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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Garg, Bindu Kasar, Manisha Kotecha, Ketan Rahmani, Mohammad Khalid Imam University College of Engineering Pune India Symbiosis Center for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9783031860713

This study addresses the burgeoning demand for website data collection and analysis in business operations, emphasizing the pivotal role of web analytics in providing crucial insights into customer behaviour. Despite the prevalence of various website traffic analysing tools, there exists a notable lack of comprehensive testing on the precision and reliability of these services. The research compares four key analytics measures from Google Analytics (GA) with SimilarWeb (SW) using average monthly statistics over a one-year period for 50+ websites across different nations and business verticals. Employing various statistical tests, including paired t-tests, Shapiro-Wilk tests, and correlation analysis, the study reveals statistically significant variations in key metrics between GA and SW. Notably, SimilarWeb tends to provide lower estimates for the number of visits and unique visitors compared to Google Analytics. However, the findings underscore the potential complementarity of using both tools in cases where direct access to a site’s analytics is not feasible, particularly for competitive intelligence and benchmarking purposes. The study’s extensive statistical analysis covers unique visitors, total visits, bounce rates, and average session duration, offering valuable insights into the nuanced variations and potential sources of error. There are statistically significant differences between the two web traffic tools in terms of unique visitors, total visits, average session time & bounce rates. In comparison to Google Analytics, SimilarWeb figures were on average, 40% less for unique visitors, 50% greater for average session time, 25% greater for bounce rate, and 20% lesser for total visits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Error statistics

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Tutorial on Using Machine Learning and Deep Learning Models for Mental Illness Detection

arXiv

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arXiv 2025年

作者： Zhang, Yeyubei Wang, Zhongyan Ding, Zhanyi Tian, Yexin Dai, Jianglai Shen, Xiaorui Liu, Yunchong Cao, Yuchen University of Pennsylvania School of Engineering and Applied Science United States New York University Center for Data Science United States Georgia Institute of Technology College of Computing United States University of California Department of EECS Berkeley United States Northeastern University Khoury College of Computer Science United States

Social media has become an important source for understanding mental health, providing researchers a way to detect conditions like depression from user-generated posts. This tutorial provides practical guidance to address common challenges in applying machine learning and deep learning methods for mental health detection on these platforms. It focuses on strategies for working with diverse datasets, improving text preprocessing, and addressing issues such as imbalanced data and model evaluation. Real-world examples and step-by-step instructions demonstrate how to apply these techniques effectively, with an emphasis on transparency, reproducibility, and ethical considerations. By sharing these approaches, this tutorial aims to help researchers build more reliable and widely applicable models for mental health research, contributing to better tools for early detection and intervention. © 2025, CC BY.

关键词： Adversarial machine learning

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Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media

arXiv

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arXiv 2025年

作者： Ding, Zhanyi Wang, Zhongyan Zhang, Yeyubei Cao, Yuchen Liu, Yunchong Shen, Xiaorui Tian, Yexin Dai, Jianglai University of Pennsylvania School of Engineering and Applied Science United States New York University Center for Data Science United States Georgia Institute of Technology College of Computing United States University of California Berkeley Department of EECS United States Northeastern University Khoury College of Computer Science United States

Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning (DL) models have been increasingly applied to classify mental health conditions from textual data, but selecting the most effective model involves trade-offs in accuracy, interpretability, and computational efficiency. This study evaluates multiple ML models, including logistic regression, random forest, and LightGBM, alongside deep learning architectures such as ALBERT and Gated Recurrent Units (GRUs), for both binary and multi-class classification of mental health conditions. Our findings indicate that ML and DL models achieve comparable classification performance on medium-sized datasets, with ML models offering greater interpretability through variable importance scores, while DL models are more robust to complex linguistic patterns. Additionally, ML models require explicit feature engineering, whereas DL models learn hierarchical representations directly from text. Logistic regression provides the advantage of capturing both positive and negative associations between features and mental health conditions, whereas tree-based models prioritize decision-making power through split-based feature selection. This study offers empirical insights into the advantages and limitations of different modeling approaches and provides recommendations for selecting appropriate methods based on dataset size, interpretability needs, and computational constraints. © 2025, CC BY.

