2020 2nd International Conference on Computer and Information Sciences (ICCIS)最新文献_第5页

An Adaptive Image Processing Model of Plant Disease Diagnosis and Quantification Based on Color and Texture Histogram 基于颜色和纹理直方图的植物病害诊断与定量自适应图像处理模型

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257650

Waqar Ismail, M. A. Khan, S. A. Shah, M. Javed, A. Rehman, T. Saba

{"title":"An Adaptive Image Processing Model of Plant Disease Diagnosis and Quantification Based on Color and Texture Histogram","authors":"Waqar Ismail, M. A. Khan, S. A. Shah, M. Javed, A. Rehman, T. Saba","doi":"10.1109/ICCIS49240.2020.9257650","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257650","url":null,"abstract":"In this paper, a new approach for the detection and classification of potato plant disease is implemented using computer vision techniques. Most of the existing algorithms based on plant disease detection and classification are limited to common types of feature extraction methods. However, feature extraction is an important area as the classification of diseases of any leaf. The proposed method is based on color and texture features. The implemented method processed in four steps- In the preprocessing and segmentation, LAB color space and Delta E color difference method are applied. Later, features are extracted based on RGB, HSV and Local Binary Patterns (LBP). The extracted patterns are finally classified by Multi Support Vector Machine (SVM). Moreover, we compare the results of feature subsets of RGB and HSV color features with the addition of LBP texture features and found a classification difference of 3.6% between RGB and HSV color feature extractors. The overall results show our method outperforms as compared to existing techniques.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123760490","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: 7

Optimal Location Management Service for RSUs Placement in VANETs 基于位置管理服务的车载系统定位优化

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257632

Esraa Al-Ezaly, Ahmed Abou-Elfetouh, A. Elashry, A. Shehab

引用次数: 2

A Non-invasive Automatic Skin Cancer Detection System for Characterizing Malignant Melanoma from Seborrheic Keratosis 一种用于脂溢性角化病恶性黑色素瘤特征的非侵入性自动皮肤癌检测系统

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257712

Mai. R. Ibraheem, Mohammed M Elmogy

引用次数: 7

Statistical Analysis of Clustering Performances of NMF, Spectral Clustering, and K-means: With Gene Selection 基于基因选择的NMF、谱聚类和k均值聚类性能统计分析

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257702

Andri Mirzal

{"title":"Statistical Analysis of Clustering Performances of NMF, Spectral Clustering, and K-means: With Gene Selection","authors":"Andri Mirzal","doi":"10.1109/ICCIS49240.2020.9257702","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257702","url":null,"abstract":"The using of statistical test to determine significances of performance differences between clustering algorithms is not yet common even until recently. This is an important task because the test can determine whether one algorithm is statistically better than the other one. Moreover, using statistical test to determine significances of performance gains/losses after applying some processing steps to datasets such as feature selection is even much less common. The first task has been addressed in our other work [1], and the second task is the topic of this paper. In this study, nonnegative matrix factorization (NMF), spectral clustering, and k-means are utilized as clustering methods; LS (Laplacian Score), SPEC (SPECtral), and SPFS (Similarity Preserving Feature Selection) are utilized as feature selection mechanisms; and eleven microarray gene expression datasets are used to evaluate performances of the clustering methods. The experimental results show that in average only LS can significantly improve performances of the clustering methods statistically, SPEC seems to offer no advantage, and SPFS instead lowers clustering performances. As it is expensive to apply selection mechanisms, these results raise a question whether it is worth to utilize them for selecting genes in microarray datasets.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"20 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120813122","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: 1

Statistical Analysis of Clustering Performances of NMF, Spectral Clustering, and K-means NMF、谱聚类和k均值聚类性能的统计分析

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257641

Andri Mirzal

{"title":"Statistical Analysis of Clustering Performances of NMF, Spectral Clustering, and K-means","authors":"Andri Mirzal","doi":"10.1109/ICCIS49240.2020.9257641","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257641","url":null,"abstract":"Nonnegative matrix factorization (NMF), spectral clustering, and k-means are the most used clustering methods in machine learning research. They have been used in many domains including text, image, and cancer clustering. However, there is still a limited number of works that discuss statistical significance of performance differences between these methods. This issue is epecially important in NMF as this method is still very actively researched with a sheer number of new algorithms are published every year, and being able to demonstrate newly proposed algorithms statistically outperform previous ones is certainly desired. In this paper, we present statistical analysis of clustering performance differences between NMF, spectral clustering, and k-means. We use ten NMF algorithms, six spectral clustering algorithms, and one standard k-means algorithm for benchmark. For data, eleven publicly available microarray gene expression datasets with numbers of classes range from two to ten are used. The experimental results show that statistically performance differences between NMF algorithms and the standard k-means algorithm are not significant, and spectral methods surprisingly perform less well than NMF and k-means.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122667765","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

