Sparse Feature-Weighted Double Laplacian Rank Constraint Non-Negative Matrix Factorization for Image Clustering by Hu Ma, Ziping Ma, Huirong Li, Jingyu Wang

“…Additionally, many NMF variants face challenges when dealing with complex data distributions and are vulnerable to noise and outliers. …”
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Exergetic calculation of the efficiency of heat pump installations for a private building by О. Стенін, В. Пасько, М. Солдатова, І. Дроздович

“…This article offers an exergetic calculation of the coefficient of the degree of thermodynamic perfection for the selection of the appropriate heat pump for the selected scheme of the heat pump installation and real operating conditions. …”
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Near infrared reflectance spectroscopy-driven chemometric modeling for predicting key quality traits in lablab bean (Lablab purpureus L.) Germplasm by Simardeep Kaur, Naseeb Singh, Ernieca L. Nongbri, Mithra T, Veerendra Kumar Verma, Amit Kumar, Tanay Joshi, Jai Chand Rana, Rakesh Bhardwaj, Amritbir Riar

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Comparative Evaluation of Decision Tree (M5) and Least Square Support Vector Machine (LS-SVM) Models for Groundwater Level Prediction in the Mashhad Plain by Vajihe Ramezani saani, Mahdi Zarei, Seyed Morteza Seyedian

“…Nowadays, the application of intelligent models for estimating groundwater levels is increasing due to their ease of use and high accuracy in estimating complex and nonlinear mathematical equations. The aim of the present study is to estimate the groundwater table level of the Mashhad plain aquifer using the decision tree model (M5) and to compare it with the least squares support vector machine model (LS-SVM) under 10 different scenarios.Method: For this purpose, monthly climatic data (precipitation, evaporation, and temperature) and groundwater level information from 60 piezometric wells over a 10-year statistical period were utilized, and the employed models were evaluated using statistics such as the coefficient of determination (R2), RMSE, and MBE.Results: The results of the LS-SVM model indicated that the highest simulation accuracy belonged to scenario 4, followed by scenario 9, while the other scenarios exhibited very low accuracy in simulating the water level. …”
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A clean self-diverting acid for carbonate reservoir in deep wells at high temperatures and its performance evaluation by Xiangsen Gao, Min Wang, Xian Shi, Bin Li, Jian Mao, Poplygin Vladimir Valerievich

“…The innovative clean self-diverting acid fulfills all requirements for deep acid fracturing in deep high-temperature oil and gas reservoirs with complex geological characteristics.…”
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Multitask semantic change detection guided by spatiotemporal semantic interaction by Yinqing Wang, Liangjun Zhao, Yueming Hu, Hui Dai, Yuanyang Zhang

“…STGNet enhances the ability to capture spatial details by introducing a Detail-Aware Path (DAP) and designs a Bidirectional Guidance Module for Spatial Detail and Semantic Information for adaptive feature selection, improving feature extraction capabilities in complex scenes. …”
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Advanced Cancer Classification Using AI and Pattern Recognition Techniques by Haddou Bouazza Sara, Haddou Bouazza Jihad

“…We applied feature selection techniques such as the F Test, Signal-to-Noise Ratio (SNR), T-test, ReliefF, Correlation Coefficient, Mutual Information, and minimum redundancy maximum relevance, along with classifiers including K-Nearest Neighbors, Support Vector Machines, Linear Discriminant Analysis, Decision Tree Classifiers, and Naive Bayes. …”
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Two New Approaches (RAMS-RATMI) in Multi-Criteria Decision-Making Tactics by Reda M. S. Abdulaal, Omer A. Bafail

“…When a decision must be made, a tool called multi-criteria decision-making (MCDM) is used to assess and select alternatives among numerous criteria. For a wide variety of complex problems, MCDM methods have demonstrated usefulness in finding the optimal solutions. …”
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Robust Classification of UWB NLOS/LOS Using Combined FCE and XGBoost Algorithms by Shoude Wang, Nur Syazreen Ahmad

“…The method begins with feature selection using the Pearson Correlation Coefficient to filter less correlated features of the UWB Channel Impulse Response (CIR) data, followed by outlier handling. …”
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Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting by Tutun Juhana, Hajiar Yuliana, . Hendrawan, . Iskandar, Yasuo Musashi

“…The findings reveal that Glmnet consistently outperforms ARIMA and Prophet across all categories of traffic forecasting on the selected performance metrics. Its ability to handle complex data structures, manage multicollinearity, and deliver robust and accurate predictions makes it the preferred choice for forecasting cellular network traffic in the telecommunications domain.   …”
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Prediction of Enthalpy of Mixing of Binary Alloys Based on Machine Learning and CALPHAD Assessments by Shuangying Huang, Guangyu Wang, Zhanmin Cao

