Статьи журнала - International Journal of Information Engineering and Electronic Business

Все статьи: 530

A Blockchain based Framework for Property Registration System in E-Governance

A Blockchain based Framework for Property Registration System in E-Governance

Siddhartha Sen, Sripati Mukhopadhyay, Sunil Karforma

Статья научная

In recent years, the most cutting edges and promising technology emerged is Blockchain. It has huge potential to impact various industries. The append-only distributed ledger technology (DLT) and the consensus mechanism of Blockchain can also change the dimension of E-Governance. The Electronic Property Record (EPR) systems of government have challenges like data security, integrity, secure storage of data and automated service delivery. In this paper, we discuss how Smart Contract based Blockchain technology can effectively be used to address the challenges of EPR System over the existing available systems. We propose a Smart Contract based permissioned blockchain framework which is an innovative approach, especially in the Electronic Property Registration domain of E-Governance in India. The objective of our proposed framework is firstly to implement Smart Contract solving the security problems like confidentiality, integrity, authentication, and secondly to ensure secure storage of electronic records by defining access rules for the stakeholders of the proposed framework. Moreover, we address the issues of single-point-of-failure, data inter-operability between the organizations involved for sharing and verification of property information among various stakeholders.

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A Brief Literature Review of some Efficient Human Gait Analysis Based Gender Classification Techniques

A Brief Literature Review of some Efficient Human Gait Analysis Based Gender Classification Techniques

Satyam Rawat

Статья научная

Gait based gender classification is an emerging area in the field of biometrics that has received a lot of interest from researchers mainly due to its advantages over the other methods and its potential application. Gait based gender classification helps a vision based biometric analysis system by focusing the gender-unique features. This helps to improves the performance of the model by limiting the authentication database searching to only one gender. Through the years, researchers have tried a wide variety of techniques and their combinations to improve the accuracy of gait based biometric systems in varying use-cases. In this study, we have given a brief overview of some of the recent and pioneering works done in the field of gait-based gender classification.

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A Case of Mobile App Reviews as a Crowdsource

A Case of Mobile App Reviews as a Crowdsource

Mubasher Khalid, Usman Shehzaib, Muhammad Asif

Статья научная

Crowdsourcing is a famous technique to get innovative ideas and soliciting contribution from a large online community particularly in e-business. This technique is contributing towards changing the current business techniques and practices. It is also equally famous in analysis and design of m-business services. Mobile app stores are providing an opportunity for its users' to participate and contribute in the growth of mobile app market. App reviews given by users usually contain active, heterogeneous and real life user experience of mobile app which can be useful to improve the quality of app. Best to our knowledge, the strength of mobile app reviews as a crowdsource is not fully recognized and understood by the community yet. In this paper, we have analysed a crowdsourcing reference model to find out which features of crowdsource are present and are related to our case of mobile app reviews as a crowdsource. We have analyzed and discussed each construct of the reference model from the perspective of mobile app reviews. Moreover, app reviews as a crowdsourcing technique is discussed by utilizing the four pillars of the reference model: the crowd, the crowdsourcer, the crowdsourcing, and the crowdsourcing platform. We have also identified and partially validated certain constructs of the model and highlighted the significance of app reviews as a crowdsource based on existing literature. In this study, only one crowdsourcing reference model is used which can be a limitation of our study. The study can be further investigated and compared with other crowdsourcing reference models to get better insights of app reviews as a crowdsource. We believe that the understanding of app reviews as a crowdsourcing technique can lead to the further development of the mobile app market and can open further research opportunities.

