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Research on Financial Fraud Identification Model Based on Privacy-Preserving machine learning 23

Research on Financial Fraud Identification Model Based on Pr...

引用

Proceedings of the 2023 4th international conference on machine learning and Computer Application

作者： Li Jiao Hui Li Shandong Institute of Commerce & Technology China

ISBN: (纸本)9798400709449

Financial fraud will not only mislead investors and reduce investor confidence, but also disrupt market order and hinder the orderly development of the capital market. In order to solve the problem of financial fraud, based on the data mining process, a process framework of the financial fraud identification method based on machine learning is proposed, which includes three steps: sample and feature selection, data preparation, and model construction and analysis. Based on the process framework of the financial fraud identification method based on machine learning, GBDT methods are used to build models respectively, and the model effect is evaluated based on indicators such as recall rate. Due to the particularity of financial reporting fraud, the model should aim to identify fraud samples as much as possible, so the recall rate is a major evaluation index in this article. the recall rate of the GBDT model is better when based on fewer indicators. therefore, after comprehensively considering factors such as data acquisition costs, this article recommends using the GBDT model to identify financial fraud.

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An Occlusion-aware Multi-target Multi-camera Tracking System

An Occlusion-aware Multi-target Multi-camera Tracking System

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Specker, Andreas Stadler, Daniel Florin, Lucas Beyerer, Juergen Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning St Augustin Nrw Germany

ISBN: (纸本)9781665448994

Multi-camera tracking of vehicles on a city-scale level is a crucial task for efficient traffic monitoring. Most of the errors made by such multi-target multi-camera tracking systems arise due to tracking failures or misleading visual information of detection boxes under occlusion. therefore, we propose an occlusion-aware approach that leverages temporal information from tracks to improve the single-camera tracking performance by an occlusion handling strategy and additional modules to filter false detections. For the multi-camera tracking, we discard obstacle-occluded detection boxes by a background filtering technique and boxes overlapping with other targets using the available track information to improve the quality of extracted visual features. Furthermore, topological and temporal constraints are incorporated to simplify the re-identification task in the multi-camera clustering. We give detailed insights into our method with ablative experiments and show its competitiveness on the CityFlowV2 dataset, where we achieve promising results ranking 4th in Track 3 of the 2021 AI City Challenge.

关键词： Visualization Target tracking conferences Urban areas Feature extraction Information filters data mining

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An Approach to pattern recognition for Identification of Devnagari Script Based on Fingertips and Palm 4

An Approach to Pattern Recognition for Identification of Dev...

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4th international conference on Intelligent Circuits and Systems, ICICS 2022

作者： Tantarpale, Ms. Sharvari Deshmukh, C.N. Amravati India PRMIT & R Badnera India

In this paper, we present an finger point based signed language symbol to text identification and classification algorithm that is based upon RGB image datasets. the palm sized images based upon different sizes, backgrounds, orientation are captured to be preprocessed as per the requirements of developing a convolution neural network based algorithm. this algorithm utilizes Alexnet for the preprocessing requisites where in 47 symbols of Devanagari script are augmented based on the reference rulebook created for our requirements as highlighted in the paper. At the primary level this algorithm provides an excellent classification rate which promises upliftment for our research in the upcoming future. We have provided detailed steps and discussion on the classification parameters considered for our algorithm which is implemented on MATLAB platform with the help of machine learning solution libraries. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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Research on Transferable Patent recognition Based on machine learning 2021

Research on Transferable Patent Recognition Based on Machine...

引用

4th international conference on data Science and Information Technology, DSIT 2021

作者： Hu, Yifei Yang, Shiwei Shi, Anjie Beijing University of Technology China

ISBN: (纸本)9781450390248

the construction of a transferable patent recognition model is of great significance for increasing patent transformation. To construct the index system affecting patent transformation, this paper adds cross-border dimension index to the existing three-dimensional patent value evaluation index. the results show that the recognition accuracy of Gradient boosting decision tree and Random forest is as high as 85.92%, which indicates that machine learning method can support transferable patent recognition in massive data. © 2021 ACM.

