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Vol 12, Issue 9 (September 2026) Computer Science And Engineering

An Interpretable Deep Learning Framework For MRI-Based Alzheimer’s Disease Detection And Automated Clinical Reporting

Alzheimer’s Disease (AD) Is A Progressive Neurodegenerative Disorder That Affects Memory, Cognitive Abilities, And Overall Brain Function, Particularly In The Aging Population. Early And Reliable Diagnosis Is Essential For Timely Intervention, Diseas... Alzheimer’s Disease (AD) Is A Progressive Neurodegenerative Disorder That Affects Memory, Cognitive Abilities, And Overall Brain Function, Particularly In The Aging Population. Early And Reliable Diagnosis Is Essential For Timely Intervention, Disease Management, And Improved Patient Care. Magnetic Resonance Imaging (MRI) Provides Non-invasive Structural Information About The Brain And Has Become An Important Source Of Data For Computer-aided Alzheimer’s Disease Assessment. Although Deep Learning Techniques Have Demonstrated Considerable Potential For MRI-based Disease Classification, Many Existing Approaches Primarily Emphasize Predictive Performance While Providing Limited Insight Into The Reasoning Behind Their Decisions. Furthermore, Conventional Diagnostic Models Generally Do Not Provide An Integrated Mechanism For Transforming Model Predictions Into Understandable Clinical Information. To Address These Limitations, This Paper Proposes An Interpretable Hybrid Deep Learning And Generative Artificial Intelligence (AI) Framework For MRI-based Multi-stage Alzheimer’s Disease Diagnosis With Automated Clinical Report Generation. The Proposed Framework Combines A Convolutional Neural Network (CNN) And A Swin Transformer To Learn Complementary Local Structural And Global Contextual Representations From MRI Brain Images. The Extracted Features Are Fused For Multi-stage Classification Of Normal, Mild, And Moderate Conditions. Grad-CAM Is Incorporated To Identify And Visualize The Brain Regions That Contribute To The Classification Decision, Thereby Improving Model Transparency. Subsequently, A Generative AI-based Large Language Model (LLM) Converts The Verified Classification Result, Confidence Information, And Visual Explanation Into A Human-readable Clinical-style Report. Unlike Prediction-only Systems, The Proposed Framework Establishes A Unified Pipeline Connecting Diagnosis, Visual Explanation, And Evidence-guided Report Generation. The Framework Is Intended To Improve Diagnostic Interpretability, Communication, And Usability While Providing A Foundation For Trustworthy AI-assisted Decision Support In Alzheimer’s Disease Assessment. Read more Read less

Authors: KEERTHIKA V | Vanitha J

Vol 12, Issue 9 (September 2026) Embedded Systems

ML-Based Anomaly Detection In Healthcare Systems Using ESP32 Microcontroller

Continuous Monitoring Of Patient Vital Signs Through Wireless Body Area Networks (WBANs) Is Increasingly Central To Modern Healthcare, Yet Validating Anomaly-detection Pipelines On Real Hospital Networks Is Costly, Ethically Constrained, And Difficul... Continuous Monitoring Of Patient Vital Signs Through Wireless Body Area Networks (WBANs) Is Increasingly Central To Modern Healthcare, Yet Validating Anomaly-detection Pipelines On Real Hospital Networks Is Costly, Ethically Constrained, And Difficult To Reproduce. This Project Presents A Simulation-first Framework For ML-based Anomaly Detection In Healthcare Systems, Built Entirely In OMNeT++ With The INET Framework. The System Models Sensor Nodes (ECG, SpO2, Blood Pressure, Temperature, And Glucose), A Gateway, And A Central Server As A Discrete-event WBAN, In Which A VitalSignGenerator Module Produces Realistic Vital-sign Traffic And An AnomalyInjector Module Introduces Controlled Anomalies — Physiologically Abnormal Readings, Sensor Faults, Spoofed Packets, And DoS-style Flooding — At A Known, Configurable Probability. Because The Anomaly-injection Rate Is A Simulation Parameter, Every Generated Reading Carries An Automatic Ground-truth Label, Removing The Need For Manual Annotation. Simulation Results Are Exported Via Scavetool Into A CSV Pipeline And Used To Train And Compare Three Unsupervised Machine-learning Models — Isolation Forest, One-Class SVM, And An LSTM Autoencoder — Evaluated Against The Known Ground Truth Using Precision, Recall, F1-score, And ROC-AUC. The Framework Further Defines A Physical Sensor-hardware Layer (AD8232, MAX30100/MAX30102, MLX90614, And MPU6050 Interfaced To An ESP32 Microcontroller) As The Real-world Counterpart Of The Simulated WirelessHost Nodes, And Outlines Two Patterns — A Socket Bridge And An Embedded ONNX Runtime — For Optionally Closing The Loop With Real-time, In-simulation Detection. The Proposed Approach Offers A Reproducible, Extensible, And Ground-truth-evaluated Path From A Blank Simulation Workspace To A Deployable Anomaly Detector, Addressing Key Gaps In Existing WBAN And IoT Anomaly-detection Literature. Read more Read less

