2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)最新文献_第4页

Adaptive FLAME based segmentation and classification for bone cancer detection 基于自适应FLAME的骨癌检测分割与分类

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083670

Augustine George, B. Ayshwarya

引用次数: 0

A One-Dimensional Convolutional Neural Network and Long Short-Term Memory Model for Limb Movement Detection 基于一维卷积神经网络和长短期记忆的肢体运动检测模型

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083919

Blessy, K. Neela, A. Rajalakshmi, Almaria Joseph, C. Muralidharan, A. G

{"title":"A One-Dimensional Convolutional Neural Network and Long Short-Term Memory Model for Limb Movement Detection","authors":"Blessy, K. Neela, A. Rajalakshmi, Almaria Joseph, C. Muralidharan, A. G","doi":"10.1109/ICECONF57129.2023.10083919","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083919","url":null,"abstract":"Based on the information from an electrocardiogram (ECG), this research demonstrates that a deep learning model known as deepPLM may automatically diagnose periodic limb movement syndrome (PLMS). The deepPLM model that was built has a total of five layers: a completely connected layer, two long-term memory units, four 1D convolutional layers, and one FCL. The dataset from the MrOS project was utilized in the process of developing the model, in which the model was trained, validated, and tested. Each of the 52 people who participated in the MrOS dataset had a single-lead electrocardiogram (ECG) signal based on the polysomnographic tape. After being normalized and segmented, the electrocardiogram signal was then split into three different sets: the training set, the validation set, and the test set. The deepPLM model's effectiveness was evaluated using the following metrics: Fl-score (93%), precision (91%), and recall (94.2%) for the controlling set; Fl-score (93%), precision (91%), and recall (94.2%) for the treatment set. The results show that autonomous PLMS categorization may be performed on sufferers by utilizing the deepPLM model, that is based on a single-lead ECG. This has the potential to be an effective tool for delivering treatment to seniors in the comfort of their own homes and a different approach to testing for PLMS.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"30 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":"125362678","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

Facial Expression Recognition using Convolutional Neural Network and Haar Classifier 基于卷积神经网络和Haar分类器的面部表情识别

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083838

Arjun Dinesh S, S. R, Anand A

引用次数: 0

Coviguard - Intelligent shirt using Arduino for protection against covid Coviguard -使用Arduino的智能衬衫，用于预防covid

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084022

A. S, B. J, A. M. Kumar, Sindu Divakaran, K. S, S. A

引用次数: 0

Anomaly Based Detection for Identifying R2L (Remote to Local) Attacks Using RNN-LSTM in Comparison with ANN for Reducing False Alarm Rate 基于异常的RNN-LSTM识别R2L (Remote to Local)攻击，并与人工神经网络进行比较，以降低虚警率

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084242

B. Hemasree, D. N

引用次数: 0

Early-Stage Timing Prediction in SoC Physical Design using Machine Learning 基于机器学习的SoC物理设计早期时序预测

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084105

Madhusudan Kulkarni, Jehan Kadhim Shareef Al-Safi, S. M. K Sukumar Reddy, S. B. G Tilak Babu, Pankaj Kumar, Pushpa P

{"title":"Early-Stage Timing Prediction in SoC Physical Design using Machine Learning","authors":"Madhusudan Kulkarni, Jehan Kadhim Shareef Al-Safi, S. M. K Sukumar Reddy, S. B. G Tilak Babu, Pankaj Kumar, Pushpa P","doi":"10.1109/ICECONF57129.2023.10084105","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084105","url":null,"abstract":"During the late CMOS period, companies that manufacture semiconductors and electronics are subject to extreme product schedule tension notwithstanding different types of competitive strain. Inside this system, electronic plan automation (EDA) is expected to convey “plan based comparable scaling” to help with keeping up with crucial industry trajectories. The execution of machine learning techniques “inside” as well as “around” plan devices and work processes will act as a powerful main thrust in such manner. The valuable open doors for machine learning are discussed in this paper, with a particular accentuation on the physical execution of ICs. Instances of applications include eliminating unnecessary plan and demonstrating edges through correlation mechanisms, accomplishing quicker plan convergence through predictors of downstream stream outcomes that comprehend the two instruments and configuration instances, and (3) corollaries such as enhancing the utilization of plan resources licenses and accessible schedules. The limits of machine learning in coordinated circuit physical plan are discussed in the last section of the paper.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"73 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":"126776123","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

Lung Cancer Identification System to Improve the Accuracy Using Novel K Nearest Neighbour in Comparison with Logistic Regression Algorithm 基于新K近邻的肺癌识别系统与Logistic回归算法的比较

