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6th International Conference on Cloud and b.g Data Computing, ICCb.C 2022

作者： Patil, Gouri b.thu, Srikanth b.avani, Y.V.K. Durga Harikrishna, b. Vijayalakshmi, Adluri b.Sankara b.b. Department of It Muffakham Jah College of Engg and Tech Telangana Hyderabad500034 India Department of Cse - Aiml and Iot Vnrvjiet Telangana Hyderabad500090 India Department of Cse National Institute of Technology Telangana Warangal506004 India Department of It Vijaya Institute of Technology for Women VijayawadaAP-521108 India Department of Cse Mallareddy Engineering College Telangana Hyderabad500100 India Department of Cse B v Raju Institute of Technology Telangana Hyderabad502313 India Department of Computer Science and Engineering Griet Telangana Hyderabad500090 India

ISBN: (纸本)9781450396578

Semantic Index, Human Action Detection, and Event Detection are video surveillance packages that assist automate surveillance tasks. Video surveillance structures have entered the generation of virtual surveillance structures in which virtual video is used to traverse the course of different virtual facts. Advances in storage, telecommunications, and facts compression have enab.ed the increase of more than one technology in virtual surveillance structures. Using more than one video surveillance fashion in risky conditions extends the competencies of rule implementation organizations. In addition, video surveillance enab.es the form of a motion to carry out in one-of-a-kind conditions. In addition, spotting a particular man or woman in a video is essential for added security, multimedia, and multimedia packages, including offline, seek, and online monitoring of involved humans within the video. The proposed IoT- Cloud b.sed Face detection application is designed to find a person from a huge size dataset and that can generate more accurate results. This application uses Deep Learning methods to find exactness when classifying the images. IoT here is used for liveness detection b. comparing all the images present in cloud systems. For results comparison, we used Haar Cascade and DenseNet architectures. © 2022 ACM.

关键词： Face recognition

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Internet of things (IoT) applications in dentistry

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Current Medicine Research and Practice 2020年第2期10卷 80-81页

作者： Ab.d Haleem Mohd Javaid Ib.ahim Haleem Khan Department of Mechanical Engineering Jamia Millia Islamia New Delhi India B. Tech (Computer Engineering) Jamia Hamdard New Delhi India

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Expression of Concern for: An Innovative Internet of Things (IoT) Computing-b.sed Health Monitoring System with the Aid of Machine Learning Approach

Expression of Concern for: An Innovative Internet of Things ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： M. Dhinakaran P. Krishna Priya Joel Alanya-b.ltran Vaib.av Gandhi Sushma Jaiswal Devesh Pratap Singh Department of Electronics and Communication Engineering Government College of Engineering Salem Tamil Nadu India KL Business School Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Electronic Universidad Tecnológica Del Perú Perú Department of IT Engineering and MCA B. H. Gardi College of Engineering and Technology Rajkot Gujarat Technological University Ahmedabad Gujarat India Department of Computer Science & Information Technology (CSIT) Guru Ghasidas Vishwavidyalaya (A Central University) Bilaspur C.G India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350398267

ISBN: (纸本)9798350398274

The community health area comprises an enormous measure of data, and specific methodologies are utilized to deal with that data. One of the most common approaches is handling as well as processing. This technique forecasts the likely consequences of cardiovascular disease. The result of this strategy is to foresee the former heart disease. The work controls IOT utilizing a sensor (a heartb.at sensor to screen b.ats) and Arduino, and the outcomes might b. seen on a successive screen. IFTTT is utilized to b.eak down sensor readings in Google Sheets, which are accordingly changed over into CSV go-like information. The datasets utilized are characterized b. treatment b.undaries, in addition to b.ing used for data preparation and testing. This technique assesses those b.undaries utilizing the data arrangement request strategy. With artificial intelligence calculations and order work. The dataset is first taken apart, analyzed, and screened, after which the accumulated information is handled in Python programming utilizing AI Calculations, specifically Choice Tree Calculation with Irregular Woodlands Arrangement Calculation. SVM (b.cking vector machine) creates the b.st outcomes concerning identifying coronary illness. Thus, the recommended worldview is demonstrated to b. a solid one for foreseeing past coronary illness. The recommended equipment and programming innovation help patients in anticipating heart illness in its underlying stages.

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Eye Tracking as a form of Alternate and Augmentative Communication for people with Speech, Language and Communication Needs

Eye Tracking as a form of Alternate and Augmentative Communi...

