Бидний тухай
Багш ажилтан
Abstract The most recent study reveals a high prevalence of dental caries among preschoolers in Mongolia, with rates as high as 89.3% in both urban and rural areas. This worrisome situation is exacerbated by a lack of trained dentists, dental facilities in Mongolia's remote rural areas, and dental assistants in Ulaanbaatar, the capital city. These disparities in the infrastructure of dental care have increased the workload of practicing dentists. In response to these obstacles, the Mongolian National University of Medical Sciences (MNUMS) and the National University of Mongolia (NUM) have commenced implementation of Pilot Case 3 with European partner universities as part of the Erasmus+ DigiHealth-Asia project funded by the European Union (2021-2024) in order to provide oral health education, caries prevention in children, and remote counseling to parents and caregivers using artificial intelligence.
Managing high-demand concurrent requests in web-based systems such as a university management system's course selection module is crucial to database management, especially in centralized databases where multiple users(students) may select and modify data concurrently. In a centralized database system, concurrency control mechanisms are implemented to ensure that transactions are executed orderly and controlled, preventing issues such as lost updates, uncommitted data, and inconsistent reads. Concurrency control systems in centralized databases play a crucial role in managing data consistency and ensuring that transactions are executed correctly in a multi-user environment. In this paper, we will introduce the course reservation module of the university management system and solutions for managing high concurrent access to the centralized web-based database system. Course reservations is an end-to-end booking, distribution, and fulfillment solution for managing students’ personalized curriculum plans, including course selection, classroom seat reservations, pricing, and payment services. Pricing allows us to build and deliver the total tuition fee for various course rates based on local university rules and government taxes.
Ulaanbaatar Gigapixel Panorama Photography To promote the country's history, culture, natural and tangible heritage we are proposing to implement Ulaanbaatar Gigapixel Panorama Photography. This work may attract tourists and make them feel like they are in a high-quality place from anywhere in the world. Gigapixel Equi-rectangular Panorama is used to represent major cities that are international tourism hubs. Because images are high-resolution, they can be used to create (wear multiple images), visualize (give users a realistic feel for easy, fast, and high resolution), experiment with large data processing and visualization techniques, and compare gigapixel image processing methods. An appropriate methodology needs to be identified and used. Outcome: A Gigapixel Equi-rectangular Panorama map of all parts of Ulaanbaatar will be developed, and this map will be the first Gigapixel Equi-rectangular Panorama map in Mongolia
The generation of a substantial volume of data is occurring due to advancements in medical science, population expansion, and environmental and occupational health issues. Hospitals are currently utilizing many software tools to document and manage this data. Additionally, various devices, especially sensor devices, have gained widespread use for recording human biological rhythm information at both healthy and pathological levels. By utilizing personal data, including factors such as age, gender, the prevalence of disease symptoms, medical history, lifestyle behaviours, and other relevant information, there is a growing ability to forecast an individual's vulnerability to certain illnesses. As a result, there is an urgent requirement to gather, retain, analyze, and employ the extensive volume of data produced due to the intricate procedures associated with healthcare provision and public health measures to inform clinical decision-making. Consequently, there is a growing need for professionals with interdisciplinary expertise, including medical acumen and computer science, to analyze and utilize raw data for informed decision-making effectively. This demand exhibits a consistent upward trajectory on an annual basis. Thus, the main aim of this project is to investigate the viability of training professionals with a dual background in medicine and computer science in Mongolia and establish an interdisciplinary joint Master's program as part of the Erasmus+ "DigiHealth" Project. The National University of Mongolia (NUM) has launched a postgraduate program in Data Science to provide individuals with the necessary skills to analyze health-related data in conjunction with medical graduates, physicians, and healthcare practitioners affiliated with the Mongolian National University of Medical Sciences (MNUMS). To enhance medical students and healthcare professionals' knowledge and skills in artificial intelligence, mandatory foundational courses titled "AI in Healthcare" have been incorporated into the undergraduate and graduate programs offered by the School of Biomedicine at MNUMS. This initiative aims to empower local students and specialists in the field, providing them with essential expertise in the intersection of healthcare and artificial intelligence. In addition, the Mongolian National University of Medical Sciences (MNUMS) and the National University of Mongolia (NUM) have collaboratively designed a specialized postgraduate program focused on vocational training. In this context, an ongoing pilot project is being conducted to examine the feasibility of remotely assessing, diagnosing, and delivering oral and dental health counselling through an intraoral camera. This study aims to identify the presence of dental caries among children residing in geographically isolated regions of sparsely inhabited Mongolia.
