Mapping the landscape of Artificial intelligence for serious games in Health: An enhanced meta review by Xiya Tao, Nicolás Sáenz-Lechón, Martina Eckert

“…Game control algorithms adapt the game environment and difficulty, while user assessment algorithms gather information about the player's state, such as performance, mood, or physiological data, to evaluate the treatment progress. …”
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Digital Colorimetry of Non-steroidal Anti-inflammatory Drugs: Identification Using Principal Component Method by A. A. Chaplenko, O. V. Monogarova, K. V. Oskolok

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In-Process Monitoring of Inhomogeneous Material Characteristics Based on Machine Learning for Future Application in Additive Manufacturing by André Jaquemod, Marijana Palalić, Kamil Güzel, Hans-Christian Möhring

“…The algorithms are trained to recognize patterns, anomalies, or deviations from expected behavior, which can aid in evaluating the effect of detected defects on the machining process and the resultant component quality. …”
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BESS-Set: A Dataset for Cybersecurity Monitoring in a Battery Energy Storage System by Giovanni Battista Gaggero, Alessandro Armellin, Giulio Ferro, Michela Robba, Paola Girdinio, Mario Marchese

“…In this context, there is a need to develop datasets of attacks on these systems to evaluate the risks and allow researchers to develop proper monitoring algorithms. …”
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Metabolic pathway activation and immune microenvironment features in non-small cell lung cancer: insights from single-cell transcriptomics by Yanru Liu, Yanru Liu, Yanru Liu, Hanmin Liu, Hanmin Liu, Ying Xiong, Ying Xiong

“…Four highly activated metabolic pathways were identified within malignant cell subpopulations, which were further divided into seven distinct subgroups showing significant differences in differentiation potential and metabolic activity. …”
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Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learn... by Ting Ge, Guixin He, Qian Cui, Shuangcui Wang, Zekun Wang, Yingying Xie, Yuanyuan Tian, Juyue Zhou, Jianchun Yu, Jinmin Hu, Wentao Li

“…A LUAD-CSRS-integrated nomogram was constructed to provide a quantitative tool for predicting prognosis in clinical practice. Finally, the difference of immune infiltration and response to immunotherapy in patients with high and low risk of LUAD were evaluated. …”
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Consumer Happiness in the Purchase of Electric Vehicles: a Fuzzy Logic Model by Fernando Lámbarry-Vilchis, Aboud Barsekh Onji, Leticia Refugio Chavarría López, Paola Judith Maldonado Colín

“…This research was conducted using a fuzzy Delphi method survey targeting a specific consumer group and two fuzzy inference systems: a multi-input single-output FIS model and an FIS Tree employing a hierarchical fuzzy inference structure, which leverages the survey's training data to optimize the models using different machine learning algorithms. The FIS tree model demonstrated superior efficacy in predicting the consumer satisfaction index, achieving an average forecast error of 0.65%. …”
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Forest cover restoration analysis using remote sensing and machine learning in central Malawi by Jabulani Nyengere, Precious Masuku, Sylvester Chikabvumbwa, Weston Mwase, Msaiwale Kathewera, Allena Laura Njala, Wilson Tchongwe, Isaac Tchuwa, Tiwonge I Mzumara, Chikondi Chisenga, Wilfred Kadewa, Emmanuel Chinkaka, Harineck Tholo

“…Utilizing a Support Vector Machine (SVM) classification algorithm applied to time-series Landsat and high-resolution imagery (2003–2023), we quantify land cover changes, while Normalized Difference Vegetation Index (NDVI) trends serve as indicators of ecological recovery. …”
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Do Calibrated Recommendations Affect Explanations? A Study on Post-Hoc Adjustments by Paul Dany Flores Atauchi, André Levi Zanon, Leonardo Chaves Dutra da Rocha, Marcelo Garcia Manzato

“…Our study investigates two key research gaps: (1) the impact of graph embeddings in model-agnostic knowledge graph explanations, exploring their under-researched potential compared to syntactic approaches to produce meaningful explanations; and (2) the effect of calibration on recommendation explanations, assessing whether calibrated recommendation reordering influences the outcomes of explanation algorithms. We evaluate the quality of explanations using a set of metrics, such as diversity, which measures how well different interests of the user are covered; popularity, which assesses how well explanations avoid favoring already popular items; and recency, which examines the inclusion of recently interacted items. …”
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Deep reinforcement learning for inverse inorganic materials design by Christopher Karpovich, Elton Pan, Elsa A. Olivetti

