A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…The first section addresses process prediction, showcasing how AI techniques predict welding outcomes using historical data and process parameters, which enhances decision-making prior to actual implementation. …”
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AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes by José E. Valerio, Guillermo de Jesús Aguirre Vera, Maria P. Fernandez Gomez, Jorge Zumaeta, Andrés M. Alvarez-Pinzon

“…Machine learning (ML) algorithms and big data analytics enable precise patient stratification, risk assessment, and outcome prediction, accelerating the development of novel therapeutic approaches. …”
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Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods by Bilal Cemek, Yunus Kültürel, Emirhan Cemek, Erdem Küçüktopçu, Halis Simsek

“…This study compares two modeling approaches for predicting soil temperature at various depths: (i) fuzzy logic-based systems, including the Mamdani fuzzy inference system (MFIS) and the adaptive neuro-fuzzy inference system (ANFIS); (ii) supervised machine learning algorithms, such as multilayer perceptron (MLP), support vector regression (SVR), random forest (RF), extreme gradient boosting (XGB), and k-nearest neighbors (KNN), along with multiple Linear regression (MLR) as a statistical benchmark. …”
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Enhancing piezoelectric control of cracks in thin solid aluminum plates using finite element data and a neural networks approach by Abdul Aabid, Muneer Baig, Mohammad Abdul Malik

“…Later, these data have been arranged to train the algorithms. As a result, this combined approach highlights the feasibility of active crack repair using piezoelectric actuators for improved structural integrity. …”
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Real-Time Algal Monitoring Using Novel Machine Learning Approaches by Seyit Uguz, Yavuz Selim Sahin, Pradeep Kumar, Xufei Yang, Gary Anderson

“…We evaluated the performance of Decision Trees (DTS), Random Forests (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (k-NN) algorithms using RGB color histograms extracted from images of <i>Scenedesmus dimorphus</i> cultures. …”
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Wide-Angle Image Distortion Correction and Embedded Stitching System Design Based on Swin Transformer by Shiwen Lai, Zuling Cheng, Wencui Zhang, Maowei Chen

“…Experiments on synthetic and real-world datasets show that the method outperforms mainstream algorithms, with PSNR gains of 3.28 dB and 2.18 dB on wide-angle and fisheye images, respectively, while maintaining real-time performance. …”
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Characterization of the immune cell infiltration patterns in lung adenocarcinoma to facilitate immunotherapy by Haili Li, Zanmei Qin, Xuemei Huang, Zuyou Wei, JingMin Deng

“…Herein, ESTIMATE and CIBERSORT algorithms were conducted to characterize the ICI patterns of 1155 patients with LUAD. …”
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Comprehensive VR dataset for machine learning: Head- and eye-centred video and positional dataThe Adaptive Mind Datahub by Alexander Kreß, Markus Lappe, Frank Bremmer

“…Researchers can leverage this extensive dataset to develop and refine algorithms requiring comprehensive and annotated video and positional data. …”
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Target identification of natural products in cancer with chemical proteomics and artificial intelligence approaches by Guohua Li, Qian Shi, Qibiao Wu, Xinbing Sui

“…Recent advances in artificial intelligence (AI) have further enhanced the field by improving target prediction and streamlining data analysis. AI-driven models, especially machine learning algorithms, have proven effective in processing complex proteomic data and predicting potential NP-protein interactions. …”
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MW-UNet: Multi-Scale Weighted Connection UNet for Identification and Classification of Non-Meteorological Clutter over Big Radar Data by Mengmeng Cui, Chen Zeng, Xiaolong Xu, Muhammad Bilal, Xiaoyu Xia

“…The field of weather forecasting makes extensive use of radar big data to extract information about precipitation, storms, lightning, and other weather phenomena to aid in the prediction and monitoring of weather changes. To improve the quality of radar data, machine learning and fuzzy logic algorithms are often used to identify and classify non-meteorological clutter in weather data. …”
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Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks by Wei Lin, Meitao Zou, Mingrui Zhao, Jiaqi Chang, Xiongyao Xie