This paper explores in detail the methods, techniques, and technologies employed to capture a high-definition gigapixel panoramic image of Ulaanbaatar, the capital city of Mongolia. Using specialized equipment including a Canon 6D Mark II camera, a Sigma 50-500mm lens, and a Benro Polaris BR209 panoramic head, a series of 1260 photographs were taken, each with a resolution of 3120 x 2080 pixels. These images were then expertly stitched and edited to produce a seamless panoramic display. We further delve into the complexities of storing such a large image file and the software solutions for presenting this on a web platform
Өнөөдөр дэлхийн олон улс орон хөгжлийн бэрхшээлтэй хүний эрх, оролцоо, хамгааллыг дэмжих, тэгш хамруулан сургах боловсролын хүртээмжийг нэмэгдүүлэх, үр бүтээлтэй суралцах орчныг бүрдүүлэх, ялангуяа МХХТ-ийн мэдлэг, ур чадварыг эзэмшүүлэхэд ихээхэн анхаарах болжээ. Манай улс энэ чиглэлээр олон талт арга хэмжээг авч хэрэгжүүлж байгаа хэдий ч хөгжлийн бэрхшээлтэй иргэдэд зориулсан МХХТ-ийн сургалтыг зохион байгуулахад суралцагчдын ялгаатай шинж байдлыг тусгасан тодорхой хөтөлбөр, агуулга байхгүй байна. Бид энэхүү өгүүлэлд хөгжлийн бэрхшээлтэй иргэдийн МХХТ-ийн хэрэглээ, тулгарч буй асуудлууд, тэдгээрийн сурах арга барилын талаар авч үзэн МХХТ-ийн сургалтыг хөгжлийн бэрхшээлтэй иргэдийн бодит хэрэгцээнд тулгуурлан үр ашигтай зохион байгуулах, сургалтын үр өгөөж ба үр дүнг нэмэгдүүлэх зорилгоор сургалтын ерөнхий загвар болон сургалтын үйл ажиллагааны хувилбар загварыг танилцуулж байна.
МХХТ, интернэт хувь хүний өдөр тутмын амьдралын наад захын хэрэглээ болсон өнөө үед хөгжлийн бэрхшээлтэй иргэд нийгмийн болон цахим харилцаанд тэгш оролцох, цахим орчинд аюулгүй ажиллахад МХХТ-ийн мэдлэг, ур чадвартай байх зайлшгүй шаардлага тулгарч байна. Бид энэхүү өгүүлэлд хөгжлийн бэрхшээлтэй иргэдийн МХХТ-ийн хэрэгцээ, зохицуулалт, тэдний мэдээллийн хүртээмж ба МХХТ-ийн ур чадвар, хэрэглээнд тулгарч буй асуудлыг авч үзэж, хөгжлийн бэрхшээлтэй иргэдийн МХХТ-ийн ур чадварыг дээшлүүлэх чиглэлээр авч хэрэгжүүлэх арга хэмжээг санал болгосон болно.
In this paper, we presented two POS taggers for Mongolian, namely Neural Networks - Multilayer Perceptron and Hidden Markov Model with Viterbi. The accuracy of the former tagger is 95.6%, whereas the latter tagger is 85.6%. Also, we compared the performance of our taggers with the previous works. The comparison shows that the Neural Network tagger performs better for Mongolian POS tagging than other approaches. Our dataset consists of about 5000 sentences and includes almost 100,000 words for training and testing.
