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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Dalal, Renu Khari, Manju Pandey, Priyank Jatana, Samanvay Joshi, Vijay Usar Guru Gobind Singh Indraprastha University East Delhi Campus Surajmal Vihar New Delhi India Jawaharlal Nehru University Department of Computer and Systems Science New Delhi India Graphic Era Deemed to Be University Department of Computer & Engineering Dehradun India Maharaja Surajmal Institute of Technology Ggsipu Department of Information Technology Janakpuri Delhi India

ISBN: (纸本)9798350319125

Facial emotion recognition is now becoming a hot topic in previous years as it helps in human computer interaction. As human emotions can be detected, there are various studies on it in the computer vision field. Humans can easily recognize the type of emotion from someone's facial expressions but it's tough for a computer machine. FER can be used in vast areas for applications such as education, social media, healthcare, Al based application, etc. In this research paper, the Convolutional Neural Network (CNN) technique has been built. The methodology is mainly divided into two steps: Haar cascade features from the face are extracted by using Viola Jones algorithm leading to facial identification and then facial emotion is recognised by using the help of neural network. This technique is built on conventional neural networks (CNN) and it has used FER2013 and CK+ dataset for detection of human emotions. This model consists of 3 completed connected layers and six convolutional layers. On the basis of our experimental results, this model has a validation accuracy of 68.26% with FER 2013 dataset and 91.58% with CK+ dataset. © 2023 IEEE.

关键词： CNN Deep Learning Facial Emotion Recognition Human computer Interaction

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Vulnerability Testing on IoT Networks Using Machine Learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

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Advances in Data engineering and Intelligent Computing systems (ADICS), International Conference on

作者： R. Mohandas D.S.John Deva Prasanna Baiju B V S. Nathiya T. Dhiliphan Rajkumar C. Sivashankar Department of Computational Intelligence SRM Institiute of Science and Technology Chennai India Department of Datascience and Business systems SRM Institiute of Science and Technology Chennai India School of Computer Science and Engineering(SCOPE) VIT University Vellore TamilNadu India Department of Electronics and Communication Engineering Prince shri Venkateshwara Padmavathi Engineering College Chennai Dept. Of Computer Science and Engineering Kalasalingam Academy Of Research And Education Tamilnadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennnai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks; therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities.

关键词： Learning systems Industries Pathology Machine learning algorithms Intrusion detection Machine learning Internet of Things

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Enhanced Prognostics and Health Management in Automated Driving systems: Using Graph Neural Networks to Recognize Operational Contexts

Enhanced Prognostics and Health Management in Automated Driv...

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Prognostics and System Health Management Conference (PHM)

作者： Peng Su Yeqi Wang Conglei Xiang Erik Wendel Madhav Mishra DeJiu Chen Department of Engineering Design Mechatronics Division KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program of Electrical Engineering and Computer Science (EECS) School KTH Royal Institute of Technology Stockholm Sweden Digital Systems Division RISE Research Institutes of Sweden Gothenburg Sweden

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data.

关键词： Graph neural networks Data models Safety Complexity theory Data mining Prognostics and health management Intelligent systems

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Recursive Least-Square-Based Parameter Estimation for Dynamic State Estimation in Power Grids

Recursive Least-Square-Based Parameter Estimation for Dynami...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Shahin Riahinia Amir Ameli Mohsen Ghafouri Abdulsalam Yassine Department of Electrical and Computer Engineering Lakehead University Thunder Bay ON Canada Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montreal QC Canada Department of Software Engineering Lakehead University Thunder Bay ON Canada

Nowadays, Dynamic State Estimation (DSE) has become integral to power system control and real-time contingency analysis. The efficacy of DSE in wide-area power systems is contingent upon several factors, most notably the accuracy of information regarding the system topology, generation, and load. Given that these parameters can vary dynamically, it becomes imperative to accurately estimate ongoing changes; since misestimations can compromise control and protective actions within power grids. This paper introduces a Recursive Least Squares (RLS) approach, which is grounded on the inverse power flow problem, to effectively estimate the reduced admittance matrix (Y-bus) for DSE applications based on the measurements received from Phasor Measurement Units (PMUs). The proposed RLS estimation technique offers reliable estimates of system parameters despite their dynamic behavior (both smooth and sudden changes) in the presence of measurement noise. The efficacy of the proposed method is validated on the IEEE 14-bus test system using diverse DSE scenarios.

