [ ] | 2017 | Fully attention-based transformer models for superior translation performance |
| Yang et al. [ ] | 2017 | Hybrid model integrating RNNs into the transformer architecture |
| Song et al. [ ] | 2019 | Incorporating BERT into translation models for enhanced understanding and fluency |
| Kang et al. [ ] | 2023 | Bilingual attention-based machine translation model combining RNN with attention |
| Zulqarnain et al. [ ] | 2024 | Multi-stage feature attention mechanism model using GRU |
Application Domain | Reference | Year | Methods and Application |
---|
| Hinton et al. [ ] | 2012 | Deep neural networks, including RNNs, for speech-to-text systems |
| Hannun et al. [ ] | 2014 | DeepSpeech: LSTM-based speech recognition system |
| Amodei et al. [ ] | 2016 | DeepSpeech2: Enhanced LSTM-based speech recognition with bidirectional RNNs |
| Zhang et al. [ ] | 2017 | Convolutional RNN for robust speech recognition |
| Chiu et al. [ ] | 2018 | RNN-transducer models for end-to-end speech recognition |
| Dong et al. [ ] | 2018 | Speech-Transformer: Leveraging self-attention for better processing of audio sequences |
| Bhaskar and Thasleema [ ] | 2023 | LSTM for visual speech recognition using facial expressions |
| Daouad et al. [ ] | 2023 | Various RNN variants for automatic speech recognition |
| Nasr et al. [ ] | 2023 | End-to-end speech recognition using RNNs |
| Kumar et al. [ ] | 2023 | Performance evaluation of RNNs in speech recognition tasks |
| Dhanjal et al. [ ] | 2024 | Comprehensive study of different RNN models for speech recognition |
| Nelson et al. [ ] | 2017 | Hybrid CNN-RNN model for stock price prediction |
| Bao et al. [ ] | 2017 | Combining LSTM with stacked autoencoders for financial time series forecasting |
| Fischer and Krauss [ ] | 2018 | Deep RNNs for predicting stock returns, outperforming traditional ML models |
| Feng et al. [ ] | 2019 | Transfer learning with RNNs for stock prediction |
| Rundo [ ] | 2019 | Combining reinforcement learning with LSTM for trading strategy development |
| Devi et al. [ ] | 2024 | RNN-based model for weather prediction and capturing sequential dependencies in meteorological data |
| Anshuka et al. [ ] | 2022 | LSTM networks for predicting extreme weather events by learning complex temporal patterns |
| Lin et al. [ ] | 2022 | Integrating attention mechanisms with LSTM for enhanced weather forecasting accuracy |
| Marulanda et al. [ ] | 2023 | LSTM model for short-term wind power forecasting and improving prediction accuracy |
| Chen et al. [ ] | 2024 | Bidirectional GRU with TCNs for energy time series forecasting |
| Hasanat et al. [ ] | 2024 | RNNs for forecasting energy demand in smart grids and optimizing renewable energy integration |
| Asiri et al. [ ] | 2024 | Short-term renewable energy predictions using RNN-based models |
| Yildiz et al. [ ] | 2024 | Hybrid model of LSTM with CNN for accurate electricity demand prediction |
| Luo et al. [ ] | 2024 | Attention-based CNN-BiLSTM model for improved financial forecasting |
| Gao et al. [ ] | 2023 | Dynamic ensemble deep ESN for wave height forecasting |
| Bhambu et al. [ ] | 2024 | Recurrent ensemble deep random vector functional link neural network for financial time series forecasting |
Application Domain | Reference | Year | Methods and Application |
---|
Signal processing | Mastoi et al. [ ] | 2019 | ESNs for real-time heart rate variability monitoring |
| Valin et al. [ ] | 2021 | ESNs for speech signal enhancement in noisy environments |
| Gao et al. [ ] | 2021 | EWT integrated with ESNs for enhanced time series forecasting |
Bioinformatics | Li et al. [ ] | 2019 | RNNs for gene prediction and protein-structure prediction |
| Zhang et al. [ ] | 2020 | Bidirectional LSTM for predicting DNA-binding protein sequences |
| Xu et al. [ ] | 2021 | RNN-based model for predicting protein secondary structures |
| Yadav et al. [ ] | 2019 | Combining BiLSTM with CNNs for protein sequence analysis |
| Aybey et al. [ ] | 2023 | Ensemble model for predicting protein–protein interactions |
Autonomous vehicles | Altché and de La Fortelle [ ] | 2017 | LSTM for predicting the future trajectories of vehicles |
