Advanced Technologies in Electronics, Communications and Signal Processing

First EAI International Conference, ICATECS 2024, Hyderabad, India, July 26–27, 2024, Proceedings, Part II

AvKrishna Kishore Koganti,Sreenivasa Rao E.

Häftad, Engelska, 2025

Del i serien Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Beskrivning

This book LNICST 620 constitutes the proceedings of the First EAI International Conference on Advanced Technologies in Electronics, Communications and Signal Processing, ICATECS 2024, held in Hyderabad, India, during July 26–27, 2024. The 65 full papers were carefully reviewed and selected from 210 submissions.

Produktinformation

Utgivningsdatum:2025-08-19
Mått:155 x 235 x 23 mm
Vikt:633 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Antal sidor:399
Förlag:Springer International Publishing AG
ISBN:9783031942822
Originaltitel:Advanced Technologies in Electronics, Communications and Signal Processing

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Innehållsförteckning

Machine Learning and Deep Learning Applications.- Optimizing Blood Donation Operations with a PHP Driven Management System.- FIT FORMA: live fitness tracking application with blaze pose.- Genetic Algorithm Driven Hyperparameter Optimization for Precision Sickle Cell Disease Prognosis.- Enhanced Weed Classification with a Custom CNN: Evaluating Optimizer Performance.- A STRATEGY FOR SAFEGUARDING THE MOST FRAGILE RAIL TRANSPORT SYSTEM IN THE EVENT OF AN ACCIDENT - “TRACK SENTIAL”.- Leveraging Transfer Learning for Plant Identification with Limited Data: A MobileNet V2 Based Approach.- Airline Passenger Satisfaction Analysis using Classifiers of Computation Models and Explainable AI.- Classify Object Behavior to Enhance the Safety of Autonomous Vehicles.- Bottom Bounce Analysis of Multipath Acoustic Propagation in Deep Water.- Comparative Analysis of Self-Supervised Monocular Depth Estimation and ORB-SLAM2 in Visual Perception and Robotics.- Acoustic based Drone Detection using Machine Learning.- Bifunctional Tunable Metasurface for Terahertz Shielding and Refractive Index Sensing Using a Machine Learning Based Prediction Model.- Temporal Analysis in Deep Learning-Based Recommender Systems for Predicting Plant Disease Outbreaks.- Enhancing IoT Security: Multiclass Traffic Classification with Advanced Machine Learning Algorithms.- Detection of Artificial Profiles on Social Media Platforms Using Machine Learning and Natural Language Processing.- Towards sustainable weed management using lightweight deep learning model.- Enhancing Safety in Autonomous Vehicles Using Advanced Deep Learning-Based Pothole Detection.- CalMeter: Food Recognition and Calorie Estimation using Deep Learning.- Enhancing Web-Based Advanced Persistent Threat Detection through Deep Learning Techniques.- Application of Machine Learning Algorithms for Vegetable Quality Prediction.- Diabetic Retinopathy Detection using Deep Learning.- Optimized Feature Selection for Motion Sensor Based Human Activity Recognition with Machine Learning Techniques.- Enhancing Spectrum Awareness in Cellular Networks through Deep Learning Approaches for Efficient 5G-NR and LTE Signal Classification.- Advancements in Diabetes Prediction: Integrating Machine Learning with Smart Sensor.- A Hybrid Deep Learning Approach to X-Ray Diagnosis of Lung Diseases.- Transparency in Lung Cancer Prediction: Integrating Explainable AI Techniques with Machine Learning Models.- Accurate Prediction and Classification of Heart failure using Machine learning algorithms and Interpretation using Explainable AI.- Electric Vehicle Charging Stations: A Comparative Study of Multiple Machine Learning Classifiers and Interpretation Using Explainable AI.- Deep Learning for Urban Sound Classification: Using CNN and YAMNet Model Integration.- MelanomaNet: Deep Learning for Skin Cancer Diagnosis through Inception V3.- Deciphering Chronic Kidney Disease Diagnosis: A Comparative Exploration of Computational Approaches with LIME and SHAP Interpretation.- Exploring Seed Quality Assessment through Convolutional Neural Networks and Generative Adversarial Networks.- Tuberculosis Detection in Chest X-rays using Deep Learning Algorithms with segmentation and data augmentation techniques.