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Experimental Evaluation of Cross-Platform Recognition of Identical and Unknown Users over Social Networking Environment

Experimental Evaluation of Cross-Platform Recognition of Ide...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Nagababu, Kancharla Ananthi, S. Sunheriya, Neeraj Venkata Siva Prasad, Ch. Jayanthi, J. Fawareh, Hamed Aditya University Department of Computer Applications Surampalem India St. Joseph's College of Engineering Department of Cse Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Jeppiaar Engineering College Department of Ece Rajiv Gandhi salai Chennai119 India Sona College of Technology Department of Cse Salem India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The capacity to individually identify and categorize people across sites and apps is vital for trustworthiness, fraud prevention, and personalized experiences in online social networking. In this study, strategies for platform identification among similar and unknown social networking users are evaluated. We study strategies that employ user attributes, behavior history, and inter-network connections to match profiles on multiple platforms and uncover new, previously connected people. This cross-validation on the proposed method uses Social Linkage Identification Strategy (SLIS) and Friend Relationship Evaluator. According to quantitative experiment results, our approach improves search people across multiple social networks more than FRE. The extensive social connection web in many online social networks is used to enhance user identification and verification via the Friend Relationship Evaluator (FRE) system. Our process uses advanced machine learning and data fusion to handle a wide variety of natural ventilation phenomena from different sensors and platform formats. We evaluate attribute-based matching, graph approaches, and behavior analytics for accuracy, scalability, and computing cost. This study advances user identification systems across digital platforms and helps service providers and end-users establish more sophisticated online identity management strategies. © 2024 IEEE.

关键词： Anonymous Identical Users FRE Identity Resolution SLIS Social Media Network User Identification

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Enhancing Hybrid Electric Vehicle Speed Controller based on Fractional Order PID using Jaya Optimization Algorithm 4

Enhancing Hybrid Electric Vehicle Speed Controller based on ...

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4th IEEE International Conference in Power engineering Applications, ICPEA 2024

作者： Naidu, Kanendra Rajendran, Gowthamraj Mohamad, Hasmaini Awalin, Lilik Jamilatul Kaliannan, Jagatheesan Embrandiri, Manoj School of Electrical Engineering College of Engineering Selangor Malaysia University of Catania Department of Electrical Electronics Engineering and Computer Science Catania Italy Univeritas Airlangga Faculty of Advanced Technology and Multidicipline Surabaya Indonesia Paavai Engineering College Department of EEE Pachal Namakkal India Barium Selat Sdn. Bhd Sungai Besi Kuala Lumpur Malaysia

ISBN: (纸本)9798350344578

The drive for sustainability has pushed forward various avenues in meeting the zero-carbon target. Electrification of vehicles has seen rapid growth, especially in replacing current internal combustion engine vehicles which relies on fossil fuel. The hybrid electrical vehicles (HEVs) that catalyze complete vehicle electrification provide a transition platform for vehicle users. The losses experienced by the HEVs which is contributed by the motor, generator, and battery have to be minimized to ensure efficient hybrid EV performance. The motor controller in the simulation model of the power-split hybrid transmission architecture is optimized using Jaya Optimization Algorithm (JOA). Two objective functions, which are speed regulation error and total electrical power loss are evaluated. Three different analyses for the Fractional order PID controller (FOPID) are carried out. The investigation comprises of comparative analysis with PID controller for first objective function, an analysis on FOPID controller based on second objective function, and an analysis of three different driving patterns. Overall improvement is observed in the integration of FOPID for HEV to enhance the performance of speed regulation and total electrical losses. © 2024 IEEE.

关键词： Optimization

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Coordinated Operational Optimization of Water and Power Systems Under Emergency Conditions

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SSRN 2024年

作者： Perelman, Gal Shmaya, Tomer Navon, Aviad Vrachimis, Stelios Panteli, Mathaios Eliades, Demetrios G. Ostfeld, Avi Faculty of Civil and Environmental Engineering Technion – Israel Institute of Technology Israel Faculty of Electrical Engineering and Computer Engineering Technion – Israel Institute of Technology Haifa Israel KIOS Research and Innovation Center of Excellence University of Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus

This research explores the integrated management of Water Distribution Systems (WDS) and Power Distribution Systems (PDS) for improved operational efficiency and resilience under extreme scenarios. Traditionally, these systems are operated independently leading to sub-optimal operational efficiency. Alternatively, some studies proposed a joint operation of WDS and PDS but assumed that decisions are made simultaneously by a single decision-maker, which is impractical. This study offers a novel approach for emergency control based on sparse communication between WDS and PDS operators to enhance resilience and improve the integrated systems’ functionality. The primary objective is to minimize the load shedding in the PDS through strategic demand management in the interconnected WDS, demonstrating the potential of collaborative decision-making between these two critical infrastructure systems. The paper highlights the significance of cross-sectoral collaboration, presenting a viable pathway for managing local infrastructure systems under extreme conditions while ensuring uninterrupted service delivery to communities. © 2024, The Authors. All rights reserved.

