Investigation of oil palm fruit bunch ripeness classification using machine learning classifiers by Zulkhoiri Muhammad Arif, Ali Hasimah, Ahmad Zaidi Ahmad Firdaus, Mohd Kanafiah Siti Nurul Aqmariah, Jusman Yessi, Elshaikh Mohamed, Tuan Noor Tuan Muhammad Taufiq Aiman

“…The mean and standard deviation of colour-based features were then subjected to k-Nearest Neigbour (kNN) and Support Vector Machine (SVM) classifier utilizing two different strategies of hold-out and 10-fold cross-validation. …”
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Malicious Attack Correlation Analysis Method of Source-Grid-Load System under Specific Attack Scenarios by Rui ZHANG, Jiaxuan FEI, Congcong SHI, Xiaojian ZHANG, Xiuli HUANG, Qi WANG

“…Firstly, an attribute-based multi-source event fusion model is constructed to perform multi-source data fusion processing on abnormal events of cyber layer and electrical layer. …”
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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

“…Meanwhile, the near-field RCS is analyzed based on the magnitude of the Poynting vector, obtained through High Frequency Structure Simulator (HFSS) shooting and bouncing ray plus (SBR+) simulation. …”
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Intelligent Photolithography Corrections Using Dimensionality Reductions by Parag Parashar, Chandni Akbar, Tejender S. Rawat, Sparsh Pratik, Rajat Butola, Shih H. Chen, Yung-Sung Chang, Sirapop Nuannimnoi, Albert S. Lin

“…Additionally, we demonstrate the automation in SEM images preprocessing using feature detection, and this facilitates intelligent manufacturing in semiconductor processing. The input vector dimensions are effectively reduced by two orders of magnitude while the observed mean squared error is not affected significantly. …”
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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 refined data are then fed into advanced predictive models, including a bidirectional neural network for capturing long-term dependencies, an extreme learning machine, and a support vector regression model. These models address nonlinear patterns in the historical data. …”
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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

“…Human melanoma cell line SK-MEL-28 was transfected with a SOX3 expression vector to evaluate the impact of SOX3-induced expression on EMT markers, cellular viability, and wound healing. …”
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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 results demonstrated that the LSTM outperformed the convolutional neural network (CNN) and multi-class support vector machine (MCSVM) approaches. Also, features selected by the DAE showed better performance compared to features extracted using both genetic algorithms (GAs) and sequential forward selection (SFS). …”
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Preclinical Evaluation of ⁶⁸Ga-labeled RGD Peptide for Detection of Malignant Angiogenesis by O. E. Klementyeva, A. B. Bruskin, A. S. Lunev, M. G. Rakhimov, K. A. Luneva, G E. Codina

“…The molecular participants in the process are gallium-68 labeled vector to the delivery of radionuclides and positron-emission tomography (PET) imaging.Aim. …”
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Anderson impurity solver integrating tensor network methods with quantum computing by François Jamet, Lachlan P. Lindoy, Yannic Rath, Connor Lenihan, Abhishek Agarwal, Enrico Fontana, Fedor Simkovic IV, Baptiste Anselme Martin, Ivan Rungger

“…Solving the Anderson impurity model typically involves a two-step process, where one first calculates the ground state of the Hamiltonian and then computes its dynamical properties to obtain Green’s function. …”
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Rapid screening and optimization of CO2 enhanced oil recovery operations in unconventional reservoirs: A case study by Shuqin Wen, Bing Wei, Junyu You, Yujiao He, Qihang Ye, Jun Lu

“…Three different methods, namely random forest (RF), support vector regression (SVR), and artificial neural network (ANN), were used to establish proxy models using the data from a specific unconventional reservoir, and the RF model demonstrated a preferable performance. …”
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A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities by José Luis Uc Castillo, Ana Elizabeth Marín Celestino, Diego Armando Martínez Cruz, José Tuxpan Vargas, José Alfredo Ramos Leal, Janete Morán Ramírez

“…It observed that Artificial Neural Networks (ANN) models were preferred, probably due to their capability to learn and model non-linear and complex relationships in addition to other popular models such as Random Forest (RF) and Support Vector Machines (SVM). It identified that the selection and application of the algorithms rely on the study objective and the data patterns. …”
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Convolutional Neural Networks for Automatic Identification of Individuals at Terrestrial Terminals by Darwin Yarango-Farro, Alex Mondragon-Fernandez, Heber I. Mejia-Cabrera, Juan Arcila-Diaz

“…In FAISS, the embeddings were stored in vector format to facilitate fast and efficient querying. …”
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Grape vine (Vitis vinifera) yield prediction using optimized weighted ensemble machine learning approach by Nobin Chandra Paul, Pratapsingh S. Khapte, Navyasree Ponnaganti, Sushil S. Changan, Sangram B. Chavan, K. Ravi Kumar, Dhananjay D. Nangare, K. Sammi Reddy

“…A diverse set of machine learning (ML) models, including Random Forest (RF), Artificial Neural Network (ANN), Extreme Gradient Boosting (XgBoost), Support Vector Regression (SVR), Gaussian Process Regression (GPR), Cubist and Multivariate Adaptive Regression Splines (MARS), were employed to model the grapevine yield. …”
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Generative AI-Enhanced Cybersecurity Framework for Enterprise Data Privacy Management by Geeta Sandeep Nadella, Santosh Reddy Addula, Akhila Reddy Yadulla, Guna Sekhar Sajja, Mohan Meesala, Mohan Harish Maturi, Karthik Meduri, Hari Gonaygunta

“…At its core, the anomaly detection engine integrates machine learning models, such as Random Forest and Support Vector Machines (SVMs), alongside deep learning techniques like Long Short-Term Memory (LSTM) networks, delivering robust performance across diverse domains. …”
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A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis by Fatima Hasan Al-bakri, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Zulkifli Tahir, The Alzheimer’s Disease Neuroimaging Initiative

“…The methodology involves training an ensemble model that integrates Random Forest, Support Vector Machine, XGBoost, and Gradient Boosting classifiers, with meta-logistic regression used for the final decision. …”
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Ferritin1-mediated ferroptosis participates in granular acini degeneration of Haemaphysalis longicornis salivary glands by Dongqi Yuan, Songqin Chen, Yongzhi Zhou, Jie Cao, Houshuang Zhang, Yanan Wang, Jinlin Zhou

“…Our findings are important for developing novel preventive measures against H. longicornis as a disease vector.…”
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Adapting a global plant identification model to detect invasive alien plant species in high-resolution road side images by Vincent Espitalier, Jean-Christophe Lombardo, Hervé Goëau, Christophe Botella, Toke Thomas Høye, Mads Dyrmann, Pierre Bonnet, Alexis Joly

“…Recent advancements in vehicle-mounted equipment enable automatic analysis of high-resolution images to detect invasive plants along roadsides, a primary vector for their spread. Deep learning technologies show promise for processing this data efficiently, but the choice of approach significantly affects both computational and human resource costs. …”
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Visual Impairment in Stroke Patients: a Two-Part Review. Part II — Rehabilitation Methods by Tatyana V. Marfina, Tatiana V. Konchugova, Tatiana V. Apkhanova, Detelina B. Kulchitskaya, Anastasiya A. Mukhina

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Research on green supply chain finance risk identification based on two-stage deep learning by Ying Liu, Sizhe Li, Chunmei Yu, Mingli Lv

“…Finally, to model low-order feature interactions, we integrate the Support Vector Machine (SVM) algorithm. The paper is grounded in the green innovation production of enterprises, collecting financial data of 176 upstream and downstream enterprises and corresponding core enterprise green indicators from 2013 to 2022. …”
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