2023 Second International Conference on Electronics and Renewable Systems (ICEARS)最新文献_第8页

Salt Segment Identification in Seismic Images of Earth Surface using Deep Learning Techniques 基于深度学习技术的地表地震图像盐段识别

2023 Second International Conference on Electronics and Renewable Systems (ICEARS) Pub Date : 2023-03-02 DOI: 10.1109/ICEARS56392.2023.10085475

Lakshmi Devi N, Rajasekhar Reddy Bochu, Naveen Kumar Buddha

{"title":"Salt Segment Identification in Seismic Images of Earth Surface using Deep Learning Techniques","authors":"Lakshmi Devi N, Rajasekhar Reddy Bochu, Naveen Kumar Buddha","doi":"10.1109/ICEARS56392.2023.10085475","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085475","url":null,"abstract":"Salt segmentation is the process of identifying whether a subsurface target is salt or not. There are several places on Earth where there are significant amounts of salt as well as oil and gas. For businesses engaged in oil and gas development, finding the exact locations of significant salt deposits is crucial. Also, lands that have been impacted by salt are not useful for farming. The absorption capacity of the plant reduces due to the presence of salt in the soil solution. So, in order to identify the land that contains salt, salt segmentation is being done. The seismic image of a particular pixel is analysed to classify it either as salt or sediment. TGS Salt Identification Challenge dataset is used which consists of 4,000 seismic image patches of size (101x101x3) and corresponding segmentation masks of size (101x101x1) in training set. 18,000 seismic image patches are present in the test set which are used for evaluation of the model. The existing models have less detection rate. So, this study has proposed two models for identifying the salt region with high detection rate. The primary model used here is a combination of UNET with ResNet-18 and ResNet-34. The secondary model achieves segmentation results by ensembling UNET with ResNet-34, VGG16 and Inceptionv3. Using these two models, the salt region can be determined from the seismic data. IoU is used as performance metric in order to evaluate the model. The outcomes demonstrate that the ensemble model outperforms individual network models and achieves better segmentation results.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134085173","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

Survey on Customized Diet Assisted System based on Food Recognition 基于食物识别的定制饮食辅助系统研究

2023 Second International Conference on Electronics and Renewable Systems (ICEARS) Pub Date : 2023-03-02 DOI: 10.1109/ICEARS56392.2023.10085456

K. Makanyadevi, P. S, S. R, S. S

{"title":"Survey on Customized Diet Assisted System based on Food Recognition","authors":"K. Makanyadevi, P. S, S. R, S. S","doi":"10.1109/ICEARS56392.2023.10085456","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085456","url":null,"abstract":"Across the world, people are growing more dietary sensitive today. An unbalanced diet can result in a variety of issues, including weight gain, obesity, diabetes, etc. As a result, many techniques were created to analyse images of food and determine factors like calories as well as nutrition content. One of the most essential needs of every living thing on earth is food. Humans demand that the food they eat be of standard quality, freshness, and purity. Food quality is taken care of by the standards set and automation implemented in the food processing business. The idea of using food as medicine has gained traction in recent years, in part due to doctors' and practitioners' increased understanding of the importance of including food in the treatment of chronic illnesses alongside drugs. Food measurement is crucial for a good healthy diet. One of the difficult tasks in maintaining diet is calorie and nutritional content measurement in daily eating. In today's technology age, the smartphone exacerbates the problem with nutritional intake. The meal image recognition algorithm for calculating the nutritional and calorie values has been established in this survey analysis. The system classifies the meal once the user takes a picture of it to determine the type of food, the portion size, and the expected number of calories. This approach uses food area, size, and volume to accurately compute calories and nutrition. Due to the difficulty in achieving accuracy to classify food images, many images have been trained to attain high accuracy.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134166368","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

Artificial Neural Network and Process Optimization of Electrical Discharge Machining of Al 6463 电火花加工Al 6463的人工神经网络及工艺优化

2023 Second International Conference on Electronics and Renewable Systems (ICEARS) Pub Date : 2023-03-02 DOI: 10.1109/ICEARS56392.2023.10085204

A. Pugazhenthi, R. Thiyagarajan, P. Srividhya, R. Udhayasankar, S. R

{"title":"Artificial Neural Network and Process Optimization of Electrical Discharge Machining of Al 6463","authors":"A. Pugazhenthi, R. Thiyagarajan, P. Srividhya, R. Udhayasankar, S. R","doi":"10.1109/ICEARS56392.2023.10085204","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085204","url":null,"abstract":"A silicon carbide strengthened aluminium 6463 composite was formed by stir casting. To assess crucial process parameters, the composite was machined. At three different levels, three variables—current, pulse ON time, and feed of the wire—were incorporated in Taguchi's experimental setup. The components that impact the process were found using a statistical analysis. The ON time of the pulse of 160 s, the current of 18 A, and the feed of the wire of 2 mm/min had the highest removal rate. The pulse on-time of 100 s, the current of 12 A, and the feed of the wire rate of 2 mm/min remained the most effective factors for obtaining a good surface quality. Feed of the wire had minimal impact on output characteristics, but pulse duty cycle and current were important elements in achieving high material removal rates with acceptable surface quality. The experimental Taguchi design improved machinability characteristics while milling the synthesized composites by maintain the higher value of the ON time of the pulse and current. The artificial neural network model is developed to predict the experimental outcome and the model predicts the result with an accuracy of 100%.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134456323","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

