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Evolutionary Artificial Intelligence de David Asirvatham

Evolutionary Artificial Intelligence: Algorithms for Intelligent Systems

Editat de David Asirvatham, Francisco M. Gonzalez-Longatt, Przemyslaw Falkowski-Gilski, R. Kanthavel
en Limba Engleză Hardback – 14 mar 2024

Diferența majoră între Evolutionary Artificial Intelligence și documentația tehnică standard rezidă în natura sa empirică; nu ne aflăm în fața unui manual de sintaxă, ci în fața unui compediu de soluții validate prin experimente riguroase prezentate la conferința ICEAI 2023. Găsim în această carte o tranziție clară de la arhitecturile statice de inteligență artificială către sisteme dinamice, capabile de auto-optimizare prin mecanisme bio-inspirate. Dacă Artificial Intelligence and Evolutionary Computations in Engineering Systems v-a oferit cadrul teoretic al disciplinelor conexe, acest volum oferă instrumentele practice necesare pentru implementarea unor algoritmi precum optimizarea prin roi de particule (PSO) sau algoritmi memetici în scenarii de stres computațional.

Notăm cu interes structura aplicativă a volumului, care progresează de la procesarea limbajului natural (sumarizarea multilingvă a textelor) către aplicații critice în medicină și securitate. Putem afirma că rigoarea editorială a celor de la Springer se reflectă în modul în care sunt prezentate studiile de caz: de la utilizarea rețelelor ProGAN pentru identificarea bolilor la plante, până la optimizarea XGBoost pentru securitatea dispozitivelor IoT în sănătate. Volumul nu se limitează la algoritmi izolați, ci explorează hibridizarea — cum ar fi combinarea auto-encoderelelor deep stacked cu optimizarea biogeografică pentru procesarea Big Data. Este o resursă care documentează stadiul actual al inteligenței computaționale, punând accent pe rezolvarea problemelor „dificile” care sfidează metodele liniare tradiționale.

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Specificații

ISBN-13: 9789819984374
ISBN-10: 9819984378
Pagini: 584
Ilustrații: XIV, 568 p. 286 illus., 211 illus. in color.
Dimensiuni: 160 x 241 x 27 mm
Greutate: 1.14 kg
Ediția:2024
Editura: Springer
Colecția Algorithms for Intelligent Systems
Seria Algorithms for Intelligent Systems

Locul publicării:Singapore, Singapore

De ce să citești această carte

Această carte este esențială pentru inginerii de date și cercetătorii care doresc să aplice algoritmi evolutivi în domenii precum sănătatea, finanțele sau securitatea cibernetică. Cititorul câștigă acces la metodologii avansate de optimizare și studii de caz care demonstrează cum algoritmii bio-inspirați pot îmbunătăți precizia modelelor de învățare automată în medii complexe și instabile.

Despre autor

Volumul este coordonat de un colectiv de editori de prestigiu, printre care se numără David Asirvatham și Francisco M. Gonzalez-Longatt. Experiența lor vastă în sisteme inteligente și inginerie electrică asigură o selecție riguroasă a lucrărilor incluse. Alături de Przemyslaw Falkowski-Gilski și R. Kanthavel, aceștia au curatoriat contribuțiile de la ICEAI 2023 pentru a reflecta cele mai noi tendințe în computația evolutivă, oferind o perspectivă globală asupra algoritmilor de optimizare și a aplicațiilor lor în infrastructurile critice moderne.

Descriere scurtă

This book gathers a collection of selected works and new research results of scholars and graduate students presented at International Conference on Evolutionary Artificial Intelligence (ICEAI 2023) held in Malaysia during 13-14 September 2023. The focus of the book is interdisciplinary in nature and includes research on all aspects of evolutionary computation to find effective solutions to a wide range of computationally difficult problems. The book covers topics such as particle swarm optimization, evolutionary programming, genetic programming, hybrid evolutionary algorithms, ant colony optimization, evolutionary neural networks, evolutionary reinforcement learning, genetic algorithms, memetic algorithms, novel bio-inspired algorithms, evolving multi-agent systems, agent-based evolutionary approaches, and evolutionary game theory.

Cuprins

Empowering Multilingual Abstractive Text Summarization: A Comparative Study of Word Embedding Techniques.- A more effective ensemble ML method for detecting breast cancer.- Model Accuracy Test for Early Stage of Diabetes Risk Prediction with Data Science Approach.- Financial Statement Fraud Detection using Optimized Deep Neural Network.- Managing Operations in Chaotic Environments with Evolutionary Software Agents.- Identification of Plant Leaf Disease using Synthetic Data Augmentation ProGAN to Improve the Performance of Deep Learning Models.- IoT and Satellite Image Driven Water Quality Monitoring and Assessment Method in Coastal Region.- Machine Learning-powered Cloud-based Text Summarization.- Image Classification Using Few Shot Learning.- A Survey on Thyroid Nodule Detection and Classification.- XGBoost tuned by hybridized SCA metaheuristics for intrusion detection in healthcare 4.0 IoT systems.- Chaotic Biogeography Based Optimization using Deep Stacked Auto Encoder for Big DataClassification.- CigaretteCNN: A Convolutional neural network for detecting cigarette smoking activity.- Predicting Customer Churn in Subscriptio n-Based Enterprises Using Machine Learning.- Improved Edge Detection for Brain Tumor using Multi-Threading and Advanced Parallelism.- Impact of Artificial Intelligence on Investment: A Narrative Review.- Power Quality Conditioner with Hybrid Ant Colony Optimization.- Diagnosis of Early Cardiac Disease by Applying Machine Learning Algorithms.- Fault-Tolerant Mobile Agent System using eXtensible Volunteer Algorithm enabled Dynamic Role Based Access Control in a Conclave Environment.- Hyperparameter Tuning by Evolutionary Algorithm for Object Detection on Multimodal Satellite Imagery.- Comparative Examination of Credit Card Fraud Detection Using Machine Learning Algorithms.- Wagging Based Whale Optimization Algorithm To Enhance The Prediction of Intrusions In IoT Network.- Redefining Leadership in the Age of AI: Tools, Applications, and Limitations.- Happiness Index: Prediction with Machine Learning.- Malicious Domain Detection using Random Indexing and Machine Learning.- Multi Objective Neuro Evolution Based Xception For Fault Detection In Edge System.- Deep Neuro Evaluation With Stacked Auto Encoders Opimization For Biomedical Cancer Text Classification.

