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Violence Detection System Using Resnet 5

Violence Detection System Using Resnet

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5th International Conference on Electronics, Communication and Aerospace technology, ICECA 2021

作者： Shripriya, C. Akshaya, J. Sowmya, R. Poonkodi, M. Bachelors in Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Chennai India SRM Institute of Science and Technology Dept. of Computer Science and Engineering Vadapalani Chennai India

ISBN: (纸本)9781665435246

In recent years, human motion analysis has become the center of video processing, especially in motion detection and security surveillance. When performing specific tasks, it is always a tool to minimize human resources. However, earlier methods of detection often descriptors are extracted around spatiotemporal attention points or statistical features of motion regions are extracted, resulting in limited ability to effectively detect violence in videos. This paper is intended to use a Pre-trained Convolutional Neural Network Model (ResNet), where the video stream is sent to the ResNet together with a single shot detector (S S D) to detect violence and objects in the video. In addition, if an intense incident is identified, the relevant departments will be notified. This proposed methodology would be beneficial in monitoring scenarios such as detention center asylum. Also, on mobile phones with cameras. © 2021 IEEE.

关键词： Object detection

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ECG Guided Automated Diagnostic Intervention of Cardiac Arrhythmias with Extra-Cardiac Noise Detection using Neural Network 7

ECG Guided Automated Diagnostic Intervention of Cardiac Arrh...

引用

7th International Conference on Optimization and Applications, ICOA 2021

作者： Sasmal, Binoy Roy, Sayan SRM Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665441032

The electrocardiogram's (ECG) cyclic activity provides information about a person's emotional, behavioural, and cardiovascular health. Noise that occurs during acquisition and symptomatic patterns produced by pathologies are two examples of irregular behaviours that affect the analysis of these signals. This paper presents a Deep Neural Network algorithm that learns the normal behaviour of an ECG when identifying irregular events, which is studied in two different settings: noise detection and symptomatic events triggered by multiple pathologies. Two noise detection algorithms were developed using an auto-encoder and Convolution Neural Networks (CNN), with the binary class model achieving 98.18 percent accuracy and the multi-class model achieving 70.74 percent accuracy in distinguishing between base wandering, muscle artefact, and electrode motion noise. Recurrent Neural Networks and an autoencoder with Gated Recurrent Units (GRU) configuration were used to create the arrhythmia detection algorithm. With a 56.85 percent accuracy and a 61.13 percent overall sensitivity for a 7-class model. It was determined that the machine's learning mechanism learned characteristics of a regular ECG signal, sacrificing precision for greater generalisation at the moment. In the ECG, the frequency of sporadic events is more discriminatory than the classification of different types of events. © 2021 IEEE.

关键词： Electrocardiograms

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Graph Convolutional Network-based Centre-Object Tracking for the multi-dynamic agents for Autonomous Vehicles 5

Graph Convolutional Network-based Centre-Object Tracking for...

引用

5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Singh, Divya Srivastava, Rajeev Indian Institute of Technology BHU Dept. of Computer Science and Engineering Varanasi India

ISBN: (纸本)9781665403412

Multi-Object tracking goals to localize, classify and track all object instances of each class throughout an image sequence. It is very useful to understand the video scenes and very desirable for computer vision-based applications that require detailed pixel-level information, such as video editing and autonomous driving. Our method involves Detection-based tracking, which generally requires detected objects with a tracker that links the boundary boxes (bboxes) to generate target trajectory. To optimize the similarity between tracked and detected objects are the basic part of multi-object tracking (MOT) frameworks. To mark this issue, ongoing works concurrently enhance the matching technique of data modules with joint MOT structure, which has expressed improved execution. The key thought behind our GCNN approach is to take into consideration and learn the distinctive or selective features for local and global matching. It takes into account the relations between nearby objects in a frame sequence and thus provides a better tracking performance as compared to the existing models. We demonstrated our model on the MOT datasets that showed the viability of our GCNN-based joint MOT approach which includes the Hungarian algorithm to assign the identity of the object in the next frame. © 2021 IEEE.

关键词： Autonomous vehicles

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A Case Study of Techniques to Select UMVI in Health Care System 4

A Case Study of Techniques to Select UMVI in Health Care Sys...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Aadithyan, G. Al-Attabi, Kassem Shareef, Hayder Bordoloi, Dibyahash Kadeem, Sahar R. Abdul Saadoun, Osama Nazim Jawad, Ahmed Qusay Hindustan Institute of Technology and Science Department of Computer Applications India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Al-Farahidi University Baghdad Iraq Graphic Era Hill University Dept of Cse India Graphic Era Deemed to Be University Uttarakhand Dehradun248007 India National University of Science and Technology College of Engineering Dhi Qar Iraq Al-kunooze University College Law Department Basra Iraq Kut University College Department of Electrical Engineering Technologies Wasit Iraq

