IDDMSLD: An image dataset for detecting Malabar spinach leaf diseasesMendeley Data by Adnan Rahman Sayeem, Jannatul Ferdous Omi, Mehedi Hasan, Mayen Uddin Mojumdar, Narayan Ranjan Chakraborty

“…This dataset will help the researchers for further research on Malabar Spinach disease detection implementing various efficient computational models and applying advanced machine learning techniques.…”
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A Review of Stall Detection in Subsonic Axial Compressors by Kellie N. Wilson, Golam Gause Jaman, Anish Thapa, Amirthavarshini Vivekananda, Mitchell Lowe, Zachary Grima, Marco P. Schoen

“…In addition, a forward-looking projection is presented that includes the use of machine learning algorithms to further the understanding and the capability of stall precursor detection.…”
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Artificially intelligent nursing homes: a scoping review of palliative care interventions by Isabel Ronan, Sabin Tabirca, Sabin Tabirca, David Murphy, Nicola Cornally, Mohamad M. Saab, Patrice Crowley

“…A broad range of machine learning solutions remain unused within the context of nursing home palliative care. …”
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Data-Driven Technologies for Energy Optimization in Smart Buildings: A Scoping Review by Joy Dalmacio Billanes, Zheng Grace Ma, Bo Nørregaard Jørgensen

“…The key technologies identified included big data analytics, Artificial Intelligence, Machine Learning, the Internet of Things, Wireless Sensor Networks, Edge and Cloud Computing, Blockchain, Digital Twins, and Geographic Information Systems. …”
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Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model by Wenwen Cheng, Chen Yu, Xiaohui Liu

“…We aimed to build better machine learning models and provide useful tools to provide better guidance and assistance for COPD patients' treatment and care.MethodsA total of 863 COPD patients from a local general hospital were collected and screened, and they were separated into two groups: cognitive impairment (356 patients) and cognitively normal (507 patients). …”
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Multiscale guided attention network for optic disc segmentation of retinal images by A Z M Ehtesham Chowdhury, Andrew Mehnert, Graham Mann, William H. Morgan, Ferdous Sohel

“…This paper presents a deep machine-learning method for semantically segmenting OD from retinal images. …”
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Discovery of new topological insulators and semimetals using deep generative models by Tao Hong, Taikang Chen, Dalong Jin, Yu Zhu, Heng Gao, Kun Zhao, Tongyi Zhang, Wei Ren, Guixin Cao

“…In this study, we propose an efficient inverse design method CTMT (CTMT: CDVAE, Topogivity, interatomic potentials (IAPs) as realized in M3GNet, and TQC) utilizing deep generative machine learning models to discover novel topological insulators and semimetals in a much-fast and low-cost manner. …”
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Eco-innovative approaches for recycling non-polyester/cotton blended textiles by Chandra Manivannan Arun, Logeshwaran Panneerselvan, Gunasekhar Nachimuthu, Meredith Conaty, Thava Palanisami

“…In the sorting stage, integrating artificial intelligence (AI) and machine learning (ML) enhances efficiency and accuracy. …”
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Surprise Bug Report Prediction Utilizing Optimized Integration with Imbalanced Learning Strategy by Hui Li, Yang Qu, Shikai Guo, Guofeng Gao, Rong Chen, Guo Chen

“…In order to overcome the imbalanced category distribution of the bugs, a method based on machine learning to predict surprise bugs is presented in this paper. …”
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Method for Evaluating Urban Building Renewal Potential Based on Multimachine Learning Integration: A Case Study of Longgang and Longhua Districts in Shenzhen by Dengkuo Sun, Yuefeng Lu, Yong Qin, Miao Lu, Zhenqi Song, Ziqi Ding

“…A regression analysis based on building characteristics and locational factors was conducted using a stacking ensemble machine learning model. In addition, buildings were categorized into residential, industrial, and commercial types based on their usage, enabling both overall- and category-specific predictions of building renewal. …”
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Modelling current and future forest fire susceptibility in north-eastern Germany by K. H. Horn, K. H. Horn, S. Vulova, S. Vulova, H. Li, B. Kleinschmit

“…We applied a random forest (RF) machine learning (ML) algorithm to model current and future FFS in the federal state of Brandenburg (Germany) using topographic, climatic, anthropogenic, soil, and vegetation predictors. …”
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Therapeutic potential of phytocompounds of Bacopa monnieri (L.) Wettst (literature review) by O.O. Nikitina, D.S. Tsyba

“…The mechanism of action of bacoside on β-amyloid is characterized and illustrated. Using machine learning, the prospects for using the main compounds of bacopa to create drugs were calculated taking into account the ATC classification, their biological effects and alternative plant sources were given. …”
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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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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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