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Automated System for Detection of Suspicious Activity in Examination Hall 7

Automated System for Detection of Suspicious Activity in Exa...

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7th IEEE international conference on Electronics, computing and Communication Technologies, CONECCT 2021

作者： Kulkarni, Aishwarya S Naresh, E. Swetha, Merla Kusuma, S.M Department of ISE Bengaluru India Reva University School of Management Studies Bengaluru India

ISBN: (纸本)9781665428491

One among the curses in the education institution of the country is malpractice during written examination, either online or off line. Some of the suspicious activities during written examination includes, copying from neighbouring candidates, other written materials, exchange of answer scripts, etc. These activities lead to physical movement of the face and other parts of the body than the normal writing posture of the candidate(s) seen at the initial stage of the examination. Any deviation to normal posture can be an indicative of suspicious activities. Main objective of this work is to build a real time edge computing-based video analytics and techniques to identify the suspicious activities relating to malpractice case booking as evidence and proofs during written examination. The proposed model includes a low-cost webcam video frame capturing and edge computing for analytics to detect the face and behaviour recognition using the following techniques. Firstly, the CNN is used to detect human faces. Secondly, candidates are recognised using YOLO and features are extracted using Local Binary patterns Histogram (LBPH). After feature extraction, the activity classification is performed and is used to detect whether the activity is suspicious or not based on the face movement, hand contact detection and body posture using SVM. Lastly, the system will set an alert based on the threshold set for movement pattern of the candidate(s) in the given area focussed by the web cam or any other camera used in an examination centre or for individual online examination for suspicious activities. © 2021 IEEE.

关键词： Local binary pattern

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Study on Cluster-Based pattern recognition Using Recommendation System 2nd

Study on Cluster-Based Pattern Recognition Using Recommendat...

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2nd international conference on Communication, Networks and computing, CNC 2020

作者： Chaurasia, Shailendra Shrivastava, Prashant Kamble, Megha Chouksey, Bhanupriya Computer Science Department LNCT Excellence Bhopal Bhopal India

ISBN: (纸本)9789811688959

The recommendations system has growing relevance to internet service and overload of information is one of the most critical incidents that users meet on the Internet. To make suggestions based on similar interests between users or items, Recommender Systems uses collective filtering. Clusters shall classify population or data points so that data points in the same category are more comparable to other data points in the same category and differ from data points in other categories. pattern detection and matching include parameters such as average, correlation, and mutual information, etc. The PR can be used as a classification method for extracting and grouping patterns into various groups. PR is a classification method. PR is an intermediary process. In this review, we researched data mining, Clustering, Recommendation systems, pattern recognition, and described different types of recommendation systems as well as defined various methods of clustering & pattern recognition. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Toward reducing the effect of force variations on electromyography pattern recognition by Mel-frequency spectrum

Toward reducing the effect of force variations on electromyo...

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2021 IEEE international conference on Real-Time computing and Robotics, RCAR 2021

作者： Liu, Yan Tian, Lan Zheng, Yue Zhou, Xiaomeng Li, Xiangxin Li, Guanglin Wuhan Research Institute of Posts and Telecommunications Wuhan430070 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

ISBN: (纸本)9781665436786

Currently, the electromyography pattern recognition (EMG-PR) is considered as a promising approach to control the human-machine interaction systems such as multifunctional prostheses. However, the robustness of EMG-PR method is still not strong enough to against some issues such as different arm positions, electrode shift, muscle fatigue and force variation in the clinical application. And among these issues, the force variation is an important problem that greatly affects the performance of EMG-PR based systems. In this study, a feature of log-Mel-frequency spectrum (log-MFS) was proposed to reduce the effects of force variations on the classification performance of the EMG-PR method. Eight channels of EMG signals were recorded from the upper limbs of eight subjects when performing different hand motions at low, medium and high force levels, respectively. Then the proposed feature of log-MFS was extracted from the EMG signals and used to classify the motions. Compared with the commonly used time domain feature set, the feature of log-MFS achieved the higher classification accuracies for all the three force levels. Especially for the un-trained high and low force levels, the average classification accuracies increased by about 27% and 11%. These results demonstrated that the feature of log-MFS is effectiveness to enhance the robustness of the EMG-PR based systems to against force variations in practical application. © 2021 IEEE.

关键词： pattern recognition

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2019 Joint 8th international conference on Informatics, Electronics and Vision, ICIEV 2019 and 3rd international conference on Imaging, Vision and pattern recognition, icIVPR 2019 with international conference on Activity and Behavior computing, ABC 2019

2019 Joint 8th International Conference on Informatics, Elec...

引用

Joint 8th international conference on Informatics, Electronics and Vision and 3rd international conference on Imaging, Vision and pattern recognition, ICIEV and icIVPR 2019

ISBN: (纸本)9781728107868

The proceedings contain 64 papers. The topics discussed include: performance analysis and mitigating the challenges of constraint-based local search;MR-anonymization: a relationship-based privacy model;challenges in sensor-based human activity recognition and a comparative analysis of benchmark datasets: a review;interpretable machine learning with boosting by Boolean algorithm;effects of illumination color on autonomic response to exercise;implementing an efficient SAT solver for structured instances;a comparative study on missing data handling using machine learning for human activity recognition;and prediction of gender and age from inertial sensor-based gait dataset.

