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检索条件"任意字段=International Conference on Intelligent and Smart Computing in Data Analytics, ISCDA 2020"

共 3243 条记录，以下是11-20 订阅

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Benefits and Limitations of Using data-Driven Approach to Develop Inclusivity Networks for Young Disabled People: A Case Study of Assemble by the National Youth Theatre 12th

Benefits and Limitations of Using Data-Driven Approach to De...

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12th international conference on Frontiers of intelligent computing: Theory and Applications, FICTA 2024

作者： Yu, Qicheng Singh, Uttam Stirbu, Diana Nyby, Lina Digital Media and Computing London Metropolitan University London United Kingdom Social Sciences and Professions Holloway Road London LondonN7 8DB United Kingdom

ISBN: (纸本)9789819601387

Inclusivity networks bridge the gap between disabled and non-disabled communities, empowering individuals. Organisations like the National Youth Theatre have run its local inclusivity networks programme to promote collaboration, reduce isolation, and empower young disabled voices. A data-driven approach is a powerful way to make informed decisions and solve complex problems based on empirical evidence and quantitative data. This paper explores the benefits and limitations of using data-driven approaches to support the development of local inclusivity networks to enhance young disabled people’s voices and lived experience into decision-making and discuss the creation of a dynamic data dashboard, enhancing research efficiency by drawing on existing good practice examples from a London local authority. By combining meaningful findings with data-driven recommendations, we contribute to building more inclusive networks for young disabled people. However, its limitations such as limited data sources, data quality must be addressed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data analytics data dashboard data visualisation data-driven Disability Inclusivity network Quantitative Young people

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international IoT, Electronics and Mechatronics conference, IEMTRONICS 2024

International IoT, Electronics and Mechatronics Conference, ...

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international IoT, Electronics and Mechatronics conference, IEMTRONICS 2024

ISBN: (纸本)9789819747795

The proceedings contain 40 papers. The special focus in this conference is on IoT, Electronics and Mechatronics. The topics include: Exploring Mangrove Complexity with Gate-Based Fractal Analysis Through AND Circuitry;enhancing Paralympic Athlete Performance: Kinematic Analysis and Computer Information Systems for Optimal Training Load Adjustment;Enhancing ECG Heartbeat Signal Classification Through Multi-Step Preprocessing and Machine Learning Model Optimization;features of data Collection and Software Tool Architecture for Performing Predictive Analysis of Phenomena Leading to Forest Fires;augmenting Reliability in Speaker Recognition Systems Through Low Density Parity Check Codes;an Analytical Study of Text Summarization Techniques;MAPExeCPS: A Platform to Support the Commercial Value and Production Condition of Mud Crabs of the Genus Scylla spp. In the Philippines;ioT Guardian: An intelligent Framework for Multi-Class Intrusion Detection with Machine Learning;Development of an Approach to Analysis and Classification of EMG Signals for Prosthesis Control;high Impedance Surfaces: Unique Properties and Applications;big data analytics Approach with Multiple Text Types: The Case of the Computer Gaming;dynamic Change of Three-Dimensional Soil in Game Simulators;an Approach to Design a Cost-Effective Thermocycler for Polymerase Chain Reaction;ioT Machine Learning-Based Health Compliance Monitoring System;an IoT-Based Mobile Air Pollution Monitoring System;data Allocation and Replication Strategies in Edge computing;structure of Control computing Facilities of data Transmission Networks and Features of Dispatch Algorithm Programs;Enhancing Power Allocation in MIMO-OFDM Systems: Latest Approaches and Algorithms;supercapacitor-Based Power Supply for Embedded System Applications;an Ideal Pulsed Electromagnetic Field Device Based on a Multidimensional Model of Light.

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international IoT, Electronics and Mechatronics conference, IEMTRONICS 2024

International IoT, Electronics and Mechatronics Conference, ...