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A Comparative Study of ANN and GEP Model to Predict the Pressure Drop in the Water Transportation System

A Comparative Study of ANN and GEP Model to Predict the Pressure Drop in the Water Transportation System

Rajesh Chakraborty, Uttam Kumar Mandal, Rabindra Nath Barman

Статья научная

In the present study, the parameter responsible to find out pressure drops in a pipeline network system has been modeled by Gene Expression Programming Based on the experimental data. The different factors like Pipe diameter, Particle diameter, liquid density, Solid density liquid Viscosity, Volume fraction, Velocity, Solid concentration are taken into consideration as the input parameter. GEP model was developed to predict the pressure drop within the pipeline system. GEP model predicts the pressure drop with an accuracy of mean R-Square 0.999153373.As the input parameter is responsible for the selection of soft computing method and both ANN and GEP model is considered in order to validate the output parameters. The result of GEP has been compared with an ANN model, to observe the level of accuracy of the predicted pressure drop with a correlation to predict pressure drop shown by equation 6. The obtained results of both GEP and ANN models are being compared and GEP predicted results are found to be better in predicting the output parameter. The mean absolute error is found to be 15.566 % by the ANN model wherein the GEP model predicts with an accuracy of 8.993 %.The results indicate that the GEP is better tool to predict pressure drop with more accuracy.

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A Comparative Study of Arabic Text-to-Speech Synthesis Systems

A Comparative Study of Arabic Text-to-Speech Synthesis Systems

Najwa K. Bakhsh, Saleh Alshomrani, Imtiaz Khan

Статья научная

Text-to-speech synthesis is the process of converting written text to speech. The lack of research on the growth of and the need for the Arabic language is notable. Therefore, this paper reports an empirical study that systematically compares two screen readers, namely, NonVisual Desktop Access (NVDA) and IBSAR. We measured the quality of these two systems in terms of standard pronunciation and intelligibility tests with visually impaired or blind people. The results revealed that NVDA outperformed IBSAR on the pronunciation tests. However, both systems gave competitive performance on the intelligibility tests.

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A Comparative Study of Eigenface and Fisherface Algorithms Based on OpenCV and Sci-kit Libraries Implementations

A Comparative Study of Eigenface and Fisherface Algorithms Based on OpenCV and Sci-kit Libraries Implementations

Ismail Aliyu, Muhammad Ali Bomoi, Maryam Maishanu

Статья научная

Facial Recognition is the task of processing an image or video content in order to identify and recognize the faces of individuals. Its area of applications are wide and a lot of research efforts have been invested which led to introduction of techniques/algorithms and programming language libraries for implementation of those techniques. Facial recognition relies heavily on the use of machine learning techniques. Convolutional Neural Network (CNN), a deep learning algorithm has been successfully applied for face recognition task. However, because of its requirements, it may not be applicable in all cases. Where application scenario cannot cope with CNN, it is necessary to resort to other techniques that use traditional Machine Learning (ML) techniques. Previous studies that performed comparison on face recognition algorithms that use traditional ML techniques only disclosed the best algorithm without revealing the best image processing library used. Considering the fact that people now depend on these libraries to build face recognition systems, it is important to empirically show the best library. In this paper an experiment was conducted with aim of assessing the performance of Fisherface and Eigenface algorithms, and that of Scikit-learn and OpenCV libraries. Eigenface and Fisherface algorithms were combined with K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) classifiers respectively. The algorithms were evaluated using LFW dataset, and implemented in two Python libraries for image processing Scikit-learn and OpenCV. This is to enable us determine the best performing technique/algorithm and at the same time the best library, thereby achieving dual aims. Experimental results show that Scikit-learn implementation of Fisherface with KNN recorded the highest F-score of 67.23% while the OpenCV implementation of Eigenface with SVM recorded the lowest F-score of 14.53%. Comparing the algorithms, Fisherface with SVM produced better results than Eigenface with SVM. The same story holds for Fisherface with KNN, and Eigenface with KNN. This suggests that irrespective of classifier, Fisherface outperform Eigenface in terms of accuracy of recognition. Comparing the libraries, Scikit-learn implementations of Fisherface with SVM and Eigenface with SVM, outperform the OpenCV implementation of the same algorithms. This means scikit-learn implementation produces better results than its counterpart, the OpenCV.

