Adapting a style based generative adversarial network to create images depicting cleft lip deformity by Abdullah Hayajneh, Erchin Serpedin, Mohammad Shaqfeh, Graeme Glass, Mitchell A. Stotland

“…Abstract Training a machine learning system to evaluate any type of facial deformity is impeded by the scarcity of large datasets of high-quality, ethics board-approved patient images. We have built a deep learning-based cleft lip generator called CleftGAN designed to produce an almost unlimited number of high-fidelity facsimiles of cleft lip facial images with wide variation. …”
Get full text

Filtering Approaches and Mish Activation Function Applied on Handwritten Chinese Character Recognition by Zhong Yingna, Kauthar Mohd Daud, Kohbalan Moorthy, Ain Najiha Mohamad Nor

“…Furthermore, online character recognition enables stronger involvement and flexibility than offline characters. Deep learning techniques, such as convolutional neural networks (CNN), have superseded conventional Handwritten Chinese Character Recognition (HCCR) solutions, as proven in image identification. …”
Get full text

XI2S-IDS: An Explainable Intelligent 2-Stage Intrusion Detection System by Maiada M. Mahmoud, Yasser Omar Youssef, Ayman A. Abdel-Hamid

“…The challenge is further compounded by the fact that most IDS rely on black-box machine learning (ML) and deep learning (DL) models, making it difficult for security teams to interpret their decisions. …”
Get full text

A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants by Ahmad Shaheryar, Xu-Cheng Yin, Hong-Wei Hao, Hazrat Ali, Khalid Iqbal

“…In order to address poor regularization and robust learning issues, here, we propose a denoised autoassociative sensor model (DAASM) based on deep learning framework. Proposed DAASM model comprises multiple hidden layers which are pretrained greedily in an unsupervised fashion under denoising autoencoder architecture. …”
Get full text

An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO by Qiaoqiao Xiong, Qipeng Chen, Saihong Tang, Yiting Li

“…Abstract Deep learning has achieved significant success in the field of defect detection; however, challenges remain in detecting small-sized, densely packed parts under complex working conditions, including occlusion and unstable lighting conditions. …”
Get full text

Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees by Ünal Kızıl, Hakkı Fırat Altınbilek, Sefa Aksu, Hakan Nar

“…Sensor data were scaled between 0-1 with min-max normalization before being subjected to deep learning and machine learning training. In the Decision Tree (DT) a model score of 0.96 was obtained by choosing the maximum depth of 20. …”
Get full text

Active Hard Sample Learning for Violation Action Recognition in Power Grid Operation by Lingwen Meng, Di He, Guobang Ban, Guanghui Xi, Anjun Li, Xinshan Zhu

“…Traditional methods often rely on manual supervision and rule-based detection, leading to inefficiencies. Existing deep learning approaches, while powerful, require fully labeled data and long training times, thereby increasing costs. …”
Get full text

A Multi-Source Domain Adaptation Method for Bearing Fault Diagnosis with Dynamically Similarity Guidance on Incomplete Data by Juan Tian, Shun Zhang, Gang Xie, Hui Shi

“…In actual industrial scenarios, collecting a complete dataset with all fault categories under the same conditions is challenging, leading to a loss in fault category knowledge in single-source domains. Deep learning domain adaptation methods face difficulties in multi-source scenarios due to insufficient labeled data and significant distribution differences, hindering domain-specific knowledge transfer and reducing fault diagnosis efficiency. …”
Get full text

Pore Space Reconstruction of Shale Using Improved Variational Autoencoders by Yi Du, Hongyan Tu, Ting Zhang

“…The recent branch of deep learning, variational auto-encoders (VAEs), has good capabilities of extracting characteristics for reconstructing similar images with the training image (TI). …”
Get full text

Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries by Azeez Okikiola Lawal, Tolutope Joseph Ogunniyi, Oriire Idunnuoluwa Oludele, Oluwaloseyi Ayomipo Olorunfemi, Olalekan John Okesanya, Jerico Bautista Ogaya, Emery Manirambona, Mohamed Mustaf Ahmed, Don Eliseo Lucero-Prisno

