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Creative Communication and Innovative technology (ICCIT), IEEE

作者： Novi Cholisoh Rio Wahyudin Anugrah Muhammad Faizal Fazri Syahrul Mu’Arif Wahid Reza Dani Pramudya Dept. of Information Management University of Raharja Tangerang Indonesia Dept. of Computer Systems Pandawan Sejahtera Indonesia Tangerang Indonesia Dept. of Business Intelligence Bosindo Tangerang Indonesia Dept. of Digital Business Sesindo Tangerang Indonesia Dept. of Information Technology Pandawan Sejahtera Indonesia Tangerang Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

This study explores the factors influencing student engagement in e-learning and proposes strategies for improvement. It incorporates a mixed-method approach, combining quantitative data from a learner survey with qualitative insights from interviews and focus groups involving educators and students. The survey investigates technological, pedagogical, and personal factors that impact engagement, while qualitative data provide deeper insights into experiences and perceptions. The findings underscore that student engagement in e-learning is shaped by the quality of digital content, opportunities for interaction, feedback mechanisms, and individual learner characteristics. Personalized learning experiences and robust support systems emerge as crucial for enhancing engagement. The study recommends practical strategies to strengthen e-learning engagement, including adopting adaptive learning technologies, promoting collaborative learning communities, and providing targeted support for diverse learner needs. These insights contribute to optimizing engagement within digital learning environments.

关键词： Surveys Adaptive learning Electronic learning Federated learning Collaboration Communication channels Complexity theory Interviews

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Enhancing Credit Card Fraud Detection through LSTM-Based Sequential Analysis with Early Stopping

Enhancing Credit Card Fraud Detection through LSTM-Based Seq...

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Networking and Communications (ICNWC), International Conference on

作者： S. Jhansi Ida K. Balasubadra Skandarsini R R Lakshmi Narayanaa T Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India Department of Information Technology RMD Engineering College Thiruvallur India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

There is still a serious problem with credit card fraud in the world of financial transactions, requiring innovative solutions to effectively detect and halt illicit behavior. This research presents a unique method of detecting credit card fraud by strategically implementing early pausing in a Long Short-Term Memory (LSTM) neural network. Leveraging the sequential dependencies inherent in transaction data, the LSTM model demonstrates remarkable efficacy in discerning fraudulent from legitimate transactions. The early stopping mechanism ensures that the model’s training halts at the optimal epoch, preventing over fitting and enhancing generalization. Our thorough analysis highlights the model’s versatility by demonstrating a dynamic compromise between recall and accuracy throughout training. Practical implications include enhanced fraud detection accuracy, reduced false alarms, and real-time decision support for financial institutions. The model’s adaptability and generalization make it a valuable asset in the ongoing battle against credit card fraud, contributing to a more secure financial environment. Future research directions may explore additional features and alternative architectures to further advance fraud detection capabilities.

关键词： Training Adaptation models Sequential analysis Credit cards Feature extraction Harmonic analysis Real-time systems

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Leveraging Artificial Intelligence for Enhanced Data Generation in Addressing Imbalance in Binary Classification System

Leveraging Artificial Intelligence for Enhanced Data Generat...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Srijita Bandopadhyay Srimonti Dutta Imran Haider Bhavaraju Anuraag Jerry Zhu Saad Ahmed Bazaz Dept. of Electronics and Communication Engineering University of Engg. & Management Kolkata India School of Agro and Rural Technology Indian Institute of Technology Guwahati Guwahati India Dept. of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. of Electronics and Communication Engineering Vellore Institute of Technology Vellore India Faculty of Mathematics University of Waterloo Ontario Canada Dept. of Computer Science National University of Computer & Emerging Sciences Islamabad Pakistan

This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority Over-sampling Technique (SMOTE), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) to enhance classification outcomes. Traditional classification models tend to favor the majority class, while the impact of imbalanced misclassification costs is often overlooked. The integration of SMOTE, GANs, and VAEs in binary classification, or SMOTE-GAN-VAE, addresses these challenges by generating synthetic instances, refining data representations, and capturing latent features. To evaluate the effectiveness of various data generation methods, a credit card fraud dataset is used. The performance metrics considered include F0.5-score, F1-score, and F2-score, which account for both precision and recall. The results indicate that SMOTE-GAN-VAE outperforms individual methods, such as SMOTE, GANs, and VAEs, demonstrating its potential to enhance data representation and classification accuracy, and outperformed the β- VAE filtered approach employed in previous literature.