关键词： Contrastive Learning

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Deep Learning-Driven Cryptanalysis in Modern Encryption Systems

Deep Learning-Driven Cryptanalysis in Modern Encryption Syst...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Venkateswaran Radhakrishnan Vijayakumari Rodda S. Sowmiya Abdur Rahman Sarker R. Palanikumar R. Asokan Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Dhofar Sultanate of Oman Department of Computer Science Krishna University Rudravaram Machilipatnam Sri Eshwar College of Engineering COIMBATORE Applied Statistics and Data Science Jahangirnagar University Dhaka Bangladesh Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India Department of ECE Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

With the rapid advancements in artificial intelligence, deep-learning-based cryptanalysis has emerged as a promising approach to evaluating the security of modern encryption algorithms. Traditional cryptanalysis methods, such as linear and differential attacks, often require extensive mathematical modelling and computational effort. In contrast, deep learning enables automated feature extraction and pattern recognition, potentially identifying cryptographic weaknesses more efficiently. This paper presents an advanced neural network-based cryptanalysis framework targeting modern encryption schemes, including AES, RSA, and lightweight block ciphers. We employ deep learning architectures such as Convolutional Neural Networks (CNNs) and Transformer models to analyse ciphertext-plaintext relationships and recover cryptographic keys. Additionally, we explore the impact of data preprocessing, hyperparameter optimization, and model interpretability on cryptanalysis accuracy. Our experiments demonstrate that deep learning can achieve significant improvements in key recovery probability compared to conventional approaches. However, we also highlight the computational challenges and practical limitations of deep-learning-based cryptanalysis. This study provides insights into the applicability of AI-driven techniques in cryptanalysis and their implications for cryptographic security.

关键词： Deep learning Ciphers Accuracy Computational modeling Visual analytics Neural networks Transformers Encryption Pattern recognition Convolutional neural networks

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SOCIOTECHNICAL EFFECTS OF MACHINE TRANSLATION

arXiv

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arXiv 2025年

作者： Moorkens, Joss Way, Andy Lankford, Séamus ADAPT Centre School of Applied Language and Intercultural Studies Dublin City University Dublin Ireland ADAPT Centre School of Computing Dublin City University Dublin Ireland ADAPT Centre Department of Computer Science Munster Technological University Cork Ireland

While the previous chapters have shown how machine translation (MT) can be useful, in this chapter [Moorkens et al., 2024d] we discuss some of the side-effects and risks that are associated, and how they might be mitigated. With the move to neural MT and approaches using Large Language Models (LLMs), there is an associated impact on climate change, as the models built by multinational corporations are massive. They are hugely expensive to train, consume large amounts of electricity, and output huge volumes of kgCO2 to boot. However, smaller models which still perform to a high level of quality can be built with much lower carbon footprints, and tuning pre-trained models saves on the requirement to train from scratch. We also discuss the possible detrimental effects of MT on translators and other users. The topics of copyright and ownership of data are discussed, as well as ethical considerations on data and MT use. Finally, we show how if done properly, using MT in crisis scenarios can save lives, and we provide a method of how this might be done. © 2025, CC BY.

关键词： computer aided language translation

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Dataset Evaluation Framework for Secure Electric Vehicle Systems

