2021 8th International Conference on Smart Computing and Communications (ICSCC)最新文献_第8页

A Cognitive Information Processing Pipeline for Multiple–Access Vehicular Camera Communication 多址车载摄像头通信的认知信息处理管道

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528281

Khadija Ashraf, A. Ashok

引用次数: 0

Class Imbalance Issue in Software Defect Prediction Models by various Machine Learning Techniques: An Empirical Study 基于不同机器学习技术的软件缺陷预测模型中的类不平衡问题的实证研究

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528170

Sushant Kumar Pandey, A. Tripathi

{"title":"Class Imbalance Issue in Software Defect Prediction Models by various Machine Learning Techniques: An Empirical Study","authors":"Sushant Kumar Pandey, A. Tripathi","doi":"10.1109/ICSCC51209.2021.9528170","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528170","url":null,"abstract":"Software practitioners are continuing to build advanced software defect prediction (SDP) models to help the tester find fault-prone modules. However, the Class Imbalance (CI) problem consists of uncommonly few defective instances, and more non-defective instances cause inconsistency in the performance. We have conducted 880 experiments to analyze the variation in the performance of 10 SDP models by concerning the class imbalance problem. In our experiments, we have used 22 public datasets consists of 41 software metrics, 10 baseline SDP methods, and 4 sampling techniques. We used Mathews Correlation Coefficient (MCC), which is more useful when a dataset is highly imbalanced. We have also compared the predictive performance of various ML models by applying 4 sampling techniques. To examine the performance of different SDP models, we have used the F-measure. We found the performance of the learning models is unsatisfactory, which needs to mitigate. We have also found a few surprising results, some logical patterns between classifier and sampling technique. It provides a connection between sampling technique, software matrices, and a classifier.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128438967","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: 3

A Novel Approach to Solve Sporadic Interrogation in Tracking System during Transonic Ascent Flight of Satellite Launch Vehicles 一种解决卫星运载火箭跨音速上升飞行跟踪系统中零星询问的新方法

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528089

Indu Gopan, M. S, S. Joy, Mukundan Kk

{"title":"A Novel Approach to Solve Sporadic Interrogation in Tracking System during Transonic Ascent Flight of Satellite Launch Vehicles","authors":"Indu Gopan, M. S, S. Joy, Mukundan Kk","doi":"10.1109/ICSCC51209.2021.9528089","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528089","url":null,"abstract":"Tracking a launch vehicle is of paramount importance, not only for knowing the instantaneous position of the vehicle, but also for taking appropriate and critical real time decisions related to Range safety. Tracking System also provides antenna positioning information for Telemetry, Telecommand and Optical Tracking stations. Active tracking systems consisting of C Band Radar ground station and onboard C Band Transponder provide the necessary input data in real time to the Range Safety Officer for taking necessary decisions. In the ascent phase of Launch Vehicle mission, during the transonic regime, sporadic interrogation is registered on onboard C-Band Transponders and large number of extra echo pulses are observed on Radar displays. Large fluctuations are also observed in signals received onboard and at ground during this time period. Response of the onboard transponders to the additional interrogation pulses lead to the original uplink pulse being missed, thereby causing loss of track by Radars. This paper discusses on the tracking system, the observations during transonic regime, its analysis and the unique solution implemented. Pulse position coding technique has been successfully employed in a novel way in the tracking system to overcome the difficulties caused due to erroneous interrogation and the design has been effectively demonstrated in flight.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128489646","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

Lite-Deep : Improved Auto Encoder-Decoder for Polyp Segmentation Lite-Deep:改进的息肉分割自动编码器-解码器

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528124

G. S, G. C., Shahid Haseem C., Arun Sreenivas, Aleena Maria John, Arathy A. S.

{"title":"Lite-Deep : Improved Auto Encoder-Decoder for Polyp Segmentation","authors":"G. S, G. C., Shahid Haseem C., Arun Sreenivas, Aleena Maria John, Arathy A. S.","doi":"10.1109/ICSCC51209.2021.9528124","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528124","url":null,"abstract":"Colorectal cancer(CRC) or colon cancer, is fatal cancer seen in males and females. Colorectal polyps usually develop on the mucosal layer of the colon or rectal part of the large intestine. They may later turn malignant and become cancerous. Diagnosis of colorectal polyps in the initial stages is a key factor in reducing the mortality rate due to CRC. Colonoscopy is considered the golden standard in CRC detection. Automation of polyp detection, localization and segmentation in the screening stage can help the clinicians to a great extent. However, detection, localization and segmentation of polyps of various morphological structures and textures have been proved to be very challenging. Deep neural networks (DNNs) have emerged as a powerful subset of machine learning and recorded a tremendous boost in many visual recognition tasks including medical imaging. Deep learning models often need an immense number of annotated images, which is difficult to collect in the medical domain and these models are computationally expensive and memory intensive. Hence a lot of works are going on to have model compression and acceleration in deep neural networks without significantly decreasing the performance. This work suggests a lightweight deep learning model rooted on auto-encoder decoder architecture for the segmentation of colorectal polyps of various morphological structures and textures. This model can be trained at full length from a considerably less number of images and shows par performance in terms of essential metrics used in semantic segmentation.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128598549","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

Detection and Grading of Multiple Fruits and Vegetables Using Machine Vision 多种果蔬的机器视觉检测与分级

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528165

Renju Rachel Varghese, Pramod Mathew Jacob, Sooraj S, Daniel Mathew Ranjan, Jino Cherian Varughese, Hegsymol Raju

引用次数: 5