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9th International Conference on Computational Data and Social Networks, CSoNet 2020

作者： Hakak, Saqib Khan, Wazir Zada Bhattacharya, Sweta Reddy, G. Thippa Choo, Kim-Kwang Raymond Faculty of Computer Science Canadian Institute for Cybersecurity University of New Brunswick Fredericton Canada Faculty of CS and IS Jazan University Jazan Saudi Arabia School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249-0631 United States

ISBN: (纸本)9783030660451

Fake news, particularly with the speed and reach of unverified/false information dissemination, is a troubling trend with potential political and societal consequences, as evidenced in the 2016 United States presidential election, the ongoing COVID-19 pandemic, and the ongoing protests. To mitigate such threats, a broad range of approaches have been designed to detect and mitigate online fake news. In this paper, we systematically review existing fake news mitigation and detection approaches, and identify a number of challenges and potential research opportunities (e.g., the importance of a data sharing platform that can also be used to facilitate machine/deep learning). We hope that the findings reported in this paper will motivate further research in this area. © 2020, Springer Nature Switzerland AG.

关键词： Information dissemination

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Accuracy enhancement during suspicious object detection using machine learning in IoT environment

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AIP Conference Proceedings 2023年第1期2587卷

作者： Rituraj Jain Amit Kumar Kaushik Amogh Shukla Ranjeesh R. Chandran Sushma Jaiswal Ankur Gupta Dept. of Electrical and Computer Engineering Wollega University Nekemte Ethiopia Dept. of Electrical Engineering Vaish College of Engineering Rohtak Haryana India Vellore Institute of Technology Vellore Tamil Nadu India Dept. of Robotics and Automation AdiShankara Institute of Engineering & Technology Kalady Kerala India Dept. of Computer Science & Information Technology Guru GhasidasVishwavidyalaya Bilaspur Chhattisgarh India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India

The term “Internet of Things” refers to gadgets that are primarily focused on communication and computing. IoT refers to the interconnection of physical things. Vehicles, structures, and electronics are examples of these things. It may also take into account the network that allows devices to communicate with one another. To collect the signal, researchers used an IoT model. On the other hand, prediction and detection were done using a trained neural network. Before performing a training operation, image processing allows for the capture and preparation of graphical information. On the basis of a trained neural network, the proposed work has an integrated IoT model to capture the signal (Fire, Humans, Vehicles, etc.) and forecast and detect it. This work enables prediction to be shown on a web page linked to a web interface. Previous studies have limitations in terms of time consumption and accuracy. It was discovered that a lot more work needed to be done on the performance and accuracy factors in order to make the IoT system more dependable. Furthermore, prior studies only provided restricted solutions. As a result, work that is capable of delivering excellent solutions is still required. By combining image processing and neural networks in an IoT system, the suggested study provides the optimum answer. The usage of neural networks has made the system smarter, while image processing has improved the system’s performance. The usage of IoT, on the other hand, has made the system scalable and adaptable. The neural network’s prediction and detection, with the help of image processing, may be shown on a distant site through a web interface. The suggested study is designed to offer a scalable, adaptable, and effective solution for detecting suspicious behaviours in organizations, such as the existence of a fire, an unauthentic vehicle or person, and so on. The suggested work is designed to make the most of limited resources while also performing operations more precisel

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Multi-Channel FLANN Adaptive Filter for Speckle & Impulse Noise Elimination from Color Doppler Ultrasound Images

Multi-Channel FLANN Adaptive Filter for Speckle & Impulse No...

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Emerging Trends in Communication, Control and Computing (ICONC3), International Conference on

作者： Manish Kumar Sunil Kumar Jangir Sudhansu Kumar Mishra Sumit Kumar Choubey Dilip Kumar Choubey Biomedical Engineering School of Engineering & Technology Mody University of Science & Technology Lachhamangarh India Computer Science & Engineering School of Engineering & Technology Mody University of Science & Technology Lachhamangarh India Electrical Engineering Birla Institute of Technology Mesra Ranchi India Electronics & Telecommunication Engineering Pune Institute of Engineering Technology Pune India School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9781728114200

ISBN: (纸本)9781728114217

The conventional fixed filters cannot be employed for removing the mixed noise of Color Doppler Ultrasound (CDUS) images because it affects the features of the image awkwardly. Consequently, identifying an internal blockage or hemorrhage of the patient become arduous in such conditions. Hence, the evolutionary multi-channel Functional Link Artificial Neural Network (M-FLANN) has been proposed to get rid of Speckle noise from the CDUS images. In this paper, the performance of the M-FLANN and other five competitive filters is evaluated in terms of qualitative and quantitative measures.