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A Comparative Study of Flat and Hierarchical Classification for Amharic News Text Using SVM

A Comparative Study of Flat and Hierarchical Classification for Amharic News Text Using SVM

Alemu Kumilachew Tegegnie, Adane Nega Tarekegn, Tamir Anteneh Alemu

Статья научная

The advancement of the present day technology enables the production of huge amount of information. Retrieving useful information out of these huge collections necessitates proper organization and structuring. Automatic text classification is an inevitable solution in this regard. However, the present approach focuses on the flat classification, where each topic is treated as a separate class, which is inadequate in text classification where there are a large number of classes and a huge number of relevant features needed to distinguish between them. This paper aimed to explore the use of hierarchical structure for classifying a large, heterogeneous collection of Amharic News Text. The approach utilizes the hierarchical topic structure to decompose the classification task into a set of simpler problems, one at each node in the classification tree. An experiment had been conducted using a categorical data collected from Ethiopian News Agency (ENA) using SVM to see the performances of the hierarchical classifiers on Amharic News Text. The findings of the experiment show the accuracy of flat classification decreases as the number of classes and documents (features) increases. Moreover, the accuracy of the flat classifier decreases at an increasing number of top feature set. The peak accuracy of the flat classifier was 68.84 % when the top 3 features were used. The findings of the experiment done using hierarchical classification show an increasing performance of the classifiers as we move down the hierarchy. The maximum accuracy achieved was 90.37% at level-3(last level) of the category tree. Moreover, the accuracy of the hierarchical classifiers increases at an increasing number of top feature set compared to the flat classifier. The peak accuracy was 89.06% using level three classifier when the top 15 features were used. Furthermore, the performance between flat classifier and hierarchical classifiers are compared using the same test data. Thus, it shows that use of the hierarchical structure during classification has resulted in a significant improvement of 29.42 % in exact match precision when compared with a flat classifier.

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A Comparison of Opinion Mining Algorithms by Using Product Review Data

A Comparison of Opinion Mining Algorithms by Using Product Review Data

Sumaiya Sultana, Sumaiya Rahman Eva, Nayeem Hasan Moon, Akinul Islam Jony, Dip Nandi

Статья научная

After release of Web 2.0 in 2004 user spawned contents on the internet eminently in abundant review sites, online forums, online blogs, and many other sites. Entire user generated contents are considerable bunches of unorganized text written in different languages that encompass user emotions about one or more entities. Mainly predictive analysis exerts the existing data to forecast future outcomes. Currently, a massive amount of researches are being engrossed in the area of opinion mining, also called sentiment analysis, opinion extraction, review analysis, subjective analysis, emotion analysis, and mood extraction. It can be an utmost choice whilst perceiving the meaning and patterns in prevailing data. Most of the time, there are various algorithms available to work with polling. There are contradictory opinions among researchers regarding the effectiveness of algorithms. We have compared different opinion mining algorithms and presented the findings in this paper.

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A Content Assist based Approach for Providing Rationale of Method Change for Object Oriented Programming

A Content Assist based Approach for Providing Rationale of Method Change for Object Oriented Programming

Amit S. Ami, Shariful Islam

Статья научная

Software engineering requires modification of code during development and maintenance phase. During modification, a difficult task is to understand rationale of changed code. Present Integrated Development Environments (IDEs) attempt to help this by providing features integrated with different types of repositories. However, these features still consume developer's time as he has to switch from editor to another window for this purpose. Moreover, these features focus on elements available in present version of code, thus increasing the difficulty of finding rationale of an element removed or modified earlier. Leveraging different sources for providing information through code completion menus has been shown to be valuable, even when compared to standalone counterparts offering similar functionalities in literature. Literature also shows that it is one of the most used features for consuming information within IDE. Based on that, we prepare an Eclipse plug-in and a framework that allows providing reason of code change, at method granularity, across versions through a new code completion menu in IDE. These allow a software engineer to gain insight about rationale of removed or modified methods which are otherwise not available in present version of code. Professional software engineers participated in our empirical evaluation process and we observed that more than 80% participants considered this to be a useful approach for saving time and effort to understand rationale of method change. Later, based on their feedback, the plug-in and framework is modified to incorporate chronological factors. We perform quasi experimental evaluation with professional software engineers. It is found that time required to find rationale of method change is reduced to at least half compared to usual amount of time required for all the software engineers who participated in the quantitative evaluation.

