2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)最新文献

{"title":"An Effective Security Protocol Design for IRIS based Credential Evaluation using Intensive Deep Learning Scheme","authors":"R. R, H. K, K. Malathy, G. Sivagamidevi, C. V. Sudhakar, V. Indhumathi","doi":"10.1109/ACCAI58221.2023.10199928","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199928","url":null,"abstract":"The component of a system charged with ensuring the security of its users is among its most crucial parts. It has been demonstrated that using a password or login that is too basic makes you vulnerable to hackers and does not ensure a high level of security. Authentication using biometric methods can be achieved in several ways. Some of the most advanced and reliable are facial recognition technology and iris recognition. Because it relies heavily on detection of patterns, it is able to reliably identify the rightful owner of an Iris scan. Accuracy as well as effectiveness have both been greatly enhanced in the resultant recognition system. Security breaches and other identification scams are on the rise, making it all the more crucial to implement a robust biometric system. The option that has gained a lot of attention is biometric identification. The iris's potential as a biometric has gained traction in recent years. This quantifiable quality ensures great productivity and precision, which is what triggered the phenomenon. We present a complete ResNet50-based deep learning system for iris identification in this study. Utilizing only a small number of training photos from every class, we train our algorithm on a popular identification of iris dataset, achieving significant gains over prior methods. In addition, we introduce a visualization method that may identify key features of iris pictures that have a significant bearing on the precision of recognition. We anticipate that this approach will be utilized extensively in the future to improve the scalability and precision of various biometrics identification jobs","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124748218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Design of Smart Bus Management Scheme using Internet of Things and Location Indicator","authors":"A. P, A. Veeramuthu, R. Anandhi, Ata Kishore Kumar, R. Lakshmi, D. M. Sohan","doi":"10.1109/ACCAI58221.2023.10200570","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200570","url":null,"abstract":"It's impossible to live without time. Waiting at bus stops for extended periods of time without knowing the bus's precise arrival time or its current state is a common source of frustration for many commuters. There are a lot of buses going in the same direction, but there isn't a good place to find out information about them. Bus schedule changes can occur for a variety of reasons. People's faith in public transportation may be bolstered if they had access to real-time information on the next bus, as well as an estimate of when it would arrive under typical traffic circumstances and how many people were already on board. In order to better serve the general public, this article focuses on the use of embedded systems and IoT to smart seating management. The article delves into what this cutting-edge innovation can do now and where it can go in the future. Due to the increasing number of connected devices, a large amount of data is being generated. So, in order to develop effective frameworks, it is necessary to manage and convert this massive amount of data into useful information. In this study, we focus on an urban IoT framework that is used to build an ITS. The Smart City idea, which seeks to utilize the most up-to-date and competent communication technology in the service of municipal administration and individual people, will be bolstered by the IoT-based smart transportation system.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129539889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Evaluation of Vedic Multiplier Using Hybrid Improvised High Utility Item Set Mining Using Fuzzy","authors":"Binu Siva Singh S K, K. Karthikeyan","doi":"10.1109/ACCAI58221.2023.10199321","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199321","url":null,"abstract":"The main goal of High Utility Itemset Mining (HUIM) is to find all High Utility Itemset (HUI) given a user-defined min util criteria. The utility of an itemset is expressed as a proportion of the database's overall utility. HUIM has a variety of uses. Finding all sets of items that have created a profit more than or equal to min util for business purposes is the process of high-utility itemset mining. To find HUI, many methods have been presented. Because the HUIM issue is broader than the FIM problem, any approach for identifying HUI may also be used to find frequent itemsets in a transaction database. By this approach the Performance Evaluation of Vedic Multiplier is analysed.