Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP by Florian Birk, Lucas Mahler, Julius Steiglechner, Qi Wang, Klaus Scheffler, Rahel Heule

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Implementing an Outgoing Longwave Radiation Climate Dataset from Fengyun 3E Satellite Data with a Machine-Learning Algorithm by Yanjiao Wang, Feng Yan

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Two-Step Deep Learning Approach for Estimating Vegetation Backscatter: A Case Study of Soybean Fields by Dong Zhu, Peng Zhao, Qiang Zhao, Qingliang Li, Jinpeng Zhang, Lixia Yang

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AnomLite: Efficient binary and multiclass video anomaly detection by Anna K. Zvereva, Mariam Kaprielova, Andrey Grabovoy

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A Comparison of Physical-Based and Statistical-Based Radiative Transfer Models in Retrieving Atmospheric Temperature Profiles from the Microwave Temperature Sounder-II Onboard the... by Qiurui He, Xiao Guo, Ruiling Zhang, Jiaoyang Li, Lanjie Zhang, Junqi Jia, Xuhui Zhou

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RFMVDA: An Enhanced Deep Learning Approach for Customer Behavior Classification in E-Commerce Environments by Kwanhee Kim, Mingyu Jo, Ilkyeun Ra, Sangoh Park

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A Framework for Early Detection of Acute Lymphoblastic Leukemia and Its Subtypes From Peripheral Blood Smear Images Using Deep Ensemble Learning Technique by Sajida Perveen, Abdullah Alourani, Muhammad Shahbaz, M. Usman Ashraf, Isma Hamid

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Hybrid Frequency–Spatial Domain Learning for Image Restoration in Under-Display Camera Systems Using Augmented Virtual Big Data Generated by the Angular Spectrum Method by Kibaek Kim, Yoon Kim, Young-Joo Kim

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A New Deep Learning-Based Method for Automated Identification of Thoracic Lymph Node Stations in Endobronchial Ultrasound (EBUS): A Proof-of-Concept Study by Øyvind Ervik, Mia Rødde, Erlend Fagertun Hofstad, Ingrid Tveten, Thomas Langø, Håkon O. Leira, Tore Amundsen, Hanne Sorger

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A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE by Vanlalruata Hnamte, Hong Nhung-Nguyen, Jamal Hussain, Yong Hwa-Kim

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Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-motion Signals from Millimeter-wave Radar and Pulse Wave Data by Xiang ZHAO, Wei WANG, Chenyang LI, Jian GUAN, Gang LI

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AdaMoR-DDMOEA: Adaptive Model Selection with a Reliable Individual-Based Model Management Framework for Offline Data-Driven Multi-Objective Optimization by Subhadip Pramanik, Abdalla Alameen, Hitesh Mohapatra, Debanjan Pathak, Adrijit Goswami

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Prediction of OCT contours of short-term response to anti-VEGF treatment for diabetic macular edema using generative adversarial networks by Xueying Yang, Fabao Xu, Han Yu, Zhongwen Li, Xuechen Yu, Zhiwen Li, Li Zhang, Jie Liu, Shaopeng Wang, Shaopeng Liu, Jiaming Hong, Jianqiao Li

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The Theory and Applications of Hölder Widths by Man Lu, Peixin Ye

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Advanced Fractional Mathematics, Fractional Calculus, Algorithms and Artificial Intelligence with Applications in Complex Chaotic Systems by Dumitru Baleanu, Yeliz Karaca

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Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network by Miao Zhang, Yao Zhang, Qian Cen, Shixun Wu

“…The advantages of the proposed deep neural network are the capabilities to achieve low complexity and latency resource allocations. …”
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Survey on intellectual property protection for deep learning model by Xinya WANG, Guang HUA, Hao JIANG, Haijian ZHANG

“…With the rapid development of deep learning technology, deep learning models have been widely used in many fields such as image classification and speech recognition.Training a deep learning model relies on a large amount of data and computing power, thus selling the trained model or providing specific services (DLaaS, e.g.) has become a new business.However, the commercial interests of model trainers and the intellectual property rights of model developers may be violated if the model is maliciously stolen.With deep neural network watermarking becoming a new research topic, multimedia copyright protection techniques were used for deep learning model protection.Numerous methods have been proposed in this field and then a comprehensive survey is needed.the existing deep neural network watermarking methods were elaborated and summarized and the future research directions of this field were discussed.The overall framework of neural network watermarking was presented, whereby the basic concepts such as classification model and model backdoor were introduced.Secondly, the existing methods were divided into two types according to the mechanism of watermark embedding, one is to embed the watermark bits into the carrier of internal information of the network, and the other one uses the established backdoor mapping as the watermark.These two existing deep neural network watermarking methods were analyzed and summarized, and attacks to the watermarks were also introduced and discussed.By analyzing the white-box and black-box conditions in watermarking scenario, it comes to the conclusion that the model is difficult to be effectively protected when it is distributed in the white-box manner, and the neural network watermark defenses in the black-box distribution and black-box verification are both worthy for further research.…”
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Emei Martial Arts Promotion Model and Properties Based on Neural Network Technology by Cheng Xing, N.E. Zainal Abidin, Yudong Tang

“…The upgraded deep neural network’s recommendation model has a recall rate that is 4% greater than the baseline model. …”
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Evaluating structural safety of trusses using Machine Learning by Tran-Hieu Nguyen, Anh-Tuan Vu

“…Three popular machine learning classifiers including Support Vector Machine, Deep Neural Network, and Adaptive Boosting are used for evaluating the safety of structures. …”
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Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge by Haicheng Qu, Jiangtao Guo, Yanji Jiang

“…Overfitting in a deep neural network leads to low recommendation precision and high loss. …”
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