Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 968
Optimizing Memory using Knapsack Algorithm
Статья научная
Knapsack problem model is a general resource distribution model in which a solitary resource is allocated to various choices with the aim of amplifying the aggregate return. Knapsack problem has been broadly concentrated on in software engineering for a considerable length of time. There exist a few variations of the problem. The study was about how to select contending data/processes to be stacked to memory to enhance maximization of memory utilization and efficiency. The occurrence is demonstrated as 0 – 1 single knapsack problem. In this paper a Dynamic Programming (DP) algorithm is proposed for the 0/1 one dimensional knapsack problem. Problem-specific knowledge is integrated in the algorithm description and assessment of parameters, with a specific end goal to investigate the execution of finite-time implementation of Dynamic Programming.
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Outlier Reduction using Hybrid Approach in Data Mining
Статья научная
The Outlier detection is very active area of research in data mining where outlier is a mismatched data in dataset with respect to the other available data. In existing approaches the outlier detection done only on numeric dataset. For outlier detection if we use clustering method , then they mainly focus on those elements as outliers which are lying outside the clusters but it may possible that some of the unknown elements with any possible reasons became the part of the cluster so we have to concentrate on that also. The Proposed method uses hybrid approach to reduce the number of outliers. The number of outlier can only reduce by improving the cluster formulation method. The proposed method uses two data mining techniques for cluster formulation i.e. weighted k-means and neural network where weighted k-means is the clustering technique that can apply on text and date data set as well as numeric data set. Weighted k-means assign the weights to each element in dataset. The output of weighted k-means becomes the input for neural network where the neural network is the classification and clustering technique of data mining. Training is provided to the neural network and according to that neurons performed the testing. The neural network test the cluster formulated by weighted k-means to ensure that the clusters formulated by weighted k-means are group accordingly. There is lots of outlier detection methods present in data mining. The proposed method use Integrating Semantic Knowledge (SOF) for outlier detection. This method detects the semantic outlier where the semantic outlier is a data point that behaves differently with other data points in the same class or cluster. The main motive of this research work is to reduce the number of outliers by improving the cluster formulation methods so that outlier rate reduces and also to decrease the mean square error and improve the accuracy. The simulation result clearly shows that proposed method works pretty well as it significantly reduces the outlier.
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P300 Detection Algorithm Based on Fisher Distance
Статья научная
With the aim to improve the divisibility of the features extracted by wavelet transformation in P300 detection, we researched the P300 frequency domain of event related potentials and the influence of mother wavelet selection towards the divisibility of extracted features, and then a novel P300 feature extraction method based on wavelet transform and Fisher distance. This can select features dynamically for a particular subject and thereby overcome the drawbacks of no systematic feature selection method during traditional P300 feature extraction based on wavelet transform. In this paper, both the BCI Competition 2003 and the BCI Competition 2005 data sets of P300 were used for validation, the experiment results showed that the proposed method can increase the divisibility by 121.8% of the features extracted by wavelet transformation, and the classification results showed that the proposed method can increase the classification accuracy by 1.2% while reduce 73.5% of the classification time. At the same time, integration of multi-domain algorithm is proposed based on the research of EEG feature extraction algorithm, and can be utilized in EEG preprocessing and feature extraction, even classification.
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PSICO-A: A Computational System for Learning Psychology
Статья научная
PSICO-A is a new educational system, based on the web, for learning psychology. Its computational architecture consists of a front-end and a back-end. The first one contains a design mode, a reflective mode, a game mode and a simulation mode. These modes are connected to the back-end, which is composed of a rule engine, an evaluation module, a communication module, an expert module, a student module and a metacognitive module. The back-end is the heart of the system analysing the performance of pupils. PSICO-A assembles Boolean equations introducing algorithms such as those of Levenshtein, Hamming, Porter and Oliver. The system design used the programming language PHP5 for a clear and fast interface. PSICO-A is an innovative system because it is the first system in psychology designed for assessing the value of computer-based learning games compared with simulations for teaching the subject. Other systems use virtual environments for teaching subjects like mathematics, physics or ecology to children but the role of digital games and simulations in learning psychology is to date an unexplored field. A preliminary analysis of the motivational value of the system has been performed with sample of undergraduate students, verifying its advantages in terms of to encouraging scientific exploration. An internal evaluation of the system, using the game mode, has been conducted.
