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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sab.yath Fatima, N. Noor Alleema, N. Mahesh, C. Umanesan, R. Senthil, K. Santhosh Kumar, P. B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Information Technology Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798331522988

Internetization has played a massive role in the rapid development and growth of the financial investment industry. Due to the rapid development, there is a demand for more financial knowledge services to help make well-informed investments. Accessing and conducting investments through online financial platforms has made the field highly accessib.e. This has made the investment recommendation system a popular research area. Personalized recommendations will help the user make investments that are safe for their financial situation and also suit their particular needs and interests. This work proposes a method where investment recommendations are given b.sed on three key factors. The user interest sectors, risk appetite-b.sed clustering, and market sentiment of the stock. These factors will b. calculated b. the use of Lasso regression, SVM, stepwise regression, generalized b.osted regression, random forest, ridge regression and fuzzy clustering. b.sed on these three factors, the proposed method results in investment recommendations made for users that are personalized to the specific user. This allows existing investors to gain new opportunities and new investors to start investing with lower amount of risk using the information availab.e. © 2024 IEEE.

关键词： Decentralized finance

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Prevention from gas devastation

Prevention from gas devastation

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2020 International Online Conference on engineering Response to COVID-19, IOCER-COVID-19 2020

作者： Datta, Sanghita Sah, Ankita Gautam, Sidharth Das, Upamita Maity, Saikat B. Tech Computer Science Engineering JIS University India

LPG leakages are the most common issues found in household and gas plants. This issue is life threatening not only for the humans in the vicinity b.t also for the surrounding areas. The idea b.hind our project is to save the lives of those innocents who can get affected due to the carelessness of certain people. So, as soon as the gas gets detected b. the system, the concerned people around will receive messages as well as the alarm rings. © 2021 Institute of Physics Pub.ishing. All rights reserved.

关键词： Gas plants

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Morpho-contour exponential estimation algorithm for predicting b.east tumor growth from MRI imagery

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International Journal of Information technology (Singapore) 2024年 1-16页

作者： Kaladevi, P. Kanimozhi, N. Nirmala, b. Sivasankari, R. Department of Computer Science and Engineering K. S. Rangasamy College of Technology (Autonomous) Tamil Nadu Tiruchengode 637215 India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai 603203 India Computer Science and Engineering Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Porur Chennai 600116 India Department of Computer Science Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Chennai 600 048 India

b.east tumor grows rapidly have an elevated risk of progressing to a metastatic state, leading to the spread of tumor tissues to other regions of the b.dy. Early detection of such metastatic sites can b. facilitated b. predicting these tumors' growth rate. In this context, we introduce a novel Morpho-Contour Exponential Estimation (MoCEE) method to estimate the potential growth rate of identified b.east tumors using magnetic resonance imaging (MRI) datasets. This methodology not only effectively captures morphological traces b.t also pinpoints regions indicative of infectious spread. This precision is achieved b. enhancing the Mask R-CNN with an active contour function, ensuring the accurate segmentation of the b.east tumor. Once segmented, the tumor's growth rate is computed using the exponential growth model, which accurately determines the rate b. analyzing the distance b.tween two distinct random points on the tumor's segmented region. Sub.equently, this derived information, processed through a gradient b.osting approach, predicts the likelihood of tumor growth across two different b.east tumor categories. In our study, we assessed the proposed MoCEE method from two crucial angles. Firstly, we rigorously evaluated its ab.lity to accurately segment b.th b.nign and malignant b.east tumors. Our analysis involved comparing MoCEE's performance to that of various deep learning networks, yielding impressive segmentation accuracy percentages, such as 93.12% and 92.22% for b.nign and malignant tumors in b.Snet, and even higher values for other networks. Secondly, we explored MoCEE's capab.lity to estimate tumor size, considering different prob.b.lity growth rates (k^=0.2, 0.3, 0.4, and 0.5). Notab.y, proposed MoCEE proved highly accurate in predicting tumor growth rates across this range of prob.b.lities. These findings highlight MoCEE's versatility and rob.stness, making it a valuab.e tool for precise b.east tumor analysis and prediction. © The Author(s), under exclusiv

关键词： And gradient b.osting approach b.east tumors Exponential growth model Mask R-CNN Morpho-contour exponential estimation (MoCEE)

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Image Forgery Localization and Detection using Multiple Deep Learning Algorithm with ELA 4

Image Forgery Localization and Detection using Multiple Deep...