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International Conference on Smart Systems and Inventive technology (ICSSIT)

作者： S. Adithyan G. Dhyaneshwar S. Shivani Varshini Karthik Ashok Kumar Biomedical Engineering (B. Tech) SRM Institute of Science and Technology Chennai Biomedical Engineering SRM Institute of Science and Technology Chennai

ISBN: (数字)9781728158211

ISBN: (纸本)9781728158228

Diagnosing a disease or a condition has b.come easy and convenient for the clinician, in this digital era. However, the focus on rehab.litative medicine and recovery oriented technology is not as popular as diagnostics. The current trends in eye-tracking involve the tracking of the progression of cognitive disorders in the elderly patients. The tracking of eye motion can give deterministic data ab.ut the patient's ab.lity to focus, socially interact, track moving ob.ects (muscle movement), and so on. With the help of eye-tracking, the proposed system helps in designing an efficient system for alternative and augmentative communication (AAC), b. utilizing a camera and a screen (with visual prompt display) to determine the accuracy, and speed of response, to categorize the efficiency of AAC method. The system in design can help to improve the latency of patient response and can help to determine the recovery status of the patient.

关键词： Tracking Cameras Gaze tracking Tools Time factors b.omedical engineering Portab.e computers

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Multi-lingual code-mixed machine translation system

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AIP Conference Proceedings 2022年第1期2424卷

作者： Amit Khaparde Akshat Kumar Mohit Kumar Suyash Gupta Department of Computer Science and Engineering G. B. Pant Government Engineering College Okhla New Delhi India

The Machine Translation System has b.en lately attracting considerab.e recognition due to its extensive use in multiple fields. This study focuses on ab.ndant raw code-mixed textual data on microb.ogging sites, which can't b. processed directly b.cause it's multilingual. This raises a language b.rrier in communication and research. In order to use this immense data source, all unwanted symb.ls are removed to reduce noise in the input text. Also, the emoticon adding up the intention or emotion is permuted with the corresponding textual meaning. Recurrent Neural Network in one form or the other can b. used for translating multilingual text from source to target language. A large neural network is used in the process of generating the expected noise-free output in the targeted language. The extensive experiments demonstrate that removing noise and transmuted emoticons improve the result and sustain the intention or semantics b.tter. The proposed system can save anywhere from 35% to 98% of the cost of a human translator b.cause of higher accuracy and desired output. It also finds a place as a sub.ystem to real-time multi-lingual translators, manuscript translators, other systems for providing input as required targeted language thus enlarging to input domain for that system.

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An Innovative Internet of Things (IoT) Computing-b.sed Health Monitoring System with the Aid of Machine Learning Approach

An Innovative Internet of Things (IoT) Computing-Based Healt...

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International Conference on Contemporary Computing and Informatics (IC3I)

关键词： Temperature sensors Support vector machines Heart Wireless sensor networks Heart b.at Medical services Machine learning

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Research on Dynamic Data Flow Anomaly Detection b.sed on Machine Learning

arXiv

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arXiv 2024年

作者： Wang, Liyang Cheng, Yu Gong, Hao Hu, Jiacheng Tang, Xirui Li, Iris Olin Business School Washington University St. LouisMO63130 United States The Fu Foundation School of Engineering and Applied Science Columbia University New YorkNY10027 United States A. B. Freeman School of Business Tulane University New OrleansLA70118 United States College of Computer Sciences Northeastern University BostonMA02115 United States NYU Courant Institute of Mathematical Sciences New YorkNY10012 United States

The sophistication and diversity of contemporary cyb.rattacks have rendered the use of proxies, gateways, firewalls, and encrypted tunnels as a standalone defensive strategy inadequate. Consequently, the proactive identification of data anomalies has emerged as a prominent area of research within the field of data security. The majority of extant studies concentrate on sample equilib.ium data, with the consequence that the detection effect is not optimal in the context of unb.lanced data. In this study, the unsupervised learning method is employed to identify anomalies in dynamic data flows. Initially, multi-dimensional features are extracted from real-time data, and a clustering algorithm is utilised to analyse the patterns of the data. This enab.es the potential outliers to b. automatically identified. b. clustering similar data, the model is ab.e to detect data b.haviour that deviates significantly from normal traffic without the need for lab.lled data. The results of the experiments demonstrate that the proposed method exhib.ts high accuracy in the detection of anomalies across a range of scenarios. Notab.y, it demonstrates rob.st and adaptab.e performance, particularly in the context of unb.lanced data. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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A Koopman-b.sed Digital Twin Approach for Fault Detection in Cab.e Slab.Dynamics of Wafer Scanners

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IEEE Sensors Journal 2025年

作者： Pumphrey, Michael Saaideh, Mohammad Al Al-Rawashdeh, Yazan M. Alatawneh, Natheer Aljanaideh, Khaled F. b.ker, Almuatazb.llah Janaideh, Mohammad Al University of Guelph School of Engineering Guelph N1G 2W1 ON Canada Memorial University Department of Electrical and Computer Engineering St. John’s A1B 3X5 NL Canada Al-Zaytoonah University of Jordan Department of Electrical Engineering Amman Jordan Jordan University of Science and Technology Department of Aeronautical Engineering Irbid 22110 Jordan Virginia Tech Bradley Department of Electrical and Computer Engineering Blacksburg VA United States University of Jordan Amman Jordan University of Toronto Toronto M5S 1A1 ON Canada Institute for Applied Optics and Precision Engineering Jena Germany

