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2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Wijiyanto Marjuni, Aris Fanani, Ahmad Zainul Basuki, Ruri Suko Dian Nuswantoro University Department of Informatics Engineering Semarang Indonesia Duta Bangsa University Fakulty of Computer Science Surakarta Indonesia

ISBN: (纸本)9798350366648

Prediction systems for students' performance are crucial in improving educational outcomes in higher education institutions. Big data can be sourced from various channels, such as examination centers, academic systems, registration departments, and e-learning platforms. Extracting meaningful insights from educational data is challenging, necessitating data dimensionality reduction. This study introduces an enhanced recursive feature elimination (RFE) algorithm as a wrapper feature selection technique. We adopt machine learning by combining the proposed RFE with five different classifiers, including Gradient Boosting Classifier (GB), Random Forest Classifier (RF), Support Vector Classifier (SVC), K-Neighbors Classifier (KNN), and Decision Tree Classifier (DT). The dataset was originally intended to influence factors in dropout students, and it has 329 records and 9 features. The AUC score improved by 8% compared to the model tested without the proposed feature selection method by testing the proposed methodology with the Decision Tree Classifier. Educators and academic institutions can utilize this information to develop targeted intervention strategies, tailored learning experiences, and personalized recommendations for students, ultimately promoting a more effective learning environment and improving overall educational outcomes. © 2024 IEEE.

关键词： Adversarial machine learning

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

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computers, Materials & Continua 2024年第8期80卷 2301-2330页

作者： Uddagiri Sirisha Parvathaneni Naga Srinivasu Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravatiAndhra Pradesh522503India Department of Tele informatics Engineering Federal University of CearáFortaleza60455-970Brazil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturAndhra Pradesh522302India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea School of CSIT Symbiosis Skills and Professional UniversityPune412101India Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia School of IT and Engineering Melbourne Institute of TechnologyMelbourne3000Australia

Fetal health care is vital in ensuring the health of pregnant women and the *** check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential *** know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,***,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during *** researchers have proposed various methods for classifying the status of fetus *** processing of CTG data is time-consuming and ***,automated tools should be used to classify fetal *** study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal *** strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also *** study easily provides valuable interpretations for healthcare professionals in the decision-making process.

关键词： Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering

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Quaternionic multilayer bottleneck autoencoder for color image compression 27

Quaternionic multilayer bottleneck autoencoder for color ima...

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27th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2024

作者： Petrovsky, Nick Vashkevich, Maxim Belarusian State University of Informatics and Radioelectronics Department of Computer Engineering 6 P. Brovky st Minsk220013 Belarus

ISBN: (纸本)9788362065486

This paper presents a study on the multilayer bottleneck autoencoder applied for thumbnail color image compression task using quaternionic neural nets (QNN). Existing state-of-the-art real-valued autoencoder architectures are weak for reconstruction of images with high-frequency components. However, experiments shows that hypercomplex representation of color and activation functions, gives better results in terms of pixellevel quality metrics PSNR and SSIM. Designed hypercomplex autoencoders are simplest type of the QNN - fully connected quaternionic layers. Investigated one- and two-layer QNN structures for encoder/decoders in compare with real-valued ones provides better PSNR by approx. 2.5 dB and 0.11 SSIM (Real valued: PSNR - 26.36 dB, SSIM - 0.856;Quaternionic: PSNR 31.28 dB, SSIM - 0.951) for 3:1 compression ratio and equal or less number of hyperparameters. © 2024 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词： Image compression

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A spatio-temporal matrix representation for trajectory classification 24

A spatio-temporal matrix representation for trajectory class...

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

作者： Kontopoulos, Ioannis Varlamis, Iraklis Makris, Antonios Tserpes, Konstantinos Department of Informatics and Telematics Harokopio University of Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9798400711077

Fish piracy remains widespread globally despite national and international efforts. Experts estimate it accounts for about 20% of the total seafood catch worldwide. Technology is playing a key role in detecting illegal fishing, with satellite imagery and sensors being used to track vessels and monitor fishing practices. Since fishing boats broadcast their positions using a vessel tracking system, this data can be processed to detect illegal activity. This study focuses on classifying fishing vessel trajectories using only positional data. A novel trajectory representation and a Convolutional Neural Network is employed, showing promising results compared to traditional methods. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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New Perspectives in e-Learning: EEG-Based Modelling of Human Cognition Individual Differences 13th

New Perspectives in e-Learning: EEG-Based Modelling of Hum...

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Trigka, Maria Dritsas, Elias Mylonas, Phivos Athena Research and Innovation Center Patras26504 Greece Department of Informatics and Computer Engineering University of West Attica Aigaleo Greece

ISBN: (纸本)9783031632266

Learning is the process of acquiring knowledge, skills and values, through lifelong education. Learning abilities are affected by the subject’s cognitive skills showing how they think, process information, pay attention and remember things. The dynamic nature of a human’s mental state usually impacts the aforementioned structural characteristics of human cognition, hindering learning performance as well. Nowadays, technological advances in Brain-computer Interface (BCI) systems, in combination with advanced processing methods, have paved the way for the highly accurate capturing of human brain activity, helping to decode cognitive and mental status to adapt the learning process. This paper aims to present the first research outcomes of a study in progress. More specifically, the main proposition lies in extending the basic functionalities of our electroencephalography (EEG)-based e-learning prototype system to deliver personalized solutions by taking into account the individual cognitive differences of the learners. Some primary findings are showcased that investigate the potential association of cognitive style with the power spectral features of brain activity in a specific context where the subjects execute a visual task. It is anticipated that the real-time recognition of the learner’s cognitive style will help educators adapt and advance the learning process. © IFIP International Federation for Information Processing 2024.

