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smart Grid Tolerance Invasion Control Under False data Injection Attacks 8th

Smart Grid Tolerance Invasion Control Under False Data Injec...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Li, Ming Wei, Lisheng Wang, Ruiren Anhui Key Laboratory of Electric Drive and Control Anhui Polytechnic University Wuhu241000 China School of Electrical Engineering Anhui Polytechnic University Wuhu241000 China

ISBN: (纸本)9789819602247

Aiming at the problem of how to maintain stable and secure operation of the smart grid after a false injection attack, the tolerant intrusion control of the system after a false data injection attack is investigated. First, since there is no publicly available FDIA dataset, two datasets are generated: one contains random single-node attacks and the other contains random multi-node attacks. Second, combining AE and Generative Adversarial Network (GAN), and introducing Wasserstein distance and gradient penalty term to avoid the problems of gradient vanishing and pattern collapse that GAN is prone to in adversarial training;at the same time, adversarial training is used to achieve Nash equilibrium, learn spatial relationship between data, and complete the data reconstruction;the method does not need labeled data from measurement instruments, and only requires attack-free historical state data to complete the training, thus it is unsupervised learning. Finally, the simulation results show that the adopted recovery scheme can accurately restore the contaminated state quantities to their pre-attack values, and has high recovery accuracy for FDIA contamination. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Multi-sensor data Fusion for Accurate Prediction of Suspicious Activities in Video 3rd

Multi-sensor Data Fusion for Accurate Prediction of Suspicio...

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3rd international conference on Advancements in smart computing and Information Security, ASCIS 2024

作者： Radhika, R. Muthukumaravel, A. Chennai India Chennai India

ISBN: (纸本)9783031863011

Video analytics for surveillance enhances safety of the public across a range of public spaces, such as shopping centers, railroad stations, and daycares. Conventional closed-circuit television systems have proven to be inefficient and prone to errors as they rely heavily on human operators. The proposed intelligent video surveillance system utilizes deep learning algorithms to promptly notify the authorities of any suspicious activity. The system has been trained to identify a wide range of human actions and potential hazards. It achieves this by utilizing datasets like UCF-Crime, HMDB51, and UCF101. The architecture combines LSTM (Long Short Term Memory) with CNN (Convolution Neural Network). The CNN component effectively identifies suspicious activity by extracting important features from video frames using the Inception V3 model. The LSTM examines these characteristics to establish connections over time. The system demonstrated exceptional performance on UCF-Crime, with an F1 score of 99.1%, precision of 99.8%, recall of 98.5%, and an impressive accuracy of 99.6%, as observed during the evaluation across datasets. HMDB51 achieves an impressive F1 score of 99.1%, with precision at 98.7%, recall at 97.9%, and accuracy at 99.4%. On the other hand, UCF101 achieves a commendable F1 score of 98.6%, with accuracy at 99.2%, precision at 98.7%, and recall at 97.9%. The detection durations for UCF-Crime, HMDB51, and UCF101 are 0.25 s, 0.30 s, and 0.28 s, respectively. The proposed model streamlines the procedure of improving safety by utilizing intelligent video analytics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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AI Integrated Pedagogical Techniques for Higher Education 1st

AI Integrated Pedagogical Techniques for Higher Education

引用

1st international conference on intelligent Technology for Educational Applications, ITEA 2024

作者： Dhasarathan, Jayalakshmi Thangasamy, Suresh Balakrishnan Uthirapathy, Samson Ebenezar Rattinamangalam Sakthivel, Arunkumar Devaraj, Santhakumar Fernandez, Mary Herin Fernandez Subbiah, Priyanga Narayanan, Lakshmi Kanthan R.M.D Engineering College KavaraipettaiGummidipoondi Thiruvallur India Saveetha University Tamilnadu Chennai602105 India Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu 603203 India

