Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1064
A Match or Mismatch Between Learning Styles of the Learners and Teaching Styles of the Teachers
Статья научная
It is important to study learning styles because recent studies have shown that a match between teaching and learning styles helps to motivate students´ process of learning. That is why teachers should identify their own teaching styles as well as their learning styles to obtain better results in the classroom. The aim is to have a balanced teaching style and to adapt activities to meet students´ style and to involve teachers in this type of research to assure the results found in this research study. Over 100 students complete a questionnaire to determine if their learning styles are auditory, visual, or kinesthetic. Discovering these learning styles will allow the students to determine their own personal strengths and weaknesses and learn from them. Teachers can incorporate learning styles into their classroom by identifying the learning styles of each of their students, matching teaching styles to learning styles for difficult tasks, strengthening weaker learning styles. The purpose of this study is to explain learning styles, teaching styles match or mismatch between learning and teaching styles, visual, auditory, and kinesthetic learning styles among Iranian learners, and pedagogical implications for the EFL/ESL classroom. A review of the literature along with analysis of the data will determine how learning styles match the teaching styles.
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A Metaheuristic Algorithm for Job Scheduling in Grid Computing
Статья научная
These days the number of issues that we can not do on time is increasing. In the mean time, scientists are trying to make questions simpler and using computers. Still, more problems that are complicated need more complex calculations by using highly advanced technology. Grid computing integrates distributed resources to solve complex scientific, industrial, and commercial problems. In order to achieve this goal, an efficient scheduling system as a vital part of the grid is required. In this paper, we introduce CUckoo-Genetic Algorithm (CUGA), which inspired from cuckoo optimization algorithm (COA) with genetic algorithm (GA) for job scheduling in grids. CUGA can be applied to minimize the completion time of machines, and it could avoid trapping in a local minimum effectively. The results illustrate that the proposed algorithm, in comparison with GA, COA, and Particle Swarm Optimization (PSO) is more efficient and provides higher performance.
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Статья научная
Code clone detection plays a vital role in both industry and academia. Last three decades have seen more than 250 clone detection techniques with lack of single framework that can detect and classify all 4 basic types of code clones with high precision. This serious lack of clone classification impacts largely on the universities and online learning platforms that fail to validate the projects or coding assignments submitted online. In this paper, we propose a complete and language agnostic technique to detect and classify all 4 clone types of C, C++, and Java programs. The method first generates the parse tree then extracts the functional tree to eliminate the need for the preprocessing stage employed by previous clone detection techniques. The generated parse tree contains all the necessary information for detecting code clones. We employ TF-IDF cosine similarity for the proper classification of clone types. The proposed technique achieves incredible precision rate of 100% in detecting the first two types of clones and 98% precision in detecting type-3 and type-4 clones for small codes of C, C++, and Java containing an average line count of 5. The proposed technique outperforms the existing tree-based clone detection tools by providing the average precision of 98.07% on the C, C++, and Java programs crawled from Github with an average line count of 15 which signifies that cosine similarity measure on ANTLR functional tree accurately detects all 4 types of small clones and act as proper validation tools for identifying the learning level in the submitted programming assignment.
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A Methodology for Teaching Computer Programming: first year students’ perspective
Статья научная
The teaching of computer programming is one of the greatest challenges that have remained for years in Computer Science Education. A particular case is computer programming course for the beginners. While the traditional objectivist lecture-based approaches do not actively engage students to achieve their learning outcome, we believe that integrating some cutting-edge processes and practices like agile method into the teaching approaches will be leverage. Agile software development has gained widespread popularity and acceptance in the software industry and integrating the ideas into teaching will be constructive. In the educational system, while the positive impact of agile principles has been felt on students’ projects, none has been experienced on the teaching aspect. Therefore, this paper proposes the use of agile process in the teaching of first year programming courses. The goal is to help the beginners develop their programming skills, proffer a teaching technology that maximizes students’ chances of engagement, improve teaching as teachers reflects on what they are teaching and what the students are learning. Additionally, beginners will be able to operate the computer, program, and improve their programming skills through active team collaboration as well as managing large classes effectively by the teacher.
