International Journal of Education and Management Engineering @ijeme
Статьи журнала - International Journal of Education and Management Engineering
Все статьи: 643
Design of a Teaching Process Monitoring and Management System
Статья научная
To solve the emerging problems in network courses, we propose the design of a teaching process monitoring and management system, based on the education technology related theories, such as cooperative learning, environment learning, etc. Through the interaction between the monitoring, management, evaluation, and feedback, the system is able to increase the utilization rate of the network course, improve the learning interest and efficiency. Finally, the teaching management and quality are maximally improved.
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Desktop Virtualization: Benefits, Challenges, and Future Trends
Статья научная
A thorough overview of desktop virtualization, a growingly popular technology that allows centralized and effective IT management, is provided in this review paper. Exploring desktop virtualization's benefits, drawbacks, various forms, impact on contemporary workplaces, and potential future trends are the main objectives. The advantages of desktop virtualization are emphasized in the report, including increased productivity, cost savings, and improved security. It explores the numerous forms of virtualization, such as client-based, server-based, and application virtualization, highlighting their special qualities and applicability for varied organizational purposes. The article also addresses how virtual desktop infrastructure (VDI) supports bring-your-own-device (BYOD) rules, allowing workers to access their work environments from any place and device. In today's dynamic workplace, this feature improves collaboration and overall efficiency. The study concludes by examining the potential of desktop virtualization and offering information on new trends and advancements that have the potential to influence the field. This review paper provides a thorough overview, making it a useful tool for businesses wishing to use desktop virtualization in their IT infrastructure.
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Detection of Infeasible Paths in Software Testing using UML Application to Gold Vending Machine
Статья научная
Software testing is an integral part of the software development cycle. Software testing involves designing a set of test cases. In white box testing, test cases are usually designed based using path testing. The basis path testing approach involves generation of test cases from a set of independent paths. Each test case is forced to execute a certain test path of the control flow graph. Some cases might arise paths of the control flow graph have no test data to force execution. These paths are infeasible paths. Identification and removal of infeasible paths earlier will reduce testing efforts and cost. In the present work, we used Unified Modeling Language (UML) for detecting of these infeasible paths. For detection of these infeasible paths, the author builds the control flow graph from sequence diagram and then generated independent paths from it. Each path is converted into a set of a linear equation and solved. If there is an inconsistent solution, then the corresponding path is infeasible. The presented approach is evaluated on a case study of an automatic gold vending machine.
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Статья научная
In the age of computing, there is a vast assortment of medical equipment and software available. Software and medical equipment that can be online connected to healthcare Information Technology (IT) systems are referred to as Internet of Medical Things (IoMT). This research study elaborates healthcare connectivity and its security issues to the different dimension of IoMT. During the pandemic situation in 2020-21 Covid, importance of virtualization and its dependencies have got the momentum. The security challenge of IoMT needs to be addressed. The research analysis is evaluating the impact of security factors in IoMT. By systematically evaluating research studies based on the keywords IoMT, security of IoMT, and security in healthcare sector, security attributes and factors were discovered from the different digital library. This evaluation uses soft computing and Artificial Intelligence (AI) techniques, quantitatively elaborates the factors of IoMT and their impact based on security. The results provide guidance for the development of IoMT with security attributes that can help to ensure the security of the device and software based applications on networks or in the cloud. To assess the importance of the criteria and the ranking of the alternatives, the AI technique of Analytic Hierarchy Process (AHP) and Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS) were applied. The hybrid Fuzzy AHP, Fuzzy TOPSIS techniques are utilizing the concept of decision making in security of IoMT. The items were evaluated using a multi rules choice investigation with several standards. In this research study, eight factors and ten alternatives of IoMT were selected to determine their impact on security. The creating new funding, operating and business model factor of IoMT got the top weight and successfully navigating regulatory change got the least. The AI research on IoMT security determination helps the developer, medical practitioner, and medical device operator to consider the impact of security in IoMT.
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Determining Factors Resulting to Employee Attrition Using Data Mining Techniques
Статья научная
Business Process Outsourcing is a budding industry which currently employs millions of workers in the Philippines which draws applicants from undergraduate to professionals. It provides high-quality, well-paying jobs to millions of Filipinos while inspiring economic activity and investments all around. However, attrition rate of around 50 percent in the current year is a big challenge to predict employee turnover. This study came up with a model that can be adopted in the organization to predict possible attrition and guide the employers particularly the HR team in determining first-hand the type of applicant that they have by applying Data Mining techniques. The authors extracted significant predictors among the given data from a BPO company. Fast Correlation-Based Filtering Algorithm was performed to remove irrelevant data and increase learning accuracy. 1470 records with 21 attributes were initially provided and 17 were identified as significant after filtering and preprocessing of data was performed. The preprocessed data was used for model building with the application of Naïve Bayes Algorithm. The resulting model predicted percentage probability of hoppers and stayers. Among the 17 given variables, Total Working Years, Marital Status and Age ranked as the top predictors in determining possibility of attrition. The data was split into 60% training data or a total of 882 records and 40% testing data or a total of 588 records. The predicted number of stayers is 542 or 92.2% and the predicted hoppers or who likely to resign are 46 or 7.8% The model was tested and evaluated to check accuracy of result through confusion matrix cross validation technique which yielded an accuracy percentage of 84.69%.
