2020 2nd International Conference on Computer and Information Sciences (ICCIS)最新文献_第4页

Real Time Implementation of Neuro-Fuzzy MPPT Control of PV Systems 光伏系统神经模糊MPPT控制的实时实现

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257706

A. Mansouri, F. Krim

引用次数: 0

Solving Optimization Problems using Hybrid Metaheuristics: Genetic Algorithm and Black Hole Algorithm 用混合元启发式算法求解优化问题:遗传算法和黑洞算法

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257717

Omar Sabah Mohammed, A. Sewisy, A. Taloba

{"title":"Solving Optimization Problems using Hybrid Metaheuristics: Genetic Algorithm and Black Hole Algorithm","authors":"Omar Sabah Mohammed, A. Sewisy, A. Taloba","doi":"10.1109/ICCIS49240.2020.9257717","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257717","url":null,"abstract":"During the last two decades, many optimization algorithms have been developed for solving optimization problems. These algorithms have inspired from an intelligent behaviour of a living species, or a natural phenomenon. Black Hole (BH) algorithm has been developed recently, it as a metaheuristic that is based on population imitates the black hole event in the universe, whereby circulating solution in the search space represents an individual star. Although the original BH has shown better performance on benchmark datasets, it does not possess exploration capabilities but performs a good local search. In this paper, a new hybrid metaheuristic based on the combination of BH algorithm and Genetic Algorithm (GA) is proposed. The type of the proposed hybrid algorithm is High level hybridization, when GA represent the initialization phase (Global Search), while BH represents the searching Phase (Local Search). The proposed GA-BH is examined based on several optimization problems. The results obtained showed that the proposed algorithm is better than the original BH and GA algorithm.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"61 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":"128247656","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

Selective Data Augmentation Approach for Remote Sensing Scene Classification 遥感场景分类的选择性数据增强方法

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257710

Rowida Alharbi, H. Alhichri, Y. Bazi

{"title":"Selective Data Augmentation Approach for Remote Sensing Scene Classification","authors":"Rowida Alharbi, H. Alhichri, Y. Bazi","doi":"10.1109/ICCIS49240.2020.9257710","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257710","url":null,"abstract":"Scene classification in remote sensing (RS) images is an issue that attracted a lot of researchers' attention recently. Using CNN for scene classification has been investigated in depth. One difficulty in using CNN models in remote sensing is the limited amount of data. Data augmentation techniques have been shown to provide one solution to this problem, yet, few works have investigated these techniques in their methods. In this work, our main contribution is presenting a novel method for selective data augmentation in remote sensing. The proposed selective augmentation method tries to optimize the way we augment the training set. We do that by being selective in the new scenes that we generate and add to the training set. This will help us achieve the best results with the least amount of training data added. The method selects scenes based on a “quality” criterion. To that end we investigate two criteria for evaluating the quality of new scenes namely, one based on entropy and another one known as the breaking-ties criterion. The initial results present promising capabilities of this solution for four RS scene datasets in enhancing the accuracy of classification.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"66 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":"131190501","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

Unmanned Aerial Vehicle for Human Surveillance by Using Face and Upperbody Classifiers 基于人脸和上半身分类器的人类监视无人机

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257659

M. Alharbi, E. Alghamdi, S. Alzaid, S. Qaisar

引用次数: 1

A Review of Detection Methodologies for Quick Response code Phishing Attacks 快速响应代码网络钓鱼攻击检测方法综述

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257687

S. Subairu, J. Alhassan, S. Abdulhamid, J. Ojeniyi

引用次数: 3

Evaluation of the impact of Clinical Decision Support Systems: Descriptive Analytics 评估临床决策支持系统的影响:描述性分析

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257686

Mohammed Suliman Albahly, M. Seliaman

引用次数: 4

A Quantitative Investigation of the Correlation Between Academic Program Majors and Educational Objectives: A Data-Driven Approach 学术专业与教育目标相关性的定量研究:数据驱动的方法