关键词： Brain computer interface

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DeBERTa-GRU: Sentiment Analysis for Large Language Model

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computers, Materials & Continua 2024年第6期79卷 4219-4236页

作者： Adel Assiri Abdu Gumaei Faisal Mehmood Touqeer Abbas Sami Ullah Department of Informatics for Business College of BusinessKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Department of Computer Science and Technology Beijing University of Chemical TechnologyBeijing100029China Department of Computer Science Government College University FaisalabadFaisalabadPunjab38000Pakistan

Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

关键词： DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model

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An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms

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Multimedia Tools and Applications 2024年第37期83卷 85103-85138页

作者： Seyyedabbasi, Amir Tareq Tareq, Wadhah Zeyad Bacanin, Nebojsa Computer Engineering Deptartment Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Department of Mathematics Saveetha School of Engineering SIMATS Thandalam Tamilnadu India Faculty of Informatics and Computing Singidunum University Belgrade Serbia MEU Research Unit Middle East University Amman Jordan

Recently, the Honey Badger Algorithm (HBA) was proposed as a metaheuristic algorithm. Honey badger hunting behaviour inspired the development of this algorithm. In the exploitation phase, HBA performs poorly and stagnates at the local best solution. On the other hand, the sand cat swarm optimization (SCSO) is a very competitive algorithm compared to other common metaheuristic algorithms since it has outstanding performance in the exploitation phase. Hence, the purpose of this paper is to hybridize HBA with SCSO so that the SCSO can overcome deficiencies of the HBA to improve the quality of the solution. The SCSO can effectively exploit optimal solutions. For the research conducted in this paper, a hybrid metaheuristic algorithm called HBASCSO was developed. The proposed approach was evaluated against challenging CEC benchmark instances taken from CEC2015, CEC2017, and CEC2019 benchmark suites The HBASCSO is also evaluated concerning the original HBA, SCSO, as well as several other recently proposed algorithms. To demonstrate that the proposed method performs significantly better than other competitive algorithms, 30 independent runs of each algorithm were evaluated to determine the best, worst, mean, and standard deviation of fitness functions. In addition, the Wilcoxon rank-sum test is used as a non-parametric comparison, and it has been found that the proposed algorithm outperforms other algorithms. Hence, the HBASCSO achieves an optimum solution that is better than the original algorithms. © The Author(s) 2024.

关键词： Benchmarking

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The Association Rule Methods and K-Means Clustering for Optimization Mapping of New Students Admission

The Association Rule Methods and K-Means Clustering for Opti...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Harsono, Sugi Utami, Ema Yaqin, Ainul Universitas Amikom Yogyakarta Magister of Informatics Engineering Yogyakarta Indonesia Universitas Amikom Yogyakarta Computer Science Faculty Yogyakarta Indonesia

ISBN: (纸本)9798350350524

Educational service providers deliver information about student admission by installing banners, distributing brochures, and providing information directly to prospective new students annually. The target result can be outside expectations because there is no data analysis related to optimizing the support of the system. This study proposes the K-Means algorithm optimized with the Association Rules method to map the interests of prospective students in selecting the study program. The data collection method uses an explanatory research survey and correlational study with primary data from the questionnaires and documents for new students in the year 2022. The accuracy results of this study using the K-Means algorithm process to calculate the silhouette score, elbow methods, cross-combination experiments, and data sharing are 95.00%. The optimization accuracy with mapping relationships between variables using frequent itemset and Association Rules increased to 100%. The most significant factors influencing the student selection of study programs are religion 100%, transportation 85.71%, gender 83.33%, school status 80.95%, regency 73.81%, school origin 71.43%, the district of the school close to student life 66.67%, residence located at the parent's home 64.29%, place of birth 61.90%, and job future ambition 61.90%. © 2024 IEEE.

关键词： Association rules

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Using Machine Learning to Identify and Categorize Personally Identifiable Information and Payment Card Industry Data in Textual Content 4

Using Machine Learning to Identify and Categorize Personally...

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4th International Conference on Advanced Research in Computing, ICARC 2024

作者： Arambawela, Milinda Aponso, Achala University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350384864

The advent of the Internet has significantly stream-lined daily tasks through the rapid increase of online services. Everyday activities, such as purchasing goods and scheduling appointments with healthcare professionals, have become more speedy, efficient and user-friendly with the integration of the Internet. The continuous improvement of online services has led to many people moving towards digital activities. As a result, it has heightened the recording of personal and payment transaction data across various storage mediums, including databases and log files. The protection and regulation of this sensitive data are imperative, aligning with the guidelines outlined in GDPR and PCI-DSS compliances. Recognizing exposed personal data poses a considerable challenge. This research introduces a novel approach to identifying payment card industry data (PCI) and personally identifiable information (PII). The research project proposes a machine learning-based text classification model utilizing the Convolutional Neural Network (CNN) model to discern PII and PCI data within a given text. The CNN model has been constructed and compared against Naive Bayes, Gradient Boost, Random Forest, and Support Vector Machine (SVM) models. The CNN model achieved the highest accuracy at 0.96 (96%). Additionally, the F1 scores for each class were significant, with PII scoring 0.94, PCI scoring 0.95, and Normal scoring 0.99. Following the model's construction and training, it was employed with the saved tokenizer's word indexes and label encoders in the developed classification tool. This tool successfully delivered the promised results, identifying exposed PII and PCI data. © 2024 IEEE.

关键词： Neural network models

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