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international IoT, Electronics and Mechatronics conference, IEMTRONICS 2024

ISBN: (纸本)9789819747832

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters 1

Cloud Based Automated Control System Workshops and Rooms for...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Dinesh Kuntal, Ravinder Singh Deep, Pragya Chamoli, Anup Shekhar Singh, Pinki Mandal, Rishabh SRM Institute of Science & Technology Department of Computer Science and Engineering UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India IIMT College of Engineering Department of AI & DS Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun248001 India Accurate College of Engineering & Management Department of Computer Science and Engineering Greator Noida India Tula's Institute Department of Computer Science and Engineering UK Dehradun India

ISBN: (纸本)9798331518578

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing. © 2025 IEEE.

关键词： smart manufacturing

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Revolutionizing Residential EV Charging Through IoT-Enabled smart Energy Metering for Sustainability 6

Revolutionizing Residential EV Charging Through IoT-Enabled ...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Sivakumar, N. Charles Raja, S. Meenakshi Devi, M. Saraswathi Meena, R. Dept. of EEE Thiagarajar College of Engineering Tamilnadu Madurai India Dept. of Applied Mathematics and Computational Science Thiagarajar College of Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331522667

The rapid growth in Electric Vehicle (EV) adoption poses substantial challenges to residential energy infrastructures, especially in terms of the strain it places on local grids during charging. This paper proposes an IoT-based smart Energy Meter System (SEM) designed to efficiently manage EV charging in residential environments. The system integrates IoT technology with advanced energy metering features, enabling real-time monitoring and control of charging operations. The system is composed of intelligent energy meters for real-time monitoring, an IoT-enabled hub for data collection and processing, and an intuitive consumers interface for customizing and managing EV charging schedules. By continuously monitoring energy consumption and charging demands, the system optimizes charging schedules to reduce peak loads, lower energy costs, and enhance grid stability. It provides residents with insights into their energy usage and charging patterns, empowering them to make informed decisions to reduce their carbon footprint. Furthermore, the system seamlessly integrates with renewable energy sources and energy storage solutions, promoting clean energy usage for EV charging. Through cloud-based analytics and predictive maintenance, it ensures reliable and uninterrupted charging services. The proposed IoT-based SEM system offers a scalable and sustainable solution for managing EV charging in residential settings, supporting electric mobility while improving energy efficiency and promoting environmental sustainability. © 2025 IEEE.

关键词： Predictive maintenance

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Internet of Energy (IoE): A Comprehensive Review of Design, Principles, and Architectural Frameworks 15th

Internet of Energy (IoE): A Comprehensive Review of Design, ...

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15th international conference on e Infrastructure and e Services for Developing Countries

作者： Abdalla, Rania Salih Ali, Elmustafa Sayed Mahbub, Sara A. Mokhtar, Rania A. Ahmed, Zeinab E. Taif Univ Dept Comp Engn Al Taif Saudi Arabia Red Sea Univ Dept Elect & Elect Engn Port Sudan Sudan Sudan Univ Sci & Technol Dept Elect Engn Khartoum Sudan Int Islamic Univ Malaysia Dept Elect & Comp Engn Kuala Lumpur Malaysia Univ Gezira Dept Comp Engn Wad Madani Sudan

ISBN: (纸本)9783031815690;9783031815706

Design of energy resources, transmission, distribution, and consumption in network architecture is becoming a challenging energy optimization issue. The demand for power analysis becomes a key pillar in sustainable renewable energy and adaptation to climate change. State-of-the-art technologies can play a vital role in realizing the new architectural design for smart grids and cities. The 4th generation mobile network is considered a critical technology and enabler. The 3GPP standard body is set to a target of 35% of the deployment of the 4G to be Low Power Wide Area (LPWA) network by 2020. LPWA is an Internet of Energy (IoE) structure that can provide a comprehensive stream of energy sector applications. The IoE with intelligent computing tools can dramatically enhance energy efficiency, improve and sustain renewable energy, and diminish energy contamination's ecological effects. This paper reviews the literature on the IoE design principles and architecture models comprehensively. it also explains IoE enabling technologies, including fog computing and various interoperability and data analysis standards.

