Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 968
Person Authentication using Relevance Vector Machine (RVM) for Face and Fingerprint
Статья научная
Multimodal biometric systems have proven more efficient in personal verification or identification than single biometric ones, so it is also a focus of this paper. Particularly, in the paper, the authors present a multimodal biometric system in which features from face and fingerprint images are extracted using Zernike Moment (ZM), the personal authentication is done using Relevance Vector Machine (RVM) and feature-level fusion technique. The proposed system has proven its remarkable ability to overcome the limitations of uni-modal biometric systems and to tolerate local variations in the face or fingerprint image of an individual. Also, the achieved experimental results have demonstrated that using RVM can assure a higher level of forge resistance and enables faster authentication than the state-of-the-art technique , namely the support vector machine (SVM).
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Personalized recommendation systems (PRES): a comprehensive study and research issues
Статья научная
The type of information systems used to recommend items to the users are called Recommendation systems. The concept of recommendations was seen among cavemen, ants and other creatures too. Users often rely on opinion of their peers when looking for selecting something, this usual behavior of the humans, led to the development of recommendation systems. There exist various recommender systems for various areas. The existing recommendation systems use different approaches. The applications of recommendation systems are increasing with increased use of web based search for users’ specific requirements. Recommendation techniques are employed by general purpose websites such as google and yahoo based on browsing history and other information like user’s geographical locations, interests, behavior in the web, history of purchase and the way they entered the website. Document recommendation systems recommend documents depending on the similar search done previously by other users. Clickstream data which provides information like user behavior and the path the users take are captured and given as input to document recommendation system. Movie recommendation systems and music recommendation systems are other areas in use and being researched to improve. Social recommendation is gaining the momentum because of huge volume of data generated and diverse requirements of the users. Current web usage trends are forcing companies to continuously research for best ways to provide the users with the suitable information as per the need depending on the search and preferences. This paper throws light on common strategies being followed for building recommendation systems. The study compares existing techniques and highlights the opportunities available for research in this area.
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Perspective of Database Services for Managing Large-Scale Data on the Cloud: A Comparative Study
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The influx of Big Data on the Internet has become a question for many businesses of how they can benefit from big data and how to use cloud computing to make it happen. The magnitude at which data is getting generated day by day is hard to believe and is beyond the scope of a human's capability to view and analyze it and hence there is an imperative need for data management and analytical tools to leverage this big data. Companies require a fine blend of technologies to collect, analyze, visualize, and process large volume of data. Big Data initiatives are driving urgent demand for algorithms to process data, accentuating challenges around data security with minimal impact on existing systems. In this paper, we present many existing cloud storage systems and query processing techniques to process the large scale data on the cloud. The paper also explores the challenges of big data management on the cloud and related factors that encourage the research work in this field.
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Plants Disease Segmentation using Image Processing
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The image segmentation performs a significant role in the field of image processing because of its wide range of applications in the agricultural fields to identify plants diseases by classifying the different diseases. Classification is a technique to classify the plants diseases on different morphological characteristics. Different classifiers are used to classify such as SVM (Support Vector Machine), K- nearest neighbor classifiers, Artificial Neural Networks, Fuzzy Logic, etc. This paper presents different image processing techniques used for the early detection of different Plants diseases by different authors with different techniques. The main focus of our work is on the critical analysis of different plants disease segmentation techniques. The strengths and limitations of different techniques are discussed in the comparative evaluation of current classification techniques. This study also presents several areas of future research in the domain of plants disease segmentation. Our focus is to analyze the best classification techniques and then fuse certain best techniques to overcome the flaws of different techniques, in the future.
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Pragmatic evaluation of iscrum & scrum
Статья научная
Scrum has emerged as a most adopted and most desired Agile approach that provides corporate strategic competency by laying a firm foundation for project management. Scrum, being more of a framework than a rigid methodology, offers maximum flexibility to its practitioners. However, there are several challenges confronted during its implementation for which certain researchers not only adapted, but also augmented Scrum with other Agile practices. One such effort is IScrum, an Improved Scrum process model. In this paper an empirical study has been conducted for analyzing the two models i.e. classical Agile Scrum model and IScrum process model. There are two goals of this study: first is to validate the IScrum and the second goal is to evaluate it in comparison with the traditional Scrum model. Subsequently, the study will describe and highlight which characteristics of Scrum are enhanced in IScrum. Furthermore, a survey is used to investigate the teams’ experience with both models. The results of survey and case-study have been examined and compared to find out if IScrum performs well than Scrum in software development. The outcomes advocate that the improvements were quite effective in resolving most of the problem areas. The IScrum can thus be adopted by industry practitioners as best choice.