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114359268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Appropriate Detection of HAM and Spam Emails Using Machine Learning Algorithm","authors":"Dr. T. Jaya, R. Kanyaharini, Bandi Navaneesh","doi":"10.1109/ACCAI58221.2023.10200007","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200007","url":null,"abstract":"An clever and automatic anti-unsolicited mail framework is vital because of the excessive increase of unsolicited e-mail assaults and the inherent malevolent dynamic inside the ones assaults on numerous social, personal, and expert work. There is an increased risk of identity theft, theft of sensitive information, financial loss, damage to reputation, and other crimes that threaten the privacy of the victim. When taking into account the multidimensional feature set of email, current methods are rather fallible. We believe that an artificial intelligence-based strategy is the most effective one going forward, particularly unsupervised machine learning. Exploring the application of unsupervised learning for ham and spam clustering in the mail by comparing these Random Forest, Logistic, Random Tree, Bayes Net, and Naïve Bayes algorithms with LTSM Algorithms by using frequency weightage of words and validating the best accuracy is the purpose of this study.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116364327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing Medical Image Transmission using Memetic Algorithm","authors":"Thangapalani L, Dharini R, K. R","doi":"10.1109/ACCAI58221.2023.10200110","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200110","url":null,"abstract":"In recent times, medical industry faces adverse issue in terms of protecting the medical records of people all over the world. In remote areas, in case of severe health issues, if they want to contact the doctors in urban areas, the new technology has been introduced is teleradiology where you can send the data through IoT based distributed systems. Securing the medical images while transferring the medical records is an important task to maintain the privacy. Then for securing these records, we have to implement two techniques that i steganography and cryptography. These technologies act as a second layer security in the IoT based distributed systems. Here we will be using \"Memetic algorithm\", a bio-inspired genetic algorithm. This algorithm is used effectively to encrypt and decrypt the data.This paper proposes a novel approach called \"Memetic Optimization\" for intensifying the security and efficiency of Medical Data(MD) transmission through cryptography. The proposed method involves using memetic algorithms to optimize the key generation process and improve the randomness of the keys used for encryption. By incorporating memetic optimization techniques into the cryptographic process, the security of MD transmission is strengthened, making it more resistant to attacks by unauthorized users. The results of our experiments show the effectual of the proposed method, and its potential for improving the security and efficiency of MD transmission.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126325490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Human Activity Recognition Using Convolution Neural Network Algorithm in Comparison with Grid Search Algorithm","authors":"P.Ganesh, P.Jagadeesh, Josiah Samuel, R. Scholar, Junior Research Fellow","doi":"10.1109/ACCAI58221.2023.10200427","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200427","url":null,"abstract":"The objective of this piece is to find human activity recognition not through the use of grid search but rather through the application of convolution neural network algorithms. The calculation is carried out by utilising G-power 0.8 with alpha, and the confidence interval is established at 95%. Fifty people will serve as the sample size for the algorithm that uses convolution neural networks to make predictions about human activity recognition (Group 1 equals twenty-five, and Group 2 equals twenty-five). In comparison, the accuracy that can be achieved through grid search is 89.6012, while the accuracy that can be achieved through the Novel Convolution Neural Network is 98.6512. The performance of the Novel Convolution Neural Network is noticeably superior to that of grid search because it incorporates the accuracy of both methods into a single solution.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126359323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intelligent traffic management system based on the Internet of Things for detecting rule violations","authors":"V. Venkatesh, P. Raj, Anushiadevi R, Kalluru Amarnath Reddy","doi":"10.1109/ACCAI58221.2023.10199293","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199293","url":null,"abstract":"One of the top priorities in smart cities is having an effective traffic management system. Traffic management systems greatly aid in the planning of traffic flow, the prevention of traffic accidents, and the reduction of traffic congestion. However, numerous challenges must be overcome due to the high utilization of many vehicles, a lack of sufficient workers to manage traffic flow, and the fact that traffic violations are sometimes not captured. Drivers who travel at excessively high speeds and those who violate traffic laws by making unwarranted lane changes and other manoeuvres are the most significant contributors to increased collisions. This problem must be addressed immediately to reduce the number of deaths that have occurred for no apparent reason. Because they are not designed to prevent excessive congestion, today's traffic control systems, mainly developed and directed by human specialists, must be revised. Tensor Flow, a machine learning platform and you only look once (YOLO), an object identification technique, is used in this study to propose a hybrid model for real-time vehicle recognition. The hybrid model is \"you only look once\" (Yolo). The proposed technique determines the improvement of the YOLOv3 algorithm in-vehicle detection systems over the previous model