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Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

computers, Materials & Continua 2023年第6期75卷 5307-5319页

作者： Khalid M.O.Nahar Mohammad Alauthman Saud Yonbawi Ammar Almomani Computer Science Department Yarmouk UniversityIrbidJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Software Engineering Department University of JeddahJeddahKSA Research and Innovation Department Skyline University CollegeP.O.Box 1797SharjahUAE IT Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan

Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our *** is a social media network issue,a global crisis affecting the victims and society as a *** results from a misunderstanding regarding freedom of *** this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet *** addition,we used accuracy,precision,recall,and F1 score to assess prior *** show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.

关键词： Cyberbullying social media naïve bayes support vector machine natural language processing LDA

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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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Tor Network Detection by Using Machine Learning and Artificial Neural Network

Tor Network Detection by Using Machine Learning and Artifici...

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2021 International Symposium on Networks, computers and Communications, ISNCC 2021

作者： Soykan, Murat Boluk, Pinar Sarisaray Bahcesehir University Graduate Education Institute Computer Engineering Istanbul Turkey Bahcesehir University Faculty Of Engineering And Natural Sciences Software Engineering Istanbul Turkey

ISBN: (纸本)9780738113166

The Internet is a virtual world where everyone can express themselves as much as they wish and perform the operations they want. In this virtual world, some users want to experience the internet without giving their identity for certain reasons. The concept of an anonymous network has emerged so that they can use the internet without revealing their identity. The Tor project is a product that provides anonymous communication on the Internet without revealing users' identities. In this project, we aimed to determine whether network traffic is the TOR network by using machine learning and artificial neural networks. With the dataset we have, we first performed data analysis and gained more information about the data set. Categorical values were assigned to numerical values to learn the dataset. After converting the categorical data to numerical data, normalization is applied to the data set and all features are taken between -1 and 1. It was estimated whether the future traffic was TOR by learning the past data by using K Nearest Neighbor, Naive Bayes Classifiers, Random Decision Forest, Logistic Regression, Support Vector Machine, one of the machine learning classification algorithms. In addition, artificial neural networks were used. After each algorithm, confusion matrix, precision, recall, and F1-score values, which are among the model evaluation tools, were calculated, and compared which model performed better for our dataset. © 2021 IEEE.

关键词： Neural networks

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Research and development of two-factor authentication methods using neural networks 4

Research and development of two-factor authentication method...

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4th IEEE International Conference on Smart Information Systems and Technologies, SIST 2024

作者： Darbek, Temirkhan Mukasheva, Assel Bissembayev, Alibek Gnatyuk, Sergiy Mukammejanova, Dinargul The Department of Information Systems and Cybersecurity Almaty university of power engineering and telecommunications Almaty Kazakhstan The School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan The School of Information Technology and Engineering Kazakh British Technical University Almaty Kazakhstan The Faculty of Cybersecurity Computer and Software Engineering at National Aviation University National Aviation University Kyiv Ukraine The Department of Artificial Intelligence and Big Data Kazakh National University named after Al-Farabi Almaty Kazakhstan

ISBN: (纸本)9798350374865

This article is devoted to the research and development of innovative two-factor authentication (2FA) methods using neural networks. 2FA plays a key role in ensuring the security of data and accounts in the modern digital world, and the use of neural networks opens up new prospects in strengthening protection and improving the user experience. The article presents an analysis of existing 2FA methods, identifies their limitations, and discusses the advantages of using neural networks in this area. The main focus is on the development of new authentication algorithms based on deep learning, providing effective anomaly detection and support for a dynamic adaptive approach. In the context of the article, the issues of privacy and data security are also considered, as well as the possibilities of applying the developed methods in various fields, including finance, medicine and the Internet of Things. The results of the study represent a significant contribution to the field of digital. © 2024 IEEE.

