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Innovations in Power and Advanced Computing Technologies (i-PACT)

作者： Vanmathi Chandrasekaran Prabu Sevugan School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamilnassdu India School of Computer Science and Engineering Vellore Institte of Technology Vellore Tamilnadu India

In this paper, we have proposed a stenographic algorithm that advantageously disguises an instant message inside an image. The hidden message is concealed in the cover image using an amalgamation of a hybrid Least Significant Bit method and Three Modulus characters by character encryption. The entire encryption methodology is secured by a password such that only the designated receiver can decrypt the hidden message from the cover image.

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Advances in chitosan biopolymer composite materials: from bioengineering, wastewater treatment to agricultural applications

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Materials Research Express 2022年第5期9卷 052002-052002页

作者： Utkarsh Chadha Preetam Bhardwaj Senthil Kumaran Selvaraj Kanak Kumari Tassella Susanna Isaac Mahek Panjwani Kunal Kulkarni Rhea Mary Mathew Ashly Mariam Satheesh Anushka Pal Neha Gunreddy Omika Dubey Shalu Singh Srinivasan Latha Arghya Chakravorty Badrish Badoni Murali Banavoth Prashant Sonar Manikandan Manoharan Velmurugan Paramasivam School of Mechanical Engineering (SMEC) Vellore Institute of Technology (VIT) Vellore Tamil Nadu 632014 India Centre for Nanotechnology Research (CNR) School of Electronics Engineering (SENSE) Vellore Institute of Technology Vellore 632014 Tamil Nadu 632014 India School of Chemical Engineering (SCHEME) Vellore Institute of Technology (VIT) Vellore Tamil Nadu 632014 India Department of Biotechnology Pillai College of Arts Commerce and Science Navi Mumbai Maharashtra 410210 India Jawaharlal Nehru Medical College Datta Meghe Institute of Medical Sciences (Deemed to be University) Sawangi (MEGHE) Wardha 442001 Maharashtra India School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore Tamil Nadu 632014 India Srinivas Institute of Medical Sciences and Research Centre Mangalore Karnataka IN 575001 India Departement of Chemistry School of Advanced Sciences (SAS) Vellore Institute of Technology (VIT) Vellore Tamil Nadu632014 India School of Bio Sciences and Technology (SBST) Vellore Institute of Technology (VIT) Vellore Tamil Nadu 632014 India Department of Physics Bal Ganga Degree College Sendul Kemar Tehri Garhwal Uttrakhand 249155 India Solar Cells and Photonics Research Laboratory School of Chemistry University of Hyderabad Hyderabad Telangana 500046 India Center for Materials Science School of Chemistry and Physics Queensland University of Technology (QUT) 2 George Street Brisbane QLD 4001 Australia School of Mechanical and Automotive Engineering College of Engineering and Technology Dilla University PO Box 419 Dilla Ethiopia

Chitosan has become the most known and second abundantly available recyclable, non-hazardous and eco-friendly biopolymer after cellulose with several advantageous biomedical, agriculture, and wastewater treatment applications. As nanotechnology has progressed, researchers have begun incorporating chitosan-based carbon compounds into various compounds, elements, and carbonaceous materials to increase their efficiency and biocompatibility. Chitosan carbon compounds have also been used directly in many applications due to their inherent chelating and antibacterial features and the presence of customizable functional groups. This review widely discusses- the properties and synthesis of chitosan and chitosan composite. It also discusses the modification of chitosan with different compounds, metals, carbonaceous materials, and agriculture residues to allow their use on an industrial scale. Recent advances in the use of chitosan in biomedical, agro-waste management, agriculture, wastewater treatment, and a few other applications (such as food packaging, cosmetics, and the textile and paper sector) are briefly discussed. Furthermore, this analysis reveals that chitosan and its composite materials are potential, low-cost products for environmental clean-up that can be made with basic manufacturing procedures.

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Correction to: A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

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Complex & intelligent systems 2023年第2期9卷 2205页

作者： Tanzeela Shakeel Shaista Habib Wadii Boulila Anis Koubaa Abdul Rehman Javed Muhammad Rizwan Thippa Reddy Gadekallu Mahmood Sufiyan School of System and Technology University of Management and Technology Lahore Pakistan. Robotics and Internet of Things Lab Prince Sultan University Riyadh 12435 Saudi Arabia. Department of Cyber Security PAF Complex E-9 Air University Islamabad Pakistan. Department of Computer Science Kinnaird College for Women Lahore Pakistan. School of Information Technology and Engineering Vellore Institute of Technology Vellore India.

[This corrects the article DOI: 10.1007/s40747-022-00767-w.].

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Glioblastoma multiforme classification on high resolution histology image using deep spatial fusion network ? 5

Glioblastoma multiforme classification on high resolution hi...

