Horizontal Autoscaling of Virtual Machines in Hybrid Cloud Infrastructures: Current Status, Challenges, and Opportunities by Thushantha Lakmal Betti Pillippuge, Zaheer Khan, Kamran Munir

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Reflection of the principle - a consumer, managing relations (CMR) in the assortment policy of the existing players in the food industry by V. Yu. Karpenko, M. Yu. Tamova, T. A. Dzhum, E. V. Barashkina

“…The purpose of the study is to identify ways to form a loyalty system through keeping  records of features and consumer wishes, based on innovative solutions related to the guest relationship  management system based on the special AntiJetlag menu, based on new forms of service and  technological ideas. …”
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Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos by Shadi Jaradat, Mohammed Elhenawy, Huthaifa I. Ashqar, Alexander Paz, Richi Nayak

“…Our approach highlights the importance of leveraging near-miss incidents to proactively enhance road safety, thereby reducing the likelihood of crashes through early intervention and better event understanding.…”
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Explaining the unexplainable: data sharing and privacy in Web 3.0 by Shim Jieun, Kim Jieun

“…Web 3.0 transforms data ownership by empowering users to monetize their personal information, yet this shift amplifies complex privacy challenges. Understanding the features influencing data-sharing decisions in this evolving context aligns with the proactive philosophy of designing legal frameworks that anticipate and mitigate conflicts. …”
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Smart train control and monitoring system with predictive maintenance and secure communications features by Alfian Akbar Gozali, Muhammad Faris Ruriawan, Andry Alamsyah, Yudha Purwanto, Ade Romadhony, Febry Pandu Wijaya, Fifin Nugroho, Dewi Nala Husna, Agri Kridanto, Anang Fakhrudin, Mu’ammar Itqon, Sri Widiyanesti

“…Predictive maintenance is a proactive and data-driven approach to service maintenance that aims to identify potential problems before they occur. …”
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Dynamic experiences generated by sensory features through smart material driven design by Marta González-Colominas

“…Dynamic products are those that show sensory features that change over time in a proactive and reversible way, activating one or more user’s sensory modalities and aiming at enhancing the user’s experience (Colombo 2016). …”
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Predictive Analysis of Maritime Congestion Using Dynamic Big Data and Multiscale Feature Analysis by Yalin Wu

“…This study proposes an innovative congestion prediction approach using dynamic big data analysis of vessel trajectories and multiscale feature analysis. First, the dynamic analysis of vessel trajectories aims to extract valuable information from ships’ data as they navigate the oceans, enabling proactive traffic management and optimized routing. …”
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Towards Transparent Deep Learning in Medicine: Feature Contribution and Attention Mechanism-Based Explainability by Thanveer Shaik, Xiaohui Tao, Haoran Xie, Lin Li, Niall Higgins, Juan D. Velásquez

“…Attention weights and Shapley values were computed for each input feature to provide global and local explanations, offering insights into the models’ behavior and feature importance. …”
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A Hybrid Brain Stroke Prediction Framework: Integrating Feature Selection, Classification, and Hyperparameter Optimization by Mohammad Amin, Khalid M. O. Nahar, Hasan Gharaibeh, Rabia Emhamed Al Mamlook, Ahmad Nasayreh, Nesrine Atitallah, Ali Gharaibeh, Raneem Hamad, Raed Abu Zitar, Aseel Smerat, Laith Abualigah

“…We used a publicly available Harvard Stroke Prediction Data Warehouse dataset, applying multiple feature selection methods: ANOVA, chi‐square, mutual information classification, and analysis of variance to identify relevant features. …”
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Advancing Artificial Intelligence of Things Security: Integrating Feature Selection and Deep Learning for Real-Time Intrusion Detection by Faisal Albalwy, Muhannad Almohaimeed