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Predicting Student Program Completion Using Naïve Bayes Classification Algorithm
Статья научная
Data mining approaches provide different educational institutions opportunities to find hidden patterns from the data stored in the database. Many researchers have used these data to develop a model that would assist the institution administrators in decision-making. This study was performed to predict student program completion using the Naïve Bayes classifier technique. The dataset utilized in this study was obtained from Bulacan State University – Sarmiento Campus in the Philippines under BS Information Technology program from five-year graduates’ data for Academic Year 2012-2016. This dataset was pre-processed, cleansed, transformed, and balanced before constructing the model. Ten predictors were used for predicting student completion. The feature selection technique was used to filter and evaluate the significance of each factor. The significant variables assessed by the feature selection technique (Weight by Correlation) were the final parameters in creating the model. The Naïve Bayes classifier was applied to predict the students’ completion using the 70:30 ratios for training and testing dataset distribution. Correlation analysis identified the weight of individual attributes to the label attribute. From 10 possible predictor variables, only four (4) predictor variables were selected after correlation analysis. The identified significant attributes affecting program completion, namely (in order of significance): parents' monthly income, mother and father's educational attainment, and High School GPA attributes. The significant attributes identified in correlation analysis splitted into 70% training data or 447 records and 30% testing data or 191 records. There were 84 out of 191 data samples, or 44% of students were predicted to complete the program. On the other hand, 107 out of 191 data samples, or 56%, were predicted as not completing the program. The accuracy values performed an 84% rating with 80.46% class precision, and 83.33% class recall in the testing dataset (n=191). The outcomes of this study have a significant impact on HEIs, particularly on college completion rates. This study shall be highly significant and beneficial specifically to university administrators as this be a tool for them to identify students who will complete college based on variables included in the model.
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Статья научная
This study compared two neural network models (Multilayer Perceptron and Generalized Regression Neural Network) with a view to identifying the best model for predicting students’ academic performance based on single performance factor. Only academic factor (students’ results) was considered as the single performance factor of the study. One cohort of graduated students’ academic data was collected from the Computer Science and Engineering Department of Obafemi Awolowo University, Nigeria using documents and records technique. The models were simulated using MATLAB version 2015a and evaluated using mean square error, receiver operating characteristics and accuracy as the performance metrics. The results obtained show that although Multilayer Perceptron had prediction accuracy of 75%, Generalized Regression Neural Network had a better accuracy. The response time of Generalized Regression Neural Network (0.016sec) was faster than Multilayer Perceptron (0.03sec) and its memory consumption size (5kb) lower than that of Multilayer Perceptron (8kb). The simulated models were further compared with t-test method using a confidence interval of 95%. The attained t-test result from p-value (0.6854) suggests acceptance of null hypothesis, which shows that there is no significant difference between the predicted Grade Point Average and the actual Grade Point Average. The findings therefore reveal that the overall performance of Generalized Regression Neural Network outperforms the Multilayer Perceptron model with an accuracy of 95%. The study concluded that Generalized Regression Neural Network model which was simulated and with 95 % accuracy could be deployed by educationists to predict students’ academic performance using single performance factor.
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Predictive analytic game-based model for Yoruba language learning evaluation
Статья научная
Be it indigenous or foreign language, languages are core for communicating messages from one person to another or group of persons. Primarily, it is learnt at home, schools, through the media like television and radio programmes. However, most of these language-teaching approaches do not measure the percentage growth of people who have gained the knowledge of the language over the years; they also lack the capacity to foretell the range of people that will acquire the knowledge of the language in the latest future. This is because several of the language teaching aids do not have the required dataset to describe and effectively predict the state of the language (a category of people who can speak and write the language) now, and against the future. Here, the research proposed an analytic game based model for Yoruba language evaluation. The essence is first to ascertain the user’s initial knowledge of a language, train users through difference fun filled game stages and levels, evaluate the user at the end of every level and analyse the clustered dataset of users game points to describe and predict the state of the language by using a dual level predictive analytics technique.
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Статья научная
Literature show that there are limited factors for existing models in e-learning systems’ adoption. This has raised an increasing sensible debate about factors affecting successful adoption of e-leaning systems in universities in developing world particularly in Tanzania. This preliminary study aimed at exploring multiple factors for successful adoption of e-learning systems in universities in learner perspective, using DeLone and McLean (2003) IS success model as a base model. This study was conducted by collecting data randomly, using the questionnaire from students of Open Universities of Tanzania (OUT) with response rate of 0.83 in a cross-sectional study and later analyzed through content validity, reliability, and criterion-based predictive validity. The preliminary analysis shows that there are twelve distinctive factors affecting e-learning systems’ adoption in universities in Tanzania. This finding suggests more empirical research studies to follow it up, to cement and generalize this case and validate the proposed model in large scale. The novelty of this research lies on the number and uniqueness of factors found.
