Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Pena et al. (2021) employed a fuzzy convolutional deep learning model to estimate the maximum operational risk value at a 99.9% confidence level. …”
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A high-precision segmentation network for industrial surface defect detection by Hao Chen, Byung-Won Min

“…Accurate surface defect detection is essential for improving product quality and reducing manufacturing costs, particularly in high-precision industries. …”
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Analisis Kinerja Pegawai Pada Kantor Camat by Muhammad Reza Syahputra, Isnaini Isnaini, Adam Adam

“…The results showed that the work quality of the employees of the East Medan District Head Office, Medan City was still low in terms of handling documents and procedures were still convoluted and slow and not transparent in terms of costs. …”
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Design and experimental research of on device style transfer models for mobile environments by Igeon Hwang, Taeyeon Oh

“…To address this challenge, we propose a set of lightweight NST models incorporating depthwise separable convolutions, residual bottlenecks, and optimized upsampling techniques inspired by MobileNet and ResNet architectures. …”
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SHARP-Net: A Refined Pyramid Network for Deficiency Segmentation in Culverts and Sewer Pipes by Rasha Alshawi, Md Meftahul Ferdaus, Md Tamjidul Hoque, Kendall Niles, Ken Pathak, Steve Sloan

“…SHARP-Net combines multiscale feature fusion, depthwise separable convolutions, and fine-tuned Haar-like features to enhance performance while reducing computational complexity. …”
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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

“…The integration of linear attention mechanisms with depthwise separable convolutions significantly reduces the network’s computational complexity while maintaining a comparable performance. …”
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Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices by Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Sabina Umirzakova, Abror Shavkatovich Buriboev, Azizjon Meliboev, Bahodir Muminov, Heung Seok Jeon

“…Furthermore, the model significantly reduces computational costs, making real-time analysis feasible on low-power hardware. …”
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Real-time dental caries segmentation with an efficient Deformable U-Net (DU-Net) for teledentistry system by Zendi Iklima, Trie Maya Kadarina, Ketty Siti Salamah, Arrival Dwi Sentosa

“…Additionally, AI advancements enhance diagnostic accuracy and streamline clinical decision-making, reducing costs and resource disparities in dental care. This study presents an improved U-Net architecture, Deformable U-Net (DU-Net), for semantic dental caries segmentation, leveraging deformable convolutions to dynamically adjust sampling points for improved feature extraction and reduced computational redundancy. …”
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Commercial Biomaterial-Based Products for Tendon Surgical Augmentation: A Scoping Review on Currently Available Medical Devices by Marta Pluchino, Leonardo Vivarelli, Gianluca Giavaresi, Dante Dallari, Marco Govoni

“…However, scientific innovations must navigate convoluted clinical regulatory paths, which, due to high costs for investors, long development timelines, and funding shortages, hinder the translation of many scientific discoveries into routine clinical practice.…”
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Dynamic atrous attention and dual branch context fusion for cross scale Building segmentation in high resolution remote sensing imagery by Yaohui Liu, Shuzhe Zhang, Xinkai Wang, Rui Zhai, Hu Jiang, Lingjia Kong

“…Furthermore, we fused triplet attention with depth-wise separable convolutions, reducing computational requirements and mitigating potential overfitting scenarios. …”
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Predicting the Imbalanced Impact of Drugs on Microbial Abundance Using Multi-View Learning and Data Augmentation by Bei Zhu, Haoyang Yu, Bingxue Du, Hui Yu, Jianyu Shi

“…Traditional Microbe-Drug Association (MDA) determination through biological assays is time-consuming and costly. With the accumulation of MDA data, computational methods have become a promising approach to infer potential MDAs. …”
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SMART DELAY PREDICTION: SUPERVISED MACHINE LEARNING SOLUTIONS FOR CONSTRUCTION PROJECTS by Pramodini Sahu, Dillip Kumar Bera, Pravat Kumar Parhi, Meenakshi Kandpal

“…These can relate to convoluted relationships in construction data, which makes them suitable for yet another application in project risk management. …”
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MCIDN: Deblurring Network for Metal Corrosion Images by Jiaxiang Wang, Meng Wan, Pufen Zhang, Sijie Chang, Hao Du, Peng Shi, Hongying Yu, Dongbai Sun, Jue Wang, Yangang Wang

“…While self-attention is widely used in visual tasks, its quadratic complexity often leads to high computational costs. To address this issue, we introduce a new spatial channel attention module (SCAM) that employs dynamic group convolutions to achieve self-attention, effectively integrating information from local regions and enhancing representation learning capabilities. …”
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A lightweight mechanism for vision-transformer-based object detection by Yanming Ye, Qiang Sun, Kailong Cheng, Xingfa Shen, Dongjing Wang

“…XFA simplifies the attention mechanism’s computational process and reduces complexity through L2 normalization and two one-dimensional convolutions applied in different directions. This design reduces the computational complexity from quadratic to linear while preserving spatial context awareness. …”
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