IEEE Projects on Big Data
IEEE Projects on Big Data offer enlightened environment to gain more and more adventures in this scientific forest. We are introducing our IEEE Projects on Big Data service for national and international level students and research colleges who pursuing from BE, BTech, ME, MTech, MCA, MPhil, MS and PhD. Our primary intension is disseminating our high genius and virtuoso scientific research notions amidst of students and research philosophers in all over the universe. For that grand vision, our experts are conducting workshops, seminars and faculty training programs on various colleges and universities. Trust the process only moves us through your destination and believe all outcomes. If you aspire to acquiring our service, approach our big data experts without any delay.
IEEE Projects on Big Data
IEEE Projects on Big Data service is began for scholars to making their solid achievements instead of running. We are always dedicating our scientific profession to grant admirable support and guidance for students and research profs in each and every part of their intellectual journey including topic/domain selection support, project designing & implementation support, project documentation/report preparation support, internal & external review support, PPT preparation support etc.
Let’s see some specific challenges of Big Data Analytics,
- Streaming Data
- High Dimensional Data
- Scalability of Models
- Distributed Computing
- Semantic Indexing
- Data Tagging
- Fast Information Retrieval
- Discriminative Tasks Simplifying
- Complex Patterns Extraction from massive volumes of data
Future Research Interests on Big Data:
- Defining data sampling criteria
- Useful Data Abstractions Definition
- Domain Adaption Modeling
- Improving Semantic Indexing
- Active and Semi-Supervised Learning
Big Trends of IEEE Projects on Big Data Analytics:
- Big Data Hadoop Framework
- More better NoSQL
- Big Data Lakes: Data base design
- Deep learning
- More Predictive Analytics
- SQL on Hadoop
- In-Memory Analytics
- Big Data Analytics in Cloud
Nowadays, Deep learning is an extremely active research area in Data Mining, Machine Learning and Pattern Recognition. Due to its emergence, it receives huge successes in a wide range of applications such as computer vision, speech recognition and natural language processing.
Challenges of Deep learning in Big Data Analytics,
- Real-time Non Stationary Data
- Data Parallelism
- Multimodal Data
- High Dimensional Data
- Large Scale Models
Deep learning Algorithms:
- Stacked Autoencoders
- Restricted Boltzman Machines
- Deep Belief Networks
- TF-IDF and BM25 algorithms
- Semantic Hashing Strategy
- Word2Vector Model
- Artificial Neural Networks
- Convolutional Neural Networks
- Sparse coding [Edge and blob detection]
- Recursive Neural Networks
- Deep Belief Networks (DBN)
- Deep Boltzmann Machines
- Deep Stacking Networks
- Back-Propagation DBN
- Hidden Markov Models
- Gaussian Mixture Models
- Associative Memory DBN
- Conditional Random Field
Deep learning Applications in Big Data Analysis:
- Cyber Security Systems
- Fraud Detection
- Object Detection
- Speech Recognition
- Cyber Traffic Monitoring
- Sensor Data Analysis
- Cyber Physical Systems
- Network Intrusion Detection
- Trend Analysis
- Management Information Systems
Current Topics in IEEE Projects on Big Data:
- Machine Learning and Data Analytics in IC Semiconductor Manufacturing and Electronic Design Automation for Design Process Yield Optimization
- Multi Path TCP Path Management in NorNet Testbed Based Internet Scenarios
- SSD Performance Improvement by Using Request Internal Parallelism and Characteristics
- Revocable Identity Based Access Control for Verifiable Outsourced Computing Big Data
- Collaborative Cloud and Edge Processing Live Data Analytics in Wireless Internet of Things Networks
- Hierarchical Paradigm Using Large Scale Smart Meter Data for Smart Grid Anomaly Detection
- Distributed Information Estimation Using Adaptive Fusion Scheme Through Cooperative Multi Agent Networks
- Data Visualization and Data Fusion Application for Investigate Network Forensic
- Extreme Learning Machine for Big Data Using Parallel Multi Classification Algorithm
- Design Fault Observer for Homogeneous Polynomials Parameter Dependent Lyapunov Utility Based Discrete Time Takagi Sugeno Fuzzy Frameworks
- Automatic Smart City Paradigm Configuration for Support User Centric Decision
- Hybrid MPI OpenMP Scheme to Speed Up Big Data Generation Sequencing Datasets Compression
- Energy Consumption Characteristic Patterns Mining in Smart Grid Applications for Households
- Load Feedback Based Data Locality Based on Dynamic Migration and Resource Scheduling in Openstack Based Clouds for Virtual Hadoop Clusters
- Dynamic Stopping for Event Oriented Potential Brain Computer Interfaces Using eSVM Scores
