Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1064
A feature selection based ensemble classification framework for software defect prediction
Статья научная
Software defect prediction is one of the emerging research areas of software engineering. The prediction of defects at early stage of development process can produce high quality software at lower cost. This research contributes by presenting a feature selection based ensemble classification framework which consists of four stages: 1) Dataset selection, 2) Feature Selection, 3) Classification, and 4) Results. The proposed framework is implemented from two dimensions, one with feature selection and second without feature selection. The performance is evaluated through various measures including: Precision, Recall, F-measure, Accuracy, MCC and ROC. 12 Cleaned publically available NASA datasets are used for experiments. The results of both the dimensions of proposed framework are compared with the other widely used classification techniques such as: “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)”. Results reflect that the proposed framework outperformed other classification techniques in some of the used datasets however class imbalance issue could not be fully resolved.
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A framework for ensuring consistency of Web Services Transactions based on WS-BPEL
Статья научная
Transaction processing, as the key technology of web service composition (WSC), has obtained wildly concern. WS-BPEL[1] as a primary web service composition description language, which couldn’t coordinate these web service transactions that distribute in a distributed computing environment reach consistent agreement on the outcome. This paper proposed two kinds of transaction types and coordination mechanisms by analyzing the features of WSC transaction, and a transaction processing coordination model based on BPEL was lastly proposed, by which extending the structure of BPEL firstly and then introduced the coordination mechanism into it. The model was validated by an instance at last.
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A fuzzy based parametric approach for software effort estimation
Статья научная
Accurate Software effort estimation is an ongoing challenge for the modern software engineers in computer science engineering since last 30 years due to the dynamic behavior of the software [1] [2][14]. This is only because of the time and cost estimation during the early stage of the software development is quite difficult and erroneous. So many algorithmic and non algorithmic techniques are used such as SLIM (Software life cycle management), Halstead Model, Bailey-Basil Model, COCOMO model and Function point analysis, etc, but does not estimate all kinds of software accurately. Nowadays these traditional techniques are not acceptable. This research work proposes a new fuzzy model to achieve higher accuracy by multiplying a fuzzy factor with the effort equation predicted empirically. As comparison to both model based and equation based, Model based estimation focused on specific models where as equation based techniques are based on traditional equations. Fuzzy logic is more suitable and flexible to meet the realistic challenges of today’s software estimation process.
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Статья научная
Many approaches to sentiment analysis benefit from polarity lexicons. Existing methods proposed for building such lexicons can be grouped into two categories: (1) Lexicon based approaches which use lexicons such as dictionaries and WordNet, and (2) Corpus based approaches which use a large corpus to extract semantic relations among words. Adjectives play an important role in polarity lexicons because they are better polarity estimators compared to other parts of speech. Among natural languages, Turkish, similar to other non-English languages suffers from the shortage of polarity resources. In this work, a hybrid approach is proposed for building adjective polarity lexicon, which is experimented on Turkish combines both lexicon based and corpus based methods. The obtained classification accuracies in classifying adjectives as positive, negative, or neutral, range from 71% to 91%.
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Статья научная
This paper focuses on the personality traits of students and stress scale they had to face in undergraduate level. With the advancement of computer science and machine learning based applications, we have tried to inter-correlate the terms. In the area of computational psychology, it is important to understand participants’ psychological behavior using personality traits and predict how he/she is going to react on a certain level of the stressed situation. For the experiment, we have collected data of around 150 participants. The personality traits data are collected using the standard survey named The Big Five Personality Test created by IPIP organization and stress scale measurements are collected using scale devised by Sheldon Cohen named as Perceived Stress Scale hosted by Mind garden. The data are taken from Bangladeshi computer science undergraduate students and kept anonymous. In this paper, we have applied nine different machine learning based classification models are built for mapping the traits with stress scales. For performance evaluation, we have utilized precision, recall, f1-score, and accuracy. From the experimental findings, we found that Sequential Minimal Optimization (SMO) and k-NN classifier gives the highest prediction accuracy which is approximately 70%.