Notă biografică

David Asirvatham is currently the Executive Dean for the Faculty of Build Environment, Engineering, Technology and Design, Taylor's University, Malaysia. He has held numerous posts such the Associate Dean for Faculty of Information Technology (Multimedia University), Project Manager for the Multimedia and IT Infrastructure Development for a university campus, Secretary for the Artificial Intelligence Society Malaysia and Country Representative for the Asia E-learning Network (AEN). Dr. David completed his Ph.D. from Multimedia University, M.Sc. (Digital System) from Brunel University (U.K.), and B.Sc. (Hons) Ed. and Post-grad Diploma in Computer Science from University of Malaya. He has been lecturing in the area of ICT for the past 20 years. He has been responsible for the development and implementation of various ICT projects such as E-learning Systems, Smart Card Systems, Interactive Voice Response Systems, E-Procurement Systems and Document Management System. At International level, he worked on various ICT Projects and Workshop in South Africa, Sudan, Iran, Ghana, Vietnam, Maldives, Bangladesh, UAE, India and Brunei.
Francisco M. Gonzalez-Longatt is currently a full professor in electrical power engineering at the Department of Electrical Engineering, IT and Cybernetics, University of Southeast Norway, Norway. His academic qualifications include first Class Electrical Engineering of Instituto Universitario Politécnico de la Fuerza Armada Nacional , Venezuela (1994), Master of Business Administration ( Honors ) of Universidad Bicentenaria de Aragua , Venezuela (1999), PhD in Electrical Power Engineering from the Universidad Central de Venezuela (2008) and Postgraduate Certificate in Higher Education Professional Practice from Coventry University (2013) and Diploma in Leadership and Management (ILM Level 3), Loughborough University (2018). He is the author or editor of several books (Spanish and English) including: “Power Factory Applications for PowerSystem Analysis'', Springer; “Advanced Smart Grid Functionalities based on PowerFactory” Springer, and “Dynamic Vulnerability Assessment and Intelligent Control for Sustainable Power Systems”, Wiley.
Przemyslaw Falkowski-Gilski is a graduate of the Faculty of Electronics, Telecommunications and Informatics (ETI), Gdansk University of Technology. He graduated 1st degree B.Sc. studies (in Polish) and 2nd degree M.Sc. studies (in English) in 2012 and 2013, respectively. Between 2013-2017, during Ph.D. studies, he pursues his interests in the field of electronic media, particularly digital broadcasting systems and quality of networks and services. In 2018 he receives the title of Doctor of Technical Sciences with distinction, discipline Telecommunications, specialty Radio communication. Currently he works as an Assistant Professor. Member of a number of organizational, scientific, technical and program committees of national and international conferences, indexed in DBLP, IEEE Xplore, Scopus, Springer, Web of Science, as well as a reviewer in numerous Polish and foreign English-language journals, from publishing houses including Elsevier, Inderscience, IPPT-PAN, MDPI, Wiley, etc.
R. Kanthavel is presently working as a Full Professor, Dept. of Computer Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia. He Completed his M.E (Communication Systems Engineering) in 1999 and Ph.D from Anna University, Chennai under the faculty of Information and Communication Engineering. He has been over 21 Years teaching and research experiences in both Government and reputed Engineering Colleges. As an administrative part, He has taken up positions effectively as Overall in-charge for Part-time B.E program, Regular P.G program, Head of the Department, Vice Principal and Principal in institutions, NITS-Chennai, Government Engg. College- Tirunelveli, Velammal Engg. College -Chennai, Rajalakshmi Engineering College-Chennai. As an academic side, he has been anexpert member in the areas of Wireless Communication, Advanced Embedded Systems, Image Classification, Big data and Computing Techniques. He has published more than hundred and fifty Research articles in reputed Journals and International Conferences which includes 40 Scopus indexed and 14 SCI indexed publications. He delivered a Keynote address and also Chaired for a technical session in the International Conference held in India and Abroad. In addition to the above, he has already produced 12 Ph.D Scholars under Anna University. Also, he has completed many funded research projects from Funding Agencies of Government of India and Ministry of Education, Saudi Arabia. He is a lifetime member in professional societies like IEEE, CSI, IETE and ISTE. As a proof of his academic achievement, he was the recipient of the National Citizenship Gold Medal Award for Outstanding Research in Engineering Field in 2015 and also received an Outstanding Faculty award in the field of Wireless Networks in 2016.

Caracteristici

Presents research works in the field of evolutionary artificial intelligence Provides original works presented at ICEAI 2023 held in Malaysia Serves as a reference for researchers and practitioners in academia and industry