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257707

A. Yahya, Ibrahim Alyami

{"title":"A Quantitative Investigation of the Correlation Between Academic Program Majors and Educational Objectives: A Data-Driven Approach","authors":"A. Yahya, Ibrahim Alyami","doi":"10.1109/ICCIS49240.2020.9257707","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257707","url":null,"abstract":"In tertiary education, program education objects (PEDs) are a core component around which all program's activities revolve. This paper presents a data-driven approach to uncover an important aspect of this component that is its correlation to program majors (PMs). It does so by applying three well-known data correlation metrics, namely Pointwise Mutual Information, Correlation Coefficient, and Odds Ratio, to a dataset extracted from self-study reports of a set of Engineering programs. The collected dataset has undergone a preprocessing step to transform it into a suitable representation. This involves data cleaning, data annotation using a set of PEDs labels, and data projection to break down each multi-PEDs label data instances into a number of single PEDs data instances. The results obtained from the application of the three correlation metrics show a remarkable consistency among the three metrics in their evaluation of the correlation between PMs and PEDs. In a subsequent step, a ranking procedure of the PEDs within each PM, based on the obtained PMs-PEDs correlation strength, is applied and then a majority vote among the ranks of the three metrics is performed to obtain an overall rank of the PEDs within each PM. The obtained results show that each PM has a unique pattern of PEDs ranks, which suggests that PM nature plays a key role in determining the PM-PEDs correlation pattern. As a general conclusion, although the obtained results need further investigation on their causality correlation, the obtained quantitative correlations are very beneficial to the academicians particularly when designing new programs or reviewing existing ones.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"68 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":"132143636","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

Emotion Specific Human Face Authentication Based on Infrared Thermal Image 基于红外热图像的情感特异性人脸认证

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257683

Mohammad Alamgir Hossain, Basem Assiri

{"title":"Emotion Specific Human Face Authentication Based on Infrared Thermal Image","authors":"Mohammad Alamgir Hossain, Basem Assiri","doi":"10.1109/ICCIS49240.2020.9257683","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257683","url":null,"abstract":"Facial emotion authentication is an emergent topic during the last few decades. It depicts a human's mood and reflects his activity that he is doing, going to do and thinking to do. Activity-mapping is possible to establish by analyzing emotions' sequence from the classification of facial expressions. To identify and recognize emotion the whole face is divided into four classes and into eight regions namely forehead (left, right), eyes (left, right), lips (left, right), and chin (left, right). However, the importance is being given to the region of interest (ROI). Based on the ROI four regions are been chosen (Nose-tip, Left-eye, Right-eye and Lip). Once classification and recognition are completed a database termed as image-data-mask is maintained. The correlation between variances and standard deviations is established based on one identified image. In the process of classification and recognition, and Optimized Probability Density Function (OPDF) is proposed. The centralized database (image-data-mask) is being checked before registration of a new image into the system to avoid redundancy. Nose-tip is taken as the central-point and rest regions are being detected based on it. In this investigation, the emotions (normal, fear, and smiley) are considered and the infrared thermal images are also recorded concurrently. A calibration technique is implemented to establish a matching between vectors of face-ROI and its features. The investigational result illustrates the supremacy of the proposed method as compared to other investigators.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"205 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":"132164889","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: 5

Edge IoT-cloud Framework based on Blockchain 基于区块链的边缘物联网云框架

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257647

S. Ismail, R. Almayouf, Shaden Chehab, Shatha J. Alghamdi, Albandari Almutairi, Bshair Alasmari, Rawan Altherwy

引用次数: 3

Detection And Classification Of Apple Diseases using Convolutional Neural Networks 基于卷积神经网络的苹果病害检测与分类

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI: 10.1109/ICCIS49240.2020.9257640

Asmaa Ghazi Alharbi, Muhammad Arif

引用次数: 9