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A Corpus Based Approach to Build Arabic Sentiment Lexicon

A Corpus Based Approach to Build Arabic Sentiment Lexicon

Afnan Atiah Alsolamy, Muazzam Ahmed Siddiqui, Imtiaz Hussain Khan

Статья научная

Sentiment analysis is an application of artificial intelligence that determines the sentiment associated sentiment with a piece of text. It provides an easy alternative to a brand or company to receive customers' opinions about its products through user generated contents such as social media posts. Training a machine learning model for sentiment analysis requires the availability of resources such as labeled corpora and sentiment lexicons. While such resources are easily available for English, it is hard to find them for other languages such as Arabic. The aim of this research is to build an Arabic sentiment lexicon using a corpus-based approach. Sentiment scores were propagated from a small, manually labeled, seed list to other terms in a term co-occurrence graph. To achieve this, we proposed a graph propagation algorithm and compared different similarity measures. The lexicon was evaluated using a manually annotated list of terms. The use of similarity measures depends on the fact that the words that are appearing in the same context will have similar polarity. The main contribution of the work comes from the empirical evaluation of different similarity to assign the best sentiment scores to terms in the co-occurrence graph.

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A Data-Fusion-Based Method for Intrusion Detection System in Networks

A Data-Fusion-Based Method for Intrusion Detection System in Networks

Xiaofeng Zhao, Hua Jiang, LiYan Jiao

Статья научная

Hackers’ attacks are more and more intelligent, which makes it hard for single intrusion detection methods to attain favorable detection result. Therefore, many researches have carried out how to combine multiple security measures to provide the network system more effective protection. However, so far none of those methods can achieve the requirement of the practical application. A new computer information security protection system based on data fusion theory is proposed in this paper. Multiple detection measures are “fused” in this system, so that it has lower false negatives rate and false positive rate as well as better scalabilities and robust.

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A Dependency Graph Generation Process for Client-side Web Applications

A Dependency Graph Generation Process for Client-side Web Applications

Tajkia R. Toma, Mohayeminul Islam, Mohammad Shoyaib, Shariful Islam

Статья научная

The prolific growth of the Internet density has replaced native applications with web based applications. Current trend of web applications is moving towards fat client architecture, which results in a large codebase of the client side of web applications. Manual management of this huge code is tedious and time consuming for de-velopers. We present a technique to construct a depend-ency graph to provide an overview of the code showing the inter-dependency of the code elements. We conduct a dynamic analysis to make the JavaScript call graph to address the dynamic nature of JavaScript. We further integrate HTML and CSS with the JavaScript call graph to make a dependency graph. Because we can accurately identify the HTML and CSS relations, the result of the dependency graph depends on the JavaScript call graph. Our evaluation of the JavaScript call graph on six web applications demonstrates that the precision is high for the large applications and relatively low for small applications. The recall is low for large applications and relatively higher for small applications.

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A Design High Impact Lyapunov Fuzzy PD-Plus-Gravity Controller with Application to Rigid Manipulator

A Design High Impact Lyapunov Fuzzy PD-Plus-Gravity Controller with Application to Rigid Manipulator

Farzin Piltan, Mohammad Javad Rafaati, Fatima Khazaeni, Ali Hosainpour, Samira Soltani

Статья научная

The control problem for manipulators is to determine the joint inputs required to case the end-effector execute the commanded motion. The nonminimum phase characteristic of a rigid manipulator makes the design of stable controller that ensure stringent tracking requirements a highly nontrivial and challenging problem. A useful controller in the computed torque family is the PD-plus-gravity controller. Methodology. To compensate the dynamic parameters, fuzzy logic methodology is used and applied parallel to this method. when the arm is at rest, the only nonzero terms in the dynamic is the gravity. Proposed method can cancels the effects of the terms of gravity. In this case inorder to decrease the error and satteling time, higher gain controller is design and applied to nonlinear system.

