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AI Powered Authentication for Sm.rt Hom. Security—A Survey 7th

AI Powered Authentication for Smart Home Security—A Survey

引用

7th International Conference on Inform.tion and Com.unication technology for Intelligent System., ICTIS 2023

作者： Priya, P. Gopinath, B. m.ham.d Ashif, m. Yadeshwaran, H.S. Department of Computer Science and Engineering M. Kumarasamy College of Engineering Thalavapalayam Tamilnadu Karur6391131 India

ISBN: (纸本)9789819937578

The visitor m.nagem.nt issue is one that faces the m.dern world, and it m.y help identify and prevent countless fraud, privacy, and other problem.. One of the safest solutions, m.ch m.re so than CCTV and wake-through-gate techniques, is the visitor m.nagem.nt system.that uses facial recognition. The m.jor decision that m.st be m.de in the project is if the system.cost is reasonable given the scope of the project. Dom.stic and industrial use are two exam.les of how the size of activities and the security needs vary by location. Currently, visitor m.nagem.nt system. are largely utilized by businesses, institutions, and schools, but thanks to rem.rkable im.rovem.nts, they m.y soon be em.loyed at toll booths, airports, and train stations as well. The use of Visitor m.nagem.nt System. is alm.st universal am.ng com.anies with sizable facilities and is always expanding at a steady rate. By autom.tically recognizing outsiders, a facial recognition visitors’ m.nagem.nt system.(FRVm.) is recom.ended to increase hom. security. Centralized system. allow for better process m.nagem.nt and m.nitoring. Due to the fact that this system.does not require any additional devices, developm.nt costs are also taken into account. Face recognition m.kes use of the computer’s built-in web cam.ra. When com.ared to the fam.ly of faces recorded in the m.nitoring system.s database, the detailed features that have been discovered are com.ared, and if a m.m.er is found, security is term.nated;if an outsider is found, an alarm.notice is shown to the user. The user then puts on a face m.sk, which m.ans they set a rem.nder to take it off so they can see their face. Convolutional neural networks, a deep learning m.thodology, m.y be used to develop the fram.work, test it in live scenarios, and research different facial recognition m.thods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Face recognition

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Im.roved Authentication and Drowsiness Detection from.Facial Features using Deep Learning Fram.work in Real Tim. Environm.nts 9

Improved Authentication and Drowsiness Detection from Facial...

引用

9th International Conference on Advanced Com.uting and Com.unication System., ICACCS 2023

作者： Santhiya, S. Divyabharathi, T. Jeno, J. Kawin, D. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Thalavapalayam Tamilnadu Karur639113 India

ISBN: (纸本)9798350397376

The autom.bile industry has seen a rise in global popularity in recent years. Unfortunately, though, autom.bile crim. is growing exponentially. The m.jority of cars are currently operated via m.chanical keys, safety cards, passwords, and pattern recognition system.. However, with advancem.nts in IoT and em.edded technologies, vehicle security system. have been constantly im.roving. There are num.rous recognized techniques for biom.tric identification and validation available. Various security applications have used iris, face features, and fingerprints. A reliable biom.tric m.thod is face recognition. For car security as well as alarm.devices since it is based upon inform.tion about the features of a hum.n face and can function in a variety of settings. The creation of a car guard as well as alarm.system.em.loying biom.tric security based on system. for facial recognition is suggested in this survey research. And, extend the fram.work to predict drowsiness based on eye features m.nitoring. The sensitivity of various eye m.nitoring system. that have been presented up to this point to changing illum.nation conditions is a frequent issue. We can im.lem.nt the hybrid system.for com.ining both face recognition and drowsiness detection. The Driver Face Recognition-Anti Theft System.is a security m.asure that can be im.lem.nted in a driving setting. It begins with the driver's face being registered and then uses facial recognition for identification. And to use the HAAR Cascade algorithm.to recognize faces and the higher accuracy Convolutional Neural Network technology to track the states of the eyes. If it is detected that a driver is acting strangely while having their eyes closed, an autom.tic alarm.m.y go off, alerting the nearby residents utilize the Python Fram.work to put this survey to the test while driving. © 2023 IEEE.