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A Modernized Voting System Using Fuzzy Logic and Blockchain Technology
Статья научная
Voting is a formal expression of one’s choice. Though the process is simple, it has far-reaching and deep-lying impacts. Through a vote, people get a channel to voice their opinion anonymously. There are issues with the orthodox traditional voting system, which is used across the world today. Studies, presented throughout the paper, would highlight how millions of people have missed out on voicing their opinion, or get proper representation, due to the many short-comings of the dated traditional voting systems. Blockchain is a comparatively new technology. There have been advances and research made to make use of blockchains in the world of finance and ledger management. But precious little has been done to tackle simpler but wider-reaching problems of voting. The novel approach suggested here would give the voters a chance to vote from the comfort of their homes, or, without adjusting their busy everyday schedules, and make sure everyone gets a proper representation as well. A combination of blockchain technology and fuzzy logic has been used here, to achieve a solution, that we think would help modernize the voting system and ensure greater satisfaction among the voters that their views have been represented in one way or the other. Using this novel approach, we believe that more people would be encouraged to vote and a greater number of voices would get proper representation.
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A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants
Статья научная
Particle Swarm Optimization (PSO) is one of most widely used metaheuristics which is based on collective movement of swarm like birds or fishes. The inertia weight (w) of PSO is normally used for maintaining balance between exploration and exploitation capability. Many strategies for updating the inertia weight during iteration were already proposed by several researchers. In this paper, a Modified Particle Swarm Optimization (MPSO) algorithm based on self-adaptive acceleration constants along with Linear Decreasing Inertia Weight (LDIW) technique is proposed. Here, in spite of using fixed values of acceleration constants, the values are updated themselves during iteration depending on local and global best fitness value respectively. Six different benchmark functions and three others inertia weight strategies were used for validation and comparison with this proposed model. It was observed that proposed MPSO algorithm performed better than others three strategies for most of functions in term of accuracy and convergence although its execution time was larger than others techniques.
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A Module Coupling Slice Based Test Case Prioritization Technique
Статья научная
Regression testing is a process that executes subset of tests that have already been conducted to ensure that changes have not propagated unintended side effects. Test case prioritization aims at reordering the regression test suit based on certain criteria, so that the test cases with higher priority can be executed first rather than those with lower priority. In this paper, a new approach for test case prioritization has been proposed which is based on a module-coupling effect that considers the module-coupling value for the purpose of prioritizing the modules in the software so that critical modules can be identified which in turn will find the prioritized set of test cases. In this way there will be high percentage of detecting critical errors that have been propagated to other modules due to any change in a module. The proposed approach has been evaluated with a case study of software consisting of ten modules.
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A Naïve Based approach of Model Pruned trees on Learner’s Response
Статья научная
Appraisal and feedback have a strong positive influence on teachers and their work. Teachers report that it increases their job satisfaction and, to some degree, their job security, and it significantly increases their development as teachers. Student’s appraisal towards a teacher plays a vital role in building a very good teaching-learning environment in an educational institution. The evaluation report of the student helps the stakeholders to retain qualified teachers for the course. It will also help the teacher to understand the need of the student and the course. Therefore it becomes necessary to evaluate the teacher using appropriate tool to improve the quality of the education. Teacher evaluation can be measured based on the technical knowledge, communication skills, clarity, attitude towards the student etc. Regression trees can be considered as a tool to analyze the teacher appraisal scores. Two regression trees namely the REP tree and M5P algorithms are applied on the data set to bring out new knowledge from it. The algorithms have identified Parameter A as an important factor in teacher’s appraisal. Pruning has been taken as parameter to find the accuracy of the algorithm. The performance of the algorithm is measured using the mean absolute error and the time taken by the algorithms to derive the regression tree. The REP tree algorithm performs better than the M5P algorithm in terms of accuracy as well as the performance.
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A Network-Based Peer Evaluation Strategy
Статья научная
The purpose of this study was to design a network-based peer evaluation strategy -which included two types of peer evaluation (known and unknown) to investigate the effect of these two types on developing problem-solving and critical thinking skills for students in Java object oriented programming language course (OOP). The participants of this study were twenty-four (n=24) students at College of Science and Arts, Qassim university. The results revealed that the two types of peer evaluation (in both known and unknown) had a positive effect on developing problem-solving and critical thinking skills for students. After, the comparison between the post-application of the two experimental groups in the problem solving and critical thinking skills, although there is a slightly higher between mean ranks in the sake of the unknown group, the results showed that there were no significant differences between the two groups. Finally, the researcher recommended to using a network-based peer evaluation strategy with other specializations and a large sample.
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A New Approach for Dynamic Virtual Machine Consolidation in Cloud Data Centers
Статья научная
Cloud computing environments have introduced a new model of computing by shifting the location of computing infrastructure to the Internet network to reduce the cost associated with the management of hardware and software resources. The Cloud model uses virtualization technology to effectively consolidate virtual machines (VMs) into physical machines (PMs) to improve the utilization of PMs. Studies however have shown that the average utilization of PMs in many Cloud data centers is still lower than expected. The Cloud model is expected to improve the existing level of utilization by employing new approaches of consolidation mechanisms. In this paper we propose a new approach for dynamic consolidation of VMs in order to maximize the utilization of PMs. This is achieved by a dynamic programing algorithm that selects the best VMs for migration from an overloaded PM, considering the migration overhead of a VM. Evaluation results demonstrate that our algorithms achieve good performance.
