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11th International Conference on Electrical Engineering, computer Science and informatics, EECSI 2024

作者： Bachtiar, Fitra Abdurrachman Nainggolan, Cesilia Natasya Soetedjo, Riza Setiawan Rokhmawati, Retno Indah Muflikhah, Lailil Brawijaya University Department of Informatics Engineering Faculty of Computer Science Malang Indonesia Brawijaya University Faculty of Computer Science Department of Information Systems Malang Indonesia

ISBN: (纸本)9798350355314

The e-Iearning system or distance learning system has become important, especially during the COVID-19 pan-demic. Several tertiary institutions have made the e-Iearning system an alternative teaching and learning activity. Research on the use of e-Iearning needs to be explored to obtain innovative, creative, and fun learning. One aspect that can be examined in e-learning is students' emotional response to the learning activities. The emotional state of students directly or indirectly affects the learning process. The student's emotional responses can be measured by their facial expressions. In this study, an exploration was carried out on the facial expressions shown by students in HSS Learning, an e-Iearning for learning web programming. The student's facial expressions were extracted using 68 pairs of facial landmarks and additional 6 facial feature vectors. The total number of features used is 142. Machine learning methods for classification are applied to determine the performance of several classification models, including the k-Nearest Neighbor (k- NN) algorithm, Logistic Regression, and Support Vector Machine (SVM). Machine learning performance evaluation is done using the Fl-score and confusion matrix. The results show that the accuracy of the k-NN, Logistic Regression, and SVM are 91.4%, 92.2%, and 92.1 %, respectively. However, the Fl-score macro values are relatively low due to data imbalance with the k- NN, Logistic Regression, and SVM models are 0.250, 0.272, and 0.243, respectively. While the study provides insight into the student learning experience in e-Iearning settings, this study needs to be explored more. © 2024 IEEE.

关键词： Contrastive Learning

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A Comparative Study of Automatic Hate Speech Detection Using Machine Learning 1

A Comparative Study of Automatic Hate Speech Detection Using...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Siddiqui, Jawaid Ahmed Yuhaniz, Siti Sophiayati Memon, Zulfiqar Ali Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Computer Science Department Karachi Pakistan

ISBN: (纸本)9798350373042

There is no denying that social media's ubiquitous use and the knowledge it seamlessly disseminates have improved humanity. But despite its many benefits, this growth has also given rise to urgent worries, including the spread of hate speech. Modern research have embraced a variety of feature engineering techniques and machine learning algorithms as a remedy to this increasing difficulty inside the world of social media platforms. These initiatives aim to automatically identify hate speech across several datasets, offering a promising way to lessen this pervasive problem. As far as we are aware that no research has directly compared different feature engineering methods with different machine learning algorithms to determine which method produces the best results on a representative public dataset. This article's goal is to evaluate how well three different feature engineering approaches work with eight different machine learning algorithms using an open source free publicly available datasets that include 03 different classes. The results of the experiment revealed that the combination of bigram features and the (SVM) support vector machine algorithm performed the best overall, with an amazing accuracy rate of '79%'. Our research consequences touch on actual situations, making it a landmark study that potentially laid the foundation for future efforts aimed at automatically identifying hate speech. Further, the results of these comparisons will serve as state-of-the-art approaches against which further studies of automated text classification can be measured. © 2024 IEEE.

关键词： Social networking (online)

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Decoding Digital Hostility: Examining and Addressing Hate Speech on Social Media Platforms 1

Decoding Digital Hostility: Examining and Addressing Hate Sp...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Siddiqui, Jawaid Ahmed Yuhaniz, Siti Sophiayati Memon, Zulfiqar Ali Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Computer Science Department Karachi Pakistan

ISBN: (纸本)9798350373042

There is no denying that the widespread usage of social media and the sharing of information have greatly benefited humanity. However, a number of issues have also emerged as a result of this increase in online engagement, most notably the spread of hate speech. Recent research has addressed this growing problem on social media platforms by automatically detecting hate speech in a variety of datasets using a variety of feature engineering approaches and machine/deep learning algorithms. It is interesting, though, that many of these studies - to the best of our knowledge - resort to identifying hate speech messages using traditional feature engineering techniques, which leads to less-than-ideal classification results. This is explained by the shortcomings of the feature engineering techniques now in use, specifically their vulnerability to the word order and word context problems. Therefore, more advanced strategies are desperately needed to address these issues and improve the precision of hate speech identification on social media platforms. To create fundamental lexical benchmarks is the goal. As distinguishing characteristics, our methodology makes use of the power of n-grams, which cover both character and word levels, and skip-grams, which cover both character and word levels. Notably, we successfully identify posts in all three defined categories with an impressive 78% accuracy. The results highlight how difficult it is to discriminate between vulgarities and hate speech. We also conduct a thorough exploration of potential directions for future research projects. © 2024 IEEE.