关键词： biometric authentication deep learning digital security identification Neural networks

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Soft Computing Based Metaheuristic Algorithms for Resource Management in Edge Computing Environment

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computers, Materials & Continua 2022年第9期72卷 5233-5250页

作者： Nawaf Alhebaishi Abdulrhman M.Alshareef Tawfiq Hasanin Raed Alsini Gyanendra Prasad Joshi Seongsoo Cho Doo Ill Chul Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Sejong UniversitySeoulKorea School of Software Soongsil UniversitySeoul06978Korea Artificial Intelligence Education Hankuk University of Foreign StudiesDongdaemun-guSeoul02450Korea

In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end *** computing is considered a method of forwarding the processing and communication resources in the cloud towards the *** of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved *** this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing *** SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource *** goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy *** problem is formulated based on the availability of VMs,task characteristics,and queue *** integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data *** experimental validation,a comprehensive set of simulations were performed using the CloudSim *** experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.

关键词： Resource scheduling edge computing soft computing fitness function virtual machines

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Reinforcement-Learning Based Method for Accelerating Functional Coverage Closure of Traffic Light Controller Dynamic Digital Design 32

Reinforcement-Learning Based Method for Accelerating Functio...

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32nd International Conference on computer Theory and Applications, ICCTA 2022

作者： Halim, Youstina M. Ismail, Khaled A. Abd El Ghany, Mohamed A. Ibrahim, Sameh A. Halim, Youssef M. Ain Shams University Faculty of Engineering Mixel Egypt. Electronics and Communications Engineering Dept. Cairo Egypt Germany Electronics Department German University in Cairo Egypt Integrated Electronic Systems Lab Tu Darmstadt Germany Ain Shams University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt Ain Shams University Faculty of Engineering Computer Engineering and Software Systems Dept. Cairo Egypt

ISBN: (纸本)9798350320190

Functional verification of digital designs is an increasingly complex and time-consuming endeavor. One of the major challenges in functional verification is achieving functional coverage closure in a timely manner. Verification experts have moved from using directed stimulus, towards constrained random stimulus and finally, towards using machine learning to direct stimulus. In this work, reinforcement learning (RL) was used, incorporating three different kinds of RL agents to direct stimulus generation for a traffic light controller system. Two of the agents are based on Q-learning (DQN and SARSA), and one is based on Monte-Carlo method (CEM). The maximum reduction in simulation time needed to achieve a targeted percent of coverage was 97%. The average reduction of simulation time across all testcases and agents used was 70%. The solution is highly reusable, the architecture was integrated with different agents and testcases. © 2022 IEEE.

关键词： Reinforcement learning

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study

The Correlation Between Architecture Smells, Package Stabili...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mrwan BenIdris Ehab A. Omar Elfallah Marwa Aslam Cheema Mohammed Salah Addin Alsma Tareq Ziad Kadiki Abdelfattah Elbarsha Dep. of Computer Science Faculty of Information Technology University of Benghazi Dept. of Software Engineering Faculty of Information Technology Libyan International Medical University Health Informatics Department Faculty of Public Health University of Benghazi

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-depth research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability.

关键词： Correlation coefficient Correlation Codes Conferences computer architecture software systems software development management

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Prediction of Solar Energy Potential with Machine Learning and Deep Learning Models

Prediction of Solar Energy Potential with Machine Learning a...

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International Conference on Applied and Industrial Mathematics, ICAIM 2023

作者： İşler, Buket Şener, Uğur Tokgözlü, Ahmet Aslan, Zafer Baumann, Peter Department of Software Engineering Istanbul Topkapi University Istanbul Turkey Department of Computer Information Systems and Business Analytics Metropolitan State University of Denver DenverCO United States Faculty of Science and Literature Süleyman Demirel University Isparta Turkey Faculty of Engineering Department of Computer Engineering Istanbul Aydin University Istanbul Turkey School of Computer Science and Engineering Constructor University Bremen GmbH Bremen Germany

