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Soft Computing for Problem Solving de Manoj Thakur

Soft Computing for Problem Solving: Lecture Notes in Networks and Systems, cartea 547

Editat de Manoj Thakur, Samar Agnihotri, Bharat Singh Rajpurohit, Millie Pant, Kusum Deep, Atulya K. Nagar
en Limba Engleză Paperback – 2 mar 2023

În domeniile inteligenței artificiale și ale științei datelor, volumul Soft Computing for Problem Solving reprezintă o resursă tehnică riguroasă ce documentează progresele prezentate la conferința SocProS 2022. Observăm o orientare clară către rezolvarea problemelor complexe din lumea reală, unde metodele computaționale tradiționale devin ineficiente. Lucrarea este structurată pentru a evidenția atât inovațiile algoritmice, cât și implementările lor practice, acoperind un spectru larg de tehnici, de la rețele neuronale și Deep Learning, până la metode statistice și optimizări euristice precum algoritmul genetic sau optimizarea prin roiuri de particule.

Remarcăm progresia logică a conținutului, care începe cu metodologii de benchmarking pentru segmentarea imaginilor medicale și avansează spre aplicații de nișă, cum ar fi analiza emoțiilor studenților prin CNN sau clasificarea malware-ului bazată pe Transfer Learning. Complementar volumului Soft Computing: Theories and Applications, care pune un accent deosebit pe managementul lanțului de aprovizionare și criptanaliză, acest titlu se distinge prin profunzimea studiilor de caz din domeniul sănătății (analiza semnalelor EEG pentru somn și detectarea crizelor epileptice) și al viziunii computerizate aplicate. Structura capitolelor indică o abordare experimentală solidă, oferind detalii despre preprocesarea datelor și arhitecturile neurale hibride, precum modelele CNN-LSTM.

În contextul evoluției rapide a tehnologiilor de calcul, Soft Computing for Problem Solving servește drept punte între cercetarea academică și ingineria aplicată. Volumul nu se limitează la teorie, ci explorează validarea modelelor în condiții de date imbalansate și medii cu resurse computaționale scăzute, fiind o referință esențială pentru cei care dezvoltă soluții de forecasting sau sisteme de diagnostic automatizat.

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Specificații

ISBN-13: 9789811965241
ISBN-10: 9811965242
Pagini: 756
Ilustrații: XXII, 733 p. 237 illus., 197 illus. in color.
Dimensiuni: 155 x 235 x 38 mm
Greutate: 1.26 kg
Ediția:1st ed. 2023
Editura: Springer
Colecția Lecture Notes in Networks and Systems
Seria Lecture Notes in Networks and Systems

Locul publicării:Singapore, Singapore

De ce să citești această carte

Această carte este indispensabilă cercetătorilor și inginerilor care lucrează la intersecția dintre Machine Learning și Data Science. Cititorul câștigă acces la metodologii de ultimă oră pentru optimizarea algoritmilor și soluții concrete pentru probleme de clasificare și predicție în domenii critice precum medicina și finanțele. Este un ghid practic pentru implementarea arhitecturilor Deep Learning în scenarii complexe unde precizia și eficiența sunt vitale.

Despre autor

Volumul este coordonat de un colectiv de editori de prestigiu, printre care se numără Manoj Thakur, Kusum Deep și Millie Pant, experți recunoscuți în domeniul optimizării numerice și al inteligenței computaționale. Kusum Deep este profesor la IIT Roorkee, având o experiență vastă în algoritmi de optimizare inspirați din natură, în timp ce Millie Pant s-a specializat în aplicarea tehnicilor de soft computing în inginerie. Expertiza lor combinată asigură rigoarea selecției lucrărilor incluse în seria Lecture Notes in Networks and Systems.

Descriere scurtă

This book provides an insight into the 11th International Conference on Soft Computing for Problem Solving (SocProS 2022). This international conference is a joint technical collaboration of the Soft Computing Research Society and the Indian Institute of Technology Mandi. This book presents the latest achievements and innovations in the interdisciplinary areas of Soft Computing, Machine Learning, and Data Science. It brings together the researchers, engineers, and practitioners to discuss thought-provoking developments and challenges, in order to select potential future directions. It covers original research papers in the areas including but not limited to algorithms (artificial neural network, deep learning, statistical methods, genetic algorithm, and particle swarm optimization) and applications (data mining and clustering, computer vision, medical and healthcare, finance, data envelopment analysis, business, and forecasting applications). This book is beneficial for young as well as experienced researchers dealing across complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task.