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A New Classification Algorithm for Data Stream
Статья научная
Associative classification (AC) which is based on association rules has shown great promise over many other classification techniques on static dataset. Meanwhile, a new challenge have been proposed in that the increasing prominence of data streams arising in a wide range of advanced application. This paper describes and evaluates a new associative classification algorithm for data streams AC-DS, which is based on the estimation mechanism of the Lossy Counting (LC) and landmark window model. And AC-DS was applied to mining several datasets obtained from the UCI Machine Learning Repository and the result show that the algorithm is effective and efficient.
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Статья
With the rapidly growing use of digital images in medical archival and communication, image compression technology, especially diagnosis lossless compression technology, plays a more and more important role for medical applications. In this thesis, a novel diagnosis loseless compression algorithm is presented for digital mammography. The mammogram is divided into breast region, pectoral muscle and background using the CAD technology. Then mutiple arbitrary shape ROIs coding framework is used to compress the mammogram in which the breast region and pectoral muscle are compressed losslessly and lossily respectively, and the background can be discarded or compressed lossily as user’s will. Experimental results show that the proposed method offer potential advantage in medical applications of digital mammography compression.
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A New High-Performance Bridge Structure for 4-to-2 Compressor using CMOS and CNFET Technology
Статья научная
In this paper, a new high-speed and energy efficient 4-to-2 compressor cell was presented using carbon nanotube field effect transistors (CNFETs). CNFET is very suitable for high-frequency and low-voltage applications. In addition, in this paper several conventional and state-of-the-art 4-to-2 compressor cells are surveyed and compared. In order to evaluate the proposed designs, computer simulations are carried out using 32nm-CMOS and 32nm-CNFET technologies. Simulations are conducted using various low voltage power supplies, different temperatures, frequencies and load capacitors. Results of simulation demonstrate predominance of the proposed design in terms of power consumption, delay, and power-delay product (PDP) compared to other 4-to-2 compressor cells and they confirm that the proposed design is the fastest 4-to-2 compressor in various working circumstances.
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Статья научная
In large data sets data pre-processing always has been the most essential data processing stages. Sampling and using small volumes of data has been an integrated part of data pre-processing to decrease training errors and increase speed of learning. In this study, instead of sampling from all data and using small parts of them, a method has been proposed to not only benefit from sampling but all data be used during training process. In this way, outliers would be detected and even used in completely different way. Using artificial neural networks, new features for instances will be built and the problem of intrusion detection will be mapped as a 10- feature problem. In fact, such a classification is for feature creation and as features in new problem only have discrete values, in final classification decision tree will be used. The results of proposed method on KDDCUP'99 datasets and Cambridge datasets show that this has improved classification in many classes dramatically.
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A New Method for Content based Image Retrieval using Primitive Features
Статья научная
The diminishing expenditure of consumer electronic devices such as digital cameras and digital camcorders along with ease of transportation facilitated by the internet, has lead to a phenomenal rise in the quantity of multimedia data. The need to find a desired image from a collection is shared by many professional groups, including journalists, design engineers and art historians. While the requirements of image users, it can be characterize image queries into three levels. The proposed method based on primitive features such as color and shapes. These features are extracted and used as the basis for a similarity check between images. The shape and color features are extracted through Gradient Edge Detection and color histogram the combination of these features is robust. The experiment results show that the proposed image retrieval is more efficient and effective in retrieving the user interested images.
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A New Method for Graph Queries Processing without Index Reconstruction on Dynamic Graph Databases
Статья научная
Graphs play notable role in daily life. For instance, they are used in variety fields such as social networks, malware detection, and biological networks. Graph data processing performed to extract useful information is known as graph mining. A critical field of graph mining is graph containment problem, in which all graphs containing the query are returned by a graph query q. Scanning the whole database (graph query as a subgraph) for a query is a time consuming process. To improve query performance, an inverted index is constructed on the graph database and then the query is performed based on the query. The problem in this process is that when a graph is added to or removed from a database, the inverted index must be reconstructed. The present study proposes a method in which index updating is not needed upon a change in the database. This feature enables simultaneous inverted index updating and querying. The assessment results showed optimum and satisfactory performance of the proposed method.