| Codevilla et al. [ ] | 2018 | RNNs with imitation learning for autonomous driving |
| Li et al. [ ] | 2020 | RNNs for path planning and object detection |
| Lee et al. [ ] | 2020 | Integrating LSTM with CNN for end-to-end autonomous driving |
| Li et al. [ ] | 2024 | Attention-based LSTM for video object tracking |
| Liu and Diao [ ] | 2024 | GRU with deep reinforcement learning for decision-making |
Anomaly detection | Zhou and Paffenroth [ ] | 2017 | RNNs in unsupervised anomaly detection with deep autoencoders |
| Munir et al. [ ] | 2018 | Hybrid CNN-RNN model for anomaly detection in time series |
| Ren et al. [ ] | 2019 | Attention-based RNN model for anomaly detection |
| Li et al. [ ] | 2023 | RNNs with Transfer learning for anomaly detection in manufacturing |
| Mini et al. [ ] | 2023 | RNNs for detecting anomalies in ECG signals |
| Matar et al. [ ] | 2023 | BiLSTM for anomaly detection in multivariate time series |
| Kumaresan et al. [ ] | 2024 | RNNs for detecting network traffic anomalies |
| Altindal et al. [ ] | 2024 | LSTM for anomaly detection in time series data |
| The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Share and Cite
Mienye, I.D.; Swart, T.G.; Obaido, G. Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications. Information 2024 , 15 , 517. https://doi.org/10.3390/info15090517
Mienye ID, Swart TG, Obaido G. Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications. Information . 2024; 15(9):517. https://doi.org/10.3390/info15090517
Mienye, Ibomoiye Domor, Theo G. Swart, and George Obaido. 2024. "Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications" Information 15, no. 9: 517. https://doi.org/10.3390/info15090517
Article Metrics
Article access statistics, further information, mdpi initiatives, follow mdpi.
![MDPI Open Access Journals MDPI](https://pub.mdpi-res.com/img/design/mdpi-pub-logo-white-small.png?71d18e5f805839ab?1724334283)
Subscribe to receive issue release notifications and newsletters from MDPI journals
![](//sektorel.online/777/templates/cheerup/res/banner1.gif)
IMAGES
COMMENTS
Author Talks: Writing Groups, Book Groups, Schools, and Libraries. In person or online. Meet Lauren Elise Daniels, a Bram Stoker Award® nominee [2023], Queensland Johnno Award nominee [2021, 2022, 2023], Aurealis Award winning editor [2021], Aurealis finalist [] and Australian Shadows Award finalist [2022]. Meet with Lauren for a chat about writing or a workshop tailored to your needs.
The Brisbane Writers Workshop offers small-group creative and business writing courses and editing for all levels and ambitions. A collective of qualified and published professionals, we stand behind our publications list and commitment to share our knowledge. We are awarded writers and editors of numerous acclaimed titles published for the ...
Address: 1167 South Pine Road, Arana Hills 4054. Phone Number: (07) 3205 0555. Website: moretonbay.qld.gov.au. 4. Travel and Lifestyle Writing. This course focuses on creative writing for Blogs, Articles, Books, and other forms of digital communication and has a start date on the 2nd of August from 10 am to 4 pm.
Creative Writing Courses in Brisbane. Whether you're a budding writer or a seasoned wordsmith, our courses are designed to ignite your creativity, refine your skills, and help you craft literary masterpieces.
Creative writing workshops in Brisbane for ages 8 - 17 years | Email me | Young Writers Club Workshops 10 - 12 years | Young Writers Club Workshops 8 & 9 years | Instagram | Write and Explore 10 - 12 years: Creating Stories | Young Writers Club Workshops
2 weeks. Part time 1 day per week 9.30am - 4.30pm. Saturday. Full fee: $280. This is the total course fee. Read more about student loans and payment plans. More Apply. Studying on campus is a great way to enjoy the benefits of face-to-face education in a way that is far removed from a typical school.
Writing wORKSHOPS & EVENTS. Queensland Writers Centre presents a diverse annual Program of events, workshops and courses on the art, craft and business of writing. ... South Brisbane, QLD, 4101, Australia (07) 3842 9922 [email protected]. Hours. Mon 10:00am - 4:00pm. Tue 10:00am - 4:00pm. Wed 10:00am - 4:00pm.