Investors aim to increase their profits by investing in the stock market. One possible strategy for minimization of risk is by enlarging or varying its field of operation for the portfolio. In this paper, we propose a six-step stocks portfolio selection model. This model is based on data mining clustering techniques that reflect the impact of Mongolian political, economic, legal, and corporate governance. As a dataset, we have selected stock exchange trading price, financial statements, and operational report information of TOP-20 highly capitalized stocks traded at the Mongolian Stock Exchange from 2013-to 2017. To cluster stock returns and risks, we have used K-means clustering techniques. We have combined both K-means clustering with Markowitz's portfolio theory to create an optimal, efficient portfolio. We constructed an efficient frontier, 15 portfolios created, and calculated the weight of stocks in each portfolio. From these portfolio options, the investor can choose according to his behavior.
Abstract—The article presents the European evaluation within the scope of the project SMARTCITY: Innovative Approach Towards a Master Program on Smart Cities Technologies. The evaluation covers assessment of teachers from PC universities (SSTU, NSTU, NUM, MUST,KAZNU, ENU), who have been selected to participate in the projectrelated mobilities to increase knowledge in the field of SCT. In the project is planned developing of 18 teaching materials published with e-ISBN by PC teachers involved in the project. The article also covers quality assessment of teaching materials developed within the project by PC universities and assessment of students’ knowledge which is done at the beginning of their training in the EU Universities and during the Master theses defense. Keywords—Engineering Education; smartcity; online education; information resources; practical work laboratory; video content.
Хөрөнгө оруулагч нь хөрөнгийн зах зээлд хөрөнгө оруулалт хийж өөрийнхөө ашиг орлогыг нэмэгдүүлэх зорилготой байдаг. Энэхүү судалгааны ажлаар бид үнэт цаасны багц сонголтод өгөгдлийн уурхайн арга техникийг хэрхэн ашиглаж болохыг судлан, зургаан алхамт арга зүй боловсруулсан. Уг арга зүйн дагуу өөрийн орны улс төр, эдийн засаг, хууль эрх зүй болон компанийн засаглалын нөлөөллийг тусгасан багц сонголт хийхэд туслах системийг хөгжүүлсэн бөгөөд хөрөнгө оруулагчид болон санхүүгийн зуучлагчид системээс оновчтой багцыг хувилбаруудыг харж сонголт хийх боломжтой. Судалгааны өгөгдлөөр Монголын хөрөнгийн биржид бүртгэлтэй үйл ажиллагаа явуулж буй TOP-20 индекстэй компаниудын компаниудын 2013 оны 1-р сарын 1-нээс 2017 оны 12-р сарын 31-ний өдрийн хувьцааны ханшийн мэдээлэл, санхүүгийн тайлангууд болон үйл ажиллагааны тайлангийн мэдээллийг авлаа.
This paper determined the predefining factors of loan repayment behavior based on psychological and behavioral economics theories. The purpose of this research is to identify whether an individual’s credit risk can be predicted based on psychometric tests measuring areas of psychological factors such as effective economic decision-making, self-control, conscientiousness, selflessness and a giving attitude, neuroticism, and attitude toward money. In addition, we compared the psychological indicators to the financial indicators, and different age and gender groups, to assess whether the former can predict loan default prospects. This research covered the psychometric test results, financial information, and loan default information of 1118 borrowers from loan-issuing applications on mobile phones. We validated the questionnaire using confirmatory factor analysis (CFA) and achieved an overall Cronbach’s alpha reliability coefficient greater than 0.90 (α = 0.937). We applied the empirical data to construct prediction models using logistic regression. Logistic regression was employed to estimate the parameters of a logistic model. The outcome indicates that positive results from the psychometric testing of effective financial decision-making, self-control, conscientiousness, selflessness and a giving attitude, and attitude toward money enable individuals’ debt access possibilities. On the other hand, one of the variables—neuroticism—was determined to be insignificant. Finally, the model only used psychological variables proven to have significant default predictability, and psychological variables and psychometric credit scoring offer the best prediction capacities.
