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Clustering Approach for Modeling Course Difficulty Level in Adaptive Learning

Clustering Approach for Modeling Course Difficulty Level in ...

2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2024

作者： Imamah Yuhana, Umi Laili Djunaidy, Arif Pardede, Eric Purnomo, Mauridhi Hery Departement of Electrical Engineering Faculty of Intelligent Electrical Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Informatics Faculty of Intelligent Electrical Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Information Systems Faculty of Intelligent Electrical Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Computer Science Information Technology La Trobe University BundooraVIC3086 Australia

ISBN: (纸本)9798350322996

The variations in students' abilities present challenges in achieving desired learning outcomes. Students exhibit different levels of comprehension regarding the complexity of learning materials. In this study, we aim to model the difficulty levels of learning materials using a clustering approach to offer personalized learning material recommendations within an adaptive learning framework. Data were collected from student scores of Data Structure course obtained between 2020 and 2022 at the University of Trunojoyo Madura. The dataset modeling process adheres to the CRISP-DM Methodology, employing the K-Means clustering method and Euclidean Distance. We employed the Within-Cluster Sum of Squares (WCSS) metric and silhouette score to identify the optimal K-value, which resulted in a value of 5, accompanied by a silhouette score of 0.629. Our analysis pinpointed Stack and Deque as the easiest, while Linked List emerged as the most challenging course material. Accordingly, we will assign a label of 1 to the easiest materials, 2 to those of medium difficulty, and 3 to the most challenging ones. These labels will serve as criteria for recommending suitable learning materials to students based on their knowledge level. We will assess the ratio between the overall knowledge level score and the difficulty level of the material. The ratio value will serve as a reference for determining material recommendations for students. Materials categorized with the highest difficulty levels are exclusively suggested to students demonstrating faster learning abilities with a high value of ratio, and vice versa. This approach is feasible for implementation in adaptive learning. © 2024 IEEE.

关键词： Contrastive Learning

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The adoption of human resources analytics in construction projects in Jordan:antecedents and consequences

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Journal of Management Analytics 2025年第1期12卷 134-173页

作者： Ahmad Abujraiban Gabriel J.Assaf Aladeen Yousef Rashid Hmoud Department of Civil Engineering and Construction École de Technologie Supérieure(ÉTS)University of QuebecMontrealCanada Faculty of Information Technology and Computer Science Department of Information TechnologyYarmouk UniversityIrbidJordan

The construction industry is increasingly using analytics tools to enhance decision-making and streamline project ***,human resource analytics(HRA)adoption has been slow due to concerns about cost and *** studies investigating HRA adoption rely on conceptual models and are in their early *** address this gap,this study takes an empirical approach by examining the antecedents and impacts of HRA adoption on project performance in the Jordanian construction industry.A deductive conceptual framework based on technology-organisation-environment(TOE)and resource-based view(RBV)theories is developed,and 198 individuals are *** structural equation modelling(PLS-SEM),the study identifies eight factors that significantly impact HRA adoption and shows that adoption leads to significant project performance *** study provides valuable insights into HRA adoption in the construction industry,with implications for human resource management,project performance,and the industry as a whole.

关键词： HR Analytics HRM project performance construction industry

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Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

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computers, Materials & Continua 2023年第5期75卷 3065-3089页

作者： Ali Arshad Muhammad Nadeem Saman Riaz Syeda Wajiha Zahra Ashit Kumar Dutta Zaid Alzaid Rana Alabdan Badr Almutairi Sultan Almotairi Department of Computer Science National University of TechnologyIslamabadPakistan Department of Computing Abasyn UniversityIslamabadPakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic university of MadinahMedinah42351Saudi Arabia Department of Information Systems Faculty of Computer and Information Sciences CollegeMajmaah University11952Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah 11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data,but these techniques and algorithms cannot be used to protect data from an *** cryptography is the best way to transmit data in a secure and reliable *** researchers have developed various mechanisms to transfer data securely,which can convert data from readable to unreadable,but these algorithms are not sufficient to provide complete data *** algorithm has some data security *** some effective data protection techniques are used,the attacker will not be able to decipher the encrypted data,and even if the attacker tries to tamper with the data,the attacker will not have access to the original *** this paper,various data security techniques are developed,which can be used to protect the data from attackers ***,a customized American Standard Code for information Interchange(ASCII)table is *** value of each Index is defined in a customized ASCII *** an attacker tries to decrypt the data,the attacker always tries to apply the predefined ASCII table on the Ciphertext,which in a way,can be helpful for the attacker to decrypt the *** that,a radix 64-bit encryption mechanism is used,with the help of which the number of cipher data is doubled from the original *** the number of cipher values is double the original data,the attacker tries to decrypt each *** of getting the original data,the attacker gets such data that has no relation to the original *** that,a Hill Matrix algorithm is created,with the help of which a key is generated that is used in the exact plain text for which it is created,and this Key cannot be used in any other plain *** boundaries of each Hill text work up to that *** techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorit

