Turkish Journal of Computer and Mathematics Education (TURCOMAT)

Journal Information
ISSN / EISSN : 1309-4653 / 1309-4653
Published by: Auricle Technologies, Pvt., Ltd. (10.17762)
Total articles ≅ 2,228
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Latest articles in this journal

Et. Al. Dr. Saikumari V
Turkish Journal of Computer and Mathematics Education (TURCOMAT), Volume 12, pp 411-416; doi:10.17762/turcomat.v12i11.5892

In today’s corporate world, many companies and organization are increasingly focusing on human capital as a competitive advantage in rapidly changing environment. Many successful companies realize that their employees are their greatest asset. Therefore, companies are increasingly investing in educating their own employees so that they can grow and change within the company and make it more profitable. The range of training opportunities varies considerable from company to company so, when researching potential employers, it is important for job seekers who care about this to investigate the level and type of training provided to employees. After employees have been selected for various positions in an organization, training them for specific tasks to which they have been assigned assumes greater importance. This study suggests the organization to implement more modern training methodologies, then to provide practical training to the employees and to provide specific learning assignments or projects for participants to improve on their competency gap.
Et. Al. Sarthika Dutt
Turkish Journal of Computer and Mathematics Education (TURCOMAT), Volume 12, pp 1886-1891; doi:10.17762/turcomat.v12i11.6142

Dysgraphia is a disorder that affects writing skills. Dysgraphia Identification at an early age of a child's development is a difficult task. It can be identified using problematic skills associated with Dysgraphia difficulty. In this study motor ability, space knowledge, copying skill, Visual Spatial Response are some of the features included for Dysgraphia identification. The features that affect Dysgraphia disability are analyzed using a feature selection technique EN (Elastic Net). The significant features are classified using machine learning techniques. The classification models compared are KNN (K-Nearest Neighbors), Naïve Bayes, Decision tree, Random Forest, SVM (Support Vector Machine) on the Dysgraphia dataset. Results indicate the highest performance of the Random forest classification model for Dysgraphia identification.
Et. Al. N.Bhaskar
Turkish Journal of Computer and Mathematics Education (TURCOMAT), Volume 12, pp 1892-1897; doi:10.17762/turcomat.v12i11.6143

This paper is made based on the requirement of different domain users for effective hearing system. The system uses different devices to enhance the audio capability of different users at different application environment. The system uses IoT devices, sensors and different gateways for effective use of IoT based systems. The system will enhance the hearing capability of all users at different conference or personal or professional environments for the effective hearing capability among different hearing capabilities. It provides the environment to increase or decrease the volume of the source system based on the hearing capability of participants in the hearing environment. It can be used in any domain like conferences, home environment, education institutions and at public locations. It also adopts a security algorithm to protect the customer’s data in encrypted format at cloud system. It recognizes the candidate at any time based on the ID assigned to an individual.
Et. Al. Tamanna Siddiqui
Turkish Journal of Computer and Mathematics Education (TURCOMAT), Volume 12, pp 1916-1924; doi:10.17762/turcomat.v12i11.6144

Sarcasm is well-defined as a cutting, frequently sarcastic remark intended to fast ridicule or dislike. Irony detection is the assignment of fittingly labeling the text as’ Sarcasm’ or ’non- Sarcasm.’ There is a challenging task owing to the deficiency of facial expressions and intonation in the text. Social media and micro-blogging websites are extensively explored for getting the information to extract the opinion of the target because a huge of text data existence is put out into the open field into social media like Twitter. Such large, openly available text data could be utilized for a variety of researches. Here we applied text data set for classifying Sarcasm and experiments have been made from the textual data extracted from the Twitter data set. Text data set downloaded from Kaggle, including 1984 tweets that collected from Twitter. These data already have labels here. In this paper, we apply these data to train our model Classifiers for different algorithms to see the ability of model machine learning to recognize sarcasm and non-sarcasm through a set of the process start by text pre-processing feature extraction (TF-IDF) and apply different classification algorithms, such as Decision Tree classifier, Multinomial Naïve Bayes Classifier, Support vector machines, and Logistic Regression classifier. Then tuning a model fitting the best results, we get in (TF-IDF) we achieve 0.94% in Multinomial NB, Decision Tree Classifier we achieve 0.93%, Logistic Regression we achieve 0.97%, and Support vector machines (SVM) we achieve 0.42%. All these result models were improved, except the SVM model has the lowest accuracy. The results were extracted, and the evaluation of the results has been proved above to be good in accuracy for identifying sarcastic impressions of people.
Et. Al. Djumanova Aijan Baxtiyarovna
Turkish Journal of Computer and Mathematics Education (TURCOMAT), Volume 12, pp 693-696; doi:10.17762/turcomat.v12i11.5951

The article deals with the problem of implementing management accounting in the domestic practice of logistics systems. Which is of paramount importance due to the need for in-depth research of the economic nature, essence and content of management accounting, its fundamental theoretical foundations, for making informed management decisions.
Et. Al. K.Ranga Narayana
Turkish Journal of Computer and Mathematics Education (TURCOMAT), Volume 12, pp 697-703; doi:10.17762/turcomat.v12i11.5952

