Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition

Tianbo, Song; Weijun, Hu; Jiangfeng, Cai; Weijia, Liu; Quan, Yuan; Kun, He

doi:10.1109/iccece58074.2023.10135464

Cited by 25 publications

(10 citation statements)

References 7 publications

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“…Because it is extremely difficult to obtain labels of fraudulent transactions, some scholars regard fraudulent samples as outliers and separate them from normal samples by anomaly detection technology. Van et al [9][10]. H used unsupervised anomaly detection technology to identify fraud samples of medical insurance claims, and the experimental results showed that potential new fraud patterns could be detected through anomaly detection technology.…”

Section: Related Workmentioning

confidence: 99%

The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm

Zheng,

Yang,

Xin

et al. 2024

FCIS

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Because of its close relationship with information technology, every change in the field of information technology will have an important impact on the means, time and space distribution of the final fraud of the credit card network. The rapid development and popularization of artificial intelligence (AI) software based on Deepfake technology has greatly promoted the intelligent transformation of credit card network fraud. Through the use of advanced hardware and software equipment, criminals have iterated on their own fraud tools, from the use of traditional phone calls and text messages to the use of the latest AI software. Therefore, for the problem of credit card fraud detection with missing data set labels and highly unbalanced category distribution, A credit card approval anomaly detection model DES-HBOS (Dynamic Ensemble Selection based on Histogram-based Outlier Score) is proposed. Firstly, the unsupervised anomaly detection algorithm is used to construct the false label of the training set customer. Then, the customer capability area to be measured is determined, and the classifier performance is evaluated according to Pearson correlation coefficient. Finally, a set of optimal classifiers is selected to integrate the test customers. Experiments on real credit card customer data sets show that DES-HBOS has a higher Recall and can identify more fraudulent customers than other 6 classical anomaly detection models. Comparative experiments were carried out on 4 unbalanced data sets, and the experimental results showed that DES-HBOS had stronger anomaly detection ability than HBOS.

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Section: Related Workmentioning

confidence: 99%

The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm

Zheng,

Yang,

Xin

et al. 2024

FCIS

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show abstract

“…In this paper, we optimized and improved the Dense Net model, increased the traditional 2D image input to 3D, improved the Dropout mechanism and Soft max loss function, and applied the improved 3D-DensenET model to the auxiliary diagnosis of gallbladder cancer [13][14].…”

Section: Related Workmentioning

confidence: 99%

Effective Combination of 3D-DenseNet's Artificial Intelligence Technology and Gallbladder Cancer Diagnosis Model

Zhao,

Liu,

Lin

et al. 2024

FCIS

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Gallbladder cancer is the most common malignant tumor in the biliary system. It has the characteristics of low early diagnosis rate, strong invasiveness and high lymphatic metastasis rate. In recent years, with the rapid development of artificial intelligence technology, relevant technologies based on machine learning and deep learning algorithms have been applied to the diagnosis and treatment of malignant tumors, prognosis assessment and medical image processing, bringing revolutionary changes to the diagnosis and treatment mode of malignant tumors. At present, artificial intelligence technology has been preliminarily studied in the early screening and diagnosis of gallbladder cancer, preoperative lymph node status assessment, intraoperative lymph node dissection, surgical treatment and prognosis assessment, showing certain clinical value. In this paper, in order to assist clinical diagnosis of gallbladder cancer, an improved 3D-DenseNet was used to establish an assisted diagnosis model of gallbladder cancer based on enhanced CT images of patients. Firstly, multiple arterial CT images of patients were converted into 3D images, and the regions of interest were cut out using the gallbladder area marked by doctors. Then, the traditional Dense Net network is optimized, the Dropout mechanism and Soft max loss function are improved, and the cross-entropy function is replaced by Focal loss in the output part for imbalance correction, so as to establish the auxiliary diagnosis model of gallbladder cancer.

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“…On the basis of the R-CNN also developed in addition to better performance of the Fast R-CNN! And Faster RCNN [9][10][11].…”

Section: Related Workmentioning

confidence: 99%

Grasp and Inspection of Mechanical Parts based on Visual Image Recognition Technology

Liu,

Duan,

Shen

et al. 2023

JTPES

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In the process of industrial production, whether the machine can work stably is an important issue related to production efficiency and production safety. The cleaning of mechanical parts is an important factor affecting the stable operation of mechanical equipment, so the cleaning of mechanical parts is one of the important manufacturing links before assembly and manufacturing. The important value of the application of image recognition technology in the quality inspection of mechanical parts is briefly analyzed, and then the common problems in the quality inspection of mechanical parts are studied, the relevant measures of the application of image recognition technology in the quality inspection of mechanical parts are discussed, and finally the case analysis of the quality inspection of mechanical parts is carried out. The main purpose is to rationally use image recognition technology to complete the quality inspection of mechanical parts, so as to improve the overall level of production automation and give full play to the maximum role of this technology. At present, a variety of mechanical parts classification methods based on deep learning are studied. According to the demand of industrial machine parts, the corresponding machine parts data set is created. According to the production requirements, the SPP structure in the traditional YOLOV5 network is improved, and a new SPC structure is proposed: The YOLOv5 network with SPC structure is characterized by no pooling layer, which improves the accuracy and recall rate compared with the traditional YOLOv5 network. Secondly, the control method of robot grasping mechanical parts is studied. Using the camera to capture the image and the upper computer to calculate the mechanical parts under the camera coordinate system; Through camera calibration and hand-eye calibration, the transformation relationship between the grasping coordinates of mechanical parts in the camera coordinate system and the grasping coordinates of mechanical parts in the robot base coordinate system is obtained. The grasping coordinates and part type information of the mechanical parts in the robot base coordinate system are transmitted from the host computer to the robot through Modbus/TCP communication protocol.

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Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition

Cited by 25 publications

References 7 publications

The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm

The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm

Effective Combination of 3D-DenseNet's Artificial Intelligence Technology and Gallbladder Cancer Diagnosis Model

Grasp and Inspection of Mechanical Parts based on Visual Image Recognition Technology

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