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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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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The Value of Biomarkers in the Diagnosis and Prognosis of Heart Failure in Older Age by V. N. Larina, V. I. Lunev

“…The search for reliable algorithms for diagnosing heart failure with preserved left ventricular ejection fraction (LVEF) in elderly patients is an urgent problem due to the low specificity of clinical manifestations and the peculiarities of involutive processes occurring in the human body. …”
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Retinal revelations: Seeing beyond the eye with artificial intelligence by John Davis Akkara

“…This field, known as oculomics, leverages AI and deep learning algorithms to process vast amounts of data from imaging techniques such as fundus photography, optical coherence tomography (OCT), OCT angiography, infrared iris imaging, slit-lamp photography, and external eye photography. …”
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Optimised Implant Selection Using Digital Volume Tomography (DVT) Osteodensitometry by Manfred Nilius, Jörg Winterhoff

“…At the same time, the point at which loading occurs can already be defined at the planning stage, thanks to the predictable primary stability. A standard integration of DVT osteodensitometry in the algorithms of implant planning software thus seems desirable.…”
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Data-Driven Modeling and Enhancement of Surface Quality in Milling Based on Sound Signals by Paschalis Charalampous

“…These signals are then transformed into Mel-Frequency Cepstral Coefficients (MFCCs), which yield a compact time–frequency representation optimized for human auditory perception. Next, several AI algorithms were trained in order to classify these MFCCs into predefined surface roughness categories. …”
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Immune-Mediated Adverse Events Associated with Ipilimumab CTLA-4 Blockade Therapy: The Underlying Mechanisms and Clinical Management by Ahmad Tarhini

“…Implementation of these irAE management algorithms will help ensure that patients are able to benefit from ipilimumab therapy with adequate control of toxicities.…”
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The impact of artificial intelligence on research efficiency by Mitra Madanchian, Hamed Taherdoost

“…However, challenges such as bias in algorithms, concerns about data privacy, and deficiencies in the infrastructure impede wide-scale application. …”
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