Credit scoring is a process of determining whether a borrower is successful or unsuccessful in repaying a loan using borrowers’ qualitative and quantitative characteristics. In recent years, machine learning algorithms have become widely studied in the development of credit scoring models. Although efficiently classifying good and bad borrowers is a core objective of the credit scoring model, there is still a need for the model that can explain the relationship between input and output. In this work, we propose a novel partially interpretable adaptive softmax (PIA-Soft) regression model to achieve both state-of-the-art predictive performance and marginally interpretation between input and output. We augment softmax regression by neural networks to make it adaptive for each borrower. Our PIA-Soft model consists of two main components: linear (softmax regression) and non-linear (neural network). The linear part explains the fundamental relationship between input and output variables. The non-linear part serves to improve the prediction performance by identifying the non-linear relationship between features for each borrower. The experimental result on public benchmark datasets shows that our proposed model not only outperformed the machine learning baselines but also showed the explanations that logically related to the real-world
Компьютерын хэл шинжлэл нь дэлхийн хэл бүрд харилцан адилгүй хөгжсөөр байна. Аливаа хэлний хувьд хэл боловсруулалтын судалгаа, арга, техникүүд хөгжих нь мэдээллийн технологийнх нь салбарт чухал ач холбогдолтой. Хэдийгээр манай Монгол хэл боловсруулалт харьцангуй хөгжиж байгаа хэдий ч зарим нэг чухал судалгаанууд хийгдээгүй хэвээр байна. Учир нь зарим тохиолдолд эдгээр судалгааг хийх нөөц ба хэрэгслүүд хараахан бүрдээгүйд оршино. Эдгээр судалгаануудын нэг нь Нээлттэй Мэдээлэл Задлал юм. Энэхүү өгүүллийн зорилго нь ийм системийг монгол хэл дээр байгуулах боломж, нөхцөл бүрдсэн эсийг судалж ашиглагдаж болох аргуудыг танилцуулах юм.
The article covers some experiences of using and testing virtual ma-chines in computer networking classes. The experiments include building local network topology and architecture, configuration of operating systems, working with virtual network devices. There is a difficulty of implementing these experi-ments in laboratories at universities and colleges. Therefore, laboratories are shared by different field students at higher education institutions. This problem can be solved by the use of virtual machines and virtual networks that means students to work on their own virtual machines in own virtual networks. The use of virtual machines and network enables the other everyday activities and pro-cesses at the institutions run continuously. The article presents some experiments in the use virtual machines at laboratory works.
We developed the six-step model of portfolio selection in the Mongolian stock exchange considering the political, economic, legal, and corporate governance impacts. We obtained the stock price information of the companies listed in the TOP-20 index between January 1, 2014, and December 31, 2016, their financial statements, operational reports, and processed quantitative input following the methodology we proposed in this paper. Established hierarchical clusters based on the correlation matrix of share return and applied the k-means method of clustering by share return and risk appraisals. To select the efficient portfolio package, we solved the return maximization target on selected shares of the companies, based on X. Markowitz’s mean–variance model. To achieve the maximum return of the portfolio, we estimated the share purchase ratio per each type of share and created 11 different portfolio packages based on these shares. The investor may select one of these portfolios depending on his own characteristics.
As virtual tour and virtual reality technology advances, resolution with gigapixel panoramic images become available. Visualization technique for gigapixel panoramic image needs to perform fast without any delay to access gigapixel ultra high resolution image data through wired or wireless internet by a Mobile, Tablet or a standard PC. Also, several popular visualization formats like equirectangular projection were developed to map a real scene to a panoramic image. However, Equirectangular projection may not the best choice when considering an image quality because of the distortion on both poles of the sphere. In this paper, we show the result of the evaluation for the projection formats using various comparison method to find which projection format (Equirectangular Projection, Cubemap Projection and Octahedron projection) and visualization technologies provides better image quality and speed of loading over internet. We exclude other format that has lower quality than the equirectangular projection format used in this experiment.
The most important problems (facing higher education institutions) are enhancing quality assurance. Quality assurance is at the heart of academic activities in higher education institutions (HEI) in today’s world. One of the methods to determine the quality of HEI is analyzing and summarize the satisfaction of stakeholders. The most important problems (facing higher education institutions) are enhancing quality assurance. One of the best ways to overcome this problem is by using a decision support framework for quality assurance. That framework is analyzing HEI’s historical data and supporting decision-making activities.
