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5th international conference on advanced research in Computing, ICARC 2025

作者： Sandeepa, A.G.R. Mohottala, Sanka General Sir John Kotelawala Defence University Dept. of Computer Engineering Ratmalana Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Science Malabe Sri Lanka

ISBN: (纸本)9798331530983

This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations while for train set, it was 99.45%. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach. © 2025 IEEE.

关键词： Students

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Comparative Analysis of Ensemble Techniques for Bone Fracture Detection from X-Ray Images Using HOG Features

Comparative Analysis of Ensemble Techniques for Bone Fractur...

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2025 international conference on Advancement in Data science, E-learning and information System, ICADEIS 2025

作者： Muzakki, Abyan Farrel Prasetyo, Simeon Yuda Purwanto, Eko Setyo School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

Musculoskeletal conditions encompass 150 varying conditions and approximately affect 1.71 billion people worldwide, according to the World Health Organization. Bone fractures are among the musculoskeletal conditions that significantly impact individuals, families, societies, and healthcare systems, as they can lead to work absences, decreased productivity, disability, and a host of other life-limiting conditions. This study aims to evaluate the performance of superior-performing machine learning (ML) models that are fed with histograms of oriented gradients (HOG). The bestperforming ML models were chosen and implemented toward ensemble models in aims to reduce overfitting and address resource-limited settings. The results indicate that by using KNearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF), which yield the highest scores, the proposed ensemble models were able to increase the score, with the stacking ensemble model achieving accuracy score of 99.30%. Future research could focus on integrating advanced machine learning techniques, hybrid feature extraction methods, and larger datasets to enhance diagnostic accuracy and applicability in clinical environments. © 2025 IEEE.

关键词： Musculoskeletal system

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Predicting Multiclass Java Code Readability: A Comparative Study of Machine Learning Algorithms

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international Journal of advanced computer science and Applications 2025年第4期16卷 1055-1064页

作者： Susanto, Budi Ferdiana, Ridi Adji, Teguh Bharata Department of Electrical and Information Engineering Faculty of Enginering Universitas Gadjah Mada Indonesia

The classification of program code readability has traditionally focused on two target classes: readable and unreadable. Recently, it has evolved into a multiclass classification task in three categories: readable, neutral, and unreadable. Most of the existing approaches rely on deep learning. This study investigated the multiclass classification of Java code readability using four feature metric datasets and 14 supervised machine learning algorithms. The dataset comprises 200 labeled Java function declarations. Readability features were extracted using Scalabrino’s tool, generating three datasets: Scalabrino, Buse-Weimer, a combined set (Dall), and a fourth (Dcorr) via feature selection based on interfeature correlation. Each model underwent hyperparameter tuning via a Randomized Search and was evaluated through 30 iterations of a five-fold cross-validation. Scaling techniques (MinMax, Standard, Robust, and None) were also compared. The best performance, with an average accuracy of 61.1% and minimal overfitting, was achieved by Random Forest with MinMax scaling on Dcorr. Feature importance analysis using permutation methods identified 22 key metrics related to comments: code complexity, syntax, naming, token usage, and density. Despite its moderate accuracy, the findings offer valuable insights and highlight essential features for advancing code readability research. © (2025), (science and information Organization). All rights reserved.

关键词： Random forests

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Sentiment classification of MOOC courses by merging local context focus and Bi-directional gated recurrent unit 4

Sentiment classification of MOOC courses by merging local co...

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4th international conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Jin, Jiayong Dong, Yongquan He, Bugui Zhou, Nan Yan, Linke School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou221116 China Xuzhou Cloud Computing Engineering Technology Research Center Jiangsu Xuzhou221116 China Jiangsu Engineering Technology Research Center of ICT in Education Jiangsu Xuzhou221116 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Existing MOOC review sentiment classification methods do not fully utilize the local context information associated with aspect, and they ignore the connection between local and global contexts, while resulting in modeled features that lack the information connection between aspect and contexts. In this paper, we propose a model that incorporates Local Context Focus (LCF) and Bi-Directional Gated Recurrent Unit (Bi-GRU). First, the BERT model is used to dynamically encode course reviews. Then, global semantic features are extracted using the Bi-GRU model to strengthen the connection between the preceding and following texts. Then, the LCF model based on multi-head self-attention is used to obtain local contextual features and splice them with global semantic features. Finally, the Softmax function is utilized to output the classification results. The experimental accuracies of the proposed model on the three MOOC course review datasets reach 97.96%, 96.76%, and 94.16%, respectively, which are improved by 0.70%, 0.42%, and 0.03% over the suboptimal baseline model. The proposed model significantly improves the effectiveness of MOOC course review sentiment classification, and provides a useful reference for the optimization and improvement of MOOC courses. © 2025 SPIE.