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A neoteric optimization methodology for cloud networks
Статья научная
Cloud computing is distinctively marked by its capability of providing on demand virtualized IT resources in a pay as you go fashion. Due to its popularity, the cloud computing users are increasing day by day which has become an important challenge for cloud providers. They need to serve their users in a best possible manner. The providers should not only provide their users a secure access to resources but also need to maintain a proper balance of QOS parameters like throughput, end-to-end delay, packet delivery ratio, jitter, response time, etc. The paper proposes an approach of using a meta-heuristic algorithm called Genetic Algorithm (GA) to optimize QOS parameters like packet delivery ratio and end to end delay in cloud networks. The intelligent optimization algorithms address several shortcomings of existing protocols by improving QOS parameters in an optimum manner. The results are simulated through MATLAB based simulator and the simulated results of proposed approach exhibit optimized parameters when compared to conventional method of shortest path cloud routing approach.
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A novel approach to predict high blood pressure using ABF function
Статья научная
High Blood Pressure (HBP) is a state in the biological system of human beings developed due to physical and psychological changes. Nowadays, it is a most prevalent problem in human beings irrespective of age, place, and profession. The HBP victims are increasing rapidly across the globe. HBP is undiagnosed in the majority of the patients because most of the affected people are not aware of it. To overcome this problem, this paper proposes a new approach that uses ABF (Arterial Blood Flow)-function to predict a person is prone to HBP. In this approach, the impact factor for each attribute is calculated based on the attribute value. Both attribute value and corresponding impact factor are used by ABF function to predict a person is prone to HBP. We experimented proposed approach on real-time data set, which consists of 1100 patient records in the age group between 18 and 65. Our approach outperforms regarding predictive accuracy over j48, Naive Bayes and Rule-based classifiers.
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A novel optimization based algorithm to hide sensitive item-sets through sanitization approach
Статья научная
Association rule hiding an important issue in recent years due to the development of privacy preserving data mining techniques for hiding the association rules. One of the mostly used techniques to hide association rules is the sanitization of the database. In this paper, a novel algorithm MPSO2DT has been proposed based on the Particle Swarm Optimization (PSO) in order to reduce the side effects. The aim is to reduce the side effects such as Sensitive item-set hiding failure, Non-sensitive misses, extra item-set generations and Database dissimilarities along with the reduction of running time and complexities through transaction deletion.
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A novel system for generating simple sentences from complex and compound sentences
Статья научная
In the field of natural language processing, simple sentence has a great importance; especially for multiple choice question generation, automatic text summarization, opinion mining, machine translation and information retrieval etc. Most of these tasks use simple sentences and include a sentence simplification module as pre-processing or post-processing task. But dedicated tasks for sentence simplification are hardly found. Here we have proposed a novel system for generating simple sentences from complex and compound sentences. Our proposed system is an initiative for simplifying sentence by converting complex and compound sentences into simple ones. Along with this the system classifies the simple sentences of an input corpus from other types of sentences. To generate simple sentences from complex and compound sentences we have proposed a novel algorithm which takes the dependency parsing of the input text and produce simple sentences as output. The experimental result demonstrates that the proposed technique is a promising one.
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A partial string matching approach for named entity recognition in unstructured Bengali data
Статья научная
In today's data driven, automated and digitized world, a significant stage of information extraction is to look for special keywords, more formally known as 'Named Entity'. This has been an active research topic for more than two decades and significant progresses have been made. Today we have models powered by deep learning that, although not perfect, have near human level accuracy on certain occasions. Unfortunately these algorithms require a lot of annotated training data, which we hardly have for Bengali language. This paper proposes a partial string matching approach to identify a named entity from an unstructured text corpus in Bengali. The algorithm is a partial string matching technique, based on Breadth First Search (BFS) search on a Trie data structure, augmented with dynamic programming. This technique is capable of not only identifying named-entities present on a text, but also estimating the actual named-entities from erroneous data. To evaluate the proposed technique, we conducted experiments in a closed domain where we employed this approach on a text corpus with some predefined named entities. The texts experimented on was both structured and unstructured, and our algorithm managed to succeed in both the cases.
