Global journal of computer science and technology最新文献_第9页

Simulation and Design of University Area Network Scenario(UANS) using Cisco Packet Tracer 基于Cisco数据包跟踪器的大学局域网场景仿真与设计

Global journal of computer science and technology Pub Date : 2019-08-07 DOI: 10.34257/GJCSTGVOL19IS3PG7

Md. Anwar Hossain, Mahabuba Zannat

引用次数: 3

Implementation of Back Propagation Neural Network with PCA for Face Recognition 基于PCA的反向传播神经网络在人脸识别中的实现

Global journal of computer science and technology Pub Date : 2019-08-07 DOI: 10.34257/GJCSTGVOL19IS3PG21

M. Ahmed, Z. Abadin, Md Anwar Hossain, Md. Imran Hossain, Rabindra Maitree

{"title":"Implementation of Back Propagation Neural Network with PCA for Face Recognition","authors":"M. Ahmed, Z. Abadin, Md Anwar Hossain, Md. Imran Hossain, Rabindra Maitree","doi":"10.34257/GJCSTGVOL19IS3PG21","DOIUrl":"https://doi.org/10.34257/GJCSTGVOL19IS3PG21","url":null,"abstract":"Face recognition is truly one of the demanding fields of biometric image processing system. Within this paper, we have implemented Back Propagation Neural Network for face recognition using MATLAB, where feature extraction and face identification system completely depend on Principal Component Analysis (PCA). Face images are multidimensional and variable data. Hence we cannot directly apply Back Propagation Neural Network to classify face without extracting the core area of face. So, the dimensionality of face image is reduced by the Principal Component Analysis algorithm then we have to explore unique feature for all stored database images called eigenfaces of eigenvectors. These unique features or eigenvectors are given as parallel input to the Back Propagation Neural Network (BPNN) for recognition of given test images. Here test image is taken from the integrated webcam which is applied to the BPNN trained network. The maximum output of the tested network gives the index of recognized face image. BPNN employing PCA is more robust and reliable than PCA based face recognition system.","PeriodicalId":340110,"journal":{"name":"Global journal of computer science and technology","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132453813","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

Implementation and Performance Analysis of Different Hand Gesture Recognition Methods 不同手势识别方法的实现与性能分析

Global journal of computer science and technology Pub Date : 2019-07-17 DOI: 10.34257/GJCSTDVOL19IS3PG13

M. Ahmed, Md Anwar Hossain, A. Abadin

{"title":"Implementation and Performance Analysis of Different Hand Gesture Recognition Methods","authors":"M. Ahmed, Md Anwar Hossain, A. Abadin","doi":"10.34257/GJCSTDVOL19IS3PG13","DOIUrl":"https://doi.org/10.34257/GJCSTDVOL19IS3PG13","url":null,"abstract":"In recent few years, hand gesture recognition is one of the advanced grooming technologies in the era of human computer interaction and computer vision due to a wide area of application in the real world. But it is a very complicated task to recognize hand gesture easily due to gesture orientation, light condition, complex background, translation and scaling of gesture images. To remove this limitation, several research works have developed which is successfully decrease this complexity. However, the intention of this paper is proposed and compared four different hand gesture recognition system and apply some optimization technique on it which ridiculously increased the existing model accuracy and model running time. After employed the optimization tricks, the adjusted gesture recognition model accuracy was 93.21% and the run time was 224 seconds which was 2.14% and 248 seconds faster than an existing similar hand gesture recognition model. The overall achievement of this paper could be applied for smart home control, camera control, robot control, medical system, natural talk, and many other fields in computer vision and human-computer interaction.","PeriodicalId":340110,"journal":{"name":"Global journal of computer science and technology","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116944194","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

Review Paper on Various Software Testing Techniques & Strategies 各种软件测试技术与策略综述

Global journal of computer science and technology Pub Date : 2019-05-21 DOI: 10.34257/gjcstcvol19is2pg43

Nahid Anwar, Susmita Kar

引用次数: 20

Optical Character Recognition based on Template Matching 基于模板匹配的光学字符识别

Global journal of computer science and technology Pub Date : 2019-05-21 DOI: 10.34257/GJCSTCVOL19IS2PG31

Md Anwar Hossain, S. Afrin

引用次数: 9

Recognition of Handwritten Digit using Convolutional Neural Network (CNN) 基于卷积神经网络(CNN)的手写数字识别

Global journal of computer science and technology Pub Date : 2019-05-18 DOI: 10.34257/GJCSTDVOL19IS2PG27

Md Anwar Hossain, M. Ali

引用次数: 19

An Optimized Recursive General Regression Neural Network Oracle for the Prediction and Diagnosis of Diabetes 一种用于糖尿病预测和诊断的优化递归广义回归神经网络Oracle

Global journal of computer science and technology Pub Date : 2019-05-18 DOI: 10.34257/GJCSTDVOL19IS2PG1

