Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California by Jason G. Su, Eahsan Shahriary, Emma Sage, John Jacobsen, Katherine Park, Arash Mohegh

“…These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. …”
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How does artificial intelligence shape the productivity and quality of research in business studies? A systematic literature review and future research framework by Sugandha Agarwal, Qian Long Kweh, Dima Jamali, Walton Wider, Syed Far Abid Hossain, Muhammad Ashraf Fauzi

“…We show that AI helps reduce research time and improve data management. Methods like machine learning and natural language processing can effectively uncover patterns and trends that conventional research methods may overlook. …”
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Artificial intelligence model in the cognitive and learning activities of university subjects by N. Abishev, R. Ramazanov, M. Abaideldanova, K. Chesnokova, A. Baizhumayeva

“…The authors design this model using algorithms–sets of rules that enable programs to make decisions, recognize patterns, and generate predictions based on input data relevant to the learning and cognitive processes of university subjects. …”
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Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training by Kamelia Sepanloo, Daniel Shevelev, Young-Jun Son, Shravan Aras, Janine E. Hinton

“…The simulation consists of six segments, during which critical events like hypotension and hypoxia occur, and the patient’s condition changes based on the nurse’s clinical decisions. Machine learning algorithms were then used to analyze the nurse’s physiological data and to classify different levels of stress. …”
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Automatic Fault Classification in Photovoltaic Modules Using Denoising Diffusion Probabilistic Model, Generative Adversarial Networks, and Convolutional Neural Networks by Carlos Roberto da Silveira Junior, Carlos Eduardo Rocha Sousa, Ricardo Henrique Fonseca Alves

“…Deep convolutional neural networks (CNNs) are machine learning algorithms that perform tasks involving images, such as image classification and object recognition. …”
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Identifying Suicidal Ideation Through Automatic Extraction of Emotional Traces in Suicide Notes by Angel Hernandez-Castaneda, Rene Arnulfo Garcia-Hernandez, Yulia Ledeneva

“…The primary objective of this study is to classify suicide notes based on their emotional content using machine and deep learning algorithms. We propose an innovative approach to automatically identify emotional changes in a suicide note’s content, leveraging these shifts as key indicators of suicidal ideation. …”
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A novel ensemble model for fall detection: leveraging CNN and BiLSTM with channel and temporal attention by Sarita Sahni, Sweta Jain, Sri Khetwat Saritha

“…Despite the proliferation of machine learning and deep learning algorithms for fall detection, their efficacy remains hampered by resilience, robustness, and adaptability challenges across varied input scenarios. …”
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BharatSim: An agent-based modelling framework for India. by Philip Cherian, Jayanta Kshirsagar, Bhavesh Neekhra, Gaurav Deshkar, Harshal Hayatnagarkar, Kshitij Kapoor, Chandrakant Kaski, Ganesh Kathar, Swapnil Khandekar, Saurabh Mookherjee, Praveen Ninawe, Riz Fernando Noronha, Pranjal Ranka, Vaibhhav Sinha, Tina Vinod, Chhaya Yadav, Debayan Gupta, Gautam I Menon

“…BharatSim uses a synthetic population created by applying statistical methods and machine learning algorithms to survey data from multiple sources, including the Census of India, the India Human Development Survey, the National Sample Survey, and the Gridded Population of the World. …”
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Blood biomarker discovery for autism spectrum disorder: A proteomic analysis. by Laura Hewitson, Jeremy A Mathews, Morgan Devlin, Claire Schutte, Jeon Lee, Dwight C German

“…Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and social interaction and restricted, repetitive patterns of behavior, interests, or activities. Given the lack of specific pharmacological therapy for ASD and the clinical heterogeneity of the disorder, current biomarker research efforts are geared mainly toward identifying markers for determining ASD risk or for assisting with a diagnosis. …”
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Unraveling the oxidative stress landscape in diabetic foot ulcers: insights from bulk RNA and single-cell RNA sequencing data by Jialiang Lin, Linjuan Huang, Weiming Li, Haijun Xiao, Mingmang Pan

“…Drug prediction highlighted Thymoquinone and Erlotinib as potential therapeutic candidates. Machine learning algorithms (SVM-RFE, LASSO and RF) identified BCL2 and 和FOXP2 as candidate hub DORGs for DFU diagnosis. …”
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Spatial Regionalization of the Arctic Ocean Based on Ocean Physical Property by Joo-Eun Yoon, Jinku Park, Hyun-Cheol Kim