关键词： Decision trees

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machine learning Driven Islanding Detection using Random Forest and Scalogram

Machine Learning Driven Islanding Detection using Random For...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Somila Hashunao Shalu Peshwani Khushbu Modi Palak Hariyaliyan Payal Roy Mostaque Md. Morshedur Hassan Kumaresh Pal Department of Electrical Engineering North Eastern Regional Institute of Science and Technology Itanagar India Department of Computer Engineering Aditya Silver Oak Institute of Technology Silver Oak University Ahmedabad India Department of Electrical Engineering Aditya Silver Oak Institute of Technology Silver Oak University Ahmedabad India Department of Computational Sciences Brainware University Kolkata India Department of Electrical Engineering National Institute of Technology Jamshedpur India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Islanding is a problem caused by distributed generation sources, causing potential damage to clients and equipment. To meet IEEE 1547 DG interconnection requirements, it must be detected within two seconds. this research introduces a novel method using image pattern recognition and machine learning classification algorithms, using random forest (RF) classifiers and histogram of oriented gradient (HOG) features for identifying islanding. the scalogram images are derived from the time-series signal collected from the point of common coupling. the images are utilised to extract HOG features, which are subsequently used as feature vector inputs for training and testing the RF classifiers. the scalogram image is derived using the rate of change of the voltage parameter. the proposed method achieved 98.62% accuracy and a detection time of 142 milliseconds.

关键词： Training Histograms Accuracy Voltage Feature extraction Vectors pattern recognition Classification algorithms Random forests Classification tree analysis

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A data-driven Approach to Survival Prediction in Middle-aged and Elderly Primary Ovarian Cancer Patients: an Application of machine learning Based on the SEER database 24

A Data-driven Approach to Survival Prediction in Middle-aged...

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4th international conference on Big data, Artificial Intelligence and Risk Management, ICBAR 2024

作者： Pan, Yiting Yuan, Min Zhu, Liye Huang, Wunan School of Information Science and Engineering Lanzhou University Gansu Lanzhou 730000 China Department of Obstetrics and Gynecology The First Hospital of Lanzhou University Gansu Lanzhou China Gansu Provincial Clinical Research Center for Gynecological Oncology Gansu Lanzhou China

ISBN: (纸本)9798400709753

Ovarian cancer (OC) is one of the primary causes of death from malignant tumors of the female reproductive system, which presents unique challenges due to the fact that, since it usually has no obvious clinical symptoms in the early stages, the majority of cases have already progressed to terminal stages at the time of diagnosis and have a poor prognosis, especially among middle-aged and elderly women. this study leverages advanced data mining techniques and machine learning algorithms to undertake a novel analysis of the relationship between clinical parameters and survival outcomes in primary ovarian cancer patients within these demographics, utilizing the extensive dataset provided by the SEER (Surveillance, Epidemiology, and End Results) database. Innovatively applying both the Cox proportional hazard model and Pearson correlation coefficients to examine multifaceted clinical interactions, this research breaks new ground in understanding how treatment variables correlate with survival rates. Further, this study pioneers the use of a variety of predictive models including Logistic Regression, Decision Tree, Naive Bayes, and an AdaBoost classifier, the latter of which demonstrates superior predictive performance. By adopting a randomized search for hyper-parameter optimization, the AdaBoost model's predictive accuracy for three- and five-year survival rates significantly improved from initial benchmarks of 0.89 and 0.91 to 0.91 and 0.93, respectively. the model's precision, recall, and F1 score metrics all exceeded 0.92, underscoring its robustness and precision. Moreover, ROC curve analysis further highlights the model's enhanced discriminatory power with an average AUC value of 0.94, a notable increase from the baseline of 0.87. this study not only advances our statistical methodologies in oncological prognostication but also provides a more reliable tool for clinicians in the personalized treatment planning for ovarian cancer patients. © 2024 Copyright held by

关键词： data mining machine learning Ovarian cancer survival analysis

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Model of Recommendation System to Enhance the Employability: machine learning Approach 4

Model of Recommendation System to Enhance the Employability:...

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4th international conference on Information Management and machine Intelligence, ICIMMI 2022

作者： Shah, Priyanka Virparya, Paresh V. Pandit, Hardik B. S. K. Patel Institute of Management and Computer Studies Kadi Sarva Vishwavidyalaya University Gujarat Gandhinagar India Department of Computer Science Sardar Patel University Vallabh Vidyanagar Gujarat India Faculty of Computer Science and Applications Charotar University of Science and Technology Gujarat Changa India

ISBN: (纸本)9781450399937

In the recent era, various types of data are available on the Web in various forms such as text, images, audio, and video. Extracting information of interest from this web content is one of the applications of Web Content mining. Computer Science is one of the most demanding fields where students can get the best recruitment in the IT (Information Technology) industry through job portals. Still, there is a gap of skills between academia and industry. One of the prominent factors is non-competent syllabus. this Research Paper introduces the Industry Institute Interaction Recommendation System-IIIRS model of a Web mining technique. It is based on a recommendation system to enhance the employability of students, by bridging the gap between the institution's curriculum and the required skills set at the industry end. the proposed model includes application of data extraction, data pre-processing, classification, and clustering. the model presents the state of the art skills required by the software industry, and gives recommendations about required updates in the syllabus of academic institute. the model serves the purpose of both industry and academia by bridging the gap of skill sets due to incompetent syllabus. © 2022 ACM.

