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7th International Conference of computer and informatics engineering, IC2IE 2024

作者： Putra, I Putu Edy Suardiyana Hermawan, Indra Kusuma, I Komang Arya Adi Puridiasta, I Gede Deindra Dwija Pramudya, Dewa Gede Bhaskara Kowaas, Clerencia Isabell Institut Pariwisata Dan Bisnis Internasional Digital Business Department Denpasar Indonesia Politeknik Negeri Jakarta Informatics and Computer Engineering Department Depok Indonesia Ganesha University of Education Faculty of Engineering and Vocational Computer Science Department Singaraja Indonesia

ISBN: (纸本)9798331505530

This study proposes a machine-learning-based sentiment classification approach for Bahasa Indonesia, where Gojek App's reviews are used for a case study. The sentiment classification is important since users often give 5-star ratings but leave negative reviews. A total of 19,772 reviews from The Google Play Store are used in this study. We use five machine learning algorithms to train the model, such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and Stochastic Gradient Descent (SGD). The best results of our approach are 66.6% precision, 88.2% recall, and 69.2% F-score, where an SVM-based classifier is used. These results show a promising future for using machine-learning algorithms for sentiment classification. © 2024 IEEE.

关键词： Adversarial machine learning

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An EEG-Based Application for Real-Time Mental State Recognition in Adaptive E-Learning Environment 18

An EEG-Based Application for Real-Time Mental State Recognit...

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18th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2023

作者： Chrysanthakopoulou, Amalia Dritsas, Elias Trigka, Maria Mylonas, Phivos Open University of Cyprus Cyprus University of Patras Department of Electrical and Computer Engineering Patras Greece University of West Attica Department of Informatics and Computer Engineering Athens Egaleo Greece

ISBN: (纸本)9798350327717

This paper presents the basics of non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCIs) and a system that could support the learning process in an e-learning environment. More specifically, we focus on the user's mental state and how this can be detected and captured using EEG in order to provide a positive user experience through appropriate adaptation. Mental state is a multidimensional term that includes among else relaxation and concentration, which, in this paper, are captured using a low-cost EEG device that the users wear throughout the interaction with the online environment. The accurate identification of these states is made with the aid of Machine Learning (ML) and enables notifications and proper adaptation to automatically match the detected mental state. Finally, the results from the user experience during the system usage are presented assuming 10 participants. The evaluation showed that such a system would provide a positive user experience. © 2023 IEEE.

关键词： Electroencephalography

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Improving the Effectiveness of Image Classification Structural Methods by Compressing the Description According to the Information Content Criterion

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computers, Materials & Continua 2024年第8期80卷 3085-3106页

作者： Yousef Ibrahim Daradkeh Volodymyr Gorokhovatskyi Iryna Tvoroshenko Medien Zeghid Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Informatics Kharkiv National University of Radio ElectronicsKharkiv61166Ukraine Electronics and Micro-Electronics Laboratory Faculty of SciencesUniversity of MonastirMonastir5000Tunisia

The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint *** main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification *** class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric *** is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content *** informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other *** developed method determines the relevance of the full description of the recognized object with the reduced description of the *** practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are *** results of the experimental modeling of the proposed methods for a database including images of museum jewelry are *** test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of *** practical problems of determining the threshold for the number of votes,based on which a classification decision is made,have been *** has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification *** the descriptions by twenty times in the experiment leads to slightly decreased *** speed of the analysis increases in proportion to the degree of *** use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification,which guarantees a decent

关键词： Description reduction description relevance descriptor image classification information content keypoint processing speed

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Enhancing Software Cost Estimation Using Feature Selection and Machine Learning Techniques

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computers, Materials & Continua 2024年第12期81卷 4603-4624页

作者： Fizza Mansoor Muhammad Affan Alim Muhammad Taha Jilani Muhammad Monsoor Alam Mazliham Mohd Su’ud Department of Software Engineering FAST-National University of Computer&Emerging SciencesKarachi75030Pakistan Faculty of Engineering Science and Technology IQRA UniversityKarachi72500Pakistan Department of Computer Science Bahria UnivesityKarachi74800Pakistan Faculty of Computing Riphah International UniversityIslamabad44600Pakistan Faculty of Computing and Informatics Multimedia University(MMU)Cyberjaya63100SelangorMalaysia

Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.

关键词： Machine learning software cost estimation PCA hyper parameter feature selection

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A Multi-Scale Control Framework for Electric and Automated Buses in Intercity Lines 63

A Multi-Scale Control Framework for Electric and Automated B...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Graffione, S. Bracco, S. Pasquale, C. Sacone, S. Siri, S. Ferrara, A. University of Genova Department of Informatics Bioengineering Robotics and Systems Engineering Italy University of Genova Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Italy

ISBN: (纸本)9798350316339

This paper proposes a multi-scale control scheme aimed at controlling speed, dwell time, and charging time of electric and automated buses performing the transport service on lines where there are no reserved lanes. The proposed control scheme has two layers characterized by different control objectives, level of detail and time scales. The high-level controller periodically solves a multi-objective optimal control problem formalized to ensure an adequate transport service, while the low-level controller, being based on a more accurate description of the bus dynamics and energy consumption, seeks to follow the high-level control actions while taking into account the effective traffic conditions encountered on the line and the effective dynamic behaviour of the bus. The paper concludes with the application to a case study in order to demonstrate the effectiveness of the proposed control scheme. © 2024 IEEE.