关键词： Color Doppler Ultasound Denoising Functional Link Artificial Neural Network Impulse Noise Speckle Noise Impulse noise Noise reduction speckle noise color Doppler ultrasound feature image images adaptive filters

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Correction to: Design and Implementation of Power-Efficient Cryptography Scheme Using a Novel Multiplication Technique

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Wireless Personal Communications 2023年第1期131卷 271-271页

作者： Srikanth, B. Ravindra, J. V. R. Ramakrishna, P. Ajitha, D. Department of Electronics and Communication Engineering Vardhaman College of Engineering Hyderabad India Department of Electronics and Communication Engineering Anurag University Hyderabad India Department of Software Systems School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore India

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Rank based pseudoinverse computation in extreme learning machine for large datasets

arXiv

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arXiv 2020年

作者： Ragala, Ramesh Bharadwaja Kumar, G. School of Computer Science and Engineering Vellore Institute of Technology - Chennai Campus Tamilnadu600127 India School of Computing Science and Engineering Vellore Institute of Technology - Chennai Campus Tamilnadu600127 India

Extreme Learning Machine (ELM) is an efficient and effective least-square-based learning algorithm for classification, regression problems based on single hidden layer feed-forward neural network (SLFN). It has been shown in the literature that it has faster convergence and good generalization ability for moderate datasets. But, there is great deal of challenge involved in computing the pseudoinverse when there are large numbers of hidden nodes or for large number of instances to train complex pattern recognition problems. To address this problem, a few approaches such as EM-ELM, DF-ELM have been proposed in the literature. In this paper, a new rank-based matrix decomposition of the hidden layer matrix is introduced to have the optimal training time and reduce the computational complexity for a large number of hidden nodes in the hidden layer. The results show that it has constant training time which is closer towards the minimal training time and very far from worst-case training time of the DF-ELM algorithm that has been shown efficient in the recent literature. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Attribute Based Collusion Resistant Controlled Secure Data Processing System Using Node Insertion Method

Research Square

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Research Square 2021年

作者： Dhanalakshmi, P. Rajarajeswari, S. Das, Nripendra Narayan Karthikeyan, C. Reddy, L. Chandra Sekhar Jain, Parita Department of Computer science and Systems Engineering Sree Vidyanikethan Engineering College A. Rangampet India School of Computer Science and Engineering Vellore Institute of technology Chennai Campus India Department of Information Technology Manipal University Jaipur Rajasthan India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Medchal501401 India Department of Computer Science and Engineering KIET Group of Institutions Delhi-Meerut Road Muradnagar Uttar Pradesh Ghaziabad India

In the fast-developing technological world, a large number of informational data is obtained out of different software applications and hardware sources. Data storing, sharing of data, and then processing is more effective through the cloud computing method. In practice, the issue faced is a lack of security where the user details are leaked due to the exposure of software and hardware conflict. Among different data sharing technique, cloud computing is the most convenient method to be adapted due to service satisfying features and economy. Attribute-Based-Encryption (ABE) has the potential to detect even a narrow-drawn data sharing. The drawback of the conventional attribute-based encryption (ABE) poor data security and high over-head computation. The customer side satisfaction is not achieved because of security lacking in narrow-drawn informational data sharing as well as processing. The difficulty in achieving a standardized form of data sharing is an unsolved challenge for secured data processing. The attribute-based data sharing method is more effective for secured data processing in cloud computing. Customer satisfaction can be achieved through the secured services of cloud. The customer’s data should be secured and also the customer’s data is under an authorized access. The protection of data in cloud computing includes the usage of hardware solutions for a secured data operation, provides support in data storage, access, sharing, and automated detecting of risk during data processing. © 2021, CC BY.