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Peculiarities of Manifestation of Student Youth' Roles and Positions in the Cyberbullying Process
Статья научная
To date, with the spread of the global pandemic, more and more student youth are involved in learning and living in cyberspace. Isolation and self-isolation contribute to the transfer of communication to cyberspace, which leads to more young people being in online space. However, as the authors point out, along with the intensification of communications, there are also destructive manifestations of behavior in the Internet space, which include cyberbullying. Cyberbullying is a systematic manifestation of destructive behavior, which contains a socio-role structure, where the initiator or group of bullies are morally and psychologically bullying in order to harm the victim, using information and communication tools. The growth of cyberbullying among student youth leads to demotivation in educational activities, low self-esteem, and even to post-traumatic stress disorder, etc. This encourages scientists, psychologists and teachers to study this phenomenon more deeply to understand its nature, causes, possible psychological diagnosis and, further, to create tools for psychoprophylaxis and psychocorrection, which aims to help the younger generation develop harmoniously as personalities, as well as future professionals. To date, the holistic method of diagnosing the manifestations of cyberbullying and uncertain features of its impact on student youth is insufficiently developed in psychology. The proposed study will expand knowledge about the means of detecting psychological violence in social networks in terms of professional training and personal development of students, which has theoretical and practical significance in psychological and pedagogical activity. The article highlights the results of the study of cyberbullying manifestations among student youth, which are due to the factors of the negative impact of information and communication technologies on the process of their formation in the digital space. The aim of the article was to identify the features of the manifestation of cyberbullying and its impact on student youth. Methods of empirical research: to detect cyberbullying among students, the method to identify roles and positions held by young people (author Norkina O.; modification of Podkopaieva Y., Hordiienko K.), method "Determination of self-esteem level" (author Kovalev S.), individual interviews, questionnaires were used. Within the survey method, the questionnaire of Makarova O. "Psychological features of cyberbullying as a form of Internet crime" (modified by K. Hordiienko) was used. To process and interpret the measurement results by comparing primary self-assessment statistic data from different groups, namely cyberbullies, assistants, defenders, victims and witnesses, the percentages of obtained data, descriptive statistics and the Kolmogorov-Smirnov criterion for one sample using IBM SPSS Statistics Base 22.0 were used. Based on the obtained results and their comparison, tables and figures of data to be analyzed were constructed. Results: the motives for the use of social networks by students are clarified; the roles and dominant types of student positions during cyberbullying are specified; the peculiarities of the response of young people to cyberbullying in social networks are determined; the correspondence between the roles and positions occupied by students during cyberbullying and their self-esteem is established. Empirically, new data on cyberbullying among students have been obtained, which will provide an impetus for the development of methods of prevention and psychocorrection of Internet violence among students of higher education institutions and improvement of knowledge and understanding in the field of cyberpsychology. The quality of the research is the new results about cyberbullying, its features, manifestations, roles and their relationship with self-esteem, which expands understanding and concretizes the problem among students, and thus gives an understanding of how to deal with this negative phenomenon.
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Perceived Gender Classification from Face Images
Статья научная
Perceiving human faces and modeling the distinctive features of human faces that contribute most towards face recognition are some of the challenges faced by computer vision and psychophysics researchers. There are many methods have been proposed in the literature for the facial features and gender classification. However, all of them have still disadvantage such as not complete reflection about face structure, face texture. The features set is applied to three different applications: face recognition, facial expressions recognition and gender classification, which produced the reasonable results in all database. In this paper described two phases such as feature extraction phase and classification phase. The proposed system produced very promising recognition rates for our applications with same set of features and classifiers. The system is also real-time capable and automatic.
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Статья научная
The introduction and application of computer as a resource in teaching and learning process in education has contributed to the achievement of educational objectives. Students at all levels of education use computers for specified purpose. Do computers really help students to achieve academic excellence? The study investigated the perception of students on computer utilization and academic performance. It is a descriptive research with emphasis on survey design. The population comprised all Colleges of Education in North Central geopolitical zone of Nigeria: made up of six states and FCT-Abuja; out of which six colleges were selected as sample. A 20-item questionnaire (CUSAPQ) was designed and validated through expert judgment and reliability co-efficient of 0.86 was obtained. The null hypotheses were tested using Chi-square and ANOVA statistical analysis at 0.05 level of significance. Findings revealed that there was positive perception of computer utilization on students' academic performance in the selected zone. Based on the findings, recommendations were made; seminar and workshops on computer utilization should be organised for the lecturers and the students to facilitate active and effective learning.
