fatima rubia (41 resultados)

Spiral Bound. Condición: New.

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Spiral Bound. Condición: New.

Paperback. Condición: Brand New. 96 pages. 8.66x5.91x0.22 inches. In Stock.

Taschenbuch. Condición: Neu. Gender recognition using facial images | Rubia Fatima (u. a.) | Taschenbuch | 96 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200230768 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Condición: New.

Paperback. Condición: New.

Paperback. Condición: New.

Condición: New.

Condición: New.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The main objective of this study is to find out which of the most-widely used machine learning algorithms perform well for gender recognition. The aim of the study is to develop a system that can recognize the gender of a human on the basis of frontal facial features only. This system will classify the unknown facial images into male or female by comparing it with the images in the training set. The comparison will be done between most commonly used techniques for gender recognition that are the Genetic Algorithm (GA) and Support Vector Machine (SVM ) based on the facial features of a static image. Our results showed that our proposed SVM is better in detecting gender as compared to Genetic Algorithm. 96 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Fatima RubiaRubia Fatima received her Master s degree in Information Technology (IT) from Bahauddin Zakariya University (B.Z.U), Multan, Pakistan in 2016. Currently, she is pursuing her Ph.D. in Software Engineering from School of So.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -El objetivo principal de este estudio es averiguar cuáles son los algoritmos de aprendizaje automático más utilizados para el reconocimiento del género. El objetivo del estudio es desarrollar un sistema que pueda reconocer el género de un ser humano basándose únicamente en los rasgos faciales frontales. Este sistema clasificará las imágenes faciales desconocidas en masculinas o femeninas comparándolas con las imágenes del conjunto de entrenamiento. La comparación se hará entre las técnicas más utilizadas para el reconocimiento de género que son el Algoritmo Genético (GA) y la Máquina de Vectores de Apoyo (SVM ) basada en las características faciales de una imagen estática. Nuestros resultados muestran que nuestra SVM propuesta es mejor en la detección del género en comparación con el Algoritmo Genético. 72 pp. Spanisch.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The main objective of this study is to find out which of the most-widely used machine learning algorithms perform well for gender recognition. The aim of the study is to develop a system that can recognize the gender of a human on the basis of frontal facial features only. This system will classify the unknown facial images into male or female by comparing it with the images in the training set. The comparison will be done between most commonly used techniques for gender recognition that are the Genetic Algorithm (GA) and Support Vector Machine (SVM ) based on the facial features of a static image. Our results showed that our proposed SVM is better in detecting gender as compared to Genetic Algorithm.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The main objective of this study is to find out which of the most-widely used machine learning algorithms perform well for gender recognition. The aim of the study is to develop a system that can recognize the gender of a human on the basis of frontal facial features only. This system will classify the unknown facial images into male or female by comparing it with the images in the training set. The comparison will be done between most commonly used techniques for gender recognition that are the Genetic Algorithm (GA) and Support Vector Machine (SVM ) based on the facial features of a static image. Our results showed that our proposed SVM is better in detecting gender as compared to Genetic Algorithm.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -L'objectif principal de cette étude est de déterminer quels sont les algorithmes d'apprentissage automatique les plus utilisés pour la reconnaissance du sexe. L'objectif de l'étude est de développer un système capable de reconnaître le sexe d'un être humain sur la base des caractéristiques faciales frontales uniquement. Ce système classera les images faciales inconnues comme masculines ou féminines en les comparant aux images de l'ensemble d'apprentissage. La comparaison sera faite entre les techniques les plus couramment utilisées pour la reconnaissance du sexe, à savoir l'algorithme génétique (GA) et la machine à vecteur de support (SVM), sur la base des caractéristiques faciales d'une image statique. Nos résultats ont montré que le SVM que nous proposons est meilleur pour détecter le genre que l'algorithme génétique. 72 pp. Französisch.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condición: new. Hardcover. The growing integration of artificial intelligence (AI) into software engineering is fundamentally changing how systems are developed, deployed, and maintained. Beyond performance and automating processes, AI now influences decision-making in ways that can deeply affect developers and societies. Ensuring that these technologies are used responsibly requires embedding ethical principles throughout software development. By integrating ethical AI practices, developers can create systems that not only advance innovation but also uphold human values. This evolving intersection of ethics and engineering defines the next frontier in responsible technology development. Ethical AI Applications for Software Engineering and Development explores and promotes ethical practices in the development and deployment of AI within software engineering. It offers a diverse and interdisciplinary collection of research, case studies, frameworks, and practical insights focused on responsible AI. Covering topics such as AI, software engineering, and ethical development, this book is an excellent resource for software engineers, AI researchers, data scientists, graduate students, educators, policymakers, industry professionals, and more. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condición: new. Hardcover. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Condición: Neu. Ethical AI Applications for Software Engineering and Development | Affan Yasin Yasin (u. a.) | Taschenbuch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337360393 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Hardcover. Condición: new. Hardcover. The growing integration of artificial intelligence (AI) into software engineering is fundamentally changing how systems are developed, deployed, and maintained. Beyond performance and automating processes, AI now influences decision-making in ways that can deeply affect developers and societies. Ensuring that these technologies are used responsibly requires embedding ethical principles throughout software development. By integrating ethical AI practices, developers can create systems that not only advance innovation but also uphold human values. This evolving intersection of ethics and engineering defines the next frontier in responsible technology development. Ethical AI Applications for Software Engineering and Development explores and promotes ethical practices in the development and deployment of AI within software engineering. It offers a diverse and interdisciplinary collection of research, case studies, frameworks, and practical insights focused on responsible AI. Covering topics such as AI, software engineering, and ethical development, this book is an excellent resource for software engineers, AI researchers, data scientists, graduate students, educators, policymakers, industry professionals, and more. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condición: Neu. Ethical AI Applications for Software Engineering and Development | Affan Yasin Yasin (u. a.) | Buch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337360386 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The growing integration of artificial intelligence (AI) into software engineering is fundamentally changing how systems are developed, deployed, and maintained. Beyond performance and automating processes, AI now influences decision-making in ways that can deeply affect developers and societies. Ensuring that these technologies are used responsibly requires embedding ethical principles throughout software development. By integrating ethical AI practices, developers can create systems that not only advance innovation but also uphold human values. This evolving intersection of ethics and engineering defines the next frontier in responsible technology development. Ethical AI Applications for Software Engineering and Development explores and promotes ethical practices in the development and deployment of AI within software engineering. It offers a diverse and interdisciplinary collection of research, case studies, frameworks, and practical insights focused on responsible AI. Covering topics such as AI, software engineering, and ethical development, this book is an excellent resource for software engineers, AI researchers, data scientists, graduate students, educators, policymakers, industry professionals, and more.