关键词： Machine learning

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A novel hybrid deep learning framework for pedestrian detection based on thermal infrared and visible spectrum images

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Multimedia Tools and Applications 2025年 1-27页

作者： Defaoui, Mahassine Koutti, Lahcen El Ansari, Mohamed Lahmyed, Redouan Masmoudi, Lhoussaine LabSIV Department of Computer Science Faculty of Science Ibn Zohr University Agadir80000 Morocco Informatics and Applications Laboratory Faculty of Science My Ismail University Meknes Morocco Conception and Systems Laboratory Faculty of Sciences Mohamed V University Rabat Morocco

For many intelligent systems, designing accurate pedestrian detection approaches is a fundamental task. This paper describes a novel hybrid system to detect pedestrians using both visible and thermal infrared sensors. The proposed method is achieved in two primary stages. The first stage, hypotheses generation (HG), aims to extract the regions that represent suspected pedestrians from the image. It is performed using both thermal and visible images of the same scene. Here, an improved deep learning-based solution with a convolutional neural network named PedTherm-U-net is proposed. It segments the thermal images and maps the segmentation results into the corresponding visible image to extract the ROIs (candidate pedestrians). The second stage, hypotheses verification (HV), classifies the ROIs extracted from the first stage into pedestrians and non-pedestrians. In the HV step, a novel PedVis-ResNet framework to classify the extracted ROIs is introduced. It is a modified version of ResNet model dedicated to classify the visible pedestrian samples. The proposed method has been tested on the publicly accessible OSU Color-Thermal dataset, and the obtained results demonstrate the effectiveness of the proposed method when compared to the recent state of the art pedestrian detection approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Intelligent systems

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Sentiment Analysis Using K-NN Algorithm Through Random Search Cross Validation Approach 4

Sentiment Analysis Using K-NN Algorithm Through Random Searc...

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4th International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Senda, Tito Afwi Sitompul, Opim Salim Amalia, Amalia Universitas Sumatera Utara Master of Informatics Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798350376111

This research discusses the application of the KNearest Neighbor (K-NN) algorithm in sentiment analysis, specifically in classifying positive, negative, or neutral sentiments from ChatGPT user opinions collected from user reviews on the Play Store. This research focuses on optimizing the key parameters of the K-NN algorithm using the Random Search cross-validation (RSCV) method. The evaluation results showed a good performance improvement after applying RSCV. Previously, the model showed unsatisfactory performance but improved significantly after optimization. Overall, RSCV successfully improved the average performance of the model for sentiment analysis. © 2024 IEEE.

关键词： Natural language processing systems

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Classification of Functions Using Machine Learning

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International Journal of Unconventional Computing 2023年第2-3期18卷 217-247页

作者： Lukac, Martin Yessenbayeva, Aigerim Lewis, Michael Podlaski, Krzysztof Nazarbayev University Department of Computer Science Astana01000 Kazakhstan University of Lodz Faculty of Physics and Applied Informatics Lodz Poland

We study the approximation of function classification by machine learn-ing. We first determine all the NPN and NPNP classes of three-bit reversible and one to five-bits irreversible functions. Then we apply machine learning to learn the labels for each set of functions. We then search the parameter space for several optimization criteria, such as the size of the neural network, the architecture, and learning parameters that would allow the network under training to learn the NPN classification problem with up to 100% accuracy. We also determine if, for n-bit functions, a classification can be performed using two smaller net-works, each classifying n-1-bit functions. Additionally, we discuss the logic solvability of all four-bit functions using the game of Nono-grams. We determine a relation between the learnability of the classification and logic solvability. We determined that while there is a set of functions that can be classified by representing a function as a prod-uct of two subfunctions, a more complex model is needed to classify a n-bit NPN functions as a function of n-1-bit NPN functions. In addi-tion, we determine that approximate classification is always possible, with certain NPN classes being perfectly classified while others are not. Moreover, we can observe a direct relationship between the learnability of certain classes and the speed of finding a solution for these classes when used in a logic game. Finally, we show that the approximation of the classification using machine learning can replace standard database-based classification. The advantages of machine learning methods are end-to-end learning, the lack of circuit design requirements for classifi-cation, and the ability to easily learn the class distinction problem. © 2023 Old City Publishing, Inc.

