Functional feature extraction and validation from twelve-lead electrocardiograms to identify atrial fibrillation by Wei Yang, Rajat Deo, Wensheng Guo

“…Abstract Background Deep learning methods on standard, 12-lead electrocardiograms (ECG) have resulted in the ability to identify individuals at high-risk for the development of atrial fibrillation. …”
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STUDENTS’ ERRORS IN TRANSLATING MATHEMATICAL REPRESENTATIONS FROM SYMBOLIC TO GRAPHICAL FORM IN QUADRATIC FUNCTIONS by Rizqi Febriana Safitri, Abd. Qohar, Sisworo Sisworo

“…Representation is an important element in mathematics learning that helps make abstract mathematical ideas more concrete. …”
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Incoherent dictionary learning and sparse representation for single-image rain removal by Hong-zhong TANG, Xiang WANG, Xiao-gang ZHANG, Xiao LI, Li-zhen MAO

“…The incoherent dictionary learning and sparse representation algorithm was present and it was applied to single-image rain removal.The incoherence of the dictionary was introduced to design a new objective function in the dictionary learning,which addressed the problem of reducing the similarity between rain atoms and non-rain atoms.The divisibility of rain dictionary and non-rain dictionary could be ensured.Furthermore,the learned dictionary had similar properties to the tight frame and approximates the equiangular tight frame.The high frequency in the rain image could be decomposed into a rain component and a non-rain component by performing sparse coding based learned incoherent dictionary,then the non-rain component in the high frequency and the low frequency were fused to remove rain.Experimental results demonstrate that the learned incoherent dictionary has better performance of sparse representation.The recovered rain-free image has less residual rain,and preserves effectively the edges and details.So the visual effect of recovered image is more sharpness and natural.…”
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Incoherent dictionary learning and sparse representation for single-image rain removal by Hong-zhong TANG, Xiang WANG, Xiao-gang ZHANG, Xiao LI, Li-zhen MAO

“…The incoherent dictionary learning and sparse representation algorithm was present and it was applied to single-image rain removal.The incoherence of the dictionary was introduced to design a new objective function in the dictionary learning,which addressed the problem of reducing the similarity between rain atoms and non-rain atoms.The divisibility of rain dictionary and non-rain dictionary could be ensured.Furthermore,the learned dictionary had similar properties to the tight frame and approximates the equiangular tight frame.The high frequency in the rain image could be decomposed into a rain component and a non-rain component by performing sparse coding based learned incoherent dictionary,then the non-rain component in the high frequency and the low frequency were fused to remove rain.Experimental results demonstrate that the learned incoherent dictionary has better performance of sparse representation.The recovered rain-free image has less residual rain,and preserves effectively the edges and details.So the visual effect of recovered image is more sharpness and natural.…”
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Evolution of DevOps: Lessons learned for success as part of digital strategy by Gabriel Silva-Atencio, Mauricio Umaña-Ramirez

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Optimization of English Online Learning Dictionary System Based on Multiagent Architecture by Ying Wang

“…Based on the hybrid multiagent cooperative algorithm, this paper will improve the disadvantages of the online English learning dictionary system and propose an appropriate dictionary application evaluation function. …”
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DOES SYSTEMIC FUNCTIONAL LINGUISTICS GENRE PEDAGOGY FACILITATE ISLAMIC SCHOOL STUDENTS' WRITING? by Fazri Nur Yusuf, Oti Rofiawati

“…This study aimed at investigating how systemic functional linguistics genre pedagogy facilitates Islamic school students' writing exposition text. …”
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Optimization of Quantitative Financial Data Analysis System Based on Deep Learning by Meiyi Liang

“…In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. …”
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Entropy-Guided Distributional Reinforcement Learning with Controlling Uncertainty in Robotic Tasks by Hyunjin Cho, Hyunseok Kim

“…This study proposes a novel approach to enhance the stability and performance of reinforcement learning (RL) in long-horizon tasks. Overestimation bias in value function estimation and high uncertainty within environments make it difficult to determine the optimal action. …”
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Constrained adversarial learning for automated software testing: a literature review by João Vitorino, Tiago Dias, Tiago Fonseca, Eva Maia, Isabel Praça

“…Automated software testing can be a promising solution to quickly analyze many lines of code and detect vulnerabilities and possible attack vectors by generating function-specific testing data. This process draws similarities to the constrained adversarial examples generated by adversarial learning methods, so there could be significant benefits to the integration of these methods in testing tools. …”
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Hybrid deep learning architecture for scalable and high-quality image compression by Mustafa Al-Khafaji, Nehad T. A. Ramaha

“…This study proposes a hybrid deep learning-based image compression framework that integrates Stationary Wavelet Transform (SWT), Stacked Denoising Autoencoder (SDAE), Gray-Level Co-occurrence Matrix (GLCM), and K-means clustering. …”
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Applying Reinforcement Learning for AMR’s Docking and Obstacle Avoidance Behavior Control by Chun-Chi Lai, Bo-Jun Yang, Chia-Jen Lin

“…In recent years, advancements in artificial intelligence (AI) have become an essential study for machine learning. The use of AI with the Robot Operating System (ROS) enables mobile robots to learn and move autonomously. …”
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A systematic review of machine learning approaches in cochlear implant outcomes by Anu Prasad Sreenivasan Nair, Srikanta K. Mishra, Pedro Andres Alba Diaz

“…Imaging-based studies demonstrated high predictive accuracy for language and speech perception outcomes. Neural function measures provided a feasible way to assess the functional status of the auditory nerve, while clinical and audiological predictors were extensively explored through data mining techniques. …”
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Investigating the protective effect of hydroxylated fullerenes on cognitive function in rats with temporal lobe epilepsy by Xiaoqing Wang, Shuang Tian, Zhenzhen Qu, Ran Meng, Guangxiao Ni, Min Liu, Huifang Cao

“…Abstract The objective of this study was to explore the protective effects of hydroxy fullerenes (HFs) on cognitive function in rats with temporal lobe epilepsy (TLE) and to elucidate the underlying mechanisms. …”
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Efficient Neural Network Control of a Brushless DC Motor by A. A. Vеlchеnko, S. A. Pauliukavеts, A. A. Radkеvich, A. K. Ibrahim

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Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning by Xiaoyun Lei, Zhian Zhang, Peifang Dong

“…The reward and punishment function and the training method are designed for the instability of the training stage and the sparsity of the environment state space. …”
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Deep Learning Joint Inversion of Electrical Data for Ahead-Prospecting in Tunneling by Peng Jiang, Benchao Liu, Chuanwu Wang, Lei Chen, Yuting Tang

“…Therefore, nonlinear techniques such as deep learning have recently become popular to directly approximate the inversion function by learning the mapping of apparent resistivity data to the geoelectric model. …”
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Forwarding Collision Assessment with the Localization Information Using the Machine Learning Method by Lei Guo, Yizhen Jia, Xianghui Hu, Feihong Dong

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Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised Learning by Zhaohui Liu, Sainan Liu, Weifeng Gu

“…As intelligent education advances and online learning becomes more prevalent, Knowledge Tracing (KT) has become increasingly important. …”
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Reinforcement Learning Based Quantum Circuit Optimization via ZX-Calculus by Jordi Riu, Jan Nogué, Gerard Vilaplana, Artur Garcia-Saez, Marta P. Estarellas

“…We propose a novel Reinforcement Learning (RL) method for optimizing quantum circuits using graph-theoretic simplification rules of ZX-diagrams. …”
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