Authors: MOHAMED ABUBAKKAR N | DR.P.SIVASANKAR | DR.G.KULANTHAIVEL

Vol 12, Issue 9 (September 2026) Computer Science And Engineering

AN EXPLAINABLE GENERATIVE AI-DRIVEN MULTIMODAL DEEP LEARNING FRAMEWORK FOR KIDNEY TUMOR LOCALIZATION AND CLASSIFICATION USING CT AND MRI

Kidney Tumors Are A Major Clinical Concern In Which Early Detection, Accurate Localization, And Reliable Classification Are Essential For Effective Treatment Planning And Improved Patient Outcomes. Computed Tomography (CT) And Magnetic Resonance Imag... Kidney Tumors Are A Major Clinical Concern In Which Early Detection, Accurate Localization, And Reliable Classification Are Essential For Effective Treatment Planning And Improved Patient Outcomes. Computed Tomography (CT) And Magnetic Resonance Imaging (MRI) Provide Complementary Anatomical And Tissue Information; However, Their Interpretation Is Time-consuming And Highly Dependent On Expert Radiologists, Which May Lead To Inter-observer Variability And Diagnostic Errors. To Address These Challenges, This Research Proposes An Explainable Generative-AI-driven Multimodal Deep Learning Framework For Automated Kidney Tumor Analysis. The Proposed System Integrates CT And MRI Images Through Preprocessing And Multimodal Registration, Followed By Generative Adversarial Network (GAN)-based Synthetic Data Augmentation To Overcome Limited Medical Imaging Data And Improve Model Generalization. An Attention-based Segmentation Network Is Employed To Localize Kidney Tumor Regions, While A Hybrid CNN–Transformer Architecture Extracts Complementary Local And Global Representations From The Multimodal Images. CNNs Capture Fine-grained Spatial And Textural Characteristics, Whereas Transformer-based Learning Models Long-range Contextual Dependencies. A Cross-Modal Attention Mechanism Is Further Proposed To Learn Interactions Between CT And MRI Representations, Followed By An Adaptive Multimodal Attention Feature Fusion (AM-AFF) Mechanism That Dynamically Weights And Integrates The Learned Features For Robust Tumor Classification. To Enhance Clinical Transparency, Explainable Artificial Intelligence (XAI) Techniques Are Incorporated To Visualize The Regions Contributing To The Model's Predictions. The Proposed Framework Is Designed To Provide Automated Tumor Localization And Classification While Improving Robustness, Multimodal Feature Representation, Interpretability, And Diagnostic Consistency. The Resulting System Aims To Serve As An Intelligent Clinical Decision-support Framework For Assisting Healthcare Professionals In Accurate And Efficient Kidney Tumor Assessment. Read more Read less