“Үнэт цаасны багц сонголтод өгөгдөл олборлолтыг ашиглах арга зүй” сэдэвт судалгааны ажлаар бид үнэт цаасны багц сонголтод өгөгдөл олборлолт /Data mining/ - ын арга техникийг ашиглах арга зүйг судлан 6 алхамт загвар гаргасан билээ. Энэхүү арга зүйн загварын дагуу Монголын хөрөнгийн биржид бүртгэлтэй үйл ажиллагаа явуулж буй TOP-20 индекстэй компаниудын хувьцааны арилжааны мэдээллийг авч боловсруулан хувьцааны хүлээгдэж буй өгөөж болон эрсдэлд тулгуурлан К-дунджийн аргаар бүлэглэлтийг үүсгэлээ. Тухайн бүлэглэлтэд санхүүгийн үзүүлэлтээрээ ойролцоо шинж чанар бүхий компаниудын хувьцаа байрлах бөгөөд бүлэглэлт бүрээс үнэт цааснуудыг сонгон авч багцын хүлээгдэж буй өгөөжийг хамгийн их, эрсдэлийг хамгийн бага байлгах үнэт цаасны багцыг Х.Марковицын загвараар тооцоолон багцад байх хувьцааны хувийн жинг гаргасан. Судалгааны өгөгдлөөр МХБ-д бүртгэлтэй ТОП-20 индекстэй компаниудын 2013 оны 01-р сарын 01-ээс 2017 оны 12-р сарын 31-ний хоорондох хувьцааны арилжааны ханшийн мэдээллийг авлаа.
“Үнэт цаасны багц сонголтод өгөгдөл олборлолтыг ашиглах арга зүй” сэдэвт судалгааны ажлаар бид үнэт цаасны багц сонголтод өгөгдөл олборлолт /Data mining/ - ын арга техникийг ашиглах арга зүйг судлан 6 алхамт загвар гаргасан билээ. Энэхүү арга зүйн загварын дагуу Монголын хөрөнгийн биржид бүртгэлтэй үйл ажиллагаа явуулж буй TOP-20 индекстэй компаниудын хувьцааны арилжааны мэдээллийг авч боловсруулан хувьцааны хүлээгдэж буй өгөөж болон эрсдэлд тулгуурлан К-дунджийн аргаар бүлэглэлтийг үүсгэлээ. Тухайн бүлэглэлтэд санхүүгийн үзүүлэлтээрээ ойролцоо шинж чанар бүхий компаниудын хувьцаа байрлах бөгөөд бүлэглэлт бүрээс үнэт цааснуудыг сонгон авч багцын хүлээгдэж буй өгөөжийг хамгийн их, эрсдэлийг хамгийн бага байлгах үнэт цаасны багцыг Х.Марковицын загвараар тооцоолон багцад байх хувьцааны хувийн жинг гаргасан. Судалгааны өгөгдлөөр МХБ-д бүртгэлтэй ТОП-20 индекстэй компаниудын 2013 оны 01-р сарын 01-ээс 2017 оны 12-р сарын 31-ний хоорондох хувьцааны арилжааны ханшийн мэдээллийг авлаа.
