2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)最新文献_第7页

A Comparison of Supervised Learning Algorithms to Prediction Heart Disease 监督学习算法在心脏病预测中的比较

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084035

Kuchlpudi Prasanth Kumar, Valaparla Rohini, Jyothi Yadla, Jonnalagadda VNRaju

{"title":"A Comparison of Supervised Learning Algorithms to Prediction Heart Disease","authors":"Kuchlpudi Prasanth Kumar, Valaparla Rohini, Jyothi Yadla, Jonnalagadda VNRaju","doi":"10.1109/ICECONF57129.2023.10084035","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084035","url":null,"abstract":"Heart disease problems are rapidly increasing day to day. Humanslose their lives at an early stage. Consequently, themain purpose of this projectis to employ supervised machine learning methods for heart disease early prediction. For the prediction and diagnosis of cardiac diseases, different techniques are used like data mining and machine learning. This would be tremendously useful to human life since, owing to a lack of cardiovascular competency and quick development in improperly diagnosed instances, heart diseases in people might develop at an early stage. As a result, developing robust and effective early-stage cardiac illness prediction by using analytical decision-making and digital patient data might alleviate this problem. To predict heart diseases, numerous supervised machine-learning techniques were used to learn about the illness, and their efficiency and accuracy were evaluated. This study used a Kaggle dataset on heart disease and found that three classification methods-, K-Nearest Neighbor (KNN), Support Vector Classifier, and Multi-Layer Perception (neural network) could accurately classify heart disease. TheKNN is given 91.8% accuracy. As a result, we discovered that KNN results can more accurately forecast the chance of patients developing heart disease.","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":"129354217","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

Design of Hearing Aid with Auto Tuning of Frequency on Comparison with IDFT Coefficients and Overlap Add Method 基于IDFT系数和叠加法的频率自动调谐助听器设计

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084167

M. Monisha, J. F. Roseline

{"title":"Design of Hearing Aid with Auto Tuning of Frequency on Comparison with IDFT Coefficients and Overlap Add Method","authors":"M. Monisha, J. F. Roseline","doi":"10.1109/ICECONF57129.2023.10084167","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084167","url":null,"abstract":"AIM:The main objective is to develop hearing aids that tune the frequency using the autotuning approach using the inverse discrete fourier transform coefficients rather than the overlap add method. The primary goal of this research is to compare the IDFT algorithm and the overlap add (OA) method to autotune the frequency to the decibel range of the affected people. Materials and Methods: A total of 40 sample values are obtained from the dataset taken from the graph in the design of hearing aids to auto tune the frequency. The values in this dataset are divided into two groups: group 1 (n = 20) and group 2 (n = 20). This sample value is derived from the frequency graph generated as a result of auto-tuning the frequency using a dataset. Results: The frequency shaping of the transfer function by a non-redundant filter to autotune the frequency according to impaired people's needs was compared to the IDFT TRANSFORM and developed OA method. The significant level is p < 0.05. Conclusion: It was discovered in this work that the IDFT transform outperformed the overlap add approach from the dataset under consideration.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"37 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":"115813348","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

Data science: simulating and development of outcome based teaching method 数据科学:基于结果的教学方法模拟与发展

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083713

K. Sridhar, Govind P. Shinde, Amrita Chaurasia, A. R.

{"title":"Data science: simulating and development of outcome based teaching method","authors":"K. Sridhar, Govind P. Shinde, Amrita Chaurasia, A. R.","doi":"10.1109/ICECONF57129.2023.10083713","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083713","url":null,"abstract":"The educational researcher has a wealth of options to apply analytics to extract meaningful insights to improve teaching and learning due to the growing availability of educational data. Teaching analytics, in contrast to learning analytics, examines the quality of the classroom environment and the efficacy of the instructional methods used to improve student learning. To investigate the potential of analytics in the classroom without jeopardizing students' privacy, we suggest a data science strategy that uses simulated data using pseudocode to build test cases for educational endeavors. Hopefully, this method's findings will contribute to creating a teaching outcome model (TOM) that can be used to motivate and evaluate educator performance. In Splunk, the study's simulated methodology was carried out. Splunk is a real-time Big Data dashboard that can gather and analyze massive amounts of machine-generated data. We provide the findings as a set of visual dashboards depicting recurring themes and developments in classroom effectiveness. Our study's overarching goal is to help bolster a culture of data-informed decision-making at academic institutions by applying a scientific method to educational data.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"17 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":"117240740","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 Prevention and Challenges in Wireless Sensor Networks for Energy and Security Concern 基于能源与安全考虑的无线传感器网络的有效预防与挑战

