Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation by Taeyeub Lee, Joram M. Posma

“…Furthermore, DILIGeNN outperformed the state-of-the-art in other graph-based molecular prediction tasks, achieving an AUC of 0.918 on the Clintox dataset, 0.993 on the BBBP dataset, and 0.953 on the BACE dataset, indicating strong generalisation and performance across different datasets. Conclusion DILIGeNN, utilising a single graph representation as input, outperforms the state-of-the-art methods in DILI prediction that incorporate both molecular fingerprint and graph-structured data. …”
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Contextual Deep Semantic Feature Driven Multi-Types Network Intrusion Detection System for IoT-Edge Networks by Shaho Hassen, Ahmed Abdlrazaq

“… Recent years have witnessed an exponential rise in wireless networks and allied interoperable distributed computing frameworks, where the different sensory units transfer real-world event data to the network analyzer for run-time decisions. …”
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Edge-based detection and localization of adversarial oscillatory load attacks orchestrated by compromised EV charging stations by Khaled Sarieddine, Mohammad Ali Sayed, Sadegh Torabi, Ribal Atallah, Chadi Assi

“…Moreover, this analysis results shed light on the impact of such detection mechanisms towards building resiliency into different levels of the EV charging ecosystem while allowing power grid operators to localize attacks and take further mitigation measures. …”
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Sugarcane leaf disease classification using deep neural network approach by Saravanan Srinivasan, S. M. Prabin, Sandeep Kumar Mathivanan, Hariharan Rajadurai, Suresh Kulandaivelu, Mohd Asif Shah

“…Methods In order to identify the diseases in sugarcane leaves, this study used EfficientNet architectures along with other well-known convolutional neural network (ConvNet) models such as DenseNet201, ResNetV2, InceptionV4, MobileNetV3 and RegNetX. …”
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Research on Bearing Fault Diagnosis Method for Varying Operating Conditions Based on Spatiotemporal Feature Fusion by Jin Wang, Yan Wang, Junhui Yu, Qingping Li, Hailin Wang, Xinzhi Zhou

“…Experimental results demonstrate that STFDAN achieves high diagnostic accuracy across different load conditions and effectively solves the bearing fault diagnosis problem under varying operating conditions.…”
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Personalized region of interest recommendation through adaptive fusion of multi-dimensional user preferences by Qing Tang, Shenghua Xu, Zhuolu Wang, Yong Wang, Shichuan Liu, Haiqin Hua

“…Finally, an adaptive weighting model is introduced to integrate the spatio-temporal, social, and category preferences, assigning individual preference weights to different users to facilitate personalized ROI recommendation. …”
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Wireless Channel Prediction Using Artificial Intelligence With Imperfect Datasets by Gowhar Javanmardi, Ramiro Samano Robles

“…This stress test leads to new conclusions on channel prediction: i) how and why algorithms behave in different ways under diverse conditions (optimality region), ii) derivation of new bounds linked to channel features (coherence time, channel correlation, etc.), iii) optimum parameter settings for ML also linked to channel statistics, and iv) proposal of potential improvements. …”
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A Model for Diagnosing Mild Nutrient Stress in Facility-Grown Tomatoes Throughout the Entire Growth Cycle by Yunpeng Yuan, Guoxiang Sun, Guangyu Chen, Qihua Zhang, Lingwei Liang

“…This study proposes a deep learning framework based on CNN + LSTM, using canopy near-infrared spectroscopy from different growth stages of tomatoes as input, to diagnose mild stress of nitrogen (N), potassium (K), and calcium (Ca) throughout the entire growth cycle of facility-grown tomatoes. …”
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Comprehensive Quantitative Analysis of Coal-Based Liquids by Mask R-CNN-Assisted Two-Dimensional Gas Chromatography by Huan-Huan Fan, Xiang-Ling Wang, Jie Feng, Wen-Ying Li

“…The Mask R-CNN accurately and rapidly segmented the GC × GC chromatograms into regions representing different compounds, thereby automatically qualitatively classifying the compounds according to their spots in the chromatograms. …”
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An enhanced lightweight model for apple leaf disease detection in complex orchard environments by Ge Wang, Wenjie Sang, Fangqian Xu, Yuteng Gao, Yue Han, Qiang Liu