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Performance Analysis and Enhancement of UTM Device in Local Area Network
Статья научная
Along with the growth of the computer system and networks, the mysterious and malicious threats and attacks on the computer systems are also increasing exponentially. There is a need of continuous evaluation of the security of a network and enhancement of the network attack detection system, which will be able to detect different attacks along with the characteristics of the attacks. In previous work, the port scan attack is considered as precursors to an attack and the target was to provide the mitigation technique for the particular port scan attack. There have been relatively few empirical studies done for port scan related attacks and those that do exist may no longer reflect the impact of such attacks on the functionalities of the UTM/network device and on the network. To address this lack of knowledge, this experiment is carried out in fully controlled test bed environment wherein a set of varieties of attack can be simulated and impact of attack(s) is analyzed and appropriate mitigation technique is suggested to mitigate the port scan attack. The experiment result indicates that the port scan mitigation implementation on UTM helps reducing the load on the UTM device and reduces network congestion effectively.
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Performance Analysis of Live and Offline VM Migration Using KVM
Статья научная
Virtualization is a core technology used for the implementation of cloud computing. It increases the utilization of resources such as processor, storage, network etc. by collecting various underutilized resources available in the form of a shared pool of resources built through the creation of Virtual Machines (VMs). The requirements in cloud environment are dynamic therefore there is always a need to move virtual machines within the same cloud or amongst different clouds. This is achieved through migration of VMs which results in several benefits such as saving energy of the host, managing fault tolerance if some host is not working properly and load balancing among all hosts. In this experimental study, effort has been made to analyze the performance of offline and live VM migration techniques with respect to total migration time and downtime of VM migration. Kernel-based Virtual Machine (KVM) hypervisor has been used for virtualization and a series of experiments have been carried out in computer service center of IIT Delhi on their private cloud Baadal. The experiment results show that downtime during live migration is very less in comparison to the offline migration while the total migration time is more in comparison to the offline migration.
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Performance Analysis of Routing Protocols for Target Tracking in Wireless Sensor Networks
Статья научная
Wireless sensor networks (WSNs) are large scale integration in large topology deployed with thousands sensor nodes. Nodes sizes are very small, with low cost, low weight, and limited battery, primary storage, processing power. Sensor nodes have wireless communication capabilities with sensor to monitor physical or environmental conditions. This paper study and evaluate performance for localization and target tracking application with proposed hierarchical localization tracking scheme based on hierarchical binary tree structure. The target detected information is stored at multiple sensor nodes (e.g. node, parent node and grandparent node) which deployed using complete binary tree structure to improve fault tolerance. This drastically reduces number of messaging in the network. Performance of proposed scheme and some existing routing scheme is evaluated using NS2. Simulation result proof increased in network lifetime by 25%, target detection probability by 25%, and reduces error rate by 20%, increased energy efficiency by 20%, fault tolerance, and routing efficiency.
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Performance Analysis of Schedulers to Handle Multi Jobs in Hadoop Cluster
Статья научная
MapReduce is programming model to process the large set of data. Apache Hadoop an implementation of MapReduce has been developed to process the Big Data. Hadoop Cluster sharing introduces few challenges such as scheduling the jobs, processing data locality, efficient resource usage, fair usage of resources, fault tolerance. Accordingly, we focused on a job scheduling system in Hadoop in order to achieve efficiency. Schedulers are responsible for doing task assignment. When a user submits a job, it will move to a job queue. From the job queue, the job will be divided into tasks and distributed to different nodes. By the proper assignment of tasks, job completion time will reduce. This can ensure better performance of the jobs. By default, Hadoop uses the FIFO scheduler. In our experiment, we are discussing and comparing FIFO scheduler with Fair scheduler and Capacity scheduler job execution time.