Аливаа байгууллагын үйл ажиллагаа чанартай явагдах нь бүтээгдэхүүн, үйлчилгээний чанарт шууд нөлөөлдөг. Тиймээс дээд боловсролын байгууллагуудад тулгардаг нэгэн томоохон асуудал нь чанарын удирдлагын үйл ажиллагааг сайжруулах арга замыг тодорхойлох юм. Тус асуудлыг шийдэх нэг арга зам нь шийдвэр гаргалтад дэмжлэг үзүүлэх тогтолцоог байгуулах, ашиглах юм. Энэхүү судалгааны ажлаараа дээд боловсролын чанарын баталгаажуулалтыг сайжруулах тогтолцооны ерөнхий загварыг тодорхойлсон. Үүний тулд эхлээд Монгол Улсын Их Сургуулийн (МУИС) сургалтын үйл ажиллагааны бүх процессыг тодорхойлон, сургалтын процесст оролцогч талууд тус бүрийн оролцоо, хоорондын хамтын ажиллагааны загварыг гаргаж, процессыг BPMN –ийг ашиглан загварчилсан. Мөн процесс тус бүр дээрх өгөгдлийн урсгалыг шинжилгээ хийсэн. МУИС-ийн чанарын удирдлагын тогтолцооны ерөнхий загварыг гаргаж, чанарыг сайжруулах аргачлалууд нь хэрхэн хэрэгжиж байгаа талаар дүгнэлтүүд гаргасан.
Энэхүү судалгааны ажлын зорилго нь үнэт цаасны багц сонголт хийхэд өгөгдөл олборлолтын арга техникүүдээс аль нь илүү тохиромжтой болохыг судалж, Монголын хөрөнгийн зах зээлийн онцлогийг тусган үнэт цаасны багц сонголтыг оновчтой хийх арга зүй боловсруулах явдал юм. Гаргасан арга зүйг шалгахдаа Монголын хөрөнгийн биржийн 2016 оны арилжааны өгөгдлийг авч ашигласан. Үнэт цаасны багц сонголтод өгөгдөл олборлолтыг ашиглах 6 алхамт арга зүйн загвар боловсруулсан.
With all the advanced technology nowadays, new data is being generated every minute. For example, the average size of the computer’s hard disk is 10 gigabytes in 2000, today on the Facebook website has increased 500 terabytes of new data per day [1]. Data is growing rapidly, but it is not enough valuable. Thus, it is important to extract information that is useful in the future from a large amount of data. Business intelligence (BI) systems make a prediction that supports a business decision by analyzing collected data [2]. However, the accuracy of prediction depends on a data quality. In practice, data is usually a very low quality that includes many incomplete and anomaly data. Moreover, another problem is if data size increases, query response will be slow. Previous research work, we proposed a framework based on open-source technologies for the BI systems that possibility to analyze big data
Early detection of fault-proneness of software components enables verification experts to concentrate their time and resources on the problem areas of the system under development. Some software metrics provide ways to evaluate the quality of software and their use in earlier phases of software development can help organizations access large software development quickly at a low-cost. The goal of this study is to empirically explore ability of the using deep learning for fault-proneness prediction during design phase. In order to meet this goal, we use design metrics of 1583 class information from four different open-source software systems and conduct experiments.
Early detection of fault-proneness of software components enables verification experts to concentrate their time and resources on the problem areas of the system under development. Some software metrics provide ways to evaluate the quality of software and their use in earlier phases of software development can help organizations access large software development quickly at a low-cost. The goal of this study is to empirically explore ability of the using deep learning for fault-proneness prediction during design phase. In order to meet this goal, we use design metrics of 1583 class information from four different open-source software systems and conduct experiments.
Machine learning and artificial intelligence have achieved a human-level performance in many application domains, including image classification, speech recognition and machine translation. However, in the financial domain expert-based credit risk models have still been dominating. Establishing meaningful benchmark and comparisons on machine-learning approaches and human expert-based models is a prerequisite in further introducing novel methods. Therefore, our main goal in this study is to establish a new benchmark using real consumer data and to provide machine-learning approaches that can serve as a baseline on this benchmark. We performed an extensive comparison between the machine-learning approaches and a human expert-based model—FICO credit scoring system—by using a Survey of Consumer Finances (SCF) data. As the SCF data is non-synthetic and consists of a large number of real variables, we applied two variable-selection methods: the first method used hypothesis tests, correlation and random forest-based feature importance measures and the second method was only a random forest-based new approach (NAP), to select the best representative features for effective modelling and to compare them. We then built regression models based on various machine-learning algorithms ranging from logistic regression and support vector machines to an ensemble of gradient boosted trees and deep neural networks. Our results demonstrated that if lending institutions in the 2001s had used their own credit scoring model constructed by machine-learning methods explored in this study, their expected credit losses …
This research paper analyzed the stock prices of Mongolia Stock Exchange TOP 20 index from 1 January 2012 to 31 December 2016, and estimated the return rate of these stocks. A hierarchical clustering was created from the correlation matrix of stock returns. From this clustering five stocks were selected for the portfolio construction and the rate of return was maximized using the Modern Portfolio Theory developed by Harry Markowitz. The weight of each stock in the portfolio was calculated for maximization the return rate of the portfolio, and 12 portfolios were constructed from these five stocks. An investor can select appropriate one of these portfolios in accordance with his or her risk and return characteristics.
