Recent Developments in Communication and Computational Algorithms for Engineering Application

This book explores the latest advancements in computational techniques and communication technologies that address engineering challenges. The focus is on leveraging modern algorithms, such as machine learning, optimization, and signal processing, alongside communication methods like 5G, IoT, and edge computing, to enhance system efficiency and decision-making.
The book is organized into distinct sections, each dedicated to a key topic in communication and computational theory and algorithms. The authors present advanced optimization methods that transform how engineers tackle complex challenges, enabling them to find solutions with greater speed and efficiency. The book highlights the transformative role of artificial intelligence and machine learning in engineering applications, showcasing how intelligent systems are revolutionizing various industries. It explores the potential of algorithms in performance optimization and intelligent decision-making, offering readers insights into technologies such as pattern recognition, data mining, deep learning, and neural networks.
Key techniques and tools from data science and big data analytics are also examined, demonstrating how vast datasets can yield actionable insights. Through topics like data visualization, predictive modeling, clustering, and anomaly detection, the book illustrates how algorithms uncover patterns and trends that empower engineers to make informed decisions. The book delves into numerical techniques, finite element analysis, computational fluid dynamics, and other simulation methods, emphasizing their impact on improving engineering system design and performance.
This book serves as a vital resource for researchers, engineers, and students eager to expand their expertise in this rapidly evolving domain. Its comprehensive coverage and diverse topics inspire readers to push the boundaries of computational theory and algorithms, fostering further innovation. Through its focus on practical applications and theoretical advancements, the book provides readers with a deeper understanding of how algorithms and computational theory are reshaping engineering landscapes, unlocking new possibilities, and driving industrial transformation.

Sommario

Benchmarking Handcrafted Feature-Based Machine Learning Models for Multi-Class Classification of Ovarian Histopathological Images.- Edge-Based Real-Time Animal Intrusion Detection Using YOLOv8 on Raspberry Pi and Coral Edge TPU for Farm and Residential Security.- Design and Implementation of an Automatic Writing Machine Using Raspberry Pi Pico.- Analysis of System and Circuit Level Components for Data Acquisition System using Electroencephalogram Signal.- Capacitor-based Non-dissipative Cell Balancing Strategy used in xEV Battery Management Systems and Comparison with Dissipative Balancing Method.- Implementation of Circular DGS in a Swastika Patch Antenna for 7 GHz Applications.- A Novel VLSI Architecture for Two’s Complement Based Data Comparator Architecture for Non-Linear Image Applications.- ECG Signal Based Stress Detection System Using Deep Learning Algorithm.- Smart Irrigation Solution Using Low-Cost Sensors for Water-Scarce Regions: A Study in Nagaland.- Optimizing Street Lighting: Integration of Motion and Ambient Light Sensors For Energy Efficiency.- Real-Time Underwater Image Enhancement for Autonomous Vehicles Using Deep Learning.- Skin Cancer Classification Using T2T-ResNet Model.- Detection of Fake News Using Transformer Based RNNs.- Financial Time Series Forecasting Using Hybrid Metaheuristic Models: Performance Evaluation.- A Modified Quad-Band Sierpinski Rectangular Shaped Fractal Antenna for Wireless Communication Systems.- Learning Beyond Features: Graph-Based vs. Traditional Models for Anemia Classification.- Design of Hexagonal GPS Patch Antenna Mounted on Quadcopter for L-Band Applications.- Deep Learning Approach for Analysis of OFDM-IM over Fading Channels with CSI Uncertainty.- Efficient Biomedical NER with Traditional Sequence Labeling Models.- Design and Implementation of Flash ADC Architecture.- Forensic Face Sketch Generation and AI-driven Recognition.- Automatic Blood Vessel Segmentation in Retinal Images Using Deep Learning.- A Real-Time IoT and Machine Learning Framework for Soil-Based Crop Recommendation in Semi-Arid Indian Regions.- Fusion Based Predictive Modeling of Mold Temperature in Plastic Injection Molding Using Hybrid Machine Learning Models.- Machine Learning-Based Prediction of Treatment Classes Using Arterial Blood Gas Analysis in Critically Ill Patients.- Design and Analysis of PLA and PAL Circuits using Pass Transistor Logic for Low Power Applications.- Real-Time Fact-Checking and Credibility Assistant System.- Improving Kidney Vessel Segmentation with GLUNet11.- SimCLR Based Self-Supervised Learning for Benign and Malign Lung Nodule Detection.- Enhanced Image Fusion through Multi-scale Adaptive Weighting and Post-Fusion Optimization.- Liver Tumor Segmentation Using ResUNet on LiTS17 and 3D-IRCADb.- Coverage Performance Analysis of STAR-RIS-Aided NOMA and MIMO-NOMA Networks.- Early Detection and Classification of Potato Leaf Diseases Using Deep Learning and CNN for Sustainable Agriculture and Global Food Security.- Satellite Image Dehazing using DCP-CLAHE Fusion and Laplacian Pyramid Blending.- Deep Emission – AI-Powered Prediction of Engine Pollutants using CatBoost.- Behavioral Biometric Gait Authentication with Deep Learning Using Sensors.- AI-Driven Adaptive Beamforming for Energy-Efficient 5G Communication.- Self-Tuning Randomness Generator Utilizing TRNG with Polynomically Tunable LFSR.- Novel Approach to LFSR State Extension in a High Speed Counter.- Enhancing Mental Health Detection: A Support Vector Machine Approach Optimized by the Adaptive Giant Trevally Algorithm.- Random Selection Search (RSS): A novel strategy Optimization.- Whispers of Breath: Lens Enhanced mm Wave MIMO Antenna Design for Neonatal Respiration Sensing.- Bayesian-Tuned Machine and Deep Learning Approaches for Multiclass Thyroid Disease Detection.- Optimizing Multiplier Efficiency: A Comprehensive study of Approximate 4-2 Compressors for Energy-Efficient Computing.- Artificial Intelligence-Based Vaccine Hesitancy Prediction: A Machine Learning Approach.- Effective Restoration of Underwater Images Using Green Channel Prior and Double-Opponency Light Estimation.- Halo-Free and Color-Consistent Exposure Fusion via Gradient Domain Guided Filtering.- Fail-Safe Multi-Modal CAN Security: A Resilient Approach to Intra-Vehicular Intrusion Detection.- An Optimized Volterra Framework for Nonlinear System Identification.

