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Informatica (Slovenia) 2025年第1期49卷 193-206页

作者： Ilham, Ahmad Nagara, Thahta Ardhika Prabu Kamaruddin, Mudyawati Khikmah, Laelatul Mantoro, Teddy Department of Informatics Universitas Muhammadiyah Semarang Semarang Indonesia Intelligent Data Science Research Group Universitas Muhammadiyah Semarang Semarang Indonesia Postgraduate Program of Medical Laboratory Science Universitas Muhammadiyah Semarang Semarang Indonesia Interdisciplinary Research Laboratory for Experimental Plasma Medicine Universitas Muhammadiyah Semarang Semarang Indonesia Department of Statistics Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang Semarang Indonesia School of Computer Science Nusa Putra University West Java Indonesia

Fetal mortality and newborn health issues require urgent attention because of high maternal and infant mortality rates during labor, highlighting the critical need for accurate fetal condition monitoring to reduce complications. This study proposes the development of a fetal health risk classification model based on Important Feature Selection (IFS) and a Classification and Regression Tree (CART) using cardiotocography (CTG) data from the UCI Machine Learning Repository. The IFS method was used to select the most relevant features, reduce model complexity, and increase generalization to prevent overfitting. The IFS-CART model was tested with 10-fold cross-validation and showed an accuracy of 94.50%, superior to the conventional CART, which only reached 93.83%. In addition, the average values of the True Positive Rate (TPR) and True Negative Rate (TNR) also increased, indicating that this model is effective in distinguishing normal, suspected, and pathological fetal conditions. Evaluation using the area under the curve receiver operating characteristic (AUC-ROC) showed that the model had high performance in detecting at-risk conditions, with an AUC of 0.981 for the"suspect" class. This finding confirmed that IFS-CART is not only accurate but also has high interpretability, making it easy for medical personnel to use for clinical decision support. The results of this study show that IFS-CART can serve as a reliable decision support system for real-time fetal health monitoring. Further implementation is expected to improve diagnostic accuracy and prevent complications during pregnancy and labor © 2025 Slovene Society Informatika. All rights reserved.

关键词： Fetal monitoring

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Phishing Email Detection Model Using Deep Learning

Phishing Email Detection Model Using Deep Learning

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Cybernetics and Innovations (ICCI), International Conference on

作者： Natthaphong Pimpason Pranodnard Viboonsang Somkiat Kosolsombat Data Science and Innovation Program College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

Phishing via email continues to be a concern in the cybersecurity realm as it exposes both individuals and companies to levels of risk due to clever social engineering methods employed by cybercriminals. This study introduces an extensive deep learning strategy for identifying phishing emails using a blend of various models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Encoder Representations from Transformers (BERT), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). The suggested approach utilizes natural language processing and learning techniques to evaluate and categorize email material by identifying subtle attributes that differentiate valid from harmful communication effectively. Through testing and careful evaluation of the model performance, achieved results; the LSTM model attained a notable accuracy of 99.92% in detecting phishing emails. This progress highlights the promise of learning methods in addressing complex phishing threats and improving email security measures. The research offers insights into the effectiveness of neural network approaches in detecting phishing emails by comparing the performance of multiple deep learning architectures. The high level of accuracy attained indicates a path for creating smarter email security solutions that can evolve to counter cyber threats effectively.

关键词： Deep learning Accuracy Phishing Feature extraction Natural language processing Encoding Electronic mail Security Convolutional neural networks Long short term memory

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Optimizing Malware Detection with Random Forest, XGBoost, LightGBM, and LLM-Reporting

Optimizing Malware Detection with Random Forest, XGBoost, Li...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Aticha Charoenthanakitkul Pranodnard Viboonsang Somkiat Kosolsombat Data Science and Innovation Program College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This study presents a comprehensive framework for malware detection that integrates traditional machine learning algorithms with advanced large language models (LLMs) to enhance both classification accuracy and automated report generation. The proposed approach leverages three widely used machine learning models-Random Forest, XGBoost, and LightGBM-to classify different types of malwares based on extracted features from the EMBER dataset, a well-known benchmark dataset for malware analysis. Each of these models was trained and evaluated to determine their effectiveness in identifying malicious software with high accuracy. Among the tested algorithms, LightGBM exhibited the best performance, achieving an impressive classification accuracy of 83%. XGBoost followed closely with 81%, while Random Forest achieved an accuracy of 79%. To further enhance the usability of the system for cybersecurity analysts, the study incorporates a large language model, specifically LLaMA, which is accessed via an API. This model is employed to generate detailed, human-readable reports that provide insights into detected malware threats. By automating the reporting process, the framework aims to reduce the manual effort required for malware analysis, allowing security professionals to focus on high-priority threats. Future research will focus on improving classification performance through advanced feature engineering techniques and exploring deep learning-based neural network architectures to further enhance the accuracy and efficiency of malware detection and analysis.