关键词： Semantics

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Enhancing Robot Navigation Through advanced DRL and Cellular Automata Integration

Enhancing Robot Navigation Through Advanced DRL and Cellular...

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2025 international conference on Electronics, information, and Communication, ICEIC 2025

作者： Al Farouq, Ardiansyah Tuan Tran, Dinh Lee, Joo-Ho Graduate School of Information Science and Engineering Ritsumeikan University Osaka Japan College of Information Science and Engineering Ritsumeikan University Osaka Japan

ISBN: (纸本)9798331510756

Autonomous navigation in dynamic environments poses significant challenges, particularly in enhancing learning efficiency and obstacle avoidance. This paper introduces a novel framework that combines Deep Reinforcement Learning (DRL) methods, including Deep Q-Networks (DQN) and Asynchronous Advantage Actor-Critic with Intrinsic Curiosity Module (A3C-ICM), with Cellular Automata (CA) to address these issues. The integration of CA allows for real-time recalibration of state values, which accelerates the learning process and improves decision-making under complex environmental conditions. Extensive simulation experiments demonstrate that the proposed approach significantly outperforms traditional DRL methods in terms of learning speed, obstacle avoidance, and navigation accuracy. This research contributes to the advancement of autonomous navigation systems by offering a robust, adaptive framework capable of improving robotic performance in challenging, dynamic settings. © 2025 IEEE.

关键词： Deep reinforcement learning

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Exploring Models Approach for Real-Time Hand Gesture Recognition

Exploring Models Approach for Real-Time Hand Gesture Recogni...

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2025 international conference on Advancement in Data science, E-learning and information System, ICADEIS 2025

作者： Asih, Nurma' Hantono, Bimo Sunarfri Hidayah, Indriana Department of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9798331513320

Real-time hand gesture recognition technology has become an important research area in Human-computer Interaction (HCI) due to its ability to enable more natural and intuitive interactions between humans and digital devices. By relying on hand gestures as input, this technology offers a new way to control devices without physical contact. In recent years, deep learning and computer vision-based approaches have developed rapidly, making significant contributions to improving the accuracy and speed of hand gesture recognition systems. Using the Systematic Literature Review (SLR) method, this study identifies and analyzes research trends, model architectures and algorithm-based approaches, and challenges faced in the development of this technology. This study also analyzes various technical challenges, including such as handling complex backgrounds, varying lighting conditions, and variations in hand shape and size, are also analyzed to illustrate the difficulties in creating a reliable and responsive system. The purpose of this review is to provide a comprehensive overview of current research trends, identify gaps that still need to be filled, and make recommendations for future research and development directions. Through this analysis, it is hoped that this research can help researchers and practitioners understand the existing developments and challenges and encourage innovation for more efficient and effective applications to assist people with disabilities. © 2025 IEEE.

关键词： Palmprint recognition

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Privacy enhanced course recommendations through deep learning in Federated Learning environments

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international Journal of information Technology (Singapore) 2025年第1期17卷 629-635页

作者： Kolli, Chandra Sekhar Seelamanthula, Sreenivasu Reddy V, Venkata Krishna Babu, Padamata Ramesh Reddy, Mule Rama Krishna Gumpina, Babu Rao Department of Information Technology Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India Department of Information Technology Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering Narasaraopeta Engineering College Narasaraopeta India Department of Compute Science GITAM School of Science GITAM (Deemed to be University) Andhra Pradesh Visakhapatnam India