ISBN: (纸本)9789819640898

In recent times, the incorporation of Artificial Intelligence (AI) into higher education has surged, marking a significant shift in teaching and learning methodologies. These include customized tutoring platforms, adaptive and smart tutoring systems, data analytics tools, virtual assistants tailored for academic use, curriculum design and development software, collaborative learning platforms, assessment, and feedback tools, as well as discussions on the ethical implications of AI adoption in higher education. The discussion emphasizes that the thoughtful inclusion of AI not only refines pedagogical methodologies but also facilitates the extensive coverage of contemporary educational strategies. The chapter strategically examines current applications of AI tools, presents successful case studies illustrating their impact, and envisions potential future developments. Educators also prioritize fostering critical thinking and digital literacy skills in students to enable them to engage with AI responsibly. Additionally, collaborative projects, hands-on learning opportunities, and interdisciplinary approaches are encouraged to prompt students to explore AI’s societal impacts and applications across various fields. Through these strategies, higher education institutions seek to equip students for the AI-driven workforce while fostering innovation and creativity in educational practices. This paper aims to throw the limelight on the possibility of integrating advanced computational technologies such as AI, ML, and IoT towards empowering the modern education system to attain par excellence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Architecture to Recreate Immersive Scenarios for Practicing Sports in Gyms 9th

Architecture to Recreate Immersive Scenarios for Practicing ...

引用

9th international conference on smart City Applications, SCA 2024

作者： Pereira, Hélder Matos, Paulo Polytechnic University of Bragança Campus de Santa Apolónia Bragança 5300-253 Portugal Research Centre in Digitalization and Intelligent Robotics (CeDRI) Polytechnic University of Bragança Campus de Santa Apolónia Bragança 5300-253 Portugal Associate Laboratory for Sustainability and Technology in Mountains Regions (SusTEC) Polytechnic University of Bragança Campus de Santa Apolónia Bragança 5300-253 Portugal

ISBN: (纸本)9783031886522

This paper proposes an architectural solution to integrate immersive features into fitness devices, enhancing user experience and athletic performance. The architecture is designed to be open and compatible with a broad range of fitness equipment, across different brands and models. Our approach seeks to recreate real-world environments through video playback of actual routes, enriched with contextual data such as speed and altitude variations. This data facilitates the dynamic control of compatible fitness equipment by adjusting parameters like resistance, slope, and inertia, to synchronize with the video, thereby creating an authentic and immersive experience. This approach ensures that the immersive experience can be consistently achieved, regardless of the device’s inherent capabilities. The architecture is divided into two subsystems. The first is focused on video capture and collecting contextual route data, including real-time recording of speed, altitude, and environmental factors. These recordings are processed and uploaded to a cloud-based platform, to foster a community-driven repository of shared routes. The second subsystem is supported by combining cloud and edge computing, integrating artificial intelligence for personalized adjustments, SIG BLE standards for communication with fitness devices and wearables, and the mobile application. This subsystem aspires to enable real-time adaptation of fitness equipment based on the athlete’s historical performance data and current exercise metrics. The user interface for both subsystems is provided through the mobile application, although the roles of the user who creates and publishes new routes and the user who utilizes these routes are separated. This paper details the architecture, discussing the rationale behind the selected components and technologies. It also outlines the ongoing implementation process and presents preliminary results that suggest the potential effectiveness of the proposed system. © The

关键词： Artificial Intelligence Athletic Performance Cloud computing Edge computing Fitness Immersive Environments IoT SIG BLE standards

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A Managerial Framework for intelligent Big data analytics 2019

A Managerial Framework for Intelligent Big Data Analytics

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2nd international conference on Software Engineering and Information Management (ICSIM) / 2nd international conference on Big data and smart computing (ICBDSC)

作者： Sun, Zhaohao Huo, Yanxia PNG Univ Technol Dept Business Studies Private Mail Bag Lae 411 Morobe Papua N Guinea PNG Univ Technol Teaching Learning Methods Unit Private Mail Bag Lae 411 Morobe Papua N Guinea

