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6th International Conference on Cloud and big Data Computing, ICCbDC 2022

作者： Patil, Gouri bethu, Srikanth bhavani, Y.V.K. Durga Harikrishna, b. Vijayalakshmi, Adluri b Sankara babu 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 enabled 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 enables 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 based 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 by 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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A Koopman-based Digital Twin Approach for Fault Detection in Cable 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. boker, Almuatazbellah 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 cable slab in wafer scanners or other precision motion systems. Developed as a digital twin, our approach not only approximates the complex behavior of the cable slab but also facilitates early fault detection by comparing predicted dynamics with real-time sensor data. To address the absence of a definitive analytical model for cable slab dynamics, we systematically evaluate various Koopman observable functions to minimize tracking errors. The resulting model achieves an error margin of approximately ±1% over the specified motion range and demonstrates robust performance in predicting untrained, acyclic randomized cable slab motion and reaction forces. The system was also compared against the state-of-the-art neural network model for the cable 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-based noise fault (signal-to-noise of 30) was detected in 0.04 s using position-based fault detection. A hardware malfunction fault test is also performed by removing some tubing material inside the cable slab and is detected in 0.38 s through position based fault detection with the Koopman model. Experimental results confirm that the Koopman-based framework effectively approximates the nonlinear dynamics and detects faults in the cable slab, thereby enhancing the reliability of precision motion systems. © 2001-2012 IEEE.

关键词： Cable slab Koopman operator precision motion systems

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Detection of White blood Cell Cancer using Image Processing

Detection of White Blood Cell Cancer using Image Processing

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2019 International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019

作者： Agrawal, Rohit Satapathy, Sachinandan bagla, Govind Rajakumar, K. VIT University B. Tech Computer Science and Engineering Vellore India

ISBN: (纸本)9781538693537

Detection of White blood Cell (WbC) cancer diseases like Acute Myeloid Leukemia (AML), Acute Lymphoblastic Leukemia (ALL), and Myeloma is a complex task in medical field because they are sudden in onset. Our proposed method consists of designing and developing an automated system which will assist the medical professionals in correctly diagnosing all the types and sub-types of this disease. In this paper, we have proposed a novel method in which we have taken microscopic blood images as an input image. A dataset of 100 images in which 62 training and 38 testing images is taken. After that we have converted the image to proper format (YCbCr) for segmentation. For segmenting, we have used the combination of Gaussian Distribution, Otsu Adaptive Thresholding and for clustering we have used K-Means method. Using Gray Level Co-occurrence Matrix (GLCM), the features are extracted and were used for classification using Convolutional Neural Network (CNN). The overall accuracy of the system obtained after processing is 97.3%. © 2019 IEEE.

关键词： Image segmentation

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Remote sensing of urbanization using machine learning and variational quantum regression

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AIP Conference Proceedings 2024年第1期2802卷

作者： G. balamurugan Karthiganesh Durai S. Dhamotharan A. S. Aravintakshan Amal Salilan M. K. Aabid Assistant Professor Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur 603203 Chengalpattu Tamil Nadu India Chief Quantum Architect BosonQ Psi Bangalore India B.Tech Manakula Vinayagar Institute of Technology Pondicherry University Puducherry India

Urbanization plays an important role in the field of environmental science and for the growth of green society. Urbanization scaled up with the economical benefits in developing countries and in countries making sustainable development process on ecological paths with higher efficiency on the growth of the ecosystem. In civilization due to urbanization there are problems such as air excellence corrosion, limitations on the space for survival, issues in the health of urban. Towards the perspective advancement of urban ecology it is distorted from a speculative study in the field of interdisciplinary. This article provides a new idea using Machine Learning and Quantum Computing to produce an output where it gives an efficient building idea of integrating plants and trees with or around the building. The dataset are emission and absorption of carbon dioxide. In order to create the X_features, we either use approximation factors or real-time remote sensing. The main goal of the system is to train them with different machine learning models and show that if the data set time complexity increases exponentially, Quantum computers can be used to run the models.

