2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)最新文献

Probability of a Device Failure using Support Vector Machine by comparing with Random Forest Algorithm to improve the accuracy 通过与随机森林算法的比较，利用支持向量机计算设备故障概率，提高准确率

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083595

Degala Lokesh, Femila Roseline J

引用次数: 0

XSS Attack Detection using Convolution Neural Network 基于卷积神经网络的XSS攻击检测

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083807

G.S. Nilavarasan, T. Balachander

{"title":"XSS Attack Detection using Convolution Neural Network","authors":"G.S. Nilavarasan, T. Balachander","doi":"10.1109/ICECONF57129.2023.10083807","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083807","url":null,"abstract":"Web applications are at a significant risk of being attacked by a diverse range of malicious actors because they are so widely used. These assaults can come from a variety of directions and can be of varying degrees of sophistication and severity, depending on the perpetrator. The development of Internet technology, together with advances in science and technology, has allowed it to permeate a number of different industries in today's society. However, with such rapid expansion comes the risk of compromised information security. In this group, the XSS vulnerability, which is often referred to as cross site scripting, has emerged as one of the most serious flaws in modern Internet applications. The most important task for network security is web attack detection. In order to address this challenging issue, this research investigates deep learning techniques and analyses them using convolutional neural networks. Convolutional neural networks are advantageous for XSS classification applications because of their architecture, which necessitates less pre-processing for feature extraction In this particular investigation, the Convolutional Neural Network (CNN) method was applied in order to categories and identify XSS scripts as either malicious or benign., and we almost exclusively used XSS script characters during feature creation. We achieved accuracy, precision, and recall values of 97.95, 99.30, and 96.66.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125511546","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}

引用次数: 0

An Efficient Classification of Gait Analysis Model using Modified Hybrid Neural Network 基于改进混合神经网络的步态分析模型分类

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083892

Yesodha. P, J. Mohana

{"title":"An Efficient Classification of Gait Analysis Model using Modified Hybrid Neural Network","authors":"Yesodha. P, J. Mohana","doi":"10.1109/ICECONF57129.2023.10083892","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083892","url":null,"abstract":"The Recognition of Gait Identifying people by the way they walk is one of the most under-utilized but effective forms of biometric identification. The premise of this identification method is that each individual has a distinct walk. In addition, it has been widely observed that a person's stride may be used to identify them from a distance if they are familiar with them. Researchers have begun to utilize gait recognition skills due to the growing importance of biometrics in modern personal recognition demands. The purpose of this study is to develop a novel approach to gait detection that use a combination of Artificial Neural Network and Support Vector Machine in order to better understand human gaits (ANNSVM). Background subtraction may be performed in two ways: the first is a recursive technique that uses a Gaussian mixture approach. The second technique is the non-recursive technique, and it employs a sliding-window strategy. Gait recognition consists of a training phase and a testing phase. This paper's concluding portion offers appropriate verification of validation results, presented graphically and with precise description.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125695034","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}

引用次数: 0

Pilotcontamination analysis of Massive MIMO 5G networks based on HetNets weighted scheduling with reinforcement markov encoder model 基于HetNets加权调度和强化马尔可夫编码器模型的海量MIMO 5G网络先导污染分析

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084169

Tirupathaiah Kanaparthi, R. S. Yarrabothu

引用次数: 0

Big Data analysis based intrusion detection in WSN with reduced features 基于大数据分析的约简特征WSN入侵检测

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083735

D. N, Sruthi Priya. D. M, Sakthi Sneghaa. V. A

{"title":"Big Data analysis based intrusion detection in WSN with reduced features","authors":"D. N, Sruthi Priya. D. M, Sakthi Sneghaa. V. A","doi":"10.1109/ICECONF57129.2023.10083735","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083735","url":null,"abstract":"Energy and Security remain two of the biggest obstacles that Wireless Sensor Networks (WSNs) must overcome. Protecting WSNs from Denial of Service (DoS) attacks are some of the security challenges associated with WSNs. The Intrusion Detection System (IDS) should guarantee the security of the WSN services. This IDS must be able to recognize as many security risks as it can and be compatible with WSN features. In this paper, intruder node detection is accomplished using various machine learning approaches. Our work focuses on Big Data analysis based attack detection in WSN with the reduced dataset. In this work, we utilized the WSN - DS dataset. To increase classification accuracy and reduce processing complexity, feature selection is done on the dataset and a reduced dataset is created. Flooding, Blackhole, Grayhole, and TDMA attacks are the four forms of DoS attacks that are taken into consideration in this work. The parameters used to assess the attack detection are the training time to build the machine learning model, and the number of Instances that are Correctly- Classified and Incorrectly- Classified. The outcomes demonstrate that Random forest outperforms other classifiers with a high accuracy rate of 98.17% for the reduced dataset. The Bagging classifier takes less time to train the model than Random forest as well as gives an accuracy of 98% for the reduced dataset.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115985253","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}

