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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Rao, Chirag Prabhu, Gautham Manuru Kumar, Ajay Rajendra Gupta, Shourya Shetty, Nisha P. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Information Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India

Vaccine hesitancy is a growing concern in public health, with increasing numbers of individuals expressing skepticism or outright refusal to receive vaccines. This factor was significantly highlighted during the COVID-19 pandemic, with large populations refusing to take the vaccine and prolonging the pandemic. This paper presents and compares two transformer-based approaches i.e. XLNET and BERT to classify vaccine misinformation on Twitter using the standard COVID-19 ANTi-Vax dataset. Subsequently, an analysis of vaccine discourse on Reddit is carried out following a user association mapping algorithm. The resultant graph was subsequently analyzed. The XLNET model outperformed BERT by showing a high accuracy of 0.9484, with an F1 score of 0.9353. The methodology can be used in multiple other scenarios to address concerns with regard to the usage of social media by analyzing network interactions. © 2024 Elsevier B.V.. All rights reserved.

关键词： Vaccines

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Optimizing Water Distribution Pump Scheduling with SVM-Based Machine Learning Using IoT Sensor Data 2

Optimizing Water Distribution Pump Scheduling with SVM-Based...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Basavaraddi, Chethan Chandra S. Jegan Amarnath, J. Muthukannan, K. Choudri, Subramani Roy Malathi, N. Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur Karnataka Tumkur India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Chennai India Department of Information Technology Sri Shanmugha College of Engineering and Technology Tamil Nadu Salem India Department of Computer Science and Engineering Vsardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Water distribution is essential to urban infrastructure, and pump scheduling is crucial to water reliability. Traditional pump scheduling uses preset or manual modifications, wasting resources and water. This paper introduces a unique strategy that uses Machine Learning (ML) techniques, notably Support Vector Machines (SVM) and Internet of Things (IoT) data for dynamic pump scheduling in water distribution systems to overcome these difficulties. The proposed method uses IoT sensors throughout the water distribution network to gather real-time water flow, pressure, and demand data. After processing, an SVM-based ML model predicts water demand and system behavior using this data. SVM, which can handle non-linear and high-dimensional data, captures complicated IoT variable interactions. The system provides optimum pump schedules in real time using SVM for prediction. These schedules adapt to changing circumstances to maximize energy efficiency and reduce pipeline breakdowns. SVM integration also detects abnormalities and leaks, improving system dependability and decreasing water loss. It advances IoT and ML in water distribution management. The SVM-based strategy improves water distribution network operating efficiency, saving money, reducing environmental impact, and enhancing service quality. The suggested method improves water supply system sustainability and resilience in smart cities and urban areas by being scalable and adaptive. © 2023 IEEE.

关键词： Operational Efficiency Real-time Data Water Distribution Water Loss Reduction

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Vector control design and analysis for PMSM using real-Time HIL simulation 4

Vector control design and analysis for PMSM using real-Time ...

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4th International Conference on Smart Grid Metrology, SMAGRIMET 2023

作者： Knol, Kristian Bensic, Tin Barukcic, Marinko Osijek Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Department of Electromechanical Engineering Osijek Croatia

ISBN: (纸本)9798350323597

During the development of control strategies for permanent magnet synchronous machines (PMSM) used in wind turbines, usage of wide range of simulation allows for more detailed, more safe and less expensive development. This paper presents type of HIL simulation, where instead of using real controller and real-Time simulator, two real-Time simulators are used and form a co-simulation environment where vector control of PMSM is conducted. The whole process of designing and simulating is a part of model-based design (MBD). The PMSM is described and controller for vector control is derived. Created co-simulation environment with its advantages and problems is described. An experiment with different scenarios is conducted to test created co-simulation. © 2023 IEEE.

关键词： Vectors

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Assessment of Non-Intrusive Load Monitoring as a Blind Source Separation Problem

Assessment of Non-Intrusive Load Monitoring as a Blind Sourc...

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2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023

作者： Herath, G.M. Liyanage, M.H. Angammana, C.J. Sri Lanka Institute of Information Technology Faculty of Engineering Malabe Sri Lanka University of Waterloo Department of Electrical and Computer Engineering WaterlooON Canada

ISBN: (纸本)9781728170251

Non-Intrusive Load Monitoring (NILM) allows consumers to monitor appliances' power consumption without installing appliance-level sensors. NILM has become popular with the rapid deployment of smart energy meters. Neural network-based disaggregation approaches have provided more promising results in NILM. However, most of them must have labeled energy data for model training. The availability of energy data is inadequate, and the data must be updated frequently for higher disaggregation performances. However, none of the existing datasets is periodically updated. Therefore, training data has become a limiting factor for commercializing disaggregation solutions. This study addresses the issue by proposing an Independent Component Analysis (ICA) based solution to perform energy disaggregation as a blind source separation problem for the first time in the NILM domain. The aggregate energy signal has been used to prepare the independent input signals with a novel signal preprocessing approach. The results revealed the effectiveness of ICA in NILM applications. © 2023 IEEE.