Cuprins

Chapter 1. Benchmarking state-of-the-art methodologies for optic disc segmentation.- Chapter 2. Automated Student Emotion Analysis During Online Classes using Convolutional Neural Network.- Chapter 3. Transfer Learning based Malware Classification.- Chapter 4. A Study on Metric-Based and Initialization-Based Methods for Few-Shot Image Classification.- Chapter 5. A Fast and Efficient Methods for Eye pre-processing and DR Level Detection.- Chapter 6. A deep neural model CNN-LSTM network for automated sleep staging based on a single-channel EEG signal.- Chapter 7. An Ensemble Model for Gait Classification in Children and Adolescent with Cerebral Palsy: A Low-Cost Approach.- Chapter 8. Imbalanced Learning of Regular Grammar for DFA Extraction from LSTM Architecture.- Chapter 9. Medical Prescription Label Reading using Computer Vision and Deep Learning.- Chapter 10. Autoencoder-based Deep Neural Architecture for Epileptic Seizures Classification.- Chapter 11. Stock Market Prediction using Deep Learning Techniques for Short and Long Horizon.- Chapter 12. Improved CNN Model for Breast Cancer Classification.- Chapter 13. Performance Assessment of Normalization in CNN with Retinal Image Segmentation.- Chapter 14. A novel multi-day ahead index price forecast using multi-output based deep learning system.- Chapter 15. Automatic Retinal Vessel Segmentation using BTLBO. etc.

Notă biografică

Dr. Manoj Thakur is Associate Professor at the School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, India. His research interests include optimization, machine learning, and computational finance.
 
Dr. Samar Agnihotri received the M.Sc. (Engg.) and Ph.D. degrees in electrical sciences from IISc Bangalore. From 2010 to 2012, he was Postdoctoral Fellow with the Department of Information Engineering, the Chinese University of Hong Kong. He is currently Associate Professor with the School of Computing and Electrical Engineering, IIT Mandi. His research interests include communication and information theory.  Dr. Bharat Singh Rajpurohit received the M.Tech. degree in power apparatus and electric drives from the Indian Institute of Technology Roorkee, Roorkee, India, in 2005 and the Ph.D. degree in electrical engineering from the Indian Institute of Technology Kanpur, Kanpur, India, in 2010. He is currently Professor with the School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, India. His major research interests include electric drives, renewable energy integration, and intelligent and energy-efficient buildings. He is Member of the International Society for Technology in Education, the Institution of Engineers, India, and the Institution of Electronics and Telecommunication Engineers.
 
Dr. Millie Pant is Professor at the Department of Applied Mathematics and Scientific Computing, Indian Institute of Technology Roorkee (IIT Roorkee) in India. Her areas of interests include numerical optimization, operations research, decision-making techniques, and artificial intelligence.  
Dr. Kusum Deep is Professor at the Department of Mathematics, Indian Institute of Technology Roorkee. Her research interests include numerical optimization, nature-inspired optimization, computational intelligence, genetic algorithms, parallel genetic algorithms, and parallel particle swarm optimization.   Prof. Atulya K. Nagar holds Foundation Chair as Professor of Mathematical Sciences and is Pro-Vice-Chancellor (Research) at Liverpool Hope University, UK. He is responsible for developing Sciences and Engineering and has been the Head of the School of Mathematics, Computer Science, and Engineering which he established at the university. He received a prestigious Commonwealth Fellowship for pursuing his doctorate (D.Phil.) in Applied Nonlinear Mathematics, which he earned from the University of York (UK) in 1996. He holds B.Sc. (Hons), M.Sc., and M.Phil. (with distinction) in Mathematical Physics from the MDS University of Ajmer, India. Prior to joining Liverpool Hope, he was with the Brunel University, London. He is an internationally respected scholar working at the cutting edge of nonlinear mathematics, theoretical computer science, and systems engineering. He has edited volumes on intelligent systems andapplied mathematics. He is well published with over 450 publications in prestigious publishing outlets. He has an extensive background and experience of working in universities in the UK and India. He has been an expert reviewer for the Biotechnology and Biological Sciences Research Council (BBSRC) grants peer-review committees for Bioinformatics Panel; Engineering and Physical Sciences Research Council (EPSRC) for High Performance Computing Panel; and served on the Peer-Review College of the Arts and Humanities Research Council (AHRC) as Scientific Expert member. Prof. Nagar sits on the JISC Research Strategy group, and he is Fellow of the Institute of Mathematics and its applications (FIMA) and Fellow of the Higher Education Academy (FHEA).

Caracteristici

Presents research works in the field of soft computing Provides original works presented at SocProS 2022 held in Mandi, India Serves as a reference for researchers and practitioners in academia and industry