This paper presents a higher-dimensional state-space model that employs Koopman operators to predict and monitor the nonlinear dynamics of a cab.e slab.in wafer scanners or other precision motion systems. Developed as a digital twin, our approach not only approximates the complex b.havior of the cab.e slab.b.t also facilitates early fault detection b. comparing predicted dynamics with real-time sensor data. To address the ab.ence of a definitive analytical model for cab.e slab.dynamics, we systematically evaluate various Koopman ob.ervab.e functions to minimize tracking errors. The resulting model achieves an error margin of approximately ±1% over the specified motion range and demonstrates rob.st performance in predicting untrained, acyclic randomized cab.e slab.motion and reaction forces. The system was also compared against the state-of-the-art neural network model for the cab.e slab. showing a reduction in reaction force error prediction of 75.4%. A sensor noise fault with a signal-to-noise of 20 was detected in 0.35 s using only the reaction force measurement. A position-b.sed noise fault (signal-to-noise of 30) was detected in 0.04 s using position-b.sed fault detection. A hardware malfunction fault test is also performed b. removing some tub.ng material inside the cab.e slab.and is detected in 0.38 s through position b.sed fault detection with the Koopman model. Experimental results confirm that the Koopman-b.sed framework effectively approximates the nonlinear dynamics and detects faults in the cab.e slab. thereb. enhancing the reliab.lity of precision motion systems. © 2001-2012 IEEE.

关键词： Cab.e slab Koopman operator precision motion systems

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Efficient Method for Personality Prediction using Hyb.id Method of Convolutional Neural Network and LSTM

Efficient Method for Personality Prediction using Hybrid Met...

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Electronics and Sustainab.e Communication Systems (ICESC), 2020 International Conference on

作者： N Sujatha S Pramod Shruti b.atla T Thulasimani Ravi Kant Amit Chauhan Department of Mathematics B. M. S. College of Engineering Bengaluru Karnataka India Department of Mathematics Nitte Meenakshi Institute of Technology Yelahanka Bangalore Karnataka India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India CSE Department Shoolini University Solan Himachal Pradesh India Department of life Sciences CHRIST (Deemed to be University) Bengaluru Karnataka India

Users’ contrib.tions and the emotions conveyed in status updates may prove invaluab.e to studies of human b.havior and character. A numb.r of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user’s personality traits b.sed on their social media profiles. Among the many customer services now availab.e on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users’ inner personalities, is significant. However, there hasn’t b.en much work done on the psychological analysis that’s needed to deduce the user’s inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is b.tter at capturing long-term dependencies. comb.nation of features for personality prediction, the LSTM-CNN model is superior to the individual models.

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Music Tagging and Similarity Analysis for Recommendation System 1st

Music Tagging and Similarity Analysis for Recommendation Sys...

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1st International Conference on Computational Intelligence in Pattern Recognition, CIPR 2019

作者： Kumar, Laxman Mitra, Anirb.n Mittal, Mamta Sanghvi, Vatsal Roy, Sudipta Setua, Sanjit Kumar Department of Computer Science and Engineering U.V. Patel College of Engineering Ganpat University Mehsana Kherva384012 India Department of Computer Science and Engineering Academy of Technology AdisaptagramWest Bengal712121 India Department of Computer Science and Engineering Calcutta University Technology Campus JD-2 Sector-III Salt Lake Kolkata700098 India Department of Computer Science & Engineering G. B. Pant Government Engineering College Okhla New Delhi110020 India Mallinckrodt Institute of Radiology Department Washington University in St. Louis 510 South Kingshighway Boulevard St. LouisMO63110-1076 United States

ISBN: (纸本)9789811390418

Many of the web.ites follow the system of retrieving and recommending music b.sed on the metadata. Metadata is generally a text file that attached to the music file has title and genre. Without attached metadata, it is very difficult for such web.ites to recommend or retrieve music. A regularly utilized rundown of the fundamental components incorporates pitch, timb.r, surface, volume, span, and frame. In the proposed methodology to process such a vast amount of data, the distrib.ted storage and data processing systems like Hadoop and Spark has b.en used. Hadoop Distrib.ted File System has b.en used for storing the music files and extracting feature information. Kafka queues has b.en used for asynchronous feature extraction in the b.ckground and finally Spark has b.en used for feature analysis using machine learning algorithms. This Proposed automated system for assigning genres for music provides very promising accuracy with a high true positive value. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Metadata

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