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Relevant bibliographies by topics / Machine learning, ubiquitous and mobile computing, fixing mislabeling, mobile information processing system

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Academic literature on the topic 'Machine learning, ubiquitous and mobile computing, fixing mislabeling, mobile information processing system'

Author: Grafiati

Published: 11 March 2023

Last updated: 31 July 2025

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Journal articles on the topic "Machine learning, ubiquitous and mobile computing, fixing mislabeling, mobile information processing system"

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Gandhi, Asst Prof Shivangi. "Android Accident Detection and Alert System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29863.

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Increasing concern for road safety has led to the search for new technological methods to reduce the incidence of traffic accidents. This article describes the design, development, and evaluation of an Android-based incident detection and notifi- cation system that aims to improve emergency response capabili- ties and reduce incident severity. The system leverages ubiquitous smartphones and their built-in sensors by combining accelerom- eters, GPS, and image processing technology to instantly capture and share events. When detected, the system triggers an alarm to notify emergency services and

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Gandhi, Asst Prof Shivangi. "Android Accident Detection and Alert System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29846.

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Increasing concern for road safety has led to the search for new technological methods to reduce the incidence of traffic accidents. This article describes the design, development, and evaluation of an Android-based incident detection and notifi- cation system that aims to improve emergency response capabili- ties and reduce incident severity. The system leverages ubiquitous smartphones and their built-in sensors by combining accelerom- eters, GPS, and image processing technology to instantly capture and share events. When detected, the system triggers an alarm to notify emergency services and

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Nayyar, Anand, Pijush Kanti Dutta Pramankit, and Rajni Mohana. "Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions." Scalable Computing: Practice and Experience 21, no. 3 (2020): 347–48. http://dx.doi.org/10.12694/scpe.v21i3.1568.

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Internet of Things (IoT) is regarded as a next-generation wave of Information Technology (IT) after the widespread emergence of the Internet and mobile communication technologies. IoT supports information exchange and networked interaction of appliances, vehicles and other objects, making sensing and actuation possible in a low-cost and smart manner.  On the other hand, cyber-physical systems (CPS) are described as the engineered systems which are built upon the tight integration of the cyber entities (e.g., computation, communication, and control) and the physical things (natural

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Fan, Mo, Haddadi Hamed, Katevas Kleomenis, Garcia Marin Eduard, Perino Diego, and Kourtellis Nicolas. "PPFL: Enhancing Privacy in Federated Learning with Confidential Computing." ACM GetMobile: Mobile Computing and Communications 25, no. 4 (2022). https://doi.org/10.1145/3529706.3529715.

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Mobile networks and devices provide the users with ubiquitous connectivity, while many of their functionality and business models rely on data analysis and processing. In this context, Machine Learning (ML) plays a key role and has been successfully leveraged by the different actors in the mobile ecosystem (e.g., application and Operating System developers, vendors, network operators, etc.). Traditional ML designs assume (user) data are collected and models are trained in a centralized location. However, this approach has privacy consequences related to data collection and processing. Such con

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De Boisboissel, G. "Արհեստական բանականություն. կիրառման նոր ձևերը և ազդեցությունը զորքերի մարտական կառավարման վրա / Artificial intelligence: new uses and impacts on military command and control". Հայկական բանակ / Armenian Army, 2024, 36–70. https://doi.org/10.61760/18290108-ehb24.2-36.

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General information and background on AI 1.1 The three battlefield revolutions The digitisation of the battlefield is a major revolution in combat, which needs to be assessed on a long-term scale as it will profoundly change military operating methods. First of all, it will mean that all the equipment deployed in the field will be interconnected with a tactical bubble that enables secure data exchanges to reduce the fog of war. What is already true for many armoured vehicles* will be true in the future for the dismounted soldier himself, who will be carrying advanced technologies. Processing t

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Dissertations / Theses on the topic "Machine learning, ubiquitous and mobile computing, fixing mislabeling, mobile information processing system"

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Zhang, Wanyi. "Personal Context Recognition from Sensors." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/339993.

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Machine learning has become one of the most emerging topics in a lot of research areas, such as pervasive and ubiquitous computing. Such computing applications always rely on the supervised learning approach to recognize user’s context before a suitable level of services are provided. However, since more and more users are involved in modern applications, the monitored data cannot be guaranteed to be always true due to wrong information. This may cause the mislabeling in machine learning and so affects the prediction. The goal of this Ph.D. thesis is to improve the data quality and solve the m

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