The application of artificial intelligence techniques in predicting game outcomes of professional basketball league: A systematic review. by Chuxuan Li, Hao Zhang, Yuan Zhang, Jing Shen, Ruopeng An

“…The findings reveal that artificial intelligence models, particularly the multilayer perceptron neural network, achieved a high prediction accuracy of 98.90%. …”
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Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel by Fatemeh Mobasheri, Mostafa Khajeh, Mansour Ghaffari-Moghaddam, Jamshid Piri, Mousa Bohlooli

“…Two machine learning models, LSBoost with Random Forest (LSBoost/RF) and LSBoost with K-Nearest Neighbors Neural Network (LSBoost/KNN-NN), were developed and compared for predicting extraction outcomes. …”
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A Comprehensive Survey of Explainable Artificial Intelligence Techniques for Malicious Insider Threat Detection by Khuloud Saeed Alketbi, Abid Mehmood

“…Tools such as SHAP and LIME are examined for their role in revealing feature contributions and improving analyst insight. …”
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NIR-RGB-M²Net: A Fusion Model for Precise Agricultural Field Segmentation Using Multisource Remote Sensing Data by Zhankui Tang, Xin Pan, Xiangfei She, Jian Zhao

“…Multisource remote sensing combines near-infrared (NIR) and visible light (RGB) data to leverage complementary features, but fusing these modalities often requires complex networks that risk losing vegetation signals and boundary details. …”
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Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning by Min Liang, Zhiwen Zhang, Langming Wu, Mafeng Chen, Shifan Tan, Jian Huang

“…Predictive models were built using Random Forest, XGBoost, Decision Trees, and Artificial Neural Networks, with their performance evaluated via metrics including the area under the receiver operating characteristic curve (AUC), calibration plots, brier score, and decision curve analysis (DCA). …”
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Ground Segmentation for LiDAR Point Clouds in Structured and Unstructured Environments Using a Hybrid Neural–Geometric Approach by Antonio Santo, Enrique Heredia, Carlos Viegas, David Valiente, Arturo Gil

“…This paper introduces a hybrid framework that synergizes multi-resolution polar discretization with sparse convolutional neural networks (SCNNs) to address these challenges. The method hierarchically partitions point clouds into adaptive sectors, leveraging PCA-derived geometric features and dynamic variance thresholds for robust terrain modeling, while a SCNN resolves ambiguities in data-sparse regions. …”
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A low-complexity M-shaped reconfigurable intelligent meta-surface for mitigating pathloss in wireless systems by Maira Khafagy, Sherief Fathi, Ahmed Magdy

“…These findings underscore the potential of the proposed LCM-RIM design for practical deployment in future 6G networks, offering an efficient and scalable solution to address mmWave path loss in enclosed environments.…”
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Integrative bulk and single-cell transcriptomic analysis identifies a migrasome-associated lncRNA signature predictive of prognosis and immune landscape in clear cell renal cell ca... by Junlin Shen, Chun Wang, Mingpeng Zhang, Bin Chen, Liwei Liu, Jing Tian, Zhiqun Shang

“…The associations between the model and overall survival (OS), functional enrichment, tumor mutation burden (TMB), tumor microenvironment (TME) characteristics, immune evasion, and drug sensitivity were evaluated. Single-cell transcriptomic analysis was performed to determine cell type–specific expression patterns and intercellular communication networks. …”
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Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification by Soumyarashmi Panigrahi, Dibya Ranjan Das Adhikary, Binod Kumar Pattanayak

“…We also evaluated five additional pre-trained models-VGG19, InceptionV3, Xception, MobileNetV2, and ResNet50V2 and incorporated Multi-Head Self-Attention (MHSA) and Squeeze-and-Excitation Attention (SEA) blocks individually to improve feature representation. …”
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ECG Signal Analysis for Detection and Diagnosis of Post-Traumatic Stress Disorder: Leveraging Deep Learning and Machine Learning Techniques by Parisa Ebrahimpour Moghaddam Tasouj, Gökhan Soysal, Osman Eroğul, Sinan Yetkin

“…These images were classified using deep learning-based convolutional neural networks (CNNs), including AlexNet, GoogLeNet, and ResNet50. …”
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Different pixel sizes of topographic data for prediction of soil salinity. by Shima Esmailpour, Ebrahim Mahmoudabadi, Mohammad Ghasemzadeh Ganjehie, Alireza Karimi