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Privacy in the age of Pervasive Internet and Big Data Analytics – Challenges and Opportunities
Статья научная
In the age of pervasive internet where people are communicating, networking, buying, paying bills, managing their health and finances over the internet, where sensors and machines are tracking real-time information and communicating with each other, it is but natural that big data will be generated and analyzed for the purpose of "smart business" and "personalization". Today storage is no longer a bottleneck and the benefit of analysis outweighs the cost of making user profiling omnipresent. However, this brings with it several privacy challenges – risk of privacy disclosure without consent, unsolicited advertising, unwanted exposure of sensitive information and unwarranted attention by malicious interests. We survey privacy risks associated with personalization in Web Search, Social Networking, Healthcare, Mobility, Wearable Technology and Internet of Things. The article reviews current privacy challenges, existing privacy preserving solutions and their limitations. We conclude with a discussion on future work in user controlled privacy preservation and selective personalization, particularly in the domain of search engines.
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Procedure for Processing Biometric Parameters Based on Wavelet Transformations
Статья научная
The problem of the article is related to the improvement of means of covert monitoring of the face and emotions of operators of information and control systems on the basis of biometric parameters that correlate with two-dimensional monochrome and color images. The difficulty in developing such tools has been shown to be largely due to the cleaning of images associated with biometric parameters from typical non-stationary interference caused by uneven lighting and foreign objects that interfere with video recording. The possibility of overcoming these difficulties by using wavelet transform technology, which is used to filter images by combining several identical, but differently noisy monochrome and color images, is substantiated. It is determined that the development of technology for the use of wavelet transforms is primarily associated with the choice of the type of basic wavelet, the parameters of which must be adapted to the conditions of use in a particular system of covert monitoring of personality and emotions. An approach to choosing the type of basic wavelet that is most effective in filtering images from non-stationary interference is proposed. The approach is based on a number of the proposed provisions and efficiency criteria that allow to ensure when choosing the type of basic wavelet taking into account the significant requirements of the task. A filtering procedure has been developed, which, due to the application of the specified video image filtering technology and the proposed approach to the choice of the basic wavelet type, allows to effectively clean the images associated with biometric parameters from typical non-stationary interference. The conducted experimental studies have shown the feasibility of using the developed procedure for filtering images of the face and iris of operators of information and control systems.
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Professional Courses for Computer Engineering Education
Статья научная
A sequence of professional courses of study in Computer Engineering at the authors’ university was initiated. These included Digital Fundamentals, Programmable Logic Design, Computer Hardware and Digital Systems Design. This paper presents a study on how the problem based learning has been used for these courses. It also describes how CDIO (Conceive, Design, Implement, Operate) concepts have been applied with an overview of all the hardware resources necessary to support the degree.
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Статья научная
There were a lot of methods that introduced in the information search field; one of those methods is the wireless sensor networks; and one of the most famous protocols in WSNs is LEACH protocol. And because of that protocol suffering from some defects like sometimes the node attaching to C.H. near from it, but that C.H. far from the B.S. even the node itself near to the B.S. than its C.H.; to solve that problem a new method will introduce in this research which basing on: Allocation of 5 meters (0-5) and prevent the election of any C.H. on it. Division of the Network area into four parts (near, mid, far, and very far) according to the node`s distance from B.S. Restriction of the attachment between the nodes and the C.Hs. in the same part. If a particular part is empty from the C.H. so the nodes will attach to C.H. from the upper parts, But with a condition (the distance between the C.H. and the node <= the distance between node and B.S. /2) Through these improvements, good results were gotten in the simulation, which showed that the improved LEACH was more efficient than the original LEACH.
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Proposal for a mutual conversion relational database-ontology approach
Статья научная
Whereas ontologies are formal knowledge representations, conveying a shared understanding of a given domain, databases are a mature technology that describes specifications for the storage, retrieval, organization, and processing of data in information systems to ensure data integrity. Ontologies offer the functionality of conceptual modeling while complying with the web constraints regarding publication, querying and annotation, as well as the capacity of formality and reasoning to enable data consistency and checking. Ontologies converted to databases could exploit the maturity of database technologies, and databases converted to ontologies could utilize ontology technologies to be more used in the context of the semantic web. This work aims to propose a generic approach that enables converting a relational database into an ontology and vice versa. A tool based on this approach has been implemented as a proof of a concept.
