Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 968
Hackathon for Learning Digital Theology in Computer Science
Статья научная
Hackathon is an event where programmers and subject field specialists collaborate intensively in teams with the ultimate aim to create and design fresh ICT (information and communication technology) based solutions to a given task in a limited time. In this study, we analyzed students' perceptions and experience in a hackathon where they were to design a concept for an application aimed at people that are preparing for their own death. The hackathon was part of a Digital Theology (DT) course at the university for Computer Science (CS) students. A group of 12 students participated in the event. The participants were divided into three groups. The assignment was presented to all the groups to brainstorm and create a mock-up artefact suitable to tackle the challenge. In the end, each group presented their solution. Due to the limited number of students, we applied descriptive statistics rather than exploring into inferential statistics to analyze the data. By collecting data through questionnaires and interviewing the participants, we concluded that the use of hackathon helped to achieve the learning goals of learning DT. The students expressed their satisfaction in the fact that it provided them with motivation to learn through practice. Also, students agreed that the event helped them to think collaboratively for a refined ideas. The overwhelming satisfaction expressed by the students goes to confirm that hackathon brings out the best creative skills from people through problem-solving.
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Heuristic Evaluation of the use of Blackboard & Facebook Groups in Computing Higher Education
Статья научная
The features of social media sites make them potentially effective as a learning platform for student communication and collaboration in higher education. Moreover it has become apparent that student Facebook users have been repurposing its features to fit their academic requirements. This study aims to determine if Facebook Groups and the Blackboard Learning Management System (LMS) can enhance the learner experience, and if so, in what way. The study made use of a heuristic evaluation with an educationally relevant criteria set [1]. The results, amongst other things, indicate that Facebook Groups are more useful for peer-to-peer communication than Blackboard, probably due to the notification system in Facebook. Analysis indicated that in some instances the strengths and weaknesses of Blackboard and Facebook were complementary and therefore could, arguably, improve the overall student experience.
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Hidden Markov model for identification of different marks on human body in forensic perspective
Статья научная
This paper proposes a computational forensic methodology which identify and classify different marks on the human body using Hidden Markov model. The methodology gives an efficient and effective computerized approach for the characteristics of different marks such as birthmarks, burntmarks, tattoos and weapons’ wounds found on human body. This proposed method will be a computationally effective substitution for the traditional forensic method in identifying the body marks in crime investigation of homicidal cases. Hidden Markov Model (HMM) is statistical and logical tool suitable for this identification. The marks on human body describe different patterns with characteristics that are helpful in identification. The experimental results achieved for identification of different marks with an average accuracy of 94.6%, on the available database of 400 images that includes four categories: Birthmarks, Burntmarks, Tattoos and weapons’ wounds (100 images of each marks). The methodology gives the better combination of features (color, texture and shape), which are extracted for the identification of marks on human body for the purpose of computational forensic science.
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High Performance FPGA Based Digital Space Vector PWM Three Phase Voltage Source Inverter
Статья научная
This paper focuses on the design of a low power and high performance FPGA based Digital Space Vector Pulse Width Modulation (DSVPWM) controller for three phase voltage source inverter. A new method is proposed to realize easy, accurate and high performance DSVPWM technique based on FPGA with low resource consumption and reduced execution time than conventional methods. Equations of SVPWM are relatively complicated and need a considerable time to execute on a typical microcontroller, therefore a simple method is presented to minimize run time of instructions, e.g. the multiplication operation used in these equations is replaced by a proposed signed and unsigned shifter using 2 to 1 multiplexer unit. Total power consumption of controller is reduced to 37 mW at 100MHz clock frequency. The proposed DSVPWM technique algorithm was synthesized and implemented using Quartus II 9.1V and Cyclone II FPGA, to target device EP2C20F484C6. Also power is analyzed using XPower analyzer. Experimentation and results demonstrate that proposed method have high performance than other works.
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High rate outlier detection in wireless sensor networks: a comparative study
Статья научная
The rapid development of Smart Cities and the Internet of Thinks (IoT) is largely dependent on data obtained through Wireless Sensor Networks (WSNs). The quality of data gathered from sensor nodes is influenced by abnormalities that happen due to different reasons including, malicious attacks, sensor malfunction or noise related to communication channel. Accordingly, outlier detection is an essential procedure to ensure the quality of data derived from WSNs. In the modern utilizations of WSNs, especially in online applications, the high detection rate for abnormal data is closely correlated with the time required to detect these data. This work presents an investigation of different outlier detection techniques and compares their performance in terms of accuracy, true positive rate, false positive rate, and the required detection time. The investigated algorithms include Particle Swarm Optimization (PSO), Deferential Evolution (DE), One Class Support Vector Machine (OCSVM), K-means clustering, combination of Contourlet Transform and OCSVM (CT-OCSVM), and combination of Discrete Wavelet Transform and OCSVM (DWT-OCSVM). Real datasets gathered from a WSN configured in a local lab are used for testing the techniques. Different types and values of outliers have been imposed in these datasets to accommodate the comparison requirements. The results show that there are some differences in the accuracy, detection rate, and false positive rate of the outlier detections, except K-means clustering which failed to detect outlier in some cases. The required detection time for both PSO and DE is very long as compared with the other techniques meanwhile, the CT-OCSVM and DWT-OCSVM required short time and also they can achieve high performance. On the other hand CT and DWT technique has the ability to compress its used dataset where in this paper, CT can extract much less number of coefficients as compared DWT. This makes CT-OCSVM more efficient to be utilized in detecting outliers in WSNS.