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5th International Workshop on Experimental Economics and Machine Learning, EEML 2019

作者： Sumi, P. Sobana Delhibabu, Radhakrishnan School of Computer Science and Engg. Vellore Institute of Technology Vellore India Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Faculty of Electrical and Electronics Engineering Ton Due Thang University Ho Chi Minh City Viet Nam

Brain tumor is a growth of abnormal cells in brain, which can be cancerous or non-cancerous. The Brain tumor have scarce symptoms so it is very difficult to classify. Diagnosing brain tumor with histology images will efficiently helps us to classify brain tumor types. Sometimes, histology based image analysis is not accepted due to its variations in morphological features. Deep learning CNN models helps to overcome this problem by feature extraction and classification. Here proposed a method to classify high resolution histology image. InceptionResNetV2 is an CNN model, which is adopted to extract hierarchical features without any loss of data. Next generated deep spatial fusion network to extract spatial features found in between patches and to predict correct features from unpredictable discriminative features. 10-fold cross-validation is performed on the histology image. This achieves 95.6 percent accuracy on 4-class classification (benign, malignant, Glioblastoma, Oligodendroglioma). Also obtained 99.1 percent accuracy and 99.6 percent AUC on 2-way classification (necrosis and non-necrosis). Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Histology

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RETRACTED ARTICLE: Prewitt Logistic Deep Recurrent Neural Learning for Face Log Detection by Extracting Features from Images

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Arabian Journal for science and engineering 2021年第2期48卷 2589-2589页

作者： Krishnan Nair, Sreekumar Chinnappan, Sathiya Kumar Dubey, Anil Kumar Subburaj, Arjun Subramaniam, Shanthi Balasubramaniam, Vivekanandam Sengan, Sudhakar Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computational Intelligence School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Computer Science and Engineering ABES Engineering College Ghaziabad Uttar Pradesh India Tranxit Technology Solutions Private Limited Chennai India Department of Computer Science and Engineering Kongu Engineering College Perundurai India Faculty of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

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Android Malware Detection and Classification using LOFO Feature Selection and Tree-based Models

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Journal of Physics: Conference Series 2021年第1期1911卷

作者： S Abijah Roseline S Geetha School of Computer Science and Engineering Vellore Institute of Technology-Chennai Campus Vandalur-Kelambakkam Road Chennai India

Cybersecurity threats on mobile devices are also growing substantially with the subsequent rise in the usage of smartphones and mobile applications. Cybercriminals inevitably have expanded their malicious operations to Google's Android mobile operating system. Due to the limitations of traditional signature-based approaches and the constant evolution of new malware, current malware detection systems are turned to be empowered by intelligent machine learning models. In this aspect, malware defense techniques strive to integrate data science and cybersecurity. This paper presents an Android malware detection system that incorporates the Leave One Feature Out (LOFO) approach and uses tree-based learning models to classify malware applications based on the top selected features. The experimental evaluation is conducted on the DREBIN dataset to demonstrate the efficacy of the proposed method. The prediction accuracy of the XGBoost classifier is observed to outperform other tree-based models, but with higher computational costs.

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Preface

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Journal of Physics: Conference Series 2021年第1期1979卷

作者： Rajesh, M. Gnanasekar, J.M. Sitharthan, R. Department of Computer Science and Engineering Sanjivani College of Engineering Maharashtra Kopargaon India Department of Computer Science and Engineering Sri Venkateswara College of Engineering Chennai Tamil Nadu India Vellore Institute of Technology School of Electrical Engineering Tamilnadu Vellore India

International Conference on Recent Trends in Computing (ICRTC 2021) Prof. Dr. M. Rajesh1, Prof. Dr. J. M. Gnanasekar2, and Dr. Sitharthan R31Department of computer science and engineering, Sanjivani College of engineering, Kopargaon, Maharashtra. India. 2Department of computer science and engineering, Sri Venkateswara College of engineering Chennai, Tamil Nadu, India 3School of electrical engineering, vellore institute of technology, vellore, Tamilnadu, India. Email: tnprajesh@***, jmg@***, sitharthan.r@*** We are glad to introduce you to the 2021 1St International Conference on Recent Trends in Computing (ICRTC 2021), which was successfully held on May 21-22. Different from the previous four times, ICRTC 2021 was carried out in the form of virtual conference due to the impact of COVID-19. Because there is a worldwide travel restriction, we held this flexible online conference to gather experts and scholars around the globe with the aim to continue disseminating the latest advanced research in the field of Recent Trends in Computing and developing the academic exchange among researchers. The committee of ICRTC 2021 consists of about hundred and thirty-four experts in the area of Computing science from home and abroad. With its professional and powerful influence, we are honored to have invited five renowned experts as our keynote speakers. Prof. Fadi Al-Turjman, is a Professor at Near East University Applied Artificial Intelligence Research Centre, Turkey., held a speech on recent computing trends in artificial intelligence. In addition, *** Shishodia, CEO, Ecode Networks, UK., performed his speech entitled A 5G and Research Opportunities. *** Mahalle, BOS Member, SPPU Professor & Head, computer ***, SKN COE, Pune., performed his speech entitled Future of Data science. *** Kumar, Software Quality Analyst, Glencore Information Services Pvt Ltd, UK., performed his speech entitled Emerging Trends in the area of Exte

