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[null,"1.","Record Nr.",null,"UNINA9911019753903321"]
[null,null,"Autore",null,"Swarnkar Suman Kumar"]
[null,null,"Titolo",null,"SUPERVISED AND UNSUPERVISED DATA ENGINEERING FOR MULTIMEDIA DATA / / edited by Suman Kumar Swarnkar, JP Patra, Sapna Singh Kshatri, Yogesh Kumar Rathore and Tien Anh Tran"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, NJ, : John Wiley & Sons, Inc., 2024"]
[{"$nulls":4},"Newark : , : John Wiley & Sons, Incorporated, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"9781119786436"]
[{"$nulls":4},"1119786436"]
[{"$nulls":4},"9781119786443"]
[{"$nulls":4},"1119786444"]
[{"$nulls":4},"9781119786429"]
[{"$nulls":4},"1119786428"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource"]
[null,null,"Collana",null,"Advances in data engineering and machine learning"]
[null,null,"Disciplina",null,"005.1"]
[null,null,"Soggetti",null,"Software engineering"]
[{"$nulls":4},"Multimedia systems"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Title Page -- Copyright Page -- Dedication Page -- Book Description -- Contents -- List of Figures -- List of Tables -- Preface -- Chapter 1 SLRRT: Sign Language Recognition in Real Time -- 1.1 Introduction -- 1.2 Literature Survey -- 1.3 Model for Sign Recognition Language -- 1.4 Experimentation -- 1.5 Methodology -- 1.6 Experimentation Results -- 1.7 Conclusion -- Future Scope -- References -- Chapter 2 Unsupervised/Supervised Feature Extraction and Feature Selection for Multimedia Data: (Feature extraction with feature selection for Image Forgery Detection) -- 2.1 Introduction -- 2.2 Problem Definition -- 2.3 Proposed Methodology -- 2.4 Experimentation and Results -- 2.5 Feature Selection &amp -- Pre-Trained CNN Models Description -- 2.6 Bat ELM Optimization Results -- Conclusion -- Declarations -- Consent for Publicaton -- Conflict of Interest -- Acknowledgement -- References -- Chapter 3 Multimedia Data in Healthcare System -- 3.1 Introduction -- 3.2 Recent Trends in Multimedia Marketing -- 3.3 Challenges in Multimedia -- 3.4 "]
[{"$nulls":4},"Opportunities in Multimedia -- 3.5 Data Visualization in Healthcare -- 3.6 Machine Learning and its Types -- 3.7 Health Monitoring and Management System Using Machine Learning Techniques -- 3.8 Health Monitoring Using K-Prototype Clustering Methods -- 3.9 AI-Based Robotics in E-Healthcare Applications Based on Multimedia Data -- 3.10 Future of AI in Health Care -- 3.11 Emerging Trends in Multimedia Systems -- 3.12 Discussion -- References -- Chapter 4 Automotive Vehicle Data Security Service in IoT Using ACO Algorithm -- Introduction -- Literature Survey -- System Design -- Result and Discussion -- Conclusion -- References -- Chapter 5 Unsupervised/Supervised Algorithms for Multimedia Data in Smart Agriculture -- 5.1 Introduction -- 5.2 Background."]