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GAN-Based Data Imbalance Techniques for ECG Synthesis to Enhance Classification Using Deep Learning Techniques and Evaluation

GAN-Based Data Imbalance Techniques for ECG Synthesis to Enh...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), international conference on

作者： Shaik Janbhasha S Nagakishore Bhavanam Kalva Harshita Department of CSE Acharya Nagarjuna University Andhra Pradesh India Department of ECE Acharya Nagarjuna University Andhra Pradesh India Department of CSE CBIT Hyderabad Andhra Pradesh India

The prompt detection of irregularities on Electrocardiograms can be crucial in patient monitoring, deep learning-based strategy that combines segmentation and classification as one procedure has recently been effectively applied to ECG signal analysis. In the gathered ECG signals, it frequently results in an unbalanced ratio of small and large cases of numerous abnormalities. The deep network is unable to learn how to recognize minority groups because of this data imbalance. Various physiological signal analyses, medical imaging datasets have all been balanced using GANs. GAN and Deep Learning integrate well together in the healthcare, particularly for the recognition and characterization of ECG patterns. In order to improve categorization and efficiency, this study presents a comprehensive overview of the GAN strategies for data imbalance and deep learning strategies for physiological signal classification. With Cardio GAN, CNN obtained accuracy greater than 99.0 percent and performs superior classification.

关键词： Deep learning Patient monitoring Arrhythmia pattern classification Medical services Electrocardiography Generative adversarial networks

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Research on Vehicle Reputation Management Scheme Based on Blockchain Application

Research on Vehicle Reputation Management Scheme Based on Bl...

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Information Processing and Network Provisioning (ICIPNP), international conference on

作者： Weijie Bao Jie Han Qian Liu School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Mobile Communications Technology College of Electronical and Information Engineering Nanchang Institute of Technology Nanchang China

ISBN: (数字)9798350363470

ISBN: (纸本)9798350363487

Vehicular edge computing networks are a promising Internet of Vehicles (IoV) paradigm to provide abundant computing and storage resources by mobile edge computing technology which enables the calculation, storage, and sharing of massive amounts of data among vehicles. However, edge computing service nodes are vulnerable to various network attacks resulting in substantial hidden dangers in data storage and sharing. Blockchain technology effectively solves the above problem due to its distribution, immutability, and traceability advantages. Therefore, this paper proposes a blockchain-enabled reputation management scheme for Vehicular edge computing networks. Firstly the system architecture is based on the consortium blockchain to ensure the security of data storage in the vehicular edge computing network. Then, a multi-source three-weight subjective logic algorithm is proposed to ensure the quality of information exchange is safe and efficient for shared data sources. The algorithm fuses direct and indirect reputation evaluation opinions between nodes. Finally, the simulation and analysis based on the real vehicle trajectory data set verify that the proposed blockchain-enabled reputation management scheme can effectively improve the discrimination and detection rate of abnormal nodes. Finally, the simulation shows that the proposed blockchain-based reputation management scheme can effectively improve the recognition and detection rates of abnormal nodes.

关键词： Multi-access edge computing soft sensors Simulation Systems architecture Memory Blockchains Trajectory Logic Secure storage Internet of Vehicles

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Synthetic Image Generation for Improved Detection of Bone Defects Using GAN

Synthetic Image Generation for Improved Detection of Bone De...

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international conference on computing and Networking Technology (ICCNT)

作者： Brajesh Singh Pundir Rakesh Kumar Meenu Gupta Ahmed J. Obaid Department of CSE Chandigarh University Punjab India Dept of Computer Science Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Automated Fractures of Bone Detection in orthopedic radiographs is crucial in assisting doctors to correctly and properly diagnose fractures. For this assignment, we investigate a Generative Adversarial Networks (GAN)-based solution that employs sophisticated artificial intelligence models capable of producing realistic photos while detecting anomalies. It display efficiency of GAN-based models in boosting fracture detection performance by applying multiple assessment criteria, such precision, accuracy, and F1-score, and detection speed, through thorough testing and evaluation of musculoskeletal radiograph datasets (MURA). GANs function by retraining networks of generators to produce artificial pictures and a network of discriminators. This antagonistic process encourages the formation of realistic radiography with fractures, enabling precise and automatic detection. Our proposed approach makes use of GANs’ capability to produce artificial pictures with cracks and recognize, abnormal patterns, thereby improving fracture diagnosis These findings highlight the prospect of introducing GANs into automated evaluation, which might assist with develop fracture detection systems in orthopaedics. Our findings advance fracture detection approaches, paving the way for improved and more accurate orthopaedic diagnostic tools.