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A Domain Knowledge Based Approach for Medical Image Retrieval

A Domain Knowledge Based Approach for Medical Image Retrieval

Haiwei Pan, Xiaolei Tan, Qilong Han, Guisheng Yin

Статья научная

The high incidence of brain disease, especially brain tumor, has increased significantly in recent years. It is becoming more and more concernful to discover knowledge through mining medical brain image to aid doctors’ diagnosis. Image mining is the important branch of data mining. It is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Image clustering and similarity retrieval are two basilic parts of image mining. In this paper, we introduce a notion of image sequence similarity patterns (ISSP) for medical image database. ISSP refer to the longest similar and continuous sub-patterns hidden in two objects each of which contains an image sequence. These patterns are significant in medical images because the similarity for two medical images is not important, but rather, it is the similarity of objects each of which has an image sequence that is meaningful. We design the new algorithms with the guidance of the domain knowledge to discover the possible Space-Occupying Lesion (PSO) in brain images and ISSP for similarity retrieval. Our experiments demonstrate that the results of similarity retrieval are meaningful and interesting to medical doctors.

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A Frame Work for Classification of Multi Class Medical Data based on Deep Learning and Naive Bayes Classification Model

A Frame Work for Classification of Multi Class Medical Data based on Deep Learning and Naive Bayes Classification Model

N. Ramesh, G. Lavanya Devi, K. Srinivasa Rao

Статья научная

From the past decade there has been drastic development and deployment of digital data stored in electronic health record (EHR). Initially, it is designed for getting patient general information and performing health care tasks like billing, but researchers focused on secondary and most important use of these data for various clinical applications. In this paper we used deep learning based clinical note multi-label multi class approach using GloVe model for feature extraction from text notes, Auto-Encoder for training based on model and Navie basian classification and we map those classes for multi- classes. And we perform experiments with python and we used libraries of keras, tensor flow, numpy, matplotlib and we use MIMIC-III data set. And we made comparison with existing works CNN, skip-gram, n-gram and bag-of words. The performance results shows that proposed frame work performed good while classifying the text notes.

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A Framework for Development of Recommender System for Financial Data Analysis

A Framework for Development of Recommender System for Financial Data Analysis

Pradeep Kumar M. Kanaujia, Manjusha Pandey, Siddharth Swarup Rautaray

Статья научная

The huge amount of data is being created every day by various organisations and users all over the world. Structured, semi-structured and unstructured data is being created at a very rapid speed from heterogeneous sources like reviews, ratings, feedbacks, shopping details, etc., it is termed as Big Data. This data generated from different users share many common patterns which can be filtered and analysed to give some recommendation regarding the product, goods or services in which a user is interested. Recommendation systems are the software tools used to give suggestions to users on the basis of their requirements. Today no system is available for suggesting a person on how to use their money for saving, where to invest and how to manage expenditures. Few consulting systems are available which provide investment and saving tips but they are not much effective and are much complex. The presented paper proposed a collaborative filtering based recommender system for financial analysis based on Saving, Expenditure and Investment using Apache Hadoop and Apache Mahout. Many savings and investment consulting systems are available but no system provides effective and efficient recommendation regarding management and beneficial utilisation of salary. The advantage of proposed recommender system is that it provides better suggestion to a person for saving, expenditure and investment of their salary which in turns maximises their wealth. Due to enormous amount of data involved, Apache Hadoop framework is used for distributed processing. Collaborative filtering and Apache Mahout is used for analysing the data and implementation of the recommender system.

