The impact of online food delivery applications on dietary pattern disruption in the Arab region by Radwan Qasrawi, Radwan Qasrawi, Suliman Thwib, Ghada Issa, Malak Amro, Razan AbuGhoush, Maha Hoteit, Maha Hoteit, Sahar Khairy, Narmeen Jamal Al-Awwad, Narmeen Jamal Al-Awwad, Khlood Bookari, Sabika Allehdan, Dalal Alkazemi, Haleama Al Sabbah, Salima Al Maamari, Asma H. Malkawi, Reema Tayyem

“…Model performance was evaluated using multiple metrics, including sensitivity, precision, F1-score, and AUC. …”
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Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network by Chen Zhou, Fan Zhang, Edric John Cruz Nacpil, Zheng Wang, Fei-Xiang Xu

“…Experimental results demonstrated that the proposed CNN-GRU model performs significantly better than baseline models. …”
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Hierarchical attention transformer provides assistant suggestions for orbital rejuvenation surgery by Xiang Lian, Xiang Lian, Xiang Lian, Xin Hu, Guannan Li, Siqi Wu, Yihao Liu, Ke Qin, Kai Liu, Kai Liu, Kai Liu

“…The model’s robust performance supports its potential for assisting clinical decision-making in cosmetic surgery, facilitating accessible and timely treatment recommendations.…”
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Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients by Abdou Khadir Dia, Alona Kolnohuz, Sevinj Yolchuyeva, Marion Tonneau, Fabien Lamaze, Michele Orain, Andréanne Gagné, Florence Blais, François Coulombe, Julie Malo, Wiam Belkaid, Arielle Elkrief, Drew Williamson, Bertrand Routy, Philippe Joubert, Mathieu Laplante, Steve Bilodeau, Venkata SK. Manem

“…From the histological images of tumors, pathomics features were extracted. We employed six machine learning models and seven feature selection methods to predict expression of the programmed death-ligand 1 (PD-L1), a current biomarker used to select patients for immunotherapy, and progression-free survival (PFS). …”
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Transformer network for time series prediction via wavelet packet decomposition by Zhichao Wu, Aiye Shi, Yan Ping Tao

“…We evaluated WPFormer on four publicly available data-sets and compared its performance against the Informer, LogTrans, Reformer,LSTMa, LSTNet, and DeepAR models using MSE and MAE metrics. …”
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Ai for anomaly detection in glacier movement identifying climate change effect using machine learning by Kaur Sukhmeen, Kumar Sunil

“…Extensive preprocessing techniques, such as missing data handling, feature engineering, and outlier detection, were utilized to enhance model performance. …”
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Towards a Scalable and Adaptive Learning Approach for Network Intrusion Detection by Alebachew Chiche, Million Meshesha

“…Experimental result shows that 99.91% performance is registered after connection. Interestingly, significant knowledge rich learning for intrusion detection differs as a fundamental feature of intrusion detection and prevention techniques. …”
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Multi-Conv attention network for skin lesion image segmentation by Zexin Li, Hanchen Wang, Haoyu Chen, Chenxin Lin, Aochen Yan

“…To address the trade-off between segmentation performance and model lightweighting in computer-aided skin lesion segmentation, this paper proposes a lightweight network architecture, Multi-Conv Attention Network (MCAN). …”
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PD-Free Design of Insulation Systems: An Application to Laminated Busbars by Gian Carlo Montanari, Pasquale Cambareri

“…The model is validated through PD measurements performed on a laminated busbar, using new automatic software that can identify the type of source generating PD. …”
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Explainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent. by Alejandra de-la-Rica-Escudero, Eduardo C Garrido-Merchán, María Coronado-Vaca

“…In this work, driven by the motivation of making DRL explainable, we developed a novel Explainable DRL (XDRL) approach for PM, integrating the Proximal Policy Optimization (PPO) DRL algorithm with the model agnostic explainable machine learning techniques of feature importance, SHAP and LIME to enhance transparency in prediction time. …”
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Enhanced energy management in smart microgrids using hybrid optimization and demand response strategies by Lei Yang, Lile Wu, Genzhu Li

“…The proposed model advances microgrid performance by minimizing operational costs, reducing pollution emissions, and maximizing RES utilization. …”
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An Approach to Truck Driving Risk Identification: A Machine Learning Method Based on Optuna Optimization by Zhaofei Wang, Hao Li, Qiuping Wang

“…Combining the running time, precision, recall, and F1-Score, the LightGBM model optimized based on the Tree-structured Parzen Estimator (TPE) algorithm has the best performance with a precision of 0.98. …”
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An Optimized Deep-Learning-Based Network with an Attention Module for Efficient Fire Detection by Muhammad Altaf, Muhammad Yasir, Naqqash Dilshad, Wooseong Kim

“…While various traditional machine learning and deep learning techniques have been proposed, their detection performances remain poor, particularly due to low-resolution data and ineffective feature selection methods. …”
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Multi-target personnel tracking algorithm for coal mine based on improved YOLOv7 and ByteTrack by Pengcheng QU, Jingzhao LI, Zechao LIU

“…Then, the CBAM attention mechanism was introduced into Neck to improve the feature perception ability of the model in complex scenes. …”
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Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color Space by Bagus Raffi Santoso, Christy Atika Sari, Eko Hari Rachmawanto

“…Specifically, leveraging pre-trained CNN models as a foundation for feature extraction, while simultaneously fine-tuning specific layers to adapt to the nuances of coffee bean classification tasks, could offer improved model performance and scalability.…”
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IHML: Incremental Heuristic Meta-Learner by Onur Karadeli, Kıymet Kaya, Şule Gündüz Öğüdücü

“…The results show that the proposed model achieves more accuracy (in average % 10 and at most % 71 improvements) compared to the baseline machine learning models in the literature.…”
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Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

“…Measures including the R2 score, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) helped to measure their performance. With an R2 value of 0.996, the Extra Trees model displayed remarkable accuracy and consistency, so showing better performance than other models. …”
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Deep Separable Hypercomplex Networks by Nazmul Shahadat, Anthony S. Maida

“…Our proposed model achieves almost 2% higher performance for CIFAR and SVHN datasets and more than 3% for the ImageNet-Tiny dataset and takes 84%, 35%, and 51% fewer parameters than the ResNets, quaternion, and vectormap networks, respectively. …”
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MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF). by Hasim Khan, Ahmed Ibrahim Alutaibi, Ghanshyam G Tejani, Sunil Kumar Sharma, Ahmad Raza Khan, Fuzail Ahmad, Seyed Jalaleddin Mousavirad

“…Furthermore, an effective feature selection method is proposed using the Dipper- grey wolf optimization (DGWO) algorithm to improve the performance of the proposed model. …”
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Semantic Segmentation with Multispectral Satellite Images of Waterfowl Habitat by Mateo Gannod, Nicholas Masto, Collins Owusu, Cory Highway, Katherine Brown, Abigail Blake-Bradshaw, Jamie Feddersen, Heath Hagy, Douglas Talbert, Bradley Cohen

“…Unharvested flooded corn is a popular management tool on public and private lands that greatly increases landscape-level energy compared to other wetlands; thus, landscape bioenergetic models are particularly sensitive to these habitat features. …”
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