Anwarul Azim (53 results)

Hardcover. Condition: Good. Contents: Preface. Abbreviations. 1. Introduction. 2. Technology and its growth. 3. Technology and development. 4. Technology Transfer (TT). 5. Technology based development planning. 6. Technology and the future. Index. From the Foreword: "The present book puts forward the technology concepts from the management perspectives. It would give a general as well as specific understanding of technological management. The author has contributed his sincere efforts towards the book with his professional skill and wisdom. The book will be of immense benefit to the technologists and the technology teachers and students.".…

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Taschenbuch. Condition: Neu. Privacy Preserving Support Vector Machine Classification in WSN | Muhammad Anwarul Azim (u. a.) | Taschenbuch | 60 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139846603 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. …

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Taschenbuch. Condition: Neu. Classification par machine à vecteurs de support préservant la confidentialité dans les réseaux sans fil. | Muhammad Anwarul Azim (u. a.) | Taschenbuch | Französisch | 2023 | Editions Notre Savoir | EAN 9786205565155 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Taschenbuch. Condition: Neu. Sohranenie konfidencial'nosti pri klassifikacii s pomosch'ü wspomogatel'noj wektornoj mashiny w WSN | Muhammad Anwarul Azim (u. a.) | Taschenbuch | Russisch | 2023 | Sciencia Scripts | EAN 9786205565186 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

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Taschenbuch. Condition: Neu. Classificazione con macchine vettoriali di supporto a tutela della privacy nelle WSN | Muhammad Anwarul Azim (u. a.) | Taschenbuch | Italienisch | 2023 | Edizioni Sapienza | EAN 9786205565162 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Taschenbuch. Condition: Neu. Classificação de Máquinas Vectoriais de Apoio à Preservação da Privacidade na WSN | Muhammad Anwarul Azim (u. a.) | Taschenbuch | Portugiesisch | 2023 | Edições Nosso Conhecimento | EAN 9786205565179 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Condition: Neu. Privatsphäre erhaltende Support-Vektor-Maschinen-Klassifizierung in WSN | Muhammad Anwarul Azim (u. a.) | Taschenbuch | 52 S. | Deutsch | 2023 | Verlag Unser Wissen | EAN 9786205565131 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. …

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The increasing prominence of Wireless Sensor Network (WSN) is stimulating greater interest in developing many application areas. WSNs promise viable solutions aiming at many monitoring problems despite energy, communication, computation & storage constraints. The security issues, data privacy, confidentiality and integrity become vital when the sensors are deployed in a hostile environment. Support Vector Machines (SVM) classification is one of the most widely used classifications having advantage of accuracy and sparse representation that SVMs provide for decision boundaries. It is important to achieve energy efficient data mining in WSN while preserving privacy of data. In this thesis we introduce SVM classification for WSN consisting energy efficiency advantage by distributed incremental learning for the training and construction of global SVM classification model without disclosing the data to others. We show security analysis and energy estimation for preserving privacy and energy efficiency in WSN using SVM. 60 pp. Englisch.…

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Rastuschaq populqrnost' besprowodnyh sensornyh setej (WSN) stimuliruet powyshennyj interes k razwitiü mnogih oblastej primeneniq. WSNs obeschaüt zhiznesposobnye resheniq, naprawlennye na reshenie mnogih problem monitoringa, nesmotrq na ogranicheniq po änergii, swqzi, wychisleniqm i hraneniü dannyh. Voprosy bezopasnosti, konfidencial'nosti, sekretnosti i celostnosti dannyh stanowqtsq zhiznenno wazhnymi, kogda datchiki razwernuty wo wrazhdebnoj srede. Klassifikaciq s pomosch'ü opornyh wektornyh mashin (SVM) qwlqetsq odnoj iz naibolee shiroko ispol'zuemyh klassifikacij, imeüschej preimuschestwo w tochnosti i razrezhennom predstawlenii, kotoroe SVM obespechiwaüt dlq granic prinqtiq reshenij. Vazhno dobit'sq änergoäffektiwnogo poiska dannyh w WSN pri sohranenii konfidencial'nosti dannyh. V ätoj dissertacii my predstawlqem SVM klassifikaciü dlq WSN, sostoqschuü iz preimuschestwa änergoäffektiwnosti putem raspredelennogo inkremental'nogo obucheniq dlq obucheniq i postroeniq global'noj modeli SVM klassifikacii bez raskrytiq dannyh drugim. My pokazywaem analiz bezopasnosti i ocenku änergii dlq sohraneniq konfidencial'nosti i änergoäffektiwnosti w WSN s ispol'zowaniem SVM. 52 pp. Russisch.…

