Muhammad atta othman khfagy (31 risultati)
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Editore: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
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Taschenbuch. Condizione: Neu. Diversity Role in Designing Multiple Classifier Systems Using MATLAB | Designing of MCS: A Diversity Approach | Muhammad Atta Othman Ahmed Khfagy | Taschenbuch | 104 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9783659522406 | 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. Condizione: Neu. Artificial Neural Networks Applied For Digital Images with Matlab Code | The Applications of Artificial Intelligence In Image Processing Field Using Matlab | Muhammad Atta Othman A. Khfagy | Taschenbuch | 152 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659538179 | Verantwortlich…e Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.
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Taschenbuch. Condizione: Neu. Artificial Neural Networks Applied to Digital Images with MATLAB Code | 2nd Edition | Muhammad Atta Othman A. Khfagy | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209788802 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA,…LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Multiple Classifiers Systems (MCS) perform in formation fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. We address one of the main open issues abo…ut the use of Diversity in Multiple Classifier Systems: the effectiveness of the explicit use of diversity measures for creation of classifier ensembles. So far, diversity measures have been mostly used for ensemble pruning, namely, for selecting a subset of classifiers out of an original, larger ensemble. Here we focus on pruning techniques based on forward selection, since they allow a direct comparison with the simple estimation of accuracy of classifier ensemble. We empirically carry out this comparison for several diversity measures and bench mark data sets, using bagging as the ensemble construction technique, and majority voting as the fusion rule. 104 pp. Englisch.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Artificial Neural Networks have broad applications to the real world business problems. They have already been successfully applied in many industries. Since neural networks are best at identifying patterns or trends in data, they…are well suited for prediction or forecasting. These include Sales forecasting, Industrial process control, Customer research, Data validation, Risk management, Target marketing. The work studies the use of Artificial Neural Network in the field of Image Processing. One of the applications studied is the edge detection process. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Edges detection of digital images is used in a various fields of applications ranging from real-time video surveillance and traffic management to medical imaging applications. The work demonstrates both entropy and Neural Network based edge detection methods, where Renyi s Entropy and Convolutional Neural Network based edge detection is proposed and their results are compared. 152 pp. Englisch.
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Paperback. Condizione: new. Paperback. Artificial Neural Networks have broad applications in real-world business problems and have been successfully applied across many industries. As they excel at identifying patterns and trends in data, they are well suited for prediction and forecasting tasks such as sales forecasting, indust…rial process control, customer research, data validation, risk management, and target marketing. This work examines the use of Artificial Neural Networks in the field of image processing. One of the applications studied is edge detection, which significantly reduces data volume and filters out irrelevant information while preserving essential structural features of an image. Edge detection is widely used in various fields, ranging from real-time video surveillance and traffic management to medical imaging. The study presents both entropy-based and neural network-based edge detection methods, specifically using Renyi's entropy and convolutional neural networks, and compares their performance. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial Neural Networks have broad applications to the real world business problems. They have already been successfully applied in many industries. Since neural networks are best at identifying patterns or trends in data, they are w…ell suited for prediction or forecasting. These include Sales forecasting, Industrial process control, Customer research, Data validation, Risk management, Target marketing. The work studies the use of Artificial Neural Network in the field of Image Processing. One of the applications studied is the edge detection process. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Edges detection of digital images is used in a various fields of applications ranging from real-time video surveillance and traffic management to medical imaging applications. The work demonstrates both entropy and Neural Network based edge detection methods, where Renyi s Entropy and Convolutional Neural Network based edge detection is proposed and their results are compared.
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ahmed Khfagy Muhammad Atta OthmanDr. Muhammad AOA Khfagy is a Lecturer of Computer Science. He received the PhD degree (2018) in Computer Engineering at the University of Cagliari, Italy. He awarded th…e MSc and the BSc degrees from S.