“…The integration of artificial intelligence, particularly deep learning and convolutional neural networks, has enhanced the diagnostic accuracy and data analysis capabilities. …”
Get full text

Enhancing depression recognition through a mixed expert model by integrating speaker-related and emotion-related features by Weitong Guo, Qian He, Ziyu Lin, Xiaolong Bu, Ziyang Wang, Dong Li, Hongwu Yang

“…Thus, it outperforms state-of-the-art deep learning methods that use speech features. Additionally, our approach shows strong performance across Chinese and English speech datasets, highlighting its effectiveness in addressing cultural variations.…”
Get full text

Parameter-Efficient Vehicle Trajectory Prediction Based on Attention-Enhanced Liquid Structural Neural Model by Ruochen Wang, Yue Chen, Renkai Ding, Qing Ye

“…Due to advances in sensor techniques and deep learning, autonomous vehicular technologies have become more reliable and practical. …”
Get full text

Optimizing Security in IoT Ecosystems Using Hybrid Artificial Intelligence and Blockchain Models: A Scalable and Efficient Approach for Threat Detection by William Villegas-Ch, Jaime Govea, Rommel Gutierrez, Aracely Mera-Navarrete

“…Blockchain ensures device authentication and data integrity through a lightweight consensus protocol, while AI enables real-time intrusion detection using deep learning models. The simulations demonstrate that the proposed system improves the precision of detecting phishing attacks by up to 95.2%. …”
Get full text

Achieving Faster and Smarter Chest X-Ray Classification With Optimized CNNs by Hassen Louati, Ali Louati, Khalid Mansour, Elham Kariri

“…However, building accurate and efficient deep learning models for X-ray image classification remains challenging, requiring both optimized architectures and low computational complexity. …”
Get full text

Hybrid generative adversarial network based on frequency and spatial domain for histopathological image synthesis by Qifeng Liu, Tao Zhou, Chi Cheng, Jin Ma, Marzia Hoque Tania

“…Abstract Background Due to the complexity and cost of preparing histopathological slides, deep learning-based methods have been developed to generate high-quality histological images. …”
Get full text

An Enhanced LSTM Approach for Detecting IoT-Based DDoS Attacks Using Honeypot Data by Arjun Kumar Bose Arnob, M. F. Mridha, Mejdl Safran, Md Amiruzzaman, Md. Rajibul Islam

“…The contribution of this paper will be an addition to the deep learning techniques applied for the solution of intrusion detection systems (IDS), which will also allow the building and implementation of more efficient security mechanisms in IoT environments.…”
Get full text

Distributed training of foundation models for ophthalmic diagnosis by Sina Gholami, Fatema-E Jannat, Atalie Carina Thompson, Sally Shin Yee Ong, Jennifer I. Lim, Theodore Leng, Hamed Tabkhivayghan, Minhaj Nur Alam

“…Here we propose a distributed deep learning framework that integrates self-supervised and domain-adaptive federated learning to enhance the detection of eye diseases from optical coherence tomography images. …”
Get full text

An overlapping sliding window and combined features based emotion recognition system for EEG signals by Shruti Garg, Rahul Kumar Patro, Soumyajit Behera, Neha Prerna Tigga, Ranjita Pandey

“….; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.…”
Get full text

Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs by Hanlin Feng, Zitong Zhang, Chunlei Zhang, Chengcheng Zhong, Qiaoyu Ma

“…Compared with machine learning and deep learning methods, the SVA-TCN demonstrates a remarkable accuracy of 99.00%, surpassing the accuracy of the comparison methods by 0.37–17.69%. …”
Get full text

Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions by Ying Jia He, Pin Lin Liu, Tao Wei, Tao Liu, Yi Fei Li, Jing Yang, Wen Xing Fan

“…Key research themes include AI-driven advancements in donor matching, deep learning for post-transplant monitoring, and machine learning algorithms for personalized immunosuppressive therapies. …”
Get full text