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083625

Sumathy G, K. Geetha, Maheshwari A, AR Arunarani, Prithi Samuel

{"title":"An Efficient Prevention and Challenges in Wireless Sensor Networks for Energy and Security Concern","authors":"Sumathy G, K. Geetha, Maheshwari A, AR Arunarani, Prithi Samuel","doi":"10.1109/ICECONF57129.2023.10083625","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083625","url":null,"abstract":"To have the good quality design of applications for Wireless Sensor Network (WSN), it's is much important for considering the challenges. WSN emerged with some common criteria with wireless network; they have the technical challenges, which is different from the traditional WSNs. The algorithm and protocol, which was proposed for traditional WSN, is not comparably good for the modern WSN. WSN has multiple type of sensors depending on the wide range of parameters. The deployment of nodes in WSN is in a random manner or the nodes will the manually planted in the desired location. The deployment of node in the areas where manual attending is difficult, there are chances of energy deficiency due to regular discharge of battery or by the short circuit. For minimizing these data loss issues, the affected nodes should be detected. The deployment of Sensor Node (SN) makes congestion in network by continuous transmissions by many SN. The other could be by the length of the link physically. Like, two nearby nodes cannot communicate with each other due to the physical interference (whereas distance nodes can be connected). The SN could have larger number of the sensor nodes which has the ability of sense, processing the data and commutation abilities. This paper describes the simulation tool for the implementation. Finally, the performance of Efficient Prevention and Challenges in Wireless Sensor Networks for Energy and Security Concern is provided. From the experimental results, security management system is estimated.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"19 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":"115309594","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: 2

An Innovation Development of Light Weight Deep Learning Algorithm for Smart Healthcare Neural Science Management 面向智能医疗神经科学管理的轻量级深度学习算法创新发展

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083638

M. Chithambarathanu, V. V. Kumar, P. Sreelekha, J. Samson Isaac, Mandeep Kaur Ghumman, D. A. Subhahan

{"title":"An Innovation Development of Light Weight Deep Learning Algorithm for Smart Healthcare Neural Science Management","authors":"M. Chithambarathanu, V. V. Kumar, P. Sreelekha, J. Samson Isaac, Mandeep Kaur Ghumman, D. A. Subhahan","doi":"10.1109/ICECONF57129.2023.10083638","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083638","url":null,"abstract":"Healthcare occupies a special place in the structure and complexity of economic relations, due to the objective features of the main object of medical activities - an individual. Of these aspects, the key is the pervasive uncertainty in all medical activities: the uncertainty of the dynamics of human health, and the uncertainty of the outcome of medical intervention. The most important issue in healthcare is the problem of the quality of medical care, which is difficult to overestimate because it is related to health, and sometimes human life. In this paper, the neural science-based light weight deep learning algorithm was proposed to manage healthcare issues. The quality problem can only be solved if the neural science management of the medical care system is optimal at all levels by using computational intelligence. In solving these problems, priority is given to the administrative staff of health institutions. The growth and development of neural science management are one of the key levers to improving the performance of its adaptive medical institutions in a particular situation.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"92 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":"114806377","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

Deep Learning Based Network Traffic Analysis Using Modified Hybrid Methodology Comparing with SVM to Improve Accuracy 基于深度学习的网络流量分析改进混合方法与支持向量机的比较

