Manjubala Bisi – författare

This book provides a starting point for software professionals to apply artificial neural networks for software reliability prediction without having analyst capability and expertise in various ANN architectures and their optimization.Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process are presented. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.

This book provides a starting point for software professionals to apply artificial neural networks for software reliability prediction without having analyst capability and expertise in various ANN architectures and their optimization.

Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process are presented. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.

This book provides a starting point for software professionals to apply artificial neural networks for software reliability prediction without having analyst capability and expertise in various ANN architectures and their optimization.

Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process are presented. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.

This volume constitutes the refereed proceedings of the Second International Conference on Computing, Communication and Learning, CoCoLe 2023, held in Warangal, India, in August 29–31, 2023.The 25 full papers presented were carefully reviewed and selected from 120 submissions. The CoCoLe conference focuses on Application of Supervised Learning in Computing; Application of Unsupervised Learning in Computing; and Computing in Communication Networks.

This volume constitutes the refereed proceedings of the Second International Conference on Computing, Communication and Learning, CoCoLe 2023, held in Warangal, India, in August 29–31, 2023.

The 25 full papers presented were carefully reviewed and selected from 120 submissions. The CoCoLe conference focuses on Application of Supervised Learning in Computing; Application of Unsupervised Learning in Computing; and Computing in Communication Networks.

This book constitutes the refereed proceedings of the Third International Conference on Computing, Communication and Learning, CoCoLe 2024, held in Warangal, India, in September 2024.The 24 full papers and 10 short papers presented here were carefully reviewed and selected from 149 submissions. These papers have been categorized under the following topical sections: Advancements in AI for Predictive Modeling, Quality Enhancement, and Real-Time Detection Across Various Domains; Machine Learning Advances in Medical Imaging, Agricultural Monitoring, and Multimedia Processing; Advancements in Privacy-Preservation and Intelligent Detection Systems for Federated Learning and Edge Computing.