Robust survival model for the prediction of Li-ion battery lifetime reliability and risk functions by Rasheed Ibraheem, Timothy I. Cannings, Torben Sell, Gonçalo dos Reis

“…The signature method with both desirable mathematical and machine learning properties was adopted as a feature extraction technique.The developed models are tested rigorously using application-driven strategies involving model robustness to the number of cycles of data required for model training and prediction, different fractions of training samples, and systematic data sparsity. …”
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Domain-Invariant Label Propagation With Adaptive Graph Regularization by Yanning Zhang, Jianwen Tao, Liangda Yan

“…As an effective machine learning paradigm, domain adaptation (DA) learning aims to enhance the learning performance of the target domain by utilizing other relevant but distinct domain(s) (referred to as the source domain(s)). …”
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Robust and Sparse Kernel-Free Quadratic Surface LSR via L_2,p-Norm With Feature Selection for Multi-Class Image Classification by Yongqi Zhu, Zhixia Yang, Junyou Ye, Yongxing Hu

“…Least Squares Regression (LSR) is a powerful machine learning method for image classification and feature selection. …”
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Community-engaged artificial intelligence research: A scoping review. by Tyler J Loftus, Jeremy A Balch, Kenneth L Abbott, Die Hu, Matthew M Ruppert, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Philip A Efron, Patrick J Tighe, William R Hogan, Parisa Rashidi, Michelle I Cardel, Gilbert R Upchurch, Azra Bihorac

“…Embase, PubMed, and MEDLINE databases were searched for articles describing artificial intelligence or machine learning healthcare applications with community involvement in model development, validation, or implementation. …”
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Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. by Rabia Asghar, Sanjay Kumar, Arslan Shaukat, Paul Hynds

“…Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. …”
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Distribution of trial registry numbers within full-text of PubMed Central articles: implications for linking trials to publications and indexing trial publication types by Arthur M. Holt, Ang Michael Troy, Neil R. Smalheiser

“…A variety of natural language processing (NLP)-based and machine learning-based models have been developed to assist users in identifying these connections. …”
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A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure by Sardar Muhammad Ali, Abdul Razzaque, Muhammad Yousaf, Sardar Sadaqat Ali

“…These threats affect both businesses and individuals. Machine learning (ML) and deep learning (DL) have emerged as vital tools in cybersecurity, enabling the analysis of extensive datasets to identify potential cyber threats. …”
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Applications of unmanned vehicle systems for multi-spatial scale monitoring and management of aquatic ecosystems: A review by Xingzhen Liu, Long Ho, Stijn Bruneel, Peter Goethals

“…The systematic analysis highlights the gap in UVS applications for multi-spatial scale monitoring and reveals significant opportunities for integrating UVS with Artificial Intelligence (AI), machine learning, and Internet of Things (IoT) technologies, which are improving UVS integration, security, and efficiency, and enabling better resource management and navigation accuracy. …”
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Deep Learning Based Dual Channel Banana Grading System Using Convolution Neural Network by Raghavendra S, Souvik Ganguli, P. Thirumarai Selvan, Mitali Madhusmita Nayak, Sushovan Chaudhury, Randell U. Espina, Isaac Ofori

“…Classification of various stages of fruit maturity using machine learning algorithms is a difficult task since it is difficult to distinguish the visual features of the fruits at different maturity stages. …”
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Characteristics and Cluster Analysis of 18,030 Sepsis Patients Who Were Admitted to Thailand’s Largest National Tertiary Referral Center during 2014–2020 to Identify Distinct Subty... by Phuwanat Sakornsakolpat, Surat Tongyoo, Chairat Permpikul

“…This study aimed to investigate the demographic, clinical, and laboratory characteristics of sepsis patients who were admitted to our center during 2014–2020 and to employ cluster analysis, which is a type of machine learning, to identify distinct types of sepsis in Thai population. …”
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Pilot study: Initial investigation suggests differences in EMT-associated gene expression in breast tumor regions by Kylie L. King, Hamed Abdollahi, Zoe Dinkel, Alannah Akins, Homayoun Valafar, Heather Dunn

“…This pilot study aimed to evaluate gene expression in TNBC samples from patients who identified as African American and Caucasian using traditional statistical methods and emerging Machine Learning (ML) approaches. ML enables the analysis of complex datasets and the extraction of useful information from small datasets. …”
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Real-world clinical multi-omics analyses reveal bifurcation of ER-independent and ER-dependent drug resistance to CDK4/6 inhibitors by Zhengyan Kan, Ji Wen, Vinicius Bonato, Jennifer Webster, Wenjing Yang, Vladimir Ivanov, Kimberly Hyunjung Kim, Whijae Roh, Chaoting Liu, Xinmeng Jasmine Mu, Jennifer Lapira-Miller, Jon Oyer, Todd VanArsdale, Paul A. Rejto, Jadwiga Bienkowska