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Performance Comparison of Routing protocols Using Different Mobility Models
Статья научная
Communication in Mobile Ad Hoc Network (MANET) is accomplished using routing protocols. These protocols provide an efficient and reliable path for data sharing. In static environment where the nodes are stationary these protocols performs exceptionally well but in an environment having mobile nodes the performance of these protocols degrade drastically. To investigate this factor various researchers developed mobility models using simulation tools such as QUALNET, NS-2 etc. These models represent the erratic movement of nodes and give us an idea regarding their location, velocity and acceleration change over time. This paper is an effort to study the effect of mobility models such as Random Way Point, File and Group Mobility Models on the performance of routing protocols using QUALNET simulator. The results show that the choice of mobility models affect the performance of routing protocol significantly.
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Performance Comparison of the Optimized Ensemble Model with Existing Classifier Models
Статья научная
The purpose of this study is to conduct an empirical investigation and comparison of the effectiveness of various classifiers and ensembles of classifiers in predicting academic performance. The study will evaluate the performance and efficiency of ensemble techniques that employ several classifiers against the performance and efficiency of a single classifier. Reducing student attrition is a serious concern for educational institutions worldwide. Educators are looking for strategies to boost student retention and graduation rates. This is only achievable if at-risk students are appropriately recognized early on. However, most commonly used predictive models are inefficient and inaccurate due to intrinsic classifier limitations and the usage of minor factors. The study contributes to the body of knowledge by proposing the development of optimized ensemble learning model that can be used for improving academic performance prediction. Overall, the findings demonstrate that the approach of employing optimized ensemble learning (OEL) model approaches is extremely efficient and accurate in terms of predicting student performance and aiding in the identification of students who are in the fear of attrition.
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Performance Evaluation of DWT Compared to DCT for Compression Biomedical Image
Статья научная
The image compression has for objective to reduce the volume of data required by the encoding of image, for applications of transmission or saving. For this we use the redundancies which exists within an image (a pixel has a good chance of having a luminance close to those of its neighbors) or between successive images in a sequence. We limit ourselves to the exploitation of redundancies within an image and we will work on gray level images of size 512x512. For image coding we chose an encoder based on progressive coding of data, coder is EZW (EMBEDDED WAVELET ZeroTree ENCODING, Shapiro 1993), the basis of this encoder a comparison is made between two types of transforms DWT (DISCREET WAVELETS TRANSFORM) and DCT (DISCRETE COSINE TRANSFORM) just to have the type of transformation that allows us to have a better visual quality of the image after decomposition. . Visual quality image is judged by two important devaluation parameters PSNR and MSSIM.
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Performance Evaluation of MANET in Realistic Environment
Статья научная
In order to facilitate communication in Mobile Ad hoc Network (MANET), routing protocols are developed. The performance of these protocols depends upon various factors such as: transmission range, number of nodes deployed and mobility of the nodes. Another factor which affects the performance of MANET routing protocols is the environment in which ad hoc network is deployed. The MANET environment may contain obstacles such as mountains lakes, buildings and river. These obstacles restrict nodes movement but may or may not obstruct the effective transmission range of nodes deployed. This paper is an effort to evaluate the performance of MANET routing protocols in presence of obstacles by designing a simulator in MATLAB-10. To make the situation more realistic obstacle of different shapes, size, number and type were introduced in the simulation region. We found significant impact of the same on the performance of routing protocols.
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Performance Evaluation of Routing Protocol with Selfish Users in Realistic Environment
Статья научная
Mobile Ad hoc Network (MANET) consists of wireless mobile nodes that can be set up any time at any place without the requirement of pre-requisite infrastructure. The nodes in these networks have several constraints such as battery power, processing capability and bandwidth. Moreover each node in MANET has to act as a relay node for others for the successful network operations. In an ideal environment in spite of above mentioned limitations, the node performs this community task faithfully but as in real world there exists nodes with selfish attitude also. Therefore, this paper is an effort to evaluate the efficacy of network with nodes having such behavior prevailing in realistic environment. Various researchers have evaluated the network performance in idealistic conditions but none has made an effort to evaluate it in practical condition such as in presence of obstacles. To make the scenario realistic different number, type and shape of obstacles were taken. The work was accomplished by designing a simulator in MATLAB-11.
