International Journal of Education and Management Engineering @ijeme
Journal articles - International Journal of Education and Management Engineering
All articles: 685
Scientific article
Forecasts of births and deaths play an important role in determining the dynamics of both population size and gender-age structure. Since population forecasts are the basis of long-term planning of socio-economic development, the statistical accuracy of forecasts is particularly important, and the applied methods play a special role here. The purpose of this study is to evaluate Autoregressive Integrated Moving Average (ARIMA) model ability to forecast the yearly number of births and deaths in Azerbaijan. In the analysis, the Box-Jenkins methodology was followed when building the suggested model. Besides, Akaike’s information criterion (AIC) and Bayesian Information Criteria (BIC) are used to select the best ARIMA model, compared to another estimated models. The prediction results of the models are evaluated using the mean absolute percentage error (MAPE) and the root mean square error (RMSE) . Comparing the predicted data from the ARIMA models shows that the correct selection of model parameters, it possible to fairly accurately predict the yearly number of births and deaths. Thus, using the advantages of the ARIMA model, it is possible to obtain forecasts of birth and death rates for the near future and it possible to observe changes that will occur in the age structure of the population. And these interpretations can guide policymakers to focus on socio-economic development and comprehensive healthcare system strengthening as crucial strategies for raising the fertility level and further reducing mortality rate.
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Analysis of 188 Cases of Laparoscopic Diagnosis of Infertility
Scientific article
In this paper, we have applied ventroscopy in diagnosing and curing of acyesis. We had gathered 188 cases of ventroscopy about acvesis from February 2006 to December 2009 in our hospital. The effect showed that there were 115 acyesis cases caused by fallopian tube factor, which ranks first. And there were 35 acyesis cases caused by endometriosis, which ranks second. Other acyesis cases number was 23. About 48.9% patients in those 188 cases were pregnant after being cured. So, we can diagnose the reason of acvesis in time by means of ventroscopy.
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Analysis of Access Control Methods in Cloud Computing
Scientific article
Cloud Computing is a promising and emerging technology that is rapidly being adopted by many IT companies due to a number of benefits that it provides, such as large storage space, low investment cost, virtualization, resource sharing, etc. Users are able to store a vast amount of data and information in the cloud and access it from anywhere, anytime on a pay-per-use basis. Many users are able to share the data and the resources stored in the cloud. Hence, there arises a need to provide access to the data to only those users who are authorized to access it. This can be done by enforcing access control schemes which allow only the authenticated and authorized users to access the data and deny access to unauthorized users. In this paper, a comprehensive review of all the existing access control schemes has been discussed along with the analysis of these schemes.
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Analysis of Current Wireless Network Security
Scientific article
Wireless technologies bring great convenience, but they also introduce many new risks and vulnerabilities. Based on explaining the most famous Wireless LAN standard, the 802.11 network security threats and preventive measures are given.
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Analysis of Human Behavior and Interests Based on Text Data
Scientific article
Information technology has revolutionized data collection and analysis, offering unprecedented opportunities to study human behavior. Various information registers, the internet of things, and electronic demographic platforms that collect and analyze user data from various online sources provide a unique opportunity to predict human behavior using machine learning methods. This study applies machine learning to analyze textual data derived from diverse sources: demographic data, scientific articles, employee documents, and social media content. The primary goal is to identify a person's area of interest and predict their behavior. We propose using Support Vector Machines (SVM) as a robust and versatile machine learning algorithm for text data analysis. SVM's ability to handle diverse data types makes it well-suited for analyzing complex human behavior patterns. By classifying documents into relevant topics, SVM can help assess how employee behavior aligns with organizational goals and performance metrics. This research aims to contribute to human behavior analysis by demonstrating the effectiveness of machine learning techniques, particularly SVM, in extracting meaningful insights from textual data.