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Feedforward Control in the Presence of Input Nonlinearities: With Application to a Wirebonder

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IFAC-PapersOnLine 2023年第2期56卷 1895-1900页

作者： Maurice Poot Jilles van Hulst Kai Wa Yan Dragan Kostić Jim Portegies Tom Oomen CST Dept. of Mechanical Engineering Eindhoven University of Technology Eindhoven The Netherlands Center of Competency ASMPT Beuningen The Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Eindhoven The Netherlands Delft Center for Systems and Control Delft University of Technology Delft The Netherlands

The increasing demands on throughput and accuracy of semiconductor manufacturing equipment necessitates accurate feedforward motion control that includes compensation of input nonlinearities. The aim of this paper is to develop a data-driven feedforward approach consisting of a Wiener feedforward, i.e., linear parameterization with an output nonlinearity, to achieve high tracking accuracy and task flexibility for a class of Hammerstein systems. The developed approach exploits iterative learning control to learn a feedforward signal from data that minimizes the error and utilizes a control-relevant cost function to learn the parameters of a Wiener feedforward parameterization. Experimental validation on a wirebonder shows that the developed approach enables high tracking accuracy and task flexibility.

关键词： Iterative and repetitive learning control Identification and control methods Nonlinear system identification Data-driven control Learning for control

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Online Complaint Management System using Image Recognition

Online Complaint Management System using Image Recognition

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International Conference on Communication and Electronics systems (ICCES)

作者： S. Balakrishnan J. Janet Rohith T Sakthivel R Sanjaiy Kumar T N Dept. of Computer Science and Business Systems Sri Krishna College of Engg. and Technology Coimbatore Tamilnadu India Dept. of Computer Science and Engineering Sri Krishna College of Engg. and Technology Coimbatore Tamilnadu India

Clean and safe environments are the responsibility of the government. Currently, people have to lodge their complaints with the Municipal Corporation, and then workers take time to respond to them. In this paper we have made a platform to overcome the communication gap between the user and the municipal corporation. This research study proposes an online complaint management system in a web application, where the citizen can lodge a complaint by taking a picture. After the picture being uploaded, image recognition is used to automatically detect the problem raised by the user and Google map API to detect the location automatically. As soon as the problem has been resolved, the municipal corporation must upload a completed status picture to verify that the work has been completed. The aim of this application is to overcome the communication gap between user and Municipal Corporation, to simplify the process of raising complaints and to maintain a clean and safe environment.

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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Robotics, Engineering, Science, and technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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Constrained Obnoxious Facility Location on a Line Segment 33

Constrained Obnoxious Facility Location on a Line Segment

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33rd Canadian Conference on Computational Geometry, CCCG 2021

作者： Singireddy, Vishwanath R. Basappa, Manjanna Computer Science & Information Systems Dept. Birla Institute of Technology & Science Pilani Hyderabad Campus India

In this paper we study two restricted variations of the obnoxious facility location problem in the plane, given as follows. Given a line segment pq and a set P = {p1, p2, p3, . . ., pn} of n demand points in the plane, and a positive integer k, pack k maximum-radius congruent disks centered on pq such that no point of P lies inside any of these disks. We first prove that the decision version of this problem can be solved in linear time and then propose an (1 − ∊)-factor approximation algorithm for the problem, that runs in O((n + k) log (2(||kpq−1)|| ∊)) time if the points are given in order, where ||pq|| is the length of the line segment pq and ∊ > 0. Another restricted problem is the minsum obnoxious facility location problem, in which we are given a line segment pq and a set P = {p1, p2, p3, . . ., pn} of n weighted demand points (having weights w1, w2, . . ., wn, respectively) in the plane, an integer k, and a distance λ, the goal is to pack k disks of radius λ centered on the segment pq such that the sum of weights of the points covered by the union of these disks is minimal. For k = 1 we show that this problem can be solved in O(n log n) time, and for any k > 0 we give a dynamic programming solution that runs in O(n3k) time. © 2021 Meng He and Don Sheehy.

