International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1080
Ontology-Alignment Techniques: Survey and Analysis
Статья научная
The ontology alignment consists in generating a set of correspondences between entities. These entities can be concepts, properties or instances. The ontology alignment is an important task because it allows the joint consideration of resources described by different ontologies. This paper aims at counting all works of the ontology alignment field and analyzing the approaches according to different techniques (terminological, structural, extensional and semantic). This can clear the way and help researchers to choose the appropriate solution to their issue. They can see the insufficiency, so that they can propose new approaches for stronger alignment. They can also adapt or reuse alignment techniques for specific research issues, such as semantic annotation, maintenance of links between entities, etc.
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Ontology-Based Semantic Annotation of Arabic Language Text
Статья научная
Semantic annotation is the process of adding semantic metadata to resources. Semantic metadata is data concerning the meaning of entities and the relationships that exist. Semantic annotation cannot be performed without an ontology suitable for the task. In this research paper, we describe the design, implementation, and evaluation of a lexical ontology for Arabic semantic relations. The main purpose of the ontology is to facilitate the task of semantic annotation of the Arabic textual content. The ontology was evaluated for usability and usefulness using a prototype system for the automated semantic annotation of Arabic text. The results of the evaluation indicated that the ontology was fit for the purpose of semantic annotation of Arabic text with lexical relations. The evaluation has also revealed important findings and recommendations for designing Arabic semantic annotation tools.
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Optimal Control of Model Reduction Binary Distillation Column
Статья научная
A binary distillation process with desired composition rate is considered. The aim is to find a control (Top and bottom compositions) which is optimal with respect to energy consumption and which is robust at the same time with respect to the response speed(less time) and minimum overshot. The solution approach is based on the formulation of two optimization techniques, Invasive Wood (IWO) and Differential Evolution (DE) with respect to Integral Square Error (ISE) and Integral Absolute Error (IAE) fitness function with using Proportinal_Integral-Derivative (PID) controller. An overall model including the dynamics of the distillation process is assumed with model reduction methods. This optimal control is compared with classical approach. The numerical results are presented and showed the effectiveness of the proposed control. MATLAB package is used for simulation and analysis.
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Статья научная
In this paper a novel flexible planning strategy based on the teaching-learning-based optimization (TLBO) algorithm and pattern search algorithm (PS) is proposed to improve the security optimal power flow (SOPF) by minimizing the total fuel cost, total power loss and total voltage deviation considering critical load growth. The main particularity of the proposed hybrid method is that TLBO algorithm is adapted and coordinated dynamically with a local search algorithm (PS). In order validate the efficiency of the proposed strategy, it has been demonstrated on the Algerian 59-bus power system and the IEEE 118-bus for different objectives considering the integration of multi SVC devices. Considering the interactivity of the proposed combined method and the quality of the obtained results compared to the standard TLBO and to recent methods reported in the literature, the proposed method proves its ability for solving practical planning problems related to large power systems.
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Статья научная
Queuing by patients in the out-patients department to access hospital services in Nigeria teaching hospitals is a teething concern to most healthcare providers. This causes inconvenience to patients and economic costs to the hospitals. Patients waiting for minutes, hours, days or months to receive medical services could result in waiting costs to them. Providing too much service could result in excessive costs. Also not providing adequate services could result in excessive waiting and costs. This study sought to determine an optimal server level and at a minimum total cost which include waiting and service costs in homogenous servers in order to reduce patients' congestions in the hospital as low as reasonably practicable. The queuing characteristics in all the twenty-three (23) teaching hospitals in Nigeria were analysed using a Multi-server Queuing Model and the waiting and service costs determined with a view to ascertaining the optimal service level. The data for this study were collected through observations and interviews. The data was analysed using Quantitative Methods, Production and Operations Management (POM QM) and Queuing Theory Calculator Software as well as using descriptive analysis. The results of the analysis demonstrated that average queue length, waiting time of patients as well as over utilization of specialist doctors at the teaching hospitals could be reduced at an optimal server level and at a minimum total cost as against their present server level with high total cost which include waiting and service costs. Therefore, this call for refocusing so as to improve the overall patient care in our cultural context and meet the patient needs in our environment.
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Статья научная
One crucial and intricate problem in the education sector that must be dealt with is children who initially enrolled in schools but later dropped out before finishing mandatory primary education. These children are generally referred to as out-of-school children. To contribute to the discuss, this paper presents the development of a robust Multilayer Perceptron (MLP) based Neural Network Model (NN) for optimal prognostic learning of out-of-school children trends in Africa. First, the Bayesian optimization algorithm has been engaged to determine the best MLP hyperparameters and their specific training values. Secondly, MLP-tuned hyperparameters were employed for optimal prognostic learning of different out-of-school children data trends in Africa. Thirdly, to assess the proposed MLP-NN model's prognostic performance, two error metrics were utilized, which are the Correlation coefficient (R) and Normalized root means square error (NRMSE). Among other things, a higher R and lower NRMSE values indicate a better MLP-NN precision performance. The all-inclusive results of the developed MLP-NN model indicate a satisfactory prediction capacity, attaining low NRMSE values between 0.017 - 0.310 during training and 0.034 - 0.233 during testing, respectively. In terms of correlation fits, the out-of-school children's data and the ones obtained with the developed MLP-NN model recorded high correlation precision training/testing performance values of 0.9968/0.9974, 0.9801/0.9373, 0.9977/0.9948 and 0.9957/0.9970, respectively. Thus, the MLP-NN model has made it possible to reliably predict the different patterns and trends rate of out-of-school children in Africa. One of the implications for counselling, among others, is that if every African government is seriously committed to funding education at the foundation level, there would be a reduction in the number of out-of-school children as observed in the out-of-school children data.
