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Study on Text mining Algorithm for Ultrasound Examination of Chronic Liver Diseases Based on Spectral Clustering

Study on Text Mining Algorithm for Ultrasound Examination of...

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2018 3rd international conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2018)

作者： Bingguo Chang Xiaofei Chen School of Information Science and Engineering Hunan University

Ultrasonography is an important examination for the diagnosis of chronic liver disease. The doctor gives the liver indicators and suggests the patient's condition according to the description of ultrasound report. With the rapid increase in the amount of data of ultrasound report, the workload of professional physician to manually distinguish ultrasound results significantly increases. In this paper, we use the spectral clustering method to cluster analysis of the description of the ultrasound report, and automatically generate the ultrasonic diagnostic diagnosis by machine learning. 110 groups ultrasound examination report of chronic liver disease were selected as test samples in this experiment, and the results were validated by spectral clustering and compared with k-means clustering algorithm. The results show that the accuracy of spectral clustering is 92.73%, which is higher than that of k-means clustering algorithm, which provides a powerful ultrasound-assisted diagnosis for patients with chronic liver disease.

关键词： component Text mining Ultrasonic examination report Spectral clustering learning algorithm Chronic liver disease

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Methodology-Driven Characteristics of Scientific Research Collaboration Networks in the Field of Economic Management: mining and Analysis Based on Big data 3rd

Methodology-Driven Characteristics of Scientific Research Co...

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3rd international conference on Big data and Security, ICBDS 2021

作者： Xu, Hao Chen, Shihui Yi, Mengxue Feng, Ke Ding, Xi Liu, Yilin Chen, Yiyang Nanjing Institute of Technology Nanjing211167 China Nanjing University Nanjing210023 China University of St. Andrews St. AndrewsKY16 9AJ United Kingdom

ISBN: (纸本)9789811908514

Objective: Research takes the methodological knowledge in the full text of academic literature as a knowledge production element, and divides it into four categories: Theory &Method, data, Model, and Tool & Software. Identify the complex network characteristics of scientific research collaboration in economic management field driven by it, in order to achieve efficient promotion of knowledge production. Methods: Develop a crawler by python, and uses CNKI as data source to obtain the full text data of 5,564 papers (about 50,076,000 characters) from 2000 to 2019 in "Management World", then combines the TF-IDF, fusion rules and manual annotation to extract 6946 records of methodological knowledge data and its subsidiary information. The relationship were visualized by Gephi, and characteristics are analyzed. Results/Conclusions: The overall collaboration network and scientific research institutions driven by Theory &Method are the most complexity and multi-mode (complete and continuous development model are included), while it has a cohesive collaborative subnet. Methodological knowledge-driven scientific research collaboration networks have different characteristics: Theory & Method driven has the highest complexity, a long duration, and much more mature in economics and management field. The network formed by institutions has a low density, means a cross-institutional, large-scale collaboration model has not yet been formed, which may be restricted by geographical factors and research topics. Among the four types of methodological knowledge, the Theory &Method and data type drive the research collaboration of institutions more obviously. Limitations: The types and numbers of data sources in this study need to be expanded, and the extraction of specific methodological knowledge for the full text of academic literature needs to be further expanded by relying on machine learning and other methods. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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Predictive Model Based on machine learning for the Detection of Physically Mistreated Women in the Peruvian Scope 19

Predictive Model Based on Machine Learning for the Detection...

