{"title":"Anshul Verma","description":null,"products":[{"product_id":"advanced-network-technologies-and-intelligent-computing-anshul-verma-ebook","title":"Advanced Network Technologies and Intelligent Computing","description":"\u003cp class=\"MsoNormal\"\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 115%; font-family: 'Calibri',sans-serif;\"\u003e.- Deep Learning based Leaf Disease Detection, Prevention, and Treatment Prediction Application for Smart Agriculture.\u003cbr\u003e.- An Attention based CNN for Plant Leaf Disease Detection.\u003cbr\u003e.- Improvising the performance of Transcutaneous Electroacupuncture Stimulation (TEAS) Induced EEG Classification using Optimal Band selection and Sequential Ridge Regression.\u003cbr\u003e.- Reinforcement Learning based on TD-3 for the Nonlinear Model of CSTR: Temperature Tracking.\u003cbr\u003e.- Advanced Short-Term Load Forecasting for the Indian Grid: A Hybrid Convolutional and Recurrent Neural Network Approach.\u003cbr\u003e.- To Be, Not to Be, or Both: A Quantum Leap for Sentiment Analysis.\u003cbr\u003e.- Deep Learning-based Defect Detection in Agricultural Crops: A Comprehensive Approach.\u003cbr\u003e.- Multi-Class Classification of Chronic Renal Disease (CRD)  using Deep Convolutional Neural Network (CNN) Model.\u003cbr\u003e.- HeLa CELL DETECTION AND SEGMENTATION USING DIGITAL IMAGE PROCESSING METHODS.\u003cbr\u003e.- An Efficient CNN-Based Method for Detecting Driver Drowsiness from Facial Images.\u003cbr\u003e.- Explainable AI-driven Transcriptome Analysis of Drug Responses for Therapeutic Discovery and Safety Evaluation in Cardiovascular Diseases.\u003cbr\u003e.- Hybrid Fractal-Deep Learning Model for Time Series Prediction: A Comparative Study.\u003cbr\u003e.- Finding the Signal: A Deep Dive into Data Augmentation  and Transformer Performance for Hope Speech Detection.\u003cbr\u003e.- An intelligent approach to artificial speech detection and identification based on KNN.\u003cbr\u003e.- ROI-RONI-Based Reversible Data Hiding Algorithm for Medical Images  Using Huffman Compression and DWT-LSB Embedding.\u003cbr\u003e.- Modeling Recurrent Neural Networks in Serial Recall Paradigm With Dynamic Self-Excitation.\u003cbr\u003e.- SegVCR-LISA: Commonsense Reasoning in VLMs through Segment-Specific Instruction Tuning.\u003cbr\u003e.- A Domain-Aware Grievance Redressal System Leveraging RAG and Open-Source LLMs.\u003cbr\u003e.- Brain Tumour Segmentation using Multi-Orientation Two-Pathway CNN with Attention-Based Fusion in MRI Images.\u003cbr\u003e.- Adaptive MOHAN Activation for Enhanced Brain Tumor Segmentation Using TransUNet and Grad-CAM++.\u003cbr\u003e.- MULTI-STRATEGY FEATURE FUSION FRAMEWORK FOR PLANT DISEASE DIAGNOSIS.\u003cbr\u003e.- LeafGuard: A Deep Learning Framework for Disease Detection in Apple Orchards.\u003cbr\u003e.- An Effective AI-Driven Prompting Approach for Video Summarization System using Flan-T5 Model.\u003cbr\u003e.- A comparative analysis of CNN, BiLSTM and Hybrid CNN-BiLSTM for Air Quality Index.\u003cbr\u003e.- Interpretable Feature Selection for Breast Cancer Diagnosis: A Comparative Study of ElasticNet and SHAP.\u003cbr\u003e.- Assessment of Brassica Napus L. Yield Attributes under Varied Concentrations of Untreated Distillery Effluent through Fuzzy Rule Based Modelling.\u003cbr\u003e.- OtoscopeNet: An Efficient and Attention-Driven Deep Learning Framework for Robust Diagnosis of Ear Diseases.\u003cbr\u003e.- Hybrid Mask Fusion and Transformer-Driven Deep Feature Analysis for Multi-Class Chest X-Ray Diagnosis.\u003cbr\u003e.- TinyEyeNet: An Efficient CNN for Classifying Anterior Segment Eye Conditions.\u003cbr\u003e.- A Comparative Analysis of Deep Learning Models for Early Prediction of Alzheimer’s Disease using structural MRI.\u003cbr\u003e.- Attention-ResUNet for Automated Fetal Head Segmentation.\u003cbr\u003e.- Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks.\u003cbr\u003e.- Explainable Hybrid Deep Learning for Medicinal Leaf Disease Classification: R50-MViT with LIME and Grad-CAM Interpretability.\u003cbr\u003e.- Green Channel Enhanced Self Attention U-Net++ for Multi-Lesion Retinal Segmentation with Uncertainty-Guided Explainability in Diabetic Retinopathy.\u003c\/span\u003e\u003c\/p\u003e","brand":"Anshul Verma","offers":[{"title":"Default Title","offer_id":54231673340231,"sku":"9783032271174","price":117.69,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0920\/5455\/2903\/files\/advanced-network-technologies-and-intelligent-comp-ebook-cover.webp?v=1782413799"}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0920\/5455\/2903\/collections\/anshul-verma-autor-kollektion.webp?v=1782413796","url":"https:\/\/www.cinebuch.de\/collections\/anshul-verma.oembed","provider":"CineBuch","version":"1.0","type":"link"}