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Determining the interests of Social Network Users
Статья научная
The article is devoted to a brief review of approaches to the analysis of social relations in social networks using comments and credentials located in the profiles of social network users. The study aims to determine the interest and behavior of each user. The approach that we propose to determine the interests of social network users requires some methods of machine learning (classification analysis and data clustering). A method based on sentiment analysis and a naive Bayesian classifier is proposed. Determining the interests of social network users based on the intellectual analysis of comments can help to understand the logic of their behavior, and determine social relations between users and problems in society.
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Статья научная
The research entitled edu-game "Ulun Smart-Kid" is a research development of puzzle game type designed to hone memory in the form of language. In this game player must arrange letters in random and create a word in Banjar language. AI technology (artificial intelligence) will also be applied to this research. Using the finite state machine model method game that is built will have game agent character that will accompany a child to play like a teacher. The results of this study make this entitled educational become more interesting and interactive to children. Game agent in the form of teachers can give a sad and happy expression accordingly from the game environment. It accompanies child like a teacher.
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Developing Markerless Augmented Reality for Furniture Mobile Application
Статья научная
In India, purchasing furniture online consistently faces a quandary regarding how one can comprehend whether the furniture is a solid match to purchase, regardless of whether it will fit in space without really contacting and looking at the furniture in detail. With such a dilemma there are chances for online stores to decline in their business. There is always a good certainty that technology can help to bridge the barrier and help one to overcome the dilemma. This study creates a unique light on how Augmented Reality can help to create and simulate buying furniture an online experience and enhancing customer experience provided uniqueness and mobility with the help of a mobile application. Our application allows users to simulate and experience furniture in 3D simulation using markerless augmented reality. Users can try a piece of furniture in their space supported by gestures and color furniture to try out diverse tints of the same. This research advances a new way of implementing markerless augmented reality for buying furniture products online provided by mobile applications and communicating with virtual objects in a real environment with an easy-to-use user interface.
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Developing a Mathematics Lesson Plan based on Visual Learning Technology
Статья научная
Various researchers have highlighted the importance of the introduction of visual learning in mathematics. Visual representation in mathematics is important for teachers and for pupils in their teaching and learning of mathematics, respectively. Various visual learning platforms are used in mathematics. This research concentrates on 2-D and 3-D visual learning technologies such as MIT App Inventor and Kudo Game Lab for elementary school.
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Developing an Intelligent Question Answering System
Статья научная
The goal of an intelligent answering system is that the system can respond to questions automatically. For developing such kind of system, it should be able to answer, and store these questions along with their answers. Our intelligent QA (iQA) system for Arabic language will be growing automatically when users ask new questions and the system will be accumulating these new question-answer pairs in its database. This will speed up the processing when the same question(even if it is in different syntactical structure but semantically same) is being asked again in the future. The source of knowledge of our system is the World Wide Web(WWW). The system can also understand and respond to more sophisticated questions that need a kind of temporal inference.
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Статья научная
Agriculture, Plantation and Forestry Commodities are the main sectors supporting household daily needs and people's income for improving the economy. District of Marangkayu is located in Kutai Kartanegara area, East Kalimantan Province, where geographical condition consists of the terrain of hilly steeps surrounding the lake of Kutai Kartanegara. The geographical contours make the sector of agriculture, plantation and forestry the people's primary choice to meet the needs of household as well as increase the standard of economy of the people. In order to maintain the stability of price and production of agricultural commodities, Commodity Information System is required to provide information of the location, coordinate of positions, area of production, as well as presenting information of prices, price fluctuations and changes, along with a display of information over the accumulation of agricultural commodity production of the Kutai Kartanegara area, with additional features of appropriate distribution and production thereof. Therefore, it is necessary to develop the Web-Based Geographic Information System (GIS) for Agricultural Commodity, Plantation and Forestry of Marangkayu Area. GIS application is built using the Rapid Application Development (RAD) method, which consists of the phase of Requirements planning, User design phase, Construction phase and Cut-over phase. Database for the implementation uses PostgreSQL and PostGIS extensions. Programming language uses PHP, JavaScript, and HTML. The interface implementation is built using Bootstrap. The testing of the application uses the Black box testing method. The results of the test show that the Web-Based GIS Application has met the needs of the requirement system and the problems.