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A proposed framework to analyze abusive tweets on the social networks
Статья научная
This paper takes Twitter as the framework and intended to propose an optimum approach for classification of Twitter data on the basis of the contextual and lexical aspect of tweets. It is a dire need to have optimum strategies for offensive content detection on social media because it is one of the most primary modes of communication, and any kind of offensive content transmitted through it may harness its benefits and give rise to various cyber-crimes such as cyber-bullying and even all content posted during the large even on twitter is not trustworthy. In this research work, various facets of assessing the credibility of user generated content on Twitter has been described, and a novel real-time system to assess the credibility of tweets has been proposed by assigning a score or rating to content on Twitter to indicate its trustworthiness. A comparative study of various classifying techniques in a manner to support scalability has been done and a new solution to the limitations present in already existing techniques has been explored.
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Статья научная
In literature, combinatorial optimization problems have been solved using several hybrid strategies. From the principles of software engineering, it is explicit that modelling enables better understanding of the problem’s solution as well as the various parts that constitute the solution. However, the literature reveals that there is less importance attached to modelling the problem’s solution while solving combinatorial optimization problems using hybrid strategies. Therefore, in order to better understand the advantages and significance of using a model based approach in solving such problems, a survey on model based approach and the various properties achieved by modelling has been carried out. A comparison of the algorithm or technique based approach, framework based approach and model based approach is done to better understand the differences between the approaches and their outcomes. From the comparison made between the approaches and the analysis made on the advantages of using a model based approach in solving combinatorial optimization problems using hybrid strategies, it is found that a model based approach gives clear and better understanding of complex problems by making their representation easily modular, understandable, adaptable, verifiable, reliable, customizable, reusable etc. Further, when hybrid strategies are used, and the problems solution is depicted in the form of a model, every part of the model could be implemented using different algorithms and frameworks, thus aiding to identify the optimal algorithm or framework for every part of the model, as well as the most efficient hybrid combination that solves the whole problem in an optimal manner.
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Статья научная
Student attrition among undergraduate students is among the most concerned issues in higher educational institutions in Malaysia and abroad. This problem arises when these students unable to complete their studies within the stipulated period when there are majoring in the Science, Technology, Engineering, and Mathematics (STEM) fields. Research findings highlight numerous factors contribute to the student attrition. These findings also suggest that the factors differ from one case to another case. Effects of student attrition not only for the student itself but also to the institutions and community. It is challenging to classify the factors based on general assumptions. Moreover, increasing students’ information makes the problem more complicated. This student information can provide a useful database for analytical analysis. Methods such as big data analytics and data mining techniques can be deployed to gain insights and pattern that related to student attrition problem. The objective of this paper (i) review the student attrition in higher education (HE) and the contributing factors; and (ii) review the existing computational model to analyze and predict student attrition in HE.
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A rule based extractive text summarization technique for Bangla news documents
Статья научная
News summarization is a process of distilling the most important information from a news document in a precise way. For the advancement of Internet nowadays almost all of the Bangla newspapers have their online versions, and people of this era like to read newspaper from website using Internet. But large amount of electronic news content is a burden for human to come out with valuable information. For mitigating this pain point, this paper proposes an automatic method to summarize Bangla news document. In this proposed approach, graph based sentence scoring feature is introduced for the first time for Bangla news document summarization. After analyzing vast amount of Bangla news document 12 sentence scoring features have been introduced for calculating score of a sentence. An improved summary generation method has also been proposed which remove the redundant information from summary. The result is evaluated using a standard summary evaluation tool called ROUGE, and found proposed method outperforms all existing methods used in Bangla news summarization.
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A speaker recognition system using gaussian mixture model, EM algorithm and K-Means clustering
Статья научная
The automated speaker endorsement technique used for recognition of a person by his voice data. The speaker identification is one of the biometric recognition and they were also used in government services, banking services, building security and intelligence services like this applications. The exactness of this system is based on the pre-processing techniques used to select features produced by the voice and to identify the speaker, the speech modeling methods, as well as classifiers, are used. Here, the edges and continuous quality point are eliminated in the normalization process. The Mel-Scale Frequency Cepstral Coefficient is one of the methods to grab features from a wave file of spoken sentences. The Gaussian Mixture Model technique is used and done experiments on MARF (Modular Audio Recognition Framework) framework to increase outcome estimation. We have presented an end pointing elimination in Gaussian selection medium for MFCC.
