Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…In this context, we propose a comprehensive framework that applies machine learning methods to enhance evidence collection and malware activity analysis. …”
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Application of machine learning with gradient descent method for load forecasting: a performance analysis by Saroj Kumar Panda, Manoj Kumar Panda

“…As a result, building reliable and effective predictive models is essential to delivering precise load predictions. One method of forecasting, short-term load forecasting (STLF) is used in this research, and machine learning like deep neural network (DNN) is the method used here for the analysis of STLF. …”
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DEEP LEARNING-DRIVEN PREDICTIVE CONTROL METHOD FOR OPTIMIZING COMBINE HARVESTER OPERATION SPEED by Jin Chen, Jiaqi Ji, Kuizhou Ji, Yuhang Chen

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Photovoltaic Array Fault Diagnosis and Localization Method Based on Modulated Photocurrent and Machine Learning by Yebo Tao, Tingting Yu, Jiayi Yang

“…To address this concern, this paper proposes a fault identification and localization approach for photovoltaic arrays based on modulated photocurrent and machine learning. By irradiating different frequency-modulated light, this method separates photocurrent and directly measures the photoelectric conversion efficiency of each panel, achieving both high accuracy and localization. …”
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Deep Reinforcement Learning-Based Distribution Network Planning Method Considering Renewable Energy by Liang Ma, Chenyi Si, Ke Wang, Jinshan Luo, Shigong Jiang, Yi Song

“…Based on the above discussion, this paper proposes a DNP method based on deep reinforcement learning (DRL). …”
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Polarization-Enhanced Underwater Detection Method for Multiple Material Targets Based on Deep-Learning by Guochen Wang, Jie Gao, Yubin Chen, Xin Wang, Jiangtao Li, Khian-Hooi Chew, Rui-Pin Chen

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A Deep Learning-Based Method for Detection of Multiple Maneuvering Targets and Parameter Estimation by Beiming Yan, Yong Li, Qianlan Kou, Ren Chen, Zerong Ren, Wei Cheng, Limeng Dong, Longyuan Luan

“…To address this issue, this paper presents a method for high-resolution multi-drone target detection and parameter estimation based on the adjacent cross-correlation function (ACCF), fractional Fourier transform (FrFT), and deep learning techniques. …”
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A Reinforcement Learning-Based Maximum Power Point Tracking Method for Photovoltaic Array by Roy Chaoming Hsu, Cheng-Ting Liu, Wen-Yen Chen, Hung-I Hsieh, Hao-Li Wang

“…A reinforcement learning-based maximum power point tracking (RLMPPT) method is proposed for photovoltaic (PV) array. …”
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Study of Antarctic Sea Ice Based on Shipborne Camera Images and Deep Learning Method by Xiaodong Chen, Shaoping Guo, Qiguang Chen, Xiaodong Chen, Shunying Ji

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Dual-context enhanced knowledge representation learning method in hyper-relational knowledge graphs by Jiahang Li, Qilong Han, Dan Lu, Lijie Li

“…This operation addresses the issue that most methods neglect the learning of relation representations, with these methods typically updating using a parameter matrix without considering the crucial information within HKGs. …”
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A Fundamental Statistics Self-Learning Method with Python Programming for Data Science Implementations by Prismahardi Aji Riyantoko, Nobuo Funabiki, Komang Candra Brata, Mustika Mentari, Aviolla Terza Damaliana, Dwi Arman Prasetya

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A Hybrid Deep Learning Method for the Estimation of the State of Health of Lithium-Ion Batteries by Shuo Cheng

“…Compared to other complex deep learning (DL) methods, the VIT-GRU significantly outperforms them, according to the RMSE and MAE of the predicted values.…”
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Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification by Long Li, Xiaoming Tao, Hui Song, Xiaolong Li, Zhilong Ye, Yao Jin, Qiuyu He, Shiyin Wei, Wenli Chen

“…The big data collected from structural health monitoring systems (SHMs), combined with the rapid advances in machine learning (ML), have enabled data-driven methods in practical SHM applications. …”
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Estimating Economic Insights: A Machine Learning Method for Estimating the Shanghai Stock Exchange by Reza Seifi Majdar, Seyed Hadi Seyed Hatami

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An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent by Serhii Semenov, Oleksii Kolomiitsev, Mykhailo Hulevych, Patryk Mazurek, Olena Chernyk

“…This paper presents an intelligent method for test case synthesis using a Q-learning agent. …”
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Reactor physics fast calculation method based on model order reduction and machine learning by Chen Zhao, Qinyi Zhang, Bin Zhang, Jiangyu Wang, Jiayi Liu, Lianjie Wang, Bangyang Xia, Xiaoming Chai, Xingjie Peng

“…The rapid development of artificial intelligence technology has provided new ideas and methods for reactor physics calculations. Based on AI technology, a fast calculation method for reactor physics has been established, which combines model order reduction and machine learning to address the challenges of excessive parameter quantities in machine learning-based parameter prediction. …”
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Research on Plant RNA-Binding Protein Prediction Method Based on Improved Ensemble Learning by Hongwei Zhang, Yan Shi, Yapeng Wang, Xu Yang, Kefeng Li, Sio-Kei Im, Yu Han

“…Accurate prediction of plant-specific RBPs is vital for understanding gene regulation and enhancing genetic improvement. (2) Methods: We propose an ensemble learning method that integrates shallow and deep learning. …”
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Research on an underwater high-speed vehicle target recognition method based on deep learning by WU Yajun, LIU Liwen

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A deep learning-based data augmentation method for marine mammal call signals by Jiaming Jiang, Jiaming Jiang, Wanlu Cheng, Wanlu Cheng, Shengwen Gong, Shengwen Gong, Jingjing Wang, Jingjing Wang

“…To address this problem, we propose MarGEN, a deep learning-based augmentation method for marine mammal call signal data. …”
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SensorDBSCAN: Semi-Supervised Active Learning Powered Method for Anomaly Detection and Diagnosis by Petr Ivanov, Maria Shtark, Alexander Kozhevnikov, Maksim Golyadkin, Dmitry Botov, Ilya Makarov

“…In this paper, we propose SensorDBSCAN, a novel semi-supervised method for anomaly detection and diagnosis. The key innovation lies in achieving good performance with minimal labeled data - less than 1% of the dataset - by leveraging active and contrastive learning techniques. …”
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