ISBN: (纸本)9798350360165

Most databases on healthcare have the problem of missing data. The most common method for handling missing data is imputation. The basic idea of imputation is to replace each missing value with a sensible guess, and then to carry on the analysis as if the missing data has never occurred. However, objective predictions with imputed datasets can only be generated if the missing mechanism is fully independent of the present or missing data. This guarantee is often broken in the construction of healthcare datasets due to bias in respondent responses or unintentional errors. The algorithm, therefore, offers an integrated approach to feature selection and missing value imputation to enhance the classification accuracy. The dataset D, of size M, is thus divided into training (D-tr) and test (D-te) sets. The operations are first conducted on the complete data subset D-complete, resulting in a subset reduced to D-complete' based on the feature selection algorithm. Similarly, incomplete samples of D-tr are reduced to D-incomplete'. Missing values of D incomplete' are imputed through a learning model trained on D-complete', after which the same operations are conducted for the test set D-te. Experiments on five UCI datasets show that, even in the presence of reduced-class instances, the feature selection significantly enhances the classification accuracy as compared to the baseline imputation models at different missing rates. Moreover, DT tends to be oversensitive in selecting the features, while GA and IG behave similarly, suggesting that one needs ad-hoc feature selection methodologies to attain improved imputation and classification performances. © 2024 IEEE.

关键词： Electronic health record

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Intelligent Face Detection and Recognition System

Intelligent Face Detection and Recognition System

引用

2021 International Conference on Intelligent Technologies, CONIT 2021

作者： Sikder, Juel Chakma, Ratna Chakma, Rana Jyoti Das, Utpol Kanti Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh

ISBN: (纸本)9781728185835

Face detection applications currently can use machine learning and equations for better identification of human faces in an image or video. In several types of applications such as recognition of face application that is processing in method of biometric for checking the human's validation which is related to facial information and different types of samples utilizing face detection system. This paper of presentation shows that system of detection and recognition in face by Viola-Jones process add together Haar Cascade and Principal Component Analysis (PCA) of technique which is proposed for gaining better accuracy level in detection of facial portions and recognition of faces. The findings show that the proposed system has performed better compared with other conventional face detection and recognition systems. The investigation has been done in a MATLAB environment on real images of the Faces94, Faces95, Faces96, and grimace databases. © 2021 IEEE.

关键词： Face recognition

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Allocation and Tracking of Public Funds using Blockchain 4

Allocation and Tracking of Public Funds using Blockchain

引用

4th Biennial International Conference on Nascent Technologies in engineering, ICNET 2021

作者： Bhole, Kalpesh Galphat, Yugchhaya Bathija, Kunal Saini, Tamanna Bhosale, Anuraj Vivekanand Education Society's Institute of Technology Dept of Computer Engineering Mumbai India

ISBN: (纸本)9781728190617

Blockchain is a technology that helps create and maintain a cryptographically secure, shared, and distributed ledger (a database) for transactions. It is a public ledger to which everyone has access but control of the ledger does not lie with a central authority. One can get transparency, accountability and trust by using blockchain to perform digital transactions. It is an enabling technology for individuals as well as companies to collaborate while ensuring that trust and transparency are maintained throughout the process. Considering these characteristics, blockchain can be used for tracking the flow of public funds assigned to a project. The paper explores the uses of blockchain technology in real-life commercial applications and proposes a method that will be used to allocate and track public funds using blockchain technology. © 2021 IEEE.

关键词： Blockchain

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Detection of malicious miner in block chain network 2

Detection of malicious miner in block chain network

引用

2nd International Conference on Mathematical Sciences, ICMS 2020

作者： Ramya, G. Franklin Sheela, A.C. Santha Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

The block chain innovation has developed as an alluring answer for address execution and security issues in disseminated frameworks. Block chain's open and dispersed friend topper record ability benefits distributed computing administrations which require capacities, for example, guaranteed information provenance, examining, the board of advanced resources, and disseminated agreement. Block chain's fundamental agreement system permits to construct a carefully designed condition, where exchanges on any advanced resources are confirmed by set of genuine members or excavators. With utilization of solid cryptographic strategies, blocks of exchanges are binded together to empower permanence on the records. Not with standing, accomplishing agreement requests computational force from the diggers in return of attractive prize. Along these lines, eager diggers consistently attempt to abuse the framework by increasing their mining power. Right now, first talk about block chain's capacity in giving guaranteed information provenance in cloud and present vulnerabilities in block chain cloud. Actualized at the mining pool chief, Silent Time stamping doesn't require organizing nor any conduct or usage changes in the diggers;the absence of the execution overheads recognize Silent Time stamping from different plans which have been proposed yet had constrained achievement by and by. We break down the ideal assailant conduct against Silent Time stamping and the adequacy of Silent Time stamping against FAW assault. Quiet Time stamping adequately invalidates the prize addition of the FAW assault and lessens the ideal FAW aggressor technique to be either the problematic block-retaining assault (which never presents the retained block in the undermined pool and gives constrained motivators to a normal assailant) or legitimate mining. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Game theory

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Multi-granularity Coal Mine Risk Conceptual Prediction Based on Time-varying Cloud Model

Multi-granularity Coal Mine Risk Conceptual Prediction Based...