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Статья научная
Product Development Life Cycle (PDLC) has been evolving from last decade from side to side unremitting progression of finest practices, process models and advance life cycles. Already present development models like rational unified process, waterfall model and spiral model have facilitated the people, who are being practicing the new models, to incalculably make their products better on the competence and effectiveness. Nevertheless, there is a need to quantify the product quality with the factors that are away from any traditional criterion like maintainability, reliability and the rest. Accomplishing a product that is technically strong only contributes a little part in sensational product market situation, but to what extent the product fulfills the major needs of the users reveals the real success of the product. Requirement Engineering (RE) and User Experience Design (UED) together make available the comprehended user requirements to product development team for successful implementation of product and further help the development team to resolve any problem that takes place. The paper emphasizes on the role of RE and UED during PDLC and discusses the challenges that comes out after coalition of RE and UED in product development. In the closing section some points are listed for determining these challenges.
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Holes Detection in Wireless Sensor Networks: A Survey
Статья научная
Now a day’s, it has been a great idea of research on using Wireless Sensor Networks (WSNs) to assist in the initial deployment of sensor nodes. Hole problem in WSNs is the most fundamental Problem in WSNs. Hole means a communication gap in WSNs. Finding an optimal sensor deployment strategy that would minimize the cost, reduce the node failure and also reduce the communication overhead. Then it provides a maximum degree of area coverage with lower cost of deployment of sensor nodes, best possible communication and maintaining the network connectivity. However, it increases the quality of service in WSNs that is extremely challenging. In this article, we present various types of holes, a comparative study of various types of holes and various types of coverage holes. At the end, we proposed an Algorithm to detect hole. In this paper, we aim to give the solution of hole problems of area coverage in WSNs.
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House Price Prediction using a Machine Learning Model: A Survey of Literature
Статья научная
Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field.
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Human Computation: Object Recognition for Mobile Games Based on Single Player
Статья научная
Smart phones and its applications gain a lot of popularity nowadays. Many people depend on them to finish their tasks banking, social networking, fun and a lot other things. Games with a purpose (GWAP) and microtask crowdsourcing are considered two techniques of the human-computation. GWAPs depend on humans to accomplish their tasks. Porting GWAPs to smart phones will be great in increasing the number of humans in it. One of the systems of human-computation is ESP Game. ESP Game is a type of games with a purpose. ESP game will be good candidate to be ported to smart phones. This paper presents a new mobile game called MemoryLabel. It is a single player mobile game. It helps in labeling images and gives description for them. In addition, the game gives description for objects in the image not the whole image. We deploy our algorithm at the University of Menoufia for evaluation. In addition, the game is published on Google play market for android applications. In this trial, we first focused on measuring the total number of labels generated by our game and also the number of objects that have been labeled. The results reveal that the proposed game has promising results in describing images and objects.
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Hybrid Ensemble Learning Technique for Software Defect Prediction
Статья научная
The reliability of software depends on its ability to function without error. Unfortunately, errors can be generated during any phase of software development. In the field of software engineering, the prediction of software defects during the initial stages of development has therefore become a top priority. Scientific data are used to predict the software's future release. Study shows that machine learning and hybrid algorithms are change benchmarks in the prediction of defects. During the past two decades, various approaches to software defect prediction that rely on software metrics have been proposed. This paper explores and compares well-known supervised machine learning and hybrid ensemble classifiers in eight PROMISE datasets. The experimental results showed that AdaBoost support vector machines and bagging support vector machines were the best performing classifiers in Accuracy, AUC, recall and F-measure.