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Development of Cloud Based Incubator Monitoring System using Raspberry Pi
Статья научная
Accurate and quantifiable measurement of light is essential in creating desired outcomes in practical day to day applications such as in Poultry Industry. Light measurement is essential in ensuring the efficiency of egg hatching process. Artificial incubation has been used in the poultry farm for hatching of the eggs. For better hatching of the eggs, the temperature and humidity has to be maintained properly. The proposed system is equipped with DHT11 sensor which monitors the temperature and humidity of the incubator and continuously updates to the cloud through Wi-Fi. Remote user checks the temperature and humidity values and controls the intensity of the bulb through an Android app in his mobile phone. A servo motor is attached to the egg turner kept inside the incubator and is rotated according to the specified delay in order to prevent the yolk from getting stick to the shell of the eggs and also to provide uniform temperature for the eggs. From the experimental results, it is inferred that better hatching of the eggs could be achieved by controlling the intensity of the bulb remotely.
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Development of GIS Application System on OCCI and MapX
Статья научная
Exploring a method of developing application system which can access and manipulate a mass of spatial data effectively under integration storage mode of GIS data in Oracle Spatial, is the research hot spot. This paper defines more compact, robust and reliable class modules for accessing spatial data, carries on the visualization processing of spatial data using MapX, and gives a method for enhancing the ability to manipulate spatial data in GIS system through using OCCI to call spatial operators in Oracle Spatial, which unites advantages of OCCI, MapX and Spatial to develop GIS application system fully.
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Development of Knowledge Graph for University Courses Management
Статья научная
The task of Allocating courses to lecturers in many tertiary institutions is done manually by typing using word processor application. Motivated by the widespread application of knowledge graphs in different domains, we present automated approach based on knowledge graph to address the problem of manual course allocation to, a task usually carried out at the beginning of every semester or academic year by departments in tertiary institutions. The development of knowledge graph in a way that enables easy manipulation and automatic generation of course allocation schedule is the core contribution of this paper. Rather than storing the data in relational database tables, the system stores data in a knowledge graph which is in RDF/XML format and refer to it to support intelligent knowledge services. In addition to automatic generation of course allocation schedule, another important feature of the system proposed in this paper is its ability to enable easy implementation of tasks similar to Question Answering that are very important to education administrators, which the existing manual approach does not provide. Testing of the proposed system reveals its ability to perform effectively. Our approach of using Knowledge graph offers advantages such as flexibility and security.
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Development of Two-factor Authentication Login System Using Dynamic Password with SMS Verification
Статья научная
Two-factor authentication is a method of security that adds an extra layer of protection by requiring users to have two different authentication factors to verify identity. In recent years, institutions and organizations have become more concerned with the security aspects of their networks and systems, and one of these aspects is ensuring that the individual seeking access to the system is who he claims to be. With the advancement in technology and science over time, it has been seen that the safety and security of sensitive information or data transferred over the internet from one network to another has become increasingly relevant. The reliance on access to login accounts and the use of static passwords makes it easy for hackers, identity thieves, and fraudsters to gain access. Therefore, there is a need to find solutions to overcome the weaknesses to provide a more secure environment, hence, adding another step of authentication to individual identity makes it more difficult for an attacker to gain access to personal data. The proposed system generates dynamic password (OTP), which helps to add another level of security to the system against Dictionary attack, Brute-force attack especially Perfect-Man-In-The-Middle attack. The project used the Obafemi Awolowo University (OAU) e-portal login system as a case study. The system was implemented using the MySQL, CSS, HTML and PHP programming language and evaluated using reliability, effectiveness, efficiency, usability, expediency, and satisfactoriness as metrics. A questionnaire was formulated using a rating scale of 1 - 5, with 1 representing extremely poor and 5 representing excellent. The questionnaire was given to twenty (20) randomly selected students of OAU. The average score was determined and all the metrics scored higher than 4.0, which signifies a good rating. The system developed is a useful starting point for future development in security applications that require two-factor authentication. The result show that with the developed system, it can be assured that all logins are legitimate and that users are safe by verifying that the individual seeking access to the system is who he claims to be. A more user-friendly GUI is planned for the future and expanding the OTP algorithm such that password can be generated based on different cryptographic functions.
