AutoDiVer: Automatically Verifying Differential Characteristics and Learning Key Conditions by Marcel Nageler, Shibam Ghosh, Marlene Jüttler, Maria Eichlseder

“…AutoDiVer implements a custom SAT modeling approach and takes advantage of a combination of features of advanced SAT solvers, including approximate model counting and clause learning. To show applicability to many different kinds of block ciphers like strongly aligned, weakly aligned, and ARX ciphers, we apply AutoDiVer to GIFT, PRESENT, RECTANGLE, SKINNY, Midori, WARP, SPECK, and SPEEDY. …”
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A Gamified Active-Learning Proposal for Higher-Education Heterogeneous STEM Courses by Mario J. Duran, Juan J. Aciego, Ignacio Gonzalez-Prieto, Juan Carrillo-Rios, Angel Gonzalez-Prieto, Alicia Claros-Colome

“…To this end, active learning (AL) helps scholars to be more autonomous, proactive, self-regulated, sociable and creative. …”
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Predicting Oil Price Trends During Conflict With Hybrid Machine Learning Techniques by Hicham Boussatta, Marouane Chihab, Mohamed Chiny, Younes Chihab

“…Using advanced machine learning techniques, we developed a hybrid system combining Random Forest, ElasticNet, K-Nearest Neighbors, Gradient Boosting, and Support Vector Regressor models. …”
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LDL estimation with equations and machine learning; variance of LDL and plasma atherogenic index by Petros Paplomatas, Marina Nikolidaki, Aristidis Vrahatis, Kostas Anagnostopoulos

“…LDL can be estimated using equations published in the literature or through machine learning (ML) methods. The package allows a comprehensive variety of approaches for estimating LDL values in patients, either through equations or through ten ML methods. …”
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Domain Adapting Deep Reinforcement Learning for Real-World Speech Emotion Recognition by Thejan Rajapakshe, Rajib Rana, Sara Khalifa, Bjorn W. Schuller

“…Although existing domain adaptation techniques have improved the performance of SER models across domains, there is a need to improve their ability to adapt to real-world situations where models can self-tune while deployed. This paper presents a deep reinforcement learning-based strategy (RL-DA) for adapting a pre-trained SER model to a real-world setting by interacting with the environment and collecting continuous feedback. …”
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A Global Deep Learning Perspective on Australia-Wide Monthly Precipitation Prediction by Luyi Shen, Guoqi Qian, Antoinette Tordesillas

“…Driven by this motivation, we present a specific spatiotemporal deep learning model that well integrates matrix factorization and temporal convolutional networks, along with essential year-month covariates and key climatic drivers, to analyze and forecast monthly precipitation in Australia. …”
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Polygraph-based deception detection and Machine Learning. Combining the Worst of Both Worlds? by Kyriakos N. Kotsoglou, Alex Biedermann

“…At a time when developments in computational approaches, often associated with the now much-vaunted terms Machine Learning (ML) and Artificial Intelligence (AI), face increasing challenges in terms of fairness, transparency and accountability, the temptation for researchers to apply mainstream ML methods to virtually any type of data seems to remain irresistible. …”
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Prediction of High-Risk Cardiac Arrhythmia Based on Optimized Deep Active Learning by Homeyra Amiri, Javad Mohammadzadeh, Seyed Mohsen Mirhosseini, Alireza Nikravanshalmani

“…The study delineates a new framework that integrates deep learning, fuzzy logic, and active learning, aiming to provide an innovative approach for the early detection of cardiac arrhythmias through Optimized Deep Active Learning (ODAL). …”
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STATE-OF-THE-ART OF MACHINE LEARNING IN NEURO DEVELOPMENT DISORDER: A SYSTEMATIC REVIEW by Lilian Yen Wei Lee, Ag Asri Ag Ibrahim, Rayner Alfred

“…This paper presents a comprehensive literature review focusing on the utilization of machine learning (ML) and deep learning (DL) methods for predicting and detecting Neurodevelopmental Disorders (NDDs), such as Intellectual Disability (ID), Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Dyslexia, among others. …”
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Transparency and participation in the global polity: lessons learned from food and water governance by Dario Bevilacqua, Francesca Spagnuolo