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Retraction Note: Advancements in optical steganography for secure medical data transmission in telehealth systems

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Optical and Quantum Electronics 2024年第11期56卷 1-1页

作者： Patange, Aparna Mahesan, K. V. Manjula, C. Donald, Cecil Babu, Suresh Peroumal, Vijayakumar Arumugam, Mahendran Department of Medicine Krishna Institute of Medical Sciences Krishna Vishwa Vidya Peeth (Deemed University) Karad India Dr. Ambedkar Institute of Technology Bangalore India Department of Mechatronics Oxford College of Engineering Bangalore India Department of Computer Science CHRIST (Deemed to be University) Bangalore India Department of Biochemistry Symbiosis Medical College for Women Symbiosis International (Deemed University) Pune India School of Electronics Engineering Vellore Institute of Technology Chennai India Center for Transdisciplinary Research Saveetha Dental College Saveetha Institute of Medical and Technical Science Chennai India

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Needle in a haystack: Detecting subtle malicious edits to additive manufacturing g-code files

arXiv

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arXiv 2021年

作者： Beckwith, Caleb Naicker, Harsh Sankar Mehta, Svara Udupa, Viba R. Nim, Nghia Tri Gadre, Varun Pearce, Hammond Mac, Gary Gupta, Nikhil The Dept. of Mechanical Engineering NYC College of Technology BrooklynNY11201 United States The School of Electronics Engineering Vellore Institute of Technology Kelambakkam Tamil Nadu Chennai600127 India The Dept. of Mechanical Engineering Indian Institute of Technology Goa Goa Ponda403401 India The Dept. of Mechanical Engineering National Institute of Technology Surathkal Karnataka Mangalore575025 India The Science Division New York University Abu Dhabi Abu Dhabi United Arab Emirates The Dept. of Mechanical Engineering Indian Institute of Technology Kanpur Uttar Pradesh Kanpur208016 India The Dept. of Electrical and Computer Engineering New York University BrooklynNY11201 United States The Dept. of Mechanical and Aerospace Engineering New York University BrooklynNY11201 United States

Increasing usage of Digital Manufacturing (DM) in safety-critical domains is increasing attention on the cybersecurity of the manufacturing process, as malicious third parties might aim to introduce defects in digital designs. In general, the DM process involves creating a digital object (as CAD files) before using a slicer program to convert the models into printing instructions (e.g. g-code) suitable for the target printer. As the g-code is an intermediate machine format, malicious edits may be difficult to detect, especially when the golden (original) models are not available to the manufacturer. In this work we aim to quantify this hypothesis through a red-team/blue-team case study, whereby the red-team aims to introduce subtle defects that would impact the properties (strengths) of the 3D printed parts, and the blue-team aims to detect these modifications in the absence of the golden models. The case study had two sets of models, the first with 180 designs (with 2 compromised using 2 methods) and the second with 4320 designs (with 60 compromised using 6 methods). Using statistical modelling and machine learning (ML), the blue-team was able to detect all the compromises in the first set of data, and 50 of the compromises in the second. Copyright © 2021, The Authors. All rights reserved.

关键词： 3D printers

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Opinion Mining using Machine Learning Approaches: A Critical Study

Opinion Mining using Machine Learning Approaches: A Critical...

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Computing and Information technology (ICCIT-1441), 2020 International Conference on

作者： Jayabrabu Ramakrishnan Dinesh Mavaluru Karthik Srinivasan Azath Mubarakali C. Narmatha G. Malathi College of Computer Science and Information Technology Jazan University Saudi Arabia College of Computing and Informatics Saudi Electronic University Saudi Arabia College of Computer Science King Khalid University Kingdom Saudi Arabia Faculty of Computer and Information Technology University of Tabuk Tabuk Saudi Arabia School of Computing Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9781728126807

ISBN: (纸本)9781728126814

With the advancement of internet technology, there is a large volume of information exists on the web for users of the internet. These users not just utilize the accessible resources on the web yet, in addition, give their feedback, consequently creating more useful data. Opinion mining (OM) is contemplated as a subfield of NLP, information retrieval, and data mining. It is the way toward extricating people's thoughts and observations from unformed content, which with respect to the development of online social media and the huge amount of user feedback, has gotten an effective, attractive and furthermore complex issue. The fundamental in OM is classifying the polarity of text as far as positive (good), neutral (surprise) or negative (bad). This paper is proposed to review the methods and applications of opinion mining. This paper presents a review of OM and related strategies and additionally discusses the applications and difficulties for OM with knowledge into previous researchers.

关键词： Machine learning Supervised learning Data mining Training Social network services computer architecture

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