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Clustering With Adaptive Graph Learning and Spectral Rotation 7

Clustering With Adaptive Graph Learning and Spectral Rotatio...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Ge Zhou, Jie Huang, Chucheng Gao, Can Wang, Yangbo Shen, Xinrui National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China College of Mathematics and Statistics Shenzhen University Guangdong Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

ISBN: (纸本)9781839539848

Prototype-based clustering algorithms have garnered considerable attention in the field of machine learning due to their efficiency and interpretability. Nonetheless, these algorithms often face performance degradation when confronted with high-dimensional or non-ellipsoidal data distributions. To surmount these challenges, this study introduces a novel clustering approach, dubbed Clustering with Adaptive Graph learning and Spectral Rotation (CAGSR). In CAGSR, the imposed spectral rotation operation mitigates the discrepancy between the membership matrix, which adheres to the notion of fuzzy clustering, and the spectral representations derived from an adaptive graph rather than a predefined one. This enables the generation of a comprehensive representation of the data across multiple spaces. Furthermore, the clustering and graph learning tasks are jointly optimized in a projected subspace, which can effectively reduce the adverse impact caused by irrelevant features in the original space. The proposed method seamlessly integrates fuzzy clustering, graph structure learning, and spectral rotation into a unified model, facilitating the detection of intrinsic structures. Experimental evaluations conducted on benchmark data sets substantiate the effectiveness of CAGSR when compared to related clustering approaches. © The Institution of engineering & Technology 2023.

关键词： Fuzzy clustering

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Analysis and Detection of Fake News Using Machine Learning

Analysis and Detection of Fake News Using Machine Learning

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： P Sathish Kumar P. Suthanthiradevi C Arul Stephen B Ebenezer Abishek S. Sivakumar M Mathiyarasu Computer and Communication Engineering Rajalakshmi institute of technology Chennai Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Electronics and Communication Engineering BIHER Chennai

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Tokenization Reliability Internet of Things

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Study on indoor pollutants emission in Akure, Ondo State, Nigeria

Arab Gulf Journal of Scientific Research

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Arab Gulf Journal of Scientific Research 2024年第4期42卷 1643-1663页

作者： Abulude, Francis Olawale Suriano, Domenico Oluwagbayide, Samuel Dare Akinnusotu, Akinyinka Abulude, Ifeoluwa Ayodeji Awogbindin, Emmanuel Environmental and Sustainable Research Group Science and Education Development Institute Akure Nigeria Department of Sustainable Development Research Center of Brindisi ENEA-Italian National Agency for New Technologies Energy and Environment Brindisi Italy Department of Agricultural and Bio-Environmental Engineering Federal Polytechnic Idah Nigeria Department of Science Laboratory Technology Rufus Giwa Polytechnic Owo Nigeria Department of Agricultural Economics Bioeconomy and Food Systems Justus – Liebig University (JLU) Giessen Germany Department of Computer Science Bamidele Olumilua University of Education Science and Technology Ekiti Ikere Nigeria

Purpose: This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2), as well as particulate matter (PM) (PM1, PM... 详细信息