关键词： Recommender systems

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Self-Training for Domain Adaptive Scene Text Detection 25

Self-Training for Domain Adaptive Scene Text Detection

引用

25th international conference on pattern recognition (ICPR)

作者： Chen, Yudi Wang, Wei Zhou, Yu Yang, Fei Yang, Dongbao Wang, Weiping Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China TAL Educ Grp Beijing Peoples R China

ISBN: (纸本)9781728188089

though deep learning based scene text detection has achieved great progress, well-trained detectors suffer from severe performance degradation for different domains. In general, a tremendous amount of data is indispensable to train the detector in the target domain. However, data collection and annotation are expensive and time-consuming. To address this problem, we propose a self-training framework to automatically mine hard examples with pseudo-labels from unannotated videos or images. To reduce the noise of hard examples, a novel text mining module is implemented based on the fusion of detection and tracking results. then, an image-to-video generation method is designed for the tasks that videos are unavailable and only images can be used. Experimental results on standard benchmarks, including ICDAR2015, MSRA-TD500, ICDAR2017 MLT, demonstrate the effectiveness of our self-training method. the simple Mask RCNN adapted with self-training and fine-tuned on real data can achieve comparable or even superior results with the state-of-the-art methods.

关键词： Deep learning

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Traffic pattern recognition Method with XGBoost Based on Multi-scale Features

Traffic Pattern Recognition Method with XGBoost Based on Mul...

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Electronic Communication and Artificial Intelligence (IWECAI), international Workshop on

作者： Yunlong Song Hao Wang Institute of Big Data and Artificial Intelligence China Telecom Corporation Limited Beijing Research Institute Beijing China

Traffic pattern recognition belongs to a branch of scene recognition and has become a hot research field. Correctly identifying the transportation mode used by users to travel plays a vital role in promoting the development of situational recognition. In the field of traffic pattern recognition research, many recognition methods use GPS, the combination of GPS and WiFi, and the combination of GSM and WiFi to obtain data, and use LR, SVM and deep learning models to make predictions. However, these methods have some disadvantages, such as poor WiFi signal in some outdoor environments, resulting in failure to obtain user data, GPS being interfered by the external environment, resulting in inaccurate data acquisition and other issues, and the models they use have problems such as low prediction accuracy or prolonged prediction consumption. In response to these problems, this paper uses multi-source sensors in mobile phones to obtain data, and preprocesses sensor data through statistics and signal processing methods to generate features at different scales, and finally uses XGBoost to identify various traffic modes. Extensive experiments are carried out on the method proposed in this paper, and the effectiveness of the proposed method is demonstrated by comparing with two state-of-the-art methods.

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Named Entity recognition in Electronic Medical Records Based on Transfer learning 22

Named Entity Recognition in Electronic Medical Records Based...

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4th international conference on Intelligent Medicine and Health, ICIMH 2022

作者： Zhang, Kunli Zhang, Chenghao Ye, Yajuan Zan, Hongying Liu, Xiaomei School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Department of Pediatrics Kunming Children's Hospital Kunming China

ISBN: (纸本)9781450397087

Named entity recognition is the first step in clinical electronic medical record text mining, which is significant for clinical decision support and personalized medicine. However, the lack of annotated electronic medical record datasets limits the application of pre-trained language models and deep neural networks in this field. To alleviate the problem of data scarcity, we propose T-RoBERTa-BiLSTM-CRF, a transfer learning-based electronic medical record entity recognition model, which aggregates the characteristics of medical data from different sources and uses a small amount of electronic medical record data as target data for further training. Compared with existing models, our approach can model medical entities more effectively, and the extensive comparative experiments on the CCKS 2019 and DEMRC datasets show the effectiveness of our approach. © 2022 ACM.

关键词： Deep neural networks

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