关键词： Classification (of information)

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Predictive Analysis of Sarajevo's AQI using Machine Learning Models for Varied Data Granularity and Prediction Windows 47

Predictive Analysis of Sarajevo's AQI using Machine Learning...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Zolota, Emina Hasić, Vahidin Mević, Amina Delić, Amra Krivić, Senka University Of Sarajevo Faculty Of Electrical Engineering Department For Computer Science And Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350382495

This study scrutinizes five years of Sarajevo's Air Quality Index (AQI) data using diverse machine learning models - Fourier autoregressive integrated moving average (Fourier ARIMA), Prophet, and Long short-term memory (LSTM) - to forecast AQI levels. Focusing on various prediction frames, we evaluate model performances and identify optimal strategies for different temporal granularities. Our research unveils subtle insights into each model's efficacy, shedding light on their strengths and limitations in predicting AQI across varied timeframes. This research presents a robust framework for automatic optimization of AQI predictions, emphasizing the influence of temporal granularity on prediction accuracy, automatically selecting the most efficient models and parameters. These insights hold significant implications for data-driven decision-making in urban air quality control, paving the way for proactive and targeted interventions to improve air quality in Sarajevo and similar urban environments. © 2024 IEEE.

关键词： Air quality

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Fine-Tuning GPT-4o-Mini for Programming Questions Generation

Fine-Tuning GPT-4o-Mini for Programming Questions Generation

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2024 International Conference on computer and Applications, ICCA 2024

作者： Hesham, Alaa Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt

ISBN: (纸本)9798350367560

Creating programming questions that are both meaningful and educationally relevant is a critical task in computer science education. This paper introduces a fine-tuned GPT4o-mini model (C2Q). It is designed to generate meaningful questions by leveraging semantic feature extraction and well- crafted prompts. The approach addresses the limitations of traditional generative models, offering a deeper understanding of programming code and producing questions that are precise, diverse, and relevant to a given code snippets. The proposed framework incorporates essential code elements, such as control structures and method attributes, to generate questions that align with programming concepts. Evaluation metrics used were BLEU, ROUGE-1, and ROUGE-L to evaluate the model's performance. The findings reveal that the model achieves better structural coherence and conceptual relevance while focusing on contextual understanding over exact term matching. This work highlights the potential of the proposed approach to advance teaching and assessment methods in computer science. © 2024 IEEE.

关键词： Program processors

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The Influence of ChatGPT on Student Learning and Academic Performance 5

The Influence of ChatGPT on Student Learning and Academic Pe...

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5th International Conference on computer and Applications, ICCA 2023

作者： Ayman, Shehab Eldeen El-Seoud, Samir A. Nagaty, Khaled Karam, Omar H. The British University In Egypt Faculty of Informatics and Computer Science Cairo Egypt

ISBN: (纸本)9798350303254

This study delves into the integration of ChatGPT, an artificial intelligence-driven language model, within undergraduate education. The research scrutinizes the potential advantages, obstacles, and ethical dimensions linked to including ChatGPT in educational practices. It assesses how ChatGPT may impact student engagement, critical thinking, problem-solving abilities, writing proficiency, and the delivery of personalized learning experiences. A survey administered to university faculty members captures their perspectives on ChatGPT usage, encompassing its influence on learning outcomes and academic performance. An internal inquiry at the British University in Egypt found high plagiarism rates in various faculties, with mass media having the highest at 66%, while political science had the lowest at 28%. A survey has been conducted on teaching staff at the university. 75% of teaching staff believes that ChatGPT should be integrated into teaching. However, they are still undecided about whether ChatGPT has affected the teaching and learning process. The study underscores the significance of responsible implementation, faculty training, and continuous assessment to optimize ChatGPT's benefits while adhering to ethical standards in education. In conclusion, this paper illuminates ChatGPT's potential as a valuable tool in undergraduate education, underscoring the imperative of preserving critical thinking skills and human interaction in the learning journey. © 2023 IEEE.

关键词： Students

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Modeling Antenna Array with Vibrator Antennas for Problem of Dead on Arrival

Modeling Antenna Array with Vibrator Antennas for Problem of...

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2023 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2023

作者： Preobrazhenskiy, Yu. Avetisyan, T. Preobrazhenskiy, A. Voronezh Institute of High Technologies Faculty of Informatics Voronezh Russia Voronezh Institute of High Technologies Faculty of Informatics Computer Voronezh Russia

ISBN: (纸本)9781665475952

The paper is devoted to the problem of dead on arrival (DoA) sources of radiation of electromagnetic waves. It is proposed to use the antenna array to solve this problem, which contains logoperiodic antennas with vibrators. The solution to an integral equation was carried out to determine the electromagnetic field, which received by the antenna structure. When determining currents on the antenna, the method of collocation was used. To determine the DoA of the sought sources, it was proposed to use the technique, which consisted of two stages. At the first stage, the criterion of the maximum of the magnitudes of the amplitudes of signals at the outputs of the logoperiodic antennas with vibrators were used. It was found that the phase difference from the angle of the electromagnetic wave arrival and on frequency obeys practically linear laws. In the second stage, the direction-finding angle can be estimated for one of the angles of the electromagnetic wave. At the same time, an approximating dependence of the difference of DoA from the angle of the wave is used. The error of the resulting angle of the direction finding does not exceed 1°. © 2023 IEEE.

关键词： Electromagnetic waves

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