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Proceedings of the 3rd international conference on Advances in Artificial Intelligence

作者： Nemias Saboya A. Angel Sullon Omar L. Loaiza Peruvian Union University Lurigancho Lima Peru Peruvian Union University Carretera Salida a Arequipa Juliaca Peru

ISBN: (纸本)9781450372534

Violence against women in recent years in Peru showed alarming results, where 65.0 % of them were victims of some type of violence at some time in their lives. The study consisted of the elaboration of a predictive model that allows the recognition of the future physically mistreated woman. The data used was from a public survey of national scope. The methodology specified the data obtained; three supervised learning models Random Forest Classifier, Decision Tree Classifier, and Extra Trees Classifier were developed with the intention of buying the results and selecting that of the best performance. Regarding the results, Random Forest Classifier was the best model to obtain a precision of 51.0 % and a recall of 40.0 %, above those obtained by the other algorithms used. Subsequently, the best model passed to a calibration process where the average score was 0.7182, the precision was 0.76, and the recall was 0.31; concluding that the model classifies 76.0% of cases as physically mistreated women of which 31.0 % are effectively mistreated.

关键词： Random Forest Physical violence Predictive model machine learning Decision Tree

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Multisensor image fusion & mining: from neural systems to COTS software

Multisensor image fusion & mining: from neural systems to CO...

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international conference on Integration of Knowledge Intensive Multi-Agent Systems (KIMAS)

作者： A.M. Waxman D.A. Fay R.T. Ivey N. Bomberger Cognitive Fusion Technology Directorate ALPHATECH Inc. Burlington MA U.S.A.

We summarize our methods for the fusion of multisensor imagery based on concepts derived from neural models of visual processing and pattern learning and recognition. These methods have been applied to real-time fusion of night vision sensors in the field, airborne multispectral and hyperspectral imaging systems, and space-based multiplatform multimodality sensors. The methods enable color fused 3D visualization, as well as interactive exploitation and data mining in the form of human-guided machine learning and search for targets and cultural features. Over the last year we have developed a user-friendly system integrated into a COTS exploitation environment known as ErdAS Imagine. We demonstrate fusion and interactive mining of low-light Visible/SWIR/MWIR/LWIR night imagery, and IKONOS multispectral imagery. We also demonstrate how target learning and search can be enabled over extended operating conditions by allowing training over multiple scenes. This is illustrated for detecting small boats in coastal waters using fused Visible/MWIR/LWIR imagery.

关键词： Image fusion Software systems Image sensors Hyperspectral sensors Sensor fusion Sensor systems Multimodal sensors pattern recognition Image recognition Real time systems

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Research on Algorithm of Online Merchant Loan P2P Platform Based on data mining 23

Research on Algorithm of Online Merchant Loan P2P Platform B...

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Proceedings of the 3rd international conference on Electronic Information Technology and Smart Agriculture

作者： Zihao Zhu Department of International Politics University of International Relations China

ISBN: (纸本)9798400716775

With the rapid development of Internet finance, P2P (Peer-to-Peer) lending platforms have become a part of the financial market that cannot be ignored. These platforms provide an innovative way to lend money by connecting borrowers and investors. In order to improve the service quality and operational efficiency of the platform, it has become particularly important to use advanced data mining technology to analyze loan applications. This study aims to explore how to use data mining algorithms to improve the credit assessment and risk management of online merchant loan P2P platforms. We conduct experiments using a real data set containing 100,000 loan application records, which is provided by a well-known P2P lending platform and has been made public on the Kaggle platform. Through detailed preprocessing and feature engineering of the dataset, we selected a variety of data mining models for training and evaluation. Experimental results show that some models can accurately predict the repayment status of loans, providing valuable risk assessment information for P2P platforms. This study not only provides scientific methodological support for the risk management of P2P lending platforms, but also lays a solid foundation for future research in this field.

