Ogala Emmanuel (62 results)

Taschenbuch. Condition: Neu. Development Of Web-Base System | Development Of Web-Base System For Processing University Student's Examination Record's | Emmanuel Ogala | Taschenbuch | 196 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9783659422775 | 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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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 80 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 76 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a complete course text for Masters Degree thesis research work for computer scientist. The many good reference text on the subject can be too intimidating and off-putting to be useful these students which is needed, and this research book aims to satisfy that need of web-base system, is a research book which assumes a knowledge of computer web- base application and provides students with the knowledge and skills to consult with confidence the advanced text to this topic. The text covers in details the research work carry out at Federal University of Technology Akure. A case study of Kogi State university, Anyigba Nigeria. 196 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Face recognition has emerged as one of the most significant biometric technologies for automated identification and authentication in modern security, surveillance, and access control systems. Traditional identification methods, such as passwords and identity cards, are increasingly vulnerable to theft, forgery and unauthorized access, thereby necessitating the development of intelligent and reliable biometric solutions. This study presents a machine learning approach to automated face recognition and identification aimed at enhancing the accuracy, efficiency, and robustness of human identity verification systems. The proposed framework employs advanced image preprocessing techniques, including face detection, normalization, feature extraction, and dimensionality reduction to improve the quality and discriminative power of facial data. Machine learning algorithms are utilized to learn distinctive facial features from a large dataset of facial images and subsequently classify individuals based on their unique biometric characteristics. The system is trained and evaluated using benchmark facial image datasets under varying conditions such as illumination changes, facial expressions.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The rapid proliferation of Internet of Things (IoT) devices has transformed modern digital ecosystems by enabling seamless connectivity and intelligent automation across various domains, including healthcare, transportation, smart cities, and industrial systems. However, the resource-constrained nature and heterogeneous architecture of IoT networks make them highly vulnerable to cyber threats, particularly Distributed Denial-of-Service (DDoS) attacks. These attacks can compromise network availability, disrupt critical services, and cause substantial economic and operational losses. Traditional signature-based intrusion detection approaches often struggle to identify evolving and sophisticated attack patterns, necessitating the development of intelligent and adaptive security solutions. This study proposes an intelligent machine learning framework for detecting DDoS attacks in IoT networks. The framework integrates data preprocessing, feature engineering, and supervised machine learning techniques to effectively distinguish malicious traffic from legitimate network activities.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ogala EmmanuelOgala, Emmanuel is a native of Unale in Ibaji L.G.A. of Kogi State, Nigeria. He holds a BSc. Degree in Mathematics, Statistics and Computer Science from Federal university of Agriculture, Makurdi. Master Degree (M. Tech.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware 76 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Face recognition has emerged as one of the most significant biometric technologies for automated identification and authentication in modern security, surveillance, and access control systems. Traditional identification methods, such as passwords and identity cards, are increasingly vulnerable to theft, forgery and unauthorized access, thereby necessitating the development of intelligent and reliable biometric solutions. This study presents a machine learning approach to automated face recognition and identification aimed at enhancing the accuracy, efficiency, and robustness of human identity verification systems. The proposed framework employs advanced image preprocessing techniques, including face detection, normalization, feature extraction, and dimensionality reduction to improve the quality and discriminative power of facial data. Machine learning algorithms are utilized to learn distinctive facial features from a large dataset of facial images and subsequently classify individuals based on their unique biometric characteristics. The system is trained and evaluated using benchmark facial image datasets under varying conditions such as illumination changes, facial expressions. 80 pp. Englisch.…

Taschenbuch. Condition: Neu. Cyber Security | An Intelligent Machine Learning Framework for Detecting Distributed Denial-of-Service Attacks in Internet of Things | Ogala Emmanuel (u. a.) | Taschenbuch | Englisch | 2026 | GlobeEdit | EAN 9786630051032 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