Dana Bani-Hani, P. Patel, Tasneem Alshaikh

{"title":"An Optimized Recursive General Regression Neural Network Oracle for the Prediction and Diagnosis of Diabetes","authors":"Dana Bani-Hani, P. Patel, Tasneem Alshaikh","doi":"10.34257/GJCSTDVOL19IS2PG1","DOIUrl":"https://doi.org/10.34257/GJCSTDVOL19IS2PG1","url":null,"abstract":"Diabetes is a serious, chronic disease that has been seeing a rise in the number of cases and prevalence over the past few decades. It can lead to serious complications and can increase the overall risk of dying prematurely. Data-oriented prediction models have become effective tools that help medical decision-making and diagnoses in which the use of machine learning in medicine has increased substantially. This research introduces the Recursive General Regression Neural Network Oracle (RGRNN Oracle) and is applied on the Pima Indians Diabetes dataset for the prediction and diagnosis of diabetes. The R-GRNN Oracle (Bani-Hani, 2017) is an enhancement to the GRNN Oracle developed by Masters et al. in 1998, in which the recursive model is created of two oracles: one within the other. Several classifiers, along with the R-GRNN Oracle and the GRNN Oracle, are applied to the dataset, they are: Support Vector Machine (SVM), Multilayer Perceptron (MLP), Probabilistic Neural Network (PNN), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor (KNN), and Random Forest (RF). Genetic Algorithm (GA) was used for feature selection as well as the hyperparameter optimization of SVM and MLP, and Grid Search (GS) was used to optimize the hyperparameters of KNN and RF. The performance metrics accuracy, AUC, sensitivity, and specificity were recorded for each classifier.","PeriodicalId":340110,"journal":{"name":"Global journal of computer science and technology","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128917780","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: 9

Classification of Image using Convolutional Neural Network (CNN) 卷积神经网络(CNN)图像分类

Global journal of computer science and technology Pub Date : 2019-05-18 DOI: 10.34257/GJCSTDVOL19IS2PG13

Md Anwar Hossain, Md. Shahriar Alam Sajib

{"title":"Classification of Image using Convolutional Neural Network (CNN)","authors":"Md Anwar Hossain, Md. Shahriar Alam Sajib","doi":"10.34257/GJCSTDVOL19IS2PG13","DOIUrl":"https://doi.org/10.34257/GJCSTDVOL19IS2PG13","url":null,"abstract":"Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. We have used Convolutional Neural Networks (CNN) in automatic image classification systems. In most cases, we utilize the features from the top layer of the CNN for classification; however, those features may not contain enough useful information to predict an image correctly. In some cases, features from the lower layer carry more discriminative power than those from the top. Therefore, applying features from a specific layer only to classification seems to be a process that does not utilize learned CNN’s potential discriminant power to its full extent. Because of this property we are in need of fusion of features from multiple layers. We want to create a model with multiple layers that will be able to recognize and classify the images. We want to complete our model by using the concepts of Convolutional Neural Network and CIFAR-10 dataset. Moreover, we will show how MatConvNet can be used to implement our model with CPU training as well as less training time. The objective of our work is to learn and practically apply the concepts of Convolutional Neural Network.","PeriodicalId":340110,"journal":{"name":"Global journal of computer science and technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130428408","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: 27

Sub-sampling Approach for Unconstrained Arabic Scene Text Analysis by Implicit Segmentation based Deep Learning Classifier 基于隐式分割的深度学习分类器的无约束阿拉伯语场景文本分析子采样方法

Global journal of computer science and technology Pub Date : 2019-03-26 DOI: 10.34257/GJCSTDVOL19IS1PG7

S. Ahmed, Z. Malik, M. I. Razzak, R. Yusof

引用次数: 3

An Integration of Deep Learning and Neuroscience for Machine Consciousness 机器意识中深度学习与神经科学的整合

Global journal of computer science and technology Pub Date : 2019-03-26 DOI: 10.34257/GJCSTDVOL19IS1PG21

A. Mallakin

{"title":"An Integration of Deep Learning and Neuroscience for Machine Consciousness","authors":"A. Mallakin","doi":"10.34257/GJCSTDVOL19IS1PG21","DOIUrl":"https://doi.org/10.34257/GJCSTDVOL19IS1PG21","url":null,"abstract":"Conscious processing is a useful aspect of brain function that can be used as a model to design artificial-intelligence devices. There are still certain computational features that our conscious brains possess, and which machines currently fail to perform those. This paper discusses the necessary elements needed to make the device conscious and suggests if those implemented, the resulting machine would likely to be considered conscious. Consciousness mainly presented as a computational tool that evolved to connect the modular organization of the brain. Specialized modules of the brain process information unconsciously and what we subjectively experience as consciousness is the global availability of data, which is made possible by a nonmodular global workspace. During conscious perception, the global neuronal work space at parieto-frontal part of the brain selectively amplifies relevant pieces of information. Supported by large neurons with long axons, which makes the long-distance connectivity possible, the selected portions of information stabilized and transmitted to all other brain modules. The brain areas that have structuring ability seem to match to a specific computational problem. The global workspace maintains this information in an active state for as long as it is needed. In this paper, a broad range of theories and specific problems have been discussed, which need to be solved to make the machine conscious. Later particular implications of these hypotheses for research approach in neuroscience and machine learning are debated.","PeriodicalId":340110,"journal":{"name":"Global journal of computer science and technology","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131492427","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