Using Machine Learning For Minimizing Mobile Phone Disruption
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Using Machine Learning For Minimizing Mobile Phone Disruption
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Author : Dan Nacht
language : en
Publisher:
Release Date : 2010*
Using Machine Learning For Minimizing Mobile Phone Disruption written by Dan Nacht and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010* with Cell phones categories.
Context Aware Machine Learning And Mobile Data Analytics
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Author : Iqbal Sarker
language : en
Publisher: Springer Nature
Release Date : 2022-01-01
Context Aware Machine Learning And Mobile Data Analytics written by Iqbal Sarker and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-01 with Computers categories.
This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision making. Thus, we have bestowed a comprehensive study on this topic that explores multi-dimensional contexts in machine learning modeling, context discretization with time-series modeling, contextual rule discovery and predictive analytics, recent-pattern or rule-based behavior modeling, and their usefulness in various context-aware intelligent applications and services. The presented machine learning-based techniques can be employed in a wide range of real-world application areas ranging from personalized mobile services to security intelligence, highlighted in the book. As the interpretability of a rule-based system is high, the automation in discovering rules from contextual raw data can make this book more impactful for the application developers as well as researchers. Overall, this book provides a good reference for both academia and industry people in the broad area of data science, machine learning, AI-Driven computing, human-centered computing and personalization, behavioral analytics, IoT and mobile applications, and cybersecurity intelligence.
Deep Learning On Mobile Devices With Neural Processing Units
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Author : Tianxiang Tan
language : en
Publisher:
Release Date : 2022
Deep Learning On Mobile Devices With Neural Processing Units written by Tianxiang Tan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Deep Neural Networks (DNN) have been successfully applied to various computer vision and natural language processing problems. Although DNNs can provide better results, they suffer from high computational overhead which means long delay and more energy consumption when running on mobile devices. To address this problem, many companies have developed dedicated Neural Processing Units (NPUs) for accelerating deep learning on mobile devices. NPU can significantly reduce the running time of these DNNs with much less energy, however it incurs accuracy loss which poses new research challenges. The goal of this dissertation is to address these challenges by developing techniques to improve the performance and the energy efficiency of running DNNs on mobile devices with NPU. First, we propose techniques to decompose the DNN architecture into different layers running on CPU and NPU to maximize accuracy or minimize processing time based on the application requirement. Based on the delay and the accuracy requirements of the applications, we study two problems: Max-Accuracy where the goal is to maximize the accuracy under some time constraint, and Min-Time where the goal is to minimize the processing time while ensuring the accuracy is above a certain threshold. To solve these problems, we propose heuristic based algorithms which are simple but only search a small number of layer combinations (i.e., where to run which DNN model layers). To further improve the performance, a machine learning based model partition algorithm is developed which searches more layer combinations and considers both accuracy loss and processing time simultaneously. Second, we propose techniques to improve the performance of running DNNs on mobile devices while avoiding the overheating problem. Compared to CPU, mobile GPU can be leveraged to improve performance. However, after running DNNs for a short period of time, the mobile device may become overheated and the processors are forced to reduce the clock speed, significantly reducing the processing speed. Compared to GPU, NPU is much faster and more energy efficient, but with lower accuracy due to the use of low precision floating-point numbers. We propose to combine these two approaches to improve performance by studying the thermal-aware scheduling problem, where the goal is to achieve a better tradeoff between processing time and accuracy while ensuring that the mobile device is not overheated. To solve the problem, we first propose a heuristic-based scheduling algorithm to determine when to run DNNs on GPU and when to run DNNs on NPU based on the current states of the mobile device, and then propose a deep reinforcement learning based scheduling algorithm to further improve performance. Third, we propose techniques to support deep learning applications through edge processing and NPU in mobile. The major challenge is to determine when to offload the computation and when to use NPU. Based on the processing time and accuracy requirement of the mobile application, we study three problems: Max-Accuracy where the goal is to maximize the accuracy under some time constraints, Max-Utility where the goal is to maximize the utility which is a weighted function of processing time and accuracy, and Min-Energy where the goal is to minimize the energy under some time and accuracy constraints. We formulate them as integer programming problems and propose heuristics based solutions. Finally, we further improve the performance of offloading by leveraging the confidence score of running DNNs on mobile devices. If the confidence score is higher than a threshold, the classification result on NPU is most likely accurate and can be directly used; otherwise, the data is offloaded for further processing to improve the accuracy. However, the confidence score of many advanced DNNs cannot accurately estimate the classification results, and then may not be effective for making offloading decisions. We propose confidence score calibration techniques, formulate the confidence based offloading problem where the goal is to maximize accuracy under some time constraint, and propose an adaptive solution that determines which frames to offload at what resolution based on the confidence score and the network condition. Through real implementations and extensive evaluations, we demonstrate that the proposed solution can significantly outperform other approaches.
Context Aware Call Management For Mobile Phones
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Author : Hsien-ming Chou
language : en
Publisher:
Release Date : 2017
Context Aware Call Management For Mobile Phones written by Hsien-ming Chou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
When a user receives a phone call, his mobile phone would notify him with a ringing sound or vibration immediately without considering whether or not the user is available to answer the call, which could be disruptive to his ongoing task or social situation. In addition, many incoming calls are unwanted for users. Although users may turn off their mobile phones in advance or just set their mobile phones in a silent mode to avoid interruptions, it would cause the problems of forcing an extra step for users, missing important phone calls, or forgetting to switch a phone back to the regular mode. Mobile call management is a type of mobile applications for coping with the problem of mobile interruption. A mobile call management system can sense context information about the situational environment of a user and choose appropriate mechanisms to handle incoming phone calls, making call management more effective, adaptive, and personalized. Existing mobile call management systems often utilize only one type of context information (e.g., location) for call management. In reality, however, mobile call management often needs diverse contextual information of individual users, such as time, location, event, and social relations. Another major challenge in mobile call management is to deal with noise and ambiguity of real-time data collected from multiple sensors embedded in mobile phones. The objective of this dissertation research is to develop a conceptual framework for mobile call management and design, implement, and empirically evaluate a novel mobile call management method called CaCM (Context-aware Call Management) that incorporates diverse context factors, including time, location, event, social relations, environment, body position, and body movement, and leverages machine learning algorithms to reduce mobile interruption and improve the effectiveness and user satisfaction with mobile call management. The results of an empirical evaluation via a field experiment indicate that CaCM shows advantages in the accuracy of interruptability prediction and user perceptions in comparison with existing context-aware mobile call management methods.