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -El objetivo principal de este estudio es averiguar cuáles son los algoritmos de aprendizaje automático más utilizados para el reconocimiento del género. El objetivo del estudio es desarrollar un sistema que pueda reconocer el género de un ser humano basándose únicamente en los rasgos faciales frontales. Este sistema clasificará las imágenes faciales desconocidas en masculinas o femeninas comparándolas con las imágenes del conjunto de entrenamiento. La comparación se hará entre las técnicas más utilizadas para el reconocimiento de género que son el Algoritmo Genético (GA) y la Máquina de Vectores de Apoyo (SVM ) basada en las características faciales de una imagen estática. Nuestros resultados muestran que nuestra SVM propuesta es mejor en la detección del género en comparación con el Algoritmo Genético.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Spanisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - El objetivo principal de este estudio es averiguar cuáles son los algoritmos de aprendizaje automático más utilizados para el reconocimiento del género. El objetivo del estudio es desarrollar un sistema que pueda reconocer el género de un ser humano basándose únicamente en los rasgos faciales frontales. Este sistema clasificará las imágenes faciales desconocidas en masculinas o femeninas comparándolas con las imágenes del conjunto de entrenamiento. La comparación se hará entre las técnicas más utilizadas para el reconocimiento de género que son el Algoritmo Genético (GA) y la Máquina de Vectores de Apoyo (SVM ) basada en las características faciales de una imagen estática. Nuestros resultados muestran que nuestra SVM propuesta es mejor en la detección del género en comparación con el Algoritmo Genético.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -L'objectif principal de cette étude est de déterminer quels sont les algorithmes d'apprentissage automatique les plus utilisés pour la reconnaissance du sexe. L'objectif de l'étude est de développer un système capable de reconnaître le sexe d'un être humain sur la base des caractéristiques faciales frontales uniquement. Ce système classera les images faciales inconnues comme masculines ou féminines en les comparant aux images de l'ensemble d'apprentissage. La comparaison sera faite entre les techniques les plus couramment utilisées pour la reconnaissance du sexe, à savoir l'algorithme génétique (GA) et la machine à vecteur de support (SVM), sur la base des caractéristiques faciales d'une image statique. Nos résultats ont montré que le SVM que nous proposons est meilleur pour détecter le genre que l'algorithme génétique.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Französisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - L'objectif principal de cette étude est de déterminer quels sont les algorithmes d'apprentissage automatique les plus utilisés pour la reconnaissance du sexe. L'objectif de l'étude est de développer un système capable de reconnaître le sexe d'un être humain sur la base des caractéristiques faciales frontales uniquement. Ce système classera les images faciales inconnues comme masculines ou féminines en les comparant aux images de l'ensemble d'apprentissage. La comparaison sera faite entre les techniques les plus couramment utilisées pour la reconnaissance du sexe, à savoir l'algorithme génétique (GA) et la machine à vecteur de support (SVM), sur la base des caractéristiques faciales d'une image statique. Nos résultats ont montré que le SVM que nous proposons est meilleur pour détecter le genre que l'algorithme génétique.