
Teil der Reihe: Springer Nature Proceedings excluding Computer Science
Emerging Trends in Microelectronics, Communication and Intelligent Systems
Inhaltsangabe
An Efficient Method for Early Detection System for Cardiovascular Disease using Ensemble Machine Learning Techniques.- Word Recognition Using DTW.- Classroom Activity Detection using TinyML.- Analysis of Different Antenna Array Patterns for Hybrid Beamforming in Massive MIMO.- Design and Implementation of Cyclic Redundancy Check for Downlink Transmission in NB-IoT.- Performance Evaluation of CPU and GPU Processors Using Advanced Data Analysis Techniques.- A Survey on Skin Disease Detection Techniques through Deep Learning.- Fatigue and Drowsiness Detection using CNN.- Neuromorphic Computing Using RRAM.- Development of wearable beat-to-beat blood pressure variability monitor using differential pulse arrival time.- Machine Learning Based Sleep Pattern Analysis Using Linear Regression Algorithm.- ViD-Vision in Dark: Object Detection in Low-Light Images.- Intelligent Energy Management Systems for Future Smart Cities.- Design and Implementation of Vedic BCD Multiplier on Artix 7 FPGA Board.- Optimizing Sentiment Analysis in Airline Feedback: Feature Selection Impact on Machine Learning Models.- Sentiment Analysis on Medical Discharge Summaries.- Navigation and Control of SCARA Robot using Automation Studio.- APP BASED LEAF DISEASE DETECTION USING IMAGE PRE PROCESSING AND DEEP LEARNING.- Voice Command Enabled Robotic Arm: Enhancing in Human-Robot Interaction.- Comparative Analysis of NLP using various Machine Learning Methods.- Diabetes Prediction Using Machine Learning Algorithms with Different Feature Scaling Techniques.- Face Recognition-Based Smart Attendance Monitoring System Using Liveness Check and Geofencing.- Nonlinear Autoregressive with exogenous input (NARX) Neural Network based Machine Learning model for Time-series Application.- An L-Band Log-Periodic Dipole antenna for Future Moroccan CubeSat Projects.- Anaemia Prediction from Conjunctiva Images: Leveraging Pre-trained CNNs for Feature Extraction and Traditional ML for Classification.- Segmentation and Classification of Pulmonary diseases using Artificial Neural Networks and Otsu-Region Based Method from Chest Radiographs.- Recent Advances in Routing Techniques for IoT-Based Networks: A Comparative Analysis.- Performance Analysis of Hybrid Metal-Graphene THz MIMO Antenna with Equivalent ECM.- Manufacturing Setup for 4nm Semiconductor Integrated Circuits in India: Current Scenario and Opportunities.- Ambulance Relocation Using Deep Reinforcement Learning.- A Graph Based Hybrid Approach for Abstractive Text Summarization using Fastformers to Study Different Performance Matrices.- Image Inpainting using Two Stage Generative Inpainting Network with Contextual Attention.- Maharashtra Engineering College Admission Prediction - A Solution Using Ensemble Machine Learning Model.- Design and Development of an Energy and Power Management Strategy for Dual Energy Storage System for E-Bicycle.- Influence of Micro Cavity Fill Factor on Sensitivity Performance of MOS-HEMT device based Biomolecule Detector.- Temporal Evaluation of SPI and RDI for Agricultural Drought Analysis in Ahmednagar from 1981-2021.- EEG-based Cognitive load detection using pre-trained CNN.- ARTIFICIAL INTELLIGENCE BASED TRUST EVALUATION FOR ATTACKS DETECTION IN THE HEALTH-CARE DOMAIN.- Harvest Insight: Crop Yield Prediction Using Machine Learning Approach.- Averaged State Space Analysis of Single Phase Vienna Rectifier.- Performance Enhancement of Multiband Terahertz Patch Antennas Using Complementary Split Ring Reso-nators.- An Automated Approach for the Detection of Synthetic and Deepfake Media using Deep Learning.
Produktdetails
- Erscheinungsdatum: 30.09.2025
- Autor/Autorin: Samaresh Das
- Reihe: Engineering (R0)
- Format: E-Book
- Dateiformat: PDF
- Kopierschutz: Wasserzeichen
- Dateigröße: 41.1 MB
- Verlag: SPRINGER
- Sprache: Englisch
- Umfang: 557 Seiten
- ISBN: 9789819664061
- Lieferung: Sofort per Download
- Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
- Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.
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