2022 3rd International Conference on Smart Electronics and Communication (ICOSEC)最新文献

Improving the Integration and Dynamic of Sentiment Analysis Prediction using Fast Vector Space Model 基于快速向量空间模型的情感分析预测集成与动态改进

2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) Pub Date : 2022-10-20 DOI: 10.1109/ICOSEC54921.2022.9952075

S. S. Subashka Ramesh, G. Jayandran, A. Rushab

引用次数: 0

MEMS based Chip for Blood Cell Sorting using Microfluidic Channel 基于MEMS的微流控通道血细胞分选芯片

2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) Pub Date : 2022-10-20 DOI: 10.1109/ICOSEC54921.2022.9952048

S. Ramya, S. Praveen Kumar, T. K. Srinivasan, T. Aravind, G. Ram, D. Lingaraja

引用次数: 0

Non-Intrusive Load Monitoring for Energy Consumption Disaggregation 面向能耗分解的非侵入式负荷监测

2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) Pub Date : 2022-10-20 DOI: 10.1109/ICOSEC54921.2022.9951891

P. R. Aravind, T. Sarath

引用次数: 1

Sports Dance Intelligent Training Correction System based on Multimedia Image Action Real-Time Acquisition Algorithm 基于多媒体图像动作实时采集算法的体育舞蹈智能训练校正系统

2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) Pub Date : 2022-10-20 DOI: 10.1109/ICOSEC54921.2022.9952152

Lu Pan

引用次数: 0

Efficient Brain and Liver Tumor Segmentation using Seagull Optimization Algorithm based Super Pixel Fuzzy Clustering 基于超像素模糊聚类的海鸥优化算法高效脑和肝肿瘤分割

2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) Pub Date : 2022-10-20 DOI: 10.1109/ICOSEC54921.2022.9952105

S. Devi, E. G. Manoharan

{"title":"Efficient Brain and Liver Tumor Segmentation using Seagull Optimization Algorithm based Super Pixel Fuzzy Clustering","authors":"S. Devi, E. G. Manoharan","doi":"10.1109/ICOSEC54921.2022.9952105","DOIUrl":"https://doi.org/10.1109/ICOSEC54921.2022.9952105","url":null,"abstract":"Now a days, medical image segmentation has been utilized in many applications with the consideration of computer aided diagnosis system. From that, brain tumour segmentation with MRI image play a main role in disease prediction. Hence, in this paper Seagull Optimization Algorithm Based Super Pixel Fuzzy Clustering (SOA-SFC)is designed for segmentation. The proposed segmentation process is designed with the combination of Super Pixel Fuzzy Clustering and Seagull Optimization Algorithm. In the Super Pixel Fuzzy Clustering, the efficient cluster center is chosen with the assistance of Seagull Optimization Algorithm. Initially, the Super Pixel Fuzzy Clustering objective function is considered with the consideration of fuzzy information extracted from the images of brain. After that, Seagull Optimization Algorithm is utilized towards optimize the cluster center in addition fuzzifier from the clustering method. The projectedtechniquecan be implemented in the MATLAB in additionpresentationiscomputed. The projectedtechniquecan becontrasted with the existing techniqueslike fuzzy c means clustering, k means clustering methods and Chimp Optimization Algorithm Based Type-2 Intuitionistic Fuzzy C-Means Clustering (COA-T2FCM). The projected method can be validated by performance metrices such as Dice similarity coefficient (DSC), Jaccard Similarity Index (JSI), accuracy, sensitivity, and specificity.","PeriodicalId":221953,"journal":{"name":"2022 3rd International Conference on Smart Electronics and Communication (ICOSEC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123461077","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 Model for Predicting Future Price of Agricultural Commodities using K-Nearest Neighbors Algorithm Compared with Support Vector Machine Algorithm 基于k -最近邻算法的农产品未来价格预测模型与支持向量机算法的比较

2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) Pub Date : 2022-10-20 DOI: 10.1109/ICOSEC54921.2022.9952132

Kuruba Bandaia, M. Gunasekaran

引用次数: 0

Optimal Band Selection and Scale based Feature Selection for Hyper Spectral Image Classification using Hybrid Neural Network 基于最优频带选择和尺度特征选择的混合神经网络高光谱图像分类

2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) Pub Date : 2022-10-20 DOI: 10.1109/ICOSEC54921.2022.9952114

N. Jeenath Shafana, Jayan K T, R. Divagar Iyyappan

引用次数: 0

Deep Learning-based Automatic 3D Printer Anomaly Detection during the Printing Process 基于深度学习的3D打印机打印过程异常自动检测

2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) Pub Date : 2022-10-20 DOI: 10.1109/ICOSEC54921.2022.9951903

