Mapping the Research Landscape of Agricultural Sciences by Dmitry Devyatkin, Elena Nechaeva, Roman Suvorov, Ilya Tikhomirov

“…Full-text mining is crucial due to (a) the poor representation of many Russian research areas in systems like Scopus or Web of Science; (b) the poor quality of Russian Science Index data; and (c) the differences between taxonomies used in different data sources. …”
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Unraveling the structural and functional consequences of non-synonymous single-nucleotide polymorphisms (nsSNPs) in human SOCS2: an in silico approach by Tanvir Hossain, Md. Nur Islam, Md. Anwar Hossain, Md. Mofizur Rahman, Mohammed Mafizul Islam, Shipan Das Gupta

“…Results In this study, we examined nsSNPs in the human SOCS2 gene to evaluate their effects on protein stability, structure, and function through in silico approaches. …”
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A Privacy-Preserving Polymorphic Heterogeneous Security Architecture for Cloud–Edge Collaboration Industrial Control Systems by Yukun Niu, Xiaopeng Han, Chuan He, Yunfan Wang, Zhigang Cao, Ding Zhou

“…Comprehensive evaluations using natural gas pipeline pressure control and smart grid voltage control systems demonstrate superior performance: the proposed method achieves 100% system availability compared to 62.57% for static redundancy and 86.53% for moving target defense, maintains 99.98% availability even under common-mode attacks (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math></inline-formula> probability), and consistently outperforms moving target defense methods integrated with state-of-the-art detection mechanisms (99.7790% and 99.6735% average availability when false data deviations from true values are 5% and 3%, respectively) across different attack detection scenarios, validating its effectiveness in defending against availability attacks and privacy leakage threats in cloud–edge collaboration environments.…”
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Identification and validation of hub genes associated with biotic and abiotic stresses by modular gene co-expression analysis in Oryza sativa L. by Izreen Izzati Razalli, Muhammad-Redha Abdullah-Zawawi, Rabiatul Adawiah Zainal Abidin, Sarahani Harun, Muhamad Hafiz Che Othman, Ismanizan Ismail, Zamri Zainal

“…A modular gene co-expression (mGCE) analysis was conducted, followed by gene set enrichment analysis to evaluate the upregulation of module activity across different stress conditions. …”
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MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma by Hong-Jian Luo, Jia-Liang Ren, Li mei Guo, Jin liang Niu, Xiao-Li Song

“…A simulated annealing algorithm was used for key feature selection. Two classification tasks were performed: task 1, the classification of HER2-positive vs. …”
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Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality by Miguel A. Becerra, Diego H. Peluffo-Ordoñez, Johana Vela, Cristian Mejía, Juan P. Ugarte, Catalina Tobón

“…First, EGMs were contaminated with different types of noise and artifacts (power-line interference, spikes, loss of samples, and loss of resolution) to assess tolerance. …”
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Utilização de redes neurais artificiais na classificação de níveis de degradação em pastagens Use of artificial neural networks in the classification of degradation levels of pastu... by César S. Chagas, Carlos A. O. Vieira, Elpídio I. Fernandes Filho, Waldir de C. Júnior

“…In this study, three different levels of pasture degradation have been identified (moderate, strong and very strong) and an image composition of 3 bands was tested (covering the visible and the near infra-red) with 15 m of spatial resolution. …”
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Optimasi Klasifikasi Sentimen Komentar Pengguna Game Bergerak Menggunakan Svm, Grid Search Dan Kombinasi N-Gram by Syahroni Wahyu Iriananda, Renaldi Widi Budiawan, Aviv Yuniar Rahman, Istiadi Istiadi

“…Grid Search (GS) was utilized for hyperparameter optimization to achieve the highest possible accuracy. To evaluate the impact of these methods, experiments were conducted across various scenarios, including different data quantities, hyperparameter settings, training and testing dataset ratios, and N-Gram configurations. …”
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Assessing the Impact of Social Media Usage and Performance of the Selected Small and Medium Businesses in Ntungamo District. by Nyamwija, Bonitah

