Research on the optimization method of inventory management of important spare parts of intercity railway. by Dongyan Wang, Ying Sun, Liang Yu, Kun Shen, Junbo Li, Xia Wu

“…This result fully demonstrates the superiority of the optimization model established in this paper in practical applications and provides new ideas and methods for the management of spare parts for other intercity railways.…”
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Gorilla troops optimization with deep learning based crop recommendation and yield prediction by A. Punitha, V. Geetha

“…The latest breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) technologies pave the way to designing effective crop recommendation and prediction models. …”
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A proficient approach for the classification of Alzheimer’s disease using a hybridization of machine learning and deep learning by Hafiz Ahmed Raza, Shahab U. Ansari, Kamran Javed, Muhammad Hanif, Saeed Mian Qaisar, Usman Haider, Paweł Pławiak, Iffat Maab

“…This paper presents a novel hybrid approach for early detection and classification of AD using structural MRI (sMRI). The proposed model employs a unique combination of machine learning and deep learning approaches to optimize the precision and accuracy of the detection and classification of AD. …”
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Combining Smartphone Inertial Sensors and Machine Learning Algorithms to Estimate Power Variables in Standing Long Jump by Beatrice De Lazzari, Giuseppe Vannozzi, Valentina Camomilla

“…Considering the state-of-the-art in SLJ biomechanics, a set of features was extracted and then selected by Lasso regression and used as inputs to several different optimized machine learning architectures to estimate the SLJ power variables. …”
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Machine learning-based prediction of proton-induced reaction cross-sections for medically relevant bromine radioisotopes by Parichehr Khalilzadeh, S. Farhad Masoudi, Hassan Yousefnia, Fatemeh S. Rasouli

“…Accurate prediction of nuclear reaction cross-sections is essential for optimizing the production of medically relevant radioisotopes. …”
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Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning by Vinu Sooriyaarachchi, David J. Lary, Lakitha O. H. Wijeratne, John Waczak

“…However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. …”
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Leveraging the Power of Hybrid and Standalone Machine Learning for Enhanced FRP-Confined Concrete Columns Strength Prediction by Mehdi Ghasri, Mohammad Ghasemi, Abdolhamid Salarnia

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Bone replacement material with optimized structure, manufactured using additive printing technology by Hergenröder Anna, Scherrieble Andreas, Linti Carsten, Gresser Götz T.

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Small Opening Flow Linearity Optimization of High-Pressure and Large-Flow Two-Way Proportional Throttle Valve by Baosheng Wang, Liu Yang, Tianxiong Gao, Qingxin Meng, Shouwen Xiao, Chao Ai

“…Secondly, a finite element model of the proportional throttle valve’s main valve flow field is developed. …”
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Review of machine learning applications for predicting the quality of biomass briquettes for sustainable and low-carbon energy solutions by Constance Nakato Nakimuli, Fred Kaggwa, Johan De Greef, David Kilama Okot, Julien Blondeau, Simon Kawuma

“…This paper reviews literature on various Machine Learning models applied for predicting and optimizing briquette quality parameters, including combustion, physical, and emission properties. …”
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Fusion of UAV-Acquired Visible Images and Multispectral Data by Applying Machine-Learning Methods in Crop Classification by Zuojun Zheng, Jianghao Yuan, Wei Yao, Paul Kwan, Hongxun Yao, Qingzhi Liu, Leifeng Guo

“…This study provides a comprehensive analysis from feature extraction to model evaluation, identifying the optimal combination of features to improve crop classification and providing valuable insights for advancing precision agriculture through data fusion and machine learning techniques.…”
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Machine learning investigation of wavy fin using penta type nanomaterial with multiple Parameters: Computational design by Badreddine Ayadi, Adnan, Sami Ullah Khan, Mutasem Z. Bani-Fwaz, Dana Mohammad Khidhir, Muhammad Mahmood Ali, Muhammad Nasir Bashir, Mohamed Bechir Ben Hamida

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Flood risk mapping and performance efficiency evaluation of machine learning algorithms: Best practice in northern Iran by M. Shirmohammadi, M. Shirmohammadi, S. Pirasteh, W. Li, D. Mafi-Gholami

“…However, challenges remain in optimizing the accuracy and reliability of machine learning (ML) algorithms for flood susceptibility assessment. …”
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Machine learning-assisted design of immunomodulatory lipid nanoparticles for delivery of mRNA to repolarize hyperactivated microglia by Mehrnoosh Rafiei, Akbar Shojaei, Ying Chau

“…The MLP model was validated by predicting the performance of four unseen LNP formulations delivering eGFP mRNA to LPS-activated BV2 cells. …”
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Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal by Erica Shrestha, Suyog Poudyal, Anup Ghimire, Shrena Maharjan, Manoj Lamichhane, Sushant Mehan

“…The suitability of both empirical and machine learning (ML) models with limited climatic variables for estimating ET0 in Nepal remains unexplored. …”
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Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data by Tongtong Jin, Ayitijiang· Halili

“…This study systematically developed machine learning (ML) models to predict depression risk in disabled elderly individuals using longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), providing a potentially generalizable tool for early screening.MethodsThis study utilized longitudinal data from the CHARLS 2011–2015 cohort. …”
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Modified LSTM based skin lesion segmentation and classification using optimization concept by S. Gomathi, N. Arunachalam

“…The proposed model categorizes skin lesions as normal, benign, or malignant. …”
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Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) by Degninou Yehadji, Degninou Yehadji, Geraldine Gray, Carlos Arias Vicente, Petros Isaakidis, Petros Isaakidis, Abdourahimi Diallo, Saa Andre Kamano, Thierno Saidou Diallo

“…Support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and four stacked models were developed. …”
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Optimizing EEG - based Emotion Recognition with a multi-modal ensemble approach by Kavitha K V, L R Sudha, J S Jayasudha

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Monitoring Canopy Height in the Hainan Tropical Rainforest Using Machine Learning and Multi-Modal Data Fusion by Qingping Ling, Yingtan Chen, Zhongke Feng, Huiqing Pei, Cai Wang, Zhaode Yin, Zixuan Qiu

“…The results showed that RH80 was the optimal choice for the prediction model regarding percentile selection, and the RF algorithm exhibited the optimal performance in terms of accuracy and stability, with R² values of 0.71 and 0.60 for the training and testing sets, respectively, and a relative root mean square error of 21.36%. …”
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