关键词： Musculoskeletal system Accuracy Image synthesis Medical services Generative adversarial networks Bones Generators pattern recognition Diagnostic radiography Testing

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Co-Speech Gesture Video Generation via Motion-Decoupled Diffusion Model

Co-Speech Gesture Video Generation via Motion-Decoupled Diff...

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conference on Computer Vision and pattern recognition (CVPR)

作者： Xu He Qiaochu Huang Zhensong Zhang Zhiwei Lin Zhiyong WU Sicheng Yang Minglei Li Zhiyi Chen Songcen Xu Xiaofei Wu Shenzhen International Graduate School Tsinghua University Huawei Noah's Ark Lab The Chinese University of Hong Kong Huawei Cloud Computing Technologies Co. Ltd

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Co-speech gestures, if presented in the lively form of videos, can achieve superior visual effects in human-machine interaction. While previous works mostly gener-ate structural human skeletons, resulting in the omission of appearance information, we focus on the direct gener-ation of audio-driven co-speech gesture videos in this work. There are two main challenges: 1) A suitable motion feature is needed to describe complex human movements with crucial appearance information. 2) Gestures and speech exhibit inherent dependencies and should be temporally aligned even of arbitrary length. To solve these problems, we present a novel motion-decoupled framework to gener-ate co-speech gesture videos. Specifically, we first intro-duce a well-designed nonlinear TPS transformation to ob-tain latent motion features preserving essential appearance information. Then a transformer-based diffusion model is proposed to learn the temporal correlation between gestures and speech, and performs generation in the latent motion space, followed by an optimal motion selection mod-ule to produce long-term coherent and consistent gesture videos. For better visual perception, we further design a refinement network focusing on missing details of cer-tain areas. Extensive experimental results show that our proposed framework significantly outperforms existing approaches in both motion and video-related evaluations. Our code, demos, and more resources are available at https://***/thuhcsi/S2G-MDDiffusion.

关键词： Human computer interaction Focusing Speech recognition Diffusion models Visual effects Transformers Skeleton

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TweetSense: Navigating Disaster Signals Using Machine Learning and Deep Learning Techniques

TweetSense: Navigating Disaster Signals Using Machine Learni...

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international conference on computing and Networking Technology (ICCNT)

作者： R. Shanthakumari E.M. Roopa Devi S. Vinothkumar N.Asifaa Sulthana B.Fahima Begum G. Kaushik Information Technology Kongu Engineering College

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

A disaster is a sudden, catastrophic event that causes significant damage, destruction, and disruption to the normal functioning of society, often resulting in loss of life, property, and livelihoods. Disasters can be natural (earthquakes, floods, wildfires) or human-made (industrial accidents, terrorist attacks, wars). Disasters can have wide-ranging impacts on communities, economies, and the environment, requiring emergency response and recovery efforts to mitigate their effects and restore normalcy. With disasters being too frequent in the modern world, the analysis of tweets cannot be an easy task for manual analysis. To overcome this, an automated model is to be built, which can reduce the manual analysis time for emergency responders. Socialmedia, with Twitter being a prime example, acts as a primary channel for disseminating information regarding disasters. The ability to predict relevant tweets holds immense potential in the development of early warning systems, facilitating timely actions by authorities and emergency responders to mitigate the impact of disasters. The approaches followed to achieve the tweet classification are Bag of Words, Term Frequency-Inverse Document Frequency, Word2Vec, followed by machine learning & deep learning methodologies. The CNN model is notably achieving a higher accuracy of $\mathbf{8 4 . 1 \%}$ compared to other models.

关键词： Deep learning Wildfires Social networking (online) Disasters Terrorism Blogs Text categorization Manuals User interfaces pattern recognition

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An improved machine learning model for data analysis in Big-data environment

An improved machine learning model for data analysis in Big-...

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international conference on computing and Networking Technology (ICCNT)

作者： Sheetal Temara Sagar Varma Samanthapudi Piyush Rohella Ketan Gupta T. Kiruthiga Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Payments &Commerce GoDaddy Edmonds Washington USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of ECE Vetri Vinayaha College of Engineering and Technology Trichy Tamil Nadu INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This technical abstract offers an outline of the ability of artificial Intelligence (AI) and device studying (ML) answers for managing leverage threats in banking. Leverage is a key element in banking danger management, as it could expose the organization to disproportionate liabilities if left unchecked. AI and ML may be used to automate the tracking system of leverage hazards, allowing extra correct and welltimed identification and management of exposures. The abstract examines how AI/ML-driven solutions can leverage to be had facts to detect subtle modifications in leverage through the years, combining techniques from supervised and unsupervised ML to generate actionable insights. It then opinions viable implementation fashions and capacity challenges and gives first-rate practices for a hit implementation. The summary concludes that AI/ML-driven hazard management structures offer a promising new approach to leverage threat management inside the banking sector. In addition, research is warranted on this location.

关键词： Analytical models Data analysis Banking Organizations Machine learning Hazards Data models pattern recognition Artificial intelligence

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