Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1064
OCR for Printed Bangla Characters Using Neural Network
Статья научная
Optical Character recognition is a buzzword in the field of computing. Artificial neural networks are being used to recognize characters for a long time. ANN has the ability to learn and model non-linear and complex relationships, which is really important because in real life, many of the relationships between inputs and outputs are non-linear as well as complex. Research in the field of OCR with Bangla language is not as vast as the English language. So, there is a scope of research in this area. It can be used to search and scan hundreds of Bangla documents within seconds and can easily manipulate the data. It is developed for various purpose like for vision impaired person where OCR software can help turn books, magazines and other printed documents into accessible files that they can listen. The limitation of traditional OCR are sufficient dataset is not available, all different font of characters are not available and there are lots of complex and similar shape characters for which accuracy not good. In our research, we first tried to make a dataset large enough so that we can train our neural network as they require big data to train. We built our own dataset of 2,97,898 Bangla single character images of different fonts . Then for implementing neural network we used Scikit-learn’s multi-layer perceptron classifier and we also implemented our own multi-layer feed forward back propagation neural network using a machine learning framework named Tensorflow. We have also built a GUI application to demonstrate the recognition of Bangla single character images.
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Offline Handwritten Gurmukhi Numeral Recognition using Wavelet Transforms
Статья научная
This paper presents an OCR (optical character recognition) system for the handwritten Gurmukhi numerals. A lot of work has been done in recognition of characters and numerals of various languages like English, Chinese, and Arabic etc. But in case of handwritten Gurmukhi script very less work has been reported. Different Wavelet transforms are used in this work for feature extraction. Also zonal densities of different zones of an image have been used in the feature set. In this work, 100 samples of each numeral character have been used. The back propagation neural network has been used for classification. An average recognition accuracy of 88.83% has been achieved.
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On Computing the Edge-balanced Index Sets of the Circle Union Graph F (3, n)
Статья научная
On the basis of power-cycle nested network graph, the edge-balanced index sets of circle union graph F(3,n) graph were investigated. A new method of changing index is provided, simplifying the proving process. It reduced the difficulty of circle union graph F(3,n) graph labeling because of the novel design of the basic graph and single-point sector subgraph. The results show that the edge-balanced index sets of circle union graph F(3,n) graph. This paper has proved the existence of the edge-balanced index sets of one class of circle union graph, the computational formulas and the construction of the corresponding graphs are also provided.
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On Correlation of Р And NP Classes
Статья научная
It is shown an incorrectness of introduction of a class of NP-complete problems, which reason is that Cook’s S.А. theorem on that the “satisfiability” problem is the universal NP-complete problem, is not true and, therefore, the issue on existence of at least one NP-complete problem remains open, that explains failures of attempts to estimate correlations between P and NP classes.
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Статья научная
Universities in South Africa (SA) are facing several challenges due to the influx of students with diverse backgrounds entering the first year. One of such challenges is the use of technologies for teaching and learning. Institutions in the rural areas are flooded with first year students characterized as under-prepared, educationally underprivileged and had little or no access to computer usage prior to their enrolment. These qualities impedes their transition into the computer-based learning system and other technologies that supports teaching and learning. Moreover, the students are not given the needed assistance when enrolled. Orientation programme that would have been a leverage is only informative and not supportive in nature. Thus, an effective solution requires orientation programme to be supportive. It should involve assessing students' profile during their first year registration to provide them with the needed assistance in terms of technologies usage. This paper conducted a pilot survey over a sample of first year entering students in the University of Venda (UNIVEN). The objective was to assess students in terms of technology-related uses, expectations, experiences, skill levels and training needs. Data collected were analyzed and the results show students' have not used computers or had experience on technologies for teaching and learning in their previous schools. Additionally, students are only technologically identified with their mobile phones. The study proposed a new programme called First Experience Computer Literacy (FECOL) to facilitate students' transition into the computer-based learning of the university.
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On the Edge-balanced Index Set of a class of Power-cycle Nested Network Graph
Статья научная
Based on the research of Power-cycle Nested Graph blob.png, the decomposition method of single point sector has come up. By the use of the process of clawed nested-cycle sub-graph, the edge-balanced index sets of the power-cycle nested graph blob.png are solved when m ≥4 and m=4(mod5) . Besides, the constructive proofs of the computational formulas are also completed. The theory can be applied to information engineering, communication networks, computer science, economic management, medicine, etc. The proving method can be a reference to solve the problem of the power-cycle nested graph blob.png.
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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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