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Статья научная
The work in this article focuses on the modelling of an intelligent digital ecosystem for educational and career guidance for students and young people seeking their first job or retraining. To do so, the multi-expert system paradigm was used to aggregate the different expertises required for a good guidance, the multi-agent system principle was used to have a modular and easily scalable ecosystem. Indeed, the agents of the system communicate with each other using the FIPA-ACL language, in a collaborative vision, throughout the orientation assistance process to perform tasks such as proposing business sectors, occupations, training, and training paths. The ontologies of the Semantic Web have been used to have a complete semantic description of the shared information and to promote communication between the different software agents of the ecosystem. Big Data principles have also been deployed to manage and exploit structured and unstructured data from different data sources related to the guidance ecosystem. The ecosystem modeled in this way has several innovative and powerful technological and scientific aspects. Thus, in terms of design and modelling, the proposed ecosystem considers all the actors and factors involved in the guidance process, including labor market trends. In technological/scientific terms, it is based on methods that allow it to be modular and scalable.
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Proposal to Teach Software Development Using Gaming Technique
Статья научная
The world today has witnessed the evolution imposes on researchers in the field of education to review the methods and strategies of teaching, since the teaching and learning system is not a collection of information and knowledge that stuffed in mind. It is a development of cognitive performance and modes of thinking in addition to the use of innovative ways and methods to help the student to adapt to its environment and to solve the problems that he/ she faces to make learning meaningful. One of the recent trends is the use of educational teaching games. Games increase the motivation of the learner and ensure the interaction with educational material which, in turn, offers fun and enjoyable manner in order to achieve the desired objectives. This paper attempts to address the need to utilize gaming to improve learning in active ways and to raise level of the learning process in an interactive environment for the students and the teachers. To evaluate the proposed solution, this paper used survey research methodology and the results are highly encouraging by the professionals working in academia.
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Proposed Automated Framework to Select Suitable Design Pattern
Статья научная
Many design patterns are available in the existing literature. Due to the availability of the enormous quantity of design patterns, it is extremely hard for a developer to find the suitable design pattern to address the problem. An experienced developer can even face problem to select the appropriate pattern for a specific problem and it is no man's land for junior developers. This paper proposes a novel framework that will generate problem-related questions to a developer to find suitable design pattern using a repository. The answers to these questions can guide developers to select the suitable design patterns. This paper uses the questionnaire as a data collection instrument to conclude the results. The results are found supportive indicating that the proposed framework will solve the problem in hand.
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Proposed framework to manage software requirements and reuse
Статья научная
Requirement elicitation and analysis form the focal point in the initial stages of the software development process. Unfortunately, in many software development projects, developers and end-users speak different languages. On one hand, end-users prefer to use natural languages while software developers who are technically perceptive, tend to use conceptual models. This difference in technical knowledge creates a communication gap, a potential cause of poor quality software products or project conflicts. The aim of this paper is to investigate the feasibility of a novel technique that seeks to foster effective elicitation of software requirements and support the implementation of structures that match particular requirements. By combining requirement elicitation and re-usable parts, the proposed solution envisages improvements in the overall software design process leading to enhanced requirement specifications. The novel idea is to incorporate an intermediate step for mapping Unified Modeling Language (UML) to Web Ontology Language (OWL) to enable the addition of ontology languages. The proposed model is validated through a survey. The validation results show that the proposed solution allows software developers to elicit software requirements and implement structures that match certain requirements.
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Provisioning Remote Lab Support for IT Programs in Distance Education
Статья научная
In the recent past Internet has become the de-facto communication network. It is being prominently used by Telecommunication, Television and other such networks as a carrier network. The current Internet technology has matured enough to support both Non-Real Time and Real-Time Streaming applications. Recently, even the speed of the access network through which an end user accesses the Internet has also increased substantially. All these have given way to newer Applications for being ported on to the Internet. A similar attempt has been made here to extend the Networking Lab infrastructure to students who have enrolled for their higher education through Distance mode. These students who are spread across the country are able to access the Network Lab to perform their Lab Exercises Live on Network devices as part of their Practical Course.
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Proximity Measurement Technique for Gene Expression Data
Статья научная
Data Mining is an analytical process intended to explore the data in search of consistent patterns. Due to its wide spread applications in biomedical industry and publicly available genomic data, data mining has become upcoming topic in the analysis of gene expression data. Clustering is the first step in understanding the complicated biological systems. The objective of clustering is to organize the samples into intrinsic clusters such that samples with high similarity belong to same cluster. The significance of clustering gene profiles is two-fold. Firstly, it assists in diagnosis of the disease condition and secondly it discloses the effect of certain treatment on genes. In this paper, we propose a new method to cluster gene expression data that is solely based on the concept of hierarchical clustering with a different method to compute the similarity between datasets and merge the pairs. The experimental results on two microarray data show the correctness and competence of proposed technique.
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