Proceedings Of The International Conference On Advanced Intelligent Systems And Informatics 2019
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Author : Aboul Ella Hassanien
language : en
Publisher: Springer Nature
Release Date : 2019-10-02
Proceedings Of The International Conference On Advanced Intelligent Systems And Informatics 2019 written by Aboul Ella Hassanien and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-02 with Technology & Engineering categories.
This book presents the proceedings of the 5th International Conference on Advanced Intelligent Systems and Informatics 2019 (AISI2019), which took place in Cairo, Egypt, from October 26 to 28, 2019. This international and interdisciplinary conference, which highlighted essential research and developments in the fields of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into several sections, covering the following topics: machine learning and applications, swarm optimization and applications, robotic and control systems, sentiment analysis, e-learning and social media education, machine and deep learning algorithms, recognition and image processing, intelligent systems and applications, mobile computing and networking, cyber-physical systems and security, smart grids and renewable energy, and micro-grid and power systems.
Mobile Disruption
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Author : Jeffrey L. Funk
language : en
Publisher: John Wiley & Sons
Release Date : 2003-12-29
Mobile Disruption written by Jeffrey L. Funk and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-12-29 with Technology & Engineering categories.
A disruptive technology is a technology or innovation that results in worse product performance different from the expected or predicted performance; an example is that the Internet accessible mobile phone was thought to be a portable substitute for the PC-the actual applications of mobile phones are far different from this Describes business models, user needs, and key technologies to create long-term strategies that are profitable in both the long- and short-term
Ethical Artificial Intelligence In Power Electronics
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Author : Tarandeep Kaur Bhatia
language : en
Publisher: CRC Press
Release Date : 2024-08-01
Ethical Artificial Intelligence In Power Electronics written by Tarandeep Kaur Bhatia and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-01 with Computers categories.
This book focuses on the techniques of artificial intelligence that are mainly used in the power electronics field for the optimization of lost vehicle power. With the intention of optimizing the powerful energy of the vehicles and producing reliable energy, the most efficient methods, algorithms, and strategies of ethical artificial intelligence (AI) are being applied. By employing machine learning methods, the optimization of power energy in vehicles can be quickly recovered and managed efficiently. In today’s bustling world, power energy is indispensable for progress, yet in congested Vehicular Ad-hoc Networks (VANETs), vehicles often face power depletion and decreased efficiency. This book explores these challenges, encompassing not only power but also other critical power electronics within vehicles. We aim to introduce innovative approaches, leveraging ethical AI methods, to optimize energy performance in the face of these difficulties. Through this exploration, we seek to provide practical insights into navigating congested VANET environments while upholding ethical principles in technological advancements. Our book will discuss the current power energy concerns faced by vehicles and also contribute a novel strategy to overcome those concerns. The employment of ethical AI in vehicular power energy will undoubtedly improve the effectiveness and production of vehicles.
Application Of Machine Learning And Deep Learning Methods To Power System Problems
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Author : Morteza Nazari-Heris
language : en
Publisher: Springer Nature
Release Date : 2021-11-21
Application Of Machine Learning And Deep Learning Methods To Power System Problems written by Morteza Nazari-Heris and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-21 with Technology & Engineering categories.
This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.
Machine Learning And Intelligent Communications
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Author : Limin Meng
language : en
Publisher: Springer
Release Date : 2018-10-12
Machine Learning And Intelligent Communications written by Limin Meng and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-12 with Computers categories.
This volume constitutes the refereed post-conference proceedings of the Third International Conference on Machine Learning and Intelligent Communications, MLICOM 2018, held in Hangzhou, China, in July 2018. The 66 revised full papers were carefully selected from 102 submissions. The papers are organized thematically in machine learning, intelligent positioning and navigation, intelligent multimedia processing and security, wireless mobile network and security, cognitive radio and intelligent networking, IoT, intelligent satellite communications and networking, green communication and intelligent networking, ad-hoc and sensor networks, resource allocation in wireless and cloud networks, signal processing in wireless and optical communications, and intelligent cooperative communications and networking.
Sustainable Developments By Artificial Intelligence And Machine Learning For Renewable Energies
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Author : Krishna Kumar
language : en
Publisher: Academic Press
Release Date : 2022-03-18
Sustainable Developments By Artificial Intelligence And Machine Learning For Renewable Energies written by Krishna Kumar and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-18 with Science categories.
Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stability with variability in renewable energy vs. traditional baseload energy generation. Providing solutions to current critical environmental, economic and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and provide sustainable development. As the use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, along with machine learning techniques and capabilities in design, modeling and for forecasting performance predictions for the optimization of renewable energy systems. It is targeted at researchers, academicians and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation. Covers the best-performing methods and approaches for designing renewable energy systems with AI integration in a real-time environment Gives advanced techniques for monitoring current technologies and how to efficiently utilize the energy grid spectrum Addresses the advanced field of renewable generation, from research, impact and idea development of new applications