“…The model performance was further optimized through Recursive Feature Elimination (RFE) and Maximal Information Coefficient (MIC) feature selection methods. Shapley Additive Explanations reveals that the primary factors affecting the mixing enthalpy, such as atomic radius and electronegativity, align with the key parameters of the Miedema model, thereby confirming the physical interpretability of our data-driven approach. …”
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Comparing the Efficiency of Sediment Rating Curve and ANN Models in Estimating River Bed-load by Jamal Mosaffaie, Amin Saleh pourjam, Mahmudreza Tabatabaei

“… Evaluation and selection of the most appropriate methods for bed-load estimation is necessary because of the sampling difficulties and inaccurate estimations of the empirical equations. …”
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A novel wind speed prediction model based on neural networks, wavelet transformation, mutual information, and coot optimization algorithm by Faezeh Amirteimoury, Farshid Keynia, Elaheh Amirteimoury, Gholamreza Memarzadeh, Hanieh Shabanian

“…To tackle this issue, this paper proposes a new wind speed prediction model that combines four techniques: Discrete Wavelet Transform, which smooths the wind speed signal; Mutual Information, which selects the most informative part of the wind speed time series; Coot Optimization Algorithm for optimal feature selection; and Bidirectional Long Short-Term Memory for capturing complex patterns. …”
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Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. by Clive J Hoggart, John C Whittaker, Maria De Iorio, David J Balding

“…A non-zero coefficient estimate was interpreted as corresponding to a significant SNP. …”
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Potential Skill Map of Predictors Applied to the Seasonal Forecast of Summer Rainfall in China by Liu Boqi, Zhu Congwen

“…Anomalous summer rainfall in China is affected by many factors, whose complex interaction restricts the predictability of Chinese summer rainfall (CSR). …”
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Nitrous oxide prediction through machine learning and field-based experimentation: A novel strategy for data-driven insights by Muhammad Hassan, Khabat Khosravi, Travis J. Esau, Gurjit S. Randhawa, Aitazaz A. Farooque, Seyyed Ebrahim Hashemi Garmdareh, Yulin Hu, Nauman Yaqoob, Asad T. Jappa

“…Applying machine learning to predict complex environmental phenomena like greenhouse gas emissions (GHG) is gaining significant attention. …”
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Future Site Suitability for Urban Waste Management in English Bazar and Old Malda Municipalities, West Bengal: A Geospatial and Machine Learning Approach by Suresh Mondal, Mst Tania Parveen, Asraful Alam, Rukhsana, Nazrul Islam, Beata Calka, Bashar Bashir, Mohamed Zhran

“…The overall accuracy and Kappa coefficient indicated that the AHP method (overall capacity of 83.83% and Kappa coefficient of 0.7894) was slightly better than the RF model (overall capacity of 80.61% and Kappa coefficient of 0.7474) for site suitability analysis. …”
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The Extraction of <i>Torreya grandis</i> Growing Areas Using a Spatial–Spectral Fused Attention Network and Multitemporal Sentinel-2 Images: A Case Study of the Kuaiji Mountain Reg... by Yanyan Lyu, Yong Wang, Xiaoling Shen

“…However, extracting forest-growing areas remains challenging due to the limited spatial and temporal resolution of remote sensing data and the insufficient classification capability of traditional algorithms for complex land cover types. This study utilized monthly Sentinel-2 imagery from 2023 to extract multitemporal spectral bands, vegetation indices, and texture features. …”
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Experimental Study on the Gas Flow Characteristics and Pressure Relief Gas Drainage Effect under Different Unloading Stress Paths by Chaolin Zhang, Jiang Xu, Enyuan Wang, Shoujian Peng

“…However, the influence of unloading stress paths on gas production was complex and time dependent. The difference coefficient parameter was proposed to characterize the influence degree of unloading stress paths on the pressure relief gas drainage effect. …”
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Evaluation of empirical relationships in carbonates by developing a 1D mechanical earth model in an oil field in Southwestern Iran by Hadi Ammarloo, Pedram Zoughy, Leili Fatahi Mehraban, Mohammadkazem Amiri

“…Accurate determination of rock mechanical parameters is crucial for safe and efficient drilling, production, and reservoir development in these complex formations. While numerous empirical relationships have been proposed to estimate these properties, there is no reliable way to guide the selection of suitable models in new studies. …”
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