关键词： P2P lending credit assessment data mining feature engineering machine learning model evaluation risk management

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Application of Deep learning in Anomaly Detection and Prediction in Intelligent Operation and Maintenance of data Assets

Application of Deep Learning in Anomaly Detection and Predic...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Taipeng Zhu Min Zhong Ye Liu Yingwei Liang Guangdong Power Grid Company Information Center Guangzhou Guangdong China Meizhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Meizhou Guangdong China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

In the intelligent operation and maintenance of data assets, the threshold triggering of network anomaly detection is mainly based on a single parameter, which has a high false positive rate and low efficiency. In order to detect network anomalies efficiently and accurately, an anomaly prediction model based on deep learning is proposed. Firstly, intelligent O&M application scenarios are designed to meet the O&M leaders' requirements for O&M support. Then, a long and short term memory autoencoder model is constructed to reduce the dimensionality of the data and lock the grid where the suspicious anomalies appear to facilitate the extraction of abnormal information. K-means clustering method is used to detect local anomalies, and finally realize anomaly detection and prediction in intelligent operation and maintenance of data asset operations. The results show that the root-mean-square error of anomaly detection is 2.2% and the average absolute error is 24.3% when the data set is 5000. The time of anomaly detection is 350ms, which has advantages and high efficiency.

关键词： Deep learning Training machine learning algorithms Predictive models data models Maintenance Object recognition Anomaly detection Monitoring Long short term memory

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Automated Dynamic Stuttered Speech recognition System and Conversion System using Mel Filter And Enhanced Logistic Regression Model

Automated Dynamic Stuttered Speech Recognition System and Co...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： K. Nirosha N. Venkatesh School of CS&AI SR University Warangal Telengana India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Confidence and communication are both negatively impacted by stuttering, a speech condition that affects more than 70 million people globally. To improve fluency and intelligibility, the Stuttered Speech recognition (SSR) system uses cutting-edge technologies such as the Weighted Mel Frequency Cepstral Coefficient algorithm and machine learning (ML) models to detect and fix speech defects. It can transform text into speech for assured communication and includes noise reduction for clarity. This goes beyond the scope of conventional speech recognition and holds great promise for helping stuttering people lead more fulfilling lives and be more integrated into society at large. The characteristic features of stuttering speech are an abundance of initial repetitions and prolongations. As a result, the stuttering episodes can be categorized using sound analysis. Finding specific stuttering occurrences, such as prolongations and repeats, in stuttered speech using a feature extraction technique is described in this study. To determine how effectively it can identify stuttering prolongations and repetitions, we apply the famous Mel Frequency Cepstral Coefficient (MFCC) feature extraction. Stuttering is a disease of neurodevelopment characterized by a lack of fluency in conventional speech patterns. Prolonged or repeated sounds, syllables, sentences, or phrases are examples of disfluency. Stuttering detection is challenging and complex since it involves pathology, psychology, acoustics, and signal processing, all of which make it an intriguing interdisciplinary domain study challenge. A thorough analysis of stuttering and disfluency classification methods is presented based on acoustic data as well as statistical and machine learning and deep learning approaches. This research is helpful for scholars, academicians and medical experts to identify the limitations in traditional models and to design efficient solutions for stuttered speech classification.

关键词： Pathology machine learning algorithms Signal processing algorithms Psychology Speech recognition Speech enhancement Signal processing Feature extraction Mel frequency cepstral coefficient Medical diagnostic imaging

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User Authentication Through Pen Tablet data Using Imputation and Flatten Function

User Authentication Through Pen Tablet Data Using Imputation...

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IEEE international conference on Knowledge Innovation and Invention (ICKII)

作者： Md. Azim Hossain Akash Nasima Begum Sayma Rahman Jungpil Shin Md Amiruzzaman Md Rashedul Islam University of Asia Pacific Dhaka Bangladesh School of Computer Science and Engineering University of Aizu Wakamatsu Fukushima Japan College of Aeronautics and Engineering Kent State University USA

