Ensemble learning-based predictor for driver synonymous mutation with sequence representation. by Chuanmei Bi, Yong Shi, Junfeng Xia, Zhen Liang, Zhiqiang Wu, Kai Xu, Na Cheng

“…Notably, the incorporation of DNA shape features and deep learning-derived features from chemical molecule represents a pioneering effect in assessing the impact of synonymous mutations in cancer. …”
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Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models. by Zenglei Liu, Ailian Gao, Hui Sheng, Xueling Wang

“…Considering these analyses, this work presents a comprehensive deep learning model that combines convolutional neural network and vision mamba models. …”
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Malware prediction technique based on program gene by Da XIAO, Bohan LIU, Baojiang CUI, Xiaochen WANG, Suoxing ZHANG

“…With the development of Internet technology,malicious programs have risen explosively.In the face of executable files without source,the current mainstream malware detection uses feature detection based on similarity,with lack of analysis of malicious sources.To resolve this status,the definition of program gene was raised,a generic method of extracting program gene was designed,and a malicious program prediction method was proposed based on program gene.Utilizing machine learning and deep-learning algorithms,the forecasting system has good prediction ability,with the accuracy rate of 99.3% in the deep-learning model,which validates the role of program gene theory in the field of malicious program analysis.…”
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Review on artificial intelligence chip technology system by Yuxia SHI

“…Artificial intelligence technology is the new focus of current countries.The development of artificial intelligence technology has put forward new requirements for computing chips.Deep learning algorithms require the training of massive data,while traditional computing architectures can’t support the large-scale computing requirements of deep learning algorithms.Therefore,artificial intelligence chips of new architectures emerge one after another.The different technical routes of artificial intelligence chips were analyzed,the characteristics of different routes were compared,the development trend of artificial intelligence chip industry and studied,the opportunities and challenges of artificial intelligence chip development in China were analyzed,and the future development of artificial intelligence chip technology was forecasted.…”
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An Efficient Frequency Domain Based Attribution and Detection Network by Junbin Zhang, Yixiao Wang, Hamid Reza Tohidypour, Panos Nasiopoulos

“…Existing deep learning methods attempt to identify and classify GM-specific artifacts but often struggle with content-independence and generalizability. …”
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SVDDD: SAR Vehicle Target Detection Dataset Augmentation Based on Diffusion Model by Keao Wang, Zongxu Pan, Zixiao Wen

“…In the field of target detection using synthetic aperture radar (SAR) images, deep learning-based supervised learning methods have demonstrated outstanding performance. …”
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An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2 by Baoquan Hu, Jun Liu, Yue Xu, Tianlong Huo

“…Deep learning has recently received extensive attention in the field of rolling-bearing fault diagnosis owing to its powerful feature expression capability. …”
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Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches by Alaa Khadidos, Adil Khadidos, Olfat M. Mirza, Tawfiq Hasanin, Wegayehu Enbeyle, Abdulsattar Abdullah Hamad

“…Classically, researchers in this field of radiomics have used conventional machine learning techniques (random forest, for example). More recently, deep learning, a subdomain of machine learning, has emerged. …”
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Detection and Classification Method for Early-Stage Colorectal Cancer Using Dyadic Wavelet Packet Transform by Daigo Takano, Hajime Omura, Teruya Minamoto

“…Incorporating deep learning into computer-aided medical diagnosis has led to significant advancements. …”
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Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN by Ning Hu, Zhenguo Liu, Shixin Jiang, Quanzhou Li, Shuqi Zhong, Bingquan Chen

“…Predicting the RUL accurately can improve machining efficiency and the quality of product. Deep learning methods have strong learning capability in RUL prediction and are extensively used. …”
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Object detection and multimodal learning for product recommendations by Karolina Selwon, Paweł Wnuk

“… This study showcases how deep learning can be applied to automated information extraction in fashion data to create a recommendation system. …”
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Privacy leakage risk assessment for reversible neural network by Yifan HE, Jie ZHANG, Weiming ZHANG, Nenghai YU