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bayesian Optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction Model

Bayesian Optimization with Stacked Sparse Autoencoder based ...

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International Conference on Smart Systems and Inventive technology (ICSSIT)

作者： Irfan Abdul Karim Shaikh P. Vamsi Krishna Swagat Gourav biswal A. Sasi Kumar S. baranidharan Kamlesh Singh Faculty of Business Bahrain Polytechnic Isa Town Bahrain School of Management Malla Reddy University Hyderabad Telangana India B. Tech Department of Electrical Engineering Odisha University of Tecnology and Research Bhubaneswar Orissa India Department of Cloud Technology & Data Science (Mentor-IT-Inurture Education Solutions Pvt Ltd Bangalore) Institute of Engineering & Technology Srinivas University Dakshina Kannada Karnataka India Department of MBA and Research Center Presidency College (Autonomous) Bangalore Karnataka India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Campus Dehradun India

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (bOSSAE-CPP) model. The major intention of the bOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the bOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the bO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the bOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the bOSSAE-CPP technique.

关键词： Training Government policies Online banking Social networking (online) Legislation Predictive models Cryptocurrency

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Variant component principal linear reduction for prediction of hypothyroid disease using machine learning

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Journal of Physics: Conference Series 2021年第1期1767卷

作者： M. Shyamala Devi P. Swathi Ayesha Jahangir A. Dhanunjai Sai Kumar A. Naga bhushan Naidu M. Raghuram Manas Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Third Year B.Tech Students Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

With the tremendous technological growth, the world is shifted to adapt the different food and life style by the people that results in the improper working of the body organs. The change in the food habits leads to a major problems that we face in the current scenario is the presence of hypothyroid in the body. The likelihood of hypothyroid still ruins as a challenging issue due to the uncertainty of proper symptoms. With this background, the machine learning can be used towards health care scenarios for the prediction of disease based on the patients past history. This paper focus on predicting the existence of hypothyroid with respect to the patients' medical parameters. The hypothyroid patient dataset is taken from the UCI Metadata repository with 24 columns and 3163 unique patient's records is used for the experimentation of hypothyroid with the following contributions. Firstly, the hypothyroid dataset from UCI machine repository is subjected with the data processing and exploratory analysis of the dataset. Secondly, the unrefined data set is fixed with different classifier algorithm to find the presence of hypothyroid and to examine the efficiency metrics before and after feature scaling. Thirdly, the data is processed to PCA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Fourth, the data is processed to LDA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Experimental results show that the Kernel Support Vector Machine classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced PCA dataset. Similarly, the Logistic Regression, Kernel Support Vector Machine and Gaussian Naïve bayes classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced LDA dataset.

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brain Tumor Segmentation and Prediction using Fuzzy Neighborhood Learning Approach for 3D MRI Images

Research Square

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Research Square 2021年

作者： Pitchai, R. Supraja, P. Razia Sulthana, A. Veeramakali, T. Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Narsapur India Department of Information Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science BITS Pilani Dubai United Arab Emirates Department of computer science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India

Segmentation of brain tumors is a daunting process comprising the delineation of heterogeneous cancerous tissues and diffuse types in anatomical representations of the brain. Deep learning techniques have recently made important strides in the segmentation of brain tumors. However, owing to the irregularity of the tumor, most of the deep learning-based segmentation techniques are not used directly for tumor detection. Although recent studies are capable of addressing the irregularity issue and retaining permutation invariance, many approaches struggle to catch the valuable high-dimensional local features of finer resolution. Inspired by the fuzzy learning methods and an analysis of the shortcomings of existing methods, an automated fuzzy neighborhood learning-based 3D segmentation technique has been proposed for the detection of cerebrum tumors in 3D images. In this technique, the fuzzy neighborhood function is deeply integrated with the proposed network architecture. This technique has been evaluated on bRATS 2013dataset. The simulation results show that the proposed brain tumor detection technique is superior to other methods in the diagnosis of brain tumors with the dice coefficient of 0.85 and the Jaccard index of 0.74. © 2021, CC bY.

