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

“…This paper presents an enhanced meta-review of artificial intelligence (AI) applications in serious games (SGs) for health, focusing on adaptation, personalisation, and real-time data processing to improve rehabilitation outcomes. A systematic review methodology, following PRISMA-ScR guidelines, was employed to analyse studies from 2017 to 2025, identifying the AI algorithms most frequently used to personalise gaming experiences, improve patient engagement, and increase treatment efficacy. …”
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Audiogmenter: a MATLAB toolbox for audio data augmentation by Gianluca Maguolo, Michelangelo Paci, Loris Nanni, Ludovico Bonan

“…Purpose – Create and share a MATLAB library that performs data augmentation algorithms for audio data. This study aims to help machine learning researchers to improve their models using the algorithms proposed by the authors. …”
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Artificial Intelligence in Cardiovascular Diagnosis: Innovations and Impact on Disease Screenings by Amber Ahmad, Sahil Ahmad, Rida Ahmad, Jahnavi Bodi, Abdulla Mohamed, Ahmad Wasim

“…Materials and methods: Various AI models as well as algorithms, such as machine learning (ML) and deep learning (DL) algorithms, have shown good results in the detection of diseases like heart failure, atrial fibrillation, coronary artery disease, and valvular heart disease. …”
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Survey on explainable knowledge graph reasoning methods by Yi XIA, Mingjng LAN, Xiaohui CHEN, Junyong LUO, Gang ZHOU, Peng HE

“…In recent years, deep learning models have achieved remarkable progress in the prediction and classification tasks of artificial intelligence systems.However, most of the current deep learning models are black box, which means it is not conducive to human cognitive reasoning process.Meanwhile, with the continuous breakthroughs of artificial intelligence in the researches and applications, high-performance complex algorithms, models and systems generally lack the transparency and interpretability of decision making.This makes it difficult to apply the technologies in a wide range of fields requiring strict interpretability, such as national defense, medical care and cyber security.Therefore, the interpretability of artificial intelligence should be integrated into these algorithms and systems in the process of knowledge reasoning.By means of carrying out explicit explainable intelligence reasoning based on discrete symbolic representation and combining technologies in different fields, a behavior explanation mechanism can be formed which is an important way for artificial intelligence to realize data perception to intelligence perception.A comprehensive review of explainable knowledge graph reasoning was given.The concepts of explainable artificial intelligence and knowledge reasoning were introduced briefly.The latest research progress of explainable knowledge graph reasoning methods based on the three paradigms of artificial intelligence was introduced.Specifically, the ideas and improvement process of the algorithms in different scenarios of explainable knowledge graph reasoning were explained in detail.Moreover, the future research direction and the prospect of explainable knowledge graph reasoning were discussed.…”
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Integrating Sentiment Analysis With Machine Learning for Cyberbullying Detection on Social Media by Maram Fahaad Almufareh, Noor Zaman Jhanjhi, Mamoona Humayun, Ghadah Naif Alwakid, Danish Javed, Saleh Naif Almuayqil

“…State-of-the-art solutions predominantly rely on pre-trained language models and machine learning algorithms; however, these methods are often associated with substantial computational overheads and the development of advanced cyberbullying detection algorithms remains limited. …”
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Current status and outlook of UWB radar personnel localization for mine rescue by ZHENG Xuezhao, MA Jiawen, HUANG Yuan, LI Qiang, REN Jing, LIU Yu

“…Future research directions of UWB radar personnel localization technology for mine rescue operations are proposed: ① optimizing the UWB radar localization system by constructing cross-modal information fusion models and developing highly adaptive signal processing methods to enhance the system's adaptability to post-mining disaster environments; ② improving the applicability of combined static and dynamic target localization by developing hybrid localization algorithms that integrate Bayesian networks or deep belief networks to fuse static and dynamic target features and establishing state-switching-based comprehensive models; ③ improving UWB radar echo processing algorithms, combining adaptive beamforming technology, Multiple Input Multiple Output (MIMO) technology, and optimized K-means++ or entropy-based hierarchical analysis algorithms, effectively distinguishing multi-target position information, and validating their adaptability and reliability in complex environments through extensive simulation experiments.…”
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A study on query terms proximity embedding for information retrieval by Ya-nan Qiao, Qinghe Du, Di-fang Wan

“…Information retrieval is applied widely to models and algorithms in wireless networks for cyber-physical systems. …”
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The influence of mind mapping on computational thinking skills and self-efficacy in students’ learning of graphical programming by Rong Guo, Yan Zheng, Haifei Miao