ISBN: (纸本)9781450366427

intelligent big data analytics is an emerging paradigm for integrating big data, analytics, and artificial intelligence. The objective of this paper is to provide a managerial framework of intelligent big data analytics. More specifically, this paper proposes a managerial framework of intelligent big data analytics, which consists of intelligent big data analytics as a science, technology, system, service and management for improving business decision making. Then it elaborates intelligent big data analytics for management taking into account main managerial functions: planning, organising, leading and controlling. The proposed approach in this paper might facilitate the research and development of business analytics, big data analytics, business intelligence, artificial intelligence and data science.

关键词： intelligent big data analytics intelligent analytics artificial intelligence management analytics

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Design and Development of Bayesian Optimization Algorithms for Big data Classification Based on MapReduce Framework 1

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international conference on intelligent and smart computing in data analytics, iscda 2020

作者： Banchhor, Chitrakant Srinivasu, N. School of Computer Engineering and Technology Dr. VishwanathKarad MIT World Peace University Pune Maharashtra India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram AP India

ISBN: (数字)9789813361768

ISBN: (纸本)9789813361751

The handling of big data refers efficient management of processing and storage requirements of very large volume of structured and an unstructured data of association. The basic approach for big data classification using naïve Bayes classifier is extended with correlation among the attributes so that it becomes a dependent hypothesis, and it is named as correlative naïve Bayes classifier (CNB). The optimization algorithms such as cuckoo search and grey wolf optimization are integrated with the correlative naïve Bayes classifier, and significant performance improvement is achieved. This model is called as cuckoo grey wolf correlative naïve Bayes classifier (CGCNB). The further performance improvements are achieved by incorporating fuzzy theory termed as fuzzy correlative naïve Bayes classifier (FCNB) and holoentropy theory termed as Holoentropy correlative naïve Bayes classifier (HCNB), respectively. FCNB and HCNB classifiers are comparatively analyzed with CNB and CGCNB and achieved noticeable performance by analyzing with accuracy, sensitivity and specificity analysis. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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"I-Care" - Big-data analytics for intelligent Systems 8

"I-Care" - Big-data Analytics for Intelligent Systems

引用

8th international conference on smart computing and Communications (ICSCC)

作者： Singh, Paras Nath CMR Inst Technol Dept CSE Bengaluru 560037 India

ISBN: (纸本)9781728196879

A very novel predicament for quantitative data science has been generated by the abundance of large, well-cured data sets in biological and social science, coupled with an extraordinary increase in computational ability. This is the possibility of sophisticated studies combined with remedial understanding. analytics for intelligent systems should cover architecture of hardware platforms and application of software methods, technique and tools. It is anticipated that adapting dynamic memory information, processing parametric values of large data sheets with optimization, would be faster. The field of Big-data analytics under recent trends of data Science studies various means of pre-processing, analyzing and filtering from huge and semi-structured data sets from different sources which are complex to be handled by traditional data processing systems. In addition to extracting and aggregating data from various main performance measures, this proposal also forecasts potential values for these KPIs (Key Performance Indicators) and alerts them when unfavorable values are about to occur. As AI and ML are implemented through different platforms and sectors including chat-bots, robotics, social media, healthcare, self-driven automobile and space exploration, large companies are investing in these fields, and the demand for ML and AI experts is growing accordingly. Python is becoming the most popular language for AI (Artificial Intelligence and Machine Learning) due to its rich supported tools. This proposed applications "I-Care" (intelligent Care) provide recommendations to improve Quality of Service of Big-data analytics. So, the proposed paper examines the methodology and requirements, architecture, modeling and analytics with implementation and describes the architectural design and the results obtained by the pilot application using Python and its powerful tools like Pandas and Scikit-Learn.