关键词： Training Analytical models Accuracy Machine learning algorithms Computational modeling Large language models Feature extraction Malware Computer security Random forests

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Phishing URL Detection Using Machine Learning and Deep Learning in NLP

Phishing URL Detection Using Machine Learning and Deep Learn...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Nutcha Srilachai Pranodnard Viboonsang Somkiat Kosolsombat Data Science and Innovation Program College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

Phishing attacks are an emerging cyber threat that makes use of fake URLs to pilfer sensitive information of users, such as their login credentials and other financial details. In line with this, the present study involves the adoption of NLP methods with machine-learning algorithms in effectively detecting phishing URLs. To this end, five different algorithms are considered for effectiveness in the study: LSTM, GRU, Random Forest, Support Vector Machine, and Logistic Regression. Each of them was trained and then tested with a complete dataset, containing phishing and legitimate URLs for correctness in distinguishing malicious URLs from the benign ones. Amongst them, the LSTM model recorded an accuracy as high as 98% due to its capability for handling sequential dependencies in addition to contextual patterns within the URLs. This research underlines the promise of LSTM-based models in fighting phishing threats and further proposes a scalable solution to enable real-time phishing URL detection and enhance cybersecurity defense.

关键词： Uniform resource locators Deep learning Support vector machines Logistic regression Accuracy Phishing Computational modeling Real-time systems Long short term memory Random forests

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Classification and Prediction of Knee Osteoarthritis by Deep Learning Approach 3rd

Classification and Prediction of Knee Osteoarthritis by Deep...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Saraswat, Amit Tooley, Tanya Shrivastav, Snigdha Srivastava, Devesh Kumar Department of Electrical Engineering Manipal University Rajasthan Jaipur303007 India Senior Analyst EY Haryana Gurgaon India Master of Science in Data Science program at the University of Rochester New York United States Department of Information Technology Manipal University Rajasthan Jaipur303007 India

ISBN: (纸本)9789819784288

The purpose of this research work on medical image analysis is to identify and diagnose knee osteoarthritis (OA) utilizing medical images by applying machine learning techniques. Knee OA is a degenerative joint disease that is becoming more prevalent in aged people worldwide. The study examines the effectiveness of several machine learning algorithms, including deep learning, to analyze medical images of knee OA, such as X-rays and magnetic resonance imaging (MRI). The research findings suggest that using machine learning algorithms might greatly improve the accuracy and efficacy of detecting knee OA, especially in its early stages. Moreover, machine learning algorithms can assist in the creation of specialized treatment plans by predicting the progression of the disease and recommending appropriate remedies. The findings of this study show how machine learning-based medical image analysis has the potential to enhance patient outcomes and quality of life for those with knee OA. Cancer detection diagnosis, neurological condition identification, and cardiovascular function analysis are a few instances of medical picture analysis employing machine learning. Moreover, image-guided treatments have been made more accurate and surgical planning has been made easier with the use of machine learning algorithms. Various methods are applied to achieve a test accuracy of 76.10% which is just 5% less than the accuracy provided by the Kellen-Lawrence Grading Scale. Overall, machine learning-based medical image analysis has the potential to revolutionize medical practice by facilitating earlier and more precise diagnosis, individualized treatment planning, and improved patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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Comparative Study of Testing Indigenous Air Filters for Fine PM Removal 1st

Comparative Study of Testing Indigenous Air Filters for Fine...

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1st International Conference on Pollution Control for Clean Environment, ICPCCE 2023

作者： Nawale, Prashant Sahu, Manoranjan Aerosol and Nanoparticle Technology Laboratory Environmental Science and Engineering Department Indian Institute of Technology Bombay Mumbai400076 India Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai400076 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai400076 India

ISBN: (纸本)9789819778454

Air pollution has become a serious global issue, which has significant impacts on both human health and the environment. Over the past decade, air quality has deteriorated due to various anthropogenic activities including construction, urbanization and industrialization. These activities release hazardous particles and harmful gases into the atmosphere, contributing to poor air quality. Developing countries such as India have been particularly affected, with some of their cities among the most polluted worldwide. To address this serious concern, it is imperative to develop and implement air pollution control technologies for both indoor and outdoor environments. This study focuses on the structured investigation of the performance of locally available air filter media in terms of their removal efficiency for submicron and micron-size particles. Various indigenous air filter media are subjected to rigorous testing to evaluate their overall and size-specific filtration efficiency and corresponding differential pressure drop. The results of these performance evaluations are then compared to their effectiveness in removing coarse and fine particles from the air. This research aims to provide valuable insights into the development of effective air filtration systems to combat air pollution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Air filters Air pollution control technology Air purification Coarse PM Fine PM