关键词： Bus transportation

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AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses

AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of O...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Lu, Xiaotian Li, Jiyi Takeuchi, Koh Kashima, Hisashi Department of Intelligence Science and Technology Graduate School of Informatics Kyoto University Kyoto Japan Department of Computer Science and Engineering University of Yamanashi Kofu Japan

ISBN: (纸本)9798891761681

Question answering (QA) tasks have been extensively studied in the field of natural language processing (NLP). Answers to open-ended questions are highly diverse and difficult to quantify, and cannot be simply evaluated as correct or incorrect, unlike close-ended questions with definitive answers. While large language models (LLMs) have demonstrated strong capabilities across various tasks, they exhibit relatively weaker performance in evaluating answers to open-ended questions. In this study, we propose a method that leverages LLMs and the analytic hierarchy process (AHP) to assess answers to open-ended questions. We utilized LLMs to generate multiple evaluation criteria for a question. Subsequently, answers were subjected to pairwise comparisons under each criterion with LLMs, and scores for each answer were calculated in the AHP. We conducted experiments on four datasets using both ChatGPT-3.5-turbo and GPT-4. Our results indicate that our approach more closely aligns with human judgment compared to the four baselines. Additionally, we explored the impact of the number of criteria, variations in models, and differences in datasets on the results. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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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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Refining Triplet Sampling for Improved Self-Supervised Representation Learning 32

Refining Triplet Sampling for Improved Self-Supervised Repre...

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32nd Italian Symposium on Advanced Database Systems, SEBD 2024

作者： Goyo, Manuel Frisoni, Giacomo Moro, Gianluca Sartori, Claudio Department of Informatics Universidad Técnica Federico Santa María Valparaíso Chile Department of Computer Science and Engineering University of Bologna Bologna Italy

Self-supervised representation learning extracts meaningful features from data without explicit supervision, building a space with desired properties. Contrastive learning has emerged as the predominant approach to clustering similar data points and separating dissimilar ones within the embedding space. Although creating different views of the same data (e.g., cropping, rotation) emphasizes similarities without labels, current methods struggle to define negative examples. Several algorithms only consider positive examples or integrate dissimilarity measures into their loss functions by computing average distances within the same batch. However, they do not capture nuanced differences effectively, risking collapsing data points in a single location. In this paper, we propose a novel technique, termed "Refined Triplet Sampling" (ReTSam), to generate synthetic negative vectors for contrastive learning. Mechanically, for each element in the batch, we identify its k-nearest neighbors and designate the centroid as a hard negative for a triplet loss methodology. We test ReTSam on two widely used image datasets, namely CIFAR-10 and SVHN, considering content-based image retrieval and classification tasks. Our findings demonstrate that, despite its simplicity, ReTSam not only promotes the learning of similarity but also significantly improves that of dissimilarity (with a +5% increase in Mean Average Precision on CIFAR10), resulting in superior performance in practical scenarios. © 2024 Copyright for this paper by its authors.

关键词： Contrastive Learning

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Plagiarism Detection Application with Modified Enhanced Confix Stripping - Levenshtein Distance Algorithm Analysis 12

Plagiarism Detection Application with Modified Enhanced Conf...

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12th International Conference on Cyber and IT Service Management, CITSM 2024

作者： Nurhayati Aditya Suryawan, Ida Bagus Wardhani, Luh Kesuma Lee, Kyungoh Suseno, Hendra Bavu Masruroh, Siti Ummi Syarif Hidayatullah State Islamic University Department of Informatics Jakarta Indonesia Sun Moon University Computer Engineering Department Asan-si Korea Republic of

ISBN: (纸本)9798331528799

This research is made to continue the research on Implementation of Levensthein Distance Algorithm on Plagiarism Detection. In that research, it is found that the application can calculate the percentage of plagiarism against 2 documents, and it is suggested for further research to include the stemming algorithm to increase the percentage of plagiarism. This study uses the stemming algorithm of Modified Enhanced Confix Stripping (MECS) to prove the suggestion written in previous research. The study was conducted by comparing plagiarism percentage, system usage, running time, and complexity of Levensthein and MECS - Levensthein algorithm. The result of this research is the use of MECS algorithm can reduce running time by 7s, but the percentage decreased by 0,8% and system usage increased by 11,72%. © 2024 IEEE.

关键词： Confix Stripping Distance Levensthein Modified Enhanced Plagiarism Stemming

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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