Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 968
SXP: Simplified Extreme Programing Process Model
Статья научная
Extreme programming is one of the widely used agile models in the software industry. It can handle unclear and changing requirements with the good level of customer satisfaction. However Lack of documentation, poor architectural structure and less focus on design are its major drawbacks that affects its performance. Due to these problems it cannot be used for all kinds of projects. It is considered suitable for small and low risk projects. It also has some controversial practices that cannot be applied in each and every situation like pair programming and on-site customer. To overcome these limitations a modified version of XP called “Simplified Extreme Programming” is proposed in this paper. This model provides solution of these problems without affecting simplicity and agility of extreme programming.
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Scheduling of Generating Unit Commitment by Quantum-Inspired Evolutionary Algorithm
Статья научная
An Quantum-Inspired Evolutionary Algorithm (QEA) is presented for solving the unit commitment problem. The proposed method has been used to achieve the schedule of system units by considering optimal economic dispatch. The QEA method based on the quantum concepts such as Q-bit, present a better population diversity compared with previous evolutionary approaches, and uses quantum gates to achieve better solutions. The proposed method has been tested on a system with 10 generating units, and the results shows the effectiveness of algorithm compared with Other previous references. Furthermore, it can be used to solve the large-scale generating unit commitment problem.
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Scrum with the spices of agile family: a systematic mapping
Статья научная
Agile mania has revolutionized the software industry. Scrum, being a widely adopted mainstream production process, has dominated other Agile family members. Both industrial and academic researchers eagerly tailored and adapted the Scrum framework in quest of software process improvement. Their burning desire for innovation drive them to integrate other software development models with it to leverage the forte of all the models combined and stifle the weaknesses. This paper aims at providing state-of-the-art insightful understanding of how practices from different Agile process models have been plugged into the Scrum framework to bring about improvements in different extents of development that ensued enhanced productivity, and product quality. To gain the in-depth perception, a systematic mapping study has been planned. This study will identify researches on hybrid models of Scrum within agile family, published between 2011 and 2017. Subsequently, these hybrid models of Scrum will be examined broadly by classifying and thematically analyzing the literature, and outcomes will be presented. This study will contribute a latest coarse-grained overview that in turn may guide researchers for future research endeavors.
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Secure and Dynamic Model for Book Searching on Cloud Computing as Mobile Augmented Reality
Статья научная
Availability of internet on different devices like smart phones like android based, IOs based, windows based and PDA etc. has brought into the evolution of mobile cloud computing, which is a vast side of research nowadays. Internet connectivity has become very easy with the evolution of Wi-Fi, everyone can access the internet using wireless connectivity. A major issue in wireless connectivity is the low level of encryption and low security. This might be a security risk for the sensitive data available on the cloud. There are mobile augmented reality systems based on cloud computing, we want to propose a dynamic framework for the security of cloud and live update data on cloud.
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Security Improvement of Object Oriented Design using Refactoring Rules
Статья научная
The main component of study is to confirm that how developed security model are helpful for security improvement of object oriented designs. Software refactoring is an essential activity during development and maintenance. It promotes the reengineering measures for improving quality and security of software. The researcher made an effort in this regard to develop security improvement guideline using refactoring activities for object oriented deign. The developed guidelines are helpful to control design complexity for improved security. A case study is adopted from refactoring example by fowler to implement the Security Improvement Guidelines (SIG). The developed Security Quantification Model (SQMOODC) is being used to calculate the quantified value of security at each step. The proposed model SQMOODC calculates the effective security index by ensuring that revised version of object oriented design is being influenced through security improvement guidelines. There is some possibility that original code segment may have some security flaws, anomalies and exploitable entities or vulnerable information that may influence security at design stage. SIG is helpful to cease the security flaws, anomalies, exploitable entities into refactored code segment. Each refactored steps of case study match the prediction of the impact for refactoring rules on security and the impact study for security through SQMOODC model legalize the effectiveness of developed model and security improvement guidelines. The validated results of statistical analysis with different case studies of object oriented designs reflect the usefulness and acceptability of developed models and guidelines.
