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Applied Intelligence and computing (AIC), IEEE World Conference on

作者： Aleksandar Petrovic Jasmina Perisic Luka Jovanovic Miodrag Zivkovic Milos Antonijevic Nebojsa Bacanin Faculty of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Informatics and Computer Science Union - Nikola Tesla University Belgrade Serbia

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

The problem of fake news can be dangerous, as widely spread misinformation can cause serious physical and psychological harm. In the age of digitization and artificial intelligence (AI), particularly deep learning methods, which are able to generated synthetic text, being able to distinguish between real and fake news has become even greater challenge. Therefore, this research proposes a metaheuristics optimized machine learning (ML) approach with natural language processing (NLP) application for detecting fake news. The main classification model that was used is the eXtreme gradient boosting (XGBoost), which was tuned by the modified variable neighborhood search (VNS) algorithm. The NLP technique that was applied to transform text into the meaningful form for ML is the term frequency-inverse document frequency (TF-IDF). The performance of the VNS-tuned XGBoost was evaluated and compared with other cutting edge metaheuristics, where the proposed method obtained superior performance.

关键词： Machine learning algorithms Image edge detection Metaheuristics Psychology Transforms Boosting Natural language processing Classification algorithms Fraud Fake news

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A Machine-Learning Based IoT Smart Home System to Detect and Reduce Urban Insecurity in Uganda: A Case of Kampala Metropolitan Area

A Machine-Learning Based IoT Smart Home System to Detect and...

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International Conference on Advances in Big Data, computing and Data Communication Systems (icABCD)

作者： Edward Miiro Ismael Kato Department of Computer Science & Engineering Faculty of Computing & Informatics Makerere University Business School Kampala Uganda Dept. of Applied Computing & Information Technology Faculty of Computing & Informatics Makerere University Business School Kampala Uganda

ISBN: (数字)9798350387902

ISBN: (纸本)9798350387919

This study sought to develop and test a machine learning based prototype of a smart home security system for Kampala Metropolitan area in Uganda. The researchers used a Design Science Research (DSR) method to execute the project. The researchers identified a problem- which was high level of insecurity and crimes around homes in Kampala and its metropolitan areas, defined objectives, collected requirements, designed and developed the artifact, then demonstrated, evaluated, and communicated the artifact to stakeholders. The team created a working prototype of a home-security system that was later tested, demonstrated, validated among potential users. Practically, this project was implemented in a simulated environment using Tinkercad Arduino software. The developed system prototype was simulated on screen and tested on at least one home to ensure its effectiveness in reducing unauthorized entry, robbery, and other forms of crimes that are widely prevalent in Kampala today. One limitation of the project was that Arduino board used was quite limited on the number of pins that were available for use. The initial project idea had featured a temperature sensor to that was intended to regulate the temperature in the home by making use of the fan. However, the researchers did not implement this due to the limited pins and the capacity of the Arduino Uno R3 board that was used. Higher capacity industrial tools could provide a more realistic design of a similar system. The project simulated an environment of a smart home security system that can be used to detect and reduce crime. This study is part of a wider project to test ML and IoT systems in building secure smart homes. The authors envisage the application on more advanced ML algorithms and techniques in improving the system.

关键词： Temperature sensors Urban areas Prototypes Smart homes Machine learning Software Pins

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Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

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Computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara Zivkovic Miodrag Zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia Informatics and Computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia Computing and Informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and Informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of Electrical and Electronics Engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： Coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

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A Hybrid Machine Learning and Metaheuristic Based Model for E-Business Risk Management

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SN Computer Science 2024年第7期5卷 1-13页

作者： Elhoseny, Mohamed Darwiesh, Abdelaziz El-Baz, A.H. Tarabia, A.M.K. College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Faculty of Computers and Information Mansoura University Mansoura Egypt Department of Mathematics Faculty of Science Damietta University New Damietta 34517 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University New Damietta 34517 Egypt

This study introduces a novel approach to enhance e-business firms by identifying potential risks through the analysis of customers’ Twitter posts. Unlike traditional lexicon-based methods, which can be complex, we propose using machine learning models and metaheuristic optimization techniques for more effective risk detection. Additionally, we employ natural language processing techniques to efficiently handle and analyze text data. This approach aims to assist firms in developing competitive e-business strategies by identifying risks based on customer indications on the platform. To achieve this, we construct three datasets: one for risk detection, one for risk assessment, and one for identifying types of risks, which are used in the training phase. We collected tweets from Amazon customers and found that over a quarter of the sample expressed risks in their tweets, with less than a quarter indicating high risks. The most common types of risks identified were operational and financial risks. Furthermore, various performance measures were calculated to evaluate the effectiveness of the proposed approach. The mean accuracy for the processes of risk detection, risk identification, and risk assessment was found to be 81.66%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： E-business firms Machine learning Metaheuristic techniques Natural language processing Risks detection Social media

