International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1147
A Performance Analysis of the Impact of Prior-Knowledge on Computational Thinking
Статья научная
Previously acquired knowledge plays a significant role to learn new knowledge and skills. Previously acquired knowledge consists of Short-term memory and Long-term memory. Though it is a well-accepted learning phenomenon, it is challenging to empirically analyse the impact of prior knowledge on learning. In this paper, we use two systems models for human thinking proposed by Nobel Laureate Prof. Daniel Kahneman. This is a model for human cognition which uses two systems of thinking—the first being quick and intuitively known as fast thinking and the second being slow and tedious known as slow thinking. While slow thinking uses long-term memory, fast thinking uses short-term memory. The impact of prior knowledge of programming language is analyzed to learn a new programming language. We assigned a learning task to two different groups with one having learnt a programming language i.e. senior students and the second group without any prior knowledge of programming language i.e. freshers. The impact of prior knowledge is measured and compared against the time taken to answer quizzes.
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Статья научная
Clustering is one of the primary functions in data mining explorations and statistical data analysis which widely used in various fields. There are two types of the clustering algorithms which try to optimize certain objective function, i.e. the hierarchical and partitional clustering. This study focuses on the achievement of the best cluster results of the hard and soft clustering (K-Mean, FCM, and SOM clustering). The validation index called GOS (Global Optimum Solution) used to evaluate the cluster results. GOS index defined as a ratio of the distance variance within a cluster to the distance variance between clusters. The aim of this study is to produce the best GOS index through the use of the proposed method called the scattered averaging technique based on datasets for the cluster center initialization. The cluster results of each algorithm are also compared to determine the best GOS index between them. By using the annual rainfall data as the dataset, the results of this study showed that the proposed method significantly improved K-Mean clustering ability to achieve the global optimum solution with a performance ratio of 69.05% of the total performance of the three algorithms. The next best clustering algorithm is SOM clustering (24.65%) followed by FCM clustering (6.30%). In addition, the results of this study also showed that the three clustering algorithms achieve their best global optimum solution at the number of even clusters.
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Статья научная
Inertial measurement units based on microelectromechanical systems are perspectives for motion capture applications due to their numerous advantages. A motion trajectory is restored using a well-known navigation algorithm, which assumes integration of the signals from accelerometers and gyroscopes. Readings of both sensors contain errors, which quickly accumulate due to integration. The applicability of an inertial measurement unit for motion capture depends on the trajectory being tracked and can be predicted due to the simulation of signals from inertial sensors. The first simulation step is prescribing a motion trajectory and corresponding velocities. The existing simulation software provides no user-friendly graphical tools for the completion of this step. This work introduces an algorithm for the simulation of accelerometer signals upon a two-dimensional trajectory drawn with a computer mouse and then vectorized. We propose a modification of the Potrace algorithm for tracing motion trajectories. Thus, a trajectory and velocities can be set simultaneously. The obtained results form a basis for simulating three-dimensional motion trajectories since the latter can be represented by three mutually orthogonal two-dimensional projections.
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Статья научная
The aim of this work is to predict high education students’ progress and achievement by forecasting their final grades in any taught courses as early as possible during a study semester or term, using an innovative neural network shrinking technique. The trained neural network NN is divided into a head and several tails in a cascaded sequential manner. Course data from previous offerings are used in a network which is trained using a feedforward BP scheme to extract input and output weight coefficients and biases. One of the input features is reduced by a fraction of its original value and used with the same input data in a tailed NN, which is initiated with the extracted coefficients and biases from the previous run. The training is continued in a cascaded manner until eliminating one input assignment. The whole process is continued for other assignments to be eliminated. This algorithm can be constituted as a dynamic process workbench for an alternative method of forecasting the achieved grades of high educational students in an easy and cost-free manner. The earlier it is to forecast final grade the easier it is to alleviate outcomes of course grading. The procedure is applied on two different courses offered by the same teacher. The input data of different batches of students attending a particular course are used. It is found that a tentative accuracy of predicting final grades from the start is possible.
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A Proposal for Improving Behavioral Adaptation of Web Services Integration
Статья научная
Reuse of existing software components is the main goal of Component-Based Software Engineering. Different organizations designed different web-services as the most famous type of component. Adaptation is a critical issue when building new applications by reusing existing services. How to adapt these services to work correctly is the main goal of most of the proposed models and techniques in software component filed. Behavioral mismatches are one of the adaptation problems. Different solutions have been written to address this problem like adapter and some other techniques. Most of the papers focused on how to create the adapter to overcome the incompatibility problem. In this paper, the authors provide a simple integrated tool that can solves ordering mismatching. The results are concluded using a survey from thirty one respondents. The proposed solution needs further validation by conducting a case study.