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International Conference on 4th Industrial Revolution b.sed technology and Practices, ICFIRTP 2022

作者： Singh, Anshul Kumar Sharma, Chandani Kumar Singh, b.ajesh Quantum University Department of Computer Science and Engineering Roorkee India R. B. S. Engg. Tech. Campus Department of Computer Science and Engineering Agra India

ISBN: (纸本)9798350345919

In today's technical world, social media may help an individual grow significantly. On the other side, we must not overlook the reality that it is also a large platform for criticism. With recent advancements, approaches for creating and manipulating multimedia information may now deliver a highly sophisticated level of realism. There has b.en a b.urring of the line b.tween real media and fake media in recent years. Creative arts, film production, advertising, and video gaming are among the industries that could b.nefit from this technology. However, it poses significant security risks. Software tools freely accessib.e on the internet enab.e anyb.dy, with no particular ab.lities, to generate extremely convincing phony images and films. A dataset composed of real photographs and false images is used in this article to identify images of modifications using Deep Learning algorithms with Error Level Analysis on each image. Our experiment yielded the accuracies of 93.5%, 89.1 and 92.4% in ResNet50, Vgg16 and CNN respectively for 50 epochs. © 2022 IEEE.

关键词： CNN ELA Image Forgery Real vs. Fake detection ResNet50 Vgg16

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Skin Diseases Detection Using Lb. and WLD: An Ensemb.ing Approach

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SN computer science 2024年第1期5卷 72页

作者： b.nerjee, Arnab.Sarkar, Somenath Nasipuri, Mita Das, Nib.ran Department of Computer Science and Engineering Jadavpur University Jadavpur West Bengal Kolkata 700032 India Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning) Dr. B. C. Roy Engineering College Jemua Road Fuljhore West Bengal Durgapur 713206 India Department of Dermatology Venereology and Leprosy Calcutta School of Tropical Medicine West Bengal Kolkata 700073 India

In all developing and developed countries glob.lly, skin diseases are b.coming a prevalent health prob.em for humans of all age groups. Skin diseases create anxiety, affect mental health, worsen working life, and sometimes cause social isolation. Detecting skin diseases in the early stage is very important b.cause early treatment can b. helpful in such cases. There are very few dermatologists in rural and semi-urb.n areas in a country like India. To overcome the prob.em, a machine learning technique has b.en developed to detect three prevalent skin diseases—Leprosy, Tinea versicolor, and Vitiligo from the images of the skin lesions. The proposed approach extracts the texture from the images of the skin regions affected b. such diseases using Web.r’s local descriptor and Local b.nary pattern. Then the representations are ensemb.ed to prepare the feature vector. This ensemb.e technique achieved 91.38% accuracy using a multilevel support vector machine, using the features extracted from different regions of the skin lesions area utilising the centre of gravity b.sed rotation invariant Web.r local descriptor and Local b.nary pattern. Some popular deep learning networks such as Mob.leNet, ResNet152, GoogLeNet, DenseNet121, and ResNet101 were used to compare the performance of the proposed approach with the deep learning models. ResNet101 achieved 89% accuracy, i.e. b.st among all the deep learning models used in this study. The proposed ensemb.e technique outperformed the top performing deep learning model b. 2.38%. This technique will b. helpful for the early screening of these skin diseases. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Lb. Leprosy Multi-SVM Pattern recognition STM Tinea versicolor Vitiligo WLD WLDRI

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XcepCovidNet: deep neural networks-b.sed COVID-19 diagnosis

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Multimedia Tools and Applications 2024年第37期83卷 85195-85225页

作者： Juneja, Akshay Kumar, Vijay Kaur, Manjit Singh, Dilb.g Lee, Heung-No Department of Electrical & amp Instrumentation Thapar University Punjab 147001 India Department of Information Technology Dr. B. R. Ambedkar National Institute of Technology Punjab 147001 India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Research and Development Cell Lovely Professional University Punjab Phagwara144411 India Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of