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Othman A. Khfagy Muhammad AttaDr. Muhammad A. Khfagy was born in 14 March 1985 in Sohag - Egypt. He received Bachelor of Science in Computer Science in 2006,Received Master in Science Major Computer Sc…ience 2013. He is interested in .
Lingua: Inglese
Editore: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
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Condizione: New. Print on Demand pp. 152 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 152 pp. Englisch.
Lingua: Inglese
Editore: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
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Condizione: New. PRINT ON DEMAND pp. 152.
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Multiple Classi¿ers Systems (MCS) perform in formation fusion of classi¿cation decisions at different levels overcoming limitations of traditional approaches based on single classi¿ers. We address one of the main open issues about the…use of Diversity in Multiple Classi¿er Systems: the effectiveness of the explicit use of diversity measures for creation of classi¿er ensembles. So far, diversity measures have been mostly used for ensemble pruning, namely, for selecting a subset of classi¿ers out of an original, larger ensemble. Here we focus on pruning techniques based on forward selection, since they allow a direct comparison with the simple estimation of accuracy of classi¿er ensemble. We empirically carry out this comparison for several diversity measures and bench mark data sets, using bagging as the ensemble construction technique, and majority voting as the fusion rule.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch.
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Multiple Classifiers Systems (MCS) perform in formation fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. We address one of the main open issues about th…e use of Diversity in Multiple Classifier Systems: the effectiveness of the explicit use of diversity measures for creation of classifier ensembles. So far, diversity measures have been mostly used for ensemble pruning, namely, for selecting a subset of classifiers out of an original, larger ensemble. Here we focus on pruning techniques based on forward selection, since they allow a direct comparison with the simple estimation of accuracy of classifier ensemble. We empirically carry out this comparison for several diversity measures and bench mark data sets, using bagging as the ensemble construction technique, and majority voting as the fusion rule.
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Artificial Neural Networks have broad applications to the real world business problems. They have already been successfully applied in many industries. Since neural networks are best at identifying patterns or trends in data, they are…well suited for prediction or forecasting. These include Sales forecasting, Industrial process control, Customer research, Data validation, Risk management, Target marketing. The work studies the use of Artificial Neural Network in the field of Image Processing. One of the applications studied is the edge detection process. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Edges detection of digital images is used in a various fields of applications ranging from real-time video surveillance and traffic management to medical imaging applications. The work demonstrates both entropy and Neural Network based edge detection methods, where Renyi's Entropy and Convolutional Neural Network based edge detection is proposed and their results are compared.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 152 pp. Englisch.
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Paperback. Condizione: new. Paperback. Artificial Neural Networks have broad applications in real-world business problems and have been successfully applied across many industries. As they excel at identifying patterns and trends in data, they are well suited for prediction and forecasting tasks such as sales forecasting, indust…rial process control, customer research, data validation, risk management, and target marketing. This work examines the use of Artificial Neural Networks in the field of image processing. One of the applications studied is edge detection, which significantly reduces data volume and filters out irrelevant information while preserving essential structural features of an image. Edge detection is widely used in various fields, ranging from real-time video surveillance and traffic management to medical imaging. The study presents both entropy-based and neural network-based edge detection methods, specifically using Renyi's entropy and convolutional neural networks, and compares their performance. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Artificial Neural Networks have broad applications in real-world business problems and have been successfully applied across many industries. As they excel at identifying patterns and trends in data, they are well suited for prediction… and forecasting tasks such as sales forecasting, industrial process control, customer research, data validation, risk management, and target marketing. This work examines the use of Artificial Neural Networks in the field of image processing. One of the applications studied is edge detection, which significantly reduces data volume and filters out irrelevant information while preserving essential structural features of an image. Edge detection is widely used in various fields, ranging from real-time video surveillance and traffic management to medical imaging. The study presents both entropy-based and neural network-based edge detection methods, specifically using Rényi's entropy and convolutional neural networks, and compares their performance.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 152 pp. Englisch.
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