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Psoriasis: Perspectives from Irwin M. Braverman, MD

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Clinics in Dermatology 2025年第2期43卷 166-169页

作者： Cohen, Jeffrey M. Bunick, Christopher G. Department of Dermatology Yale School of Medicine New Haven CT United States Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States Program in Translational Biomedicine Yale University New Haven CT United States

Dr Irwin M. Braverman is recognized as an expert on many skin diseases, including psoriasis, an immune-mediated disorder that presents with scaly erythematous plaques. The most common clinical subtype is plaque psoriasis, with scalp, inverse, guttate, pustular, and nail psoriasis representing additional subtypes. Psoriasis has been associated with several comorbidities, particularly psoriatic arthritis, which affects up to 30% of those with psoriasis. Psoriasis can have an enormous impact on patients, and Dr Braverman reflects that “it is hard to think of another disease that can be so destructive or impactful.” Over the course of his career, Dr Braverman treated many patients with psoriasis and observed several significant therapeutic advances, including the widespread use of psoralens with UV-A phototherapy and the introduction of methotrexate. He also contributed to research on the pathophysiology of psoriasis, studying the hallmark dilated capillary loops. Dr Braverman ultimately believes that “taking care of a patient with psoriasis goes into the making of a dermatologist.” © 2024 Elsevier Inc.

关键词： Goeckerman regimen blood vessels electron microscopy methotrexate phototherapy psoriasis

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Comparative Model Evaluation of Lightweight Transformer-Based and CNN-Based Architechture for Degenerative Lumbar Spine Classification

Comparative Model Evaluation of Lightweight Transformer-Base...

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Advancement in data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Jennifer Ardelia Limicia Wilbert Chandra Alexander Agung Santoso Gunawan Jeffrey Junior Tedjasulaksana Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Low back pain is a leading cause of disability globally, is often associated with degenerative lumbar spine conditions. Accurate diagnosis of these conditions is critical but challenging due to the subjective nature of MRI interpretation and the weak correlation between imaging findings and symptoms. Thus, the purpose of this study is to evaluate the performance of CNN-based architectures (VGG-16, EfficientNetB0, EfficientNetV2) and transformer-based architecture specifically Vision Transformers (ViT) in classifying lumbar spine conditions as normal, moderate, or severe. Using the RSNA 2024 Lumbar Spine Degenerative Classification dataset. The models are evaluated using the ROC-AUC and PRAUC as the performance metrics due to the dataset imbalance. Results indicate that EfficientNetB0 achieved the highest overall performance, with an average ROC-AUC of 0.784 and PR-AUC of 0.528, demonstrating strong adaptability to imbalanced datasets. EfficientNetV2 also performed competitively, while VGG-16 showed moderate effectiveness. The Vision Transformer (ViT), however, underperformed due to its reliance on larger datasets and challenges in capturing fine-grained spatial features. The findings highlight the potential of EfficientNet-based models for accurate and efficient lumbar spine diagnostics. This study underscores the potential of advanced deep learning approaches in improving diagnostic workflows for degenerative lumbar spine conditions.

关键词： Computer vision Adaptation models Accuracy Magnetic resonance imaging Soft sensors Spine Transforms Transformers data models Convolutional neural networks

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Node-Level Lymph Node Automatic Segmentation in CT Images Using Deep Parallel Structure-Related 3D U-Net Variant 21st

Node-Level Lymph Node Automatic Segmentation in CT Images Us...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Cheng, Shi Li, Quan Zhang, Guangyu Zhang, Lei Peng, Tao School of Future Science and Engineering Soochow University Suzhou China Center of Stomatology The Second Affiliated Hospital of Soochow University Suzhou China Division of Cardiology Department of Internal Medicine Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Jiangsu Kunshan China

ISBN: (纸本)9789819601240

The delineation of Lymph Nodes (LNs) is pivotal in pinpointing therapeutic targets for radiotherapy in head and neck malignancies. Nevertheless, this endeavor poses a formidable challenge, primarily stemming from the suboptimal contrast against adjacent tissues. This investigation introduces a deep learning methodology aimed at automating the segmentation of LNs within CT scans, offering the following contributions: (1) Expanding upon the 3D Unet model, we incorporate a parallel block consisting of attention gate and squeeze & excitation modules. We extensively evaluate various versions of this parallel block and achieve favorable performance. (2) To address the slow decrease in Dice loss, we introduce a lightweight boundary refinement module. Our proposed method is assessed on a dataset comprising 103 patients and 603 Lymph Nodes (LNs), with 452 nodes used for training and 151 nodes for testing. The node-level Dice similarity coefficient achieved by our method reaches an impressive 0.833. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computerized tomography

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