Taschenbuch. Condition: Neu. Cybersecurity / Biometrics | A Machine Learning Approach to Automated Face Recognition and Identification | Emmanuel Ogala | Taschenbuch | Englisch | 2026 | GlobeEdit | EAN 9786630194357 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 216 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 96 pp. Spanisch.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a complete course text for Masters Degree thesis research work for computer scientist. The many good reference text on the subject can be too intimidating and off-putting to be useful these students which is needed, and this research book aims to satisfy that need of web-base system, is a research book which assumes a knowledge of computer web- base application and provides students with the knowledge and skills to consult with confidence the advanced text to this topic. The text covers in details the research work carry out at Federal University of Technology Akure. A case study of Kogi State university, Anyigba Nigeria.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 196 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a complete course text for Masters Degree thesis research work for computer scientist. The many good reference text on the subject can be too intimidating and off-putting to be useful these students which is needed, and this research book aims to satisfy that need of web-base system, is a research book which assumes a knowledge of computer web- base application and provides students with the knowledge and skills to consult with confidence the advanced text to this topic. The text covers in details the research work carry out at Federal University of Technology Akure. A case study of Kogi State university, Anyigba Nigeria.…

Taschenbuch. Condition: Neu. A Hybrid Ensemble Machine Learning Model for Cyber Threat Detection | Emmanuel Ogala (u. a.) | Taschenbuch | Englisch | 2026 | GlobeEdit | EAN 9786630189629 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The rapid growth of digital technologies and internet-based services has significantly increased cybersecurity risks, necessitating more effective and intelligent threat detection mechanisms. Traditional cybersecurity approaches, including signature-based and behavior-based detection techniques, often struggle to cope with the increasing complexity and diversity of modern cyberattacks. To address these limitations, this study proposes a hybrid machine learning framework for cyber threat detection and classification that combines the strengths of multiple learning algorithms through a weighted voting strategy. The proposed framework integrates six machine learning classifiers: K-Nearest Neighbors (KNN), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Light Gradient Boosting (LGB). An empirical evaluation was conducted to assess their performance in both binary and multiclass classification tasks. For binary classification, the individual models achieved accuracies of 91.78% (KNN), 94.29% (RF), 82.18% (NB), 90.70% (SVM), 87.39% (LDA), and 94.24% (LGB). 216 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The rapid growth of digital technologies and internet-based services has significantly increased cybersecurity risks, necessitating more effective and intelligent threat detection mechanisms. Traditional cybersecurity approaches, including signature-based and behavior-based detection techniques, often struggle to cope with the increasing complexity and diversity of modern cyberattacks. To address these limitations, this study proposes a hybrid machine learning framework for cyber threat detection and classification that combines the strengths of multiple learning algorithms through a weighted voting strategy. The proposed framework integrates six machine learning classifiers: K-Nearest Neighbors (KNN), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Light Gradient Boosting (LGB). An empirical evaluation was conducted to assess their performance in both binary and multiclass classification tasks. For binary classification, the individual models achieved accuracies of 91.78% (KNN), 94.29% (RF), 82.18% (NB), 90.70% (SVM), 87.39% (LDA), and 94.24% (LGB).…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 96 pp. Französisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - El reconocimiento facial se ha convertido en una de las tecnologías biométricas más significativas para la identificación y autenticación automatizadas en los sistemas modernos de seguridad, vigilancia y control de acceso. Los métodos de identificación tradicionales, como las contraseñas y las tarjetas de identidad, son cada vez más vulnerables al robo, la falsificación y el acceso no autorizado, lo que hace necesaria el desarrollo de soluciones biométricas inteligentes y fiables. Este estudio presenta un enfoque de aprendizaje automático para el reconocimiento e identificación facial automatizados con el objetivo de mejorar la precisión, la eficiencia y la solidez de los sistemas de verificación de la identidad humana. El marco propuesto emplea técnicas avanzadas de preprocesamiento de imágenes, que incluyen la detección facial, la normalización, la extracción de características y la reducción de la dimensionalidad para mejorar la calidad y el poder discriminatorio de los datos faciales. Se utilizan algoritmos de aprendizaje automático para aprender rasgos faciales distintivos a partir de un gran conjunto de datos de imágenes faciales y, posteriormente, clasificar a las personas en función de sus características biométricas únicas. El sistema se entrena y evalúa mediante conjuntos de datos de imágenes faciales de referencia bajo condiciones variables, tales como cambios de iluminación y expresiones faciales.…