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084340

Y. K. Kumar, R. Priyanka

{"title":"Lung Cancer Identification System to Improve the Accuracy Using Novel K Nearest Neighbour in Comparison with Logistic Regression Algorithm","authors":"Y. K. Kumar, R. Priyanka","doi":"10.1109/ICECONF57129.2023.10084340","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084340","url":null,"abstract":"The K Nearest Neighbor (KNN) algorithm is going to be compared against the logistic regression method in an effort to determine whether one has the potential to provide a lower false detection rate of lung cancer. Both the Techniques and the Materials: A total of 304 photos were taken using data from three different lung cancer datasets found on Kaggle. Group 1 is the representation of the KNN method, while Group 2 is the representation of the logistic regression technique. The G power was calculated using a significance level of 80% and an alpha value of 0.05. The first group, Group 1, and the second group, Group 2, each had 20 samples analyzed. The results showed that KNN had an accuracy of 89.56 percent, but the accuracy of the logistic regression approach was only 80.11 percent. The KNN technique reached a level of significance of $mathbf{p}=mathbf{.042}$ when it was applied using the logistic regression methodology. The results of this research reveal that the KNN technique is much more accurate than the Logistic Regression strategy when it comes to the detection of lung cancer in the datasets that were examined for this research.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"5 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":"115248967","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

Fake News Classification using Transfer Learning 基于迁移学习的假新闻分类

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083678

Muthu Lakshmi V., K. Vijayakumar, Suthanthira Devi P., Rajin Gangadharan, D. Suresh

{"title":"Fake News Classification using Transfer Learning","authors":"Muthu Lakshmi V., K. Vijayakumar, Suthanthira Devi P., Rajin Gangadharan, D. Suresh","doi":"10.1109/ICECONF57129.2023.10083678","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083678","url":null,"abstract":"The rising complexity of information communication technology has greatly affected communication through conventional broadcast media over the past decade. Smartphone applications are increasingly emasculating the new socio-economic broadcasting environment. The trend is the same in the workplace, at home and in recreation. Social networking has stolen the game and is increasingly shifting to another age, the era of “digital relationships,” in which conventional interpersonal social interactions are replaced by mobile devices and social networks. The consequences of such false information promoted by miscreants and apologists for social media are far-reaching because it has resulted in scandals in households, communities, partnerships, organizations, and culture as a whole. The purpose of this paper is to lead to the eradication of counterfeit media by the use of technology. In this article, we proposed and built a model that incorporates neural networks to identify and eradicate false phrases posted to social media networks and web forums. Also, we compared our work Elmo VNetwith current state-of the-art models. The experimental results demonstrated that the proposed Elmo VNetmodel have better accuracy rate than the existing models.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"16 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":"125189765","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

Allocation of cloud resources based on prediction and performing auto-scaling of workload 基于工作负载的预测和自动伸缩来分配云资源

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083865

M. Jananee, K. Nimala

{"title":"Allocation of cloud resources based on prediction and performing auto-scaling of workload","authors":"M. Jananee, K. Nimala","doi":"10.1109/ICECONF57129.2023.10083865","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083865","url":null,"abstract":"Cloud Computing allows remote access to allocated services from anywhere in the world through the internet for end users. Interpretation and analysis of real-time data are one of the most challenging tasks for cloud analysts. The determination of the correct amount of resources required to match the world is difficult. On the other hand, the large configuration makes the resource underutilized, resulting in huge economic costs. In the current decade modeling and analyzing time series data across different fields has attracted researchers in cloud computing. To overcome huge economic costs allocation of cloud resources based on prediction and performing autoscaling of workload has been proposed. This prediction analysis can avoid losses such as service unavailability, maximum energy consumption, and customer loss. When the demand is large, more resources are requested from the cloud service provider to complete the task before the deadline. When the demand is less the idle resources are released. Based on predicted values, we can reduce the workload by performing autoscaling (horizontal & vertical) in the allocation of resources.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"59 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":"123007197","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

Varoka-Chatbot: An Artificial Intelligence Based Desktop Partner Varoka-Chatbot:基于人工智能的桌面合作伙伴

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083961

Penaka Sai Varshita Reddy, Tripuram Pavani Kalki, P. Roshini, S. Navaneethan

{"title":"Varoka-Chatbot: An Artificial Intelligence Based Desktop Partner","authors":"Penaka Sai Varshita Reddy, Tripuram Pavani Kalki, P. Roshini, S. Navaneethan","doi":"10.1109/ICECONF57129.2023.10083961","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083961","url":null,"abstract":"Today, more than ever, our lives have gotten faster and more frantic. Currently, artificial intelligence (AI) is improving our quality of life. We have begun to communicate, interact, and learn online. Intelligent Personal Assistants (IPAs), which enable users to converse through Natural Language Processing (NLP), enable the ultimate luxury of having a companion who always listens for you, acts when necessary, and anticipates your every need. We shall accomplish things in the era of rapidly evolving technology which we have never dreamt of, were possible in the past. A virtual personal assistant (VPA) is what this initiative seeks to develop, with the ability to automate duties and provide services for a person, allowing you to enjoy the luxury of doing so. The purpose of our personal assistant, VAROKA, which is entirely written in Python, is to provide you control over your desktop. The built-in speakers respond verbally to the user's voice request after it has been recorded via the microphone. With the hopeful rise and advent of IPAs, this voice-controlled virtual assistant helped to surpass our expectations by offering full features of employing desktop technology on customers' voice commands.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"65 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":"131502810","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