Purpose: This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO₂), ozone (O₃), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), as well as particulate matter (PM) (PM₁, PM_2.5 and PM₁₀) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations. Design/methodology/approach: The evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out. Findings: CO₂ (430.34 ppm), NO₂ (93.31 ppb), O₃ (19.94 ppb), SO₂ (40.87 ppb), PM₁ (29.31 µg/m3), PM_2.5 (43.56 µg/m³), PM₁₀ (50.70 µg/m³), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO₂ (18%) and low PM₁₀ (49%) contributions, NO₂ and PM₁₀ were the most common pollutants in all of the months. The mean air quality index (AQI) for NO₂indicated that the AQI was “moderate” (51–100). In contrast to SO₂, whose AQI ranged from “moderate” to “very unhealthy,” O₃'s AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM₁, PM_2.5 and PM₁₀ made up the majority of PC1’s contribution, both PM_2.5 and PM₁₀ were deemed “hazardous.” Practical implications: The practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs. Originality/value: Lack of literature in terms of indoor air qu

关键词： Air quality index Indoor air quality Nigeria Pollutants United States environmental protection agency (USEPA) World health organization (WHO)

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Temperature Dependence of Hot-Electron Graphene Fet Bolometric Detectors Response to Modulated Terahertz Radiation

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International Journal of High Speed Electronics and systems 2024年第4期33卷

作者： Ryzhii, Maxim Ryzhii, Victor Tang, Chao Otsuji, Taiichi Mitin, Vladimir Shur, Michael S. Department Of Computer Science And Engineering University Of Aizu Aizu-Wakamatsu Fukushima965-8580 Japan Research Institute For Electrical Communication Tohoku University Miyagi Sendai980-8577 Japan Department Of Electrical Engineering University At Buffalo Suny BuffaloNY14260 United States Department Of Electrical Computer And Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

In this paper, we analyze the modulation characteristics and the ultimate modulation frequency of the terahertz (THz) hot-electron FET bolometers with the graphene channels (GCs), metal gate (MG), and gate barrier layers (BLs) in a wide temperature range. Our results predict that the responsivity of GC-FET bolometers decreases with decreasing operating temperature. This is attributed to a dramatic drop in the thermionic GC-MG current (characterized by the relatively large activation energy) and its modulated component when the temperature lowers. A further decrease in the temperature results in a fairly strong responsivity roll-off. In contrast, the responsivity of the GC-FET detectors with the temperature-adjusted load resistance rises with decreasing temperature because the temperature lowering leads to an increase in the electron energy relaxation time and promotes more effective heating by the impinging THz radiation. In this case, the weakening of the thermionic current is compensated by the commensurate increase in the load resistance. © 2024 World Scientific Publishing Company.

关键词： Graphene

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Direct data-driven LPV control of nonlinear systems: An experimental result

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IFAC-PapersOnLine 2023年第2期56卷 2263-2268页

作者： Chris Verhoek Hossam S. Abbas Roland Tóth Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology 5600MB Eindhoven The Netherlands Institute for Electrical Engineering in Medicine Universität zu Lübeck 23558 Lübeck Germany Systems and Control Lab Institute for Computer Science and Control 1111 Budapest Hungary

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points.

关键词： Data-Driven Control Linear Parameter-Varying systems

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Reinforcement Learning for Unrelated Parallel Machine Scheduling with Release Dates, Setup Times, and Machine Eligibility

Reinforcement Learning for Unrelated Parallel Machine Schedu...

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Simulation Winter Conference

作者： Sang-Hyun Cho Hyun-Jung Kim Lars Mönch Dept. of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Dept. of Mathematics and Computer Science University of Hagen Universitätsstraße 1 Hagen GERMANY

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

This paper presents a novel approach for solving unrelated parallel machine scheduling problems through reinforcement learning. Notably, we consider three main constraints: release date, machine eligibility, and sequence- and machine-dependent setup time to minimize total weighted tardiness. Our work presents a new graph representation for solving the problem and utilizes graph neural networks combined with reinforcement learning. Experimental results show that our proposed method outperforms traditional dispatching rules and an apparent tardiness cost-based algorithm. Furthermore, since we represent and solve the problem using graphs, our method can be used regardless of the number of jobs or machines once trained.

关键词： Metalearning Adaptation models Heuristic algorithms Stochastic processes Reinforcement learning Parallel machines Dynamic scheduling Size measurement Dispatching Graph neural networks

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