“…This study was aimed to examine the accuracy of soil salinity prediction model integrating ANNs (artificial neural networks) and topographic factors with different cell sizes. …”
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THE INVERSE GAUSSIAN PLUME METHOD FOR ESTIMATING THE LEVEL OF AIR POLLUTION by Volodymyr Hura, Liubomyr Monastyrskyi

“…Its demonstrated predictive capability makes it an asset for enhancing environmental monitoring programs, potentially supplementing fixed monitoring networks and identifying areas of concern. Furthermore, the model's utility extends significantly into the domain of regulatory compliance, facilitating environmental impact assessments for proposed industrial activities and evaluating the effectiveness of emission control measures.…”
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MultiSEss: Automatic Sleep Staging Model Based on SE Attention Mechanism and State Space Model by Zhentao Huang, Yuyao Yang, Zhiyuan Wang, Yuan Li, Zuowen Chen, Yahong Ma, Shanwen Zhang

“…Sleep occupies about one-third of human life and is crucial for health, but traditional sleep staging relies on experts manually performing polysomnography (PSG), a process that is time-consuming, labor-intensive, and susceptible to subjective differences between evaluators. With the development of deep learning technologies, particularly the application of convolutional neural networks and recurrent neural networks, significant progress has been made in automatic sleep staging. …”
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The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UK by Solomon White, Tiago Silva, Laurent O. Amoudry, Evangelos Spyrakos, Adrien Martin, Adrien Martin, Encarni Medina-Lopez

“…The model incorporated Shapley values to evaluate feature importance, offering insight into the contributions of specific bands and environmental factors. …”
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A Multi-Agent and Attention-Aware Enhanced CNN-BiLSTM Model for Human Activity Recognition for Enhanced Disability Assistance by Mst Alema Khatun, Mohammad Abu Yousuf, Taskin Noor Turna, AKM Azad, Salem A. Alyami, Mohammad Ali Moni

“…<b>Results:</b> Out of the nine ML models and four DL models, the top performers are selected and combined in three stages for feature extraction. The effectiveness of this three-stage ensemble strategy is evaluated utilizing various performance metrics and through three distinct experiments. …”
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YED-Net: Yoga Exercise Dynamics Monitoring with YOLOv11-ECA-Enhanced Detection and DeepSORT Tracking by Youyu Zhou, Shu Dong, Hao Sheng, Wei Ke

“…By integrating the Mars-smallCNN feature extraction network with a Kalman filtering-based trajectory prediction module, the system attains 58.3% Multiple Object Tracking Accuracy (MOTA) and 62.1% Identity F1 Score (IDF1) in dense multi-object scenarios, representing an improvement of approximately 9.8 percentage points over the conventional YOLO+DeepSORT method. …”
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Proposing a machine learning-based model for predicting nonreassuring fetal heart by Nasibeh Roozbeh, Farideh Montazeri, Mohammadsadegh Vahidi Farashah, Vahid Mehrnoush, Fatemeh Darsareh

“…The information was acquired from the “Iranian Maternal and Neonatal Network.“A predictive model was built using four statistical ML models (decision tree classification, random forest classification, extreme gradient boost classification, and permutation feature classification with k-nearest neighbors). …”
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EmotionNet-X: An Optimized CNN Architecture for Robust Facial Emotion Analysis by Syed Muhammad Aqleem Abbas, Qaisar Abbas, Syed Muhammad Naqi

“…Existing pretrained models suffer from high computational costs, limiting real-time IoT deployment. Deep Neural Networks (DNNs), particularly Convolutional Neural Networks (CNNs), are widely used for facial expression recognition (FER). …”
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Development of a prognostic model for chemotherapy response and identification of TNFAIP2 as a target in colorectal cancer by Cheng Zhou, Chuan-Hai Xu, Min Xu, Xin-Kun Huang, Bin Zhu

“…In this study, we aimed to identify key genes associated with oxaliplatin resistance in CRC and to evaluate their potential as prognostic biomarkers. …”
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Handwritten Urdu Characters and Digits Recognition Using Transfer Learning and Augmentation With AlexNet by Aqsa Rasheed, Nouman Ali, Bushra Zafar, Amsa Shabbir, Muhammad Sajid, Muhammad Tariq Mahmood

“…The purpose of this research is to present a classification framework for automatic recognition of handwritten Urdu character and digits with higher recognition accuracy by utilizing theory of transfer learning and pre-trained Convolution Neural Networks (CNN). The performance of transfer learning is evaluated in different ways: by using pre-trained AlexNet CNN model with Support Vector Machine (SVM) classifier, and fine-tuned AlexNet for extracting features and classification. …”
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