关键词： Communications and measurement technology smart energy systems smart grid IoE Computational Tools IoE Design IoE Interoperability IoE Standards IoE Cyber Security

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smart Disinfectant Dispensers with IoT Sensors and AI for Optimal Usage in Public Spaces

Smart Disinfectant Dispensers with IoT Sensors and AI for Op...

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2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Mohandas, R. Anitha, D. Balasubadra, K. Devi, R. Aiyshwariya Murugan, S. Rajmohan, M. Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Rasipuram India R.M.D. Engineering College Department of Information Technology Kavaraipettai Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Puduvoyal Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331527495

Ensuring hygiene in public areas is of utmost importance now that there are worldwide health concerns. This research proposes a novel approach to creating smart disinfectant dispensers fitted with Internet of Things (IoT) sensors and Artificial Intelligence (AI) to maximize their use in public areas. By using IoT technology, the system can measure disinfectant levels, detect human engagement in real time, and monitor dispenser use. Machine learning models examine the data to find the best time to restock, predict when repairs will be necessary, and personalize the dispensing amount for each user according to their habits and conditions. The system also works with cloud platforms and mobile apps for remote monitoring and data analytics, giving administrators full control and visibility. Along with lowering operating expenses and ensuring a constant supply of disinfection, this method also reduces waste. smart disinfectant dispensers build healthier communities that can withstand outbreaks by encouraging regular and thorough handwashing. © 2025 IEEE.

关键词： Contrastive Learning

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smart Grid data Management and analytics Using Cloud computing: Trends and Future Directions

Smart Grid Data Management and Analytics Using Cloud Computi...

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international conference on New Trends in computing Sciences (ICTCS)

作者： Abdirizak Abdullahi Khalif Hafso Mohamed Mohamoud Abdinasir Hirsi Mohamed Jama Abdirahman Hassan Mohamud Lukman Audah Mohamed Ahmed Abdiaziz Mohamed Abdirahman Puput Dani Prasetyo Adi AI and Network Security (AiNetSec) Research Lab (Virtual Lab) Bosaso Somalia Faculty of Engineering Jamhuriya University of Science and Technology Mogadishu Somalia Faculty of Electrical & Electronics Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Faculty of Technology and Business Management UTHM University Parit Raja Malaysia Research Center for Telecom National Research and Innovation Agency (BRIN) Bandung Indonesia

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

A smart electrical grid optimizes the power flow in terms of economic efficacy, dependability, and sustainability by enabling data and energy to flow in both directions. Conversely, the analysis and interpretation of vast quantities of data generated by smart meters and other front-end intelligent devices constitute a substantial impediment to the implementation of smart grids. Cloud computing has the potential to provide numerous advantages for smart infrastructures Among these advantages are real-time data access, large-scale data analytics, enhanced system scalability, and reduced computational burden on local infrastructure, making it a crucial enabler for smart grid advancements. These attributes illustrate how cloud computing has the potential to address the challenges of data management and smart-grid analytics. This study provides unique and detailed information on cloud-based data management and analytics for smart Grid infrastructure, with emphasis on their applications, advantages, and constraints. This study also identifies critical research challenges and future directions for the detection of cloud-based data management and analytics in smart grids. This paper is structured to include a taxonomy of Trends in smart Grid data Management Using Cloud computing, a review of related studies, and a conclusion that summarizes the findings and implications.

关键词： Cloud computing data analysis Reviews Scalability Taxonomy Market research Real-time systems smart meters smart grids Sustainable development

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Implementation of Machine Learning Techniques for Predictive Security analytics 12th

Implementation of Machine Learning Techniques for Predictive...