关键词： Blind source separation

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A comprehensive Study on Security Threats in Autonomous Vehicles: Safeguarding the Future 1

A comprehensive Study on Security Threats in Autonomous Vehi...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Poddar, Srisha Deepak Murali, M. Raj Asekaran, P. Prabakaran, N. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Sona College of Technology Department of Information Technology Salem India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chengalpattu603203 India

ISBN: (纸本)9798350361780

Autonomous vehicles, commonly referred to as self- driving vehicles, have the capability to operate and perform necessary functions without the need for human intervention. This is made possible with advanced technologies such as cameras, ultrasonic and infrared sensors, INS, GPS, DSRC, RADAR, LiDAR, and onboard computers, which work together to map the vehicle's position and surroundings. However, because these technologies are easily susceptible to manipulation, autonomous vehicles are at risk of cyber-attacks if hackers succeed to find vulnerabilities in the vehicle's system or in the electronic system of the company producing the vehicle. This study aims to investigate how machine learning and deep learning methods can be utilized to improve the defence against cyber threats aimed at autonomous vehicles by analysing several computational algorithms. © 2024 IEEE.

关键词： Autonomous vehicles

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Automating the Optimal Temperature to Increase the Yield in Sericulture by Using Internet of Things 5

Automating the Optimal Temperature to Increase the Yield in ...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Hemanth Chandra, N. Harsha, R. Prithvi Veera, B. Rohan, M.N. Thriveni, J. Venugopal, K.R. University Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bengaluru India Global Academy of Technology Department of Information Science and Engineering Bengaluru India Former Vice-Chancellor Bangalore University Bengaluru India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528539

Sericulture/, the art of cultivating silk through the rearing of silkworms, earns its title as the Queen of Textiles owing to its unmatched softness, elegance, durability, and tensile strength. India, the second-largest producer of silk, favors this luxurious fabric for its exceptional water absorbency, heat resistance, and inherent luster. The intricacies of insect adaptation, particularly evident in mulberry silkworms, underscore the significance of maintaining optimal temperature and humidity levels, factors pivotal for their growth and survival. However, conventional methods of temperature regulation, such as employing wetted gunny bags, are insufficient for the precise monitoring demanded by sericulture. In this paper, a model has been proposed to revolutionize sericulture management by leveraging IoT and cloud technologies to ensure real-time monitoring of temperature and humidity within silkworm sheds. By offering insights into the interplay of humidity, airflow, and light on silkworm development, this system aims to enhance cocoon crop yields while minimizing the need for manual oversight. This holistic approach guarantees the production of superior-quality silk with consistent characteristics, heralding a new era of efficiency and excellence in the silk industry. © 2024 IEEE.

关键词： Silk

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Facial Recognition Utilizing the CLM Model and Supervised Machine Learning: A Comparative Analysis 25

Facial Recognition Utilizing the CLM Model and Supervised Ma...

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25th International Arab Conference on information technology, ACIT 2024

作者： Iqtait, Musab Ababneh, Jafar Abu-Jassar, Amer Al-Refai, Mohammad Dmour, Mohammad Arabiat, Mohammad Binsaddig, Ruaa Zarqa University Faculty of Information Technology Department of Artificial Intelligence Zarqa Jordan Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan Amman Arab University University College of of Computer Sciences and Information Amman Jordan Zarqa University Faculty of Information Technology Software Engineering Department Zarqa Jordan University of Business and Technology College of Business Administration Department of Finance Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Facial recognition is a common challenge in artificial intelligence. We extensively utilized this program in our daily life. Face recognition software was implemented on many phones to protect user identities and promptly recognize Facebook users in photographs. Despite the numerous methodologies proposed to date, the actual implementation of facial recognition continues to pose significant challenges. Distinguishing individuals by a fundamental method relies on various factors, including lighting, postural differences, and partial facial obstruction. This study aims to develop a facial recognition technique utilizing a constrained local model (CLM) and a machine learning algorithm. It is evaluated with KNN, SVR, and CCA. Furthermore, it has employed CLM and CCA to achieve a maximum recognition accuracy of 3.98 MAE. © 2024 IEEE.

关键词： Adversarial machine learning

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Ensemble Based Detection Model for DDoS Attacks in SDNs Using Advanced Feature Selection 17

Ensemble Based Detection Model for DDoS Attacks in SDNs Usin...