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2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Dai, Jin Hu, Sunling Zhang, Lei Dept. of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Dept. of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781510666597

The big data of coal mine was characterized by large scale, many influencing factors and weak correlation. The existing big data mining based on quantitative data analysis usually adopts fixed framework processing, which is easy to fall into the dilemma of overfitting and cannot acquire elastic knowledge across layers. From the perspective of coal mine monitoring and management, a multi-granularity time-varying cloud model is proposed based on a multi-granularity semantic transformation model and a grey prediction model to achieve the conceptual prediction of coal mine gas concentration risk. The new model first converts the gas concentration data into semantic concepts using a semantic transformation model, then processes the sequence of semantic concept sets for randomness and oscillation, and builds a grey prediction model for them, which is used to project future semantic concepts. The new model is compared with the existing model, and the experimental results show that the new method can not only predict coal mine risk concepts at different granularities, but also has high prediction accuracy. © 2023 SPIE.

关键词： Coal mines

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Multisport Dynamics Explored Through Advanced Activity Recognition Techniques in Athletic Performance

Multisport Dynamics Explored Through Advanced Activity Recog...

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Supriya Salian Preethi Salian K Ria D'Souza Rencita Monterio Rinette Veronica Fernandes Riya D Almeida Dept. of Computer Science and Engineering St. Joseph Engineering College Vamanjoor Mangaluru India Dept. of Information Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Udupi India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Recognition of human activities has drawn a lot of interest lately in the field of computer vision and machine learning. Group activity recognition is a significant subcategory in which several people participate in a common activity. The primary obstacle in these tasks is learning the relationships between individuals in a scene and understanding their evolution over time. The suggested study offers a new taxonomy to classify state-of-the-art (SOTA) group activity recognition approaches and subcategorizes the current literature. It also critically analyzes these techniques. To understand scenes involving multiple people, models must describe individual actions in context and infer collective activities. Accurately capturing relationships between actors and performing relational reasoning is crucial for comprehending group activities. However, modelling these relationships is challenging due to the limited availability of interaction information, relying only on individual action labels and collective activity labels. Inferring relationships from other aspects is thus essential. Group activity recognition has garnered increased research attention recently due to its importance in video understanding. The difficulties lie not only in recognizing individual actions but also in exploring scene information and collaborative relations among people. This research addresses these challenges by providing a comprehensive review of existing techniques, offering a structured approach to categorize them, and highlighting the importance of relational reasoning in understanding group activities.

关键词： computer vision Reviews Taxonomy Collaboration Machine learning Activity recognition Very large scale integration Distributed computing Context modeling

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Empirical Evaluation of Machine Learning Techniques for Autism Spectrum Disorder

Empirical Evaluation of Machine Learning Techniques for Auti...

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International Electrical engineering Congress (iEECON)

作者： Akshit Sethi Kainat Khan Rahul Katarya Thaweesak Yingthawornsuk Dept. of Computer Science & Engineering Delhi Technological University New Delhi India Dept. of Media Technology King Mongkut's University of Technology Thonburi Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

Autism spectrum disorder (ASD) is a complicated neurological system disorder that has severe effects on a person's spoken communication, logical thinking, and person-to-person interaction. The early symptoms start looking visible at the age of two to four years and the main ground of these symptoms could be hereditary or ecological. Presently, the standardized approach for detecting ASDs is usually very time-consuming and the cost of medical care is extravagant. Therefore, early detection of ASD would be helpful. Machine Learning (ML) algorithms have the required potential to predict the chances of having autism or not, based on the type of dataset provided to it and the kind of algorithms that are effectively applied to it. The focus of this study is on screening data sets and the use of machine learning algorithms, including XGBoost (XGB) (Ensemble Technique), Random Forest Classifier (RFC), Support Vector Classifier (SVC), Logistic Regression (LR), and Artificial Neural Network (ANN). Our aim for this research was to develop a model that shortens the process of identification of ASDs at an early stage. The accuracy and precision scores of 92.2% and 0.88, respectively, indicate that our model has done well. Our research could be extended in developing a large-scale model that contains a large dataset having a variety of attributes and the integration of brain and facial MRI scans could be helpful in identifying ASD.

关键词： Autism Machine learning algorithms Biological system modeling Static VAr compensators Artificial neural networks Medical services Prediction algorithms

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