关键词： Dynamic programming

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Authentic Learning Approach for Data Poisoning Vulnerability in LLMs

Authentic Learning Approach for Data Poisoning Vulnerability...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Mst Shapna Akter Md Abdur Rahman Md Mostafizur Rahman Juanjose Rodriguez-Cardenas Hossain Shahriar Fan Wu Muhammad Rahman Dept. of Intelligent Systems and Robotics University of West Florida Florida USA Dept of Cybersecurity and Information Technology University of West Florida Florida USA Dept. of Information Systems and Security Kennesaw State University Kennesaw USA Center for Cybersecurity University of West Florida Florida USA Dept of Computer Science Tuskegee University Tuskegee USA Dept. of Computer Science and Information Technology Clayton State University Georgia USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The primary goal of authentic learning is to provide students with an engaging learning environment that offers hands-on experiences in solving real-world security challenges. Each educational theme consists of prelab activities, lab activities, and hands-on lab activities. By implementing authentic learning, we design and build portable lab for data poisoning vulnerability in LLM models on Google Colab. These hands-on labs can be accessed and practiced in real-time without the need for complex installations and configurations. This allows students to focus on learning concepts and increase their hands-on problem-solving skills.

关键词： Software Real-time systems Data models Internet Security Problem-solving

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Identify Initial Nodes for Spreader in A New Diffusion Model Based on Topology (DBT) in Social Networks

Identify Initial Nodes for Spreader in A New Diffusion Model...

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International Conference on Web Research (ICWR)

作者： Ehsan Khosravi Fatemeh Yazdanifar Nikoo Salimi Hamid Ahmadi Beni Dept. of Computer Engineering Sharif University of Technology Tehran Iran Dept. of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran Dept. of the Electrical Electronics Electronics and Telecommunication Engineering and Naval Architecture University of Genoa Genoa Italy Dept. of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran

With the development of science and technology through social networks, the process of diffusion has become a controversial subject. In social networks, the process of diffusion is transmitted from one individual to another. The influence maximization problem, in which at least k nodes are chosen as the initial nodes of the spreader in the smallest amount of time, is one of the most critical aspects of the diffusion process. Furthermore, the initial nodes in the social network should generate the most influence on the other nodes in the network. However, in the algorithms proposed to solve the problem of influence maximization, the real-world factors which affect the influence probability in diffusion models are ignored for example, the influence probability is determined randomly. This problem causes influence maximization algorithms based on diffusion models to face the challenge of time-consuming Monte Carlo simulation. So, the RIM algorithm presented in this article which this method selects the initial nodes of the spreader according on their local reachability. In addition, Due to privacy in social networks, complete information about people such as gender, education, etc. is not available. For this reason, in the RIM algorithm, using the graph structure in social networks, the influence probability is calculated in the new DBT diffusion model. In addition, the RIM algorithm exhibits stable performance in the selection of the spreader's initial nodes in terms of influence spread, and execution time.

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Improving E-commerce Fraud Detection via Machine Learning: Comparative Evaluation of Model Effectiveness

Improving E-commerce Fraud Detection via Machine Learning: C...

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

作者： Gangalakshmi, T. Sathya, V. Vinodhini, S. Chandralekha, E. Usha, V. Ravikumar, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept.of CSE Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Dept.of Computer Science and Business Systems Chennai India Velammal Engineering College Dept. of Information Technology Chennai India

ISBN: (纸本)9798350319200

In the fast-growing E-commerce world, fraud has become a major concern, causing economic losses and eroding consumer trust. This work aimed to detect E-commerce fraud using machine learning on a customer dataset. The dataset contained transactional and customer data, requiring preprocessing to handle missing values and categorical features. Four models - Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Classifier - were trained and evaluated for fraud detection accuracy using metrics like Accuracy, Precision, Recall, and F1-Score. Results revealed distinct model strengths and weaknesses. Logistic Regression achieved balanced performance, while Random Forest and Gradient Boosting excelled in certain aspects. Support Vector Classifier also demonstrated competitive results. This work highlights the potential of machine learning in E-commerce fraud prevention, enhancing transaction security and trust. Further research could refine and advance fraud detection techniques for online platforms. © 2023 IEEE.

关键词： E-commerce fraud Fraud detection Hybrid approach Machine learning

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