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Performance Evaluation of TPA-HE Based Fine Grained Data Access for Cloud Computing
Статья научная
As the software technology evolves the focus of users are shifting form devices for data or information. This transformation requires reliable and scalable computing paradigms which satisfy the users processing and storage requirements. Service based, distributed, grid and web 2.0 are some of the most famous computing technologies. Conversions are occurring towards less managements and maintenance issues and despite of that the usage experience should be increased. But there are some security concerns like security, access control, privacy & isolation based trusted service delivery raises due to the data in an outsourced environment. Thus, several policies are created to define its boundaries. Also the type of user accessing the data and the service provided by the cloud needs to be verified. Thus the uses trust over the system can go down if the interoperability and security of services are satisfactory. To providing confidentiality to users data encryption is the traditional options which require decryption for reading or retrieving the data. But in outsourced environment the user is frequently accessing its data which may increase the overhead of performing such frequent encryption and then decryptions. Also for performing any operations the data need to be decrypted. It is something treating as a complex usage boundary. Thus, Homomorphic encryption is used to deal with such situations. This paper proposes a novel Third Party and Homomorphic Encryption (TPA-HE) based mechanism for secure computing. In this third party auditor and service provider is used for authentication and authorization of services & user profiles. It has three basic entities TPA, Cloud Service Provider, Encryption & Monitoring service to regularly analyze the security breaches in access & data transfer mechanism. To prove the effectiveness of suggested approach some of the results are taken which are better than the existing mechanism.
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Performance Evaluation on the Effect of Combining DCT and LBP on Face Recognition System
Статья научная
In this paper, we introduce a face recognition algorithm based on doubly truncated multivariate Gaussian mixture model with Discrete Cosine Transform (DCT) and Local binary pattern (LBP). Here, the input face image is transformed to the local binary pattern domain. The obtained local binary pattern image is divided into non-overlapping blocks. Then from each block the DCT coefficients are computed and feature vector is extracted. Assigning that the feature vector follows a doubly truncated multivariate Gaussian mixture distribution, the face image is modelled. By using the Expectation-Maximization algorithm the model parameters are estimated. The initialization of the model parameters is done by using either K-means algorithm or hierarchical clustering algorithm and moment method of estimation. The face recognition system is developed with the likelihood function under Bayesian frame. The efficiency of the developed face recognition system is evaluated by conducting experimentation with JNTUK and Yale face image databases. The performance measures like half total error rate, recognition rates are computed along with plotting the ROC curves. A comparative study of the developed algorithm with some of the earlier existing algorithm revealed that this system perform better since, it utilizes local and global information of the face.
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Performance enhancement of machine translation evaluation systems for English – Hindi language pair
Статья научная
Machine Translation (MT) is a programmed conversion in which computer software is utilized to convert manuscripts from one Natural Language (like English) to a different Language (such as Hindi). To process any such conversion, through human or through automatic means, the conversion must be established such that it reinstate the complete sense of a manuscript from its base (source) linguistic into the target language. In this paper, the study of prevailing evaluation systems along with assessing their performance is achieved through the similarity metrics. Moreover, the authors have also presented an improved technique of translation employing features of Natural Language Processing and consequently, to acquire an enhanced and more accurate assessing Machine Translation system, a corpus is selected and the outcomes are compared with the prevailing methods. Besides this, two well-known systems such as Google and Bing decoders are selected to inquire and to assess the study of metrics called similarity metrics through Assessment of Text Essential Characteristics score. This is found to provide more accuracy than prevailing methods. Furthermore, evaluations are tested under various metrics systems like Jaccard similarity metrics, cosine similarity metrics, and sine metrics to deliver enhanced accuracy than prevailing methods.
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Performing Inquisitive Study of PM Traits Desirable for Project Progress
Статья научная
The accountability of a project’s success/failure lies on shoulders of a PM (project manager). Undoubtedly, project management is tough task to bring about and this is the most challenging role within the project. The project manager role varies from project to project and may include communication & negotiation with stakeholders, along with leadership and management of the project. Therefore he must possess both hard and soft skills besides education and expertise to drive his team towards excellence. This scientific documentation presents an ideal blend of responsibilities and skills essential for a project manager to cope with the changing project environment. Technical skills necessary for an IT project manager, further elaborates this study.
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