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084206

N. Deeban, P. Bharathi

{"title":"Deep Learning Based Network Traffic Analysis Using Modified Hybrid Methodology Comparing with SVM to Improve Accuracy","authors":"N. Deeban, P. Bharathi","doi":"10.1109/ICECONF57129.2023.10084206","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084206","url":null,"abstract":"Users of networks are placing increased expectations on the speed and quality of the services provided by networks as a result of the fast advancement of network technology. As a result, one of the problems in the industry of network operation and maintenance management is to manage and regulate diverse network business traffic through efficient technological methods, differentiate between services, provide varied quality assurance, and fulfill the business demands of users. The identification of network traffic is a useful technological tool that may differentiate between the traffic generated by various applications. Through the processes of classifying, identifying, and distinguishing the application of network traffic, various types of traffic on the network may be provided with tailored network services, which in turn improves the quality of network services and the level of user satisfaction. The accurate identification of network traffic is not only a crucial foundation for the monitoring and data analysis of network traffic, but it is also the key to improving the overall quality of user service. Using a Hybrid Model that we've dubbed ANNSVM, the primary emphasis of this article is on doing an analysis of the data traffic on a 5G network. The acronym ANNSVM stands for Artificial Neural Network Support Vector Machine and combines the two terms. The term “artificial neural networks” (ANNs) refers to computer systems that utilize “learning algorithms,” which are programmes that can autonomously make modifications, or “learn,” when presented with new information. Because of this, they are a very useful tool for non-linear statistical data modeling, and SVM may function as a binary classifier. On the basis of the test data, the average classification accuracy is 98.8 percent, significantly exceeding other approaches that are already in use.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"44 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":"126153718","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

Concurrent Social Media in Collaborative Media Education 协同媒体教育中的并发社会媒体

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083780

N. Raja

引用次数: 0

Hybrid K-Means Clustering for Training Special Children using Utility Pattern Mining 基于效用模式挖掘的混合k均值聚类训练特殊儿童

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083709

J. Jeyachidra, T. Logesh, K. Nandhini, R. Krithiga

{"title":"Hybrid K-Means Clustering for Training Special Children using Utility Pattern Mining","authors":"J. Jeyachidra, T. Logesh, K. Nandhini, R. Krithiga","doi":"10.1109/ICECONF57129.2023.10083709","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083709","url":null,"abstract":"In this Hybrid K-Means Clustering for Grouping research work, the k-means methodology is a well-known process for grouping things together. Most of the time, this algorithm sorts the objects into a set number of clusters, but in this case, the user gives the number k. At first, it picks cluster centres at random and measures how far apart k points are. This kind of cluster centre is called k centroids, and it will keep changing until there are no more changes. When making applications that use machine intelligence, a machine should be able to think like a person and make the right choices. In this case, it's not possible to get k-points from the user. So, the Genetic Algorithm (GA) is used to search with heuristics to find the initial cluster centres. The goal of this research work is to look at how k-means clustering with GA can be used to optimise. The performance evaluation of hybrid k means method illustrates the precision and accuracy of the selected clustering methods. The result states that precision was 78.35% and its accuracy was 72.67% found while using the approach. The ROC curve analysis is performed with sensitivity and specificity data. The result states that the area under curve of the approach is 84.0%.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"22 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":"127212495","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

Robust Smart Face Recognition System Based on Integration of Local Binary Pattern (LBP), CNN and MTCNN for Attendance Registration 基于局部二值模式(LBP)、CNN和MTCNN集成的鲁棒智能人脸识别系统

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084103

S. B, D. Wise, S.H. Annie Silviya, Saravaanaa Kumar D, Venkat Sai Sujan K, Bruhathi S

引用次数: 0

An Enhancement of Service Function Chaining in Metro Mobile Ad-Hoc Networks in 5G Communication Using Machine Learning 利用机器学习增强5G通信中城域移动自组网的业务功能链

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084334

Komala C R, Varuna Kumara, N. Banu, S. Sumithra, S. R. Gopal, S. Mudradi

{"title":"An Enhancement of Service Function Chaining in Metro Mobile Ad-Hoc Networks in 5G Communication Using Machine Learning","authors":"Komala C R, Varuna Kumara, N. Banu, S. Sumithra, S. R. Gopal, S. Mudradi","doi":"10.1109/ICECONF57129.2023.10084334","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084334","url":null,"abstract":"A service function chaining is a location where important computer resources are stored. The hub is designed to support for sensitive applications such as mainframes, servers and server farms, and related computer resources. In addition to those server groups that support mobile network applications, there are other server groups that support network services and network applications. Network services include NTP, Telnet, FTP, DNS, DHCP, SNMP, TFTP and NFS. Network applications include IP telephony, video over IP, and video conferencing systems. Some organizational applications are logically organized into multiple layers, which are separated by the functions they perform. In this paper an algorithm was proposed to enhance the service function chaining. It focusSome layers are dedicated to supporting external functions such as client calls or serving web pages or supporting the command line interface (CLI) for applications. In some cases, external functions can be implemented based on the Internet. Other functions handle user requests and convert them into layers that can be understood by layers such as the server or database layer.","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":"124358412","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