Статьи журнала - International Journal of Education and Management Engineering
Все статьи: 613
An efficient Group Key Management Scheme for Ad Hoc Networks
Статья научная
Ad Hoc networks are characterized by frequently changing network topology. Due to the lack of central authority, forming security association among a group of members in Ad Hoc networks is more challenging than in traditional networks. With the view in mind, group key management plays an important building block of any secure group communication. In this paper, we proposed a group key management based on Identity-based Cryptosystem and Chinese Remainder Theorem. In the proposed scheme, there are no requirements of member serialization and existence of a central entity. Besides this, the scheme the protocol also many highly desirable properties such as contributory and efficient computation of group key, uniform work load for all sensor nodes and efficient support for high dynamics. Compare to other existing group key agreement protocols, the proposed protocol make no assumption about the structure of the underlying wireless network, making it suitable for Ad Hoc networks.
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An efficient approach for Web mining using semantic Web
Статья научная
The volume of data on the Web is increasing rapidly. The rapidly increased data in Web have brought an urgent need to develop a method to organize that data. At the same time, the level of user expectation of getting précised data is increased highly. Hence, it is tough to satisfy the user satisfaction through the existing system. In this paper, we proposed a model to organize the large volume of data over the Web and retrieve the more relevant data to the user. As an implementation of the proposed model, we built two demo search engine (one for RDF based semantic searching and another for existing searching). We use two different sets of data for testing. For every set of data, the RDF based searching returns more précised data than existing searching. The efficiency of the proposed model is better than the existing searching strategies. In the proposed model, we considered both traditional web and RDF based ontology library to organize the data effectively.
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Статья научная
Grid computing consists of achieving an effectual clustering of the valuable resources having dissimilar locations which will deal with real time scenarios. The grid follows the dispersed procedures having heavy workloads which can be in the form of the traffic files from different locations. Grid computing is related to the extraordinary performance systems like computer clustering or we can say nodes in the grid in such a manner that each set of the node performs different tasks and applications. Grid computers also deals with networks with topology variations and diverse geography which is not essentially to connect substantially to the cluster of computers. As the number of traffic increases day by day, is the challenging task to complete all the allocated processes in the limited time intervals. So this research deals with the efficient scheduling and optimization approach for the resource management using Ant colony optimization and round robin scheduling to obtain low execution intervals with less error rate probabilities. The whole simulation is done in MATLAB environment.
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An efficient software development life cycle model for developing software project
Статья научная
There are different life cycle models available for developing various types of software. Every Software Development Life Cycle (SDLC) model has some advantages and some limitations. In that case software developers decide which SDLC model is suitable for their product. Further, we need development of software in a systematic and disciplined manner. This is advantage of using a life cycle model. A life cycle model forms a common understanding of the activities among the software engineers and helps to develop software in a proper manner, so that time can be reduced. The objective of this paper is to compare all traditional or existing SDLC model with our Proposed SDLC model for development of software in effective and efficient manner.
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An enhanced approach for quantitative prediction of personality in Facebook posts
Статья научная
Social media is a collection of computer-mediated technologies that encourages the creation and sharing of data, thoughts and vocation interests by means of online communities. There are various kinds of web-based social networking i.e. micro-blogs, wikis and social networking sites. Different social media like Facebook, LinkedIn, Google+ and Twitter are the popular sources for connecting people all over the globe. Facebook is one of the commonly used platform where individual’s used to stay in touch, business personnel used for marketing and others used to share expedient information. Due to this lucrative nature, one’s personality can be predicted on the basis of posts created, commented on others post and likes against any posts. We have developed in-house tool using python language that defines personality in terms of psychological model of Big-5 personality traits including extraversion, neuroticism, agreeableness, openness and conscientiousness. The dictionary based approach has been used in this tool in which we have combined three dictionaries (WordNet, SenticNet and Opinion Lexicon). Our proposed technique has shown promising results as we have analyzed 213 unique Facebook profiles and their results outperforms the others. Furthermore a comparative analysis of machine learning classifiers i.e. support vector machine, na?ve bays and decision tree has performed. Our approach succeeds to predict personality traits. We are intended to predict personality from roman English posts in future.
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An intelligent distributed K-Means algorithm over Cloudera /Hadoop
Статья научная
The 21st century evolved with tsunami of data generation by the human civilization that has delivered new words like Big Data to the world of vocabulary. Digitization process has almost overtaken all the major sectors and it has played a pivotal role of dominance as for as virtual digital world is concerned. This in turn has landed us in most debated term “Big Data” in the present decade. Big Data has made the traditional relational databases (RDMS) handicapped in terms of their huge size and speed of its creation. The hunger to manage and process this gigantic complex heterogeneous data, has again followed the age old rule of “Necessity is the mother of Invention”, and came up with idea of HadoopMapReduce for the same. The given work uses K-Means clustering algorithm on a benchmark MRI dataset from OASIS database, in order to cluster the data based upon their visual similarity, using WEKA. Until a threshold size it worked out and after that compelled WEKA to prompt an emergency message “out of memory” on display. A Map/Reduce version of K-means is implemented on top of Hadoop using R, so as to cure this problem. The given algorithm is evaluated using Speedup, Scale up and Size up parameters and it neatly performed better as the size of the input data gets increased.
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Analysis and Design of University Teaching Evaluation System Based on JSP Platform
Статья научная
High quality of teaching is fundamental purpose and basic task of a university, as well as a foothold in the university. We introduce in this paper a university teaching evaluation. This system is used by students and experts via Servlet+JavaBean+ORACLE on campus network with the foundation of the system published by the teaching affairs bureau of university. The target system is divided into student evaluation, expert evaluation and management modules. The evaluation system is divided into two subsystems, namely, expert evaluation and student evaluation of courses. Database is the core of the whole system. It serves all the information processing modules. The implementation of the system can fully improve the quality control of teaching and lower the cost. A teaching evaluation system is analyzed and designed in this paper.
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Analysis of 188 Cases of Laparoscopic Diagnosis of Infertility
Статья научная
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
Статья научная
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
Статья научная
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 Implementation Effect of Increasing the Export Tax Refund Rate of China's Textiles
Статья научная
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
Статья научная
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
Статья научная
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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Статья научная
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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Статья научная
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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Статья научная
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
Статья научная
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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Статья научная
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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Статья научная
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
Статья научная
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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