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Статья научная
Software testing is the significant part of the software development process to guarantee software quality with testing a program for discovering the software bugs. But, the software testing has a long execution time by using huge number of test suites in the software development process. In order to overcome the issue, a novel technique called Hybridized Buffalo and Truncation Cyclic Gene Optimization-based Densely Connected Deep Neural Network (HBTCGO-DCDNN) introduced to improve the software testing accuracy with minimal time consumption. At first, the numbers of test cases are given to the input layer of the deep neural network layer. In the first hidden layer, the test suite generation process is carried out by applying the improved buffalo optimization technique with different objective functions namely time and cost. The improved buffalo optimization selects optimal test cases and generates the test suites. After the generation, the redundant test cases from the test suite are eliminated in the reduction process in the second hidden layer. The Truncative Cyclic Uniformed Gene Optimization technique is applied for the test suite reduction process based on thefault coverage rate. Finally, the reduced test suites are obtained at the output layer of the deep neural network The experimental evaluation of the HBTCGO-DCDNN and existing methods are discussed using the test suite generation time, test suite reduction rate as well as fault coverage rate. The comparative results of proposed HBTCGO-DCDNN technique provide lesser the generation time by 48% and higher test suit reduction rate by 19% as well as fault coverage rate 18% than the other well-known methods.
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Hypermedia E-book as a Pedagogical Tool in a Graduation Course
Статья научная
The e-book (www.design-educacao-tecnologia.com) is a support for teaching Hypermedia Design, which constitutes a didactic material to support teaching and research activities for the Design area. The book will gather issues about Design, Education and Hypermedia aimed at offering resources to enhance the use of multiple languages that converge in hypermedia environments, their applicability, techniques and methods in light of Design in Situations of Teaching-Learning. This paper is divided into five parts: the first part introduce the paper subject, the second part shows the e-book Design, Education and Technology, the third part presents the use of this hypermedia e-book as a pedagogical tool in a graduation course in Design, some of the results developed by the students, the fourth part presents the main questions observed about this digital environment and the last part is the conclusion.
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ICT in Higher Education: Wiki-based Reflection to Promote Deeper Thinking Levels
Статья научная
The main purpose of higher education is to produce skilled graduates so that they can think critically and solve real world problems. Presenting a group based solution in a face-to-face class is a common activity in the higher education classroom where other students/peers can actively participate in the follow-up question/answer sessions. Working out a solution together as a group engages students’ independent thinking ability and promotes active learning. This means, that they have the opportunity to reflect on their own thinking and take it to deeper levels of thinking. However, recent trends show that online support to the higher education class - a form of blended learning is growing day by day. This paper proposes a wiki-based (one of the ICT tools) reflection method to follow up regular existing face-to-face classroom presentation activities to promote deeper thinking levels of students in higher education. In this article, Lee’s Model of thinking levels is-used for analyzing the thinking levels of students during their wiki work. The findings of this research work (through experiments) show that the wiki-based reflection method could be an effective way to promote thinking levels of students and hence can be used as a blended learning model to promote reflective and in-depth thinking.
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INSPECT- an intelligent and reliable forensic investigation through virtual machine snapshots
Статья научная
Cloud computing is emerging as a popular paradigm that provides significant advances and utility-oriented services over shared virtualized resources. Despite the advantage of the cloud services, the majority of cloud users are reluctant to access the cloud due to unprecedented security threats in the cloud environment. The increasing cloud vulnerability incidences show the significance of cloud forensic techniques for the criminal investigation. It is challenging to gather the evidence from the abundant cloud data and identifying the source of the attack from the crime scene. Moreover, the Cloud Service Provider (CSP) confines the investigator to carry out the forensic investigation due to the prime concerns in the multi-tenant cloud infrastructure. To cope up with these constraints, this paper presents INSPECT, an investigation model that accomplishes adaptive evidence acquisition with adequate support for dynamic Chain of Custody presentation. By utilizing the VM log files, the INSPECT approach forensically acquires the corresponding evidence from the cloud data storage based on the location of malicious activity. It enhances the evidence acquisition and analysis process by optimally selecting and exploiting the required forensic fields alone instead of analyzing the entire log information. The INSPECT applies the Modified Fuzzy C-Means (M-FCM) clustering with contextual initialization method on the acquired evidence to recognize the source of the attack and improves the trustworthiness of the evidence through the submission of the chain of custody. By analyzing the Service Level Agreement (SLA) of the cloud users, it facilitates the source of attack identification from the clustered data. Furthermore, it isolates the evidence to avert deliberate modification by an adversary in the multi-tenant cloud. Eventually, INSPECT presents the evidence along with the chain of custody information regarding the crime scene. It enables the law enforcement authority to explore the evidence through the chain of custody information and to reconstruct the crime scene using the VM snapshots associated with timestamp data. The experimental results reveal that the INSPECT approach accomplishes a high level of accuracy in the investigation with the improved trustworthiness over the multi-tenant cloud infrastructure.