关键词： Cryptography

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RETRACTED ARTICLE: A study of deep learning techniques for predicting breast cancer types

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Soft Computing 2023年第2期28卷 541-541页

作者： Shanthi, D. Shinde, Swati Renjith, P. N. Nath, Srigitha. S. Thilakavathy, P. Suneetha, Vemuri Information Technology Department MVSR Engineering College Hyderabad India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Computer Science and Engineering Department Vellore Institute of Technology Chennai India ECE Saveetha Engineering College Thandalam Chennai India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of ECE Aditya Engineering College Surampalem India

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An Efficient Algorithm to Classify the Mitotic Cell using Ant Colony Algorithm

An Efficient Algorithm to Classify the Mitotic Cell using An...

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computer, Power and Communications (ICCPC), International Conference on

作者： R.G. Vidhya J. Seetha Sudhir Ramadass S. Dilipkumar Ajith Sundaram G. Saritha Department of ECE HKBK College of Engineering Bangalore India Department of Computer science and Business systems Panimalar Engineering College Chennai India Data analyst Sterck systems Chennai Department of Analytics Scope VIT Vellore Selection grade School of Business Kochi Department of ECE Sri Sai Ram institute of technology Chennai Tamilnadu India

Cancer that originates in the breast tissue then spreads to the chest wall is called breast cancer. Doctors routinely examine mammograms for signs of cancer; however, aberrant macrocalcifications and microcalcifications might appear on mammograms when the picture quality is subpar. Always get checked out if you see anything out of the ordinary, especially if it involves your breasts, such abnormal calcium deposits. For this mammographic deposit to be properly interpreted, top-notch picture quality is necessary. Many different breast cancer screening methods and the many breast cancer phases are still the subject of active study. In order to construct effective medical image processing systems, experts use methods including the Ant Colony Algorithm (ACA), the Improved Adaptive Fuzzy C-Means (IAFCM), and TNM (the size of the breast tumor (T), the lymph nodes around the tumor, and metastasized). Classes were determined using an MPIG, or a modified Poisson inverse gradient classifier. More than five hundred picture modalities are used across all methods. Medical professionals that rely on images to establish diagnoses or treatments might find the results of this research useful.

关键词： Image segmentation Sensitivity Breast tissue Medical services Feature extraction Breast cancer Mammography

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An Ensemble based Machine Learning model for Diabetic Retinopathy Classification

An Ensemble based Machine Learning model for Diabetic Retino...

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Emerging Trends in Information technology and engineering (ic-ETITE), Conference on

作者： G Thippa Reddy Sweta Bhattacharya S Siva Ramakrishnan Chiranji Lal Chowdhary Saqib Hakak Rajesh Kaluri M Praveen Kumar Reddy School of Information Technology and Engineering Vellore Institute of Technology Vellore India Faculty of Computer Science University of Northern British Columbia Vancouver Canada

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

As technology and digitization grows, there is a huge surge in digital storage of health records. Machine learning has an important role in uncovering patterns existing in these health records providing interesting insights to medical practitioners for assistance in the diagnosis of various ailments. Due to the sensitivity of the health records, the machine learning algorithms often fail to predict the diseases accurately. In present work, an ensemble based machine learning model comprising of the Machine Learning (ML) Algorithms namely Random Forest classifier, Decision Tree Classifier, Adaboost Classifier, K-Nearest Neighbour classifier, Logistic Regression classifier is experimented on diabetic retinopathy dataset. As a first step, normalization is done on the diabetic retinopathy dataset by min-max normalization method. This normalized dataset is then trained the proposed ensemble model. The performance of the proposed model is finally evaluated against the individual machine learning algorithms. The comparative analysis reveals the fact that the ensemble machine learning model outperforms the individual machine learning algorithms.

关键词： Machine Learning Algorithms Ensemble Machine Learning Diabetic Retinopathy Grid search Hyperparameter Optimization Min-Max Normalization Machine learning algorithms Diabetic Retinopathy patient information system trellis search Artificial Intelligence Machine Learning Classifiers digital storage medical practitioner digitization

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Corrigendum to: Congestion Free Transient Plane(CFTP) using bandwidth sharing during link failures in SDN

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The computer Journal 2023年第1期66卷 267-267页

作者： Vanamoorthy, Muthumanikandan Chinnaiah, Valliyammai School of Computing Science and Engineering Vellore Institute of Technology Chennai Campus VIT University Chennai - 600127 India Department of Computer Technology Madras Institute of Technology Campus Anna University Chennai - 600044 India

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