Multi-level data fusion enables collaborative dynamics analysis in team sports using wearable sensor networks by Zi Zhuo Wang, Xiaoyu Xia, Qiaonan Chen

“…The multi-level fusion architecture integrates IMU, GPS, physiological, and positioning data through adaptive weight allocation and asynchronous alignment algorithms. Experimental validation demonstrated 8.6 dB improvement in signal quality and 42.3% enhancement in positional accuracy compared to single-source approaches. …”
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HybridBranchNetV2: Towards reliable artificial intelligence in image classification using reinforcement learning. by Ebrahim Parcham, Mansoor Fateh, Vahid Abolghasemi

“…Many artificial intelligence (AI) algorithms struggle to adapt effectively in dynamic real-world scenarios, such as complex classification tasks and object relationship extraction, due to their predictable but non-adaptive behavior. …”
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Dynamics of Robo-Advisory Financial Services in Saudi Arabia by Heba Gazzaz

“…The key limitations of adopting Robo-advisory services include algorithmic bias, a lack of trust and transparency, legal and regulatory issues, privacy and data security concerns, and limited financial literacy. …”
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Forecasting the Turkish lira Exchange Rates Through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones? by M. R. Sarkandiz, S. Ghayekhloo

“…Therefore, the uncertainty about the rates has pushed algorithmic traders toward finding the best forecasting model. …”
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Recent advances and controversies in head and neck reconstructive surgery by Kuriakose Moni, Sharma Mohit, Iyer Subramania

“…Standardized reconstructive algorithms for common head and neck defects have been developed with predictable results. …”
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An Overview of the Empirical Evaluation of Explainable AI (XAI): A Comprehensive Guideline for User-Centered Evaluation in XAI by Sidra Naveed, Gunnar Stevens, Dean Robin-Kern

“…Recent advances in technology have propelled Artificial Intelligence (AI) into a crucial role in everyday life, enhancing human performance through sophisticated models and algorithms. However, the focus on predictive accuracy has often resulted in opaque black-box models that lack transparency in decision-making. …”
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Slicing Cuts on Food Materials Using Robotic-Controlled Razor Blade by Debao Zhou, Gary McMurray

“…Based on the blade sharpness properties and the specific materials, the required cutting force can be predicted. These formulation and experimental results explained the basic theory of blade cutting fracture and further provided the support to optimize the cutting mechanism design and to develop the force control algorithms for the automation of blade cutting operations.…”
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What factors enhance students' achievement? A machine learning and interpretable methods approach. by Hui Mao, Ribesh Khanal, ChengZhang Qu, HuaFeng Kong, TingYao Jiang

“…Our framework enables educators to transform predictive analytics into actionable pedagogical improvements.…”
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Disulfidptosis-related gene in acute myocardial infarction and immune microenvironment analysis: A bioinformatics analysis and validation. by Nan Huang, Chan Liu, Zheng Liu, Haibo Lei

“…Thereafter, seven hub genes were determined by machine learning algorithms, which had potential diagnostic value in AMI. …”
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Artificial intelligence as one of the key drivers of the economy digital transformation by O.I. Pizhuk

“…The prospects for using AI are huge as the algorithms that allow massive amounts of information to be processed on an hourly basis can detect cause-and-effect relationships, which are not achievable for a person, and thus make predictions more accurate and make solutions more efficient. …”
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Phylogenetic quantification of intra-tumour heterogeneity. by Roland F Schwarz, Anne Trinh, Botond Sipos, James D Brenton, Nick Goldman, Florian Markowetz

“…Accurate quantification and evolutionary inference are essential to understand the functional consequences of tumour heterogeneity. The MEDICC algorithms are independent of the experimental techniques used and are applicable to both next-generation sequencing and array CGH data.…”
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Advancements in Remote Sensing for Monitoring and Risk Assessment of Glacial Lake Outburst Floods by Serik Nurakynov, Nurmakhambet Sydyk, Zhaksybek Baygurin, Larissa Balakay

“…Central to this review is the exploration of multi-sensor data fusion for high-resolution lake dynamics mapping, machine learning algorithms for predictive risk modelling, and hydrodynamic simulations for flood propagation analysis. …”
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Effective theory of collective deep learning by Lluís Arola-Fernández, Lucas Lacasa

“…Here we introduce a minimal model of interacting deep neural nets that condenses several recent decentralized algorithms by considering a competition between two terms: the local learning dynamics in the parameters of each neural network unit, and a diffusive coupling among units that tends to homogenize the parameters of the ensemble. …”
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Data reconstruction from machine learning models via inverse estimation and Bayesian inference by Agus Hartoyo, Dominika Ciupek, Maciej Malawski, Alessandro Crimi

“…Empirical results across multiple benchmark datasets and machine learning algorithms corroborate these theoretical predictions, reinforcing the validity and robustness of our theoretical framework. …”
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Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding by Ibrahim Almubark

“…Data was utilized to train the model and subsequently generate predictions by utilizing testing data following the pre-processing of the dataset. …”
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Early Warning Systems for Plant Diseases in delta regions: Machine Learning Approaches by Biswas Debarghya, Sharma Priti

“…Some patterns and anomalies can indicate the onset of plant diseases, and the algorithms are trained to recognize them. Models that need to adapt to rapidly changing environmental conditions and those that rely on real-time data processing become very interesting for Delta regions.…”
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Review of Methods and Models for Forecasting Electricity Consumption by Kamil Misiurek, Tadeusz Olkuski, Janusz Zyśk

“…The authors conducted a comparative analysis of various models, such as autoregressive models, neural networks, fuzzy logic systems, hybrid models, and evolutionary algorithms. Particular attention was paid to the effectiveness of these methods in the context of variable input data, such as weather conditions, seasonal fluctuations, and changes in energy consumption patterns. …”
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Exploring machine learning approaches for precipitation downscaling by Honglin Zhu, Qiming Zhou, Jukka M. Krisp

“…This study critically reviewed existing spatial downscaling approaches, specifically focusing on machine learning (ML)-based algorithms. Insights into the accuracy of these downscaling techniques were provided based on findings from published validation studies. …”
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AI-Supported Shared Decision-Making (AI-SDM): Conceptual Framework by Mohammed As'ad, Nawarh Faran, Hala Joharji

“…This integration is intended to transform raw algorithmic data into actionable insights that directly support the decision-making process without supplanting human judgment.…”
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Assessment of food toxicology by Alexander Gosslau

“…Integration of food toxicology data obtained throughout biochemical and cell-based in vitro, animal in vivo and human clinical settings has enabled the establishment of alternative, highly predictable in silico models. These systems utilize a combination of complex in vitro cell-based models with computer-based algorithms. …”
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