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Analysis of Implementation Effect of Increasing the Export Tax Refund Rate of China's Textiles
Scientific article
China's textile exports was deteriorated because of the impact of the global financial crisis. State adopts some support policies in time. This paper mainly analyzes the implementation effect of increasing China's textile and apparel tax refunds to exporters, points out the active and negative effect of those policies to the enterprise export. And emphasizes state and enterprises ought to adopt other measures to pull through the crisis at same time and promote the development of textile industry.
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Analysis of Social Psychology of Higher Single Recruit Students
Scientific article
In recent years, with the vocational college enrollment expanding, vocational education in China has accounted for half of higher education, and becomes another path to university. Higher single recruit students are different form each other in every aspect, which produces many problems. This paper listed main problems facing by the students during university life, and analyzed them by social psychology as well as proposed corresponding measures and recommendations.
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Analysis of features using feature model in software product line: a case study
Scientific article
This paper shows an analysis of features of email system using feature model in a Software Product Line (SPL). The core features that can be used by different SPLs are identified using feature model. The analysis is based on two primary measures – reusability and consistency. Reusability measures the level of frequency of usage of the feature in developing a new software product line and consistency ensures that the core features are consistent in a software product line. On the basis of reusability measure, the core features are classified into four different categories. These measures help in understanding the Return on Investment in a software product line.
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Scientific article
According to the characteristic of Industrial and commercial Management specialty, especially “stronger practice”, in order to promote the transform of professional knowledge between explicit knowledge and tacit knowledge, the expansion model of SECI is introduced into the explorative analysis, and corresponding teaching methods and models are raised to facilitate the mastery and flexible application of students’ professional knowledge.
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Scientific article
The paper, beginning from the course-system setting, the teacher-education measure, the student’s Occupational Ethics, the practice ability, the communication-ability’s cultivating and so on, makes an analysis of the differences between the specialty education of Sino-American Construction Engineering Management, points out the problems and shortcomings existing in present education of the specialty education of Project Management in our country, and puts forward some means and ways to solve the problem.
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Scientific article
For almost two years, the world has been battling a global trouble- the COVID-19 pandemic. The disease, which has spread to about 225 countries around the world, has devastated the healthcare system of even the most developed countries. Governments have found the only way out is to impose a strict quarantine regime and state of emergency. Scientists immediately began testing the vaccine. Vaccination would still be the only savior of the planet's inhabitants.Because many of these pandemic infections have exactly been prevented thanks to vaccines in the past. Although the reduction in the number of infections after strict quarantine measures allowed the restrictions to be eased, the next wave was starting soon. This made it necessary the preparation of the vaccine as soon as possible. At the end of last year, the expected news came. Thus, in December 2020, the vaccination process has been launched in a number of countries. Azerbaijan is also one of the first countries to join the vaccination. The vaccination process, which began on January 18, 2021 continues, provided that 4 types of vaccines are available to the population. As a result of vaccination, the epidemiological situation in Azerbaijan is under control, as in many countries. In this article has been attempted to find a correlation between vaccination and COVID-19-confirmed cases and deaths. For this purpose, the k-means cluster-based machine learning method has been used in the Azerbaijan data collection obtained from the GitHub repository of the Center for Systems Science and Engineering at Johns Hopkins University. This research can benefit governments, stakeholders, and relevant institutions in the health care sector in monitor the vaccination process and more detally assess the epidemiological situation , and make important decisions to control and manage the spread of the disease.
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Analyzing the Performance of the Machine Learning Algorithms for Stroke Detection
Scientific article
A brain stroke is a condition with an insufficient blood supply to the brain, which causes cell death. Due to the lack of blood supply, the brain cells die, and disabilities occurs in different parts of the brain. Strokes have become one of the major causes of death and disability in recent years. Investigating the affected individuals has shown several risk factors that are considered to be causes of stroke. Considering such risk factors, many research works have been performed to classify and predict stroke. In this research, we have applied five machine learning algorithms to identify and classify the stroke from the individual’s medical history and physical activities. Different physiological factors have are considered and applied to machine learning algorithms such as Naïve Bayes, AdaBoost, Decision Table, k-NN, and Random Forest. The algorithm Decision Table performed the best to predict the stroke based on different physiological factors in the applied dataset with an accuracy of 82.1%. The machine learning algorithms can be a helpful for clinical prediction of stroke against individual’s medical history and physical activities in a better way.