“…In recent years, deep learning has emerged as a crucial technology in various fields.However, the training process of deep learning models often requires a substantial amount of data, which may contain private and sensitive information such as personal identities and financial or medical details.Consequently, research on the privacy risk associated with artificial intelligence models has garnered significant attention in academia.However, privacy research in deep learning models has mainly focused on traditional neural networks, with limited exploration of emerging networks like reversible networks.Reversible neural networks have a distinct structure where the upper information input can be directly obtained from the lower output.Intuitively, this structure retains more information about the training data, potentially resulting in a higher risk of privacy leakage compared to traditional networks.Therefore, the privacy of reversible networks was discussed from two aspects: data privacy leakage and model function privacy leakage.The risk assessment strategy was applied to reversible networks.Two classical reversible networks were selected, namely RevNet and i-RevNet.And four attack methods were used accordingly, namely membership inference attack, model inversion attack, attribute inference attack, and model extraction attack, to analyze privacy leakage.The experimental results demonstrate that reversible networks exhibit more serious privacy risks than traditional neural networks when subjected to membership inference attacks, model inversion attacks, and attribute inference attacks.And reversible networks have similar privacy risks to traditional neural networks when subjected to model extraction attack.Considering the increasing popularity of reversible neural networks in various tasks, including those involving sensitive data, it becomes imperative to address these privacy risks.Based on the analysis of the experimental results, potential solutions were proposed which can be applied to the development of reversible networks in the future.…”
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Discovery of novel TACE inhibitors using graph convolutional network, molecular docking, molecular dynamics simulation, and Biological evaluation. by Muhammad Yasir, Jinyoung Park, Eun-Taek Han, Jin-Hee Han, Won Sun Park, Mubashir Hassan, Andrzej Kloczkowski, Wanjoo Chun

“…The increasing utilization of deep learning models in drug repositioning has proven to be highly efficient and effective. …”
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Classification of ECG signals using deep neural networks by Nadour Mohamed, Cherroun Lakhmissi, Hadroug Nadji

“…The classification of ECG signals using deep learning techniques has garnered substantial interest in recent years; ECG classification tasks have exhibited promising outcomes with the application of deep learning models, particularly convolutional neural networks (CNNs). …”
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Vision-Based Object Recognition and Precise Localization for Space Body Control by Zeyu Shangguan, Lingyu Wang, Jianquan Zhang, Wenbo Dong

“…The contribution of this work is to introduce the deep-learning method for precision motion control and in the meanwhile ensure both the robustness and real time of the system. …”
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Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images by Arezoo Borji, Taha-Hossein Hejazi, Abbas Seifi

“…In this paper, three deep-learning models, namely VGG16 and AlexNet, and a custom Convolutional Neural Network (CNN) with 8-fold cross-validation, have been used for classification. …”
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Data Augmentation-Based Enhancement for Efficient Network Traffic Classification by Chang-Yui Shin, Yang-Seo Choi, Myung-Sup Kim

“…We used them as the same inputs for lightweight deep learning and tree-based machine learning models, analyzed their performance, and identified efficient models. …”
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Comparison of In Silico Tools for Splice-Altering Variant Prediction Using Established Spliceogenic Variants: An End-User’s Point of View by Woori Jang, Joonhong Park, Hyojin Chae, Myungshin Kim

“…SpliceAI and SpliceRover, tools based on deep learning, outperformed all other tools, with AUCs of 0.972 and 0.924, respectively. …”
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Metaheuristics Approach for Hyperparameter Tuning of Convolutional Neural Network by Hindriyanto Purnomo, Tad Gonsalves, Evangs Mailoa, Fian Julio Santoso, Muhammad Rizky Pribadi

“…Deep learning is an artificial intelligence technique that has been used for various tasks. …”
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Advanced TSGL-EEGNet for Motor Imagery EEG-Based Brain-Computer Interfaces by Xin Deng, Boxian Zhang, Nian Yu, Ke Liu, Kaiwei Sun

“…Deep learning technology is rapidly spreading in recent years and has been extensive attempts in the field of Brain-Computer Interface (BCI). …”
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