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A New Method of Equivalent Material Model Deformation Observation
Статья
Equivalent Material Model is a staple method that can be used to imitate the strata and the ground surface movement which caused by human’s underground activities, such as coal resources extraction. The precision of indoor imitations are mainly decided by ways of models’ deformation observation. This paper proposes a new method of measuring models after analyzing the status of deformation observation of equivalent material models. In this paper, the industrial measuring system is used to measure deformations of the model. The system includes two main parts: photographic surveying of industry instruments and structure lighting scanning devices. The first part is used to get the coordinates of the target points which are set on the model; the second one is used to scan the model surface for catching the cracks on the model surface. Due to the high accuracy of photographic surveying of industry method, it can meet the need of monitoring imperceptible movements of target points; also the structure lighting scanning has a high precision on scanning the model surface, which can get the very thick points cloud of the model surface. The system is originally used for reverse engineering, and it scarcely used for Equivalent material model until our research used, so there is no mature method on data management. This paper researches the special data procession method for the model, and results show that methods in the paper are suit for the industrial measuring system.
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Статья научная
This paper proposes a new 4×4 reversible logic gate which is named as MOG. Reversible gates are logical basic units, having equal number of input and output lines, which can reduce power dissipation in digital systems design through their reversibility feature; because there is a one-to-one corresponding between their input and outputs vectors. The most significant aspect of the MOG gate is that it is a universal gate and has the ability of calculating any logical function on its own. We have also proposed quantum representation of the MOG gate with optimal quantum cost equal to 11. Then, it has been proved that MOG gate can be used to produce a cost efficient reversible full adder/subtractor cell in terms of reversible and quantum metrics. The proposed reversible full adder/subtractor design using MOG gate is a completely optimized circuit in terms of the number of reversible gates, the number of constant inputs, and the number of garbage outputs because it can work with the minimum possible amounts of these reversible metrics. Additionally, it is more efficient than the existing counterparts in terms of quantum cost. The full adder/subtractor cell is an important circuit in VLSI and digital signal processing applications. A lot of works have been done toward designing reversible full adder/subtractors in the literature; but there is no an optimized design with quantum implementation. To prove the applicability of the proposed design in large processing scales, we have constructed 8-bits reversible ripple carry full adder/subtractor circuit using MOG gates. Results have shown the superiority of our proposed design compared with other 8-bits similar designs
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A Novel Active Data Filtration for the Cloud based Architecture against Packet Flooding Attacks
Статья научная
The usage of remote servers network on the Internet to process data, store and manage, instead of using a local server or any computer" is called cloud computing. Cloud computing is that which totally based on resource sharing rather than any other device to handle applications. Today cloud computing is facing numerous challenges and one of those is Attack on the cloud environment. There are many types of hazardous attack on cloud, as the attack is always in wait for some important data or resource. The most common and most affective attack is Packet Flooding attack and there are many faces of packet flooding. EDoS Attack one of the most commonly and strong packet flood attack on the cloud to make the resources almost inaccessible to the user by flooding the unnecessary packet to the network or site more that its capacity. This paper deals with the analysis of EDoS and a mechanism is proposed to mitigate the EDoS by using filtration mechanism. The filtration is done on the basis of secure key Exchange which differentiate legitimate user from attacker. The simulation is done by cloud sim as well as Net-Beans and the performance is analyzed over time and data. Using filter the packet loss and time delay occurs in EDoS attack is much reduced.
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Статья научная
Digital footprints track online behaviors of an individual when communicating over social media platforms. In this paper, sentiment classification is carried out over online posts and tweets to pre detect whether a person is having neurological disorder or not. This study proposed a Hybrid Optimized Model Ensemble STACKed (HOMESTACK) algorithm built on stacked generalization approach that uses stacking and blending ensemble learning technique. The model is then evaluated over two datasets (Reddit Dataset1 & Twitter Dataset2) that include varied number of tweets. The pre-processing of the data and feature extraction is carried out to get cleaned text and vector corpus. The proposed HOMESTACK algorithm is then applied over training data using four base classifiers as Support Vector, Random Forest, K-Nearest Neighbor and CatBoost along with a Meta classifier as Logistic Regression. The testing data is then fed to the tuned model to compare the classification results and analysis. Also, Stacking and Blending ensemble frameworks and algorithms are proposed in this study. Execution time and metric evaluation are calculated in respect of Accuracy, Precision, Recall and F1-score. The experimental results clearly show that the proposed HOMESTACK algorithm performed better over chosen datasets as compared to blending ensemble and standalone machine learning classifiers.
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