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Q-Rung Orthopair Fuzzy Sets-Enhanced FMEA for COVID-19 Risk Assessment

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 81-92页

作者： Abdullah, Lazim Awang, Noor Azzah Qiyas, Muhammad Special Interest Group of Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Mathematics Riphah International University Failsabad Campus Pakistan

Failure Modes and Effects Analysis (FMEA) is a widely used tool for risk analysis, primarily to identify risk factors affecting system quality. Due to the limitations of the traditional FMEA model, several recent models incorporating advanced fuzzy set extensions have been developed to enhance the reliability of risk assessment outcomes. However, most of these models limit expert flexibility in expressing preferences and often overlook the impact of unequal expert weights and the stability of risk ranking results. This study introduces a new FMEA model based on Q-Rung Orthopair Fuzzy Sets (Q-ROFSs), termed Q-ROFSs-FMEA. Q-ROFSs, an extension of intuitionistic fuzzy sets, introduce a new linguistic term. The Q-ROFSs-FMEA model considers the unequal weights of experts, enabling a dynamic representation of expert preferences. These weights and the linguistic evaluation of risk factors are integrated through an aggregation operator, facilitating consensus among experts. The model is applied to a case study on COVID-19 risk factors, revealing that ‘older age’ (risk priority number 0.000012) is the highest risk factor, while ‘gender’ (risk priority number -0.0037) is the lowest. It is found that the ranking of risk factors determined by the Q-ROFSs-FMEA model is obtained as FM1 FM3 FM6 FM4 FM5 FM7 FM8 FM2 . Furthermore, a comparative analysis indicates consistent ranking results across different models, demonstrating the reliability of the proposed model. The case study and comparative analysis validate the effectiveness and applicability of the Q-ROFSsbased risk assessment model. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Failure modes

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Twitter-Based Early Depression Detection Through Machine Learning 4th

Twitter-Based Early Depression Detection Through Machine Lea...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Ting, Kee Hui Majid, Mazlina Abdul Ibrahim, Ashraf Osman Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Centre for Artificial Intelligence & Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia

ISBN: (纸本)9789819729760

Depression, a prevalent mental health disorder, significantly affects individuals’ quality of life, emphasizing the critical need for early detection and intervention to ensure successful treatment. However, conventional methods for early depression detection have limitations that can impact accuracy and effectiveness. The proposed research advocates integrating technology to overcome these challenges, explicitly leveraging social media and machine learning. The focus is on detecting early signs of depression in Twitter users. A dataset of tweet texts is gathered and employed in machine learning classifiers, including Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). The research aims to identify the most effective algorithm for depression detection by comparing testing results and determining the classifier with the highest accuracy level. This contributes to achieving Sustainable Development Goal 3 (SDG3) by enhancing individuals’ quality of life. The result proved that the Support Vector Machine (Linear Support Vector Classifier) is best performed with the highest accuracy value of 90.57% among the three tested classification models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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Augmented Reality in Education: Transformative Innovations and Immersive Learning Experiences 4th

Augmented Reality in Education: Transformative Innovations a...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Abdul Rahim, Rini Hafzah Mohd Nizam, Dinna Nina Mohd Naim, Nur Faraha Baharum, Aslina User Experience Group Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia Department of Computing and Information System School of Engineering and Technology Sunway University No 5 Jalan University Selangor Bandar Sunway57500 Malaysia

ISBN: (纸本)9789819729760

The use of technology, such as Augmented Reality Immersive Learning (ARIL), in education to improve student learning by fusing the actual world with the virtual world is thoroughly examined. This research aims to ascertain how Augmented Reality (AR) technology might improve immersive learning for practical skills. AR has exciting opportunities for immersive learning since it can blend digital content with the real world. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and three databases—Scopus, the Institute of Electrical and Electronics Engineers (IEEE), and the Education Resources Information Center (ERIC)—206 publications have been carefully selected for the study. Three main themes emerged from the 34 articles that met the qualifying requirements: Enhancing Learning Experiences with AR in Education, Innovative AR Applications in Education, and Immersive Technologies in Education: Challenges and Innovations. The three themes’ findings point to a common theme: the effective use of technology in the classroom to improve students’ immersive learning, confidence, memory retention abilities, positive concern, creative thinking, and self-awareness. Furthermore, the usage of AR may be coupled with other technologies such as chatbots, Artificial Intelligence (AI), Internet of Things (IoT), Mixed Reality (MR), Extended Reality (XR), and others. One significant finding from the study was that certain frameworks, such as the AR Immersive Learning Design (AILeaD), the Game-Based Assessment Framework (GBAF), and AR immersion (ARI), might serve as useful recommendations for implementing AR technology for educational purposes. Further studies should be conducted and considering that every student has a particular learning style and preference, more research should investigate using this framework and approach to help students learn theory or practical in various academic topics. © The Author(s), under exclusive license to Springer Nature Singa

关键词： Contrastive Learning

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Spatio-Temporal Clustering of Forest Fire Hotspots in the Wallacea Region in 2012 – 2022 Using Chronnet 11

Spatio-Temporal Clustering of Forest Fire Hotspots in the Wa...