“…ER-dependent drug resistance mechanisms. Machine learning models predict therapeutic dependency on ESR1 and CDK4 among ER-dependent tumors and CDK2 dependency among ER-independent tumors, confirmed by experimental validation.…”
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Comprehensive multi-metric analysis of user experience and performance in adaptive and non-adaptive lower-limb exoskeletons. by Krongkaew Supapitanon, Tanyaporn Patathong, Chaicharn Akkawutvanich, Arthicha Srisuchinnawong, Worachit Ketrungsri, Poramate Manoonpong, Patarawan Woratanarat, Chanika Angsanuntsukh

“…Compared to other approaches, like model-based and machine learning-based control, the biologically inspired control provides robustness to perturbations, requires less computational power, and does not need system models or large learning datasets. …”
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Exploring happiness factors with explainable ensemble learning in a global pandemic. by Md Amir Hamja, Mahmudul Hasan, Md Abdur Rashid, Md Tanvir Hasan Shourov

“…This paper predicts happiness scores using Machine Learning (ML), Deep Learning (DL), and ensemble ML and DL algorithms and examines the impact of individual variables on the happiness index. …”
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Modeling suction of unsaturated granular soil treated with biochar in plant microbial fuel cell bioelectricity system by K. C. Onyelowe, Ahmed M. Ebid, Rosa Belén Ramos Jiménez, Viroon Kamchoom, M. Vishnupriyan, Krishna Prakash Arunachalam

“…Additionally, different machine learning models such as the “Gradient Boosting (GB)”, “CN2 Rule Induction (CN2)”, “Naive Bayes (NB)”, “Support vector machine (SVM), “Stochastic Gradient Descent (SGD)”, “K-Nearest Neighbors (KNN)”, “Tree Decision (Tree)”, “Random Forest (RF)”, and “Response Surface Methodology” (RSM), have been developed to predict SWCC based on soil suction, electric current, electrical potential, volumetric water content, temperature, and bulk density. …”
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Identifying the NEAT1/miR-26b-5p/S100A2 axis as a regulator in Parkinson's disease based on the ferroptosis-related genes. by Taole Li, Jifeng Guo

“…Bioinformatics analysis, including the gene set enrichment analysis (GSEA), consensus cluster analysis, weight gene co-expression network analysis (WGCNA), and machine learning algorithms, were employed to assess the feasible differentially expressed genes (DEGs). …”
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Identifying Incident Causal Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach by Tianxi Dong, Qiwei Yang, Nima Ebadi, Xin Robert Luo, Paul Rad

“…The solution we propose has multilabel capability and is automated and customizable, and it is more accurate and adaptable than traditional machine learning methods in extant research. This novel application of deep learning algorithms to the incident reporting system can efficiently improve aviation safety.…”
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Enhanced Learning Behaviors and Ability Knowledge Tracing by Fanglan Ma, Changsheng Zhu, Peng Lei, Peiwen Yuan

“…Knowledge tracing (KT) aims to understand the evolution of students’ knowledge states during learning using machine learning techniques. While KT has made significant strides with deep learning techniques, a gap remains reflecting students’ actual knowledge level—the significant effects of students’ learning behaviors and abilities are omitted, which can reflect their knowledge acquisition more deeply and ensure the reliability of the response. …”
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AI-assisted exposure-response data analysis: Quantifying heterogeneous causal effects of exposures on survival times by Louis Anthony Cox, Jr., R. Jeffrey Lewis, Saumitra V. Rege, Shubham Singh

“…AI-assisted data analysis can help risk analysts better understand exposure-response relationships by making it relatively easy to apply advanced statistical and machine learning methods, check their assumptions, and interpret their results. …”
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Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features by Sunawar Khan, Tehseen Mazhar, Muhammad Amir Khan, Tariq Shahzad, Wasim Ahmad, Afsha Bibi, Mamoon M. Saeed, Habib Hamam

“…Abstract This study evaluates and differentiates five advanced machine learning models—LSTM, GRU, CNN-LSTM, Random Forest, and SVR—aimed at precisely estimating solar and wind power generation to enhance renewable energy forecasting. …”
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