The increasing concerns around data security and privacy among users have significantly pushed the interest of the research community towards developing privacy-preserving recommendation systems. Amidst this backdrop, our study introduces a novel course recommendation methodology leveraging Federated Learning (FL) coupled with advanced Deep Learning techniques. This method executes the recommendation process across local nodes through several stages, including agglomerative matrix formulation, course clustering, bi-level matching, identification of learner-preferred courses, and ultimately, course recommendation. Notably, course clustering is achieved through Deep Fuzzy Clustering (DFC), while Deep Convolutional Neural Networks (DCNN) are employed for the recommendation phase. The efficacy of our DFC-DCNN-FL approach is rigorously evaluated based on several metrics: accuracy, False Positive Rate (FPR), loss function, Mean Square Error (MSE), Root MSE (RMSE), and Mean Average Precision (MAP). The results demonstrate remarkable performance with scores of 0.909, 0.116, 0.126, 0.291, 0.539, and 0.925, respectively. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Course recommendation Deep fuzzy clustering Deep learning Federated learning Privacy preservation

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Access Control Mechanisms for Secure Websites

Access Control Mechanisms for Secure Websites

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2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Bavani, K. Yarlagadda, Setu Sai Ram Yaparala, Sashi Kanth Reddy Kanuparthi, Mokshith Reddy, Obula Reddy Yemi Kalasalingam Academy of Research and Education Department of Cse 626126 India

ISBN: (纸本)9798331529833

The research developed an advanced approach toward protecting sensitive information of individuals using a multi-layered access control system in web applications. It integrates traditional authentication and post-login data protection mechanism. It follows a dual-phase security system, first username-password authentication with One-Time Passwords (OTP) for primary login and another PIN system that especially guards some sensitive information inside the account of the user. The architecture is therefore an answer to the crucial need to safeguard different levels of personal data, general account access and highly sensitive information requiring different security thresholds. Our implementation showed that it is able to prevent unnecessary access to sensitive information and maintain smooth user experience on average 8 seconds and 12 seconds in its general features and PIN protection, respectively. It describes how the use of time-based OTPs for the sole purpose of primary authentication along with a separate PIN validation is implemented in accessing the secured personal information. Thus, it is an invaluable input into how hierarchical security measures should be applied within web applications. The results show that protecting sensitive information is significantly improved from systems with uniform security levels to all data types. © 2025 IEEE.

关键词： Authentication Protocol

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The study on the circuit architecture of reconfigurable physics-based unclonable technology 5

The study on the circuit architecture of reconfigurable phys...

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5th international conference on Signal Processing and computer science, SPCS 2024

作者： He, Baigen Qiu, Ganggang Jiangsu Automation Research Institute Jiangsu Lianyungang222000 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

With the rapid development of computer technology and internet technology, the issue of information security has become increasingly prominent. Physical Unclonable Technology (PUT), based on circuit implementation, is a high-security technology that differs from traditional cryptography in that it employs logic gates with variable quantities and distinct gate circuits. PUT can be applied in various fields such as authentication, secure encryption, cryptographic computation, and more. Compared to traditional cryptography, PUT offers advantages including high security, strong resistance to attacks, and flexible operation, which has led to its widespread application in the field of information security. information security refers to the protection and maintenance of information resources from unauthorized or inappropriate access, use, modification, and other potential hazards. It is a product of the information age, encompassing any actions or events that may cause or potentially cause damage to information resources. Traditional cryptography has some drawbacks in ensuring data security, such as the ease of device loss, inconvenient key storage, and difficulties in decryption. Therefore, the development of new cryptographic technologies is necessary to address these issues. © 2025 SPIE.

关键词： Digital storage

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Attention Span and it's Correlation with Mental Health 1

Attention Span and it's Correlation with Mental Health

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1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Shah, Richa Bewoor, Laxmi Rak, Sarthak Patne, Ayush Vishwakarma Institute of Information Technology Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Information Technology Computer Engineering Pune India

ISBN: (纸本)9798350355611

This paper delves into the correlation between attention span and mental health. Attention span is the ability to focus on a task before being distracted by certain factors. It ranges from 2 seconds to more than 20 minutes. Meanwhile, mental health constitutes a person's psychological, sociological, and emotional well-being. Using a clustering algorithm, this research seeks to provide a concrete relation between the two across an age group of 18-25. © 2025 IEEE.

关键词： Electronic health record

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