Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning by Abdul Razzaq, Sharaiz Shahid, Muhammad Akram, Muhammad Ashraf, Shahid Iqbal, Aamir Hussain, M. Azam Zia, Sulman Qadri, Najia Saher, Faisal Shahzad, Ali Nawaz Shah, Aziz-ur Rehman, Sven-Erik Jacobsen

“…Stomata are the main medium of plants for the trade of water, regulate the gas exchange, and are responsible for the process of photosynthesis and transpiration. The stomata are surrounded by guard cells, which help to control the rate of transpiration by opening and closing the stomata. …”
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Enhancing drilling performance in 3D printed PLA implants application of PIV and ML models by K Shunmugesh, M Ganesh, R Bhavani, M. Adam Khan, M. Saravana Kumar, L. Rajeshkumar, Priyanka Mishra, Rajesh Jesudoss Hynes Navasingh, Angela Jennifa Sujana J, Jana Petru, Čep Robert

“…This work employs the Proximity Indexed Value (PIV) tool to predict the near-optimal value for improving hole quality and enhancing the drilling process. In addition, Artificial neural network (ANN), Support vector machine (SVM), and Random Forest (RF) models were employed to predict the optimized results by PIV method. …”
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Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products by Shruthi Pandi Chelvam, Alice Jie Ying Ng, Jiayi Huang, Elizabeth Lee, Maciej Baranski, Derrick Yong, Rohan B. H. Williams, Stacy L. Springs, Rajeev J. Ram

“…Abstract We demonstrate the feasibility of machine-learning aided UV absorbance spectroscopy for in-process microbial contamination detection during cell therapy product (CTP) manufacturing. …”
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An efficient retrieval method on Google Earth Engine and comparison with hybrid methods: a case study of leaf area index retrieval by Sijia Li, Zhiguang Tang, Kaisen Ma, Zhenyi Wang, Wenjuan Li

“…The performances of LUT and hybrid methods, including random forest (RF), gradient boosting regression tree (GBRT), classification and regression tree (CART), support vector regression (SVR), and Gaussian process regression (GPR), were evaluated on GEE by simulation experiments. …”
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Prediction of Optimum Operating Parameters to Enhance the Performance of PEMFC Using Machine Learning Algorithms by Arunadevi M, Karthikeyan B, Anirudh Shrihari, Saravanan S, Sundararaju K, R Palanisamy, Mohamed Awad, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Hany S. Hussein, Mahmoud M. Hussein, Ahmed I. Omar

“…This paper is structured to study the influence of different process parameters such as system temperature, fuel supply pressure, air supply pressure, fuel flow rate and air flow rate on the output voltage of the FC. …”
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Predicting Predisposition to Tropical Diseases in Female Adults Using Risk Factors: An Explainable-Machine Learning Approach by Kingsley Friday Attai, Constance Amannah, Moses Ekpenyong, Said Baadel, Okure Obot, Daniel Asuquo, Ekerette Attai, Faith-Valentine Uzoka, Emem Dan, Christie Akwaowo, Faith-Michael Uzoka

“…This approach provided insights into the models’ decision-making process and identified key risk factors, enabling healthcare professionals to personalize treatment services. …”
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Integrating geodetic infrastructures for GNSS displacements analysis in Chile: A case study with REDGEOMIN (2019–2022) by José Antonio Tarrío Mosquera, Valeria Vásquez Tejo, Catalina Cáceres Venegas, Miguel Marten-Allendes, Juan Giglio Gutierrez, Fernando Isla Rodríguez, Marcelo Caverlotti Silva, Gabriel Jeldres Vergara, Rodrigo Urrutia Vidal, Cristian Mardones Castro

“…This work thoroughly analyses GNSS geodetic infrastructure and covers equipment specifications, post-processing data availability, and quality control measures. …”
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Egg-driven immunosuppression and granuloma zonation in Peyer’s patches of mice with Schistosoma japonicum infection by Linzhu Li, Jing Wu, Guangxu Cao, Jiakai Yao, Yanping Miao, Yanglin Zhuang, Yushen Xiang, Xiaolin Zhong, Yicong Liu, Fubo Chen, Yalei Dai, Yang Dai, Xindong Xu, Qingfeng Zhang

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On the Use of Azimuth Cutoff for Sea Surface Wind Speed Retrieval From SAR by Yuting Zhu, Giuseppe Grieco, Jiarong Lin, Marcos Portabella, Xiaoqing Wang

“…The methodology probabilistically combines SAR data with ancillary meteorological information and optimizes the retrieval process through a cost function that leverages the sensitivity of the azimuth cutoff to changes in wind vector fields. …”
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Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems by Baolei Xu, Yunfa Fu, Gang Shi, Xuxian Yin, Zhidong Wang, Hongyi Li, Changhao Jiang