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Selecting qualitative features of driver behavior via pareto analysis
Статья научная
Driver behavior is the main cause of road crashes; it is the key element that insures a better understanding and improves predictions of car accidents. The main goal of our study is to determine the set of driver behavior features that are the most encountered in literature; we were based on behavioral questionnaires as a source for these features. We selected the questionnaires that are most cited in literature and therefore proved their efficiency through many studies they were employed in. Then we extracted the features considered in their items and classified them by rate of appearance according to the Pareto & ABC principle. In the second part of our study we collaborated with the National Committee for Circulation Accident Prevention (CNPAC) of the Ministry of Transportation of Morocco in order to compare the findings we gathered from literature with the researches they administer. We prepared a questionnaire that contains the final set of features and we transmitted it to experts working in the road safety field to rate it according to their knowledge and experience. Data analysis showed significant differences in some features, which demonstrates the gap between theoretical results and field research.
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Semantic Annotation of Pedagogic Documents
Статья научная
To teach, teacher needs help for sharing these educational documents, and especially his knowledge. We present an approach to overcome the difficulty of sharing educational materials and facilitate access to content; we describe semantically these documents to make them accessible and available to different users. The main idea in our annotation approach is based on: (1) Identify key words in a document, to have a good presentation of the document, we extract the candidate words by applying a weighting process and another process using similarity measure, These keywords candidates are reconciled with ontology to determine the appropriate concepts. (2) As document reference generally other documents, we propagate the annotations of references for citing document. (3) A process of validation will be applied each time an annotation is added in order to keep the coherence of the base of annotation. After evaluation with several types of pedagogic documents, our approach achieved a good performance; this suggests that teachers can be greatly helped for the semantic annotation of their pedagogical documents.
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Semantic Management Information Modeling based on Theory of Concept Lattices
Статья научная
With the development of future Internet, it is of great significance to study how to realize unified management information modeling, in order to avoid a lot of repetitive work and standardize information modeling in network management domain. This paper discusses the problem from the ontology point of view and introduces the theory of concept lattices into the research on semantic management information modeling, which includes a) establishing an ontology-driven framework for semantic management information modeling, b) building unified management information modeling ontology based on concept lattices, and c) generating semantic models for network management information modeling using the theory of concept lattices.
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Semantic Question Generation Using Artificial Immunity
Статья научная
This research proposes an automatic question generation model for evaluating the understanding of semantic attributes in a sentence. The Semantic Role Labeling and Named Entity Recognition are used as a preprocessing step to convert the input sentence into a semantic pattern. The Artificial Immune System is used to build a classifier that will be able to classify the patterns according to the question type in the training phase. The question types considered here are the set of WH-questions like who, when, where, why, and how. A pattern matching phase is applied for selecting the best matching question pattern for the test sentence. The proposed model is tested against a set of sentences obtained from many sources such as the TREC 2007 dataset for question answering, Wikipedia articles, and English book of grade II preparatory. The experimental results of the proposed model are promising in determining the question type with classification accuracy reaching 95%, and 87% in generating the new question patterns.
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Semi-Supervised Personal Name Disambiguation Technique for the Web
Статья научная
Personal name ambiguity in the web arises when more than one person shares the same name. Personal name disambiguation involves disambiguating the name by clustering web page collection such that each cluster represents a person having the ambiguous name. In this paper, a personal name disambiguation technique that makes use of rich set of features like Nouns, Noun phrases, and frequent keywords as features is proposed. The proposed method consists of two phases namely clustering seed pages and then clustering the actual web page collection. In the first phase, seed pages representing different namesakes are clustered and in the second phase, web pages in the collection are clustered with the similar seed page clusters. The usage of seed pages increases the accuracy of clustering process. Since it is difficult to predict the number of clusters need to be formed beforehand, the proposed technique uses Elbow method to calculate the number of clusters. The efficiency of the proposed name disambiguation technique is tested using both synthetic and organic datasets. Experimental result shows the proposed method achieves robust results across different datasets and outperforms many existing methods.