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IScrum: An Improved Scrum Process Model
Статья научная
Resolving a wide domain of issues and offering a variety of benefits to software engineering, makes the Agile process models attractive for researchers. Scrum has been recognized as one of the most promising and successfully adopted agile process models at software industry. The reason behind vast recognition is its contribution towards increased productivity, improved collaboration, quick response to fluctuating market needs and faster delivery of quality product. Though Scrum performs better for small projects but there are certain challenges that practitioners encounter while implementing it. Experts have made some efforts to adapt the Scrum in a way that could remove those drawbacks and limitations, however, no single effort addresses all the issues. This paper is intended to present a tailored version of Scrum aimed at improving documentation, team’s performance, and visibility of work, testing, and maintenance. The proposed model involves adapting and innovating the traditional Scrum practices and roles to overcome the problems while preserving the integrity and simplicity of the model.
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Статья научная
Medical imaging is the technique and process used to create images of the human body or medical science. Digital image processing is the use of computer algorithms to perform image processing on digital images. Microscope image processing dates back a half century when it was realized that some of the techniques of image capture and manipulation, first developed for television, could also be applied to images captured through the microscope. This paper presents semi-automated segmentation and identification of adenovirus particles using active contour with multi grid segmentation model. The geometric features are employed to identify the adenovirus particles in digital microscopic image. The min-max, 3 rules are used for recognition of adenovirus particles. The results are compared with manual method obtained by microbiologist.
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Статья научная
Producing skilled workforce according to industry required skills is quite challenging. Knowledge of trainee’s enrollment behavior and trainee’s course selection variables can help to address this issue. Prior knowledge of both can help to plan and target right geographic locations and right audience to produce industry required skilled workforce. Globally Technical and Vocational Education Training (TVET) is used to provide skilled workforce for the industry. TVET is an educational stream which focus learning through more practicing with less theory knowledge. In this article, we have analyzed TVET actual enrollment data of 2017 – 2018 session from a TVET training provider organization of Punjab, Pakistan. The purpose of this analysis is to understand trainee’s enrollment behavior and course selection variables which plays an important role in TVET course selection by the trainees. This enrollment behavior and course selection variables can be used to monitor and control industry required and produced skilled TVET workforce. We developed a framework which contain series of steps to perform this analysis to extract knowledge. We used educational data mining techniques of association, clustering and classification to extract knowledge. The analysis reveals that central Punjab youth is getting more TVET education as compare to south and north Punjab, Pakistan. Similarly, trainee’s ‘age group’, ‘qualification’, ‘gender’, ‘religion’ and ‘marital status’ are potential variables which can play important role in TVET course selection. By controlling these variables and integrating TVET training provider institutes, funding agencies and industry, we can smartly produce TVET skilled workforce required for industry nationally and internationally.
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Статья научная
Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence pattern recognition is very useful in identifying process problem. This paper presents a novel hybrid intelligent method for recognition of common types of control chart patterns (CCPs). The proposed method includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has very important role for its recognition accuracy. Therefore, in the optimization module, cuckoo optimization algorithm (COA) is proposed for finding of optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.
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Identifying Key Challenges in Performance Issues in Cloud Computing
Статья научная
Cloud computing is a harbinger to a newer era in the field of computing where distributed and centralized services are used in a unique way. In cloud computing, the computational resources of different vendors and IT services providers are managed for providing an enormous and a scalable computing services platform that offers efficient data processing coupled with better QoS at a lower cost. The on-demand dynamic and scalable resource allocation is the main motif behind the development and deployment of cloud computing. The potential growth in this area and the presence of some dominant organizations with abundant resources (like Google, Amazon, Salesforce, Rackspace, Azure, GoGrid), make the field of cloud computing more fascinating. All the cloud computing processes need to be in unanimity to dole out better QoS i.e., to provide better software functionality, meet the tenant’s requirements for their desired processing power and to exploit elevated bandwidth.. However, several technical and functional e.g., pervasive access to resources, dynamic discovery, on the fly access and composition of resources pose serious challenges for cloud computing. In this study, the performance issues in cloud computing are discussed. A number of schemes pertaining to QoS issues are critically analyzed to point out their strengths and weaknesses. Some of the performance parameters at the three basic layers of the cloud — Infrastructure as a Service, Platform as a Service and Software as a Service — are also discussed in this paper.
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Image Watermarking Using 3-Level Discrete Wavelet Transform (DWT)
Статья
We have implemented a robust image watermarking technique for the copyright protection based on 3-level discrete wavelet transform (DWT). In this technique a multi-bit watermark is embedded into the low frequency sub-band of a cover image by using alpha blending technique. The insertion and extraction of the watermark in the grayscale cover image is found to be simpler than other transform techniques. The proposed method is compared with the 1-level and 2-level DWT based image watermarking methods by using statistical parameters such as peak-signal-to-noise-ratio (PSNR) and mean square error (MSE). The experimental results demonstrate that the watermarks generated with the proposed algorithm are invisible and the quality of watermarked image and the recovered image are improved.
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