关键词： Decision making

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Traffic Priority-Aware Medical Data Dissemination Scheme for IoT Based WBASN Healthcare Applications

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computers, Materials & Continua 2022年第6期71卷 4443-4456页

作者： Muhammad Anwar Farhan Masud Rizwan Aslam Butt Sevia Mahdaliza Idrus Mohammad Nazir Ahmad Mohd Yazid Bajuri Department of Computer Science NFC Institute of Engineering and Fertilizer ResearchFaisalabadPakistan Department of Statistics and Computer Science Faculty of Life Sciences Business ManagementUniversity of Veterinary and Animal SciencesLahorePakistan Department of Electronics Engineering NED University of Engineering and TechnologyKarachiPakistan School of Electrical Engineering Universiti Teknologi MalaysiaJohor BahruMalaysia Institute of IR4.0 Universiti Kebangsaan MalaysiaBangiSelangorMalaysia Department of Orthopedics and Traumatology Faculty of MedicineUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia

Wireless Body Area Sensor Network(WBASN)is an automated system for remote health monitoring of *** under umbrella of Internet of Things(IoT)is comprised of small Biomedical Sensor Nodes(BSNs)that can communicate with each other without human *** BSNs can be placed on human body or inside the skin of the patients to regularly monitor their vital *** BSNs generate critical data as it is related to patient’s *** data traffic can be classified as Sensitive Data(SD)and Non-sensitive Data(ND)packets based on the value of vital *** data packets have different priority to *** ND packets may tolerate some delay or packet loss whereas,the SD packets required to be delivered on time with minimized packet loss otherwise it can be life threating to the *** this research,we propose a Traffic Priority-aware Medical Data Dissemination(TPMD2)scheme forWBASN to deliver the data packets according to their priority based on the sensitivity of the *** assessment of the proposed scheme is carried out in various *** simulation results of the TPMD2 scheme indicate a significant improvement in packets delivery,transmission delay and energy efficiency in comparison with the existing schemes.

关键词： WBANS wearable technology priority routing eHealth applications remote health monitoring

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A Sentiment Analysis Study of Twitter Users' Reactions to the COVID-19 Vaccine 14

A Sentiment Analysis Study of Twitter Users' Reactions to th...

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14th International Conference on Information and Communication Systems, ICICS 2023

作者： Alkhushayni, Suboh Alomari, Zakaria Al-Zaleq, Du'a Yarmouk University Faculty of Information Technology and Computer Science Dept. Information Systems Irbid Jordan New York Institute of Technology Dept. Computer Science VancouverBC Canada Al-Ahliyya Amman University Dept. Robotics and Artificial Intelligence Engineering Amman Jordan

ISBN: (纸本)9798350307870

This study investigates public attitudes towards the COVID-19 vaccine through Twitter data analysis. Using the Twitter API, tweets were collected, preprocessed, and labeled. Features were extracted using the Bag of Words representation, and sentiment analysis was conducted using Text Blob and Vader. Machine learning and deep learning models were trained and tested, revealing that deep learning models achieved the highest accuracy and F1 score. The research underscores the efficacy of machine learning and deep learning in analyzing COVID-19 vaccine-related tweets, shedding light on factors influencing vaccine resistance. These insights are crucial for pharmaceutical companies and public health officials, enabling them to address barriers to vaccine acceptance and enhance the societal benefits of widespread vaccination, contributing to the pandemic's resolution. © 2023 IEEE.

关键词： COVID-19

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Sequence-to-Sequence Knowledge Graph Completion Based on Gated Attention Unit 29

Sequence-to-Sequence Knowledge Graph Completion Based on Gat...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Bedi, Pradeep Goyal, S.B. Rajawat, Anand Singh Bhaladhare, Pawan Aggarwal, Alok Prasad, Ajay Computer Science and Engineering Galgotias University India Faculty of Information Technology City University Malaysia School of Computer Science and Engineering Sandip University Nashik India School of Computer Science University of Petroleum and Energy Studies Dehradun India

From the perspective of the Industry 4.0 paradigm, the machine learning (ML) discipline has had a significant influence on the manufacturing sector. The industry 4.0 concept promotes intelligent sensors, gadgets, and equipment to create technology infrastructure sectors that collect information constantly. By analyzing the obtained data, machine learning approaches allow actionable insight to boost industrial productivity without dramatically altering the necessary resources. Furthermore, the capacity of machine learning applications to provide actionable analytics has facilitated the detection of complex manufacturing trends and paved the path for an integrated intelligent process in various activities in the supply chain, including smart and constant inspection, preventative maintenance, quality enhancement, process optimization, supply chain advancement, and workflow scheduling. This paper aims to present recent advances in the field of quality inspection in Industry 4.0 and develop a framework for quality inspection that can be fully utilized in the Industry 4.0 context using adaptive bilateral filtering and Feature Correlated Auto encoder (FCA) machine learning technique. The suggested approach makes full use of information from all sources along the manufacturing chain. Therefore, it complies with quality management standards within the context of Industry 4.0. The suggested model makes use of corrective measures based on data patterns discovered through predictive analysis. Result analysis was shown on some pre-trained deep learning models such as ResNet18, Vgg19, Alexnet, Squeezenet, auto encoder, and FCA and observed that the proposed FCA(Feature Correlated Auto encoder) achieved a better result. © 2022 Elsevier B.V.. All rights reserved.