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Machine Learning for Spatiotemporal Prediction of River Siltation in Typical Reach in Jiangxi, China by Yong Fu, Jin Luo, Die Zhang, Lingjia Liu, Gan Luo, Xiaofang Zu

“…The accumulation of sediment, combined with falling water levels, was found to significantly reduce the channel depth and width, particularly in the upstream sections, posing a potential risk to navigation safety. This study presents an initial, yet promising attempt to apply machine learning for spatially explicit siltation prediction in data-constrained river systems. …”
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Small language models learn enhanced reasoning skills from medical textbooks by Hyunjae Kim, Hyeon Hwang, Jiwoo Lee, Sihyeon Park, Dain Kim, Taewhoo Lee, Chanwoong Yoon, Jiwoong Sohn, Jungwoo Park, Olga Reykhart, Thomas Fetherston, Donghee Choi, Soo Heon Kwak, Qingyu Chen, Jaewoo Kang

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Disease diagnosis and control by reinforcement learning techniques: a systematic literature review by Aditya Dev Mishra, Ajay Kumar Shrivastava, Megha Bhushan

“…Advanced reinforcement techniques including deep reinforcement, multi-agent reinforcement, hierarchical RL, inverse RL, and federated learning offer substantial promise for improving disease diagnosis and control as healthcare increasingly relies on data-driven and machine learning technologies. …”
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3-D Packing in Container using Teaching Learning Based Optimization Algorithm by Linda Fitriyani, Larissa Alva Zerinda, Asri Bekti Pratiwi, Edi Winarko

“… The paper aims to propose Teaching Learning based Optimization (TLBO) algorithm to solve 3-D packing problem in containers. …”
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Enhancing Mathematical Education Through Mobile Learning: A Problem-Based Approach by Javier Martínez-Gómez, Juan Francisco Nicolalde

“…The present study examines the intermediary function of a mobile education application, conceived under the problem-based learning approach, in the field of mathematics. …”
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Tourism Learning Resources and Development Strategies in China: A Review and Conceptual Framework by Simeng Zhang, Jia Liu, Yuxuan Li

“…However, at the present stage, Chinese tourists, tourism enterprises, and government functional departments still lack a clear and systematic understanding of the connotations and characteristics of tourism learning resources. …”
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Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control by Jibran Saleem, Umar Raza, Mohammad Hammoudeh, William Holderbaum

“…Traditional authentication systems struggle to balance security, privacy and computational efficiency, particularly in resource-constrained environments such as Industry 4.0. This research presents the SmartIoT Hybrid Machine Learning (ML) Model, a novel integration of Attribute-Based Authentication and a lightweight machine learning algorithm designed to enhance security while minimising computational overhead. …”
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Vision-Based Collision Warning Systems with Deep Learning: A Systematic Review by Charith Chitraranjan, Vipooshan Vipulananthan, Thuvarakan Sritharan

“…With recent developments in computer vision and deep learning, collision warning systems that use vision as the only sensory input have emerged. …”
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A Comparative Study of Machine Learning Models for Short-Term Load Forecasting by Etna Vianita, Henri Tantyoko

“…Short-Term Load Forecasting (STLF) was a critical task in power system operations, enabling efficient energy management and planning. This study presented a comparative analysis of five machine learning models namely XGBoost, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), and LightGBM using real-world electricity demand data collected over a four-month period. …”
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Evaluating the Effectiveness of Coxal Bone Measurements for Sex Estimation via Machine Learning by Diana Toneva, Silviya Nikolova, Gennady Agre, Nevena Fileva, Georgi Milenov, Dora Zlatareva

“…Many sexually dimorphic traits are concentrated in the coxal bones, which form the larger part of the birth canal. The present study aimed to assess the sex differences in coxal bone size and to develop machine learning (ML) models for sex estimation based on coxal bone measurements. …”
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