ISBN: (数字)9781728193335

ISBN: (纸本)9781728193342

Identifying a user or a person through handwriting-data is a popular technique. Many researches have been done in this area most of which are image or pattern-based analysis. The accuracy level depends on the quality of the image or pattern. In this paper, we proposed user authentication system using an individual's pen tablet handwriting data. The proposed system is concerned with the numerical value of a person handwriting data getting from the digital pen and tablet device. Hence, user authentication through pen tablet data ensures more accuracy by working with user's real time handwritten data. In the proposed system, 24 persons writing samples(1262 .csv files and 23 class)are used for extracting features to identify a user based on their handwriting attributes. Six completely separated features are extracted after data analysis and pre-processing. The extracted features are mainly concerned with the vital attributes of a user's handwriting. The extracted features are used for classification. With this concern, we utilized different classification algorithms such as Support Vector machine (SVM), Logistic Regression (LR), Linear Discriminant Analysis (LDA) and Random Forest (RF) classifier. From the implementation, different algorithms show different accuracy level. The testing accuracy rate of SVM, LR, LDA and RF is 87%, 85%, 76% and 77% respectively. The experimental analysis shows that we got more robust and satisfactory results which ensure the practicality of our system.

关键词： Feature extraction Writing Authentication Support vector machines Handwriting recognition data mining Radio frequency

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Balancing Security and Privacy-Preserving Counterterrorism

Balancing Security and Privacy-Preserving Counterterrorism

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： R.Tamilkodi L.Venkateswara Kiran K.Sai Soudamini P.Pratibha P.Ramu M.Praneeth Dept of CSE(AIML&CS) Godavari Global University Rajahmundry AP Dept of MCA Godavari Global University Rajahmundry AP Dept of CSE(AIML) Godavari Institute of Engineering & Technology (A) Rajahmundry AP

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Through speeches, films, and controversial websites, the internet has emerged as a key channel for the spread of *** groups use social media to indoctrinate people and spread their message, recruiting those who are most likely to join them. An effective web data mining method is suggested in this project to automatically identify and highlight such web properties for human assessment. Text, photos, videos, and audio are all mixed together on web pages, which are made with HTML (Hypertext Markup Language). We can extract textual information from these online pages and find trends by using data mining and web mining tools. Additionally, our system integrates an email filtering mechanism to detect and redirect unwanted messages that may be linked to terrorism directly to the recipient's spam folder users are protected from potentially harmful content. Our approach leverages advanced data mining algorithms to automatically flag suspicious emails and web content for further examination, enhancing digital security and countering the spread of terrorist propaganda online.

关键词： machine learning algorithms Social networking (online) Law enforcement Terrorism Unsolicited e-mail Semantics Web pages Web mining Market research Videos

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Advanced machine learning Frameworks for Multimodal Image and Speech Signal Processing

Advanced Machine Learning Frameworks for Multimodal Image an...

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Advancement in Technology (ICONAT), international conference for

作者： P Ramesh Naidu Saurabh Shandilya Srividya C N Shantanu Sudhir Gujar Supriya Devi Dankan Gowda V Department OF Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of Advance Computing Poornima College of Engineering Jaipur Rajasthan India Dept. of ECE Faculty of Engineering Management & Technology BGSIT Adichunchanagiri University B G Nagara India Department of CyberSecurity Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY Department of Mathematics Alliance University Bangalore Karnataka India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Multimodal data processing, especially the fusion of image and speech modality, is important for future human computer interface, medical applications and security surveillance. This research proposes the new machine learning approach for the efficient handling of multimodal data in which feature extraction from images and from the speech signal is obtained by employing CNN and LSTM networks, respectively. These features are combined by a hybrid fusion approach so that the resulting algorithm would be both accurate and fast. CIFAR-10 and LibriSpeech were used to assess the framework; the highest accuracy was 92% whilst the conventional methods like MDNN and EFMS could only reach 86%. Moreover, the analysis showed that the proposed method can take a processing time of 0.15 seconds to taxonomy and 0.45 seconds for speech data, thus making it preferable for real time data. These results illustrate the framework’s efficiency in predicting the multimodal input and demonstrate superiority over the prior methods.

关键词： Accuracy Surveillance Taxonomy Speech recognition machine learning Speech enhancement Signal processing Feature extraction data processing Real-time systems

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