Applied Data Analysis in Energy Monitoring System by Kychkin А.V., Mikriukov G.P.

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Decision Support System for Diagnosing Underwater Electrical Cables by Tymochko O., Tymoshchuk О., Timochko O., Boiko S., Mazhara I., Hannoshina, I., Shapran Yu.

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THE APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN MEDICAL DIAGNOSTICS by Gulchin Abdullayeva, Ulkar Alizada

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FocusDet: an efficient object detector for small object by Yanli Shi, Yi Jia, Xianhe Zhang

“…It consists of three parts: backbone, feature fusion structure, and detection head. STCF-EANet was used as the backbone for feature extraction, the Bottom Focus-PAN for feature fusion, and the detection head for object localization and recognition.To maintain sufficient global context information and extract multi-scale features, the STCF-EANet network backbone is used as the feature extraction network.PAN is a feature fusion module used in general object detectors. …”
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Privacy-Oriented Successive Approximation Image Position Follower Processing by Ying Miao, Danyang Shao, Zhimin Yan

“…In this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. …”
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Spectrum sensing method based on residual dense network by Jianxin GAI, Xianfeng XUE, Ruixiang NAN, Jingyi WU

“…Aiming at the problem that the traditional spectrum sensing method based on convolutional neural network（CNN） did not make full use of image feature and the ability of extracting the image feature was limited by the shallow network structure， a spectrum sensing method based on the residual dense network （ResDenNet） was proposed.By adding dense connections in the traditional neural network， the information reuse of the image feature was achieved.Meanwhile， shortcut connections were employed at both ends of the dense unit to implement deeper network training.The spectrum sensing problem was transformed into the image binary classification problem.Firstly， the received signals were integrated into a matrix， which was normalized and transformed by gray level.The obtained gray level images were used as the input of the network.Then， the network was trained through dense learning and residual learning.Finally， the online data was input into the ResDenNet and spectrum sensing was implemented based on image classification.The numerical experiments show that the proposed method is superior to the traditional ones in terms of performance.When the SNR is as low as -19 dB， the detection probability of the proposed method is still high up to 0.96 with a low false alarm probability of 0.1， while a better generalization ability is displayed.…”
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A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in Complex Electromagnetic Environment by Yonghua Wang, Shunchao Zhang, Yongwei Zhang, Pin Wan, Jiangfan Li, Nan Li

“…Further, the spectrum sensing problem is considered as a signal detection problem. …”
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Multi-target personnel tracking algorithm for coal mine based on improved YOLOv7 and ByteTrack by Pengcheng QU, Jingzhao LI, Zechao LIU

“…In order to solve the problems of low accuracy and poor real-time performance of existing target tracking algorithms in the complex environment of coal mines, a YOLO-FasterNet+ByteTrack coal mine personnel tracking algorithm was proposed based on the Tracking by Detection (TBD) paradigm. …”
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多尺度主元坐标变换在轴承故障声发射信号增强检测中的应用 by 张晓涛, 唐力伟, 王平, 邓士杰

“…Aiming at the problem of weak feature extraction and recognition in the bearing acoustic emission signal processing,multi-scale principal component coordinate transformation is proposed to enhance fault feature.Firstly,the signal is decomposed by wavelet package,and the sub-band reconstruction component is converted to a new linear space by principal component analysis,and the signal fault feature is enhanced.Finally,the method performance is verified by simulation signal and testing signal,the results show that multi-scale principal component coordinate transformation has obvious enhancement effect in bearing fault detection.…”
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A Mountain Summit Recognition Method Based on Improved Faster R-CNN by Yueping Kong, Yun Wang, Song Guo, Jiajing Wang

“…Traditional summit detection methods operate on handcrafted features extracted from digital elevation model (DEM) data and apply parametric detection algorithms to locate mountain summits. …”
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Lightweight grape leaf disease recognition method based on transformer framework by Ning Zhang, Enxu Zhang, Guowei Qi, Fei Li, Cheng Lv

“…This paper embeds pathological features into the generative adversarial process, which can effectively alleviate the problem of insufficient samples in intelligent agricultural detection. …”
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Analysis of Internet Marketing Forecast Model Based on Parallel K-Means Algorithm by Xiaolei Chen, Sikun Ge

“…At the same time, to solve the problem of insufficient data sets of Internet marketing nodes, the Internet data sets are artificially generated and used for detector training. …”
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Optimizing cervical cancer diagnosis with accurate cell classification using modified HDFF by Pooja Patre, Dipti Verma

“…By enhancing the feature extraction process and combining multiple layers of deep learning models, the Modified HDFF method improves classification performance across various tasks, ranging from binary to multi-class problems. …”
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Influence of Sliding Time Window Size Selection Based on Heart Rate Variability Signal Analysis on Intelligent Monitoring of Noxious Stimulation under Anesthesia by Qiang Yin, Dai Shen, Qian Ding

“…Some researches based on medical signals have failed to provide a general understanding of this problem. This paper presents a feature extraction method for heart rate variability signals, aiming at further improving the evaluation of noxious stimulation. …”
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Digits Recognition for Arabic Handwritten through Convolutional Neural Networks, Local Binary Patterns, and Histogram of Oriented Gradients by Bushra Mahdi Hasan, Zahraa Jasim Jaber, Ahmad Adel Habeeb

“…In addition, a Histogram of Oriented Gradients (HOG) is a feature extraction technique that is used in computer vision and image processing for the purpose of object detection. …”
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Multi-class fault diagnosis of BF based on global optimization LS-SVM by ZHANG Hai-gang, ZHANG Sen, YIN Yi-xin

“…Aiming at the requirement of high speed and precision in blast furnace fault diagnosis systems, a new strategy based on global optimization least-squares support vector machines (LS-SVM) was proposed to solve this problem. Firstly, the variable metric discrete particle swarm optimization algorithm was employed to optimize the feature selection and LS-SVM parameters. …”
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Adaptive malware identification via integrated SimCLR and GRU networks by Faisal S. Alsubaei, Abdulwahab Ali Almazroi, Walid Said Atwa, Abdulaleem Ali Almazroi, Nasir Ayub, N. Z. Jhanjhi

“…SimCLR-GRU provides a scalable and decisive answer to modern cyberspace’s changing malware detection problem.…”
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Ship Classification with High Resolution TerraSAR-X Imagery Based on Analytic Hierarchy Process by Zhi Zhao, Kefeng Ji, Xiangwei Xing, Wenting Chen, Huanxin Zou

“…The main idea is to apply AHP on both feature selection and classification decision. On one hand, the AHP based feature selection constructs a selection decision problem based on several feature evaluation measures (e.g., discriminability, stability, and information measure) and provides objective criteria to make comprehensive decisions for their combinations quantitatively. …”
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Classification of offshore wind grid-connected power quality disturbances based on fast S-transform and CPO-optimized convolutional neural network. by Minan Tang, Hongjie Wang, Jiandong Qiu, Zhanglong Tao, Tong Yang

“…The large-scale integration of offshore wind power into the power grid has brought serious challenges to the power system power quality. Aiming at the problem of power quality disturbance detection and classification, this paper proposes a novel algorithm based on fast S-transform and crested porcupine optimizer (CPO) optimized CNN. …”
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Multiscale Hjorth Descriptor on Epileptic EEG Classification by Achmad Rizal, Sugondo Hadiyoso, Suci Aulia, Inung Wijayanto, null Triwiyanto, Ziani Said

“…Since the characteristics of EEG signals are nonlinear and nonstationary, visual inspection becomes very difficult. To overcome this problem, digital EEG signal processing was developed. …”
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