International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1080
Genetic algorithm control of model reduction passive quarter car suspension system
Статья научная
This paper portrays the demonstrating, and testing of passive suspension control techniques. The control execution of a two-degree of-opportunity quarter car passive suspension frameworks is explored utilizing Matlab/Simulink, display. A classical Proportional Integral and Derivative (PID), Linear Quadratic Control (LQR), and H2 controller design are proposed and compared with soft computing methods, such Fuzzy logic controller (FLC) and Genetic Algorithm (GA) controller. Simulation environment was used for all design methods, investigation of the effects of the control techniques in time-domain design specifications, their comparison and verification of the results obtained. The results are shows the effectiveness of the (GA) controller to satisfied design requirements compared with others methods.
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Genetic algorithm-based curriculum sequencing model for personalised e-learning system
Статья научная
Personalised learning is a way of organising the learning content and to be accessed by the individual learner in a manner that is suitable to learner’s requirements. There are existing related works on personalised e-learning systems that focused on learner’s preference without considering the difficulty level and the relationship degree that exists between various course concepts. Hence, these affect the learning ability and the overall performance of learners. This research paper presents a genetic algorithm-based curriculum sequencing model in a personalised e-learning environment. It helps learners to identify the difficulty level of each of the curriculum or course concepts and the relationship degree that exists between the course concepts in order to provide an optimal personalised learning pattern to learners based on curriculum sequencing to improve the learning performance of the learners. The result of the implementation showed that the genetic algorithm is suitable to generate the optimal learning path using the values of difficulty level and relationship degree of course concepts. Furthermore, the system classified the learners into three different understanding levels of the course concepts such as partially, moderately and highly successful.
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Graphical Programming Environment for Performing Physical Experiments
Статья научная
This paper presents a way to improve physical experiments at the engineering university level using a graphical programming environment for data acquisition. As a case study it is presented the experimental verification of the law of the magnetic circuit. Such a working method for experimentation opens the way for the future engineer to study physical phenomena using the computer.
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Growing Importance of Distance Education
Статья научная
Distance education can be very effective use of instructional materials with visual, auditory, audiovisual and multimedia content. Visual content can be in the form of text, drawings, pictures, graphics and models, and the like. Auditory facilities are oral presentation or a speech, musical accompaniment, different sounds, etc.. Audiovisual content combine visual and auditory content, usually in the form of television shows, films or videos. Multimedia, combining text, images, sound, animation and video, and playing them before they used very different means, although in recent times for playing multimedia files commonly used multimedia computer, a data storage CD-ROM or the Internet. Using multimedia is extremely important in distance education as a lecturer is usually not physically present with the participants to draw their attention, motivate them to learn and explain the content that students are having difficulty understanding.
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Guidance and Counseling in Nigerian Secondary Schools: The Role of ICT
Статья научная
The importance of guidance and counseling programme in secondary schools, include bringing to the students an increased understanding of the educational, vocational and social information needed to make wise choices. In our society there are many influencing forces responsible for the gradual recognition of formal guidance to young people in various educational levels. This review paper focuses on the role of ICT on guidance and counseling in secondary schools. Counseling is a form of education, which the students receive from their counselors. The essence of incorporating guidance and counseling into the school system was to eliminate overwhelming ignorance of many young people on their choices of career prospects and personality maladjustment among school children. The role of ICT in guidance can be seen in three ways: as a tool, as an alternative, or as an agent of change. The growth of websites and help lines as forms of technically mediated service delivery means that the potential of ICT as a change agent is now greater than ever before. The telephone, websites and e-mail, alongside face-to-face facilities, could be alternative services; or they could be portals into a wide, flexible and well-harmonized network of services. The paper recommends that principals should make provision for guidance and counseling on the school time table. Most importantly secondary school ICT adoption should be encouraged by the ministry of education.
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H-RBAC: A Hierarchical Access Control Model for SaaS Systems
Статья
SaaS is a new way to deploy software as a hosted service and accessed over the Internet which means the customers don’t need to maintain the software code and data on their own servers. So it’s more important for SaaS systems to take security issues into account. Access control is a security mechanism that enables an authority to access to certain restricted areas and resources according to the permissions assigned to a user. Several access models have been proposed to realize the access control of single instance systems. However, most of the existing models couldn’t address the following SaaS system problems: (1) role name conflicts (2) cross-level management (3) the isomerism of tenants' access control (4) temporal delegation constraints. This paper describes a hierarchical RBAC model called H-RBAC solves all the four problems of SaaS systems mentioned above. This model addresses the SaaS system access control in both system level and tenant level. It combines the advantages of RBDM and ARBAC97 model and introduces temporal constraints to SaaS access control model. In addition, a practical approach to implement the access control module for SaaS systems based on H-RBAC model is also proposed in this paper.
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H2E: a privacy provisioning framework for collaborative filtering recommender system
Статья научная
A Recommender System (RS) is the most significant technologies that handle the information overload problem of Retrieval Information by suggesting users with correct and related items. Today, abundant recommender systems have been developed for different fields and we put an effort on collaborative filtering (CF) recommender system. There are several problems in the recommender system such as Cold Start, Synonymy, Shilling Attacks, Privacy, Limited Content Analysis and Overspecialization, Grey Sheep, Sparsity, Scalability and Latency Problem. The current research explored the privacy in CF recommender system and defined the perspective privacy attributes (user's identity, password, address, and postcode/location) which are required to be addressed. Using the base models as Homomorphic and Hash Encryption scheme, we have proposed a hybrid model Homomorphic Hash Encryption (H2E) model that addressed the privacy issues according to defined objectives in the current study. Furthermore, in order to evaluate the privacy level, H2E was implementing in medicine recommender system and compared the consequences with existing state-of-the-art privacy protection mechanisms. It was observed that H2E outperform to other models with respect to determined privacy objectives. Leading to user's privacy, H2E can be considered a promising model for CF recommender systems.
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HFIPO-DPNN: A Framework for Predicting the Dropout of Physically Impaired Student from Education
Статья научная
Education plays a significant role in individuals’ development and economic growth of the developing coun-tries like India. Dropout of students from their studies is the major concern for any order of education. Some models for predicting the dropout of students are developed with several factors. Many of them lacked consistencies as they backed their studies with the academic performance of the students. Especially, for those students suffered with physical impairment the drop out depends on several external factors. Students drop out of school for a variety of reasons, including financial difficulties, parents' unwillingness, distance and a lack of basic amenities, poor educational quality, an inadequate school environment and building, overcrowded classrooms, improper languages of instruction, carelessness on the part of teachers, and security issues in girls' schools. Hence, this work proposes a novel HFIPO-DPNN to predicting the physically handicapped student’s dropout from School also to predict the student dropout rooted on the previous semester marks. The proposed model enclosed the hybrid firefly and improved particle swarm algorithm to optimize the feature selection that influence the dropout of hearing-impaired students. The optimized feature data are used to predict the dropout with the novel DPNN. The optimized data was split and used for training the DPNN. The testing data is used to evaluate the performance of the proposed framework. The outcome for the proposed framework is evaluated on several metrics. The accuracy of the proposed model is about 99.02%. The HFIPO-DPNN framework can be enhanced for predicting the dropout for students with other disabilities. The optimization revealed that factors other than family factors should be taken into account when predicting dropout.
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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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