Info autore

Dr. Sanjeev Kumar
did his Ph.D. from Department of Electronics and Communication Engineering, Jaypee University of Engineering & Technology, Guna, Madhya Pradesh, in year 2019 with the specialization in MIMO antenna with ML and M.Tech. in 2012 from University of Delhi with specialization in RF device. He has completed his graduation in 2009 with bachelor of technology degree in electronics and communication engineering from RGPV, Bhopal. He has more than 11 years of teaching and research experience for UG and PG courses of ECE and computer science engineering. He has more than 75 research papers published in reputed international journals and conferences with a citation more than 580. He is the reviewer of various peer-reviewed journals. His current research interests include biomedical antenna design for wearable applications using machine learning. He has organized and attended many workshops, seminars, FDP, and national and international conferences in India.

Dr. Mahesh Kumar Singh
did his Ph.D. from Department of Electronics and Communication Engineering, Jaypee University of Engineering & Technology, Guna, Madhya Pradesh, in year 2020 with the specialization in speech signal processing using machine learning and M.Tech. in 2011 from Jaypee University of Engineering & Technology, Guna, with specialization in digital signal processing. He has completed his graduation in 2009 with bachelor of technology degree in electronics and communication engineering from Uttar Pradesh Technical University, Lucknow, Uttar Pradesh. He has more than 12 years of teaching and research experience for UG and PG courses of ECE and computer science engineering. He is an active researcher in the field of speech and image signal processing using machine learning. He has more than 60 research papers published in reputed international journals and conferences with a citation more than 700. He is the reviewer of various peer-reviewed journals.

Riassunto

This book explores the latest advancements in computational techniques and communication technologies that address engineering challenges. The focus is on leveraging modern algorithms, such as machine learning, optimization, and signal processing, alongside communication methods like 5G, IoT, and edge computing, to enhance system efficiency and decision-making.
The book is organized into distinct sections, each dedicated to a key topic in communication and computational theory and algorithms. The authors present advanced optimization methods that transform how engineers tackle complex challenges, enabling them to find solutions with greater speed and efficiency. The book highlights the transformative role of artificial intelligence and machine learning in engineering applications, showcasing how intelligent systems are revolutionizing various industries. It explores the potential of algorithms in performance optimization and intelligent decision-making, offering readers insights into technologies such as pattern recognition, data mining, deep learning, and neural networks.
Key techniques and tools from data science and big data analytics are also examined, demonstrating how vast datasets can yield actionable insights. Through topics like data visualization, predictive modeling, clustering, and anomaly detection, the book illustrates how algorithms uncover patterns and trends that empower engineers to make informed decisions. The book delves into numerical techniques, finite element analysis, computational fluid dynamics, and other simulation methods, emphasizing their impact on improving engineering system design and performance.
This book serves as a vital resource for researchers, engineers, and students eager to expand their expertise in this rapidly evolving domain. Its comprehensive coverage and diverse topics inspire readers to push the boundaries of computational theory and algorithms, fostering further innovation. Through its focus on practical applications and theoretical advancements, the book provides readers with a deeper understanding of how algorithms and computational theory are reshaping engineering landscapes, unlocking new possibilities, and driving industrial transformation.

Dettagli sul prodotto

• Con la collaborazione di: Sanjeev Kumar (Editore), Kumar Singh (Editore), Mahesh Kumar Singh (Editore)
• Editore: Springer International Publishing
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 04.03.2026
• EAN: 9783032139641
• ISBN: 978-3-032-13964-1
• Illustrazioni: Approx. 490 p.
• Serie: Advances in Science, Technology & Innovation
• Categorie: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP; Gesellschaft und Kultur, allgemein, machine learning, Maschinelles Lernen, Internet of things, Information and Communication Technologies (ICT), wireless sensor networks, optimization algorithms, machine learning algorithms, Deep Learning for Engineering Applications, Optical and Satellite Communication