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 Deep learning techniques, such as convolutional neural networks (CNN), have superseded conventional Handwritten Chinese Character Recognition (HCCR) solutions, as proven in image identification. …”
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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. …”
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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. …”
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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. …”
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Underwater Sonar Image Classification with Image Disentanglement Reconstruction and Zero-Shot Learning by Ye Peng, Houpu Li, Wenwen Zhang, Junhui Zhu, Lei Liu, Guojun Zhai

“…With the development of intelligent technology, deep learning has brought new vitality to underwater sonar image classification. …”
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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. …”
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Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control by Rodrigo Gutiérrez-Moreno, Rafael Barea, Elena López-Guillén, Felipe Arango, Fabio Sánchez-García, Luis M. Bergasa

“…The use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. …”
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Real-Time Interference Mitigation for Reliable Target Detection with FMCW Radar in Interference Environments by Youlong Weng, Ziang Zhang, Guangzhi Chen, Yaru Zhang, Jiabao Chen, Hongzhan Song

“…Recent advancements in interference mitigation utilizing deep learning (DL) approaches have demonstrated promising results. …”
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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. …”
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Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage System by Boyu Shu, Zhiwu Huang, Wanwan Ren, Yue Wu, Heng Li

“…Three normalized terms, battery capacity loss, battery power fluctuation, and supercapacitor state-of-charge regulation, are balanced in the objective function. Finally, a deep learning algorithm is proposed to adaptively adjust the three weighting factors to meet the diverse operation conditions. …”
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Sliding-Window Dissimilarity Cross-Attention for Near-Real-Time Building Change Detection by Wen Lu, Minh Nguyen

“…The extensive adoption of deep learning in change detection has prompted a predominant emphasis on enhancing detection performance, primarily through the expansion of the depth and width of networks, overlooking considerations regarding inference time and computational cost. …”
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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. …”
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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). …”
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Unifying spatiotemporal and frequential attention for traffic prediction by Qi Guo, Qi Tan, Jun Tang, Benyun Shi

“…By integrating attention mechanisms, we comprehensively capture the hidden correlations among space, time, and frequency dimensions. By leveraging deep learning to capture spatial correlations in traffic flow and applying spectral analysis to fuse time series data with underlying periodic correlations in both the time and frequency domains, we develop an innovative traffic prediction model called the Space-Time-Frequency Attention Network (STFAN). …”
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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. …”
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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.…”
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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. …”
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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%. …”
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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. …”
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CFP-AL: Combining Model Features and Prediction for Active Learning in Sentence Classification by Keuntae Kim, Yong Suk Choi

“…Active learning has been a research area conducted across various domains for a long time, from traditional machine learning to the latest deep learning research. Particularly, obtaining high-quality labeled datasets for supervised learning requires human annotation, and an effective active learning strategy can greatly reduce annotation costs. …”
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