Prediction of Brain Stroke in Human Beings using Machine Learning 利用机器学习预测人类脑中风

2023 Second International Conference on Electronics and Renewable Systems (ICEARS) Pub Date : 2023-03-02 DOI: 10.1109/ICEARS56392.2023.10085128

T. N. Deepthi, S. Sharmila, M. Swarna, M. Gouthami, C. Akshaya

引用次数: 1

An Ingenious Deep Learning Approach for Home Automation using Tensorflow Computational Framework 使用Tensorflow计算框架的家庭自动化巧妙的深度学习方法

2023 Second International Conference on Electronics and Renewable Systems (ICEARS) Pub Date : 2023-03-02 DOI: 10.1109/ICEARS56392.2023.10084944

P. Ilampiray, A. Thilagavathy, Challa Sai Hari Uma Sahith, Penumathsa Girish Sai Varma, Bhuvanendra Chowdary V, M. Dhanush

引用次数: 0

Prediction of Cervical Cancer using Multilayer Perceptron Algorithm 基于多层感知器算法的宫颈癌预测

2023 Second International Conference on Electronics and Renewable Systems (ICEARS) Pub Date : 2023-03-02 DOI: 10.1109/ICEARS56392.2023.10085636

S. Sujanthi, A. S, H. K, S. S

引用次数: 0

Effective Management of IoT Devices that can Withstand Attacks on Cloud Data 有效管理可抵御云数据攻击的物联网设备

2023 Second International Conference on Electronics and Renewable Systems (ICEARS) Pub Date : 2023-03-02 DOI: 10.1109/ICEARS56392.2023.10085408

A. M, Thirumalai A

{"title":"Effective Management of IoT Devices that can Withstand Attacks on Cloud Data","authors":"A. M, Thirumalai A","doi":"10.1109/ICEARS56392.2023.10085408","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085408","url":null,"abstract":"First, with regards to attribute-based encryption (ABE), it is an approach to access control that allows data to be encrypted and decrypted based on certain attributes, such as a user's role, location, or other characteristics. This approach provides granular control over who can access specific data, which is particularly useful for IoT applications where sensitive data is being generated by many devices. However, as I mentioned earlier, ABE can be computationally intensive, which may not be suitable for low-power IoT devices. One possible solution to this challenge is to use edge computing, where some of the computing tasks are performed at the edge of the network, closer to the devices generating the data. This can help reduce the amount of data that needs to be sent to the cloud and can improve overall system performance. Another challenge with ABE is that it does not provide protection against key sharing. If a user shares their decryption key with an unauthorized party, that party could potentially gain access to sensitive data. To address this challenge, it's important to have strong access controls in place to prevent unauthorized sharing of keys. In terms of data storage security, while outsourcing to cloud servers can certainly help with complex computing tasks, it's still important to implement sophisticated security measures. This might include encrypting the data at rest and in transit, implementing access controls, and monitoring the system for potential security breaches. Finally, it's important to follow regulations and best practices for key sharing to prevent unauthorized access to sensitive data. This might include policies around key management, user authentication, and data governance.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122805623","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

Study on Conveyor Belt System enabled with IoT in Postal and Courier Services 邮政和快递服务中物联网输送带系统的研究

2023 Second International Conference on Electronics and Renewable Systems (ICEARS) Pub Date : 2023-03-02 DOI: 10.1109/ICEARS56392.2023.10085155

Kanagaraj Venusamy, Abdul Hafeel M, K. M, Muthukkaruppan S, Chandramohan P

引用次数: 0

Cotton Leaf Disease Detection using Convolutional Neural Networks (CNN) 卷积神经网络(CNN)棉花叶病检测

2023 Second International Conference on Electronics and Renewable Systems (ICEARS) Pub Date : 2023-03-02 DOI: 10.1109/ICEARS56392.2023.10085551

S. Sharmila, R. Bhargavi, R. Anusha, K. Anusha, B. Divya

引用次数: 1

Prediction of Diabetic Patients with High Risk of Readmission using Smart Decision Support Framework 应用智能决策支持框架预测糖尿病高危再入院患者

2023 Second International Conference on Electronics and Renewable Systems (ICEARS) Pub Date : 2023-03-02 DOI: 10.1109/ICEARS56392.2023.10085491

N. Kumar, N. Sathyanarayana

{"title":"Prediction of Diabetic Patients with High Risk of Readmission using Smart Decision Support Framework","authors":"N. Kumar, N. Sathyanarayana","doi":"10.1109/ICEARS56392.2023.10085491","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085491","url":null,"abstract":"Patients with diabetes are more likely to be readmitted to the hospital than those who are nondiabetic. The earlier patients with a strong probability of readmission are monitored and cared for, the better. The goal of this research is to develop a decision - making framework that can identify diabetes patients who are at risk of early readmission. Many data analysis approaches have been employed to perform this. Computer vision is used to create a novel model in this study. Individuals at high risk of complications to be readmitted are prioritized in the early stages, which in turn reduces healthcare costs and improves the reputation of the hospital, thus enhancing the health service and saving money. Predictions made using machine learning are more accurate than those made using traditional methods. In this study, patients' hospital readmissions may be predicted by utilizing a standard scaler, a decision tree, and random forests for classification, CATboost for categorical features, and XGBoost classifiers. When applied to real-world data, a machine learning method that incorporates deep learning technique has outperformed the other methods. As a response to a number of modules, including extracting features, the analysis has been enhanced and a more useful framework has been created.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121428801","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