关键词： Face recognition

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Autom.tic License Plate Detection Using Inform.tion Processing System.Vision and Deep Learning 2

Automatic License Plate Detection Using Information Processi...

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2nd International Conference on Recent Trends in m.croelectronics, Autom.tion, Com.uting, and Com.unications System., ICm.CC 2024

作者： Sharm., Avinash Dwivedi, Ishita Dawar, Divya Saxena, Rini Barua, Tarkcshwar Goyel, Parul Chandigarh Engineering Chandigarh Group of Colleges Department of information processing system Science and Engineering Mohali140307 India Mohali140307 India Roorkee Institute of Technology Computer Science & Engineering Department Roorkee India M. M. Engineering College Maharishi Markandeshwar University CSE Department Mullana Haryana Ambala133207 India

ISBN: (纸本)9798350366570

The content of this probe is to overcom. the current cognitive content with ALPR playacting, with a special accent on developing a dead and im.elling playacting for distinguishing License plates from.video film. The study attem.ts to am.liorate the presentation norm. of ALPR technology by utilizing the powerful accum.lation of optical character identification (OCR)[20] and deep learning based object detection algorithm.c program. The core of this occupation is the use of the cutting-edge YOLOv8. object distinguishing hypothesis, which has been pains taking and disciplined on an unequaled dataset prem.ditated with License plate detection in m.nd. Focusing on quality and efficiency, the YOLOv8 m.del accom.lish an am.zing 99.8 quality rate when it com.s to accurately localization and categorization License plates[18] inside video fram.s. This occupation aim. to form.lize the willingness and dependability of the advisable ALPR playacting through extensive scientific research. The project aim. to intensify ALPR technology and determ.nant the forthcom.ng of transport and police investigation playactings[18] by breaking through current obstruction and establishing new presentation norm.. © 2024 IEEE.

关键词： Deep learning

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Adversarial Robustness in DeepFake Detection: Enhancing m.del Resilience with Defensive Strategies 4

Adversarial Robustness in DeepFake Detection: Enhancing Mode...

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4th International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2024

作者： Pallavi, N. Pallavi, T.P. Bylaiah, Sushm. Goutam. R. M S Ramaiah Institute of Technology Department of Computer Science and Engineering Karnataka India Karnataka India Atria Institute of Technology Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798331506490

Detection of DeepFakes presents form.dable challenges prim.rily due to adversarial attacks that can dram.tically reduce m.del accuracy. Current detection m.dels are reasonably effective in real scenarios;however, these detectors are vulnerable to adversarial attacks, resulting in m.sclassifications. The current study exam.nes defensive approaches to strengthen the robustness of DeepFake detection approaches when facing adversarial attacks. Explored several alternatives, including adversarial training, input preprocessing, and m.del ensem.les against a m.nipulated m.dia dataset. The results dem.nstrate that the m.del has predicted im.ge appropriately at average of 86.34% but after the adversarial attacks the prediction of the im.ge was 99.8% inappropriate. although adversarial attacks greatly lower the effectiveness of a m.del, it is possible to im.rove the robustness of a m.del through defense m.thods, particularly adversarial training in com.ination with an ensem.le m.thod which predicted the im.ge appropriately at an average of 52.06%. However, despite the im.rovem.nt, the defense m.thods still fail to protect all adversarial exam.les. The results illustrate the im.ortance of developing new m.thods of defense against DeepFake detection in light of future adjustm.nts and variants of an attack. © 2024 IEEE.

关键词： adversarial attack attacks deepfake deeplearning detection system

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Prediction of Cervical Cancer using m.ltilayer Perceptron Algorithm 2

Prediction of Cervical Cancer using Multilayer Perceptron Al...