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Статья научная
A way to make job matching work better in Nigeria, where the jobless rate is consistently high. Businesses and users alike might gain from the app's user-friendly layout, which makes it simple to publish jobs. Post jobs and submit resumes. The foundation of the program is the SVM algorithm, which searches job ads and user profiles for appropriate matches depending on parameters like education, experience, and the kind of role. This system learns from user interactions and comments to produce even better matches than job boards, which have significantly lower prediction accuracy. We develop secure and scalable applications using front-end and back-end methodologies with React Native and Node.js. This article outlines the system architecture, algorithmic implementation, and first testing results, illustrating how machine learning might transform the employment sector in poor countries such as Nigeria.
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Статья научная
The accuracy of population forecasts is one of the most important calculations in demography statistics. However, traditional demographic methods used in population projections are tend to produce biased results. The need for accurate prediction of future behavior in a number of areas require the application of reliable and efficient methods. Recently, machine learning (ML) models have emerged as a serious competitor to classical statistical models in the forecasting community. In this study, the performance and capacity of the four different ML models such as Random forest (RF), Decision tree (DT), Linear regression (LR) and K-nearest neighbors (KNN) to the prediction of population has been examined. The aim of the study is to find the best performing regression model among these machine learning algorithms for forecasting of population. The data were collected from the State Statistical Committee of the Republic of Azerbaijan website were used for the analysis. We used five metrics such as mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE) and R-squared to compare the predictive ability of the models. As the result of the analysis, it has been known that the all ML models showed high results with correlation coefficient of 0.985 - 0.996. Also the KNN and RF prediction models showed the lowest root mean square deviation, means square error and mean absolute error values compared to other models. By effectively using the advantage of the ML algorithms, the forecast of population growth the near future can be observed objectively, and it can provide an objective reference to the strategic planning in the public and private sectors, particularly in education, health and social areas.
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Development of relevance feedback system using regression predictive model and TF-IDF algorithm
Статья научная
Domain-specific retrieval systems developed for a homogenous group of users can potentially optimise the recommendation of relevant web documents in minimal time as compared to generic systems built for a heterogeneous group of users. Domain-specific retrieval systems are normally developed by learning from users’ past interactions, as a group or individual, with an information system. This paper focuses on the recommendation of relevant web documents to a cohort of users based on their search behaviour. Simulated task situations were used to group users of the same domain. The motivation behind this work is to help a cohort of users find relevant documents that will satisfy their information needs effectively. An aggregated implicit predictive model derived from correlating implicit and explicit feedback parameters was integrated with the traditional term frequency/inverse document frequency (tf-idf) algorithm to improve the relevancy of retrieval results. The aggregated model system was evaluated in terms of recall and precision (Mean Average Precision) by comparing it with self-designed retrieval system and a generic system. The performance of the three systems was measured based on the relevant documents returned. The results showed that the aggregated domain-specific system performed better in returning relevant documents as compared to the other two systems.
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Diagnosis of Skin Cancer Using Machine Learning and Image Processing Techniques
Статья научная
Skin Lesion is a part of the skin that can be caused by abnormal growth in the epithelium layer on the skin. There are nine types of skin lesion like Actinic Keratoses (AK), Basal Cell Carcinoma (BCC), Dermatofibroma (DF), Melanoma (MEL), Melanocytic Nevi (MV), Benign Keratosis (BK), Vascular Lesions (VASC), Squamous Cell Carcinoma (SCC), and Pigmented Benign Keratosis (PBK). The aim of this study is to spotlight on the problem of skin lesion classification based on early detection of the disease using deep learning techniques. This approach is used to work out the problem of classifying a dermoscopic image. The dermoscopic is a digital device; in this case Smartphone is attached to a lens and collects the images through the device. The proposed spotlight is built in the region of using Convolutional neural network architecture and ResNet-50 module is used to predict Skin-Lesion classification. The dataset used in this research was taken from kaggle repository. The proposed work uses ResNet-50 CNN model which has yielded 93% of accuracy for detecting Skin Cancer, previous work was carried out using Visual Geometry Group model which yielded 73% accuracy. In the proposed work we have considered 25,000 images of skin lesion. Hence we are able to attain this accuracy with more reliable Machine Learning algorithms compared to the previous work.
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Digital Audio Watermarking Based on Artificial Neural Networks
Статья научная
A digital audio watermarking based on artificial neural networks is proposed in this paper. Utilizing the learning and adaptive capabilities of artificial neural networks, the relationship between audio signals and embedded watermark is established by using the important characters of audio signals as the input vector of artificial neural networks, and the watermark were embedded into original audio signals without modifying the audio data. The experimental results show that the embedded watermark is robust to audio signal processing, and the watermarking method does not require the original audio signals for watermarking extraction.
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