“…Additionally, we design a detail-enhanced shared convolutional scaling detection head (DESCS-DH) to enable the model to effectively capture edge information of diseases and address issues such as poor performance in object detection across different scales. …”
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Assessing Revisit Risk in Emergency Department Patients: Machine Learning Approach by Wang-Chuan Juang, Zheng-Xun Cai, Chia-Mei Chen, Zhi-Hong You

“…Furthermore, this study evaluates different ML models, feature sets, and feature encoding methods in order to build an effective prediction model. …”
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Deep Learning Technology for Weld Defects Classification Based on Transfer Learning and Activation Features by Chiraz Ajmi, Juan Zapata, Sabra Elferchichi, Abderrahmen Zaafouri, Kaouther Laabidi

“…The main objective of this work is to explore the capacity of AlexNet and different pretrained architecture with transfer learning for the classification of X-ray images. …”
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An Efficient Semantic Segmentation Framework with Attention-Driven Context Enhancement and Dynamic Fusion for Autonomous Driving by Jia Tian, Peizeng Xin, Xinlu Bai, Zhiguo Xiao, Nianfeng Li

“…The encoder leverages a high-efficiency backbone, while the decoder introduces a dynamic fusion mechanism designed to enhance information interaction between different feature branches. Recognizing the limitations of convolutional networks in modeling long-range dependencies and capturing global semantic context, the model incorporates an attention-based feature extraction component. …”
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MCGFE-CR: Cloud Removal With Multiscale Context-Guided Feature Enhancement Network by Qiang Bie, Xiaojie Su

“…The experimental results demonstrate that this method has higher spectral fidelity and richer structural texture information in reconstructing various types of ground information and optical images with different cloud coverage areas.…”
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Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture by Junaid Aftab, Muhammad Attique Khan, Sobia Arshad, Shams ur Rehman, Dina Abdulaziz AlHammadi, Yunyoung Nam

“…The proposed architecture was tested on five publically available datasets of different imaging modalities and obtained improved accuracy of 98.6 (INBreast), 95.3 (KVASIR), 94.3 (ISIC2018), 95.0 (Lung Cancer), and 98.8% (Oral Cancer), respectively. …”
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MDFT-GAN: A Multi-Domain Feature Transformer GAN for Bearing Fault Diagnosis Under Limited and Imbalanced Data Conditions by Chenxi Guo, Vyacheslav V. Potekhin, Peng Li, Elena A. Kovalchuk, Jing Lian

“…To improve classification performance, an Enhanced Hybrid Visual Transformer (EH-ViT) is constructed by coupling a lightweight convolutional stem with a ViT encoder, enabling robust and discriminative fault identification. …”
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Thyroid nodule segmentation in ultrasound images using transformer models with masked autoencoder pre-training by Yi Xiang, Rajendra Acharya, Quan Le, Jen Hong Tan, Chiaw-Ling Chng

“…The difficulty arises from factors such as the absence of prior knowledge about the thyroid region, low contrast between anatomical structures, and speckle noise, all of which obscure boundary detection and introduce variability in nodule appearance across different images.MethodsTo address these challenges, we propose a transformer-based model for thyroid nodule segmentation. …”
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MCPA: multi-scale cross perceptron attention network for 2D medical image segmentation by Liang Xu, Mingxiao Chen, Yi Cheng, Pengwu Song, Pengfei Shao, Shuwei Shen, Peng Yao, Ronald X. Xu

“…Abstract The UNet architecture, based on convolutional neural networks (CNN), has demonstrated its remarkable performance in medical image analysis. …”
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Feasibility of the Anchor-Free Deep Learning Method in Coronary Stenosis Automatic Detection by Hanlin Yue, Wei Yu, Ji Dong, Yunfei Lai, You Wu, Haixia Zhao, Yiwei Song, Li Zhao, Hui Wang, Jing Zhang, Xinping Xu, Binwei Yao, Jianghao Zhao, Kexian Wang, Yue Sun, Haoyu Wang, Ruiyun Peng

“…Finally, the performances of different models in the detection of stenosis were compared in either single or multiple lesion scenarios using statistical tests. …”
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Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning by G Muni Nagamani, Chanumolu Kiran Kumar

“…In this paper, we propose an advanced hybrid model for Convolutional and Long Short-Term Memory (CNN-LSTM), which exploits the main advantages of convoluted neural networks and LSTM networks. …”
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