A VVC intra coding method based on fast partition for coding unit by ZHONG Hui, LU Yu, YIN Haibing, HUANG Xiaofeng

“…To reduce encoding complexity, a fast intra coding method combining deep learning methods and early decision in the MTT direction was proposed. …”
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Human Pose Estimation: Single-Person and Multi-Person Approaches by Tang Wan

“…According to their respective characteristics and application scenarios, single-person pose estimation is categorized into traditional methods and deep learning methods, while multi-person pose estimation is classified into top-down and bottom-up aspects. …”
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Smart contract vulnerability detection method based on pre-training and novel timing graph neural network by ZHUANG Yuan, FAN Zekai, WANG Cheng, SUN Jianguo, LI Yaolin

“…To address the limitations of current deep learning-based methods in extracting contract bytecode features and representing vulnerability semantics, as well as the shortcomings of the traditional graph neural networks in learning temporal information from contract statements, a method for detecting vulnerabilities in contracts was proposed based on pre-trained and temporal graph neural network. …”
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A New Type-3 Fuzzy PID for Energy Management in Microgrids by Weiping Fan, Ardashir Mohammadzadeh, Nasreen Kausar, Dragan Pamucar, Nasr Al Din Ide

“…In this study, for the first time, a new T3-FLS-based PID scheme with deep learning approach is introduced. In addition to rules, the parameters of fuzzy sets are also tuned such that a fast regulation efficiency is obtained. …”
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Attention and sentiment of Chinese public toward rural landscape based on Sina Weibo by Jinji Zhang, Guanghu Jin, Yang Liu, Xiyue Xue

“…This study proposes a deep learning-based model for Weibo data analysis aimed at exploring the development direction of rural landscapes from the perspective of the Chinese public. …”
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A Vehicle Detection Algorithm Based on Deep Belief Network by Hai Wang, Yingfeng Cai, Long Chen

“…In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN) is proposed. …”
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Automated Stellar Spectra Classification with Ensemble Convolutional Neural Network by Zhuang Zhao, Jiyu Wei, Bin Jiang

“…Classification of stellar spectra by applying deep learning is an important research direction for the automatic classification of high-dimensional celestial spectra. …”
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Multi-feature fusion classification method for communication specific emitter identification by Zunwen HE, Shuai HOU, Wancheng ZHANG, Yan ZHANG

“…A multi-feature fusion classification method based on multi-channel transform projection, integrated deep learning and generative adversarial network (GAN) was proposed for communication specific emitter identification.First, three-dimensional feature images were obtained by performing various transformations, the time and frequency domain projection of the signal was constructed to construct the feature datasets.GAN was used to expand the datasets.Then, a two-stage recognition and classification method based on multi-feature fusion was designed.Deep neural networks were used to learn the three feature datasets, and the initial classification results were obtained.Finally, through fusion and re-learning of the initial classification result, the final classification result was obtained.Based on the measurement and analysis of the actual signals, the experimental results show that the method has higher accuracy than the single feature extraction method.The multipath fading channel has been used to simulate the outdoor propagation environment, and the method has certain generalization performance to adapt to the complex wireless channel environments.…”
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Explicit Content Detection System: An Approach towards a Safe and Ethical Environment by Ali Qamar Bhatti, Muhammad Umer, Syed Hasan Adil, Mansoor Ebrahim, Daniyal Nawaz, Faizan Ahmed

“…The proposed ECD system is based on residual network (i.e., deep learning model) which returns a probability to indicate the explicitness in media content. …”
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Extracting neutron skin from elastic proton-nucleus scattering with deep neural network by G.H. Yang, Y. Kuang, Z.X. Yang, Z.P. Li

“…Based on the relativistic impulse approximation of proton-nucleus elastic scattering theory, the neutron density distribution and neutron skin thickness of 48Ca are estimated via the deep learning method. The neural-network-generated neutron densities are mainly compressed to be higher inside the nucleus compared with the results from the relativistic PC-PK1 density functional, resulting in a significant improvement on the large-angle scattering observables, both for the differential cross section and analyzing power. …”
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Computational intelligence in the identification of Covid-19 patients  by using KNN-SVM Classifier by shaymaa adnan