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A Framework for an E-government Based on Service Oriented Architecture for Jordan

A Framework for an E-government Based on Service Oriented Architecture for Jordan

Zakaria I. Saleh, Rand A. Obeidat, Yaser Khamayseh

Статья научная

E-government is growing to a size that requires full attention from governments and demands collaboration and facilitation between private sectors and Non-Government Organizations (NGOs). In order to reach successful e-government applications, governments have to provide services to citizens, businesses and government agencies. In Jordan, e-government applications are limited to an informative goal; they essentially offer information and no services. Moreover, it is found that the traditional peer-to-peer integration of applications will result in a tightly coupled system that reduces the agility and expansibility of the E-Government system. This paper proposes a novel integration mechanism based on the web service of the Service Oriented Architecture (SOA) for the various E-government systems. We propose a stage model for E-Government interoperability based on Service Oriented Architecture (SOA). In addition, a framework for E-government based on SOA is proposed. The proposed architectures are being examined using case study in the context of implementing environmental license web service in the Jordanian ministry of environment.

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A Genetic Approach Based Solution for Seat Allocation during Counseling for Engineering Courses

A Genetic Approach Based Solution for Seat Allocation during Counseling for Engineering Courses

Ashwani Chandel, Manu Sood

Статья научная

Genetic Algorithm (GA) is one of the most popular optimization solutions for scheduling problems and has already been used to implement variety of applications. In this paper, we describe a heavily constrained seat allocation problem experienced during counseling for seat allocation in college/universities based upon the merit of students computed on the basis of an entrance test. Manual process of allocating seats is not just inconvenient but proves expensive in terms of time and money. The application of GA involves using selection, crossover or mutation operators applied to populations of chromosomes. We propose a powerful technique using genetic algorithm (GA) in scheduling as a potential solution to the seat allocation process which has been supported with the help of an illustrative example.

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A Hybrid Data Mining Technique for Improving the Classification Accuracy of Microarray Data Set

A Hybrid Data Mining Technique for Improving the Classification Accuracy of Microarray Data Set

Sujata Dash, Bichitrananda Patra, B.K. Tripathy

Статья научная

A major challenge in biomedical studies in recent years has been the classification of gene expression profiles into categories, such as cases and controls. This is done by first training a classifier by using a labeled training set containing labeled samples from the two populations, and then using that classifier to predict the labels of new samples. Such predictions have recently been shown to improve the diagnosis and treatment selection practices for several diseases. This procedure is complicated, however, by the high dimensionality of the data. While microarrays can measure the levels of thousands of genes per sample, case-control microarray studies usually involve no more than several dozen samples. Standard classifiers do not work well in these situations where the number of features (gene expression levels measured in these microarrays) far exceeds the number of samples. Selecting only the features that are most relevant for discriminating between the two categories can help construct better classifiers, in terms of both accuracy and efficiency. This paper provides a comparison between dimension reduction technique, namely Partial Least Squares (PLS)method and a hybrid feature selection scheme, and evaluates the relative performance of four different supervised classification procedures such as Radial Basis Function Network (RBFN), Multilayer Perceptron Network (MLP), Support Vector Machine using Polynomial kernel function(Polynomial- SVM) and Support Vector Machine using RBF kernel function (RBF-SVM) incorporating those methods. Experimental results show that the Partial Least-Squares(PLS) regression method is an appropriate feature selection method and a combined use of different classification and feature selection approaches makes it possible to construct high performance classification models for microarray data.

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A Knowledge-based PSEE with the Ability of Project Monitoring

A Knowledge-based PSEE with the Ability of Project Monitoring

Shih-Chien Chou, Chiao-Wei Li

Статья научная

Process-centered software engineering environments (PSEEs) facilitate managing software projects. According to the change of enactment environments and the increment of software development complexity, PSEE features should be enhanced. We designed a knowledge-based PSEE named KPSEE. It offers the features: (1) maximizing the degree of process parallelism, (2) enhancing process flexibility, (3) managing product consistency, (4) integrating PSEEs, (5) keeping pace with significant process change, (6) preventing technique leakage, and (7) offering project monitoring ability.

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