Advanced Network Technologies and Intelligent Computing

.- Deep Learning based Leaf Disease Detection, Prevention, and Treatment Prediction Application for Smart Agriculture.
.- An Attention based CNN for Plant Leaf Disease Detection.
.- Improvising the performance of Transcutaneous Electroacupuncture Stimulation (TEAS) Induced EEG Classification using Optimal Band selection and Sequential Ridge Regression.
.- Reinforcement Learning based on TD-3 for the Nonlinear Model of CSTR: Temperature Tracking.
.- Advanced Short-Term Load Forecasting for the Indian Grid: A Hybrid Convolutional and Recurrent Neural Network Approach.
.- To Be, Not to Be, or Both: A Quantum Leap for Sentiment Analysis.
.- Deep Learning-based Defect Detection in Agricultural Crops: A Comprehensive Approach.
.- Multi-Class Classification of Chronic Renal Disease (CRD)  using Deep Convolutional Neural Network (CNN) Model.
.- HeLa CELL DETECTION AND SEGMENTATION USING DIGITAL IMAGE PROCESSING METHODS.
.- An Efficient CNN-Based Method for Detecting Driver Drowsiness from Facial Images.
.- Explainable AI-driven Transcriptome Analysis of Drug Responses for Therapeutic Discovery and Safety Evaluation in Cardiovascular Diseases.
.- Hybrid Fractal-Deep Learning Model for Time Series Prediction: A Comparative Study.
.- Finding the Signal: A Deep Dive into Data Augmentation  and Transformer Performance for Hope Speech Detection.
.- An intelligent approach to artificial speech detection and identification based on KNN.
.- ROI-RONI-Based Reversible Data Hiding Algorithm for Medical Images  Using Huffman Compression and DWT-LSB Embedding.
.- Modeling Recurrent Neural Networks in Serial Recall Paradigm With Dynamic Self-Excitation.
.- SegVCR-LISA: Commonsense Reasoning in VLMs through Segment-Specific Instruction Tuning.
.- A Domain-Aware Grievance Redressal System Leveraging RAG and Open-Source LLMs.
.- Brain Tumour Segmentation using Multi-Orientation Two-Pathway CNN with Attention-Based Fusion in MRI Images.
.- Adaptive MOHAN Activation for Enhanced Brain Tumor Segmentation Using TransUNet and Grad-CAM++.
.- MULTI-STRATEGY FEATURE FUSION FRAMEWORK FOR PLANT DISEASE DIAGNOSIS.
.- LeafGuard: A Deep Learning Framework for Disease Detection in Apple Orchards.
.- An Effective AI-Driven Prompting Approach for Video Summarization System using Flan-T5 Model.
.- A comparative analysis of CNN, BiLSTM and Hybrid CNN-BiLSTM for Air Quality Index.
.- Interpretable Feature Selection for Breast Cancer Diagnosis: A Comparative Study of ElasticNet and SHAP.
.- Assessment of Brassica Napus L. Yield Attributes under Varied Concentrations of Untreated Distillery Effluent through Fuzzy Rule Based Modelling.
.- OtoscopeNet: An Efficient and Attention-Driven Deep Learning Framework for Robust Diagnosis of Ear Diseases.
.- Hybrid Mask Fusion and Transformer-Driven Deep Feature Analysis for Multi-Class Chest X-Ray Diagnosis.
.- TinyEyeNet: An Efficient CNN for Classifying Anterior Segment Eye Conditions.
.- A Comparative Analysis of Deep Learning Models for Early Prediction of Alzheimer’s Disease using structural MRI.
.- Attention-ResUNet for Automated Fetal Head Segmentation.
.- Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks.
.- Explainable Hybrid Deep Learning for Medicinal Leaf Disease Classification: R50-MViT with LIME and Grad-CAM Interpretability.
.- Green Channel Enhanced Self Attention U-Net++ for Multi-Lesion Retinal Segmentation with Uncertainty-Guided Explainability in Diabetic Retinopathy.

• Erscheinungsdatum: 02.08.2026
• Autor/Autorin: Anshul Verma
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 96.6 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 614 Seiten
• ISBN: 9783032271174
• Lieferung: Download ab 02.08.2026
• Hinweis: Vorbestellung. Verfügbar per Download ab Erscheinungstag. Kein physischer Versand.
• Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.