2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)最新文献

Deep Learning based Betelvine leaf Disease Detection (Piper BetleL.) 基于深度学习的甜菜叶病害检测(Piper BetleL.)

2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250911

R. Kavitha Lakshmi, S. Nickolas

{"title":"Deep Learning based Betelvine leaf Disease Detection (Piper BetleL.)","authors":"R. Kavitha Lakshmi, S. Nickolas","doi":"10.1109/ICCCA49541.2020.9250911","DOIUrl":"https://doi.org/10.1109/ICCCA49541.2020.9250911","url":null,"abstract":"Environment influences plants in various forms of stress that affects plant’s growth and development. Biotic Stress causes damage to plants with the help of living organisms, pests, and pathogens. This leads to a huge loss of agricultural productivity and sustainability that has a serious impact on the world economy. Thus, at the outset of growth, it is highly desirable to automate the process of disease identification and diagnosis. At present, computer vision using deep learning methods have achieved considerable success with object detection tasks. The goal of this work is to develop an innovative, ground-breaking framework for the detection of diseases based on a deep convolution neural network through the transfer learning technique. For this work, initially, we collected a dataset that contains images of 1,014 Betelvine leaves with healthy and diseased categories in the cultivated betel vine crops and annotated manually for training. Mask-RCNN instance segmentation model is adopted for detecting Betelvine diseased leaves effectively. Eventually, the trained model achieved mean Average Precision (mAP) of 84%, using an updated Mask-RCNN model by fine-tuning the parameters with a swish as activation function and RAdam as the optimizer.","PeriodicalId":260353,"journal":{"name":"2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116782519","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: 4

An Improved Ultrasound Tumor Segmentation Using CNN Activation Map Clustering and Active Contours 基于CNN激活图聚类和活动轮廓的超声肿瘤分割

2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250909

R. Sivanandan, J. Jayakumari

{"title":"An Improved Ultrasound Tumor Segmentation Using CNN Activation Map Clustering and Active Contours","authors":"R. Sivanandan, J. Jayakumari","doi":"10.1109/ICCCA49541.2020.9250909","DOIUrl":"https://doi.org/10.1109/ICCCA49541.2020.9250909","url":null,"abstract":"Ultrasound (US) imaging has become a primary tool for diagnosis of tumors mainly owing to its non-ionizing procedure but effective analysis is challenging due to the presence of inhomogeneties, speckle, low contrast and complex echogenic patterns. Manually defined predicates for feature extraction will not suffice, where deep learning can play a pivotal role. In this work, Convolutional Neural Networks (CNN) have been used for US breast tumor feature extraction and classification as benign/ malignant. To make the network robust to noise variations in the image and as an improvement over existing methodologies, neutrosophic preprocessing and enhancement were performed and the enhanced images were appended to the dataset and the original image during training. Using neutrosophic preprocessing alone had increased the validation accuracy of the CNN from 0.84 to 0.92, while using neutrosophic preprocessing and augmentation had increased the accuracy to 0.99. GoogLeNet with pretrained parameters were chosen as the preferred architecture for the work, with the predictions being validated using class activation maps that showed that the CNN was able to learn the features related to the tumor accurately. The final layers’ activation maps are clustered using fuzzy c-means clustering, which act as an initial contour for active contour segmentation using localized energies. The clustered outputs also qualify as a validation method for the prediction accuracy of the CNN. Using the proposed segmentation, time and computational complexity can be reduced which otherwise is high for traditional encoding cum decoding based CNN segmentation architectures.","PeriodicalId":260353,"journal":{"name":"2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127291935","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

Implementation of Backtracking Algorithm for navigation in LoRa-enabled wearable device for Dementia patients 基于lora的痴呆患者可穿戴设备导航回溯算法的实现

2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250891

Urja Kulkarni, Siddhesh Badhani, Harit Bandit, Siddhant Bhagat, Manish Parmar

引用次数: 0

Battery Energy Storage System to Improve Reliability due to Under Frequency Load Shedding 低频率减载提高电池储能系统可靠性的研究

2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250889

C. Aluthge, K. U. Udayanga Hemapala, J. R. Lucas

引用次数: 2

On the Importance of the DCT Phase for Image Steganography Schemes 论DCT相位对图像隐写方案的重要性

2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250849

Mohammed Baziyad, M. Obaidat

引用次数: 4

A Study of Indian Stock Market using Matrix Variate Dynamic Model 基于矩阵变量动态模型的印度股市研究

2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250845

P. Dwivedi, Ajit Kumar

引用次数: 0

Fractal Load Balancing Method in Distributed Systems 分布式系统的分形负载均衡方法

2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250776

Anuraj Kathait, Sudhir Dhage

引用次数: 0

An Illustration of a novel All Dielectric 3D FSS based Band-Pass Filter 一种新型全介电三维FSS带通滤波器的设计

2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250756

Priyanka Das, K. Mandal

引用次数: 0

The Pipeline Process of Signature-based and Behavior-based Malware Detection 基于签名和基于行为的恶意软件检测的流水线流程

2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250879

Manish Goyal Research Scholar, Raman Kumar

引用次数: 3

Position Sensorless BLDC Motor Drive for Single Stage PV Based Water Pumping 单级光伏水泵无位置传感器无刷直流电机驱动

2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Pub Date : 2020-10-30 DOI: 10.1109/ICCCA49541.2020.9250774

Rajan Kumar, Bhim Singh

引用次数: 5