Contact Us By Email. Or you can phone the Studio between 11.30am and 3pm Monday to Friday on (02) 9386 4994. We'd love to hear from you! Join an online creative writing course with like-minded writers and own online tutor. Specialising in Short Stories, Novels and Screenplays.
The Creative Writing major provides you with an understanding of the practice of creative writing, as well as introduce you to the ideas, theories, and philosophies that inform professional writers and the writing industry. Experiment with diverse forms, genres, and styles of writing and learn how to conceive, write, edit, and proofread your ...
7 March - 19 September 2023. Avid Reader - Brisbane. Avid Reader. 193 Boundary St. West End QLD 4101. Get directions. $6,500/$5.525 alumni. This is a past course. Presented by Australia's leading independent publishing house Allen & Unwin, in partnership with the prestigious UK Publishers Faber & Faber, Writing a Novel is a highly ...
Creative Writing Course Brisbane, Australia, journalism course, learn to write fiction, creative writing lessons. Request Any Service, Anywhere with Intently.co 7 Creative Writing Courses in Brisbane - Available Right Now
The Master of Creative Writing is comprised of four core units and two dissertation units. Your first four units are broken down into 12 modules with each module focusing on a particular topic and skills development, with each unit representing six credit points and designed to require a time commitment of a total of approx 144 hours. Your ...
About creative writing. Unlock your creativity through exposure to multiple forms of writing, including short stories, poetry, script writing and creative non-fiction. Make creative writing the focus of your degree and you'll explore styles, methods and techniques and get a greater understanding of genre and writing for a range of audiences.
Your version of Internet Explorer is not longer supported. ... upgrade your browser. Eventbrite Eventbrite
Part time 1 day per week 9.30am - 4.30pm. Saturday. Full fee: $280. This is the total course fee. Read more about student loans and payment plans. More Apply. Studying on campus is a great way to enjoy the benefits of face-to-face education in a way that is far removed from a typical school.
Creative Writing Course for Teenagers. Jessica Miller is a children's writer from Brisbane. Her first novel, Elizabeth and Zenobia, was shortlisted for the Text Prize and the Readings Prize and was a CBCA Notable Book, as was her second book, The Republic of Birds. Her third novel, The Hotel Witch will be released in February 2023.
Grab a cup of tea and join us in the Museum for an opportunity to explore the generative art of Ekphrastic writing. Ekphrasis has long been a vital craft tool used by poets, fiction writers, dramatists and essayists as a springboard for creative imaginings. It is the process of employing a slow and meditative response to a piece of art.
69 jobs - Creative Writing Internship in Brisbane QLD. Page 1 of 5. New to you Seen Viewed details Started applying Applied. New to you. Software Programming Intern Software Programming Intern. Study and Work. Brisbane QLD. Relevant degree or recent graduate in programming;
Content Writer and Document Control Assistant. Skin Deep Learning. Brisbane QLD. $52,000 - $52,000 a year. Full time. Missing: creative. Support editorial tasks like proofreading, editing, and fact-checking. Maintain document control system ensuring accuracy and compliance.
176 jobs - Creative Writing in Brisbane QLD. Page 1 of 15. New to you Seen Viewed details Started applying Applied. New to you. Copywriter Copywriter. Aspect Health. Acacia Ridge QLD. $60,000 - $70,000 a year. Full time. Create engaging content for various platforms and marketing materials;
Established Provider of Writing Services Envision your website visitors captivated by each sentence, your emails sparking enthusiasm, and your social media posts fueling engaging discussions. Picture your brand evolving into a storytelling phenomenon, with content that etches an enduring impression on both hearts and minds.
Writing ( 59) Young Writers Award ( 11) Young Writers Award is open for 2024 and we're excited to find and celebrate short stories and a diversity of young and exciting new Queensland voices. Wherever you are in our state - Mackay, Toowoomba, Hervey Bay, Gold Coast, Bundaberg, Cairns, Goondiwindi, Brisbane, or somewhere else - you can enter!
Find your ideal job at SEEK with 1526 Writer jobs found in All Brisbane QLD. View all our Writer vacancies now with new jobs added daily! ... Creative thinker with a strategic mindset - responsible for planning, creating & managing content across various channels ... Be the go-to person for coordinating the bid period and providing writing ...
Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state ...