引用次数: 0

An Evaluation of Performance of Change Detection of Land Use/Land Cover in Hyderabad city using Artificial Neural Network and Mahalanobis Classification to improve Accuracy 基于人工神经网络和马氏分类的海得拉巴市土地利用/土地覆盖变化检测效果评价

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084090

Rakesh Kumar Appala, V. Sivakumar

{"title":"An Evaluation of Performance of Change Detection of Land Use/Land Cover in Hyderabad city using Artificial Neural Network and Mahalanobis Classification to improve Accuracy","authors":"Rakesh Kumar Appala, V. Sivakumar","doi":"10.1109/ICECONF57129.2023.10084090","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084090","url":null,"abstract":"In this current research the aim of study is to predict changes happening in land by comparing an Innovative Artificial Neural Network (ANN) classifier and Mahalanobis Classifier (MC) by digital image processing and also comparing which algorithm gives more accuracy. For the years 2001 and 2011 Landsat7 ETM+(Enhanced Thematic Mapper plus) is used and Landsat 8 is used for 2021 of study region. These satellite images were classified into two groups which are ANN classifier and Mahalanobis classifier, each group contains 3 samples with a total of N=6 samples. The pretest power is assumed to be 80% and with alpha value of 0.05 and Confidence Interval of 95%. The land use and land cover changes have been analyzed with supervised classifiers and percentages of different types of region has been noted. An independent samples-t test from SPSS statistical analysis it was observed that from a single tail test $mathrm{p} > 0.05$ hence there is no significance difference between two groups of classifiers, namely, ANN and MC. The mean and standard deviation of overall classification accuracy is $98.69pm 1.24$ and $91.13pm 6.47$ respectively. The mean and standard deviation for kappa coefficient is $0.97pm 0.016$ and $0.87pm 0.076$ for ANN and MC respectively. From this research, within the limits of the study, it can be concluded that Innovative Artificial Neural Network has performed better than Mahalanobis based classifier.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122653063","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}

引用次数: 0

Advanced Semiconductor Classifiers Using Machine Learning Techniques 使用机器学习技术的先进半导体分类器

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083525

Oviya G, Kishore M, P. S, P. S., A. R

{"title":"Advanced Semiconductor Classifiers Using Machine Learning Techniques","authors":"Oviya G, Kishore M, P. S, P. S., A. R","doi":"10.1109/ICECONF57129.2023.10083525","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083525","url":null,"abstract":"Due to the greater dimensional faults that appeared on the wafers earlier in the industry's history, human operators were able to execute inspection activities manually using optical microscopes. The fabrication of semiconductors is a sector that is continually expanding and becoming more significant. The STATISTA website estimates that the worldwide semiconductor sector generated roughly 429 billion USD in revenue in 2019. Testing semiconductors is a crucial step in the production process, especially as the complexity of integrated circuit (IC) designs and the competitive pressure on the market rise. An innovative method to perform advanced semiconductor classification using logistic regression and a random forest classifier is proposed. Semiconductors are found in practically all of the electronics we use on a daily basis. The proposed approach is a unique method in respect of semiconductor testing strategies. Thus, the increased number of test types of devices can significantly increase the cost of manufacturing a single semiconductor chip. This work provides an examination on how to perform automated testing using machine learning techniques.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122735095","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}

引用次数: 0

A Safe and Reliable Digital Fingerprint Recognition Method for Internet of Things (IoT) Devices 一种安全可靠的物联网设备数字指纹识别方法