“…The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using “MIFS” feature selection criterion, scaled feature using “MIFS” feature selection criterion, and scaled feature using “mRMR” feature selection criterion. …”
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Detection of Psychomotor Retardation in Youth Depression: A Machine Learning Approach to Kinematic Analysis of Handwriting by Vladimir Džepina, Nikola Ivančević, Sunčica Rosić, Blažo Nikolić, Dejan Stevanović, Jasna Jančić, Milica M. Janković

“…The feature selection process revealed that velocity-related features were most effective in distinguishing patients with depression from controls, expectedly reflecting a slowdown in psychomotor functioning among the patients. …”
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Research on Reservoir Identification of Gas Hydrates with Well Logging Data Based on Machine Learning in Marine Areas: A Case Study from IODP Expedition 311 by Xudong Hu, Wangfeng Leng, Kun Xiao, Guo Song, Yiming Wei, Changchun Zou

“…This article selects six ML methods, including Gaussian process classification (GPC), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), extreme gradient boosting (XGBoost), and logistic regression (LR). …”
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Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction by Varun Shinde, Vineet Dhanawat, Ahmad Almogren, Anjanava Biswas, Muhammad Bilal, Rizwan Ali Naqvi, Ateeq Ur Rehman

“…The aim to use GCN is due to its ability to improve the feature extraction process by utilizing the spatial and structural affiliation between elements in the digital images. …”
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A comparative performance analysis of machine learning models for compressive strength prediction in fly ash-based geopolymers concrete using reference data by Muhammad Kashif Anwar, Muhammad Ahmed Qurashi, Xingyi Zhu, Syyed Adnan Raheel Shah, Muhammad Usman Siddiq

“…This study will have made attempt to address the complexities involves in the concrete mix designs process with the aim of achieving the desired 28-day compressive strength for FAGP. …”
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A Comparative Study of Machine Learning Models for Accurate E-Waste Prediction by Mohammed Algafri, Mohammed Sayad, Mohammad A.M. Abdel-Aal, Ahmed M. Attia

“…This study evaluates six Machine Learning (ML) models, Linear Regression, Regression Tree, Support Vector Regression, Ensemble Regression, Gaussian Process Regression (GPR), and Artificial Neural Networks, for e-waste forecasting. …”
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FiSC: A Novel Approach for Fitzpatrick Scale-Based Skin Analyzer’s Image Classification by Guillermo Crocker Garcia, Muhammad Numan Khan, Aftab Alam, Josue Obregon, Tamer Abuhmed, Eui-Nam Huh

“…Our method involves modeling image features as a nine-dimensional feature vector, followed by a dimensionality reduction process to identify the most influential features and dominant areas within the feature space, enabling deployment on low-power devices. …”
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Calculation and Measurement of Near-Field RCS and Received Power Using a Downscaled Model of Precision-Guided Munition by Kyuhwan Hwang, Daeyeong Yoon, Kyounghwan Jo, Hyounjoon Joo, Inbok Kim, Honghee Kim, Hongsun Yoon, Jeongsub Kim, Yong Bae Park

“…Measuring the reflective properties between actual-sized targets and PGMs is a very costly and time-consuming process. Therefore, to facilitate such measurements even in a simple laboratory environment, the target encounter scenario is configured using a fixed target and a moving horn antenna in this study. …”
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Hybrid AI-Based Framework for Renewable Energy Forecasting: One-Stage Decomposition and Sample Entropy Reconstruction with Least-Squares Regression by Nahed Zemouri, Hatem Mezaache, Zakaria Zemali, Fabio La Foresta, Mario Versaci, Giovanni Angiulli

“…The framework uses a one-stage decomposition strategy, applying variational mode decomposition and an improved empirical mode decomposition method with adaptive noise. This process effectively extracts meaningful components while reducing background noise, improving data quality, and minimizing uncertainty. …”
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SOX3 protein is associated with epithelial-mesenchymal transition (EMT) changes in human melanoma cell by Ana Paula Gomes dos Santos Miranda, Bruna Mendes Lima, Bárbara Andrade de Carvalho, Emanuele Tadeu Pozzolini, Diego Crimi de Castro, Fábio Eduardo dos Santos, Luciana de Oliveira Andrade, Jeremy W. Prokop, Adam Underwood, Helen Lima Del Puerto, Enio Ferreira

“…Epithelial-mesenchymal transition (EMT) is a biological phenomenon related to increasing invasion and metastasis, and SOX family proteins may be involved in this process. Thus, the study aims to investigate the role of the transcription factor SOX3 in EMT in human melanoma cells. …”
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Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants by Mohamed Farag Taha, Hanping Mao, Samar Mousa, Lei Zhou, Yafei Wang, Gamal Elmasry, Salim Al-Rejaie, Abdallah Elshawadfy Elwakeel, Yazhou Wei, Zhengjun Qiu

“…The suggested methodology presents a pathway to automating the process of nutrient status diagnosis throughout the entire plant life cycle, with the LSTM technique poised to assume a pivotal role in forthcoming time-series analyses for precision agriculture.…”
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