“…Three machine learning algorithms—Multilayer Perceptron, Random Forest, and Extreme Gradient Boosting—were evaluated to determine the optimal implementation. …”
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Raising awareness of potential biases in medical machine learning: Experience from a Datathon. by Harry Hochheiser, Jesse Klug, Thomas Mathie, Tom J Pollard, Jesse D Raffa, Stephanie L Ballard, Evamarie A Conrad, Smitha Edakalavan, Allan Joseph, Nader Alnomasy, Sarah Nutman, Veronika Hill, Sumit Kapoor, Eddie Pérez Claudio, Olga V Kravchenko, Ruoting Li, Mehdi Nourelahi, Jenny Diaz, W Michael Taylor, Sydney R Rooney, Maeve Woeltje, Leo Anthony Celi, Christopher M Horvat

“…<h4>Objective</h4>To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.…”
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Uncertainty Quantification in Data Fusion Classifier for Ship-Wake Detection by Maice Costa, Daniel Sobien, Ria Garg, Winnie Cheung, Justin Krometis, Justin A. Kauffman

“…Using deep learning model predictions requires not only understanding the model’s confidence but also its uncertainty, so we know when to trust the prediction or require support from a human. …”
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Few-shot learning for novel object detection in autonomous driving by Yifan Zhuang, Pei Liu, Hao Yang, Kai Zhang, Yinhai Wang, Ziyuan Pu

“…Perception, a critical component, extracts real-time traffic information essential for various system functionalities, such as agent behavior prediction. However, the quality of information derived from perception greatly influences overall system performance. …”
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MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers by Huaxu Yu, Jun Ding, Tong Shen, Min Liu, Yuanyue Li, Oliver Fiehn

“…We present MassCube, a Python-based open-source framework for MS data processing that we systematically benchmark against other algorithms and different types of input data. From raw data, peaks are detected by constructing mass traces through signal clustering and Gaussian-filter assisted edge detection. …”
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Potential Health Risks of Chloroacetanilide Herbicides: An In Silico Analysis by Nihan Akıncı Kenanoğlu, Ahmet Ali Berber, Şefika Nur Demir

“…This article proposes to use tools to predict their potential toxicities based on their chemical structure. …”
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Explainable few-shot learning workflow for detecting invasive and exotic tree species by Caroline M. Gevaert, Alexandra Aguiar Pedro, Ou Ku, Hao Cheng, Pranav Chandramouli, Farzaneh Dadrass Javan, Francesco Nattino, Sonja Georgievska

“…While few-shot learning algorithms can address the first problem, they still lack sufficient explanations for the results. …”
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Low-latency neuromorphic air hockey player by Juan P Romero B, Dimitrios Korakovounis, Jens E Pedersen, Jorg Conradt

“…We believe our air-hockey platform provides a versatile testbed for evaluating neuromorphic systems and invites further exploration of advanced algorithms, such as those incorporating trajectory prediction or adaptive learning, which could significantly enhance real-time decision-making and control.…”
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Digital health technologies in Kurdistan region of Iraq: a narrative review of enhancing healthcare accessibility and quality by Zhinya Kawa Othman, Shuaibu Saidu Musa, Mohamed Mustaf Ahmed, Olalekan John Okesanya, Omar Abdulkarim Saeed Alhammadi, Don Lucero-Prisno Eliseo

“…This review describes several digital health technologies in Kurdistan, including software implementation for data management, such as District Health Information Software 2, the establishment of telemedicine services, and the utilization of machine learning algorithms for mortality prediction, particularly during the COVID-19 pandemic. …”
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State-Dependent Transcranial Magnetic Stimulation Synchronized with Electroencephalography: Mechanisms, Applications, and Future Directions by He Chen, Tao Liu, Yinglu Song, Zhaohuan Ding, Xiaoli Li

“…Future directions emphasize adaptive algorithms for neural state prediction, phase-specific stimulation protocols, and standardized methodologies to enhance reproducibility. …”
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