“…The study included different retail shop owners such as; clothing, cosmetics, electronic accessories, make-up services, home appliances, and food stuffs. …”
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Livestock Biometrics Identification Using Computer Vision Approaches: A Review by Hua Meng, Lina Zhang, Fan Yang, Lan Hai, Yuxing Wei, Lin Zhu, Jue Zhang

“…We also highlight the challenges faced in this field, such as data quality and algorithmic efficiency, and introduce the baseline models and innovative solutions developed to address these issues. …”
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Feature Selection and Hyper-parameter Tuning Technique using Neural Network for Stock Market Prediction by Karanveer Singh, Rahul Tiwari, Prashant Johri, Ahmed A. Elngar

“…The existing stock market prediction focused on forecasting the regular stock market by using various machine learning algorithms and in-depth methodologies. The proposed work we have implemented describes the new NN model with the help of different learning techniques like hyperparameter tuning which includes batch normalization and fitting it with the help of random-search-cv. …”
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Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids by Shadfar Davoodi, Mohammed Al-Rubaii, David A. Wood, Mohammed Al-Shargabi, Mohammad Mehrad, Valeriy S. Rukavishnikov

“…Traditional FV measurement relies on human-centric experimental evaluation, which is time-consuming. Recently, machine learning (ML) proved itself as a promising approach for FV prediction. …”
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USE OF ARTIFICIAL INTELLIGENCE TO IDENTIFY AND CORRECT MISCONCEPTIONS ABOUT RADIATION by Oleksandr Tymoshchuk

“…The experiment involved presenting students with a series of statements designed to identify misconceptions related to factual knowledge (e.g., radiation units, background levels), conceptual understanding (e.g., the difference between radiation and radioactivity, effects of low-dose exposure), and application/evaluation (e.g., risk assessment, protective measures). …”
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Machine learning approaches for predicting energy and exergy efficiency in solar still by Somayeh Davoodabadi Farahani, Mohammad Javad Zarei, Hakan F. Oztop

“…This study further explores the energy efficiency and exergy of solar panels through machine learning algorithms aimed at enhancing their performance. A comprehensive database was created by solving thermodynamic equations, followed by an evaluation of five machine learning models: multilayer perceptron (MLP), MLP BAGGING, EXTRATREES, KNEIGHBORS, and RANDOM FOREST, along with a deep neural network model for regression tasks. …”
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Fine-Tuned Machine Learning Classifiers for Diagnosing Parkinson’s Disease Using Vocal Characteristics: A Comparative Analysis by Mehmet Meral, Ferdi Ozbilgin, Fatih Durmus

“…This study seeks to assess the effectiveness of machine learning algorithms optimized to classify PD based on vocal characteristics to serve as a non-invasive and easily accessible diagnostic tool. …”
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Exploiting K-Space in Magnetic Resonance Imaging Diagnosis: Dual-Path Attention Fusion for K-Space Global and Image Local Features by Congchao Bian, Can Hu, Ning Cao

“…The findings indicate robust performance across different datasets, highlighting strong generalizability and favorable algorithmic complexity. …”
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New Predictive Models for the Computation of Reinforced Concrete Columns Shear Strength by Anthos I. Ioannou, David Galbraith, Nikolaos Bakas, George Markou, John Bellos

“…Significantly improved predictive models are proposed herein through the implementation of machine learning (ML) algorithms on refined datasets. Three ML models, LREGR, POLYREG-HYT, and XGBoost-HYT-CV, were used to develop different predictive models that were able to compute the shear strength of RC columns. …”
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Advancing Alzheimer’s disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis by Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li

“…This framework contains EEG data collection, pre-processing for noise removal, temporal segmentation, convolutional neural network (CNN) model training and classification, and finally, evaluation. We have tested different segment lengths to test the impact on AD detection. …”
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Relationship Between Weight Status and Health-Related Quality of Life in School-age Children in China by Mandana Zanganeh, Peymané Adab, Bai Li, Miranda Pallan, Wei J. Liu, Lin Rong, Wei Liu, James Martin, Kar K. Cheng, Emma Frew

“…However, the relationship between weight status and HRQOL is not well established in China, where obesity trends follow a different pattern compared with high-income countries. …”
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Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu, Guangyu Bin

“…Our evaluation revealed that functional connectivity features contribute the most to classification at 70%. …”
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