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A Proposed Algorithm for Assessing and Grading Automatically Student UML Diagrams
Статья научная
Digital technologies and innovative methods have shown a significant impact on educational systems, and have made work easier for both learners and teachers. Additionally, they have improved the quality and the capability to digitize the assessment of student work produced during a learning process. Assessing and scoring students’ UML diagrams has become a challenging task for teachers, especially with the growing number of students, as well as the necessity to better manage their time. Consequently, there will be a necessity to automate the assessment of these learners. This paper presents an approach for assessing and grading automatically the student’s UML diagrams. The approach uses an algorithm implemented in Java, which takes the tutor's and student's solution diagrams as input, then provides the student's scores and identifies differences and errors made. Our algorithm was tested and evaluated in a real case within a web platform, and the results obtained demonstrate the effectiveness of our solution.
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A Proposed Model for IT Disaster Recovery Plan
Статья научная
IT disaster recovery planning is no longer an option. Reliable IT services have become an integral part of most business processes. To ensure the continued provision of information technology, firms must engage in IT disaster recovery planning. Surprisingly, there is little research on this topic. IT disaster recovery planning has not been fully conceptualized in mainstream IT research. A previously framework for assessing the degree of IT disaster recovery planning. Practitioners can use this study to guide IT disaster recovery planning. Our Disaster Recovery Plan is designed to ensure the continuation of vital business processes in the event that a disaster occurs. This plan will provide an effective solution that can be used to recover all vital business processes within the required time frame using vital records that are stored off-site. This Plan is just one of several plans that will provide procedures to handle emergency situations. These plans can be utilized individually but are designed to support one another. The first phase is a Functional Teams and Responsibilities the Crisis Management Plan. This phase allows the ability to handle high-level coordination activities surrounding any crisis situation. We will also discuss the development, finally maintenance and testing of the Disaster Recovery Plan.
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A Proposed Modification of K-Means Algorithm
Статья научная
K-means algorithm is one of the most popular algorithms for data clustering. With this algorithm, data of similar types are tried to be clustered together from a large data set with brute force strategy which is done by repeated calculations. As a result, the computational complexity of this algorithm is very high. Several researches have been carried out to minimize this complexity. This paper presents the result of our research, which proposes a modified version of k-means algorithm with an improved technique to divide the data set into specific numbers of clusters with the help of several check point values. It requires less computation and has enhanced accuracy than the traditional k-means algorithm as well as some modified variant of the traditional k-Means.
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A Pure EBMT Approach for English to Hindi Sentence Translation System
Статья научная
The paper focuses on Example Based Machine Translation (EBMT) system that translates sentences from English to Hindi. It uses the parallel corpus for translating sentences. Development of a machine translation (MT) system typically demands a large volume of computational resources. Requirement of computational resources (for example, rules) is much less in respect of EBMT. This makes development of EBMT systems for English to Hindi translation feasible, where availability of large-scale computational resources is still scarce. Example based machine translation relies on the database for its translation. The frequency of word occurrence is important for translation in EBMT in the following research.
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A Quantitative Analysis of Postgraduate Programmes Admission of the Hong Kong Institute of Education
Статья научная
This paper analyzed 8 years' student admission trend of Hong Kong Institute of Education (HKIEd) postgraduate programmes in 2 time slots. In the first time period, year 2007-2010, we use the waterfall and competing pattern model for the quantitative analysis of the postgraduate programmes in a holistic manner of HKIEd, and further study the general student admission trend of year 2011-2013, due to the sharp increase of mainland China students and the 3-3-4 Scheme Education System Reform in 2011 and 2012. In this paper, we utilize this rich collection of programmes to identify usable admission patterns by analyzing admission data of the past years for reinforcing the institute's strength and reputation, whose programme spectrum was expanded and enhanced quite drastically since 2005 with the introduction of 1st Master in Education cohort. In particular, this paper is focused to locate: 1) if there is any internal competition and how severe the competition is. 2) If there is any programme that can be utilized as strategic recruitment? and 3) the general student admission trend of the 8 years. With this study, management can better understand where the sources of applicants come from. New insight can be drawn and new strategy can be defined to recruit more quality students which are also aligned with our strategic move to transform the students. The methods used in this paper can be easily applied in other University for analyzing the admission issues, and can demonstrate to management how operation data can be utilized to form valuable insights and significant improvement can then be made based on these identified facts.