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A sustainability theme for introductory programming courses
Статья научная
Sustainability is an important topic for modern college and universities, many of whom are actively promoting sustainable practices and integration of sustainability topics into course curricula. The inclusion of socially-relevant projects and course “themes” has been shown to assist in attracting students to Computer and Information Science, and practical, problem-based applications have also been shown to attract females and underrepresented groups to the discipline. In Computer and Information Science education, most documented approaches attempt to integrate sustainable computing topics either as learning modules, open-ended project topics, or as concentrated courses. This paper describes a lightweight, non-intrusive pedagogical approach to integrating sustainability education in introductory programming courses. By creating introductory programming projects focused on sustainability topics, students are exposed to the general concepts and terminology involved with the important scientific and societal topic. This approach also allows students to see the practical applications of computing in a socially relevant context. Results of a two-year study of this approach have been encouraging, though more work is needed to assess the full impact of this approach and to overcome the limitations of the implementation context.
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A web based approach for teaching and learning programming concepts at middle school level
Статья научная
One of the major concerns in teaching and learning programming concepts is the complexity of syntax and precision of semantics of programming languages. Traditional teaching methods are static and passive i.e. they do not engage students in an interactive manner thereby making it difficult for students to grasp the contents and instructors to convey the instruction. This obstacle even becomes challenging when programming courses are to be taught to beginners. To cope up with this challenge, this work has proposed and prototyped a system that is aimed to focus on students at their middle level of education. Multimedia technology i.e. videos have been used to plunge the students in an interactive environment where learning JavaScript programming becomes fun instead of a mind-burden. Visualization concepts have been incorporated to provide visual learning for variables, loops, control structures, functions etc. This application is dynamic in nature that is user can not only understand the programming concepts but can also run the codes using code panel. The designed system has been tested to ensure the functionality, performance and feedback from the targeted users as discussed in results section.
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AISQA - An Artificial Immune Question Answering System
Статья научная
Question answering (QA) is the task of automatically answering a question posed in natural language. At this time, there exists several QA approaches, and, according to recent evaluation results, most of them are complementary. Some of them use the evolutionary algorithms, such as the genetic algorithm, in itself. In this paper we propose a question answering system that uses the artificial immune algorithms, for searching in the knowledge base to find the right answer. This algorithm is one of the evolutionary algorithms. Search is based on two features: (i) the compatibility between question and answer types, (ii) the overlap and non-overlap information between the question-answer pair. Experimental results are encouraging; they indicate significant increases in the accuracy of proposed system, in comparison with the previous systems.
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AMMAS: Ambient Mobile Multi-Agents System: Simulation of the M-Learning
Статья научная
Recent years have witnessed the increase of the field of mobile learning, fostered by the continuous development of mobile computing and wireless technology, today the mobile learning presents a fundamental approach to satisfy our daily needs and requirements. Specifically, in this work, we aim to study as model and simulate the ambient mobile system which is based on intelligent agents. The mobile agent is not based on the traditional client server however it is based on the distributed ones. The present article proposes a mobile intelligent agent based architecture for the M-Learning that aims to facilitate the teacher and student acquisition. M-Learning is a new research area which became a principal tool for our education system. So we produced an adapted agent based approach for an efficient flexible. In our work we proceed as follows: first, we introduce the scope and the genesis of our research, second, we hold out the m-learning is the next generation of e-learning, afterwards, we present our AMMAS (Ambient Mobile Multi-Agents System) model for the M-Learning and an overview of the system implementation, and finally we conclude our work and give some perspectives.
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Статья научная
Quality of cognitive abilities and increased positive character of students in following a learning process during social distancing can still be realized even though the implementation is done from home. This can happen if a good learning design has been formed. Based on that situation, the purpose of this research was to show the existence of innovation in the form of a learning design that combines asynchronous and synchronous learning strategies by inserting ANEKA concepts. The method for developing this learning design was R&D which uses 4D design (Define, Design, Develop, and Disseminate). Subjects who were involved in evaluation toward learning design were four experts. The location of this research was conducted at one of the IT Vocational School in the North of Bali region. Data collection techniques used questionnaires. Data analysis was conducted through a comparison technique between the effectiveness percentages of learning design with categorization based on the range of effectiveness percentages. The research results showed the effectiveness level of asynchronous and synchronous learning design based on ANEKA was included in the very high category with a percentage was 89.00%.
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