Some of the concepts of deep learning that have gained attention and suggested below. All the details related to your work with up-to-date ideas will be shared for our scholars. Here we explain about deep learning theories, like design, architecture, workflow and algorithms. We carry on research work and attain a value-added application to the selected areas.
- Capsule Networks:
- Through the utilization of Capsule networks rather than using conventional neural network, we can understand the spatial hierarchical relationships among various features.
- Transformers beyond NLP:
- We developed transformers for NLP based procedures and it provides an efficient performance in various platforms like computer vision, protein folding and others.
- Methods for augmenting data:
- To build an effective framework, we utilized innovative ideas for data augmentation like CutMix, MixUp, etc., particularly in vision domain.
- Explainable AI (XAI):
- We properly developed our DL techniques for making understandable decisions because, initially, DL approaches are considered as black boxes.
- Self-supervised learning:
- To minimize the requirement for manually labeled data, we employed self supervised learning technique to train the framework by utilizing obtained labeled data from the input data.
- Neural Radiance Fields (NeRF):
- NeRF offers an effective outcome for high fidelity 3D redevelopment. So, we also used it for 3D scene visualization and rendering.
- Federated learning:
- For secure and reliable AI model, we train our model on distributed data through the deployment of federated learning techniques.
- Pros and cons in AI:
- We need to check whether the AI framework is robust and efficient by solving the problems like disadvantages of framework and discriminatory decisions.
- Neural Architecture Search (NAS):
- To find out the best network architectures, we apply automated techniques.
- Integrated frameworks:
- We ensembled DL method with other conventional techniques or integrated various kinds of neural networks to enhance the model’s efficiency.
- Out of Distribution (OOD) identification:
- To identifying and managing inputs that are contrasted from the data utilized we train the framework.
- Multimodal and Cross modal learning:
- To train and represent the data more precisely, we combined several types of data such as text pattern, image and audio patterns.
- Temporal and lifelong learning:
- By this, our model can adapt and learn in an actual time without forgetting the skills that are learned before.
- Knowledge Distillation:
- From the utilization of knowledge distillation more effectively, we trained our small model (i.e student) to obtain the characteristics of complicate and huge model (i.e staffs).
- Quantum Neural Networks:
- The common facts of quantum computing and neural network approaches will be analyzed.
- Techniques for effective training:
- By analyzing several energy efficient training techniques, we can minimize the environmental effect of DL.
To know about the current researches related to AI, we analyse the investigations of popular AI conferences like NeurIPS, ICML, ICLR, CVPR, or ACL for the current year and we research about other evolving concepts in various particular conferences.
DEEP LEARNING MPhil DISSERTATION TOPICS
All types of MPhil dissertation topics on deep learning will be guided by us. Trending topics and its work structure will be briefly explained. We stay updated on current topics to satisfy our customer needs. If you are looking for genuine research guidance for your doctoral research on deep learning matlabsimulation.com serves as a best idea. Some of the latest topics has been discussed below, while we tailored out your own topics.
- Deep Learning-Based Receiver Energy Prediction in Energy Harvesting Wireless Sensor Network
- An Autonomic Deep Learning Artificial Neural Network based Algorithm for Effective Re-generation
- A novel deep learning method for application identification in wireless network
- A New Deep Learning Method for Multi-label Facial Expression Recognition based on Local Constraint Features
- A multi-view deep learning approach for predictive business processes monitoring
- An Improved Kubernetes Scheduling Algorithm for Deep Learning Platform
- Deep Learning-based Action Recognition for Pedestrian Indoor Localization using Smartphone Inertial Sensors
- Towards 6G Networks: Ensemble Deep Learning Empowered VNF Deployment for IoT Services
- A New Deep Learning Method for Underwater Target Recognition Based on One-Dimensional Time-Domain Signals
- Bitcoin Price Prediction: A Deep Learning Approach
- Performance Comparison of Fuzzy Logic and Deep Learning algorithms for fault detection in electrical power transmission system
- Prediction of Mortality and Length of Stay with Deep Learning
- A Deep Learning Module Design for Workspace Identification in Manufacturing Industry
- Incident Detection based on Multimodal data from Social Media using Deep Learning Methods
- Exploiting 2D Coordinates as Bayesian Priors for Deep Learning Defect Classification of SEM Images
- Using Deep Learning Network for Fault Detection in UAV
- Simulation of Temperature Distribution During HIFU Therapy Using Physics Based Deep Learning Method
- Experimental Design for Multi-task Deep Learning toward Intelligence Augmented Visual AI
- Comparison of Semantic Segmentation Deep Learning Methods for Building Extraction
- Application of Advanced Deep Learning Techniques for Face Detection and Age Estimation
- All You Need is Transformer: RTT Prediction for TCP based on Deep Learning Approach
- Salient Region Detection in Images Based on U-Net and Deep Learning
- Modelling of Wireless OFDM System with Deep Learning-based Modulation Detection
- Xonar: Profiling-based Job Orderer for Distributed Deep Learning
- A Deep Learning Method for Pneumonia Detection Based on Fuzzy Non-Maximum Suppression
- Optimization of Deep Learning based Tone Reservation
- Intelligent Repair Method of Old Movie Speckle Noise Based on AI Deep Learning
- A Deep Learning Approach for Stress Detection Through Speech with Audio Feature Analysis
- A Hybrid Deep Learning Spectrum Sensing Architecture for IoT Technologies Classification
- GSP Distributed Deep Learning Used for the Monitoring System
- Regional Heatwave Prediction Using deep learning based Recurrent Neural Network
- Recommendation-based Security Model for Ubiquitous system using Deep learning Technique
- Point-Cloud-based Deep Learning Models for Finite Element Analysis
- Employing Deep Learning and Discrete Wavelet Transform Approach to Classify Motor Imagery Based Brain Computer Interface System
- Prediction of regional ecological security by applying deep learning methods in spatial and temporal simulation
- Webshell Detection Technology Based on Deep Learning
- Concept Drift Detection Methods for Deep Learning Cognitive Radios: A Hardware Perspective
- Deep Learning Based Multi Modal Approach for Pathological Sounds Classification
- Design of Deep Learning Algorithm in the Control System of Intelligent Inspection Robot of Substation
- 3D Reconstruction of Forearm Veins Using NIR-Based Stereovision and Deep Learning
- CT Dataset Enhancement using Additional Feature Insertion for Automatic Femur Segmentation Model Based on Deep Learning
- A Comparative Study of Machine Learning and Deep Learning Techniques for Sentiment Analysis
- Over The Air Performance of Deep Learning for Modulation Classification across Channel Conditions
- Accurate Precipitation Prediction using Deep Learning Neural Network Compared with Space Vector Machine
- Interpreting Deep Learning Models for Multi-modal Neuroimaging
- Bitcoin Price Prediction Using Deep Learning and Real Time Deployment
- Research on green building optimization design of smart city based on deep learning
- Introduction to Deep Learning Possibilities in Communication Systems
- An Enhanced Method on Using Deep Learning Techniques in Supply Chain Management
- Impact Analysis of Incident Angle Factor on High-Resolution Sar Image Ship Classification Based on Deep Learning
