Harnessing Artificial Intelligence and Machine Learning for Identifying Quantitative Trait Loci (QTL) Associated with Seed Quality Traits in Crops by My Abdelmajid Kassem

“…This review presents an integrated overview of AI/ML applications in QTL mapping and seed trait prediction, highlighting key methodologies such as LASSO regression, Random Forest, Gradient Boosting, ElasticNet, and deep learning techniques including convolutional neural networks (CNNs) and graph neural networks (GNNs). …”
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Curated CYP450 Interaction Dataset: Covering the Majority of Phase I Drug Metabolism by Yu-Hao Ni, Yu-Wen Su, Shang-Chen Yang, Jia-Cheng Hong, Po-Wen Allen Du, Yu-Ting Hsu, Tien-Chueh Kuo, Yufeng Jane Tseng

“…Employing a combination of conventional machine learning techniques alongside advanced methodologies such as Graph Convolutional Networks (GCN), robust models have been developed to elucidate these drug-enzyme interactions. …”
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Chinese Mathematical Knowledge Entity Recognition Based on Linguistically Motivated Bidirectional Encoder Representation from Transformers by Wei Song, He Zheng, Shuaiqi Ma, Mingze Zhang, Wei Guo, Keqing Ning

“…In order to improve the accuracy of mathematical knowledge entity recognition and provide effective support for subsequent functionalities, this paper adopts the latest pre-trained language model, LERT, combined with a Bidirectional Gated Recurrent Unit (BiGRU), Iterated Dilated Convolutional Neural Networks (IDCNNs), and Conditional Random Fields (CRFs), to construct the LERT-BiGRU-IDCNN-CRF model. …”
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Training-Free VLM-Based Pseudo Label Generation for Video Anomaly Detection by Moshira Abdalla, Sajid Javed

“…Temporal modeling is enhanced through the integration of transformers and Graph Convolutional Networks (GCNs) to capture both short- and long-range dependencies. …”
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Automated violence monitoring system for real-time fistfight detection using deep learning-based temporal action localization by Baolong Qi, Baoyuan Wu, Bailing Sun

“…The proposed framework leverages both Context-Aware Encoded Transformer (CAET) for modeling interactions between individuals and their environment and Spatial–Temporal Graph Convolutional Networks (ST-GCN) for capturing intra-person and inter-person dynamics from skeletal data. …”
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Research on intelligent classification of coastal land cover by integrating remote sensing images and deep learning by Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

“…Traditional methods, like pixel-based and object-oriented classification, often struggle with high complexity and inaccurate results due to limitations in handling spatial relationships and spectral data.MethodsThis research addresses these shortcomings by integrating deep learning models, particularly convolutional neural networks (CNNs) and spatially dependent learning techniques, to develop a robust classification model for coastal land cover using remote sensing data. …”
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Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis by Alexander Maniangat Luke, Nader Nabil Fouad Rezallah

“…The research mostly uses convolutional neural networks (CNNs) for analyzing images, showing outstanding accuracy, sensitivity, and specificity in detecting caries. …”
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Beyond the Backbone: A Quantitative Review of Deep-Learning Architectures for Tropical Cyclone Track Forecasting by He Huang, Difei Deng, Liang Hu, Yawen Chen, Nan Sun

“…We categorize all DL-based TC tracking models according to the architecture, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), Transformers, graph neural networks (GNNs), generative models, and Fourier-based operators. …”
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Challenges in AI-driven multi-omics data analysis for Oncology: Addressing dimensionality, sparsity, transparency and ethical considerations by Maryem Ouhmouk, Shakuntala Baichoo, Mounia Abik

“…Non-generative approaches, such as feedforward neural networks (FFNs), graph convolutional networks (GCNs), and autoencoders, are designed to extract features and perform classification directly. …”
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Ensemble Deep Learning Object Detection Fusion for Cell Tracking, Mitosis, and Lineage by Imad Eddine Toubal, Noor Al-Shakarji, D. D. W. Cornelison, Kannappan Palaniappan

“…EDNet uses an ensemble approach for 2D cell detection that is deep-architecture-agnostic and achieves state-of-the-art performance surpassing single-model YOLO and FasterRCNN convolutional neural networks. EDNet detections are used in our M2Track multiobject tracking algorithm for tracking cells, detecting cell mitosis (cell division) events, and cell lineage graphs. …”
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Short-term wind power forecasting method for extreme cold wave conditions based on small sample segmentation by Lin Lin, Jinhao Xu, Jianfei Liu, Hao Zhang, Pengchen Gao