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11th International Conference on Computer, Control, informatics and its Applications, IC3INA 2024

作者： Ismanto, Rido Dwi Vatresia, Arie Wahyuni, Intan Nuni Utama, Ferzha Putra Gusmanto, Rizki Anees, Mohd Talha Research Center for Computing National Research and Innovation Agency Bogor Indonesia Informatics-Engineering Faculty University of Bengkulu Bengkulu Indonesia Information System-Engineering Faculty University of Bengkulu Bengkulu Indonesia Department Geology Faculty of Science Universiti Sains Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798331542313

Data from the Ministry of Environment and Forestry of Indonesia show that millions of hectares were burned by fires in Indonesia with 2015 being the greatest incidence in the last of ten years. Therefore, a research is required to examine the distribution pattern of hotspots for forest fires. This study used Chronnet to display the clustering of forest fire hotspot data in the Wallacea Region (North Maluku) between 2012 and 2022. The NASA FIRMS provided the MODIS data used in the study. Several findings have been made from the implemented Chronnet method. The degree of connectivity map shows a medium degree which suggests that the North Maluku frequently experiences forest and land fires of a moderate intensity. The strength map shows a complex pattern of fire activity, with high-intensity flames creating major concerns in some, particularly in West Halmahera Regency and low to moderate fire events occurring in most other places. The results from the Chronnet cluster show that most fires occurred in production forests, indicating the need for stronger fire prevention and regulations. While protected forests had fewer incidents, there are still several clusters that require attention. Additionally, the frequent fires in non-forest areas emphasize the need for better land-use planning and coordination. Finally, although conservation forests remained unaffected, it is crucial for the government to prioritize maintaining this record. © 2024 IEEE.

关键词： Premixed flames

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An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

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Computers, Materials & Continua 2023年第10期77卷 1227-1249页

作者： Nourmeen Lotfy Abdulaziz Shehab Mohammed Elhoseny Ahmed Abu-Elfetouh Department of Information Systems Faculty of Computers and Information ScienceMansoura UniversityMansoura35516Egypt Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates

Despite the extensive effort to improve intelligent educational tools for smart learning environments,automatic Arabic essay scoring remains a big research *** nature of the writing style of the Arabic language makes the problem even more *** study designs,implements,and evaluates an automatic Arabic essay scoring *** proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing ***,it comprises two main components:the grading engine and the adaptive fusion *** grading engine employs string-based and corpus-based similarity algorithms *** that,the adaptive fusion engine aims to prepare students’scores to be delivered to different feature selection algorithms,such as Recursive Feature Elimination and ***,some machine learning algorithms such as Decision Tree,Random Forest,Adaboost,Lasso,Bagging,and K-Nearest Neighbor are employed to improve the suggested system’s *** experimental results in the grading engine showed that Extracting DIStributionally similar words using the CO-occurrences similarity measure achieved the best correlation ***,in the adaptive fusion engine,the Random Forest algorithm outperforms all other machine learning algorithms using the(80%–20%)splitting method on the original *** achieves 91.30%,94.20%,0.023,0.106,and 0.153 in terms of Pearson’s Correlation Coefficient,Willmot’s Index of Agreement,Mean Square Error,Mean Absolute Error,and Root Mean Square Error metrics,respectively.

关键词： Arabic corpus-based similarity correlation machine learning string-based similarity text similarity

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Theft Detection and Monitoring System Using Machine Learning 6th

Theft Detection and Monitoring System Using Machine Learning

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6th International Conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2020

作者： Arora, Jatin Bangroo, Aayush Garg, Shivi Faculty of Informatics and Computing J.C. Bose University of Science and Technology YMCA Faridabad India

ISBN: (纸本)9789811613418

Theft is a common criminal activity that is prevailing over the years and is increasing day by day. To tackle this problem, many surveillance systems have been introduced in the market. Some are simply based on video surveillance monitored by a human while some are AI-based capable of detecting suspicious activity and raising an alarm. However, none of them is intelligent enough to identify what kind of suspicious activity is being carried out and what kind of protective measures should be taken in real time. This paper presents the design of an effective surveillance system using machine learning techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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