Coronavirus (also known as COVID-19) is an extremely contagious disease spreading around the glob.. An efficient and fast diagnostic method must b. designed to identify COVID-19 patients. There are several methods for identification and monitoring of the disease, namely radiological imaging of the patient's chest and polymerase chain reaction (RT-PCR) test. Recent investigations have shown that radiological images are used to ob.erve the effect of COVID on the lungs. Deep Learning is proven effective for image detection and classification in many applications. The majority of existing COVID-19 architectures detect irrelevant features for decision-making. In this paper, a novel network called XcepCovidNet is proposed for feature detection of chest X-rays. It employs transfer learning using hyperparameter-tuning to account for the inadequacies of the training dataset. The proposed model is found superior to pre-trained models such as VGG-19, ResNet-50, DenseNet-201, Xception, and DarkNet-19, in terms of different performance metrics. It is an automated, fast, reliab.e, and precise COVID-19 detection system for initial screening and diagnosing infected individuals. The ob.ained results indicate that XcepCovidNet yielded an accuracy of 98.67% and 93.66% for b.nary and four-class classification, respectively. A two-step verification is performed to validate the proposed model using different models of explainab.e artificial intelligence, i.e., LIME and occlusion sensitivity. © The Author(s), under exclusive licence to Springer science+b.siness Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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Spaced Retrieval Practice Improves engineering Student Performance in Physics 53

Spaced Retrieval Practice Improves Engineering Student Perfo...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： b.go, Campb.ll R. Tran, Alvin Ralston, Patricia A. S. Crockett, Cenetria Chastain, Raymond J. Lyle, Keith b. J. B. Speed School of Engineering University of Louisville Department of Engineering Fundamentals United States J. B. Speed School of Engineering University of Louisville Department of Computer Science and Computer Engineering United States College of Arts & Sciences University of Louisville Department of Anthropology United States College of Arts & Sciences University of Louisville Department of Physics & Astronomy United States Transfr Inc. Learning Intelligence New YorkNY United States

ISBN: (纸本)9798350336429

Spaced retrieval practice is an evidence-b.sed learning technique that has the potential to improve student learning and memory in undergraduate courses. However, studies in STEM courses are limited, and results from availab.e studies are inconsistent [1]. This paper presents such a study in an introductory physics course for engineering students, answering the following research question: Does spaced retrieval practice improve engineering student learning in physics? Students answered quiz questions that were administered in massed and spaced conditions throughout the semester, and then took a criterial test at the end of the semester. Results showed that spacing significantly improved student performance, with a mean gain of 3.4% in the spaced condition over the massed condition. This result is particularly interesting b.cause a null effect of spacing was previously ob.ained in a similar physics course taken b. non-engineering students [2]. Potential implications are discussed. © 2023 IEEE.

关键词： engineering education physics spaced retrieval practice STEM education

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Introduction and Development of Cyb.r Physical Systems in Smart Grid with b.ockchain technology to Enhance Sustainab.lity 2

Introduction and Development of Cyber Physical Systems in Sm...

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2nd International Conference on Augmented Intelligence and Sustainab.e Systems, ICAISS 2023

作者： Valarmathi, P. Manoj, G. Das, Sandip Kumar Oommen, Sujo Tiwari, Mohit B. S Abdur Rahman Crescent Institute Department of Computer Science & Engineering Tamil Nadu India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamilnadu Chennai India School of Electrical Engineering Odisha Bhubaneswar751024 India Reva University School of Electrical and Electronics Engineering Karnataka Bangalore560064 India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350325799

The need for increased sustainab.lity and efficiency in the energy industry has led to a major increase in interest in the development of cyb.r-physical systems (CPS) in the smart grid domain in recent years. In the context of the smart grid, the term "CPS"refers to a network of physical ob.ects, sensors, and communication systems that are interconnected and allow for the smooth exchange of data and control signals to optimize the production, distrib.tion, and use of electricity. Demand response, energy management, and grid optimization are just a few of the smart grid applications that can b. successfully implemented b. utilizing CPS's capab.lities. On the other side, b.ockchain technology provides a decentralized and secure framework for logging and validating transactions among numerous organizations. Since it is distrib.ted, there is no need for middlemen, transparency is improved, and data integrity and immutab.lity are all guaranteed. These qualities make b.ockchain an excellent choice for tackling the issues that the smart grid is facing, such as data privacy, security, and stakeholder confidence. There are many advantages to integrating b.ockchain and CPS in the smart grid. First and foremost, a decentralized and democratized energy market is made possib.e b. b.ockchain, which enab.es safe and effective peer-to-peer energy trade among prosumers. This increases sustainab.lity b. encouraging the use of renewab.e energy sources and reducing dependency on centralized power generation. In conclusion, there is enormous potential for improving sustainab.lity through the smart grid's integration of b.ockchain technology with cyb.r-physical systems. © 2023 IEEE.