“…Further analysis of the data showed significant enhancements in their algorithmic thinking and modeling, as well as pattern recognition and evaluation sub-skills, while abstraction and decomposition sub-skills did not show significant improvement. …”
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A survey of sand-dust image visual enhancement techniques by SI Yazhong, ZHANG Xulong, YANG Fan, WANG Jianzong, CHENG Ning, XIAO Jing

“…The visual enhancement technology of sandstorm images aims to improve the visual perception clarity of the captured data by imaging equipment under sandstorm weather, assisting high-level vision algorithms to enhance the ability to obtain key features from the data. …”
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Project quality, regulation quality by Elena Mussinelli

“…Instead, deductive design approaches seem to prevail today, due to the growing availability of algorithmic procedures that do not merely support the design process, but develop it in an almost automated manner through conditioning and prevailing indicators and parameters. …”
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Comparison of tone mapping methods during images processing on liquid crystal displays by A. V. Sycev, A. A. Stepanov, A. G. Smirnov, K. .. Blankenbach

“…Image enhancement algorithms improve readability on liquid crystal displays under bright ambient light conditions and are the theme of many researchers and developers. …”
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Outdoor Environment Design Optimization of an Office Building Based on Indoor Thermal Conditions and Building Energy Performance by Yaolin Lin, Tao Huang, Wei Yang, Melissa Chan, Chun-Qing Li, Mingqi Dai, Pengju Chen

“…Thirdly, two machine learning prediction algorithms were used to develop discomfort degree hours and energy consumption models, and the artificial neural network models were found to have better prediction performance with R-squares greater than 0.99. …”
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A systematic review of deep learning applications in database query execution by Bogdan Milicevic, Zoran Babovic

“…We categorize these approaches into three groups based on how such models are applied: improving performance of index structures and consequently data manipulation algorithms, query optimization tasks, and externally controlling query optimizers through parameter tuning. …”
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Labelling Training Samples Using Crowdsourcing Annotation for Recommendation by Qingren Wang, Min Zhang, Tao Tao, Victor S. Sheng

“…The experimental results demonstrate that crowdsourcing annotation significantly improves the performance of machine annotation. Among the ground truth inference algorithms, both HILED and HILI improve the performance of baselines; meanwhile, HILED performs better than HILI.…”
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Hybrid Machine Learning in Hydrological Runoff Forecasting: An Exploration of Extreme Gradient-Boosting and Categorical Gradient Boosting Optimization in the Russian River Basin by Reza Seifi Majdar, Ali Rahnamaei, Vahid Babazadeh

“…Recent advances in machine learning (ML) have opened new opportunities to improve prediction accuracy. This study focuses on evaluating commonly used ML methods for runoff prediction, with an emphasis on simplicity and comparability to more advanced models. …”
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A survey of sand-dust image visual enhancement techniques by SI Yazhong, ZHANG Xulong, YANG Fan, WANG Jianzong, CHENG Ning, XIAO Jing

“…The visual enhancement technology of sandstorm images aims to improve the visual perception clarity of the captured data by imaging equipment under sandstorm weather, assisting high-level vision algorithms to enhance the ability to obtain key features from the data. …”
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Unraveling the co-morbidity between COVID-19 and neurodegenerative diseases through multi-scale graph analysis: A systematic investigation of biological databases and text mining by Negin Sadat Babaiha, Stefan Geissler, Vincent Nibart, Heval Atas Güvenilir, Vinay Srinivas Bharadhwaj, Alpha Tom Kodamullil, Juergen Klein, Marc Jacobs, Martin Hofmann-Apitius

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Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities by Paul Iacobescu, Virginia Marina, Catalin Anghel, Aurelian-Dumitrache Anghele

“…This research highlights the value of advanced machine learning techniques in healthcare, addressing key challenges and laying a foundation for future studies aimed at improving predictive models for CVD prevention.…”
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Evaluation of the predictors of tooth loss using artificial intelligence-based machine learning approach: A retrospective study by Amit Rajabhau Pawar, Sankari Malaiappan, Pradeep Kumar Yadalam, P R Ganesh

“…Early intervention and accurate prediction of tooth loss are crucial for improving oral health outcomes. Conventional prognostic models have their constraints in sensitivity, prompting the exploration of alternative approaches. …”
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USING ARTIFICIAL INTELLIGENCE IN OBSTETRICS TO DIAGNOSE FETAL MALFORMATIONS AND PREVENT DISEASES by Елена Валерьевна Литвинова, Оксана Владимировна Носкова

“…Significant advances in the application of artificial intelligence in obstetrics and gynecology are shown, identifying the need for further research and improvements with respect to achieving universality and improving the effectiveness of many of the models developed. …”
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