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Sentiment Analysis of Twitter User Data on Punjab Legislative Assembly Election, 2017
Статья научная
Sentiment Analysis is the way of gathering and inspecting data based on the personal emotions, reviews, and contemplations. The sentimental analysis is also recognized as opinion mining since it mines the major feature from people opinions. There are various social networking platforms, out of which Twitter is praised by lawmakers, academicians, and journalists for its potential political values. In literature, numerous studies have been performed on the data ecstatic to elections on Twitter. The greater part of them has been on the U.S Presidential Elections where there are two main applicants who fight it out. Since individuals discuss so many political parties and candidates and their prospects too in rendered messages, the issues of distinguishing their political feeling become extensive and fascinating. Consideration of all these aspects along with a sheer volume of data propelled us to look into the data and find interesting inferences in it. To select the 117 members of the Punjab Legislative Assembly, Legislative Assembly election was held in Punjab, the State of India on 4 February 2017. As per the Election Commission, a total of 1.9 crore voters is eligible for voting in August 2016 in Punjab. The data set that is collected with the help of Twython was analyzed to find out trivial things and interesting patterns in the data. The central idea of this research paper is to carry out the sentiment analysis on Legislative Assembly election 2017 that was held in the Punjab, a state of India for the Political Parties like BJP, INC, and AAP. We have analyzed and fetch significant implications from the tweets gathered over the whole period of elections.
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Sentiment analysis on mobile phone reviews using supervised learning techniques
Статья научная
Opinion Mining or Sentiment Analysis is the process of mining emotions, attitudes, and opinions automatically from speech, text, and database sources through Natural Language Processing (NLP). Opinions can be given on anything. It may be a product, feature of a product or any sentiment view on a product. In this research, Mobile phone products reviews, fetched from Amazon.com, are mined to predict customer rating of the product based on its user reviews. This is performed by the sentiment classification of unlocked mobile reviews for the sake of opinion mining. Different opinion mining algorithms are used to identify the sentiments hidden in the reviews and comments for a specific unlocked mobile. Moreover, a performance analysis of Sentiment Classification algorithms is performed on the data set of mobile phone reviews. Results yields from this research provide the comparative analysis of eight different classifiers on the evaluation parameters of accuracy, recall, precision and F-measure. The Random Forest Classifiers offers more accurate predictions than others but LSTM and CNN also give better accuracy.
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Статья научная
It is difficult for higher education institutes to manage the growing volumes of educational data accurately and efficiently using conventional ways. Modern technology is required to deal with this difficulty and ICT is an emerging technology that can solve this problem. In this study, Service-Oriented Computing (SOC) is used to develop a Service-Oriented Registrar System (SORS) for Debre Markos University Burie campus registrar office. Web services are used to develop highly cohesive and loosely coupled subsystems that support location transparency and manage the academic records of students accurately and efficiently. Moreover, the developed system is secured, easily maintainable, expandable and open for either inter or intra-application integration on the campus.
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Shapley's Axiomatics for Lexicographic Cooperative Games
Статья научная
In classical cooperative game theory one of the most important principle is defined by Shapley with three axioms common payoff fair distribution's Shapley value (or Shapley vector). In the last decade the field of its usage has been spread widely. At this period of time Shapley value is used in network and social systems. Naturally, the question is if it is possible to use Shapley's classical axiomatics for lexicographic cooperative games. Because of this in the article for m dimensional lexicographic cooperative v= (vT ,...,vM )1T game game is introduced Shapley's axiomatics, as the principle of a fair distribution in the case of dimensional payoff functions, when the criteria are strictly ranking. It has been revealed that axioms discussed by Shapley for classical games are sufficient in lexicographic cooperative games corresponding with the payoffs of distribution. Besides we are having a very interesting case: according to the proved theorem, Shapley's classical principle simultaneously transforms on the composed scalar v1,…, vm games of a lexicographic cooperative game, nevertheless, v2,…, vm games could not be superadditive.