Authors: VANITHA J | KEERTHIKA V

Vol 12, Issue 9 (September 2026) MECHANICAL ENGINEERING MECHATRONICS

SOLAR PV PERFORMACE MAPPING UNSER URBAN ENVIRONMENTAL CONDITIONS

Solar Photovoltaic (PV) Systems Deployed In Indian Urban Environments Are Significantly Affected By Environmental Factors Such As High Temperature, Humidity, Dust Accumulation, And Air Pollution, Leading To Gradual Performance Degradation. This Proje... Solar Photovoltaic (PV) Systems Deployed In Indian Urban Environments Are Significantly Affected By Environmental Factors Such As High Temperature, Humidity, Dust Accumulation, And Air Pollution, Leading To Gradual Performance Degradation. This Project Presents A Solar PV Performance Degradation Mapping System Designed To Analyze And Monitor The Impact Of Urban Environmental Conditions On Solar Panel Efficiency. The System Is Built Using An Arduino Microcontroller Interfaced With A 9V Solar Panel To Observe Real-time Power Generation Behaviour. Environmental Parameters Including Temperature And Humidity (DHT11 Sensor), Light Intensity (LDR Sensor), Air Quality (MQ135 Sensor), Soil Moisture, And PH Levels Are Continuously Monitored To Evaluate Surrounding Environmental Stress Factors Contributing To Panel Degradation. An LCD Display Provides Live Sensor Readings And System Status, While LED Indicators Signal Normal Operation And Degradation Alerts. The Collected Data Enables Mapping Of Performance Variation Trends Under Typical Indian Urban Conditions Such As Pollution Exposure, Heat Stress, And Moisture Variation. The Prototype Offers A Cost-effective And Practical Solution For Studying PV Degradation Patterns, Supporting Preventive Maintenance Strategies, Improving Energy Yield, And Promoting Sustainable Solar Energy Implementation In Urban Regions. Read more Read less

Authors: KILLI MANOJ KUMAR | Dr. M. Senthil Kumar

Vol 12, Issue 9 (September 2026) Library And Information Science

Research Data Management In Higher Education: The Role Of Research Support Services

The Fast Growth Of Digital Research Has Made Research Data Management (RDM) Important In Higher Education Institutions. As More Data Is Created Through Research, Managing This Data Has Become A Major Concern. In The Research Process, Things Like Data... The Fast Growth Of Digital Research Has Made Research Data Management (RDM) Important In Higher Education Institutions. As More Data Is Created Through Research, Managing This Data Has Become A Major Concern. In The Research Process, Things Like Data Quality, Data Access And Long-term Reuse All Depend On Good Data Management. Because Of This, There Is A Growing Need For Strong Research Support Services. This Review Article Looks At The Role Of Research Support Services In Managing Research Data In Higher Education Institutions.The Study Reviews The Concept And The Importance Of Research Data Management (RDM), The Essential Supports And Services That Researchers Need To Have At Their Disposal, And The Changing Nature Of The Academic Library's Role In Supporting Effective Data Stewardship. The Review Identifies The Major Functions Of Research Data Management (RDM) Support Such As Data Management Planning, Data Storage And Preservation, Metadata Activities, Repository Services, Data Sharing, And Researcher Training. It Also Explores New Trends In Research Data Management Including Open Science, FAIR (Findable, Accessible, Interoperable And Reusable) Data Principles, Cloud-based Technologies, Artificial Intelligence And Research Analytics. The Results Show That Research Support Services Are Crucial To Improve Data Quality And For Long-term Preservation And Re-use Of Research Products. There Are Certain Issues Like Infrastructure, Funding, Staff, Awareness And Data Governance That Remain To Be Addressed Within The Institutions. The Study Suggests That Institutional Policies Need To Be Further Strengthened, Capital Investment In Professional Development Is Necessary And The Use Of New Technologies Must Be Strengthened To Progress Research Data Management (RDM) Knowledge And Practices. Read more Read less

Authors: Dip Ranjan | Dr. Ashutosh Agrahari