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -La creciente importancia de las redes inalámbricas de sensores (WSN) está suscitando un mayor interés en el desarrollo de muchas áreas de aplicación. Las WSN prometen soluciones viables para muchos problemas de vigilancia a pesar de las limitaciones de energía, comunicación, cálculo y almacenamiento. Los problemas de seguridad, privacidad, confidencialidad e integridad de los datos se vuelven vitales cuando los sensores se despliegan en un entorno hostil. La clasificación mediante máquinas de vectores soporte (SVM) es una de las más utilizadas y ofrece la ventaja de la precisión y la representación dispersa que las SVM proporcionan para los límites de decisión. Es importante conseguir una minería de datos energéticamente eficiente en WSN preservando la privacidad de los datos. En esta tesis introducimos la clasificación SVM para WSN que consiste en la ventaja de la eficiencia energética mediante el aprendizaje incremental distribuido para el entrenamiento y la construcción del modelo de clasificación SVM global sin revelar los datos a otros. Mostramos el análisis de seguridad y la estimación de energía para preservar la privacidad y la eficiencia energética en WSN utilizando SVM. 52 pp. Spanisch.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Azim Muhammad AnwarulMuhammad Anwarul Azim is an Associate Professor in Computer Science & Engineering Department, University of Chittagong, Chittagong-4331, Bangladesh. His B.Sc (Engg.) degree is from Computer Science & Engineering . …

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The increasing prominence of Wireless Sensor Network (WSN) is stimulating greater interest in developing many application areas. WSNs promise viable solutions aiming at many monitoring problems despite energy, communication, computation & storage constraints. The security issues, data privacy, confidentiality and integrity become vital when the sensors are deployed in a hostile environment. Support Vector Machines (SVM) classification is one of the most widely used classifications having advantage of accuracy and sparse representation that SVMs provide for decision boundaries. It is important to achieve energy efficient data mining in WSN while preserving privacy of data. In this thesis we introduce SVM classification for WSN consisting energy efficiency advantage by distributed incremental learning for the training and construction of global SVM classification model without disclosing the data to others. We show security analysis and energy estimation for preserving privacy and energy efficiency in WSN using SVM.…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -L'importance croissante des réseaux de capteurs sans fil (WSN) suscite un intérêt accru pour le développement de nombreux domaines d'application. Les WSN promettent des solutions viables visant à résoudre de nombreux problèmes de surveillance malgré les contraintes d'énergie, de communication, de calcul et de stockage. Les questions de sécurité, de confidentialité et d'intégrité des données deviennent vitales lorsque les capteurs sont déployés dans un environnement hostile. La classification par machines à vecteurs de support (SVM) est l'une des classifications les plus largement utilisées. Elle présente l'avantage de la précision et de la représentation éparse que les SVM fournissent pour les frontières de décision. Il est important de réaliser une exploration de données économe en énergie dans les WSN tout en préservant la confidentialité des données. Dans cette thèse, nous introduisons la classification SVM pour les WSN, en tenant compte de l'avantage de l'efficacité énergétique par l'apprentissage incrémental distribué pour la formation et la construction d'un modèle de classification SVM global sans divulguer les données aux autres. Nous montrons l'analyse de sécurité et l'estimation de l'énergie pour préserver la confidentialité et l'efficacité énergétique dans les WSN en utilisant le SVM. 52 pp. Französisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The increasing prominence of Wireless Sensor Network (WSN) is stimulating greater interest in developing many application areas. WSNs promise viable solutions aiming at many monitoring problems despite energy, communication, computation & storage constraints. The security issues, data privacy, confidentiality and integrity become vital when the sensors are deployed in a hostile environment. Support Vector Machines (SVM) classification is one of the most widely used classifications having advantage of accuracy and sparse representation that SVMs provide for decision boundaries. It is important to achieve energy efficient data mining in WSN while preserving privacy of data. In this thesis we introduce SVM classification for WSN consisting energy efficiency advantage by distributed incremental learning for the training and construction of global SVM classification model without disclosing the data to others. We show security analysis and energy estimation for preserving privacy and energy efficiency in WSN using SVM.…