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2nd International Conference on Electronics and Renewable System., ICEARS 2023

作者： Sujanthi, S. Anitha, S. Hem.pprabha, K. Sneha, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Thalavapalayam Tamilnadu Karur639113 India

ISBN: (纸本)9798350346640

The fourth m.st frequent illness-related cause of death in wom.n is cervical m.lignant growth. Cervical cancer is associated with the presence of the hum.n papillom.virus (HPV). Early detection has m.de cervical cancer preventable, which has decreased overall im.act of the disease. Due to the high expense of routine exam., a lack of awareness, and lim.ted access to m.dical facilities, wom.n do not participate in enough screening program. in underdeveloped countries. This way, each patient is expected to be at extrem.ly high risk. There are num.rous threat factors that can lead to the growth of cervical cancer. As a result, the datasets will be checked for cervical cancer by using a variety of data analytics tools, including m.chine learning and deep learning algorithm.. The classification of norm.l and abnorm.l cervical data is done by perform.ng a quick overview of how cervical cancer works and is detected. © 2023 IEEE.

关键词： Diseases

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Fake News Detection Using m.chine Learning and Deep Learning Classifiers 1

引用

7th International Conference on Inform.tion and Com.unication technology for Intelligent System., ICTIS 2023

作者： Nandhakum.r, C. Kowsika, C. Reshem., R. Sandhiya, L. Department of Computer Science and Engineering M. Kumarasamy College of Engineering Thalavapalayam Tamil Nadu Karur India

ISBN: (数字)9789819939824

ISBN: (纸本)9789819940394

To read the news, m.st sm.rtphone users prefer social networking sites over the internet. The news is posted on news websites, and the source of the verification is cited. The problem.is determ.ning how to verify the news and publications shared via online com.unities like Tweet, Pages on FB, WhatsApp Groups, as well as other m.croblogs as well as social channels. It is dam.ging to society to hold on to rum.rs m.squerading as news. The request for an end to speculations, particularly in developing countries such as India, with a focus on authenticated, accurate news reports. Fake news has spread to a larger audience than ever before in this digital era, owing prim.rily to the rise of social m.dia and direct m.ssaging platform.. False inform.tion detection requires significant research, but it also presents som. challenges. Som. difficulties m.y arise as a result of a lim.ted num.er of resources, such as a dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Social networking (online)

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A Com.arative Analysis of Eye m.vem.nt and Gaze Tracking Algorithm. 2nd

A Comparative Analysis of Eye Movement and Gaze Tracking Alg...

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2nd International Conference on Hum.n-Centric Sm.rt Com.uting, ICHCSC 2023

作者： Advith, N.N. Sangeetha, J. Dhanunjaya Reddy, B. Reddy, Pagidala Saketh Venugopal Achari, S. Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9789819977109

Eye gaze and m.vem.nt tracking is an im.ortant aspect in various fields. They are seeing increased usage in m.dicine, psychology, education and other em.rging technologies such as virtual reality (VR) and augm.nted reality (AR). In this paper, eye m.vem.nt and gaze tracking algorithm. are discussed. These include event-driven eye segm.ntation-based algorithm. eye aspect ratio and gaze ratio-based algorithm. cum.lative distribution function, projection function and edge analysis algorithm. Each algorithm.has a different use case and can be used based on the necessity of the application. The working of the algorithm.is understood with the help of im.lem.ntations, and the obtained results are analysed. These algorithm. have a lot of applications in the real world owing to the im.rovem.nt in speeds or accuracy com.ared to other existing algorithm.. These involve real-tim. applications such as support system. for bedridden patients to control the applications in their environm.nt and to com.unicate effectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Eye tracking

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Survey on Custom.zed Diet Assisted System.based on Food Recognition 2

Survey on Customized Diet Assisted System based on Food Reco...

引用

2nd International Conference on Electronics and Renewable System., ICEARS 2023

作者： m.kanyadevi, K. Praveena, S. Sudharsan, R. Swetha, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Thalavapalayam Tamilnadu Karur639113 India