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Scientific article
With the expansion of worldwide security concerns and a consistently expanding requirement for successful checking of open places, i.e. air terminals, railroad stations, shopping centres, crowded sports fields, army bases or smart healthcare facilities such as daily activity monitoring and fall detection in old people’s homes is increasing very rapidly. The visual occlusions and ambiguities in crowded scenes, usage of suitable method and in addition the perplexing practices and scene semantics make the investigation a challenging task. This research demonstrates comprehensive and critical analysis of crowd scene involves in object detection, tracking, feature extraction and learning from visual surveillance which helps to recognize behavioural pattern. This research refers scene understanding as scene layout, i.e. finding streets, structures, side-walks, vehicles turning, person on foot intersection and scene status such as crowd congestion, split, merge etc. The significance of the proposed comprehensive review to create crowd administration procedures and help the development of the group or people, to maintain a strategic distance from the group calamities and guarantee general society security. Based on the observation of previous research in three aspects, i.e. review based on methods, frameworks and critical existing results analysis, this research propose a framework for anomaly detection in crowded scene using LSTM (long Short-Term Method). Proposed comprehensive review is expected to contribute significantly for the investigation of behavior pattern analysis in computer vision research domains.
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Scientific article
Smaller time loss and smoother information communication mode is the urgent pursuit of the software R&D enterprise. Information communication is difficult to control and manage and it needs more technical to support. Data mining is an intelligent way tried to analyze knowledge and laws which hidden in massive amounts of data. Data mining technology together with share repositories can improve the intelligent degree of information communication mode. In this paper, the framework of intelligent information communication mode which based on data mining technology and share repositories is advanced, and data mining model for information communication of software development is designed. In view of the extant single decision tree algorithm existence the characteristics that counting inefficient and its learning based on supervise, a new semi-supervised learning algorithm three decision trees voting classification algorithm based on tri-training (TTVA) is proposed. This algorithm in training only requests a few labeled data, and can use massively unlabeled data repeatedly revision to the classifier. It has overcome the single decision tree algorithm shortcoming. Experiments on the real communicated data sets of software developmental item indicate that TTVA has the good identification and accuracy to the crux issues mining, and can apply to the decision analysis of the development and management of the software project. At the same time, TTVA can effectively exploit the massively unlabeled data to enhance the learning performance.
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Application and security issues of internet of things in Oil-Gas industry
Scientific article
Article proposes an architecture based on new Internet of Things (IoT) for easy, safe, reliable and rapid data collection from sensors installed in oil and gas industry. Use of several Wireless Sensor Networks in management of oil and gas platforms is researched. New opportunities created by processing of data collected via sensors for improvement of safety of oil platforms (deposits), optimization of operations, prevention of problems, troubleshooting and reduction of exploitation costs in oil and gas industry. At the same time, the article analyses safety issues of different layers of monitoring system with IoT architecture.
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Application of CDIO Model for "Microcomputer Principle" at Technology University
Scientific article
CDIO Model, the trend of education at present, was introduced to the course of "Microcomputer Principle". CDIO teaching is the teaching activities of teachers and students together to complete several entire projects. The CDIO education model embodies the teaching philosophy that teachers are guiders, students are subject, the combination of works and studies and "Learning by Doing". It improves teaching effectiveness and teaching quality.