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12th international conference on Frontiers of intelligent computing: Theory and Applications, FICTA 2024

作者： Moon, Pradnya S. Deshmukh, Anand B. Sarode, Harsha Jitendra Sharma, Shweta Birare, Komal Madhukar Anandpwar, Winit Nilkanth Department of Computer Technology Yashwantrao Chavan College of Engineering Maharashtra Nagpur India Department of Information Technology Sipna College of Engineering and Technology Maharashtra Amravati India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India Department of Artificial Intelligence and Data Science Arya College of Engineering Rajasthan Jaipur India Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Computer Science Dr. Ambedkar College Deekshabhoomi Maharashtra Nagpur India

ISBN: (纸本)9789819601462

Machine learning (ML)-based predictive security analytics has become an important part of modern defense. This study gives a brief overview of how machine learning can be used in predictive security analytics. Traditional security measures aren’t working as well as they used to because online risks are getting smarter and more complicated. We need to use proactive and predictive strategies instead. Machine learning systems can look through huge amounts of data and find trends and outliers that could mean an attack or security breach. Using controlled, unstructured, and reinforcement learning, predictive security analytics can spot problems early on and stop them before they become a real problem. Supervised learning algorithms, like support vector machines and neural networks, let security events be put into groups based on data that has already been named. This makes it easier to find threats. Unsupervised learning methods, like grouping and anomaly detection, make it easier to spot strange behavior that could be a sign of a security breach even without any training data. Because they learn from how they interact with their surroundings, reinforcement learning models let security systems change and get better over time. By adding machine learning models to security data tools, companies can improve their cyber defenses, which lets them proactively reduce threats and control risks. Furthermore, the ability of ML algorithms to learn new things all the time makes it possible for security measures to be flexible enough to react to changing dangerous environments. However, problems like bad data, models that are hard to understand, and threats from other people mean that ML for predictive security analytics needs to be carefully thought out and implemented using strong methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Predictive analytics for Cybersecurity Using AI and ML: An Ensemble Approach 12th

Predictive Analytics for Cybersecurity Using AI and ML: An E...

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12th international conference on Frontiers of intelligent computing: Theory and Applications, FICTA 2024

作者： Ikhar, Sharayu Mondal, Dipannita Dhage, Prashant Sharma, Shweta Patil, Milind S. Bhattacharya, Saurabh Yashika Journal Publications Pvt. Ltd Maharashtra Wardha India Artificial Intelligence and Data Science Department Dr. D. Y. Patil College of Engineering and Innovation Talegaon410507 India Nagpur Campus Pune India Artificial Intelligence and Data Science Arya College of Engineering Jaipur India EnTC VIIT Pune India Assistant Professor SCSE Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819601462

In the constantly changing field of cybersecurity, advanced and proactive defense mechanisms are crucial. This study investigates the utilization of Artificial Intelligence (AI) and Machine Learning (ML) in predictive analytics to forecast and categorize various cyberattacks, with a specific focus on Distributed Denial of Service (DDoS) and Man-in-the-Middle attacks. The study utilizes a detailed Edge-Industrial Internet of Things (IIoT) dataset that represents actual network traffic situations. The proposed method combines Isolation Forest, Random Forest, and One-Class Support Vector Machine (SVM) to improve the accuracy of attack prediction using an ensemble approach. Isolation Forest is effective at identifying anomalies in datasets, Random Forest is strong at classification, and One-Class SVM is adept at identifying normal behavior patterns. These techniques work together to create a comprehensive and efficient defense system against various cyber threats. The predictive analytics model evaluation shows an accuracy rate of 98.23%, highlighting the system’s capability to classify incoming network traffic as normal or indicate an attack with precision. The True Positive Rate (TPR) reaches a notable 98.78%, demonstrating the model’s accuracy in detecting and predicting cyberattacks. The proposed ensemble method demonstrates a high level of accuracy and sensitivity, highlighting its potential practicality and reliability in real-world cybersecurity applications. The study enhances the development of sophisticated predictive analytics models for cybersecurity and emphasizes the importance of using AI and ML techniques, along with diverse datasets, to strengthen and adapt cyber defense mechanisms against changing cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： AI and ML Cyber threat prediction Cybersecurity Edge-IIoT dataset Ensemble method Predictive analytics

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