“…Employing methods such as information mapping and pairwise multi-comparison analysis, we found that the 12 subregions of the Arctic Ocean well represent spatial heterogeneity and homogeneity of physical properties, including sea ice concentration, surface ocean currents, SST, and sea surface salinity. Spatial patterns in SST changes also matched well with the boundaries of clustered subregions. …”
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Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution by Zehao Jin, Mario Pasquato, Benjamin L. Davis, Tristan Deleu, Yu Luo, Changhyun Cho, Pablo Lemos, Laurence Perreault-Levasseur, Yoshua Bengio, Xi Kang, Andrea Valerio Macciò, Yashar Hezaveh

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AI-Driven Belt Failure Prediction and Prescriptive Maintenance with Motor Current Signature Analysis by João Paulo Costa, José Torres Farinha, Mateus Mendes, Jorge O. Estima

“…The incorporation of LSTM networks and swarm intelligence algorithms led to a significant improvement in predictive capabilities, allowing for the early detection of degradation patterns and timely intervention. …”
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Adaptive Real-Time Transmission in Large-Scale Satellite Networks Through Software-Defined-Networking-Based Domain Clustering and Random Linear Network Coding by Shangpeng Wang, Chenyuan Zhang, Yuchen Wu, Limin Liu, Jun Long

“…Existing research primarily emphasizes traffic prediction and scheduling using spatiotemporal models and machine learning. However, these approaches often depend on extensive historical data for training, making real-time adaptation to rapidly changing network topologies and traffic patterns challenging in dynamic satellite environments. …”
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MetaStackD A robust meta learning based deep ensemble model for prediction of sensors battery life in IoE environment by D. Gayathri, S. P. Shantharajah

“…This work focuses on proposing a novel framework integrating pre-processing, standardization, encoding scheme, and predictive modeling that includes two algorithms, RFRImpute and MetaStackD, for predicting the RBL of sensors in any IoE device using a meta-learning-based deep ensemble approach blue for analyzing factors such as power consumption, environmental conditions, operational frequency, and workload patterns. …”
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Methodology for Data Integration in 3D-HBIM Digital Models. Case Study: the Holy Chalice Chapel of Valencia Cathedral by Pablo Ariel Escudero, Concepción López González, Jorge Luis García Valldecabres

“…This phase involves the use of various machine learning algorithms, including Random Sample Consensus (RANSAC) and K-Means, for data classification. …”
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Data-driven intelligent productivity prediction model for horizontal fracture stimulation by Qian Li, Yiyong Sui, Mengying Luo, Bin Guan, Lu Liu, Yuan Zhao

“…Under the assumption of similar characteristics and mechanisms, correlation analysis was conducted for each fracturing interval category to identify the dominant controlling factors affecting post-fracturing productivity in each reservoir type. Machine learning algorithms were used to establish intelligent models describing the relationships between post-fracturing production enhancement effects, dominant factors, and production time for each reservoir category. …”
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Analysis of the capabilities of an information system for improving sleep quality based on biometric data analysis by M.S. Graf, A.V. Yakoniuk, D.V. Krant, I.I. Golovach

“…The system is able to function independently thanks to the use of machine learning algorithms, including LSTM for prediction, Kalman filter for data cleaning, Isolation Forest for anomaly detection, and K-means for clustering sleep patterns.…”
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Canopy Chlorophyll Content Inversion of Mountainous Heterogeneous Grasslands Based on the Synergy of Ground Hyperspectral and Sentinel-2 Data: A New Vegetation Index Approach by Yi Zheng, Yao Wang, Tayir Aziz, Ali Mamtimin, Yang Li, Yan Liu

“…The results indicated the following: (1) DRECAVI demonstrated the highest accuracy in CCC retrieval for mountainous vegetation (R² = 0.74, RMSE = 16.79, MAE = 12.50) compared to other VIs and hybrid methods, effectively mitigating saturation effects in high biomass areas and capturing a weak bimodal distribution pattern of CCC in the montane meadow. (2) The blue band B1 enhances atmospheric correction robustness by suppressing aerosol scattering, and the red-edge band B7 overcomes the sensitivity limitations of conventional red-edge indices (such as NDVI705, CIred-edge, and NDRE), demonstrating the potential application of the synergy mechanism between the blue band and the red-edge band. (3) Although the S2BP achieved high accuracy (R² = 0.73, RMSE = 19.83, MAE = 14.71) without saturation effects and detected a bimodal distribution of CCC in the montane meadow of the study area, its algorithmic complexity hindered large-scale operational applications. …”
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Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

“…To address this imbalance, an innovative data aggregation technique was applied, effectively reducing similar patterns and trends. This approach resulted in a balanced dataset consisting of 8 attack activity points and 80 normal activity points. …”
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