关键词： Artificial Intelligence Big data analytics Healthcare Machine Learning intelligent Systems Python

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An intelligent Predictive analytics System for Transportation analytics on Open data Towards the Development of a smart City 13th

An Intelligent Predictive Analytics System for Transportatio...

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13th international conference on Complex, intelligent, and Software Intensive Systems (CISIS)

作者： Audu, Abdul-Rasheed A. Cuzzocrea, Alfredo Leung, Carson K. MacLeod, Keaton A. Ohin, Nibrasul, I Pulgar-Vidal, Nadege C. Univ Manitoba Winnipeg MB Canada Univ Trieste Trieste Italy

ISBN: (纸本)9783030223540;9783030223533

As time is a precious asset, bus riders would desire to get accurate information about bus arrival time. Although different research approaches have been developed to correctly predict bus arrival time, very few of them produce highly precise and accurate results based on open data. In this paper, we present an intelligent system designed for transportation analytics on open data such as bus delay data. Specifically, the system accesses open data to analyze public transport data-such as historical bus arrival time-for urban analytics;it then conducts data analytics and mining to discover frequent patterns. Based on the discovered patterns, the system makes predictions on whether the bus arrives on time or is being late. Evaluation on real-life open data provided by a Canadian city show the effectiveness and prediction accuracy of our intelligent system in transportation analytics on open data. The results are encouraging towards the goal of developing smart cities.

关键词： intelligent system Transportation analytics Open data Public transportation Bus Bus delay data analytics Frequent pattern mining Predictive analytics smart city

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The Role of Big data analytics in smart Grid Management 2nd

The Role of Big Data Analytics in Smart Grid Management

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2nd international conference on computing, Communications and data Engineering (CCODE)

作者： Dhupia, Bhawna Rani, M. Usha Alameen, Abdalla SPMVV Dept Comp Sci Tirupati Andhra Pradesh India Prince Sattam Bin Abdulaziz Univ Al Kharj Saudi Arabia

ISBN: (纸本)9789811501357;9789811501340

data analytics is playing a vital role in the modern industrial era. Electricity is one of the industries that have adapted data analytics techniques to a great extent. The data collected in the smart grid through smart meters and other sensors installed is very huge. The processing of such a huge heterogeneous data is not possible without the use of big data analytics technique. Big data analytics and machine learning algorithms play a vital role in electricity transmission and distribution network for data collection, storage and analysis of the data, prediction for data forecasting, and maintenance of the system. These techniques can help to optimally deliver energy at a lower cost with high quality and can also improve the customer service as well as social welfare. This article will review the use of big data analysis techniques along with the machine learning for various applications that can be mapped for smart grid environment. This article will also discuss the various methods and algorithm to be used for the applications for smart grid. A comparative analysis will also be done to show the best methods and algorithm to be used for a particular application.

关键词： smart grid Machine learning Applications in smart grid data analytics algorithms and methods

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Wireless Sensor Sequence data Model for smart Home and IoT data analytics 1st

Wireless Sensor Sequence Data Model for Smart Home and IoT D...

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1st international conference on Computational Intelligence and Informatics (ICCII)

作者： Suryadevara, Nagender Kumar Geethanjali Coll Engn & Technol Hyderabad Telangana India

ISBN: (纸本)9789811024719;9789811024702

In this paper, Wireless sensor sequence data mining model is demonstrated for the smart home and Internet of Things data analytics. Exploration of the sensor data patterns by correlating with the multi stream sensor data that are fused from the wireless sensor network is presented. The effective realization of the sensor data patterns from heterogeneous sensing systems for various applications of IoT can be known from the proposed conceptual data model. The conceptual data model includes the discovering of frequent pattern item sets using various computational archetypal. Results of the explicit patterns augmented for data analytics are encouraging as the prototype was tested through real-time data rather than test bed scenario data or synthetic data.

关键词： Wireless sensor networks Activities of daily living Wellness smart home Internet of Things data analytics

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