A gallery of books by faculty in the Department of Computer Science, College of Sciences, Old Dominion University.
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Applications of Computational Learning and IoT in Smart Road Transportation System
2025Saurav Mallik (Editor), Hong Qin (Editor), Subrata Nandi (Editor), Munshi Yusuf Alam (Editor), Arup Roy, and Tanvir Habib Sardar (Editor)
This book discusses machine learning and AI in real-time image processing for road transportation and traffic management. There is a growing need for affordable solutions that make use of cutting-edge technology like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). The efficiency, sustainability, and safety of transport networks can be greatly increased by implementing an Internet of Things (IoT) and machine learning (ML)-based smart road transport system. Install sensors on roadways and intersections to gather data on traffic conditions in real time, such as vehicle density, speed, and flow. Predicting traffic patterns is done by analyzing the gathered data using machine learning algorithms. This can lessen traffic, enhance overall traffic management, and optimize traffic signal timings. Vehicles equipped with Internet of Things devices can have their health monitored in real time. Parameters including lane changes, brake condition, tire pressure, and engine performance can all be monitored by sensors. Based on the gathered data, ML models are used to forecast probable maintenance problems. By scheduling preventive maintenance, failures can be avoided and overall road safety can be increased. Create a smartphone app that would enable drivers to locate parking spots in their area. To forecast parking availability based on past data, the time of day, and special events, apply machine learning algorithms. Integrate Internet of Things (IoT) sensors into fleet vehicles to monitor their performance, location, and fuel consumption. To maximize fleet efficiency, reduce fuel consumption, and plan routes more effectively, apply machine learning algorithms. Train ML models to forecast the quickest and most efficient routes with the help of historical data analysis. Route recommendations for drivers or fleet management systems can be constantly adjusted with real-time updates, which contain real-time data on road conditions, accidents, and construction. To guarantee smooth integration and efficient implementation, government organizations, transportation providers, and technology firms must work together.
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Landscape of Pattern Learning Applied to Public Health and Social Sciences
2024Saurav Mallik, Himanish Shekhar Das, Hong Qin, Kangkana Bora, and Koushik Mallick
In this book, different machine learning and deep learning-based approaches are provided in terms of public health and social science. This book demonstrates medical imaging-based cancer detection studies. Chapter One discusses a comprehensive analysis of tissue-specific colorectal cancer classification from H&E-stained microscopic images. Chapter Two demonstrates an Ensemble-Based CNN framework for Breast Cancer Detection in Mammograms. Chapter Three provides a Deep Learning-Based Tissue-Specific Classification technique of Colorectal Cancer from H&E-Stained Microscopic Images. Chapter Four describes Parkinson's Disease Detection through machine learning technique from Speech and Imaging Data. Chapter Five describes empowering social causes, i.e., Indian Language Identification with Multimodality Strategy. Moreover, this book provides innovative information about pattern recognition, feature selection and disease classification from medical imaging datasets for public and social sciences that are benevolent for healthcare persons, doctors and social science researchers. [Frpm the publisher]
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Vehicular Networks: From Theory to Practice
2009Stephan Olariu (Editor) and Michele C. Weigle (Editor)
In spite of their importance and potential societal impact, there is currently no comprehensive source of information about vehicular ad hoc networks (VANETs). Cohesively integrating the state of the art in this emerging field, Vehicular Networks: From Theory to Practice elucidates many issues involved in vehicular networking, including traffic engineering, human factors studies, and novel computer science research.
Divided into six broad sections, the book begins with an overview of traffic engineering issues, such as traffic monitoring and traffic flow modeling. It then introduces governmental and industrial efforts in the United States and Europe to set standards and perform field tests on the feasibility of vehicular networks. After highlighting innovative applications enabled by vehicular networks, the book discusses several networking-related issues, including routing and localization. The following section focuses on simulation, which is currently the primary method for evaluating vehicular networking systems. The final part explores the extent and impact of driver distraction with in-vehicle displays.
Encompassing both introductory and advanced concepts, this guide covers the various areas that impact the design of applications for vehicular networks. It details key research challenges, offers guidance on developing future standards, and supplies valuable information on existing experimental studies. [Amazon.com]
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Numerical Simulations and Case Studies Using Visual C++.Net
2008Shaharuddin Salleh, Albert Y. Zomaya, Stephan Olariu, and Bahrom Sanugi
Master the numerical simulation process required to design, test and support mobile and parallel computing systems. An accompanying ftp site contains all the Visual C++ based programs discussed in the text to help readers create their own programs. With its focus on problems and solutions, this is an excellent text for upper-level undergraduate and graduate students, and a must-have reference for researchers and professionals in the field of simulations. [Amazon.com]
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Handbook of Bioinspired Algorithms and Applications
2005Stephan Olariu (Editor) and Albert Y. Zomaya (Editor)
The mystique of biologically inspired (or bioinspired) paradigms is their ability to describe and solve complex relationships from intrinsically very simple initial conditions and with little or no knowledge of the search space. Edited by two prominent, well-respected researchers, the Handbook of Bioinspired Algorithms and Applications reveals the connections between bioinspired techniques and the development of solutions to problems that arise in diverse problem domains.
A repository of the theory and fundamentals as well as a manual for practical implementation, this authoritative handbook provides broad coverage in a single source along with numerous references to the available literature for more in-depth information. The book's two sections serve to balance coverage of theory and practical applications. The first section explains the fundamentals of techniques, such as evolutionary algorithms, swarm intelligence, cellular automata, and others. Detailed examples and case studies in the second section illustrate how to apply the theory in actually developing solutions to a particular problem based on a bioinspired technique.
Emphasizing the importance of understanding and harnessing the robust capabilities of bioinspired techniques for solving computationally intractable optimizations and decision-making applications, the Handbook of Bioinspired Algorithms and Applications is an absolute must-read for anyone who is serious about advancing the next generation of computing. [Amazon.com]
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Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences
2000Albert Y. Zomaya (Editor), Fikret Ercal (Editor), and Stephan Olariu (Editor)
Solving problems in parallel and distributed computing through the use of bio-inspired techniques. Recent years have seen a surge of interest in computational methods patterned after natural phenomena, with biologically inspired techniques such as fuzzy logic, neural networks, simulated annealing, genetic algorithms, or evolutionary computer models increasingly being harnessed for problem solving in parallel and distributed computing. Solutions to Parallel and Distributed Computing Problems presents a comprehensive review of the state of the art in the field, providing researchers and practitioners with critical information on the use of bio-inspired techniques for improving software and hardware design in high-performance computing. [From the back cover]