“…Specifically, five feature selection methods (correlation-based feature subset selection (CFS), Pearson analysis, gain ratio (GR), information gain (IG) and symmetrical uncertainty (SU)) were integrated with PCA to optimise feature dimensionality and enhance predictive performance. …”
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FDRL: a data-driven algorithm for forecasting subsidence velocities in Himalayas using conventional and traditional soil features by Sahil Sankhyan, Ajoy Kumar, Praveen Kumar, Aaditya Sharma, K. V. Uday, Varun Dutt

“…This work bridges this gap by suggesting an interpretable data-driven model that systematically integrates traditional soil information with geotechnical features for improved prediction. A stacking ensemble regression model called Forecasting Data-Driven Regression Learning (FDRL) was developed on the basis of the last machine learning breakthroughs, including feature selection techniques such as Pearson correlation and mutual information scores. …”
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A Hierarchical Feature-Based Time Series Clustering Approach for Data-Driven Capacity Planning of Cellular Networks by Vineeta Jain, Anna Richter, Vladimir Fokow, Mathias Schweigel, Ulf Wetzker, Andreas Frotzscher

“…However, to address the evolving dynamics of cellular networks, this paper advocates for a data-driven approach that considers user behavioral analysis in the planning process to make it proactive and adaptive. We introduce a Hierarchical Feature-based Time Series Clustering (HFTSC) approach that organizes clustering in a multi-level tree structure. …”
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Development and calibration of roundabout safety performance functions using machine learning: a case study from Amman, Jordan by Diana Al-Nabulsi, Aya Hassouneh

“…The findings offer a scalable, transferable framework for proactive roundabout safety planning, particularly in regions where traditional SPF development remains infeasible due to data limitations.…”
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Smart Grid Intrusion Detection for IEC 60870-5-104 With Feature Optimization, Privacy Protection, and Honeypot-Firewall Integration by Pedamallu Sai Mrudula, Rayappa David Amar Raj, Archana Pallakonda, Yanamala Rama Muni Reddy, K. Krishna Prakasha, V. Anandkumar

“…It also utilizes important feature optimization methods, implemented in multiple ways, such as SHAP, Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA). …”
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Specific Features of Big Corporations Lobbying in the Political Dynamics of Global Megapolis (Case of the Housing Renovation Program in Moscow). Part 2 by A. A. Degtyarev, A. S. Potapkin

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A multi-layered defense against adversarial attacks in brain tumor classification using ensemble adversarial training and feature squeezing by Ahmeed Yinusa, Misa Faezipour

“…Our results highlight the importance of proactive defense strategies for maintaining the reliability of AI in medical imaging under adversarial conditions.…”
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Estimating intersections' near-crash conflicts with the drone-based image-recording data by Yen-Lin Huang, Yen-Hsiang Chen

“…The proposed new surrogate variable, featuring its direct relevance to road users’ maneuvers, e.g. braking from high-precision time-varying braking rate information uniquely available from the DIRD, has reflected crash-prone contributors attributed to driving behaviors and intersections’ overall environments. …”
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Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident Severity by Bita Ghasemkhani, Kadriye Filiz Balbal, Kokten Ulas Birant, Derya Birant

“…Road traffic accident severity prediction is crucial for implementing effective safety measures and proactive traffic management strategies. Existing methods often treat this as a nominal classification problem and use traditional feature selection techniques. …”
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Machine Learning for Early Detection of Phishing URLs in Parked Domains: An Approach Applied to a Financial Institution by Jaqueline D. Duarte, Pedro Chagas Junior, Joao Paulo Javidi da Costa, Elena J. da Costa, Laerte Peotta de Melo, Rafael Rabelo Nunes, Carlos V. N. Gabriel Soares, Thiago Erivan da Cunha Silva

“…This work contributes to proactive cybersecurity practices addressing evolving threats within digital ecosystems.…”
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Improved Crime Prediction Using Hybrid Neural Architecture Search Together with Hyperparameter Tuning by Rami Ayied Alshahrani, Tariq Jamil Saifullah Khanzada

“…The study considered the robust rank aggregation (RRA) feature selection method to rank and select the best features to predict crime behavior in some countries. …”
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