Dataset Evaluation Framework for Secure Electric Vehicle Sys...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Alauthman Ghadeer Albesani Ashraf Mashaleh Ammar Almomani Amjad Aldweesh Department of Information Security University of Petra Jordan King Abdullah II School of Information Technology Jordan University Jordan Al-Balqa Applied University Al-Salt Jordan Department of Computer Information Science Higher Colleges of Technology Al-Balqa UAE Applied University Jordan College of computing and IT Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Intrusion Detection Systems (IDS) play a critical role in safeguarding connected vehicular systems and Electric Vehicle Supply Equipment (EVSE) infrastructures against evolving cyber threats. The effectiveness of these IDS solutions largely depends on the quality and relevance of the datasets used for their training and evaluation. In this paper, we present a systematic framework for assessing datasets intended for IDS development in the automotive and EV charging domains. Our analysis covers essential criteria—such as data completeness, realism, labeling accuracy, and statistical distribution—and provides best practices for evaluating both traditional and novel datasets (e.g., CICIoV2024, CICEVSE2024). We further detail methodologies for intrinsic and extrinsic evaluations, including statistical analysis, time-based assessment, and benchmark testing using machine learning algorithms. Our approach is illustrated through a case study that demonstrates how these criteria and methods can be applied to real-world datasets, highlighting potential pitfalls and improvements. By offering concrete guidelines and metrics for dataset quality, this work aims to advance the development of robust IDS solutions in connected automotive and EV charging environments.

关键词： Training Systematics Statistical analysis Intrusion detection Electric vehicle charging Labeling computer security Standards Automotive engineering Internet of Vehicles

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

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IEEE Access 2025年 13卷 92468-92482页

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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Correction to: A comprehensive performance evaluation of container migration strategies

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computing 2025年第4期107卷 1-2页

作者： Feitosa, Leonel Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Fé, Iure Silva, Luis Guilherme Callou, Gustavo Carvalho, Juliana Leão, Erico Nguyen, Tuan Anh Rego, Paulo Silva, Francisco Airton Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piauí (UFPI) Picos Brazil Department of Computing Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Software Engineering Faculty of Information Technology University of Industry and Trade (HUIT) Ho Chi Minh City Vietnam Department of Computer Science Federal University of Ceará (UFC) Fortaleza Brazil

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Open-Ended Coaxial Probe Based Chloride Concentration Prediction in Water Using Machine Learning Algorithms

Open-Ended Coaxial Probe Based Chloride Concentration Predic...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Hanima Kannan C H Veena Venugopal Deepa Gupta Parul Mathur Dhanesh G Kurup Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Physical Science Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India School of Engineering and Applied Science Benette University Greater Noida UP India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Monitoring chloride levels in water is essential for environmental health, industrial processes, and safe drinking water, as both high and low concentrations can cause harm for living being. This paper presents a novel approach to detecting chloride concentrations using Open-Ended Coaxial Probe (OECP) sensing combined with machine learning algorithms. OECP sensing offers real-time, non-destructive, and precise monitoring, enabling early detection of contaminants. Scattering parameter ( $S$ 11) data across a broad frequency range was collected with varying chloride concentrations in controlled laboratory experiments. Thirteen machine learning algorithms were trained to predict chloride concentrations, with different datasets for training and testing to ensure robustness. Among these, CatBoost demonstrated the best performance. After fine tuning and cross-validation, the CatBoost model achieved a test Mean Square Error (MSE) of 0.00627 and Coefficient of Determination (R2) of 0.9936, showcasing its accuracy for applications in water monitoring, corrosion prevention, and safeguarding public health.

关键词： Machine learning algorithms Water quality Predictive models Real-time systems Sensors Scattering parameters Probes Public healthcare Tuning Testing

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Deep Learning-Driven Cryptanalysis in Modern Encryption Systems

Deep Learning-Driven Cryptanalysis in Modern Encryption Syst...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Radhakrishnan, Venkateswaran Rodda, Vijayakumari Sowmiya, S. Sarker, Abdur Rahman Palanikumar, R. Asokan, R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Dhofar Oman Krishna University Department of Computer Science Rudravaram Machilipatnam521004 India Sri Eshwar College of Engineering Coimbatore641202 India Applied Statistics and Data Science Jahangirnagar University Dhaka Bangladesh Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India Kongunadu College of Engineering and Technology Department of Ece Tamil Nadu Trichy India

ISBN: (纸本)9798331521394

关键词： Convolutional neural networks

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