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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Jared Tan Yi Hoong Samuel-Soma M. Ajibade Muhammed Basheer Jasser Anwar P.P. Abdul Majeed David Olayemi Alebiosu Bayan Issa Anthonia Oluwatosin Adediran Charis Shwu Chen Kwan Department of Data Science and Artificial Intelligence School of Engineering and Technology Sunway University No.5 Jalan Universiti Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Sunway University No.5 Jalan Universiti Selangor Darul Ehsan Malaysia Faculty of Informatics Engineering University of Aleppo Syria Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia Department of Smart Computing and Cyber Resilience School of Engineering and Technology Sunway University No.5 Jalan Universiti Selangor Darul Ehsan Malaysia

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

The prediction of breast cancer prognosis has been a significant challenge in medical research due to the complex nature of cancer progression and the variability in patient-specific factors. Machine learning techniques have emerged as powerful tools for enhancing prediction accuracy in cancer diagnosis and prognosis. This study evaluates the performance of three widely used machine learning classifiers—Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM)—in predicting breast cancer outcomes using the Wisconsin Breast Cancer database. A robust framework was designed to preprocess the dataset, scale features, and assess the classifiers using key evaluation metrics, including accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). The results reveal that all classifiers demonstrated high predictive accuracy, with SVM achieving the highest accuracy of 97.08%, followed by RF and ANN with 96.67%. While RF recorded superior precision (95.40%), SVM outperformed others in recall (97.70%) and F1-score (96.05%), showcasing its ability to balance prediction precision and sensitivity. These findings underscore the potential of machine learning to augment traditional cancer prognosis methods, providing healthcare professionals with data-driven insights for personalized treatment strategies. This research highlights the critical role of machine learning in transforming cancer care and lays the groundwork for future studies focused on hybrid models and real-world implementation.

关键词： Support vector machines Accuracy Machine learning Medical services Artificial neural networks Breast cancer Prognostics and health management Random forests Medical diagnostic imaging Classification tree analysis

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Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

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data Intelligence 2024年第2期6卷 320-343页

作者： Sergei Romanov Abel Soares Siqueira Jonathan de Bruin Jelle Teijema Laura Hofstee Rens van de Schoot Applied Data Science Department of Information and Computing ScienceFaculty of ScienceUtrecht University Netherlands eScienceCenter AmsterdamNL Department of Research and Data Management Services Information Technology ServicesUtrecht UniversityUtrechtthe Netherlands Department of Methodology and Statistics Faculty of Social and Behavioral SciencesUtrecht University

Active learning can be used for optimizing and speeding up the screening phase of systematic *** simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training *** paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud *** provide a technical explanation of the proposed cloud architecture and its *** addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM *** analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing *** parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.

关键词： Active learning Systematic review Simulation study Multiprocessing Cloud architecture

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Analysis of Factors influencing Early Detection of Breast Cancer 3

Analysis of Factors influencing Early Detection of Breast Ca...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Malik, Hassal Sonia Sial, Umaira Ahmad Bukhari, Faisal Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (纸本)9798331504861

Carcinoma, particularly breast cancer, poses a formidable challenge, especially in Pakistan where it stands as the most prevalent cancer among women. This research investigates the complex landscape of breast cancer in Pakistan, utilizing a dataset from local hospitals. Through comprehensive statistical analyses and advanced machine learning techniques, including XGBoost and RandomForest, the study uncovers significant associations between socio-economic and psychological factors and the timing of breast cancer diagnosis among Pakistani women. Factors such as embarrassment, fear of treatment, and lack of awareness emerge as crucial determinants. The findings provide valuable insights for tailored interventions and contribute to the global fight against breast cancer. ©2024 IEEE.

关键词： Lung cancer

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Motor imagery enhances performance beyond the imagined action

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Proceedings of the National Academy of sciences of the United States of America 2025年第20期122卷 e2423642122页

作者： Gippert, Magdalena Shih, Pei-Cheng Heed, Tobias Howard, Ian S. Jamshidi Idaji, Mina Villringer, Arno Sehm, Bernhard Nikulin, Vadim V. Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig 04103 Germany Sony Computer Science Laboratories 141-0022 Tokyo Japan Department of Psychology and Centre for Cognitive Neuroscience University of Salzburg 5020 Salzburg Austria School of Engineering Computing and Mathematics Faculty of Science and Engineering University of Plymouth Plymouth PL4 8AA United Kingdom Machine Learning Group Berlin Institute for the Foundations of Learning and Data 10587 Berlin Germany Machine Learning Group Institute of Software Engineering and Theoretical Computer Science Electrical Engineering and Computer Science Faculty Technical University Berlin 10587 Berlin Germany Department of Neurology Martin Luther University of Halle-Wittenberg Halle (Saale) 06120 Germany

