Brain Tumor Detection and Classification Using IFF-FLICM Segmentation and Optimized ELM Model by Suvashisa Dash, Mohammed Siddique, Satyasis Mishra, Demissie J. Gelmecha, Sunita Satapathy, Davinder Singh Rathee, Ram Sewak Singh

“…In recent years, machine learning classifiers have played an essential role in automatically classifying brain tumors. …”
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Comprehensive bioinformatics analysis reveals novel potential biomarkers associated with aging and mitochondria in osteoporosis by Ke Bi, Yuxi Chen, Yuhang Hu, Song Li, Weiming Li, Zhange Yu, Lei Yu

“…Then, 6 key genes were identified by WGCNA and multiple machine learning, and a novel diagnostic model was constructed. …”
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Decoding communicative action vitality forms in social contexts by Radoslaw Niewiadomski, Radoslaw Niewiadomski, Amrita Suresh, Alessandra Sciutti, Giuseppe Di Cesare, Giuseppe Di Cesare

“…Finally, we also demonstrate that automatic recognition of VFs is possible using traditional machine learning methods, with an accuracy of 87.3%. Moreover, this recognition is also feasible for action types do not present in the training set, with an accuracy of 74.2%. …”
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Proteogenomic analysis reveals Arp 2/3 complex as a common molecular mechanism in high risk pancreatic cysts and pancreatic cancer by A. K. M. Firoj Mahmud, Dina Gamaleldin Mansour Aly, Yelin Zhao, Mikael Benson, Martin Smelik, Oleg Sysoev, Hui Wang, Xinxiu Li

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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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Deep learning-enabled exploration of global spectral features for photosynthetic capacity estimation by Xianzhi Deng, Xiaolong Hu, Liangsheng Shi, Chenye Su, Jinmin Li, Shuai Du, Shenji Li

“…However, vegetation indices-based linear regression exhibits insufficient utilization of spectral information, while full spectra-based traditional machine learning has limited representational capacity (partial least squares regression) or uninterpretable (convolution). …”
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Power Transformer Health Index Using Cost-Sensitive Learning to Consider the Impact of Misclassification by Junsoo Che, Gihun Park, Jeongsik Oh, Su-Han Pyo, Byeonghyeon An, Taesik Park

“…Recent studies have explored the application of machine learning to improve accuracy. However, these studies ignore the risk of misclassification, which can result in significant financial losses when predicting the health index of power transformers. …”
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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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A framework for modeling carbon loss from rivers following terrestrial enhanced weathering by Shuang Zhang, Christopher T Reinhard, Shaoda Liu, Yoshiki Kanzaki, Noah J Planavsky

“…We map key water quality parameters using machine learning and use a DRN model to simulate changes in carbon degassing during EW. …”
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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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Innovations and barriers in sustainable and green finance for advancing sustainable development goals by Raghu Raman, Suparna Ray, Dayana Das, Prema Nedungadi

“…We use quantitative bibliometric methods with machine learning-based BERTopic modeling and case study analysis to reveal trends. …”
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