Local and Deep Features Based Convolutional Neural Network Frameworks for Brain MRI Anomaly Detection by Sajad Einy, Hasan Saygin, Hemrah Hivehch, Yahya Dorostkar Navaei

“…In this research, we proposed three different end-to-end deep learning approaches for analyzing effects of local and deep features for brain MRI images anomaly detection. …”
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Residual Vision Transformer and Adaptive Fusion Autoencoders for Monocular Depth Estimation by Wei-Jong Yang, Chih-Chen Wu, Jar-Ferr Yang

“…Through recent advancements in deep learning technologies, monocular depth estimation, with its simplicity, has surpassed the traditional stereo camera systems, bringing new possibilities in 3D sensing. …”
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Unsupervised Image Super-Resolution for High-Resolution Satellite Imagery via Omnidirectional Real-to-Synthetic Domain Translation by Minkyung Chung, Yongil Kim

“…Image super-resolution (SR) aims to enhance the spatial resolution of images and overcome the hardware limitations of imaging systems. While deep-learning networks have significantly improved SR performance, obtaining paired low-resolution (LR) and high-resolution (HR) images for supervised learning remains challenging in real-world scenarios. …”
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Traditional guidance mechanism based deep robust watermarking by Xuejing GUO, Yixiang FANG, Yi ZHAO, Tianzhu ZHANG, Wenchao ZENG, Junxiang WANG

“…With the development of network and multimedia technology, multimedia data has gradually become a key source of information for people, making digital media the primary battlefield for copyright protection and anti-counterfeit traceability.Digital watermarking techniques have been widely studied and recognized as important tools for copyright protection.However, the robustness of conventional digital watermarking methods is limited as sensitive digital media can easily be affected by noise and external interference during transmission.Then the existing powerful digital watermarking technology’s comprehensive resistance to all forms of attacks must be enhanced.Moreover, the conventional robust digital watermarking algorithm’s generalizability across a variety of image types is limited due to its embedding method.Deep learning has been widely used in the development of robust digital watermarking systems due to its self-learning abilities.However, current initialization techniques based on deep neural networks rely on random parameters and features, resulting in low-quality model generation, lengthy training times, and potential convergence issues.To address these challenges, a deep robust digital watermarking algorithm based on a traditional bootstrapping mechanism was proposed.It combined the benefits of both traditional digital watermarking techniques and deep neural networks, taking into account their learning abilities and robust characteristics.The algorithm used the classic robust digital watermarking algorithm to make watermarked photos, and the constructed feature guaranteed the resilience of traditional watermarked images.The final dense image was produced by fusing the conventionally watermarked image with the deep network using the U-Net structure.The testing results demonstrate that the technique can increase the stego image’s resistance to various attacks and provide superior visual quality compared to the conventional algorithm.…”
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Artificial intelligence in dentistry: Assessing the informational quality of YouTube videos. by Sachin Naik, Abdulaziz Abdullah Al-Kheraif, Sajith Vellappally

“…The terms used for the search were "artificial intelligence in dentistry," "machine learning in dental care," and "deep learning in dentistry." The accuracy and reliability of the information source were assessed using the DISCERN score. …”
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Sublemma-Based Neural Machine Translation by Thien Nguyen, Huu Nguyen, Phuoc Tran

“…Powerful deep learning approach frees us from feature engineering in many artificial intelligence tasks. …”
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A deep neural network for general scattering matrix by Jing Yongxin, Chu Hongchen, Huang Bo, Luo Jie, Wang Wei, Lai Yun

“…Our work proposes a convenient solution of deep learning for scattering problems.…”
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An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging by Alauddin Bhuiyan, Arun Govindaiah, R. Theodore Smith

“…Then, using CDR below 0.5 (nonsuspect) and CDR above 0.5 (glaucoma suspect), deep-learning architectures were used to train and test a binary classifier system. …”
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The Adventure of Artificial Intelligence in Educational Research from the Past to the Present by Deniz Kaya

