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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M. Arun bhavadharani K b. R. Supreeth Sumit Pundir K Pradeepa K. Abdul Razak Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering V.S.B. Engineering College Karur Tamil Nadu India Department of Computer Science Oxford Degree and PG College Bangalore India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Artificial Intelligence and Data Science KPR College of Arts Science and Research Coimbatore Tamil Nadu Department of Mathematics K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection mechanism that employs data analytics and machine learning (ML) to promote individual privacy in the digital realm. The system analyzes several data sources, including system events, network traffic, and user activity logs, using advanced ML methods like Random Forests (RF), Support Vector Machines (SVM), and Neural Networks (NN), to detect and prevent privacy violations. Ensemble learning techniques, feature engineering, and real-time monitoring enable adaptability in the face of shifting threats. According to the results, the proposed system surpassed the existing system in terms of accuracy (0.92), recall (0.89), precision (0.91), and F1 score (0.90). Its ability to detect unlawful access, unusual logins, and irregular data requests is proved by anomaly detection rates utilizing One-Class SVMs (91%) and Isolation Forest (89%). The system's low false positive rate of 5% demonstrates how effectively it can detect privacy breaches, resulting in stronger cybersecurity and greater privacy protection for both individuals and enterprises.

关键词： Support vector machines Data privacy Data analysis Accuracy Scalability Telecommunication traffic Data collection Privacy breach Protection computer security

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Predicting the Quantity of Municipal Solid Waste using XGboost Model 3

Predicting the Quantity of Municipal Solid Waste using XGBoo...

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3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Jayaraman, Vaishnavi Parthasarathy, Saravanan Lakshminarayanan, Arun Raj Singh, Hiran Kumar B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9780738146270

The quantity of Municipal Solid Waste (MSW) gets intensified, based on various factors such as population growth, monetary status and consumption patterns. The insufficiency of elementary trash data is a critical problem for managing the MSW. In this study, the goal is to forecast the MSW generation of Northern Ireland. A precise model was developed to estimate the total amount of waste produced for every quarter of the year, by employing the Machine Learning techniques. The seasonal ARIMA (s-ARIMA) and Extreme Gradient boosting (XGboost) models were employed to estimate the amount of waste produced. On comparing both the models, XGboost performed better. Thus, the parameters of the XGboost were tuned to yield the optimal outcome. The XGboost with the tuned hyperparameters achieved an optimum result with the higher coefficient of determination (R2) value as 0.5325 and lower RMSE value of 13215.97. The prediction of the MSW weight would help the decision-makers in treating and disposing solid waste appropriately. © 2021 IEEE.

关键词： Decision making

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Non-Rigid Registration with Volumetric Mesh for Liver Surgery

SSRN

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SSRN 2024年

作者： Zhang, Yanying Liu, Peter xiaoping Hou, Wenguo School of Mechanical Engineering Zhejiang Sci-Tech University Zhejiang Hangzhou China School of Information Science and Engineering Zhejiang Sci-Tech University Zhejiang Hangzhou China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

In image-guided liver surgery, the registration of preoperative reconstructed liver model with intraoperative captured surfaces is extremely challenging, due to the limited visibility of liver surfaces and the impact of soft tissue deformation. In order to solve this problem, this paper presents a novel nonrigid registration method that achieves accurate alignment between the preoperative reconstructed volumetric liver mesh and a portion of the intraoperative captured liver surface. A deformation subspace is incorporated to characterize the deformation of volumetric liver mesh during registration, ensuring volume preservation of the liver mesh. The proposed method enables to capture both surface and inside deformation of the liver, thus improving the flexibility and accuracy of the *** majorization-minimization (MM) algorithm with limited-memory broyden fletcher goldfarb shanno (L-bFGS) is introduced to solve the optimization problem in the registration process, thereby effectively improves the computational efficiency of the non-rigid registration. The experimental results show that the volume relative error is less than 2% and the registration error is less than 1mm. The proposed method can reduce mesh distortion, and maintain organ volume. It provides a promising solution for aligning the liver mesh between preoperative and intraoperative periods. © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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blockchain based Identity Verification Model

Blockchain Based Identity Verification Model

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

作者： Malik, Gunit Parasrampuria, Kshitij Reddy, Sai Prasanth Shah, Seema NMIMS MPSTME Mumbai Campus B.Tech Computer Engineering India