T. Lalitha, N. K. Anushkannan, Sarange Shreepad, S. Sasireka, Harishchander Anandaram, S. Razia

{"title":"Deep Learning-based Automatic 3D Printer Anomaly Detection during the Printing Process","authors":"T. Lalitha, N. K. Anushkannan, Sarange Shreepad, S. Sasireka, Harishchander Anandaram, S. Razia","doi":"10.1109/ICOSEC54921.2022.9951903","DOIUrl":"https://doi.org/10.1109/ICOSEC54921.2022.9951903","url":null,"abstract":"3D printing is a technology which is expected to be one of the most used technologies in the upcoming time. This technology allows to print out products that are designed using 3D modeling software. Though this is an effective technology, it also has its disadvantages. The disadvantages include anomalies. Anomaly is a defect that is often found when the printer finishes the printing process. Thus, it cannot be rectified when found during the process. To resolve this issue, this study aims in developing a deep learning model using the UNet algorithm. A dataset of pictures of various possible anomalies is gathered from Kaggle. The Kaggle data is then preprocessed using three different methods. The images are initially applied using the target format. The images are then multiplied and shrunk to keep the balance. The UNet method is employed to create a deep learning model. The preprocessed dataset is then used to train the model. To guarantee improved performance, the trained model is subsequently put to the test to assess the model’s final accuracy and loss. In all three instances, the model’s output is determined to be satisfactory. The model produced an accuracy of 98% during the testing and produced a loss value of 0.54%. This loss value is so small that it can be neglected. The model developed is found to be one of the best algorithms that can be used in anomaly detection.","PeriodicalId":221953,"journal":{"name":"2022 3rd International Conference on Smart Electronics and Communication (ICOSEC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125348307","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

Anisotropic Diffusion based Impulse Noise Removal for Remote Sensing Images 基于各向异性扩散的遥感图像脉冲噪声去除

2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) Pub Date : 2022-10-20 DOI: 10.1109/ICOSEC54921.2022.9951890

Resmi R. Nair, R. Senthamizh Selvi, Jerusha Beulah, B. Karthika Sree

{"title":"Anisotropic Diffusion based Impulse Noise Removal for Remote Sensing Images","authors":"Resmi R. Nair, R. Senthamizh Selvi, Jerusha Beulah, B. Karthika Sree","doi":"10.1109/ICOSEC54921.2022.9951890","DOIUrl":"https://doi.org/10.1109/ICOSEC54921.2022.9951890","url":null,"abstract":"In image processing and computer vision, image denoising is a crucial challenge that should be rectified by suppressing the noise-corrupted image and obtaining the image information. The random variation of brightness or colour information in acquired images is referred to as image noise. Image denoising is also useful in a variety of applications, such as image restoration, visual tracking, image registration, picture segmentation, and image classification, where recapturing the original image content is critical to achieving good results. To deal with additive noise, a myriad of image denoising methodologies have been proposed in recent times. Impulse noise, on the other hand, remains a challenging problem to solve using multiple ways. It is a sort of noise with either black or white noise pixels. We propose a novel concept of scale-space in this study, as well as a class of algorithms that implement it via a diffusion process. The primary purpose is to eliminate salt and pepper noise from remote sensing imagery using an anisotropic diffusion median filter. Our method ensures that region boundaries are kept as precise as possible. The findings of the experiments are depicted in a series of images. In terms of visual outcomes and performance metrics, the performance of the algorithm is validated by Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR)","PeriodicalId":221953,"journal":{"name":"2022 3rd International Conference on Smart Electronics and Communication (ICOSEC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126927736","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 the Frequency Bands and the Path Loss in Wireless Communication Systems using Random Forests 利用随机森林预测无线通信系统的频带和路径损耗

2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) Pub Date : 2022-10-20 DOI: 10.1109/ICOSEC54921.2022.9951963

C. Senthilkumar, P. Nirmala, S. Ahila, M. Geetha, S. Ramesh

{"title":"Predicting the Frequency Bands and the Path Loss in Wireless Communication Systems using Random Forests","authors":"C. Senthilkumar, P. Nirmala, S. Ahila, M. Geetha, S. Ramesh","doi":"10.1109/ICOSEC54921.2022.9951963","DOIUrl":"https://doi.org/10.1109/ICOSEC54921.2022.9951963","url":null,"abstract":"Proactive and predictive design in the next wireless generation is critical to avoiding the flaws of prior generations and achieving the 5G goal services pillars. Base stations are needed to perform and make judgments to maintain communication dependability as wireless devices become more commonplace. Machine Learning (ML) is used in this research to help base stations anticipate the frequency bands and the route loss. There is a comparison between the ML algorithms Multilayer Perceptron and Random Forests. In order to keep up with the demands of the new radios, systems that use various bands need an immediate reaction from devices to change bands quickly. For this reason, ML approaches are required to learn and help a radio base station in shifting between multiple bands in response to data-driven decisions. Afterwards, the findings are compared to those of different deep learning approaches. To guarantee that the projected works would succeed, these strategies are used to several case studies. Unsupervised algorithms were added to the random forests in order to improve the accuracy of the learning process.","PeriodicalId":221953,"journal":{"name":"2022 3rd International Conference on Smart Electronics and Communication (ICOSEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116098654","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