“…As a thematic change in the studies, there has been an evolution towards new technological developments such as deep learning, machine learning, ChatGPT, chatbots, learning analytics, blockchain, and generative AI. …”
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Analysis and Classification of Fake News Using Sequential Pattern Mining by M. Zohaib Nawaz, M. Saqib Nawaz, Philippe Fournier-Viger, Yulin He

“…Current machine and deep learning based methodologies for classification/detection of fake news are content-based, network (propagation) based, or multimodal methods that combine both textual and visual information. …”
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A cross‐project defect prediction method based on multi‐adaptation and nuclear norm by Qingan Huang, Le Ma, Siyu Jiang, Guobin Wu, Hengjie Song, Libiao Jiang, Chunyun Zheng

“…Existing CPDP methods based on the deep learning model may not fully consider the differences among projects. …”
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Solving Spatial Optimization Problems via Lagrangian Relaxation and Automatic Gradient Computation by Zhen Lei, Ting L. Lei

“…This paper aims to ease the development of Lagrangian relaxation algorithms for GIS practitioners by employing the automatic (sub)gradient (autograd) computation capabilities originally developed in modern Deep Learning. Using the classic <i>p</i>-median problem as an example, we demonstrate how Lagrangian relaxation can be developed with paper and pencil, and how the (sub)gradient computation derivation can be automated using autograd. …”
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Incident duration prediction through integration of uncertainty and risk factor evaluation: A San Francisco incidents case study. by Amirreza Salehi, Ardavan Babaei, Majid Khedmati

“…Finally, we employ both traditional Machine Learning (ML) and Deep Learning (DL) models to perform classification and regression tasks. …”
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A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications by M. J. Aashik Rasool, Shabir Ahmad, Sevara Mardieva, Sumaiya Akter, Taeg Keun Whangbo

“…In contemporary computer vision, deep learning-based real-time single image super-resolution approaches have gained significant attention for their ability to enhance the resolution of images in real time. …”
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Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM by Hang Wang, Min-jun Peng, Yong-kuo Liu, Shi-wen Liu, Ren-yi Xu, Hanan Saeed

“…Experiments show that the proposed method could predict RUL more accurately compared to other typical machine learning and deep learning methods. This will further enhance maintenance efficiency of any plant.…”
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Avances en el aprovechamiento de biopolímeros y productos peruanos by Erika Del Milagro Lozano-Flores

“…Asimismo, el análisis de palabras clave destaca la relevancia de técnicas como "machine learning", "deep learning" y "neural networks". Los mapas de colaboración reflejan que Estados Unidos y China son líderes en producción y coautoría. …”
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RETRACTED: Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation management by Chengran Yin, Guangming Wang, Jiacheng Liao

“…Introduction: This paper proposes a deep learning algorithm based on the VMD-SSA-BiLSTM model for time series forecasting in the smart grid financial market. …”
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Forecasting Travel Speed in the Rainfall Days to Develop Suitable Variable Speed Limits Control Strategy for Less Driving Risk by Ping Wang, Yajie Zhang, Saisai Wang, Li Li, Xiaohui Li

“…The experimental results show that a significant decrease happens in the travel speed in the rainfall day during peak hours. Furthermore, the deep learning algorithm that considers more factors such as the rainfall intensity and traffic flow could improve the prediction accuracy. …”
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Secured Wireless Network Based on a Novel Dual Integrated Neural Network Architecture by H. V. Ramachandra, Pundalik Chavan, S. Supreeth, H. C. Ramaprasad, K. Chatrapathy, G. Balaraju, S. Rohith, H. S. Mohan

“…DINN is designed for any presence of deep learning-based attack in a physical security layer. …”
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MultiChem: predicting chemical properties using multi-view graph attention network by Heesang Moon, Mina Rho

“…Recent advances in deep learning approaches have offered deeper insights into molecular structures. …”
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