An Observer-Based Event Triggered Mechanism for the Detection and Mitigation of FDI Attacks in Deep Brain Stimulation Systems by Ping Yu, Ding Yang, Khalid A. Alattas, Ardashir Mohammadzadeh, Afef Fekih

“…To achieve accurate prediction, Heuristic Adaptive Dynamic Programming (HADP) is utilized to adjust the coefficients embedded in the FOESO structure. Using a neural networks-based approach, the HADP agent regulates the parameters of the FOESO by maximizing a reward (reinforcement) signal according to the desired system requirements. …”
Get full text

Rapid literature mapping on the recent use of machine learning for wildlife imagery by Nakagawa, Shinichi, Lagisz, Malgorzata, Francis, Roxane, Tam, Jessica, Li, Xun, Elphinstone, Andrew, Jordan, Neil R., O'Brien, Justine K., Pitcher, Benjamin J., Van Sluys, Monique, Sowmya, Arcot, Kingsford, Richard T.

“…We found that an increasing number of studies used convolutional neural networks (i.e., deep learning). Typically, studies have focused on large charismatic or iconic mammalian species. …”
Get full text

Classifying Sunn Pest Damaged and Healthy Wheat Grains Across Different Species with YOLOV8 and Vision Transformers by Melike Çolak, Özgü Özkan, Ali Berkol, Nergis Pervan Akman, Murat Ardıç, Okan Sezer, Nazife Gözde Ayter Arpacıoğlu, Zekiye Budak Başçiftçi, Murat Olgun

“…This paper presents an approach using a recurrent neural networks-based transformer to identify different varieties of wheat grain that have been sunn pest-damaged and healthy. …”
Get full text

Data-driven prediction of critical diameter for deterministic lateral displacement devices: an integrated DPD-ML approach by Shuai Liu, Peng Zhang, Anbin Wang, Keke Tang, Shuo Chen, Chensen Lin

“…Four ML models are trained: Random Forest Regression (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Artificial Neural Networks (ANN). To address the low interpretability of complex ML models, the Shapley Additive Explanations (SHAP) method is introduced to clarify all input features. …”
Get full text

Single-View Depth Estimation: Advancing 3D Scene Interpretation With One Lens by Kavitha Dhanushkodi, Akila Bala, Neelam Chaplot

“…In contrast, this method leverages convolutional neural networks (CNNs) trained on large-scale datasets containing ground truth depth information to predict the relative depth of objects in a scene and visualize it in a 3D perspective. …”
Get full text

Advancing the application of the analytical renal pathology system in allograft IgA nephropathy patients by Xumeng Liu, Huiwen Fang, Dongmei Liang, Qunjuan Lei, Jiaping Wang, Feng Xu, Shaoshan Liang, Dandan Liang, Fan Yang, Heng Li, Jianghua Chen, Yuan Ni, Guotong Xie, Caihong Zeng

“…Background The analytical renal pathology system (ARPS) based on convolutional neural networks has been used successfully in native IgA nephropathy (IgAN) patients. …”
Get full text

Research on a Novel Unsupervised-Learning-Based Pipeline Leak Detection Method Based on Temporal Kolmogorov–Arnold Network with Autoencoder Integration by Hengyu Wu, Zhu Jiang, Xiang Zhang, Jian Cheng

“…Furthermore, the deployment cost of such models has increased substantially due to the use of GPU-trained neural networks in recent years. In this study, we propose a novel leak detector, which includes a new model and a sequence labeling method that integrates prior knowledge with traditional reconstruction error theory. …”
Get full text

Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis by Haoyu Wang, Xihe Qiu, Bin Li, Xiaoyu Tan, Jingjing Huang

“…The framework leverages heterogeneous graph convolutional neural networks to extract both localized and global features from these graphs. …”
Get full text

Hierarchical graph-based integration network for propaganda detection in textual news articles on social media by Pir Noman Ahmad, Jiequn Guo, Nagwa M. AboElenein, Qazi Mazhar ul Haq, Sadique Ahmad, Abeer D. Algarni, Abdelhamied A. Ateya

