bhuiyan touhid (44 risultati)

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Condizione: New. pp. 136.

Paperback. Condizione: Brand New. 115 pages. 9.00x6.25x0.50 inches. In Stock.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbors, generated from a database made up of the preferences of past users. In the absence of these ratings, trust between the users could be used to choose the neighbor for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world 'friend of a friend' recommendations. To extend the boundaries of the neighbor, an effective trust inference technique is required. This book proposes a trust interference technique called Directed Series Parallel Graph (DSPG) that has empirically outperformed other popular trust inference algorithms, such as TidalTrust and MoleTrust. For times when reliable explicit trust data is not available, this book outlines a new method called SimTrust for developing trust networks based on a user's interest similarity. To identify the interest similarity, a user's personalized tagging information is used. However, particular emphasis is given in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbors used in the automated recommender system. Through a series of case studies and empirical results, this book highlights the effectiveness of this tag-similarity based method over the traditional collaborative filtering approach, which typically uses rating data. Trust for Intelligent Recommendation is intended for practitioners as a reference guide for developing improved, trust-based recommender systems. Researchers in a related field will also find this bookvaluable.

Taschenbuch. Condizione: Neu. Trust for Intelligent Recommendation | Touhid Bhuiyan | Taschenbuch | SpringerBriefs in Electrical and Computer Engineering | xiv | Englisch | 2013 | Springer | EAN 9781461468943 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Condizione: New. In.

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Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

Condizione: New. pp. 745 1st ed. 2020 edition NO-PA16APR2015-KAP.

Taschenbuch. Condizione: Neu. Cyber Security and Computer Science | Second EAI International Conference, ICONCS 2020, Dhaka, Bangladesh, February 15-16, 2020, Proceedings | Touhid Bhuiyan (u. a.) | Taschenbuch | xvii | Englisch | 2020 | Springer | EAN 9783030528553 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed post-conference proceedings of the Second International Conference on Cyber Security and Computer Science, ICONCS 2020, held in Dhaka, Bangladesh, in February 2020.The 58 full papers were carefully reviewed and selected from 133 submissions.The papers detail new ideas, inventions, and application experiences to cyber security systems. They are organized in topical sections on optimization problems; image steganography and risk analysis on web applications; machine learning in disease diagnosis and monitoring; computer vision and image processing in health care; text and speech processing; machine learning in health care; blockchain applications; computer vision and image processing in health care; malware analysis; computer vision; future technology applications; computer networks; machine learning on imbalanced data; computer security; Bangla language processing.

Paperback. Condizione: Brand New. 764 pages. 9.25x6.10x1.73 inches. In Stock.

Paperback. Condizione: new. Paperback. This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2023), organised by Jahangirnagar University, Bangladesh, and Daffodil International University, Bangladesh, held in Dhaka, Bangladesh, during 68 September 2023. The book covers research papers in the field of big data, IoT and machine learning. The book is helpful for active researchers and practitioners in the field. This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2023), organised by Jahangirnagar University, Bangladesh, and Daffodil International University, Bangladesh, held in Dhaka, Bangladesh, during 68 September 2023. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Taschenbuch. Condizione: Neu. Proceedings of the 3rd International Conference on Big Data, IoT and Machine Learning | BIM 2025, Volume 2 | Mohammad Shamsul Arefin (u. a.) | Taschenbuch | xxii | Englisch | 2026 | Springer | EAN 9783032157638 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Proceedings of the 3rd International Conference on Big Data, IoT and Machine Learning | BIM 2025, Volume 1 | Mohammad Shamsul Arefin (u. a.) | Taschenbuch | Lecture Notes in Networks and Systems | xxiii | Englisch | 2026 | Springer | EAN 9783032153456 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2023), organised by Jahangirnagar University, Bangladesh, and Daffodil International University, Bangladesh, held in Dhaka, Bangladesh, during 6-8 September 2023. The book covers research papers in the field of big data, IoT and machine learning. The book is helpful for active researchers and practitioners in the field.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT, and Machine Learning (BIM 2025), organized by Dhaka International University, Bangladesh, during 25 27 September 2025. This book covers research papers in the field of big data, IoT and machine learning. The book is helpful for active researchers and practitioners in the field.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2025), organised by Dhaka International University, Bangladesh, during 25 27 September 2025. The book covers research papers in the field of big data, IoT and machine learning. The book is helpful for active researchers and practitioners in the field.

Paperback. Condizione: Brand New. 1182 pages. 9.25x6.10x9.30 inches. In Stock.

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbors, generated from a database made up of the preferences of past users. In the absence of these ratings, trust between the users could be used to choose the neighbor for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world 'friend of a friend' recommendations. To extend the boundaries of the neighbor, an effective trust inference technique is required. This book proposes a trust interference technique called Directed Series Parallel Graph (DSPG) that has empirically outperformed other popular trust inference algorithms, such as TidalTrust and MoleTrust. For times when reliable explicit trust data is not available, this book outlines a new method called SimTrust for developing trust networks based on a user's interest similarity. To identify the interest similarity, a user's personalized tagging information is used. However, particular emphasis is given in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbors used in the automated recommender system. Through a series of case studies and empirical results, this book highlights the effectiveness of this tag-similarity based method over the traditional collaborative filtering approach, which typically uses rating data. Trust for Intelligent Recommendation is intended for practitioners as a reference guide for developing improved, trust-based recommender systems. Researchers in a related field will also find this book valuable. 136 pp. Englisch.

Condizione: New. Print on Demand pp. 136 34 Illus.

Condizione: New. PRINT ON DEMAND pp. 136.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommen.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbors, generated from a database made up of the preferences of past users. In the absence of these ratings, trust between the users could be used to choose the neighbor for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world ¿friend of a friend¿ recommendations. To extend the boundaries of the neighbor, an effective trust inference technique is required.This book proposes a trust interference technique called Directed Series Parallel Graph (DSPG) that has empirically outperformed other popular trust inference algorithms, such as TidalTrust and MoleTrust. For times when reliable explicit trust data is not available, this book outlines a new method called SimTrust for developing trust networks based on a user¿s interest similarity. To identify the interest similarity, a user¿s personalized tagging information is used. However, particular emphasis is given in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbors used in the automated recommender system. Through a series of case studies and empirical results, this book highlights the effectiveness of this tag-similarity based method over the traditional collaborative filtering approach, which typically uses rating data.Trust for Intelligent Recommendation is intended for practitioners as a reference guide for developing improved, trust-based recommender systems. Researchers in a related field will also find this bookvaluable.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 136 pp. Englisch.

Condizione: new. Questo è un articolo print on demand.