关键词： Cryptography symmetric cipher text encryption matrix cipher encoding decoding hill matrix 64-bit encryption

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Neural Sentiment Network Model for Myanmar Language Using Attention Mechanism (AttenSentNet) 5

Neural Sentiment Network Model for Myanmar Language Using At...

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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： Khine, Win Lei Kay Zin, Thet Thet University of Computer Studies Hinthada Department of Information Technology Supporting and Maintenance Hinthada Myanmar University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

Sentiment analysis is the process of algorithmically identifying whether the opinion/emotion in social media posts, movie reviews as positive, negative, or neutral classes. It is becoming an important in making decision for business owners because they want to know the feedback of their products. It classifies the sentiment polarities into different classes in the user's review. This proposed paper aims to predict the sentiment polarity of the user comment review from YouTube channels that are written in Myanmar language by combining BiLSTM (Bidirectional LSTM) and attention mechanism. Although there are many research works of NLP tasks using Myanmar language in machine translation and question answering systems, there are few researches on sentiment analysis tasks. The paper uses the hyperparameter tuning approach that is used to find the optimal parameters in training the neural sentiment model. The AttenSentNet also solves the vanishing gradient problem, which is a long-standing issue in the training the deep learning model. The attention mechanism pays attention to the important sentiment words in the given review. Experiments are tested on the phone product review in Youtube written by Myanmar language. Finally, the experimental results show with the accuracy of AttenSentNet model. To the best of our knowledge, this is the first work for sentiment analysis task on Myanmar language using BiLSTM neural network with attention. © 2024 IEEE.

关键词： Deep learning

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Comparative Analysis of Robotic Arm Efficiency: Evaluating PID and Fuzzy-PID Control using Printed Mechanical Structure and ESP8266 Integration 6

Comparative Analysis of Robotic Arm Efficiency: Evaluating P...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Crisnapati, Padma Nyoman Susila, I. Made Darma Aryanto, I. Komang Agus Ady Krohkaew, Jaturapith Diaz, Ricky Aurelius Nurtanto Pande, I. Made Suandana Astika Department of Information Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Computer Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Information Technology Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Dept. of Big Data Management and Analytics Faculty of Science and Technology Rajamangala University of Technology Thanyaburi Thanyaburi Thailand

ISBN: (纸本)9798331539603

This study investigates the comparative effectiveness of manual PID tuning versus Fuzzy-PID control in enhancing the performance of a robot arm with two degrees of freedom (2-DOF). The experimental approach involves conducting two scenarios: manual tuning of PID parameters in Scenario 1 and utilizing a fuzzy algorithm for PID parameter tuning in Scenario 2. The novelty of this research lies in the new 3D mechanical design of the 2-DOF robot arm and the utilization of the ESP8266 microcontroller for implementing Fuzzy-PID control. Performance evaluation is conducted using Root Mean Square Error (RMSE) and Root Mean Square Percentage Error (RMSPE) calculations. The results reveal that Fuzzy-PID control significantly reduces errors compared to manual PID tuning, with lower RMSE and RMSPE values indicating more precise and stable control. These findings underscore the potential of integrating fuzzy logic with PID control to enhance system adaptability and flexibility. Additionally, the ESP8266 is capable of handling the computations required for fuzzy-PID control while being cost-effective. © 2024 IEEE.