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A Real-time DBMS System for the Immigration Processing of Large Hajj Crowd
Статья научная
Hajj is an important Islamic ritual and one of the five pillars of Islam. The Hajj event occurs in the twelfth month of the Islamic lunar calendar and requires anywhere from two to three millions of Muslims from all over the world to make pilgrimage for 10-15 days to the Holy city of Makkah in the Kingdom of Saudi Arabia. Providing quality Hajj services to such large number of pilgrims has been a significant challenge for the Saudi Arabian authorities. Among other services, immigration processing of a large Hajj pilgrim crowd arriving simultaneously at various Saudi Arabian ports during the specific Hajj days has resulted in significant delays at these ports. Unique and technology based solutions must be explored to alleviate the various Hajj related pilgrim service problems and to improve overall quality of these services. This paper reports experience with the design and development of a prototype backend DBMS system to automate the immigration processing of the large Hajj crowd. A real-time DBMS is considered for meeting the processing requirements of such a large Hajj pilgrim crowd arriving simultaneously at various ports. The purpose of this prototype was to understand the challenges and the feasibility of implementation of the backend system using a real-time DBMS.
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A Recursive Binary Tree Method for Age Classification of Child Faces
Статья научная
This paper proposes an intuitive approach to facial age classification on child faces – a recursive multi-class binary classification tree – using the texture information obtained from facial images. The face area is divided into small regions from which Local Binary Pattern (LBP) histograms were extracted and concatenated into a single vector efficiently representing a facial image. The classification is based on training a set of binary classifiers using Support Vector Machines (SVMs). Each classifier estimates whether the facial image belongs to a specified age range or not until the last level of the tree is reached where the age is finally determined. Our classification approach also includes an overlapping function that resolves overlaps and conflicts in the outputs of two mutually-exclusive classifiers at each level of the classification tree. Our proposed approach was experimented on a publicly available dataset (FG-NET) and our locally obtained dataset (FAGE) and the results obtained are at par with those of existing works.
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A Review of Fully Automated Techniques for Brain Tumor Detection From MR Images
Статья научная
Radiologists use medical images to diagnose diseases precisely. However, identification of brain tumor from medical images is still a critical and complicated job for a radiologist. Brain tumor identification form magnetic resonance imaging (MRI) consists of several stages. Segmentation is known to be an essential step in medical imaging classification and analysis. Performing the brain MR images segmentation manually is a difficult task as there are several challenges associated with it. Radiologist and medical experts spend plenty of time for manually segmenting brain MR images, and this is a non-repeatable task. In view of this, an automatic segmentation of brain MR images is needed to correctly segment White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF) tissues of brain in a shorter span of time. The accurate segmentation is crucial as otherwise the wrong identification of disease can lead to severe consequences. Taking into account the aforesaid challenges, this research is focused towards highlighting the strengths and limitations of the earlier proposed segmentation techniques discussed in the contemporary literature. Besides summarizing the literature, the paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. However, articulating a new technique is beyond the scope of this paper.
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A Review of Interventions to Recruit and Retain ICT Students
Статья научная
This article reviews and analyses the educational research literature on interventions to increase the recruitment and retention of information and communications technology (ICT) students. The results show that three changes in computing pedagogy characterize successful interventions, and consequently offer promising ways to attract and prepare more people for careers in ICT. The most important selection criterion considered when choosing papers to review was that interventions had been tested in practice and their effectiveness measured. Interventions were arranged into two groups: recruitment and retention. Recruitment interventions described initiatives to motivate interest in computing among secondary schools students, whereas retention interventions described efforts to retain students in ICT majors at universities. The three pedagogical approaches that emerged from an analysis of the successful interventions were: (1) visual programming environments to teach introductory programming, (2) inquiry learning activities to engage students in computing, and (3) integration of interdisciplinary knowledge to attract students from diverse disciplines. This review draws attention to innovative teaching practices currently shaping computer science education. Wider adoption of these pedagogical strategies has the potential to significantly increase the number of qualified ICT professionals.
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A Review of Knowledge Management Based Career Exploration System in Engineering Education
Статья научная
Knowledge sharing has become an important tool in every organization's success story today. Various knowledge management applications has been carried out recently in many sector like health care, university, banking, construction etc. and it has brought significant improvements. However minimal research is done on career development and as such Knowledge management in career exploration in Engineering Education has not been carried out till date. In India, most of the curriculum and pedagogy does not suit with the real time job environment and this caused many graduates to remain unemployed every year. To meet the gap, this paper attempts to highlight the works on the various existing career exploration and knowledge management techniques. The author collected relevant literature from 2010 to 2015 with the objective to find out a way to combine the two disciplines of career exploration and knowledge management to enhance the career awareness and career exploration system with the application of knowledge management techniques. From the study, the researcher found four conclusions: (i). Education and work/career are separated into different world; (ii). Career exploration enhances student's career awareness; (iii). Knowledge management technique adds efficiency and enhances curriculum; (iv). The research efforts in knowledge management (KM) and career exploration are sparse and no such literatures are available on knowledge management in career exploration currently. This research may add to the development of an efficient knowledge management based career exploration system for engineering education.