“…This work isolates and evaluates the effects of different RL methodologies to suggest promising, valid compounds of interest by exploring the chemical design space for materials discovery.…”
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Language task-based fMRI analysis using machine learning and deep learning by Elaine Kuan, Elaine Kuan, Elaine Kuan, Viktor Vegh, Viktor Vegh, Viktor Vegh, John Phamnguyen, John Phamnguyen, John Phamnguyen, Kieran O’Brien, Amanda Hammond, David Reutens, David Reutens, David Reutens, David Reutens

“…Their analysis necessitates the use of alternative methods such as machine learning (ML) and deep learning (DL) because task regressors may be difficult to define in these paradigms.MethodsUsing task-based language fMRI as a starting point, this study investigates the use of different categories of ML and DL algorithms to identify brain regions subserving language. …”
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Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review by Rongrong Zhong, XiaoHui Wu, Jun Chen, Yiru Fang

“… BackgroundDifferentiating bipolar disorder (BD) from unipolar depression (UD) is essential, as these conditions differ greatly in their progression and treatment approaches. …”
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Elucidating the dynamic tumor microenvironment through deep transcriptomic analysis and therapeutic implication of MRE11 expression patterns in hepatocellular carcinoma by Ruiqiu Chen, Chaohui Xiao, Zizheng Wang, Guineng Zeng, Shaoming Song, Gong Zhang, Lin Zhu, Penghui Yang, Rong Liu

“…We collected data from 92 HCC patient samples and validated MRE11 expression differences in HCC tissues through qPCR, immunohistochemistry, and Western blotting. …”
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Real-time mobile broadband quality of service prediction using AI-driven customer-centric approach by Ayokunle A. Akinlabi, Folasade M. Dahunsi, Jide J. Popoola, Lawrence B. Okegbemi

“…Three (3) classification algorithms including Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were trained using the QoS dataset and then evaluated in order to determine the most effective model based on certain evaluation metrics – accuracy, precision, F1-Score and recall. …”
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Enhancing Security of Error Correction in Quantum Key Distribution Using Tree Parity Machine Update Rule Randomization by Bartłomiej Gdowski, Miralem Mehic, Marcin Niemiec

“…A series of simulations were conducted to evaluate the security implications under various configurations, including different values of K, N, and L parameters of neural networks. …”
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Explainable Model Prediction of Memristor by Sruthi Pallathuvalappil, Rahul Kottappuzhackal, Alex James

“…The accuracy, macro average, and weighted average of both algorithms at different operational frequencies are explored.…”
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Efficient structure learning of gene regulatory networks with Bayesian active learning by Dániel Sándor, Péter Antal

“…Results We introduce novel acquisition functions for experiment design in gene expression data, leveraging active learning in both Essential Graph and Graphical Model spaces. We evaluate scalable structure learning algorithms within an active learning framework to optimize intervention selection. …”
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Metaheuristics in automated machine learning: Strategies for optimization by Francesco Zito, El-Ghazali Talbi, Claudia Cavallaro, Vincenzo Cutello, Mario Pavone

“…Despite significant advancements in this area, a comprehensive comparison of these algorithms across different deep learning architectures remains lacking. …”
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Impact of ITH on PRAD patients and feasibility analysis of the positive correlation gene MYLK2 applied to PRAD treatment by Chuanyu Ma, Chuanyu Ma, Guandu Li, Xiaohan Song, Xiaochen Qi, Tao Jiang

“…The ITH-score of PRAD samples was evaluated using the DEPTH algorithm. The optimal cut-off value of RiskScore was calculated based on the difference in survival curves, and PRAD patients were classified into high ITH and low ITH groups based on the optimal cut-off value. …”
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A Comparative Study of Breast Cancer Detection and Recurrence Prediction Using CatBoost Classifier by Rana Dhia’a Abdu-aljabar, Khansaa Dheya Aljafaar, Zinah Jaffar Mohammed Ameen, Hala A. Naman

“…Among all algorithms examined, CatBoost stood out, showcasing AUC values above 98 %, 90 %, and 83% on different datasets. …”
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