ISBN: (纸本)9781538693537

blockchain is peer-to-peer decentralized system that provides security, reliability, authenticity, immutability and transparency. It is an expandable list of records called blocks which are linked using cryptography. blockchain by design, is resilient to modifications, it consists of an open distributed ledger that records all the transactions made between two parties which cannot be forged. This paper provides a unique model for document verification with use case of the government. Currently all issuing authorities are separate cells providing identity documents to a citizen. Due to this cellular nature document verification is a tedious and long process. The problem of falsifying documents is another issue within government bodies. We discuss the use of blockchain to improve efficiency and security. The purpose of this project is to develop a private permissioned blockchain network where an individuals official documents can be shared by government bodies, organizations and educational institutions. Using the blockchain technology, our platform achieves a decentralized system to share authenticated government documents between government bodies and private organizations without the need of a certain level of human intervention. With the concluding facts of this paper, it is observed that the process of document verification and issuing process's efficiency is drastically improved with our blockchain model. Along with this we reap benefits of security, reliability and transparency. © 2019 IEEE.

关键词： Smart contract

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Crop Analysis and Seed Marketing using Regression and Association Rules of India

Crop Analysis and Seed Marketing using Regression and Associ...

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

作者： Vivank Sharma Mansi Shukla Ritika Mandal B.Tech (Information Technology) Vellore Institute of Technology Vellore India B.Tech (Computer Science) Vellore Institute of Technology Vellore India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

The agriculture plays a crucial and vital role in the country's Economy. A major population of any country across the globe depends on agriculture for survival. Many times resources are wasted when the farmers are not able to correctly predict the crop to be grown due to lack of information. We have tried to build a model that predicts the type of crop that can be cultivated in a given land on the basis of climatic conditions. Therefore, the crop with maximum production probability for a given area of land can be cultivated and thus in this way, the resources and hard work of the farmers would be optimized and will give better results. This model would also help in effective seed marketing as the predictive analysis of the suitable crop would help the suppliers supply the suitable crop seed in enough quantity to the farmers which would benefit the suppliers as well as the farmers.

关键词： regression elimination association marketing Telemarketing Farmers (2004) Regression (Psychology) crops Avian Crop agriculture Association (Psychology) Association rules elimination Seeds Globe Land

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Vision based umpire detection method for event extraction in cricket video based on HOG and color image segmentation 1

Vision based umpire detection method for event extraction in...

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1st IEEE International Conference on Advances in Electrical, Computing, Communications and Sustainable technologies, ICAECT 2021

作者： Nandyal, Suvarna Kattimani, Suvarna L. Department of Computer Science and Engineering P. D. A. College of Engineering Karnataka Kalaburgi585102 India Department of Computer Science and Engineering B.L.D.E.A's V.P. Dr.P.G.Halakatti College of Engg. and Tech. Karnataka Vijayapur586103 India

ISBN: (纸本)9781728157900

As of late, there has been expanded interest in Video Summarization and Automatic Highlights age. One of the most watched and a played sport is cricket, especially in South Asian Countries. In cricket sport video, Umpire has the force for settling on huge choices about functions in the field. In this work, the manually chosen Recorded Video of Some International Twenty-Twenty cricket match video (T20*20) is used as dataset for Umpire identification inside the sport of cricket. The proposed dataset is assessed as a starter help for creating frameworks to consequently produce cricket features. With the growing increase of the utilization of technology in sports, this paper points basically at the Umpire Identification utilizing Area-Of-interest based Histogram of Oriented Gradients (AOI-HOG) Feature Extraction strategy for Human Identification. It expects to give Color Image Segmentation technique utilizing pixel wise color information to support vector machine (SVM) classification for Umpire Identification while protecting the arrangement of highlights. Test proof shows that the proposed technique accomplishes the Accuracy of 94.57%. Proposed one efforts towards successful segmentation results and computational conduct, and lessens the time and expands the norm of color image segmentation. © 2021 IEEE

关键词： Support vector machines

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Twitter Sentiments Analysis Using Machine Learninig Methods

Twitter Sentiments Analysis Using Machine Learninig Methods

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Emerging technology (INCET), International Conference for

