Innovations in Information and Communication Technology Series最新文献_第3页

Real Time LDR Data Prediction using IoT and Deep Learning Algorithm 使用物联网和深度学习算法的实时LDR数据预测

Innovations in Information and Communication Technology Series Pub Date : 2020-12-30 DOI: 10.46532/978-81-950008-1-4_033

S. Chandraprabha, G. Pradeepkumar, Dineshkumar Ponnusamy, D. SaranyaM, S Satheeshkumar, R. Sowmya

引用次数: 0

Novel Vivaldi Antenna Design for 5G Applications 5G应用的新型维瓦尔第天线设计

Innovations in Information and Communication Technology Series Pub Date : 2020-12-30 DOI: 10.46532/978-81-950008-1-4_031

S Satheeshkumar, Pradeep Pushpanathan, B MaruthiShankar, J MuralidharanJ

引用次数: 0

Detection of Ovarian Tumor Using Machine Learning Approaches A Review 使用机器学习方法检测卵巢肿瘤综述

Innovations in Information and Communication Technology Series Pub Date : 2020-12-30 DOI: 10.46532/978-81-950008-1-4_103

Gitanjali Wadhwa, Mansi Mathur

{"title":"Detection of Ovarian Tumor Using Machine Learning Approaches A Review","authors":"Gitanjali Wadhwa, Mansi Mathur","doi":"10.46532/978-81-950008-1-4_103","DOIUrl":"https://doi.org/10.46532/978-81-950008-1-4_103","url":null,"abstract":"The important part of female reproductive system is ovaries. The importance of these tiny glands is derived from the production of female sex hormones and female gametes. The place of these ductless almond shaped tiny glandular organs is on just opposite sides of uterus attached with ovarian ligament. There are several reasons due to which ovarian cancer can arise but it can be classified by using different number of techniques. Early prediction of ovarian cancer will decrease its progress rate and may possibly save countless lives. CAD systems (Computer-aided diagnosis) is a noninvasive routine for finding ovarian cancer in its initial stages of cancer which can keep away patients’ anxiety and unnecessary biopsy. This review paper states us about how we can use different techniques to classify the ovarian cancer tumor. In this survey effort we have also deliberate about the comparison of different machine learning algorithms like K-Nearest Neighbor, Support Vector Machine and deep learning techniques used in classification process of ovarian cancer. Later comparing the different techniques for this type of cancer detection, it gives the impression that Deep Learning Technique has provided good results and come out with good accuracy and other performance metrics.","PeriodicalId":191913,"journal":{"name":"Innovations in Information and Communication Technology Series","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121560238","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

Potato Leaf Disease Classification using Transfer Learning based Modified Xception Model 基于迁移学习的改进例外模型的马铃薯叶病分类

Innovations in Information and Communication Technology Series Pub Date : 2020-12-30 DOI: 10.46532/978-81-950008-1-4_096

Rajasekaran Thangaraj, P. Pandiyan, V. K. Kaliappan, S. Anandamurugan, P Indupriya

引用次数: 0

Miniaturized Angularly Stable Dual Band Frequency Selective Surface for K and Ka Band 小型化角稳定的K和Ka波段双波段频率选择表面

Innovations in Information and Communication Technology Series Pub Date : 2020-12-30 DOI: 10.46532/978-81-950008-1-4_020

M Singaram, E KrishnaKumar, Chandraprasad, F. D. Shadrach, Gowtham Manoharan

引用次数: 0

Experimental Investigation on Wear Behaviour of AA6082 Aluminium Alloy, Tungsten Carbide and Graphite Hybrid Composites AA6082铝合金、碳化钨和石墨杂化复合材料磨损性能试验研究

Innovations in Information and Communication Technology Series Pub Date : 2020-12-30 DOI: 10.46532/978-81-950008-1-4_107

K. K, SuriyaPrakash M, R. K., V. M

引用次数: 0

The DICOM CT Image Compression Based On Enhanced Lossless Prediction And Multilevel Thresholding Based Hybrid Cuckoo Search With Hill Climbing (CS-HC) Algorithm Based Segmentation 基于增强无损预测和多级阈值混合杜鹃搜索的DICOM CT图像压缩与爬山(CS-HC)分割算法

Innovations in Information and Communication Technology Series Pub Date : 2020-12-30 DOI: 10.46532/978-81-950008-1-4_058

Mothi, Supriya

{"title":"The DICOM CT Image Compression Based On Enhanced Lossless Prediction And Multilevel Thresholding Based Hybrid Cuckoo Search With Hill Climbing (CS-HC) Algorithm Based Segmentation","authors":"Mothi, Supriya","doi":"10.46532/978-81-950008-1-4_058","DOIUrl":"https://doi.org/10.46532/978-81-950008-1-4_058","url":null,"abstract":"In computer vision applications, image segmentation is a common image processing step. It is used to separate pixels into different groups. The rise in the threshold count would hinder the segmentation phase of images. At the same time, in the field of threshold implementation in the image, it becomes an NT concern. This thesis suggests a multilevel threshold based on optimization techniques to remove ROI and uses enhanced lossless prediction algorithm to compress DICOM images in telemedicine applications. The hybrid Cuckoo search with hill climbing (CS-HC) algorithm strengthens the process used by the search agent to update the optimal solution. This algorithm calculates the threshold value. The superior results are produced by the proposed multilevel level thresholding based on CS-HC, as seen by the simulation results. Optimization is efficient and it has a high degree of convergence. Effective results are provided by the proposed lossless compression algorithm based on classification and blending estimation as compared with JPEG lossless and lossy compression techniques. With various threshold values, the algorithm 's efficiency is checked. To apply this algorithm, Matlab2010a is used and DICOM photos are used to validate it.","PeriodicalId":191913,"journal":{"name":"Innovations in Information and Communication Technology Series","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127807479","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

Fuzzy Logic in Agriculture – A Short Review 农业中的模糊逻辑综述

Innovations in Information and Communication Technology Series Pub Date : 2020-12-30 DOI: 10.46532/978-81-950008-1-4_035

Balaji Devarajan, L. Rajeshkumar, Bhuvaneswari, K. PriyaA, P. Rajesh

引用次数: 0

A Prediction of Corona Disease Transmission Using A Traditional Machine Learning Approach 利用传统机器学习方法预测冠状病毒的传播

Innovations in Information and Communication Technology Series Pub Date : 2020-12-30 DOI: 10.46532/978-81-950008-1-4_098

C. Venkatesan, E. Balan, G SumithraM, A. Karthick, Jayarajan, M AntoMerline

引用次数: 0

IIR Filter Design Using African Buffalo Optimization 基于非洲水牛优化的IIR滤波器设计

Innovations in Information and Communication Technology Series Pub Date : 2020-12-30 DOI: 10.46532/978-81-950008-1-4_019

J. B, Govindaraj V, S. Kanth

引用次数: 0