Motor imagery is frequently utilized to improve the performance of specific target movements in sports and rehabilitation. In this study, we show that motor imagery can facilitate learning of not only the imagined target movements but also sequentially linked overt movements. Hybrid sequences comprising imagined and physically executed segments allowed participants to learn specific movement characteristics of the executed segments when they were consistently associated with specific imagined segments. Electrophysiological recordings revealed that the degree of event-related synchronization in the alpha and beta bands during a basic motor imagery task was correlated with imagery-evoked motor learning. Thus, both behavioral and neural evidence indicate that motor imagery's benefits extend beyond the imagined movements, improving performance in linked overt movements. This provides decisive evidence for the functional equivalence of imagined and overt movements and suggests applications for imagery in sports and rehabilitation.

关键词： adaptation force field motor imagery motor learning motor sequence

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Peer Tutoring and its Impact on Academic Achievements

Peer Tutoring and its Impact on Academic Achievements

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Aqeel, Muhammad Hassan, Manahil Ghani, Affaf Zaheer, Manahil Anjum, Bilal Bukhari, Faisal Department of Data Science Faculty of Computing and Information Technologys University of the Punjab Lahore Pakistan

ISBN: (纸本)9798331504465

Peer tutoring is gaining attention rapidly nowadays, providing a learning environment where students have the opportunity to learn and grow through collaboration and effective communication. This study focuses on the impact of peer tutoring by collecting data from different students as well as surveys and then analyzing the pre and post-tutoring assessments to determine the impact of peer tutoring. It employs statistical techniques to study the impact of peer tutoring on both tutee and tutor, their self-confidence, and the overall academic achievement gained through tutoring. © 2024 IEEE.

关键词： Federated learning

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Closed Shop Scheduling Optimisation using Max-Plus Automata 3

Closed Shop Scheduling Optimisation using Max-Plus Automata

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3rd International Conference on science, ICOS 2019

作者： Merdekawati, Dian Ayu Subiono Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology Surabaya Indonesia

An appropriate scheduling system is needed in manufacturing activities to minimize production time and costs. Flow shop is one type of production flow that widely used in manufacturing. Based on the demand criteria, it has been known as a scheduling problem called closed shop. This scheduling emphasizes the make-to-stock (MTS) which produces a stock. We take the closed shop scheduling case studies from an existing literature and optimize scheduling using max-plus automata optimum. We compare the result with regular scheduling and the previous result in the literature. We show that the result of max-plus automata optimum scheduling are optimum compared than the existing regular scheduling and the existing previous result of the literature. © Published under licence by IOP Publishing Ltd.

关键词： Scheduling

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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IP SafeGuard - An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Daffodil Smart City Birulia Department of Software Engineering Dhaka 1216 Bangladesh Oryx Universal College with Liverpool John Moores University School of Computing and Data Sciences Cybersecurity Department Doha 34110 Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems Al-Salt 19385 Jordan Bangladesh University of Professionals (BUP) Department of Computer Science and Engineering Dhaka Bangladesh Amman Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Artificial Intelligence Cyber attack Cyber defense Cyber Security IP Address SOC Threat Intelligence

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Beware of "Explanations" of AI

arXiv

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arXiv 2025年

作者： Martens, David Shmueli, Galit Evgeniou, Theodoros Bauer, Kevin Janiesch, Christian Feuerriegel, Stefan Gabel, Sebastian Goethals, Sofie Greene, Travis Klein, Nadja Kraus, Mathias Kühl, Niklas Perlich, Claudia Verbeke, Wouter Zharova, Alona Zschech, Patrick Provost, Foster University of Antwerp Department of Engineering Management Antwerp2000 Belgium National Tsing Hua University Institute of Service Science Hsinchu30013 Taiwan INSEAD Technology and Business Fontainebleau77300 France Goethe University Frankfurt Department of Information Systems Frankfurt60629 Germany TU Dortmund University Department of Computer Science Dortmund44227 Germany LMU Munich Munich Center for Machine Learning Munich80539 Germany Erasmus University Rotterdam School of Management Rotterdam3062 Netherlands Copenhagen Business School Department of Digitalization Copenhagen2000 Denmark Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany University of Regensburg Faculty of Informatics and Data Science Regensburg93053 Germany University of Bayreuth Faculty of Law Business and Economics Bayreuth95440 Germany New York University Department of Technology Operations and Statistics New YorkNY10012 United States KU Leuven Faculty of Economics and Business Leuven3000 Belgium Humboldt-Universität zu Berlin School of Business and Economics Berlin10099 Germany Leipzig University Faculty of Economics and Management Science Leipzig04109 Germany

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations. © 2025, CC BY.

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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