作者： Lokesh Mandloi Ruchi Patel B.Tech Computer Science and Engineering Meidcaps University Indore India Dept. Computer Science and Engineering Medicaps University Indore India

ISBN: (数字)9781728162218

ISBN: (纸本)9781728162225

Analysis of sentiments is the method of deciding whether the sentiments in the text is positive, negative or neutral. It is also known as material polarity or mining of opinions. The growth and advancement in social media platforms engaged a huge number of users. Social media platform like twitter where users can post their tweets in 280 characters. because of the limited number of characters in tweets, it becomes easy for the sentiment analysis. On Twitter 550 millions of tweets are posted daily. Twitter also represents all age group people and also a fair representation of gender. Therefore, the sentiment analysis of twitter data becomes somewhat general sentiments of society. In this paper, we will compare various Machine Learning methods like the Naïve bayes Classification method, Support Vector Machine Classification Method and Maximum Entropy Classification method. We will see how sentiments analysis is done by this classification algorithm and what is the accuracy and precision in these cases.

关键词： Sentiment analysis Twitter Machine learning Feature extraction Support vector machines Entropy

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS bulletin 2025年 1-2页

作者： banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, brent Chapman, Karena Guiton, beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-brady, Lori Maruyama, benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, b. Schwarz, Udo D. Simon, Sindee L. brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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SD-measure: a social distancing detector

arXiv

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arXiv 2020年

作者： Gupta, Savyasachi Kapil, Rudraksh Kanahasabai, Goutham Joshi, Shreyas Srinivas Joshi, Aniruddha Srinivas B.Tech Department of Computer Science and Engineering National Institute of Technology Warangal Telangana506004 India

The practice of social distancing is imperative to curbing the spread of contagious diseases and has been globally adopted as a non-pharmaceutical prevention measure during the COVID-19 pandemic. This work proposes a novel framework named SD-Measure for detecting social distancing from video footages. The proposed framework leverages the Mask R-CNN deep neural network to detect people in a video frame. To consistently identify whether social distancing is practiced during the interaction between people, a centroid tracking algorithm is utilised to track the subjects over the course of the footage. With the aid of authentic algorithms for approximating the distance of people from the camera and between themselves, we determine whether the social distancing guidelines are being adhered to. The framework attained a high accuracy value in conjunction with a low false alarm rate when tested on Custom Video Footage Dataset (CVFD) and Custom Personal Images Dataset (CPID), where it manifested its effectiveness in determining whether social distancing guidelines were practiced. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A Novel Machine Learning technique for Diabetic Prediction in IoT-based Healthcare Monitoring System

A Novel Machine Learning Technique for Diabetic Prediction i...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： S Aravinth Vijay Jesuraj R. Ganesh Vinda Manjramkar M. Sridharan Vivek Ravishankar Dubey M. R. Arun Department of Pharmacy Practice Nirmala College of Health Science Poolani-Puzhpagiri Rd Meloor Kerala India Department of Computer Science and Engineering (AI &ML) Institute of Aeronautical Engineering Hyderabad Telangana India B.N.Bandodkar College of Science(Autonomous) Thane west Maharashtra India Department of Science and Humanities NPR College of Engineering and Technology Natham Dindigul Dist Tamil Nadu India Department of Computer Science and Engineering Bharat Institute of Engg & Tech (BIET) Hyderabad Telangana India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai Tamil Nadu India

The successful development of a diagnosis system to identify diabetes in the Internet of Things (IoT) e-healthcare scenario has gained considerable attention to implement accurate diabetes diagnosis. IoT is playing an increasingly important role in healthcare environment by providing a structure for evaluating medical information to detect diseases via data mining techniques. The existing diagnostic methods have some challenges, such as lengthy calculation times and inaccurate predictions. To evade the limitations, this article suggested an IOT-based diagnosis system that uses Machine Learning (ML) techniques. Through the dataset from UCI Repository with medical sensors, a novel systematic technique is utilized to treat diabetic disease, and relevant medical data is produced to precisely predict those who would be seriously impacted by the condition. For predicting the illness and its severity, a brand-new classification technique called Tuna Swarm optimization-Aided Neural Classifier (TSO-NN) is suggested (T. The experimentation is conducted in MATLAb and the performance is evaluated using accuracy, precision, and F1-score. besides, the efficiency is tested and confirmed by comparing over SOTA methods.

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