Short-Term Load Forecasting in Distribution Substation Using Autoencoder and Radial Basis Function Neural Networks: A Case Study in India by Venkataramana Veeramsetty, Prabhu Kiran Konda, Rakesh Chandra Dongari, Surender Reddy Salkuti

“…The input dataset dimensionality is reduced using autoencoder to build a light-weight machine learning model to be deployed on edge devices. …”
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Towards a minimum universal features set for IoT DDoS attack detection by Osama Ebrahem, Salah Dowaji, Suhel Alhammoud

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Prediction and Assessment of Myocardial Infarction Risk on the Base of Medical Report Text Collection by Margaryta Prazdnikova

“…This underscores the significant potential of leveraging textual data and machine learning methods in medical diagnostics. Moreover, the reduction in false predictions highlights the model's reliability and suitability for practical application. …”
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Unsupervised detection of high-frequency oscillations in intracranial electroencephalogram: promoting a valuable automated diagnostic tool for epilepsy by Wenjing Chen, Tongzhou Kang, Md Belal Bin Heyat, Jamal E. Fatima, Yuanning Xu, Dakun Lai

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Decoding Humor-Induced Amusement via Facial Expression Analysis: Toward Emotion-Aware Applications by Gabrielle Toupin, Arthur Dehgan, Marie Buffo, Clément Feyt, Golnoush Alamian, Karim Jerbi, Anne-Lise Saive

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A Methodology to Characterize an Optimal Robotic Manipulator Using PSO and ML Algorithms for Selective and Site-Specific Spraying Tasks in Vineyards by Roni Azriel, Oded Degani, Avital Bechar

“…This prediction served as decision support in selecting which configurations should be tested in the simulation, thereby reducing computational time. The integration of machine learning models in the proposed methodology resulted in an average runtime reduction of 59% while maintaining an average manipulability index score in comparison to the original approach, which did not include the XGBoost model. …”
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Efficient management of neonatal sepsis diagnosis using predictive analytics methods: a scoping review by KVKL Narasimha Rao, Pradeep Kumar Dadabada, Sanjita Jaipuria

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Skin Microbiome alterations in heroin users revealed by full-length 16S rRNA sequencing by Ruiyang Tao, Xiang Wang, Xiaoyuan Zhen, Ruocheng Xia, Kaiqin Chen, Han Zhang, Yu Deng, Suhua Zhang

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Solid Oxide Fuel Cell Voltage Prediction by a Data-Driven Approach by Hristo Ivanov Beloev, Stanislav Radikovich Saitov, Antonina Andreevna Filimonova, Natalia Dmitrievna Chichirova, Egor Sergeevich Mayorov, Oleg Evgenievich Babikov, Iliya Krastev Iliev

“…A potential solution involves using data-driven machine learning (ML) black-box models. This study examines three ML models: artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB). …”
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Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation by Amirreza Kandiri, Ramin Ghiasi, Maria Nogal, Rui Teixeira

“…The first stage involves an offline process where interconnected optimisation algorithms are employed to identify the optimal set of features and determine the most effective machine learning model architecture. In the second stage, the real-time process utilises the optimised model to predict travel times using new data from previously unseen parts of the dataset. …”
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Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers by Wentong Li, Yiyi Zhang, Chuanhong Li, Mingyang Jiang, Dong Wang, Luomeng Chao, Luomeng Chao, Luomeng Chao, Yuxia Yang

“…PPI network analysis identified HSP90AA1, HSPA1B, and DNAJB1 as core hub genes (degree centrality >20). Machine learning validation demonstrated their combined exceptional diagnostic efficacy (AUC=0.963, F1 = 0.88). …”
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Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality by Fernando García-García, Dae-Jin Lee, Mónica Nieves-Ermecheo, Olaia Bronte, Pedro Pablo España, José María Quintana, Rosario Menéndez, Antoni Torres, Luis Alberto Ruiz Iturriaga, Isabel Urrutia, COVID-19 & Air Pollution Working Group

“…We proposed a sequence of machine learning stages: feature scaling, missing data imputation, reduction of data dimensionality via Kernel Principal Component Analysis (KPCA), and clustering with the k-means algorithm. …”
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Temporal and spatial variations of urban surface temperature and correlation study of influencing factors by Lei Ding, Xiao Xiao, Haitao Wang

“…This study investigates the influence of three-dimensional urban form characteristics on LST, using ECOSTRESS sensor data and four machine learning models. Six urban morphology variables—building density (BD), mean building height (MH), building volume (BVD), gross floor area (GFA), floor area ratio (FAR), and sky view factor (SVF)—are analyzed across different seasons and times of day. …”
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A new classification algorithm for low concentration slurry based on machine vision by Chuanzhen Wang, Xinyi Wang, Andile Khumalo, Fengcheng Jiang, Jintao Lv

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Landslide Susceptibility Evaluation Based on the Combination of Environmental Similarity and BP Neural Networks by Ruiting Wang, Wenfei Xi, Guangcai Huang, Zhiquan Yang, Kunwu Yang, Yongzai Zhuang, Ruihan Cao, Dingjie Zhou, Yijie Ma

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Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach by Rahul Kumar Gupta, Asmaul Hassan, Samir Kumar Majhi, Nikhat Parveen, Abu Taha Zamani, Raju Anitha, Binayak Ojha, Abhinav Kumar Singh, Debendra Muduli

“…A stacking ensemble model is developed with support vector machine (SVM), K-nearest neighbors (KNN), and extreme learning machine (ELM) to enhance forecast accuracy. …”
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Two-decade spatiotemporal variations in ground-level ozone over Ontario, Canada by Zhou Zang, Jane Liu, Erjia Ge, Yi Zhang

“…Understanding long-term trends and spatially explicit details of O3 is important for supporting air quality management in Ontario.MethodWe construct a high-resolution (daily, 10 km) dataset of maximum daily 8-hour average O3 (MDA8 O3) over Ontario from 2004 to 2023, through a two-step machine learning model. The model has incorporated our hypothesis that accounting for transboundary influences can enhance the accuracy of O3 estimation.ResultsValidation against in-situ measurements confirms the hypothesized high accuracy of the dataset (R2 = 0.82, RMSE = 4.99 ppb), outperforming the traditional model and two existing datasets. …”
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Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records by Noorul Husna Abd Rahman, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Ayman Khallel Al-Ani, D. Jerline Sheebha Anni, Prabu Mohandas

“…This study proposes a data-driven machine-learning model for predicting medical device failure. …”
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Adaptive Remaining Capacity Estimator of Lithium-Ion Battery Using Genetic Algorithm-Tuned Random Forest Regressor Under Dynamic Thermal and Operational Environments by Uzair Khan, Mohd Tariq, Arif I. Sarwat

“…The proposed GA-optimized RF model achieved an MAE of 0.0026 at 25 °C and 0.0102 at −20 °C, showing a 41.37% to 50% reduction in the MAE compared to traditional random forest models without GA optimization. …”
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Enhancing environmental sustainability and operational efficiency in a case study of limestone quarry in an arid climate by Hussein A. Saleem, Abebe Temesgen Ayalew

“…Automated environmental monitoring systems, incorporating IoT sensors and machine-learning algorithms, provide real-time data on air quality, dust levels, and noise pollution. …”
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