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083840

S. Diwakaran, P. Vijayakumari, P. G. Kuppusamy, E. Kosalendra, K. Krishnamoorthi

{"title":"A Safe and Reliable Digital Fingerprint Recognition Method for Internet of Things (IoT) Devices","authors":"S. Diwakaran, P. Vijayakumari, P. G. Kuppusamy, E. Kosalendra, K. Krishnamoorthi","doi":"10.1109/ICECONF57129.2023.10083840","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083840","url":null,"abstract":"The proliferation of IIoT devices for control, monitoring, and processing has been spurred by the advent of 5G networks. With biometric-based user identification, IIoT devices may be protected against unwanted access, keeping production data secure. However, most IIoT biometric authentication solutions do not safeguard template data, putting at risk sensitive biometric information kept as models in centralized dataset. Furthermore, conventional biometric verification is hampered by issues with speed, database storage space, and data transfer. In order to solve these problems, we offer a safe E-fingerprint verification solution using 5G networks. The suggested method centers on the creation of a nullifying fingerprint model, which safeguards the real granular details while also guaranteeing the privacy and security of clients and the content of messages sent among devices and the server across the networks. On three public fingerprint dataset, the suggested authentication model achieves comparable performance while minimizing computational costs and providing quick online matching compared to traditional approaches.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122876853","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}

引用次数: 0

I-SPMS: IoT-Enabled Novel Smart Parking Management System with Load cell Deployment I-SPMS:具有传感器部署的物联网智能停车管理系统

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083800

L. P, Rakshith. R. K, Pavankumar. C, K. K

{"title":"I-SPMS: IoT-Enabled Novel Smart Parking Management System with Load cell Deployment","authors":"L. P, Rakshith. R. K, Pavankumar. C, K. K","doi":"10.1109/ICECONF57129.2023.10083800","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083800","url":null,"abstract":"The Internet of Things (IoT) is a large technical field that is actively being researched and implemented. Several IoT-based technologies offer extra benefits while lowering labour costs. As the world's population expands, so does the demand for transportation. As a result, the dependency on parking autos expands. People visiting public facilities in large cities, such as malls, parks, temples, and theatres, are finding it difficult to locate parking. Despite the existence of various established systems, the bulk of parking places are still controlled by hand. The only way to travel about in most smart cities is to use a well-organized indoor parking system. Regulations for automobile parking in the open air have yet to be created and implemented. Traditional parking methods are far too inefficient and inconvenient for metropolitan areas where it is difficult to find available spaces. This might result in a lot of traffic, small collisions, and public accidents. As a result, I-SPMS with weighbridgeload sensors is proposed. It may be built for the organised, well-timed, flexible, easy, and safe parking of automobiles in public spaces.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114572638","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}

引用次数: 0

Predicting Anomalous and Consigning Apprise During Heists 在抢劫过程中预测异常和委托通知

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084114

Nikkath Bushra, S. Sibi, K. Vijayakumar, M. Niveditha

{"title":"Predicting Anomalous and Consigning Apprise During Heists","authors":"Nikkath Bushra, S. Sibi, K. Vijayakumar, M. Niveditha","doi":"10.1109/ICECONF57129.2023.10084114","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084114","url":null,"abstract":"Nowadays, there are many problems due to robberies, and there are many solutions for this but this can't be stopped at that moment. The crime charge has increased because there aren't many security guards outside the cell. Consequently, there is a critical need for a sophisticated device that can detect an anomaly i.e., face occlusion and weapon detection are considered anomalous behavior. With the current system, the ongoing illegal behavior cannot be reported to the nearby protective authorities before the criminal may get away. With the aid of Convolutional neural network (CNN), YOLO algorithms, and SMTP for sending emails.this machine has provided a technique to identify and categories whether or not it's unusual behavior or not, and to send alerts to the security government. In which the MobileNetV2 model of CNN algorithm is used to detect the face mask of the burglar and YOLOv4 for detecting objects like guns, rifles, and knives. When compared to the ResNet50 model, MobileNetV2 gives high accuracy with less time consumption. With the number of crimes increasing as well as the lack of safety, there is a growing necessity to produce this kind of security gadget. This method helps in reporting the ongoing unlawful activity to the local government's concerned protection agency in order to prevent another robbery from occurring.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117247219","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}

引用次数: 1