关键词： Robotic arms

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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Sentiment Analysis on Tourism Place using Naive Bayes 17

Sentiment Analysis on Tourism Place using Naive Bayes

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17th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2023

作者： Atmadja, Aldy Rialdy Rahmawati, Aprilia Alam, Cecep Nurul Dauni, Popon Saputra, Yogi Faculty of Science and Technology UIN Sunan Gunung Djati Informatics Department Bandung Indonesia Faculty of Computer Science and Information System Universitas Kebangsaan RI Information System Department Bandung Indonesia

ISBN: (纸本)9798350309164

The method of extracting information from a text is called text mining. In contrast, sentiment analysis involves examining text data to determine whether a statement expresses good or negative conversations. In this study, a system was developed that categorizes tweets about West Bandung Regency tourism spots that contain both favorable and negative remarks. The Cross Industry Standard Process for Data Mining (CRISP-DM) technique is used to carry out the classification process utilizing the Naive Bayes algorithm. You will learn how locals feel about West Bandung Regency's tourist attractions through this method. Six scenarios were developed using the categorization findings from 250 pieces of data that included both positive and negative sentiment. The scenario has got the greatest f1-score of 82.8%. These findings demonstrate how effectively the Naive Bayes algorithm performs sentiment analysis. © 2023 IEEE.

关键词： Tourism

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Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

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computer Systems science & Engineering 2023年第9期46卷 2773-2795页

作者： Hala Ahmed Hassan Soliman Shaker El-Sappagh Tamer Abuhmed Mohammed Elmogy Information Technology Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt College of Computing and Informatics Sungkyunkwan UniversitySuwonKorea

The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage *** is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image *** this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain ***,the preprocessing was performed on the ***,the data augmentation techniques are used to handle ***,the skull is removed to lead to good *** the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of *** resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific *** emission tomography images can provide functional information and the metabolisms of particular *** characteristic helps clinicians detect diseases and tumor progression at an early ***,the feature extraction of fused images is extracted using a convolutional neural *** the case of late fusion,the features are extracted first and then ***,the proposed system performs XGB to classify Alzheimer’s *** system’s performance was evaluated using accuracy,specificity,and *** medical data were retrieved in the 2D format of 256×256 *** classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was *** best number of trees for the random forest was 60;for

关键词： Alzheimer’s disease(AD) machine learning(ML) image fusion Laplacian Re-decomposition(LRD) XGBoosting

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Exploring Knowledge Landscapes: Clustering and Topic Modeling of Sebha University Scientific Publications 4

Exploring Knowledge Landscapes: Clustering and Topic Modelin...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Abdelhamed, Roqyia Essgaer, Mansour Sebha University Faculty of Science Department of Computer Sciences Sebha Libya Sebha University Faculty of Information Technology Department of Information Technology Sebha Libya

ISBN: (纸本)9798350372632

The continuous and exponential growth in scientific publications poses a challenge in manually sorting and searching for specific topics within a university's publications. Recent advancements in artificial intelligence, natural language processing, and clustering methods, particularly the K-Means, have shown satisfactory performance in handling large volumes of textual data. Furthermore, topic modeling methods, notably praised for their effectiveness in organizing textual documents. This study focuses on evaluating the performance of the K-Means and Latent Dirichlet Allocation in clustering Sebha University scientific publications. Different term weighting is employed namely term frequency inverse document frequency, and Word2Vec to represent the tokens. K-Means demonstrated notable performance when coupled with Word2vec, as indicated by metrics such as the Davies-Bouldin Index, Silhouette Score, Calinski-Harabasz Index, and Inertia. The best performance was observed when using term frequency inverse document frequency with the Entropy metric. The evaluation metrics for K-Means did not unanimously agree on the optimal number of clusters. In contrast, Latent Dirichlet Allocation consistently performed well, with all measurements agreeing on the best results with the smallest number of clusters. Among the various combinations tested, Latent Dirichlet Allocation paired with Word2vec produced the best results across metrics, demonstrating superior cohesion strength through the topic coherence metric and minimizing noise aggregates based on the entropy metric. © 2024 IEEE.

关键词： Statistics

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Hybrid Global Optimization Algorithm for Feature Selection

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computers, Materials & Continua 2023年第1期74卷 2021-2037页

作者： Ahmad Taher Azar Zafar Iqbal Khan Syed Umar Amin Khaled M.Fouad Faculty of Computers and Artificial Intelligence Benha UniversityBenha13511Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Technology and Computer Science Nile UniversitySheikh ZaidEgypt

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization *** experimental study is performed in two stages to assess the proposed *** I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO *** based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)*** phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT *** phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical *** experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable *** from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

关键词： Particle swarm optimization(PSO) time-variant acceleration coefficients(TVAC) genetic algorithms differential evolution feature selection medical data

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