[{"$nulls":4},"5.3 Applications of Machine Learning Algorithms in Agriculture -- References -- Chapter 6 Secure Medical Image Transmission Using 2-D Tent Cascade Logistic Map -- 6.1 Introduction -- 6.2 Medical Image Encryption Using 2D Tent and Logistic Chaotic Function -- 6.3 Simulation Results and Discussion -- 6.4 Conclusion -- Acknowledgement -- References -- Chapter 7 Personalized Multi-User-Based Movie and Video Recommender System: A Deep Learning Perspective -- 7.1 Introduction -- 7.2 Literature Survey on Video and Movie Recommender Systems -- 7.3 Feature-Based Solutions for Movie and Video Recommender Systems -- 7.4 Fusing: EF - (Early Fusion) and LF - (Late Fusion) -- 7.5 Experimental Setup -- 7.6 Conclusions -- References -- Chapter 8 Sensory Perception of Haptic Rendering in Surgical Simulation -- Introduction -- Methodology -- Background Related Work -- Application -- Case Study -- Future Scope -- Result -- Conclusion -- Acknowledgement -- References -- Chapter 9 Multimedia Data in Modern Education -- Introduction to Multimedia -- Traditional Learning Approaches -- Applications of Multimedia in Education -- Conclusion -- References -- Chapter 10 Assessment of Adjusted and Normalized Mutual Information Variants for Band Selection in Hyperspectral Imagery -- Introduction -- Test Datasets -- Methodology -- Statistical Accuracy Investigations -- Results and Discussion -- Conclusion -- References -- Chapter 11 A Python-Based Machine Learning Classification Approach for Healthcare Applications -- Introduction -- Methodology -- Discussion -- References -- Chapter 12 Supervised and Unsupervised Learning Techniques for Biometric Systems -- Introduction -- Various Biometric Techniques -- Major Biometric-Based Problems from a Security Perspective -- Supervised Learning Methods for Biometric System -- Unsupervised Learning Methods for Biometric System -- Conclusion."]
[{"$nulls":4},"References -- About the Editors -- Index -- EULA."]
[null,null,null,"Sommario/riassunto","SUPERVISED and UNSUPERVISED DATA ENGINEERING for MULTIMEDIA DATA Explore the cutting-edge realms of data engineering in multimedia with Supervised and Unsupervised Data Engineering for Multimedia Data, where expert contributors delve into innovative methodologies, offering invaluable insights to empower both novices and seasoned professionals in mastering the art of manipulating multimedia data with precision and efficiency. Supervised and Unsupervised Data Engineering for Multimedia Data presents a groundbreaking exploration into the intricacies of handling multimedia data through the lenses of both supervised and unsupervised data engineering. Authored by a team of accomplished experts in the field, this comprehensive volume serves as a go-to resource for data scientists, computer scientists, and researchers seeking a profound understanding of cutting-edge methodologies. The book seamlessly integrates theoretical foundations with practical applications, offering a cohesive framework for navigating the complexities of multimedia data. "]
[{"$nulls":4},"Readers will delve into a spectrum of topics, including artificial intelligence, machine learning, and data analysis, all tailored to the challenges and opportunities presented by multimedia datasets. From foundational principles to advanced techniques, each chapter provides valuable insights, making this book an essential guide for academia and industry professionals alike. Whether you're a seasoned practitioner or a newcomer to the field, Supervised and Unsupervised Data Engineering for Multimedia Data illuminates the path toward mastery in manipulating and extracting meaningful insights from multimedia data in the modern age."]
[null,"2.","Record Nr.",null,"UNINA9910831892103321"]
[null,null,"Autore",null,"Ehret Christopher"]
[null,null,"Titolo",null,"Southern Nilotic History : : Linguistic Approaches to the Study of the Past / / Christopher Ehret"]
[null,null,"Pubbl/distr/stampa",null,"[s.l.] : , : Northwestern University Press, , 1971"]
[null,null,"ISBN",null,"9780810138339"]
[{"$nulls":4},"0810138336"]
[null,null,"Descrizione fisica",null,"1 online resource (216 p.)"]
[null,null,"Collana",null,"Humanities Open Books"]
[null,null,"Soggetti",null,"History / Africa / East"]
[{"$nulls":4},"History"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","In Southern Nilotic History, Christopher Ehret reconstructs the history of the Southern Nilotic speaking peoples of East Africa, from their earliest origins to the beginning of the colonial period. As a history, the book is a remarkable tour de force. Using mainly linguistic evidence, the author locates populations, moves them around, determines their relative influence vis-vis their neighbors, and reconstructs aspects of their culture, from basic economy to the practice of extracting incisors. - American Anthropologist"]