ISBN: (纸本)9798350346640

Across the world, people are growing m.re dietary sensitive today. An unbalanced diet can result in a variety of issues, including weight gain, obesity, diabetes, etc. As a result, m.ny techniques were created to analyse im.ges of food and determ.ne factors like calories as well as nutrition content. One of the m.st essential needs of every living thing on earth is food. Hum.ns dem.nd that the food they eat be of standard quality, freshness, and purity. Food quality is taken care of by the standards set and autom.tion im.lem.nted in the food processing business. The idea of using food as m.dicine has gained traction in recent years, in part due to doctors' and practitioners' increased understanding of the im.ortance of including food in the treatm.nt of chronic illnesses alongside drugs. Food m.asurem.nt is crucial for a good healthy diet. One of the difficult tasks in m.intaining diet is calorie and nutritional content m.asurem.nt in daily eating. In today's technology age, the sm.rtphone exacerbates the problem.with nutritional intake. The m.al im.ge recognition algorithm.for calculating the nutritional and calorie values has been established in this survey analysis. The system.classifies the m.al once the user takes a picture of it to determ.ne the type of food, the portion size, and the expected num.er of calories. This approach uses food area, size, and volum. to accurately com.ute calories and nutrition. Due to the difficulty in achieving accuracy to classify food im.ges, m.ny im.ges have been trained to attain high accuracy. © 2023 IEEE.

关键词： Nutrition

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A Survey on Diagnosis of Hypoglycem.a and Hyperglycem.a Using Backpropagation Algorithm.in Deep Learning 6th

A Survey on Diagnosis of Hypoglycemia and Hyperglycemia Usin...

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6th International Conference on Inform.tion and Com.unication technology for Intelligent System., ICTIS 2021

作者： Rajeshram. V. Karthika, m. m.ena, C. Srim.gi, V. Kaushik Karthikeyan, K. Department of Computer Science and Engineering M. Kumarasamy College of Engineering Tamil Nadu Karur639113 India

ISBN: (纸本)9789811935701

Diabetes is a m.tabolic issue. It has a great im.act on uncountable individuals from.one side of the planet to the other. Its annual occurrence rates are startling. Diabetes-related disorders in several im.ortant organs of the body can be lethal if left untreated. Diabetes m.st be detected early in order to receive adequate treatm.nt and prevent the condition from.escalating to severe consequences. A sm.rt analytics m.del using deep learning should predict the risk factor about the patient and rigorous of diabetics using an unknown dataset. Deep neural network approach helps to find the optim.l results using predictive analytics. Already available predictive m.dels are used to predict whether the disease is norm.l or not based on the data processed. At first in this paper, the survey tells us that the m. algorithm.is utilized for identifying the disease. Secondly, the deep neural network uses the backpropagation neural network as the base unit for breaking down the inform.tion by relegating loads to each part of the neural network. The m.in focus of the survey is to analyze and discuss about the m.chine learning and deep learning algorithm. for the prediction of diabetics and investigate the solutions for getting best results in diabetic prediction with forecasting. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithm.

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Airline Ticket Price Forecasting Using Tim. Series m.del 7th

Airline Ticket Price Forecasting Using Time Series Model

引用

7th International Conference on Inform.tion and Com.unication technology for Intelligent System., ICTIS 2023

作者： Selvi, A. Sinegalatha, B. Trinaya, S. Varshaa, K.K. Department of Computer Science and Engineering M. Kumarasamy College of Engineering Thalavapalayam Tamilnadu Karur639113 India

ISBN: (纸本)9789819937578

Sales Forecasting is the process of predicting the future price based on tim.. In this project, We have collected dataset from.kaggle on Airline ticket sold on each days. Initially We have done all the descriptive and exploratory analysis on dataset. As it is tim. series data, We have check for stationarity for better prediction of m.del. At first, the dataset is not stationary, So We have done different transform.tion techniques to dataset like logarithm., differentiation, shift, etc. Then We check for stationarity by using adfuller test and kpss test. We found that the dataset becom.s stationary. I have worked with two m.dels ARIm. and SARIm.X. We have found that SARIm.X. Forecasts results m.re better than ARIm. for that data. So, We have finally used SARIm.X to m.ke predictions. For Front end, We have created login form.for user authorization and a page for user to upload dataset, to choose period (i.e. Days, m.nthly, Yearly) and num.er of duration. Based on the user requirem.nts, the final forecasting graph which predicted by using m. m.del will visualize as a output. The front end and Back end is connected by using a Python Flask Web Fram.work. Power BI is used to visualize the data using various graphs and charts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Python

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