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Optimization and Tracking of Vehicle Stable Features Using Vision Sensor in Outdoor Scenario
Статья научная
Detection and tracking of stable features in moving real time video sequences is one of the challenging task in vision science. Vision sensors are gaining importance due to its advantage of providing much information as compared to recent sensors such as laser, infrared, etc. for the design of real–time applications. In this paper, a novel method is proposed to obtain the features in the moving vehicles in outdoor scenes and the proposed method can track the moving vehicles with improved matched features which are stable during the span of time. Various experiments are conducted and the results show that features classification rates are higher and the proposed technique is compared with recent methods which show better detection performance.
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Optimization of SVM Multiclass by Particle Swarm (PSO-SVM)
Статья научная
In many problems of classification, the performances of a classifier are often evaluated by a factor (rate of error).the factor is not well adapted for the complex real problems, in particular the problems multiclass. Our contribution consists in adapting an evolutionary method for optimization of this factor. Among the methods of optimization used we chose the method PSO (Particle Swarm Optimization) which makes it possible to optimize the performance of classifier SVM (Separating with Vast Margin). The experiments are carried out on corpus TIMIT. The results obtained show that approach PSO-SVM gives a better classification in terms of accuracy even though the execution time is increased.
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Статья научная
Autism Spectrum Disorder (ASD) is a neurodevelopmental syndrome which cannot be curable but can be predicted in early stage. Early prediction and cure may help to diagnose the autism. In existing methods, prediction of best feature is not identified for detecting the autism in early stage. In this proposed research, prediction of ASD has been done by identifying the best feature transformation technique with best ML classifier and finding out the most significant feature for diagnosis of autism in early age. Early-detected ASD datasets pertaining to toddler and child are collected and applied few Feature transformation techniques, comprising log, power-box-cox and yeo-Johnson transformations to these datasets. Then, using these ASD datasets, several classification approaches were applied, and their efficiency was evaluated. Adaboost given 100% accuracy for toddler dataset and whereas, Random forest showed 98.3% accuracy for child datasets. The feature transformations ensuing the best prediction was Log, Power- Box cox and Yeo-Johnson Transformation for toddler and Log transformation for children datasets. After these exploration, various feature selection techniques like univariate (UNI) and recursive feature elimination (RFE) are applied to these transformed datasets to recognize the most significant ASD risk feature to predict the autism in early stage for toddler and child data. It is found that A5 feature is most significant feature for toddler, A4 stands most significant feature for child based on univariate and RFE. This benefits the doctor to provide the suitable diagnosis in their early stage of life. The results of these logical methodologies show that ML methods can yield precise predictions of ASD when they are accurately optimised. This shows that using these models for early ASD detection may be feasible.
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Optimizing Knapsack Problem with Improved SHLO Variations
Статья научная
The Simple Human Learning Optimization (SHLO) algorithm, drawing inspiration from human learning mechanisms, is a robust metaheuristic. This study introduces three tailored variations of the SHLO algorithm for optimizing the 0/1 Knapsack Problem. While these variants utilize the same SHLO operators for learning, their distinctiveness lies in how they generate new solutions, specifically in the selection of learning operators and bits for updating. To assess their efficacy, comprehensive tests were conducted using four benchmark datasets for the 0/1 Knapsack Problem. The results, encompassing 42 instances from three datasets, reveal that both SHLO and its proposed variations yield optimal solutions for small instances of the problem. Notably, for datasets 2 and 3, the performance of SHLO variations 2 and 3 outpaces that of the Harmony Search Algorithm and the Flower Pollination Algorithm. In particular, Variation 3 demonstrates superior performance compared to SHLO and variations 1 and 2 concerning optimal solution quality, success rate, convergence speed, and execution time. This makes Variation 3 notably more efficient than other approaches for both small and large instances of the 0/1 Knapsack Problem. Impressively, Variation 3 exhibits a remarkable 14x speed improvement over SHLO for large datasets.
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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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Overview of Deaf Education in Morocco
Статья научная
This paper provides a comprehensive overview of Deaf Education in Morocco documenting its historical evolution and systematically assessing current instructional methodologies. With a focus on learning and teaching environments, the study aims to offer a wide understanding of the educational opportunities, teaching methods, and teacher training programs within Moroccan schools serving the Deaf community. The research questions guide the inquiry addressing historical paths, the influence of teaching methods, and common challenges. By identifying challenges and evaluating practices, the research makes methodological and theoretical contributions to the fields of special education and Deaf education in Morocco. This foundational resource, which is lacking in Moroccan research, serves as a basis for future investigations into instructional approaches. The study navigates through Morocco’s educational history from colonial impact to post-independence reforms emphasizing challenges like pedagogical strategies, infrastructure limitations, and social integration issues. The findings confirm the importance of shifting negative attitudes, fostering inclusivity, and reassessing policies to enhance the educational journey for Deaf learners in Morocco.
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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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