关键词： Tumors

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SOUL: Simulation of Objects in Unity for Learning

SOUL: Simulation of Objects in Unity for Learning

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International Conference on Intelligent Communication and Computational techniques (ICCT)

作者： Lavina Nagpal Meghna Jaglan Anuraj Kathait Aakil Mathur Abhishek Vichare B.Tech Computer Engineering MPSTME NMIMS University Mumbai India

This paper explores the realms of creating a user-friendly and highly interactive environment for e-learning. It offers an in depth explanation about how a web based application along with Unity can be built to allow the user to learn myriad of subjects with ease. The end user can access a farrago of e-learning modes such as basic mode, game mode, fast track mode, and certification mode for a wide range of concepts and their topics. The paper gives detailed information about the requirement specifications for building such an e-learning portal and its proposed architecture and flow diagrams. It then further explains how to build these modes in Unity using C# to write the scripts and then embed them into a web application subsequently. The paper concludes by offering critical analysis on developing such an e-learning application and the benefits of each mode.

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Effective permeability of an immiscible fluid in porous media determined from its geometric state

arXiv

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arXiv 2022年

作者： Alzubaidi, Fatimah Mostaghimi, Peyman Niu, Yufu Armstrong, Ryan T. Mohammadi, Gelareh McClure, James E. berg, Steffen School of Minerals and Energy Resources Engineering University of New South Wales Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia National Security Institute Virginia Tech BlacksburgVA24061 United States Shell Global Solutions International B.V. Grasweg 31 Amsterdam1031HW Netherlands

based on the phenomenological extension of Darcy's law, two-fluid flow is dependent on a relative permeability function of saturation only that is process/path dependent with an underlying dependency on pore structure and wettability. For applications, fuel cells to underground CO2 storage, it is imperative to determine the effective phase permeability relationships where the traditional approach is based on the inverse modelling of time-consuming experiments. The underlying reason is that the fundamental upscaling step from pore to Darcy scale, which links the pore structure of the porous medium to the continuum hydraulic conductivities, is not solved. Herein, we develop an Artificial Neural Network (ANN) that relies on fundamental geometrical relationships to determine the mechanical energy dissipation during creeping immiscible two-fluid flow. The developed ANN is based on a prescribed set of state variables based on physical insights that predicts the effective permeability of 4,500 unseen pore-scale geometrical states with R2 = *** Codes 76S05 (Primary), 76T06 (Secondary) Copyright © 2022, The Authors. All rights reserved.

关键词： Pore structure

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A Comprehensive Review of Convolutional Neural Network based Image Enhancement techniques

A Comprehensive Review of Convolutional Neural Network based...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Katyani Singh Avi Seth Harshit Singh Sandhu Krishna Samdani B.Tech Computer Engineering MPSTME NMIMS University Computer Engineering Department MPSTME NMIMS University

Image enhancement is the most important field in image research and processing. The main aim here is to artificially enhance the photo quality and visual appearance. Many photos end up with unusually high amount of noise and grain due to poor sensor quality and control, or with low sharpness. It is expensive and sometimes impossible to increase the resolution of the sensors (even counter intuitive at times) inside the cameras. Convolutional Neural Networks (CNN) rely on image structure and the texture to serve as differentiating features. In this paper, we do a literature review of CNN based techniques for Image denoising, Image super resolution, contrast enhancement, improving images in low light conditions, and then propose a method utilizing the above mechanisms, and our own devised system which uses nearest neighbor classification to rebuild images, along with an HDR photography approach similar to Google RAISR's.

关键词： Cameras Image resolution Convolutional neural networks Image enhancement Image color analysis Google Adaptation models

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