This research paper analyzed the stock prices of Mongolia Stock Exchange TOP 20 index from 1 January 2015 to 31 December 2017, and estimated the return rate of these stocks. K-means clustering was created from the of stock returns and risk. Stock were placed in the clustering rank of stocks. From this clustering five stocks were selected for the portfolio construction and the rate of return was maximized using the Modern Portfolio Theory developed by Harry Markowitz. The weight of each stock in the portfolio was calculated for maximization the return rate of the portfolio.
This research paper analyzed the stock prices of Mongolia Stock Exchange TOP 20 index from 1 January 2015 to 31 December 2017, and estimated the return rate of these stocks. K-means clustering was created from the of stock returns and risk. Stock were placed in the clustering rank of stocks. From this clustering five stocks were selected for the portfolio construction and the rate of return was maximized using the Modern Portfolio Theory developed by Harry Markowitz. The weight of each stock in the portfolio was calculated for maximization the return rate of the portfolio.
Quality assurance is at the heart of academic activities in higher education institutions (HEI) in today’s world. The most important problems facing higher education institutions) are enhancing quality assurance. One of the best ways to overcome this problem is using decision support framework for quality assurance. That framework is analyzing HEI’s historical data and supporting decision making activities. At first we have modeled university data flow. In this stage we have designed general framework for University quality management system. In the second step we used Weka to analyze student grading. More than 250 student’s record being analyzed. Similarly, quality improvement factors considered. Finally, we have proposed general framework for Enhancing Higher Education Quality Assurance. Keywords: quality assurance, decision support system, business process analysis
The 11th International Conference FITAT 2018
Paper presents a suitability analysis of motion sensor technology that is the main technology of creating the virtual environment using popular motion sensors of Kinect motion sensor and Shadow full-body motion sensor. Recently virtual environment technology that enabled people to experience imagination of human has grown rapidly. The motion sensing process can be a variety of ways, and methods of Kinect and Shadow motion sensors techniques will further explain.
Paper presents a suitability analysis of motion sensor technology that is the main technology of creating the virtual environment using popular motion sensors of Kinect motion sensor and Shadow full-body motion sensor. Recently virtual environment technology that enabled people to experience imagination of human has grown rapidly. The motion sensing process can be a variety of ways, and methods of Kinect and Shadow motion sensors techniques will further explain.
Equirectangular panoramas based virtual tours are popular tool for achieving 360-degree immersed viewing experiences. Nowadays, virtual tours are very popular and many people would like to see a virtual house before the acquisition of the real one. A virtual tour captures a scene from one point and viewers software allows the user to control the viewing direction, but usually the viewer is not allowed to move around and interact the objects in the virtual tour. This study proposes a strategy for transforming virtual tour with the effect of moving and interacting in three dimensions using full body motion sensor - Shadow. Preliminary proof of concept test shows that the strategy allows free translation within 360 virtual tours.
Equirectangular panoramas based virtual tours are popular tool for achieving 360-degree immersed viewing experiences. Nowadays, virtual tours are very popular and many people would like to see a virtual house before the acquisition of the real one. A virtual tour captures a scene from one point and viewers software allows the user to control the viewing direction, but usually the viewer is not allowed to move around and interact the objects in the virtual tour. This study proposes a strategy for transforming virtual tour with the effect of moving and interacting in three dimensions using full body motion sensor - Shadow. Preliminary proof of concept test shows that the strategy allows free translation within 360 virtual tours.
