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Papers tagged with "Two"
Deep Learning-Based Eye Disease Detection
Eye Diseases Such As Diabetic Retinopathy (DR), Glaucoma, And Age-related Macular Degeneration (AMD) Remain Major Causes Of Vision Loss Globally. Early Screening Is Crucial But Limited By The Availability Of Trained Ophthalmologists. This P...
ENHANCING STOCK PRICE FORECASTING ACCURACY USING COMPOSITIONAL RNN
Predicting Stock Prices Accurately Is Essential For Making Well-informed Decisions In Erratic Financial Markets. This Paper Introduces A Compositional Deep Learning Framework For Multivariate Time-series Forecasting That Integrates Three RN...
An Intelligent Hybrid GCN-LSTM Model For Energy Stock Price Forecasting With Temporal Dynamics And Inter-Stock Correlation
Energy Sector Stock Price Prediction Is A Critical Yet Challenging Task In Financial Forecasting, Characterized By High Volatility, Non-linearity, And Complex Interdependencies Driven By Geopolitical Events, Regulatory Shifts, And Commodity...
Advanced Gas Thermal Sensing System
The Advanced Gas Thermal Sensing System (AGTSS) Is An IoT-enabled Industrial Safety Monitoring Platform Designed To Detect Hazardous Gases And Thermal Anomalies In Real Time. Industrial Environments Such As Chemical Plants, Manufacturing Fa...
DEEP LEARNING-BASED DETECTION OF SKILLED SIGNATURE FORGERIES
Signature Verification Plays A Critical Role In Authentication Systems Used In Banking, Legal Documentation, And Financial Transactions. However, Skilled Signature Forgeries Pose A Significant Challenge For Traditional Verification Techniqu...
Machine Learning Approaches For Intelligent Intrusion Detection Systems In Cloud Networks
Cloud Computing Has Become A Fundamental Platform For Storing Data And Running Applications Due To Its Scalability, Flexibility, And Cost Efficiency. However, The Rapid Growth Of Cloud Environments Has Also Increased Security Threats Such A...
A Survey on Intelligent Skin Disease Prediction Using Deep Learning And CNN–LLM Integration
Skin Diseases Are Among The Most Prevalent Medical Issues Globally, Making Timely And Accurate Diagnosis Essential For Successful Treatment.Expert Clinical Evaluation Is The Traditional Foundation Of Dermatological Diagnosis.However, This A...
LUNG TUMOR SEGMENTATION USING VISUAL GEOMETRY GROUP NETWORKS IN MRI IMAGES
Lung Cancer Is One Of The Most Life-threatening Diseases Worldwide, And Early Diagnosis Significantly Increases The Chances Of Survival. Accurate Detection And Classification Of Lung Tumors Remain Challenging Due To Variations In Tumor Size...
An Optimized Machine Learning Model For Pavement Condition Prediction
Pavement Condition Prediction Is An Essential Aspect Of Road Maintenance Planning, Ensuring The Safety, Efficiency, And Durability Of Transportation Infrastructure. Statistical Machine Learning (ML) Techniques Have Emerged As Powerful Tools...
Cyber Intrusion Detection Using Machine Learning Techniques
Cyber Intrusions Are Becoming Increasingly Complex And Sophisticated, Making Them Difficult To Detect Using Conventional Rule-based And Signature-driven Security Mechanisms. The Rapid Growth Of Network Traffic And Evolving Attack Patterns D...
FPGA Implementation Of Brain-Inspired Neuromorphic Computing Circuits
Brain-inspired Neuromorphic Computing Has Emerged As An Efficient Approach For Implementing Cognitive And Learning-based Systems With Low Power Consumption And High Parallelism. Unlike Conventional Computing Architectures, Neuromorphic Syst...
A Structured Framework For Identifying Network Vulnerability Using NMAP
Network Security Has Become A Critical Concern For Educational Institutions Managing Complex IT Infrastructure With Hundreds Of Interconnected Devices. Manual Auditing Of Network Ports And Services Is Time-consuming, Error-prone, And Often...
Artificial Neural Network-Based Approaches For Predicting Software Development
To Get Reliable Software On Time And Under Budget, You Need To Be Able To Accurately Forecast How Much Work Will Go Into Developing It. There Have Been A Number Of Software Effort Estimating Models Made In The Last Few Years, But It's Still...
Enhancing Reactive Power Compensation Through Shunt Reactive Elements For Improved Voltage Stability
This Paper Presents The In Modern Electrical Power Systems, The Efficient Management Of Reactive Power Has Become Increasingly Critical For Ensuring System Reliability, Voltage Stability, And Energy Efficiency. Reactive Power, Though Essent...
A Hybrid GNN-LSTM Model For Feedback-Free Adaptive Modulation And Coding In Massive MIMO
Adaptive Modulation And Coding (AMC) Is A Key Enabler For Achieving High Spectral Efficiency And Reliability In Massive Multiple-Input Multiple-Output (MIMO) Systems. Conventional AMC Techniques, However, Rely Heavily On Explicit Channel St...