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A Review of NBTI Degradation and its Impact on the Performance of SRAM
Статья научная
Temporal degradation of VLSI design is a major reliability concern for highly scaled silicon IC technology. Negative Bias Temperature Instability (NBTI) in particular is a serious threat affecting the performance of both digital and analog circuits with time. This paper presents a review of NBTI degradation, its mechanism and various factors that affect the degradation caused by NBTI. Reaction Diffusion (RD) model based analytical expressions developed by various researchers are also discussed along with their features and underlying assumptions. Degradation in the Static RAM (SRAM) performance caused by NBTI is also discussed in detail along with the strategies that are employed to combat the effect of NBTI degradation in SRAM. Results of the review done for SRAM cell under NBTI degradation suggests that these design strategies are effective in improving the SRAM cell performance.
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A Review on the Suitability of Machine Learning Approaches to Facial Age Estimation
Статья научная
Age is a human attribute which grows alongside an individual. Estimating human age is quite difficult for machine as well as humans, however there has been and are still ongoing efforts towards machine estimation of human age to a high level of accuracy. In a bid to improve the accuracy of age estimation from facial image, several approaches have been proposed many of which used Machine Learning algorithms. The several Machine Learning algorithms employed in these works have made significant impact on the results and of performances of the proposed age estimation approaches. In this paper, we examined and compared the performance of a number of Machine Learning algorithms used for age estimation in several previous works. Considering two publicly available facial ageing datasets (FG-NET and MORPH) which have been mostly used in previous works, we observed that Support Vector Machine (SVM) has been most popularly used and a combination/hybridization of SVM for classification (SVC) and regression (SVR) have shown the best performance so far. We also observed that the face modelling or feature extraction techniques employed significantly impacted the performance of age estimation algorithms.
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A Routing Priority Scheduling Algorithm for MAC Layer in Wireless Sensor Networks
Статья научная
Based on the ideas of conflict-free transmission, priority to guarantee transmission quality for communication between the different clusters , this article proposes a scheduling Algorithm fit for the MAC scheme of WSNs, which has made it possible for the polling service capable of differentiating services of the cluster head node of two priority levels. The high-priority service of the cluster head is responsible for routing between the different clusters, via exhaustive service policy, while the low-priority services of the cluster head node, for communication within the cluster through limited service policy with good fairness. The theoretical model of this scheme is established through Markov chain and probability generating function. Mathematical analysis is made on the mean queue length, the mean inquiry cyclic time and the mean delay time. It turns out that the findings from theoretical analysis correspond well with those from simulated experiments.
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A Scheme for Evaluating XML Engine on RDBMS
Статья научная
There are an increasing number of DBMS vendors thinking of integrating XML data management into traditional relational database, with wider use of XML. In this case, a comprehensive evaluation methodology is needed to evaluate the XML engine in RDBMS correctly. In this paper, we analyze the characteristics of XML engine and propose an evaluation strategy of XML engine in a RDBMS. We believe that the evaluation should include functional evaluation and performance evaluation, and cover several major aspects of DB such as storage, query and update. Then we designed an evaluation scheme for the XML engine in RDBMS according the strategy. The scheme describes an evaluation scene and contains a data set, workload and index set. The data set reflects the characteristics of both data-centric and document-centric XML data. The workload covers all of the requirements of XQuery in W3C. The index set covers the aspects of storage, indexing, query and update. In the end, we complete an experiment to test an actual computer system using the proposal. The result shows that the proposal is proper.
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A Scheme to Reduce Response Time in Cloud Computing Environment
Статья научная
The area of cloud computing has become popular from the last decade due to its enormous benefits such as lower cost, faster development and access to highly available resources. Apart from these core benefits some challenges are also associated with it such as QoS, security, trust and betterresource management. These challenges are caused by the infrastructure services provided by various cloud vendors on need basis. Empirical studies on cloud computing report that existing quality of services solutions are not enough as well as there are still many gaps which need to be filled. Also, there is a dire need to develop appropriate frameworks to improve response time of the clouds. In this paper, we have made an attempt to fill this gap by proposing a framework that focuses on improving the response time factor of the QoS in the cloud environment such as reliability and scalability. We believe that if the response time are communicating effectively and have awareness of the nearest and best possible resource available then the remaining issues pertaining to QoS can be reduced to a greater extent.
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