“…Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). …”
Get full text

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). …”
Get full text

Improvement of Propeller Hydrodynamic Prediction Model Based on Multitask ANN and Its Application in Optimization Design by Liang Li, Yihong Chen, Lu Huang, Qing Hai, Denghai Tang, Chao Wang

“…A multitask learning (MTL) model based on artificial neural networks (ANNs) is proposed in this study to improve the prediction accuracy and physical reliability of marine propeller hydrodynamic performance. …”
Get full text

Multi-Cell Displacement Measurement During the Assembly of Automotive Power Batteries Based on Machine Vision by Yueda Xu, Yanfeng Xing, Hongbo Zhao, Yufang Lin, Lijia Ren, Zhihan Zhou

“…MicKey can predict the coordinates of corresponding keypoints in the 3D camera space through keypoint matching based on neural networks, and it can acquire feature point pairs of the subject to be measured through its unique depth reduction characteristics. …”
Get full text

A novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health state by R. Manikandan, S. Arunprakash, Rakan A. Alsowail, Tharani Pandiaraj

“…The system employs deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) models to automate healthcare functions such as patient monitoring, infrastructure management, and disease prediction. …”
Get full text

A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock Offsets by Tailai Wen, Gang Ou, Xiaomei Tang, Pengyu Zhang, Pengcheng Wang

“…To deal with this problem, we proposed a deep learning-based approach for BDS short-term satellite clock offset modeling which utilizes the superiority of Long Short-Term Memory (LSTM) derived from Recurrent Neural Networks (RNN) in time series modeling, and we call it QPLSTM. …”
Get full text

Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy by Jui-En Lo, Eugene Yu-Chuan Kang, Yun-Nung Chen, Yi-Ting Hsieh, Nan-Kai Wang, Ta-Ching Chen, Kuan-Jen Chen, Wei-Chi Wu, Yih-Shiou Hwang, Fu-Sung Lo, Chi-Chun Lai

“…The model was developed using 4 different convolutional neural networks (Inception-V3, DenseNet-121, VGG1, and Xception). …”
Get full text

Determination of the melanin and anthocyanin content in barley grains by digital image analysis using machine learning methods by E. G. Komyshev, M. A. Genaev, I. D. Busov, M. V. Kozhekin, N. V. Artemenko, A.  Y. Glagoleva, V. S. Koval, D. A. Afonnikov

“…Four models based on computer vision techniques and convolutional neural networks of different architectures were developed to predict grain pigment composition from images. …”
Get full text

Bearing Fault Classification Using Improved Antlion Optimizer and Extreme Learning Machine by Zhuanzhe Zhao, Yu Zhang, Qiang Ma, Yujian Rui, Guowen Ye, Mengxian Wang, Yongming Liu, Zhen Zhang, Neng Wei, Zhijian Tu

“…At present, the models based on the fusion of various optimization algorithms and neural networks have become one of the emerging techniques for accurate fault identification. …”
Get full text

Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort Search by Jürgen Landauer, Simon Maddison, Giacomo Fontana, Axel G. Posluschny

“…A semi-automated workflow employing Convolutional Neural Networks (CNNs) was developed and tested across three regions – England, Hesse (Germany), and Molise (Italy) – covering a total area of 180,000 km2. …”
Get full text

A Deep Learning-Based Approach for the Detection of Various Internet of Things Intrusion Attacks Through Optical Networks by Nouman Imtiaz, Abdul Wahid, Syed Zain Ul Abideen, Mian Muhammad Kamal, Nabila Sehito, Salahuddin Khan, Bal S. Virdee, Lida Kouhalvandi, Mohammad Alibakhshikenari

“…Leveraging advanced deep learning methods, specifically Convolutional Neural Networks (CNNs), XIoT analyzes spectrogram images transformed from IoT network traffic data to detect subtle and complex attack patterns. …”
Get full text

Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR Images by G. Savitha, S. Girisha, Pundarika Sughosh, Dasharathraj K. Shetty, Jayaraj Mymbilly Balakrishnan, Rahul Paul, Nithesh Naik

“…Furthermore, the study presents a new semantic segmentation technique that uses convolutional neural networks to automatically identify flooded areas in SAR images. …”
Get full text