Deep learning for identifying and classifying retinal diseases 用于识别和分类视网膜疾病的深度学习

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257674

Mohamed Berrimi, A. Moussaoui

{"title":"Deep learning for identifying and classifying retinal diseases","authors":"Mohamed Berrimi, A. Moussaoui","doi":"10.1109/ICCIS49240.2020.9257674","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257674","url":null,"abstract":"Vision and eye health are one of the most crucial things in human life, it needs to be preserved to maintain the life of the individuals. Eye diseases such as CNV, DRUSEN, AMD, DME are mainly caused due to the damages of the retina, and since the retina is damaged and discovered at late stages, there is almost no chance to reverse vision and cure it, which means that the patient will lose the power of vision partially and maybe entirely. Optical Coherence Tomography is an advanced scanning device that can perform non-invasive cross-sectional imaging of internal structures in biological tissues by measuring their optical reflections. which will help the ophthalmologists to take a clear look on the back of the eye and determine at early stages the damage caused to the retina, macula, and optic nerve. The aim of this study is to propose a novel classification model based on deep learning and transfer learning to automatically classify the different retinal diseases using retinal images obtained from Optical Coherence Tomography (OCT) device. We propose a deep CNN architecture and compared the obtained results with pretrained models such as Inception V3 and VGG-16, our proposed CNN architecture gave an accuracy of 98.5 % and Inception V3 model gave an accuracy up to 99.27 % on the test set while VGG-16 gave only 53% we modified VGG-16 architecture by adding more convolution layers and regularization terms to obtain a result up to 93.5%.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128822196","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: 6

Predicting Life Expectancy at Birth 预测出生时的预期寿命

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257630

K. Alsalem, Alyx Steinmetz, Nayawiyyah Muhammad, Danielle Frierson, M. Nashed

引用次数: 0

DEENR vs C3R: A Comparative Analysis of Connectivity Restoration Protocols DEENR与C3R:连接恢复协议的比较分析

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257605

Mahmood ul Hassan, Shahzad Ali, K. Mahmood, Amin Al Awady, Omar Abdul Rahman Ali Ismil

{"title":"DEENR vs C3R: A Comparative Analysis of Connectivity Restoration Protocols","authors":"Mahmood ul Hassan, Shahzad Ali, K. Mahmood, Amin Al Awady, Omar Abdul Rahman Ali Ismil","doi":"10.1109/ICCIS49240.2020.9257605","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257605","url":null,"abstract":"Due to their diverse range of applications, Wireless Sensor Networks (WSNs) have been the center of attention for research since last few decades. Due to their inherent characteristics, mostly WSNs are deployed in hostile environments where human intervention is mostly infeasible. A sensor node failure is a common phenomenon in WSNs and node failure leads to partitioning of the networks resulting in loss of inter-node connectivity. Loss of connectivity can compromise the operation of the whole sensor network. Therefore, rapid recovery of partitioned network is crucial for persistence of the operation of the sensor network. In the literature, a plethora of different approaches regarding the restoration of inter-node connectivity are present. However, these approaches have not focused on the efficient use of energy, coverage-aware mechanisms, and connectivity restoration in an integrated manner. In this paper we compare two representative approaches for connectivity restoration named C3R (Coverage Conscious Connectivity Restoration) and DEENR (Distributed Energy Efficient Node Relocation Algorithm). Both of these approaches are thoroughly evaluated with respect to multiple performance metrics by using extensive simulations in NS-2. Simulation results conclude that DEENR prove to outperform C3R with respect to multiple performance metrics.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125349191","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 Career Decisions Among Graduates of Tafseer and Hadith 预测Tafseer和Hadith学院毕业生的职业选择

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257663

K. Jamal, R. Kurniawan, Ilyas Husti, Zailani, M. Nazri, J. Arifin

{"title":"Predicting Career Decisions Among Graduates of Tafseer and Hadith","authors":"K. Jamal, R. Kurniawan, Ilyas Husti, Zailani, M. Nazri, J. Arifin","doi":"10.1109/ICCIS49240.2020.9257663","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257663","url":null,"abstract":"The overall aim of the research was to identify influential factors that best predict career decisions or job choice among graduates of the Department of Tafseer and Hadith at the Universitas Islam Negeri Sultan Syarif Kasim Riau. Instead of using a longitudinal, cohort study using statistical analysis, machine learning techniques such as Decision Tree and Naïve Bayes was applied to search for unknown patterns or rules. This study compares the performance of the machine learning methods in discovering hidden patterns or factors that influenced the alumni career decisions. One of the primary concern of the university is whether their career choice after graduation are relevant or match to their field of studies. Our studies show that CGPA, cohort, additional expertise, and gender are the main factors that influenced alumni career success. We found that a cohort of graduate students was unable to find relevant professions in their field of studies. The experimental result shows that Naïve Bayes outperforms Decision Tree with the best accuracy score of 97.1 % and 92.6% subsequently. Thus, it can be concluded that the prediction model and analysis using Naïve Bayes have the potential to be used effectively. Despite its low performance, Decision Tree able to extract the main factors that influenced an alumnus career efficiently. These findings are valuable and useful both for the institution to better understand and improve the quality of its program and graduates, and also the community of machine learning in understanding the techniques behaviors with small datasets.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127650379","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

Li-Ion Battery SoH Estimation Based on the Event-Driven Sampling of Cell Voltage 基于事件驱动电池电压采样的锂离子电池SoH估计

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257629

S. Qaisar

引用次数: 0