by integrating these two dependencies with other requirements and using Python as the programming language. The proposed hybrid model computes the data from a surveillance camera near the traffic signal. This camera is linked to the city's traffic servers. Suppose any vehicle crosses the device while violating the traffic above regulation. The information must be detected and transmitted to the server immediately. Furthermore, it will be easier to determine who was responsible for violating the traffic regulation, making it easier for the traffic department to enforce the laws strictly. Signal jumps are determined using the region of interest and the vehicle's location throughout the frames. After being optimized for accuracy, the proposed model has an approximate accuracy of 93.83%which will help reduce the number of daily accidents.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126230871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cosmetic Products Customisation and Customer Segmentation","authors":"Snekha. S, C. M, J. Krishnan, Ranjith R, V. K.","doi":"10.1109/ACCAI58221.2023.10200286","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200286","url":null,"abstract":"Every business has a responsibility to understand the needs of the consumer and produce the products accordingly. The upsurge of any sales in a product an depend on a variety of factors. To reach the targeted business goals, every company must contribute to the needs of business. But most importantly, it is required to understand the trends and analytics in the current data. The purpose of business customer segmentation is to briefly understand the progression in the recent interests of the customers. The process helps to understand the depth of what the customer really needs. The aggregation of prospective buyers into groups or segments with common needs actually ensures there is consistent profit annually. The research draws a clear picture on how to ascertain growth and minimise risks of loss in a skin care cosmetics company using the Marketing Segmentation technique.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128020472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Accuracy in Identification of Bone Fracture using Canny Edge and Prewitt Edge Detection Approach","authors":"Y. Harshavardhan, A. G","doi":"10.1109/ACCAI58221.2023.10201056","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10201056","url":null,"abstract":"In this study, we compare the New Modified Canny edge detection method to the Prewitt edge detection method to determine whether the method is more effective at identifying bone fractures. We will accomplish this by contrasting the two approaches. Methods and Materials: This study takes a ten-person sample and compares it to another ten-person sample using an innovative modified Canny edge detector (CED) and a Prewitt edge detector (PED). With the use of the g power software, we were able to compare our sample sizes using the following settings: alpha = 0.05, enrollment ratio = 0.1, 95% confidence interval = 80%, and power = 80%. The results of the study demonstrated that a customised version of the Canny edge detection method had an accuracy of 95% and a specificity of 86%. This result outperformed the Prewitt edge detection method in terms of accuracy and specificity. With an initial test statistical power of 80% in SPSS analysis and an accuracy of p = 0.006 (p 0.05) and specificity of p = 0.025 (p 0.05), it was determined that the data obtained left no room for error. The significance level was too low (p-value 0.05) to rule out this conclusion. Compared to the traditional Prewitt edge detection approach, the novel modified Canny edge detection method is significantly more accurate when diagnosing bone fractures.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128021268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An IoT enabled Malware Identification Mechanism over Digital Documents using Predictive Learning Scheme","authors":"K. Radha, G. Sivagamidevi, N. Juliet, S. Niranjana, Nimmalaharathi Nimmalaharathi, G. Dhanalakshmi","doi":"10.1109/ACCAI58221.2023.10200518","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200518","url":null,"abstract":"The ability to identify malicious software is crucial to ensuring the safety of computer networks. Nevertheless, signature-based technologies now in use are inadequate for identifying zero-day assaults and polymorphic infections. That's why we need detection methods that use machine learning. The proliferation and sophistication of malware threats have elevated the problem of automated malware identification to the forefront of network security discussions. Manually analyzing all malware in a programme using conventional malware detection techniques is laborious and resource-intensive. The proliferation of the internet has led to a meteoric rise in the number of people using IoT devices. Malware assaults are growing more common as the storage capacity of IoT devices grows; as a result, detecting malware in IoT devices has become a pressing concern. For identifying malware in IoT devices, we present an ensemble categorization approach based on deep learning. For the identification of malware, we employ ANN and LSTM outputs. Our suggested technique achieves an average accuracy of 99.49% on standard datasets, which is higher than the accuracy of state-of-the-art methods.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127909771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}