In present scenario, tracking of target in videos with low resolution is most important task. The problem aroused due to lack of discriminatory data that have low visual visibility of the moving objects. However, earlier detection methods often extract explanations around fascinating points of space or exclude mathematical features in moving regions, resulting in limited capabilities to detect better video functions. To overcome the above problem, in this paper a novel method which recognizes a person from low resolution videos is proposed. A Three step process is implemented in which during the first step, the video data acquired from a low-resolution video i.e. from three different datasets. The acquired video is divided into frames and converted into gray scale from RGB. Secondly, background subtraction is performed using LBP and thereafter Histogram of Optical Flow (HOF) descriptors is extracted from optical flow images for motion estimation. In the third step, the eigen features are extracted and optimized using particle swarm optimization (PSO) model to eliminate redundant information and obtain optimized features from the video which is being processed. Finally to find a person from low resolution videos, the features are classified by Support Vector Machine (SVM) and parameters are evaluated. Experimental results are performed on VIRAT, Soccer and KTH datasets and demonstrated that the proposed detection approach is superior to the previous method
Et. Al. R Hemalatha
Turkish Journal of Computer and Mathematics Education (TURCOMAT), Volume 12, pp 1801-1814; doi:10.17762/turcomat.v12i11.6126

This study examines the mediating role of Nonaka’s knowledge spiralsof SECI.Whetherthishasarelationshipwiththeeffectofsocialmediaonknowledgesharingandexamines whether it leads to effective learning. The effect on knowledge sharing through socialmedia which was constructed by Bock et al. (2005) has been used for measuring in this study. Themediating role of SECI on social media and knowledge sharing for effective learning has beenassessed based on the four dimensionsSECI multi-dimensional questionnaire offered by Nonakaet al. (2000) has been used for this study. The results reveal which of the four dimensions of Nonaka's that, which has a significant impact on effective learning using social media & knowledge sharing that has been brought to light from this study. The empirical findings of thisstudymayenable toenrichthetheoreticaland practicalimplications.
Et. Al. Ritu
Turkish Journal of Computer and Mathematics Education (TURCOMAT), Volume 12, pp 807-817; doi:10.17762/turcomat.v12i11.5966

: Software Quality is the key priority of today’s marketplace and software development organization to which a system, technique, or factor meets particular requirements and conditions. Soft computing techniques play a vital role in developing software engineering applications. In this paper, we have identified five parameters: Reliability, Efficiency, Usability, Maintainability, and Portability for accessing the level of quality of software. A fuzzy logic-based intelligent identification methodology has been proposed to access the quality of particular software-based on five parameters. The proposed identification scheme takes these five parameters as input and predicts the quality of the software using the fuzzy rule base which is generated using various studies. As this scheme takes five inputs and each input is divided into three regions i.e. ‘Low’, ‘Medium’, ‘High’ and thus a total of 35 i.e. 243 rules has been generated to analyze the software quality. Furthermore, Mamdani fuzzy model has been used as the reference model. To show the effectiveness of the proposed methodology, simulation results have been performed in MATLAB, which shows that the software's quality closely matches with the actual one.
Et. Al. Anantraj I
Turkish Journal of Computer and Mathematics Education (TURCOMAT), Volume 12, pp 1925-1933; doi:10.17762/turcomat.v12i11.6147

The utilization of information and data turns out to be progressively urgent for the farming area to improve profitability and manageability. Data and Communication Technology (ICT) significantly builds the viability and effectiveness of gathering, putting away, investigating and utilizing information in agribusiness. It permits agrarian experts and cultivating networks to handily acquire update-to-date data and accordingly settle on better choices in their every day cultivating. The proposed blockchain based horticulture web application assist ranchers with guaranteeing more prominent benefit through direct rancher to client correspondence. This application chips away at the premise of blockchain innovation, to stay away from the change information during transmission. This assistance helps business correspondence and acquires straightforwardness the framework. The horticultural data framework gives its clients to get data about, cultivating items and so forth Squares of time-stepped item subtleties are put away on all frameworks across a worth chain. The blockchain innovation can comply with the provenance of meals and alongside those traces makes dependable horticulture item deliver chains and assemble believe amongst ranchers and shoppers. As a confided in approach of setting away records, it really works with the usage of records pushed improvements to make cultivating extra intelligent
Et. Al. Aditya Singh
Turkish Journal of Computer and Mathematics Education (TURCOMAT), Volume 12, pp 1980-1985; doi:10.17762/turcomat.v12i11.6154

Strabismus is one of the most common vision diseases in which the eyes do not properly align with each other when looking at an object. The condition may be present occasionally or constantly and if it is present during a large part of childhood, it may result in amblyopia or loss of depth perception. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. In this paper, we propose to detect ICD-10-CM Code H50.9 unspecified strabismus using CNN and Image Processing techniques. There are four types of strabismus namely exotropia, esotropia, hypertropia and hypotropia which we aim to detect. We furthermore aim to introduce a GUI particularly into pediatric ophthalmology where obtaining relevant diagnostic information is taxing.
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