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Статья научная
Feature driven development (FDD) is a process oriented and client centric agile software development model which develops a software according to client valued features. Like other agile models it also has adaptive and incremental nature to implement required functionality in short iterations. FDD mainly focus on designing and building aspects of software development with more emphasis on quality. However less responsiveness to changing requirements, reliance on experienced staff and less appropriateness for small scale projects are the main problems. To overcome these problems a Simplified Feature Driven Development (SFDD) model is proposed in this paper. In SFDD we have modified the phases of classical FDD for small to medium scale projects that can handle changing requirements with small teams in efficient and effective manner.
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Simulation and Analysis of AODV and DSR Routing Protocol under Black Hole Attack
Статья научная
In this paper, two routing protocols (AODV and DSR) are simulated under regular operation, single and cooperative black hole attack. This work has been performed by simulator to show consequence of black hole attacks in MANET by using various graphs which are used to collect data in term of several metrics. One common method to perform most of researches in the MANET security field is to simulate and analyze the routing protocols in various scenarios. This work has been based on the implementation and experiments in the OPNET modeler version 14.5. Finally the results have been computed and compared to stumble on which protocol is least affected by these attacks.
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Статья научная
Recording of ground motions with high amplitudes of acceleration and velocity play a key role for designing engineering projects. Here we try to represent a reasonable prediction of peak ground acceleration which may create more than g acceleration in different regions. In this study, applying different structures of Neural Networks (NN) and using four key parameters, moment magnitude, rupture distance, site class, and style of faulting which an earthquake may cause serious effects on a site. We introduced a radial basis function network (RBF) with mean error of 0.014, as the best network for estimating the occurrence probability of an earthquake with large value of PGA ≥1g in a region. Also the results of applying back propagation in feed forward neural network (FFBP) show a good coincidence with designed RBF results for predicting high value of PGA, with Mean error of 0.017.
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Статья научная
In this paper, Coded Orthogonal Frequency Division Multiplexing (COFDM) based Radio over Fiber (RoF) system is simulated (for different bit rates) for different modulation techniques to observe impact on system parameter [quality factor (Q)]. Influence of increasing bit rates has been observed for 2 km long linear multimode fiber. To reduce dispersion due to multimode fiber, convolutional encoder (code rate ½, generator polynomial 1338, 1718) and viterbi hard decision decoding algorithm are chosen for simulative model of COFDM based RoF system. Transmission of 32 sub-carriers with externally modulated continuous wave laser source gave satisfied results for Q values.
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Software Defect Prediction Using Variant based Ensemble Learning and Feature Selection Techniques
Статья научная
Testing is considered as one of the expensive activities in software development process. Fixing the defects during testing process can increase the cost as well as the completion time of the project. Cost of testing process can be reduced by identifying the defective modules during the development (before testing) stage. This process is known as “Software Defect Prediction”, which has been widely focused by many researchers in the last two decades. This research proposes a classification framework for the prediction of defective modules using variant based ensemble learning and feature selection techniques. Variant selection activity identifies the best optimized versions of classification techniques so that their ensemble can achieve high performance whereas feature selection is performed to get rid of such features which do not participate in classification and become the cause of lower performance. The proposed framework is implemented on four cleaned NASA datasets from MDP repository and evaluated by using three performance measures, including: F-measure, Accuracy, and MCC. According to results, the proposed framework outperformed 10 widely used supervised classification techniques, including: “Naïve Bayes (NB), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)”
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Software Inspection in Software Industry: A Pakistan's Perspective
Статья научная
Today in software world the qualifying criterion for a software product is to be of high quality. Quality enables a software product to conform to customer's expectations. It is another name for best available services and is made acceptable through many practices like reviews, inspections and testing. Among these software inspection is the one which is cost efficient and easy to implement technique. Software inspection is composed of many activities to result in improving the underlying document better and creates consistent understanding. This research presents the different activities in the inspection and practicing of these activities in the software industry of Pakistan. This research is carried out through questionnaires. The answers demonstrate that software inspection is source of better quality products and customer satisfaction without using any proper framework of inspection.
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