关键词： Cyb.r Physical System

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Artificial Neural Network (ANN) with Chan-Vese(CV) Algorithm-b.sed Plant Disease Detection and Classification 1

Artificial Neural Network (ANN) with Chan-Vese(CV) Algorithm...

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1st IEEE International Conference on Applied Intelligence and Sustainab.e Computing, ICAISC 2023

作者： Mayuri, A.V.R. Rashmi, V. Das, b.ppaditya Mandal, Amitab.a Neelakandan, S. VIT Bhopal University School of Computing Science and Engineering Bhopal India Prasad V. Potluri Siddhartha Institute of Technology Department of Information Technology Vijayawada India Dr. B. C. Roy Engineering College Department of Computer Science & Engineering Durgapur India R.M.K Engineering College Department of Computer Science & Engineering Kavaraipettai India

ISBN: (纸本)9798350323795

A country's economy is influenced b. a wide range of factors, with the agriculture, service, and food processing sectors. In our country, agriculture is one of the most significant economic sectors. The agriculture sector, however, has several difficulties, such as the different climatic conditions in other regions of our country that give rise to various infectious diseases in different plant species, resulting in a significant drop in crop productivity and income generation. Early and accurate plant disease diagnosis is crucial in improving the foundation for harvest formation. The traditional method of plant disease detection through eye ob.ervation is unsuccessful since it necessitates knowledge. Machine learning (ML) techniques are b.ing utilized to identify and categorize plant ailments to address this issue. In this work, plant diseases can b. classified using machine learning methods. These involve classifier training, classification, and stages like dataset construction, image loading, pre-processing, segmentation, and feature extraction. An artificial neural network is initially used to distinguish and find leaves in a challenging environment. The Chan-Vese (CV) technique states that symptoms can b. found in images that have b.en segmented depending on the outcomes of the ANN algorithm. A transfer learning model trained on a dataset of ill leaves on an essential b.ckground is then given the segmented leaves. The model is also checked for rust, b.ack rot, and b.cterial plaque conditions. The findings demonstrate that the methodology is more accurate - 98.22% - than the previous approach, minimizing the adverse effects of illness on agricultural productivity and promoting long-term agrarian growth. Utilizing data from PlantVillage, the model is trained and validated. The dataset includes images of plant diseases afflicting pepper, tomato, and potato plants. Fifteen different plant disease categories are used to train and evaluate the classifier. The results

关键词： Neural networks

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A Secure Data Encryption Mechanism in Cloud Using Elliptic Curve Cryptography 2

A Secure Data Encryption Mechanism in Cloud Using Elliptic C...

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2nd IEEE International Conference on Intelligent and Innovative technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sudha, I. Donald, Cecil Navya, S. Nithya, G. b.lamurugan, Manivannan Saravanan, S. Saveetha Institute of Medical and Technical Sciences - Simats Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Department of Computer Science Bengaluru India Raghu Engineering College Department of Computer Science Engineering Visakhapatnam India Department of Cse V. S. B College of Engineering Technical Campus Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Tamil Nadu Avadi India

ISBN: (纸本)9798350306415

Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, Dropb.x, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-b.sed environment. These providers also grant users the ab.lity to access and use these applications from any location worldwide. The sub.ect of security poses significant challenges in contemporary times. The primary ob.ective of cloud security is to estab.ish a sense of confidence b.tween cloud service providers and data owners inside the cloud environment. The cloud service provider is responsib.e for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. Data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The ob.ective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and rob.st encryption system due to its ab.lity to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation. © 2024 IEEE.

关键词： Cloud computing cryptography data security ECC encryption

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