“…This research presents an advanced methodology employing deep learning techniques for the analysis of medical pictures pertaining to respiratory disorders. …”
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Artificial intelligence in green management and sustainability: A bibliometric survey by Niftiyev Ibrahim

“…The results show that the focus of AI and sustainability research has shifted from, for example, water management to broader environmental concerns and then to new analytical tools such as deep learning and blockchain. The most prolific and collaborative researchers come from India, China and the US and publish in already established international journals. …”
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Interactive Embodied Intelligence of Machines by Deyi Li

“…For “embodied intelligence” in the farming machine context, we propose (1) deep learning should be performed iteratively via real-time interactions with the external world; (2) embodied control and self-regulation can ensure coordination between behaviors of machines and their environment; (3) intelligent farming machines are characterized by the ability to interact, learn, and grow autonomously.…”
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Stock Price Prediction in the Financial Market Using Machine Learning Models by Diogo M. Teixeira, Ramiro S. Barbosa

“…This paper presents an analysis of stock price forecasting in the financial market, with an emphasis on approaches based on time series models and deep learning techniques. Fundamental concepts of technical analysis are explored, such as exponential and simple averages, and various global indices are analyzed to be used as inputs for machine learning models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and XGBoost. …”
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A Noble Classification Framework for Data Glove Classification of a Large Number of Hand Movements by Yuhuang Zheng

“…The movement classification algorithm is composed of downsampling in data preparation and a new deep learning network named the DBDF network. Bidirectional Long Short-Term Memory (BiLSTM) is the main part of the DBDF network. …”
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Evaluation of Image Enhancement Techniques for Vision-Based Navigation under Low Illumination by Mohamed Aladem, Stanley Baek, Samir A. Rawashdeh

“…The findings are relevant to a wide range of feature-based vision systems, such as tracking for augmented reality, image registration, localization, and mapping, as well as deep learning-based object detectors. As autonomous mobile robots are expected to operate under low-illumination conditions at night, evaluation is based on state-of-the-art systems for motion estimation, localization, and object detection.…”
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Performance and Analysis of FCN, U-Net, and SegNet in Remote Sensing Image Segmentation Based on the LoveDA Dataset by Yang Shuhao

“…This study utilizes the LoveDA dataset to investigate the segmentation performance of three classic deep learning models: Fully Convolutional Networks(FCN), U-Net, and SegNet, in both urban and rural scenarios. …”
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Human activity recognition system based on active learning and Wi-Fi sensing by Guangzhi ZHAO, Zhipeng ZHOU, Wei GONG, Shaoqing CHEN, Haoquan ZHOU

“…Human activity recognition system based on deep learning and Wi-Fi sensing has gradually become the mainstream research field and has been developed in recent years.However, related systems heavily rely on training with huge labeled samples to reach a high accuracy, which is labor-intensive and unrealistic for many real-world scenarios.To solve this problem, a system that combines active learning with Wi-Fi based human activity recognition—ALSensing was proposed, which was able to train a well-perform classifier with limited labeled samples.ALSensing was implemented with commercial Wi-Fi devices and evaluated in six real environments.The experimental results show that ALSensing achieves 52.83% recognition accuracy using 3.7% of total training samples, 58.97% recognition accuracy using 15% of total training samples, while the existing full-supervised system reaches 62.19% recognition accuracy.It demonstrates that ALSensing has a similar performance with baseline but requires much less labeled samples.…”
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Utilizing Statistical Tests for Comparing Machine Learning Algorithms by Hozan Khalid Hamarashid

“…With classification and regression prediction models it can be conducted by utilizing traditional machine learning and deep learning methods. The difficulty is to identify whether or not the difference between two models is accurate. …”
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Timing data visualization: tactical intent recognition and portable framework by SONG Yafei, LI Lemin, QUAN Wen, NI Peng, WANG Ke

“…Experimental results demonstrate that the proposed framework achieves over 0.99% higher accuracy compared to machine learning and deep learning methods, exhibiting superior performance, scalability, robustness, and transferability.…”
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