关键词： computer vision

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Real-time behavior recognition of animal: an IoT-based system design using acceleration data

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Multimedia Tools and Applications 2024年 1-26页

作者： Tran, Duc-Nghia Manh, Do Viet Van Thanh, Pham Shankar, A. Achyut Joshi, Kireet Tran, Duc-Tan Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam University of Fire Prevention and Fighting Hanoi100000 Viet Nam Department of Cyber Systems Engineering WMG University of Warwick CoventryCV74AL United Kingdom Department of CSE University Centre for Research & ampDevelopment Chandigarh University Punjab Mohali140413 India School of Computer Science Engineering Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Center of Research Impact and Outcome Chitkara University Punjab Chandigarh India Department of Computer Science & amp Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam

By analyzing large amounts of animal behavior data, humans can assess cattle’s condition. It leads to research and development a field using accelerometers and machine learning algorithms to ‘study’ behavior from acceleration data. It is challenging to analyze animal behavior due to the activity complexity. For example, acceleration data of some cow behaviors (e.g., feeding and standing) are similar if these behaviors contain similar gestures. In practical, real-time applications even bring more challenging since it is necessary to minimize a data window for behavior consideration. Using short data window often reduces the performance of classification algorithms. This study proposes a new solution for the mentioned challenges by combining neck-mounted and leg-mounted accelerometers, then using the vector of the dynamic body acceleration (VeDBA) and Mean features of synchronized acceleration data for the Decision Tree algorithm. The proposed method is demonstrated that performs well (0.94 accuracy, 0.91 sensitivity, 0.90 positive predictive value) within a low computational complexity algorithm and short data window (3-second data window, at the rate of one sample per second). It is particularly suited for the case of a real-time IoT-based system. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Accelerometers

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Microwave Sensor for Ethanol Intensity Determination Based on Fractal Resonators

Microwave Sensor for Ethanol Intensity Determination Based o...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Pubet Sangmahamad Tanaporn Pechrkool Thanakorn Sutham Boonyarit Kumkhet Nipont Tangthong Virote Pirajnanchai Patchadaporn Sangpet Deparment of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Deparment of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

This paper presents a fractal resonator and square patch microstrip-line structure with a central drill hole as a means of achieving ethanol concentration characterization. The liquid under test (LUT), which is a binary mixed liquid made up of ethanol and water, is used to measure and detect the effectiveness of the suggested sensor. The LUT was injected into a glass capillary tube that was positioned in the middle of the square patch microstrip-line structure for the ethanol concentration test. The sensors were designed and fabricated on an FR-4 substrate with a thickness of 0.8 mm, highlighting in general measurements of 15 × 15 mm², and operating at 2.50 GHz under an unloaded condition. The detected ethanol concentrations ranging from 10% up to 95% within the ethanol-water mixtures. Measurement comes about demonstrating the sensor's capability to identify ethanol concentration by comparing the shifted resonance frequency within the transmission coefficient.

关键词： Microwave measurement Liquids Three-dimensional displays Ethanol Resonant frequency Fractals Table lookup

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A Noval Reduced Particle Swarm Optimization With Improved Learning Strategy and Crossover Operator

A Noval Reduced Particle Swarm Optimization With Improved Le...

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International CSI computer Conference, CSICC

作者： Mina Nazari Mehdi Esnaashari Mohammadreza Parvizimosaed Amirhosein Damia Faculty of Computer Engineering Razi University Kermanshah Iran Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran

In terms of balancing the exploration and exploitation capabilities of the PSO method in order to increase its resilience, this work provides a unique particle swarm optimization with enhanced learning techniques and a crossover operator (LSCPSO). Each particle is updated depending on the simplified equations in the first stage. The proposed LSCPSO method then employs a self-learning technique in which each particle (personal best) learns from k better particles in the current population. Then, a crossover step is introduced to the algorithm in the subsequent stage. After taking the k global best (gbest particle), the crossover is performed. This method strengthens the LSCPSO algorithm's capacity for social learning and global exploration. In subsequent trials, the performance of the LSCPSO algorithm is compared to that of five sample PSO variations. The benchmark function test results show that the proposed ILSPSO algorithm has much better overall performance than the other PSO variations that were looked at.

关键词： Sociology Benchmark testing Particle swarm optimization Statistics Resilience

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