“…Then, a cold wave power loss extraction method based on Graph Convolutional Networks (GCN) and Bidirectional Gated Recurrent Units (BiGRU) is introduced for the entire time period. …”
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A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction by Wu Yan, Fu Yu, Li Tan, Li Mengshan, Xie Xiaojun, Zhou Weihong, Sheng Sheng, Wang Jun, Wu Fu-an

“…Results Here, we proposed a hybrid machine learning model based on graph convolutional neural networks (GCN) and bi-directional long short-term memory (Bi-LSTM) with attention mechanism and multidimensional multivariate feature coding for essential gene prediction, called EGP Hybrid-ML. …”
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Machine learning for classifying affective valence from fMRI: a systematic review and meta-analysis by Charith Chitraranjan, Ruwan Dayananda, Dakshitha Suriyaaratchie, Nuwan Abeynayake, Svetlana Shinkareva

“…However, we suggest that future studies also explore deep learning architectures such as convolutional and graph neural networks, which have not yet been applied to classify valence from fMRI data.…”
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Advanced Deep Learning Approaches for Forecasting High-Resolution Fire Weather Index (FWI) over CONUS: Integration of GNN-LSTM, GNN-TCNN, and GNN-DeepAR by Shihab Ahmad Shahriar, Yunsoo Choi, Rashik Islam

“…Based on this, our study developed a hybrid modeling framework to forecast FWI over a 14-day horizon, integrating Graph Neural Networks (GNNs) with Temporal Convolutional Neural Networks (TCNNs), Long Short-Term Memory (LSTM), and Deep Autoregressive Networks (DeepAR). …”
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Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting by Bihter Daş, Damla Mengus

“…This study utilizes air pollution data from the Continuous Monitoring Center of the Ministry of Environment, Urbanization, and Climate Change in Turkey to predict various pollutants using three advanced deep learning approaches: LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), and RNN (Recurrent Neural Network). …”
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Ginkgetin Alleviates Inflammation and Senescence by Targeting STING by Yadan Liu, Jialin Ye, Zisheng Fan, Xiaolong Wu, Yinghui Zhang, Ruirui Yang, Bing Jiang, Yajie Wang, Min Wu, Jingyi Zhou, Jingyi Meng, Zhiming Ge, Guizhen Zhou, Yuan Zhu, Yichuan Xiao, Mingyue Zheng, Sulin Zhang

“…To reveal the molecular mechanism of Ginkgetin's anti‐aging effect, a graph convolutional network‐based drug “on‐target” pathway prediction algorithm for prediction is employed. …”
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A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features by Xuhui ZHANG, Yunkai CHI, Yuyang DU, Junying JIANG, Wenjuan YANG, Youjun ZHAO, Jicheng WAN, Yanqun WANG, Chenhui TIAN

“…This study proposed a visual positioning method for TBMs in underground coal mines based on anchor net features.MethodsA three-stream depthwise separable convolutional neural network (TSCR-NET) for image enhancement was employed to estimate the reflection, illumination, and noise in images individually. …”
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A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure by Sardar Muhammad Ali, Abdul Razzaque, Muhammad Yousaf, Sardar Sadaqat Ali

“…We trained three ML classifiers: Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbors (KNN), along with three DL models: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). …”
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Pedestrian Crossing Direction Prediction at Intersections for Pedestrian Safety by Younggun Kim, Mohamed Abdel-Aty, Keechoo Choi, Zubayer Islam, Dongdong Wang, Shaoyan Zhai

“…The framework leverages Transformer-based models, Graph Convolutional Networks (GCNs), and a hybrid Transformer+GCN approach to extract spatial and temporal features from the pedestrian behaviors. …”
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Deep learning for property prediction of natural fiber polymer composites by Ivan P. Malashin, Dmitry Martysyuk, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Vadim Tynchenko

“…These studies demonstrate that specialized architectures, including hybrid CNN–MLP models, feedforward ANNs, graph convolutional networks, and DNNs, provide high accuracy in predicting mechanical, thermal, and chemical properties of polymer composites and biodegradable plastics. …”
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