Although the field of intelligent systems has grown rapidly in recent years, there has been a need for a book that supplies a timely and accessible understanding of this important technology. Filling this need, Case Studies in Intelligent Computing: Achievements and Trends provides an up-to-date introduction to intelligent systems.
This edited book captures the state of the art in intelligent computing research through case studies that examine recent developments, developmental tools, programming, and approaches related to artificial intelligence (AI). The case studies illustrate successful machine learning and AI-based applications across various industries, including:
- A non-invasive and instant disease detection technique based upon machine vision through the image scanning of the eyes of subjects with conjunctivitis and jaundice
- Semantic orientation-based approaches for sentiment analysis
- An efficient and autonomous method for distinguishing application protocols through the use of a dynamic protocol classification system
- Nonwavelet and wavelet image denoising methods using fuzzy logic
- Using remote sensing inputs based on swarm intelligence for strategic decision making in modern warfare
- Rainfall–runoff modeling using a wavelet-based artificial neural network (WANN) model
Illustrating the challenges currently facing practitioners, the book presents powerful solutions recently proposed by leading researchers. The examination of the various case studies will help you develop the practical understanding required to participate in the advancement of intelligent computing applications.
The book will help budding researchers understand how and where intelligent computing can be applied. It will also help more established researchers update their skills and fine-tune their approach to intelligent computing.
Table of Contents
Survey of Intelligent Computing; Kuruvilla Mathew and Biju Issac
Intelligent Machine Vision Technique for Disease Detection through Eye Scanning; Amit Laddi and Amod Kumar
Laser Promotes Proliferation of Stem Cells: A Comprehensive Case Study Consolidated by Intelligent Agent–Based Model Predictions; Aya Sedky Adly, Mohamed H. Haggag, and Mostafa-Sami M. Mostafa
Semantic Orientation–Based Approaches for Sentiment Analysis; Basant Agarwal, Namita Mittal, and Vijay Kumar Sharma
Rough Set on Two Universal Sets and Knowledge Representation; Debi P. Acharjya
Automating Network Protocol Identification; Ryan G. Goss and Geoff S. Nitschke
Intelligent and Non-Intelligent Approaches in Image Denoising: A Comparative Study; Mantosh Biswas and Hari Om
Fuzzy Relevance Vector Machines with Application to Surface Electromyographic Signal Classification; Hong-Bo Xie, Hu Huang, and Socrates Dokos
Intelligent Remote Operating System Detection; João P. Souza Medeiros, João B. Borges Neto, Gutto S. Dantas Queiroz, and Paulo S. Motta Pires
An Automated Surveillance System for Public Places; Kumar S. Ray, Debayan Ganguly, and Kingshuk Chatterjee
Nature-Inspired Intelligence: A Modern Tool for Warfare Strategic Decision Making; Lavika Goel
High-Utility Patterns Discovery in Data Mining: A Case Study; Chiranjeevi Manike and Hari Om
Bag of Riemannian Words for Virus Classification; Masoud Faraki and Mehrtash Harandi
Normalized Ordinal Distance: A Performance Metric for Ordinal, Probabilistic-Ordinal, or Partial-Ordinal Classification Problems; Mohammad Hasan Bahari and Hugo Van Hamme
Predictive Data Mining for Oral Cancer Treatment; Neha Sharma and Hari Om
Human Identification Using Individual Dental Radiograph Records; Omaima Nomir and Mohamed Abdel-Mottaleb
A Novel Hybrid Bayesian-Based Reasoning: Multinomial Logistic Regression Classification and Regression Tree for Medical Knowledge-Based Systems and Knowledge-Based Systems; Patcharaporn Paokanta
Application of Backpropagation Neural Networks in Calculation of Robot Kinematics; R. R. Srikant and Ch. Srinivasa Rao
Conceptual Modeling of Networked Organizations: The Case of Aum Shinrikyo; Saad Alqi thami , Jennifer Haegele, and Henry Hexmoor
Energy-Efficient Wireless Sensor Networks Using Learning Techniques; Sumit Tokle, Shamantha Rai Bellip ady, Rajee v Ranjan, and Shirshu Varma
Knowledge on Routing Nodes in MAN ET: A Soft Computing Approach; Senthilkumar K and Arunkumar Thangavelu
Implication of Feature Extraction Methods to Improve Performance of Hybrid Wavelet-ANN Rainfall–Runoff Model; Vahid Nourani, Tohid Rezapour Khanghah, and Aida Hosseini Baghanam
Artificial Intelligence: A Tool for Better Understanding Complex Problems in Long-Term Care; Vijay K. Mago, Ryan Woolrych, Vahid Dabbaghian, and Andrew Sixsmith
Combining Feature Selection and Data Classification Using Ensemble Approaches: Application to Cancer Diagnosis and Credit Scoring; Afef Ben Brahim, Waad Bouaguel, and Mohamed Limam
Intelligent Grade Estimation Technique for Indian Black Tea; Amit Laddi and Neelam R. Prakash
Dr. Biju Issac is a senior lecturer at the School of Computing, Teesside University, United Kingdom, and has more than 15 years of academic experience with higher education in India, Malaysia, and the United Kingdom. He earned a PhD in networking and mobile communications, along with MCA (master of computer applications) and BE (electronics and communications engineering).
He is a senior Institute of Electrical and Electronics Engineers (IEEE) member, a fellow of the Higher Education Academy, an Institution of Engineering and Technology (IET) member, and a chartered engineer (CEng). He is a CISCO-Certified Network Associate (CCNA) instructor, a Sun-Certified Java instructor, and a Lotus Notes professional. His broad research interests are in computer networks, wireless networks, computer or network security, mobility management in 802.11 networks, intelligent computing, data mining, spam detection, secure online voting, e-learning, and so forth. Dr. Issac has authored more than 60 peer-reviewed research publications, including conference papers, book chapters, and journal papers. He has supervised postgraduate research students to completion. He is in the technical program committee of many international conferences and on the editorial board of some journals and has reviewed many research papers.
Dr. Nauman Israrhas been a senior lecturer at the School of Computing, Teesside University, United Kingdom, for many years. He earned his PhD in wireless sensor networks at the University of Bradford, United Kingdom. He teaches computer networks–related subjects at the university. His areas of research expertise are wireless sensor networks, wireless networked control systems, fly-by-wireless systems, active aircraft, and wireless embedded systems. Dr. Israr was a research fellow at Queen’s University Belfast (Active Aircraft Project). The aim of that project was to design and develop a wireless nervous system for the next-generation Airbus aircrafts, where the wireless system will be used to reduce the turbulence on the aircraft, thus reducing the fuel burned. He has published a number of conference papers, book chapters, and journal papers.