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Application of DFS in the Study of Edge-connected Graph
Scientific article
In this paper a simple method is proposed to determine whether a graph is edge-connected. This method may calculate the minimum pre-order number of each vertex by back edge for the depth-first search spanning tree, and then find out the bridges in the graph. Finally, it may determine whether the graph is edge-connected. The best nature of method is to understand and hold the algorithm easily. It can help teaching improvement and practice application. It is also worth popularization.
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Scientific article
This study aims to identify digital audio forensics using the System Development Life Cycle (SLDC) method which is used as a reference in the audio forensic investigation process. The process of testing the application of the framework that was carried out succeeded in identifying audio evidence with the identification results that subject x sampling (known) was identical to recorded evidence (unknown) with the results obtained for more than 4 identical words and supporting the prosecution hypothesis. Also, the results of the feasibility test of a framework that has been developed as a reference standard for comparison of frameworks related to other audio forensics, show that the framework that has been developed has a more complete stage to be used in the audio forensic investigation process. The results of Spectrogram analysis and Pict analysis on values matrix cross similarity level of evidence with audio subject Nasri4Y has the highest similarity value 0.9575822. The results of reading the audio evidence matrix with audio subject Bakrim5Y have the lowest similarity value 0.48924464. The results of reading the matrix, audio subject- B with audio subject Bakrim3Y have the highest similarity value of 0.9287775 because it is a sample voice from the same person. The results of the reading of the matrix, Nasri2Y audio subject, and Nasri4Y audio subject have a similarity value of 0.9575822 because they are sound samples from the same person. The results of reading the matrix audio subject Nasri2Y with audio subject Nasri4Y have the highest similarity value of 0.9575822, from this result it can be said a significant value because the audio subject Nasri2Y and Nasri4Y have the most similar level of sound samples from other subjects because Nasri2Y and Nasri4Y are sound samples from the same person.
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Application of Greedy Algorithm on Traffic Violation Enforcement
Scientific article
The background of this research is how the application of the algorithm in traffic control systems, with the Greedy algorithm, the system will decide what action and what punishment will be given to traffic offenders on the highway, with the Greedy algorithm, the decisions taken will be based on data and facts. based on existing laws, so decisions made based on law and the human side cannot influence the decisions to be taken by the system based on the application of the Greedy algorithm. The research method used in this study uses literature reviews by reading many previous research journals, it will be able to add knowledge and deepen the research we are doing this time, with the literature review method, we will be able to find new problems and can be used as new research because The literature review is very helpful for our research this time. The system that is being used is using CCTV and can determine what decisions and punishments will be given to traffic offenders, through evidence based on images taken by cameras placed at red lights or corners of the capital's highway, the system this has been active effectively but with the implementation of the algorithm will increase. This research will produce a system proposal and be able to find out whether the application of the Greedy algorithm is correct and can help the current system by implementing the algorithm, so the existing system is more perfect. The main contribution of this research is that the use of the Greedy algorithm can help control the traffic system to enforce the law.
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Application of Machine Learning and Predictive Models in Healthcare – A Review
Scientific article
The use of predictive analytics or models in healthcare has the potential to revolutionize patient care by identifying high-risk patients and intervening with targeted preventative measures to improve health outcomes. This makes the application of analytics in healthcare a concept of utmost interest, which has been explored in various fashions by several scholars. From predicting patients’ ailments to prescribing appropriate drugs, predictive models have seen massive interest. This work studied published works on predictive models in healthcare and observed that the implementation of predictive models in healthcare is experiencing a notable upswing, with a particular focus on research in the United States, where a majority of the top publications originated. Surprisingly, all of the leading nations in this sector have affiliations spanning many continents, with the exception of Africa and South America, together producing a substantially larger volume of research than other countries. The United States also shone out, accounting for 60% of the top five researchers. Notably, although it was published in 2017 (relatively later), Jiang et al. had the most citations (1,346). These studies' core themes were clinical standards, machine learning terminology, and model accuracy. The Journal of Biomedical Informatics topped among journals, with 54 articles, while Luo Gang emerged as the top-performing author, with 12 publications.
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