ISBN: (纸本)9789819634590

Replacing fossil fuel-based energy with carbon-free sources is essential for achieving climate neutrality in Europe, aligned with the European Green Deal. The southern regions of Turkey, especially Serik, Antalya, are well-suited to support this strategy due to their significant solar energy potential. This research investigates the solar energy potential of Serik, a region with one of Turkey’s highest renewable energy reservoirs. Accurately forecasting solar energy potential requires understanding meteorological, geographical, and technological factors, particularly advancements in machine learning and deep learning models. This study employs Nonlinear Autoregressive Neural Networks (NAR), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Seasonal Autoregressive Moving Average (SARMA), and Discrete Wavelet Analysis, as well as the NARX model, incorporating Wavelet outputs as inputs. The integration of Discrete Wavelet Transform with neural networks has significantly enhanced forecast accuracy. The SARMA model also performed well over the test period. This paper serves as a guide for selecting optimal approaches to forecast medium- and long-term solar energy potential, contributing to improved strategies for solar energy utilization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Long short-term memory

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Classification and Evaluation Framework of Automated testing tools for agile software: Technical Review

Classification and Evaluation Framework of Automated testing...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Mogeeb A.A. Moseh Nashwan Ameen Al-Khulaidi Abdu H. Gumaei Ayman Alsabry Ali A. A. Musleh Software Engineering Dept Faculty of Engineering & Information Technology Taiz University Taiz Yemen Industrial Dept. Engineering Dep Faculty of Engineering & Information Technology Taiz Univerity Taiz Yemen Computer Science Dept. College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Science International University of Technology Twintech Sana’a Yemen Faculty of Computer Science & Information Technology International University of Technology Twintech Sana’a Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Test automation is crucial for agile software projects to enable frequent delivery of working software with cost and time and minimal bugs. However, selecting the right automated testing tool is considered challenging due to the wide range of such existing tools. Additionally, the challenges occur clearly due to several issues such as the programming code language, the categorization of the developed system, and the tester’s knowledge and skills. This paper aims to address this gap by proposing an evaluation framework for comparing and classifying the existing automated testing tools used in agile projects. The framework is developed based on an extensive literature review of existing agile testing methodologies and common commercial automation testing techniques. The key criteria for tool evaluation are identified to cover the main testing objective aspects such as test design support, testing interfaces, reporting capabilities, etc. These criteria are considered the core methodology for this study used to analyze and compare the popular open-source and commercial tools. The proposed evaluation framework provides agile practitioners with guidelines to assist in selecting the appropriate tools based on their specific project needs such as budget, timelines, and technical expertise. This study is considered a comparative evaluation of existing agile testing tools to highlight their key strengths and limitations. The findings of this study categorized the testing tools based on the interface, code, design, and report features. This research contributes to assisting the project developer and tester in selecting suitable tools for the adoption of automated testing tools in their agile software projects. It also identifies the direction for future work, such as integrations with modern development methodologies and technologies.

关键词： Codes Automation Reviews Agile software development Programming software software measurement

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MODEL CHECKING FOR MULTI-AGENT SYSTEMS MODELED BY EPISTEMIC PROCESS CALCULUS

arXiv

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arXiv 2025年

作者： Yu, Qixian Cao, Zining Hui, Zong Zhou, Yuan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China Faculty of Computer and Software Engineering Huaiyin Institute Of Technology Huaian223001 China

This paper presents a comprehensive framework for modeling and verifying multi-agent systems. The paper introduce an Epistemic Process Calculus for multi-agent systems, which formalizes the syntax and semantics to capture the essential features of agent behavior interactions and epistemic states. Building upon this calculus, we propose ATLE, an extension of Alternating-time Temporal Logic incorporating epistemic operators to express complex properties related to agent epistemic state. To verify ATLE specifications, this paper presents a model checking algorithm that systematically explores the state space of a multi-agent system and evaluates the satisfaction of the specified properties. Finally, a case study is given to demonstrate the method. © 2025, CC BY.

关键词： Multi agent systems

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