Long Short-Term Memory Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Long Short-Term Memory ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Long Short-Term Memory Projects
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Bad and Good Errors: Value-Weighted Skill Scores in Deep Ensemble Learning
This project focuses on checking how useful predictions are, not just how accurate they are. It gives more importance to predictions that matter most in real situations. The method looks at different types of mistakes and weighs them by their impact. It tests this approach on predictions for pollution, space weather, stock prices, and IoT data, showing better results overall. -
EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning
This project focuses on using brain scans to detect autism. It studies how different regions of the brain interact in complex ways. The method finds important brain areas that help distinguish autistic patients from others. The approach is explainable, meaning it shows why it makes each diagnosis and works better than earlier methods. -
Multivariate Time Series Characterization and Forecasting of VoIP Traffic in Real Mobile Networks
This project studies how voice calls over mobile networks behave in real time. The researchers collected a large amount of data from a real LTE network and analyzed it to see how different factors affect call quality. They used computer models and machine learning to predict future performance of the network. The goal is to help network operators plan better and improve the quality of voice calls. -
NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning
This project focuses on helping doctors listen to newborns’ heart and lung sounds more clearly. The researchers created a smart computer program called NeoSSNet that can separate heart sounds from lung sounds in recordings. It works faster and more accurately than older methods. This can help in early detection of heart or lung problems in babies. -
Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods
This project focuses on making Internet of Things (IoT) devices more secure. It uses a smart system to recognize each device by its unique radio signals. The method works without making the devices more complex. Tests show it can detect fake devices and correctly identify real ones with over 90% accuracy. -
Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies
This project focuses on making wireless networks smarter and more secure. It uses artificial intelligence to manage network resources for many users and base stations. The system also studies how a jammer can disrupt the network and how to defend against it. The methods are tested in simulations to show they work well. -
Simulation-Aided Handover Prediction From Video Using Recurrent Image-to-Motion Networks
This project teaches robots to work together by watching short video clips of motion. The robots learn to predict future movements and plan their actions safely. The system uses both real and simulated data to improve learning. It helps robots pass objects to each other accurately, even if they are not perfectly set up. -
CiGNN A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation
This project develops a new method to measure blood pressure without using a cuff. It finds which body signals actually affect blood pressure and then uses a smart computer model to track changes beat by beat. The method works accurately for people of different ages and health conditions. It can help monitor blood pressure continuously and more reliably than current approaches. -
TTN Topological Transformer Network for Automated Coronary Artery Branch Labeling in Cardiac CT Angiography
This project focuses on automatically labeling branches of coronary arteries in heart CT scans. The researchers developed a new AI model called a Topological Transformer Network that understands the position and relationship of all artery branches. It works well even when some branches are small or rare. This system can help doctors detect heart problems and locate blockages more easily. -
Deep Knowledge Tracing Incorporating a Hypernetwork With Independent Student and Item Networks
This project focuses on tracking how a student learns over time. It improves an existing AI method called Deep-IRT by separating student ability and item difficulty into two independent models. This makes the system more accurate and easier to interpret. Tests show it predicts student performance better than previous methods. -
E-BabyNet: Enhanced Action Recognition of Infant Reaching in Unconstrained Environments
This project develops a smart computer system called E-babyNet to detect when infants reach for objects. It uses video data to track the movements of babies’ hands and the objects they touch. The system can accurately find the start and end of each reaching action. It works well even in home or clinic settings and reduces false detections. -
Treatment of Nocturnal Enuresis Using Miniaturised Smart Mechatronics With Artificial Intelligence
This project developed a small, wearable device called MyPAD that can track how full the bladder is and alert the user before accidents happen. It uses ultrasound sensors and smart algorithms to learn and improve over time. Tests on volunteers and models show it works with very high accuracy. The device aims to help children manage bedwetting more effectively than current methods. -
Efficient Container Scheduling With Hybrid Deep Learning Model for Improved Service Reliability in Cloud Computing
This project focuses on improving how cloud systems assign computing resources to applications. It predicts how much CPU and memory each application will need in the future. Then it decides the best way to place applications on servers to avoid wasting resources or causing service problems. Tests show it makes systems more efficient and reliable than existing methods. -
A Hybrid Approach for Forecasting Occupancy of Buildings Multiple Space Types
This project focuses on predicting how many people are present in different rooms of a university building. It uses data from indoor and outdoor sensors, energy usage, and Wi-Fi connections to make predictions. By using smart machine learning models, it helps improve energy management and space use. The proposed method gives more accurate results than other approaches. -
A Novel Convolutional Neural Network Model for Automatic Speaker Identification From Speech Signals
This project focuses on identifying people by their voice using a computer model. It uses a deep learning method called CNN to recognize who is speaking from their recorded speech. The system was trained with English audio from several people and tested with other datasets. It achieved very high accuracy, showing that it can correctly identify speakers in most cases. -
Children’s Sentiment Analysis From Texts by Using Weight Updated Tuned With Random Forest Classification
This project helps computers understand emotions in text, like happy or sad feelings. It uses advanced learning methods to analyze stories more accurately. The model is faster and more reliable, useful for studying children’s emotions and online behavior. -
Comparative Analysis of Artificial Intelligence Methods for Streamflow Forecasting
This project focuses on predicting river water flow using deep learning methods. It studies 28 years of data from the Johor River in Malaysia to understand how rainfall and other factors affect streamflow. The researchers used advanced neural network models to make more accurate predictions and measure uncertainty in results. This helps improve water resource planning and flood management. -
Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework
This project focuses on improving how computers detect harmful software. It combines traditional methods that use known malware signatures with artificial intelligence to find new, unseen threats. The system uses a special AI model called GAN to create fake safe files, helping the detector learn better. Tests show that this approach detects malware more accurately than existing systems.
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How We Help You with Long Short-Term Memory Projects
At UniPhD, we provide complete guidance and support for Long Short-Term Memory ieee projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.
We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.
Our experts guide students all over India, including in Mumbai, Delhi, Bangalore, Hyderabad, Ahmedabad, Chennai, Kolkata, Pune, Jaipur, and Surat. We also assist students in the USA, UK, Canada, Australia, Singapore, Malaysia, and Thailand. They have extensive experience in computer science, electronics, electrical and all engineering domains.
Long Short-Term Memory Thesis and Dissertation Writing
UniPhD has a team of experienced academic writers who specialize in Long Short-Term Memory research and thesis development. We offer fast-track dissertation writing services to help you complete your Long Short-Term Memory thesis or dissertation smoothly and on time.
Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.
Long Short-Term Memory Research Paper Publishing Support
UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.
Project Synopsis and Presentation Support
We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Long Short-Term Memory final year project.
Long Short-Term Memory Research Support for PhD Scholars
UniPhD offers advanced Long Short-Term Memory research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.
Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.