Artificial Intelligence And Machine Learning Recent Trends And Applications
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Artificial Intelligence
Author: Marco Antonio Aceves-Fernandez
language: en
Publisher: BoD – Books on Demand
Release Date: 2018-06-27
Artificial intelligence (AI) is taking an increasingly important role in our society. From cars, smartphones, airplanes, consumer applications, and even medical equipment, the impact of AI is changing the world around us. The ability of machines to demonstrate advanced cognitive skills in taking decisions, learn and perceive the environment, predict certain behavior, and process written or spoken languages, among other skills, makes this discipline of paramount importance in today's world. Although AI is changing the world for the better in many applications, it also comes with its challenges. This book encompasses many applications as well as new techniques, challenges, and opportunities in this fascinating area.
Artificial Intelligence and Machine Learning, Recent Trends and Applications
Author: Dr. Shanmuganathan V
language: en
Publisher: RK Publication
Release Date: 2025-02-07
Artificial Intelligence and Machine Learning: Recent Trends and Applications explores the advancements, methodologies, and real-world implementations of AI and ML across various industries. It emerging trends such as deep learning, reinforcement learning, generative AI, and ethical AI, providing insights into their impact on healthcare, finance, robotics, and more. The highlights innovations, challenges, and future prospects, making it an essential resource for researchers, professionals, and students seeking to understand the evolving landscape of intelligent systems and their transformative potential in modern society.
Data Science and Interdisciplinary Research: Recent Trends and Applications
Author: Brojo Kishore Mishra
language: en
Publisher: Bentham Science Publishers
Release Date: 2023-09-27
Data Science and Interdisciplinary Research: Recent Trends and Applications is a compelling edited volume that offers a comprehensive exploration of the latest advancements in data science and interdisciplinary research. Through a collection of 10 insightful chapters, this book showcases diverse models of machine learning, communications, signal processing, and data analysis, illustrating their relevance in various fields. Key Themes: Advanced Rainfall Prediction: Presents a machine learning model designed to tackle the challenging task of predicting rainfall across multiple countries, showcasing its potential to enhance weather forecasting. Efficient Cloud Data Clustering: Explains a novel computational approach for clustering large-scale cloud data, addressing the scalability of cloud computing and data analysis. Secure In-Vehicle Communication: Explores the critical topic of secure communication in in-vehicle networks, emphasizing message authentication and data integrity. Smart Irrigation 4.0: Details a decision model designed for smart irrigation, integrating agricultural sensor data reliability analysis to optimize water usage in precision agriculture. Smart Electricity Monitoring: Highlights machine learning-based smart electricity monitoring and fault detection systems, contributing to the development of smart cities. Enhanced Learning Environments: Investigates the effectiveness of mobile learning in higher education, shedding light on the role of technology in shaping modern learning environments. Coastal Socio-Economy Study: Presents a case study on the socio-economic conditions of coastal fishing communities, offering insights into the livelihoods and challenges they face. Signal Noise Removal: Shows filtering techniques for removing noise from ECG signals, enhancing the accuracy of medical data analysis and diagnosis. Deep Learning in Biomedical Research: Explores deep learning techniques for biomedical research, particularly in the realm of gene identification using Next Generation Sequencing (NGS) data. Medical Diagnosis through Machine Learning: Concludes with a chapter on breast cancer detection using machine learning concepts, demonstrating the potential of AI-driven diagnostics.