MSBiLSTM-Attention: EEG Emotion Recognition Model Based on Spatiotemporal Feature Fusion by Yahong Ma, Zhentao Huang, Yuyao Yang, Zuowen Chen, Qi Dong, Shanwen Zhang, Yuan Li

“…The proposed model was evaluated on the SEED dataset for emotion classification. …”
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YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation by Madalena de Oliveira Barbosa, Fernanda Pereira Leite Aguiar, Suely dos Santos Sousa, Luana dos Santos Cordeiro, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

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Machine Learning Approach for Assessment of Compressive Strength of Soil for Use as Construction Materials by Yassir M. H. Mustafa, Yakubu Sani Wudil, Mohammad Sharif Zami, Mohammed A. Al-Osta

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A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model by Shouming Qi, Ao Zheng

“…The experimental results demonstrated strong alignment between the enhanced model and VISSIM simulations in vehicle speed and headway calibration, achieving classification accuracy rates exceeding 92.71%. …”
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The Arab world at a crossroads: assessing future risks under changing climate by Rasha M. Abou Samra, R. R. Ali

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Fine-tuning transformer models for M&A target prediction in the U.S. ENERGY sector by Inés Rodríguez-Muñoz-de-Baena, María Coronado-Vaca, Esther Vaquero-Lafuente

“…We provide empirical evidence on LLMs’ capability in the direct classification of M&A target companies, with FinBERT utilizing oversampling, being the top-performing model due to its high precision and minimized false positives, critical for precise financial decision-making. …”
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A Risk Analysis Model for Biosecurity in Brazil Using the Analytical Hierarchy Process (AHP) by Fillipe Augusto da Silva, Adriana Marcos Vivoni, Harrison Magdinier Gomes, Leonardo Augusto dos Santos Oliveira, Annibal Parracho Sant’Anna, Luiz Octávio Gavião

“…This study proposes a risk analysis model based on the principles of ISO 31000 and decision theory for biological agents with potential for offensive use in Brazil. …”
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Development and Optimization of a Novel Deep Learning Model for Diagnosis of Quince Leaf Diseases by A. Naderi Beni, H. Bagherpour, J. Amiri Parian

“…DCNNs improve detection or classification accuracy by developing machine-learning models with many hidden layers to extract optimal features. …”
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Exploring the process—structure–property relationship of nylon aramid 3D printed composites and parameter optimization using supervised machine learning techniques by Mohammed Raffic Noor Mohamed, Ganesh Babu Karuppiah, Dharani Kumar Selvan, Rajasekaran Saminathan, Shubham Sharma, Shashi Prakash Dwivedi, Sandeep Kumar, Mohamed Abbas, Dražan Kozak, Jasmina Lozanovic

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Integration of wing pattern morphology and deep learning to support Plusiinae (Lepidoptera: Noctuidae) pest identification by Karina M. Torres, Chenjiao Tan, Kayla A. Mollet, Allan H. Smith-Pardo, Rui Xu, Changying Li, Silvana V. Paula-Moraes

“…Five deep learning models were trained on lab-reared specimens with high-quality wing patterns and evaluated for model generalization using field-collected specimens for three classification tasks: classification of SBL and CBL; male and female SBL and CBL; and SBL, CBL, and GLM. …”
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Predicting Mesothelioma Using Artificial Intelligence: A Scoping Review of Common Models and Applications by Malihe Ram MS, Mohammad Reza Afrash PhD, Khadijeh Moulaei PhD, Erfan Esmaeeli, Mohadeseh Sadat Khorashadizadeh, Ali Garavand PhD, Parastoo Amiri PhD, Azam Sabahi PhD

“…SVM, DT, and RF emerged as prominent models, achieving high accuracies ranging from 78.3% to 99.97%. …”
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Benchmarking bias in embeddings of healthcare AI models: using SD-WEAT for detection and measurement across sensitive populations by Magnus Gray, Leihong Wu

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A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer by Ziynet Pamuk, Hüseyin Erikçi

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Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks by Muawia A. Elsadig, Abdelrahman Altigani, Yasir Mohamed, Abdul Hakim Mohamed, Akbar Kannan, Mohamed Bashir, Mousab A. E. Adiel

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Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

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Cervical Cancer Prediction Based on Imbalanced Data Using Machine Learning Algorithms with a Variety of Sampling Methods by Mădălina Maria Muraru, Zsuzsa Simó, László Barna Iantovics

“…The obtained results show that resampling methods help improve the classification ability of prediction models applied to cervical cancer data. …”
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Detection of Irrigated and Non-Irrigated Soybeans Using Hyperspectral Data in Machine-Learning Models by Izabela Cristina de Oliveira, Ricardo Gava, Dthenifer Cordeiro Santana, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Mayara Favero Cotrim, Regimar Garcia dos Santos, Rita de Cássia Félix Alvarez, Carlos Antonio da Silva Junior, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro

“…The objectives of this work are (i) to classify soybean cultivars under different irrigation managements using hyperspectral data, looking for the best machine-learning algorithm for the classification and the input that improves the performance of the models. …”
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Evaluation of the Effectiveness of the UNet Model with Different Backbones in the Semantic Segmentation of Tomato Leaves and Fruits by Juan Pablo Guerra Ibarra, Francisco Javier Cuevas de la Rosa, Julieta Raquel Hernandez Vidales

“…The task focuses on pixel-wise classification into three categories: leaves, fruits, and background, based on images of semi-hydroponic tomato crops captured in greenhouse settings. …”
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Evaluation of multiple machine learning models predicting the results of hybrid imaging in primary hyperparathyroidism by Anna Drynda, Jacek Podlewski, Karolina Kucharczyk, Grzegorz Sokołowski, Anna Sowa-Staszczak, Alicja Hubalewska-Dydejczyk, Małgorzata Trofimiuk- Müldner

“…The aim of this study is to evaluate predictive strategies for the assessment of radiotracer uptake in pre-operative [99mTc]Tc-sestamibi scintigraphy ([99mTc] Tc-MIBI SPECT-CT) among PHP patients to identify individuals with a high probability of negative results, and to develop clinical decision-making tools. MATERIAL AND METHODS: Development and evaluation of logistic regression (LR), classification trees utilizing the classification and regression trees (CART) algorithm, random forest (RF), and boosted trees employing XGBoost (XGB) predictive models. …”
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Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression by Haoshen Qin, Lal Hussain, Ziang Liu, Xu Yan, Fuad A. Awwad, Faisal Mehmood Butt, Umair Ahmad Salaria, Emad A.A. Ismail

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