Radiomic Feature Extraction Based on Computed Tomography for Stroke Patients by Alamgir Hossain, Jolly Sultana, Shahed Hossain, Hafizur Rahman

“…To improve diagnosis, classification, and prediction for the best possible patient outcomes, this study investigates how radiomics might improve computed tomography (CT)-based stroke evaluation. …”
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Multi-Feature Long Short-Term Memory Facial Recognition for Real-Time Automated Drowsiness Observation of Automobile Drivers with Raspberry Pi 4 by Michael Julius R. Moredo, James Dion S. Celino, Joseph Bryan G. Ibarra

“…The model was trained on the NTHU-DDD dataset and optimized using long short-term memory (LSTM) deep learning algorithms implemented using TensorFlow version 2.14.0. …”
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Collaborative Optimization Planning Method for Distribution Network Considering “Hydropower, Photovoltaic, Storage, and Charging” by Jinlin Liao, Jia Lin, Guilian Wu, Sudan Lai

“…Secondly, the trip chain method is used to predict the charging demand of electric vehicles. …”
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RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnosti... by Zhang Zhang, Fangfang Chen, Xiaoxiao Deng

“…Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. …”
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A combined immune and exosome-related risk signature as prognostic biomakers in acute myeloid leukemia by Zenghui Fang, Jiali Fu, Xin Chen

“…The variations in immune cell infiltrations among risk groups were assessed through four algorithms. Expression of hub gene in specific cell was analyzed by single-cell RNA seq.Results A total of 85 immune-ERGs associated with prognosis were identified, enabling the construction of a risk model for AML. …”
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State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States by Osman Ozer, Hayri Arabaci

“…To address this, deep learning algorithms such as long short-term memory (LSTM) have been focused on for SOC prediction. …”
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Revolutionizing Marine Stability: A Review of Cutting-Edge Gyro Stabilizer Designs and Techniques for Vessels by Wan Rahiman, Mohamad Hazwan Mohd Ghazali, Iskandar Al-Thani Mahmood

“…Despite these challenges, recent advancements in control algorithms and optimization techniques have improved gyro stabilizer adaptability under varying sea conditions. …”
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Development of a Wireless Sensor Network and IoT-based Smart Irrigation System by Juliana Ngozi Ndunagu, Kingsley Eghonghon Ukhurebor, Moses Akaaza, Robert Birundu Onyancha

“…The sample size used contains 5722 rows and 6 columns from the result of our machine learning algorithms using Microsoft Excel and Jupyter Notebook to process and evaluate the performance of the drip SIS, considering the soil moisture, soil temperature, sunlight, rain, and pump. …”
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Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L. by Mahboobe Hojati, Ruhollah Naderi, Mohsen Edalat, Hamid Reza Pourghasemi

“…Results showed that The RF (ROC: 0.99), BRT (ROC: 0.98), and SVM (ROC: 0.96) models were highly accurate in predicting the habitat suitability of S. marianum. The results of the RF algorithm also revealed that factors such as distance from roads, elevation, and mean annual rainfall had the most significant influence on the habitat suitability of S. marianum. …”
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Unlocking the structural, vibrational, electronic, optical and thermoelectric properties of K2X (X=S, Se, Te) monolayers via DFT and ML by G. Sneha, R.D. Eithiraj

“…AFLOW-PLMF model was deployed for the electronic band gap value predictions. Machine learning algorithms namely Decision Tree Regressor and Gradient Boosting Regressor out-performs the other ML models with least root mean square error (RMSE) and accurate R2 values. …”
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Comparing MEG and EEG measurement set-ups for a brain-computer interface based on selective auditory attention. by Dovilė Kurmanavičiūtė, Hanna Kataja, Lauri Parkkonen

“…Employing whole-scalp MEG recordings and offline classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. …”
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The inconvenient truth of ground truth errors in automotive datasets and DNN-based detection by Pak Hung Chan, Boda Li, Gabriele Baris, Qasim Sadiq, Valentina Donzella

“…Assisted and automated driving functions will rely on machine learning algorithms, given their ability to cope with real-world variations, e.g. vehicles of different shapes, positions, colors, and so forth. …”
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Attention-Based Color Difference Perception for Photographic Images by Hua Qiang, Xuande Zhang, Jinliang Hou

“…Existing deep learning-based CD measurement algorithms only focus on local features and cannot accurately simulate the human perception of CD. …”
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Artificial intelligence in environmental monitoring: in-depth analysis by Emran Alotaibi, Nadia Nassif

“…Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. …”
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Damage Scene Change Detection Based on Infrared Polarization Imaging and Fast-PCANet by Min Yang, Jie Yang, Hongxia Mao, Chong Zheng

“…Comparisons with typical PCANet-based change detection algorithms are made on a dataset of infrared-polarized images. …”
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Identification of biomarkers for the diagnosis in colorectal polyps and metabolic dysfunction-associated steatohepatitis (MASH) by bioinformatics analysis and machine learning by Ying Geng, Yifang Li, Ge Liu, Jian Jiao

“…This study investigated the biomarkers of MASH and colorectal polyps, and the prediction of targeted drugs using an integrated bioinformatics analysis method. …”
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Countermeasuring Anti-Ship Missiles for Surface Naval Platforms: A Machine Learning Approach With Explainable Artificial Intelligence by Murat Ertop, Ali Oter, Ali Kara

“…This model has been used to predict target parameters. The simulator includes parameters for the ship, missile, and chaff/flare. …”
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Cooling Load Forecasting Method for Central Air Conditioning Systems in Manufacturing Plants Based on iTransformer-BiLSTM by Xiaofeng Huang, Xuan Zhou, Junwei Yan, Xiaofei Huang

“…Meanwhile, by integrating the SHAP sensitivity analysis method, the contributions of input variables to the cooling load prediction results are systematically evaluated, thereby enhancing the interpretability of the model. …”
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Leveraging artificial intelligence in disaster management: A comprehensive bibliometric review by Arief Wibowo, Ikhwan Amri, Asep Surahmat, Rusdah Rusdah

“…Six research clusters were identified through keyword network mapping: (1) disaster monitoring and prediction using IoT networks, (2) AI-based geospatial technology for risk management, (3) decision support systems for disaster emergency management, (4) social media analysis for emergency response, (5) machine learning algorithms for disaster risk reduction, and (6) big data and deep learning for disaster management. …”
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Raising Awareness of Acute Kidney Injury: Unfolding the Truth by Aida Lydia

“…Considering the substantial impacts of AKI in ICU patients, early implementation of preventive measures should be an essential program which consists of developing AKI risk stratification in the ICU and encouraging the use of novel AKI biomarkers (TIMP-2, IGFBP-7, Cystatin C, IL-18,  KIM-1 and NGAL) as well as other risk stratification tools (clinical risk prediction scores, computer algorithms, furosemide stress test). …”
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