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17th International Conference on Signal Processing and Communication Systems, ICSPCS 2024

作者： Alashhab, Abdussalam Ahmed Edrah, Aisha Mohd Zahid, Mohd Soperi Siddikur Rahman, Md. Department of Computer and Information Sciences Universiti Teknologi Petronas Seri Iskandar Malaysia Faculty of Information Technology Alasmarya Islamic University Zliten Libya Western University Faculty of Engineering Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada Universiti Teknologi Petronas Department of Electrical and Electronics Engineering Seri Iskandar Malaysia

ISBN: (纸本)9798350389630

Software-Defined Networking (SDN) enhances flexibility, scalability, and innovation by decoupling the control plane from the data plane, managed through streamlined controller operations. However, Distributed Denial of Service (DDoS) attacks pose significant cybersecurity threats to SDNs, disrupting services by flooding targeted systems with traffic from multiple sources. Real-time detection of these attacks remains challenging, as traditional methods often lack the ability to accurately identify complex attack patterns due to limited feature sets. To address this, we propose an ensemble-based model that combines three classifiers (SGD, EBM, and MLP) for effective DDoS attack detection and mitigation in SDNs. Our approach integrates a reliable feature selection methodology, leveraging Principal Component Analysis (PCA) to identify the most informative features for attack detection. This enhancement significantly improves the accuracy and efficiency of the ensemble model. Evaluated using the CIC-DDoS2019 and InSDN datasets, our model demonstrates substantial improvements in detection rates and computational efficiency. Results show that the proposed approach achieves 99% accuracy, underscoring its potential as a resilient solution for DDoS attack detection in SDNs. © 2024 IEEE.

关键词： Denial-of-service attack

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Scheduling with Time Aware Shaping in Integrated Ethernet and Wi-Fi Networks

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SN computer Science 2024年第8期5卷 1-10页

作者： Gopalakrishnan, Abhilash Duttagupta, Subhasri Nadig, Deepak Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam 690525 India Department of Computer and Information Technology Purdue University 401 N Grant St West Lafayette 47907-2021 IN United States

Communication technologies play a significant role in systems engineering. The proliferation of connected embedded systems has resulted in Internet of Things applications. Many of these systems are safety or mission critical. For example, in smart manufacturing or health, reliability and low latency are significantly important. A deterministic communication system with real-time capability is essential for ensuring safety in those circumstances. Patient monitoring and remote surgery are two such examples. The maximum permitted delay for video in remote surgery is 200 ms and the minimum allowed latency for tactile feedback is 10 ms. The prior efforts of VLAN tagging over Ethernet are insufficient to ensure determinism and reliability. The upcoming IEEE Time-Sensitive Networking (TSN) standards have enabled determinism by regulating latency, jitter, and packet loss across different types of flows. However, currently, it is only widely available on Ethernet. The paper explores a scheduling approach for an integrated Ethernet and WiFi scenario dealing with mixed-priority traffic that leads to minimal violations of the end-to-end delay requirement. With WiFi TSN bridges and access points equipped with Time Aware Shapers, the proposed scheduling strategy is able to offer determinism while satisfying the requirements of a specific class of IoT applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ethernet Reinforcement learning Scheduling Simulation Time sensitive networking WiFi

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Data Analytics Using Machine Learning for Cyber Security Safeguards Individual Privacy

Data Analytics Using Machine Learning for Cyber Security Saf...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Sruthy, S. Ravindran, Zina Akhilkk Gouri, M.S. Waheeduddin Hussain, Mohammed Jothikumar, R. Kerala Palai India Baselios Mathews II College of Engineering Department of Computer Science and Artificial Intelligence Engineering Sasthamcotta690520 India Vimal Jyothi Engineering College Department of Computer Science and Design Chemperi India Baselios Mathews II College of Engineering Lake View Department of Computer Science and Engineering Kerala India Shadan College of Engineering and Technology Department of Information Technology Peerancheru Hyderabad India Shadan College of Engineering and Technology Department of Computer Science and Engineering Peerancheru Hyderabad India

ISBN: (纸本)9798350319200

The merging of data analytics and machine learning offers tremendous potential for boosting cyber security and protecting individual privacy in an age when personal data and cyber dangers are abundant. The purpose of this study was to create, test, and publish a model that takes into account this junction. Accuracy, precision, recall, F1-Score, and area under the receiver operating characteristic curve (AUC-ROC) are only a few of the indicators that show how well the framework can identify cyber threats. Further, the use of privacy-preserving methods like differential privacy highlights the moral need to guard sensitive information. Readers are given a concrete knowledge of the framework's real-world effect via vivid depictions of its practical use based on hypothetical data sets. This study serves as a helpful resource for a varied audience, from consumers worried about data privacy to organisations looking to better their cyber security defenses and legislators aiming to safeguard citizens' data. It also provides a springboard for further study in areas such as privacy enhancement, more sophisticated machine learning models, and sector-specific framework development. The digital ecosystem is always changing, but this study provides a guide for overcoming obstacles and capitalizing on possibilities at the intersection of cyber security and personal privacy. © 2023 IEEE.

关键词： Cyber Security Cyber Threat Detection Data Analytics Differential Privacy Digital Privacy Framework Development Individual Privacy Performance Metrics Privacy-Preserving Techniques User-Centric Security

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