International Journal of Education and Management Engineering @ijeme
Статьи журнала - International Journal of Education and Management Engineering
Все статьи: 676
An Improved Flower Pollination Algorithm with Chaos
Статья научная
Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, a new method is developed based on the flower pollination algorithm combined with chaos theory (IFPCH) to solve definite integral. The definite integral has wide ranging applications in operation research, computer science, mathematics, mechanics, physics, and civil and mechanical engineering. Definite integral has always been useful in biostatistics to evaluate distribution functions and other quantities. Numerical simulation results show that the algorithm offers an effective way to calculate numerical value of definite integrals, and it has a high convergence rate, high accuracy and robustness.
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An Improved Multi-objective Evolutionary Algorithm with the Hybrid Strategies
Статья научная
An improved multi-objective evolutionary algorithm with the hybrid strategies is presented in this paper for multi-objective optimization problems. The evolution process is divided into initial exploration stage, the middle feedback stage and the accelerating convergence stage by the amount of non-dominated individuals in the population. The hybrid strategies and adaptive population structure are employed to improve the behavior of the algorithm at the different stages. The proposed algorithm is validated by 3 benchmark test problems. Compared with three other famous multi-objective algorithms by two quality indicators, the proposed algorithm achieves competitive results.
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Статья научная
The mobility of Migrating Instance（MI） brings many risks to the migrating workflow system. Especially, the codes of MI face the risk of being maliciously manipulated by the hostile working places. This paper presents an initiative mechanism of safe-guarding the codes of MI based on danger theory: for MI consists of several modules in order to complete relative tasks, MI will detect whether the block MI determines to run changed before executing. If there are some changes, MI will then perceive whether the changes dangerous; if dangerous, appropriate measures will be taken to amend the damaged codes and then obfuscate them lest this module will be attacked easily again. This mechanism improves the efficiency of MI compared with other mechanism, and makes MI avoid temporary attacks, which are illustrated with the experimental results at the end of this paper.
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An Intelligent Survey of Personalized Information Retrieval using Web Scraper
Статья научная
In this paper we aim to do an intelligent background survey of Personalized Information Retrieval, a specialized and crucial subsection of Information Retrieval or IR. We have chosen the method of IR as Web Scraping, a technique that is extremely popular and is proven to have multi-domain usage.
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An Intelligent, Bilingual Pregnancy Health Monitoring System
Статья научная
This research implements an intelligent, bilingual pregnancy health monitoring system for expectant mothers. A significant problem commonly experienced by expectant mothers in rural areas in Nigeria is the unavailability of a decent antenatal system and a shortage of experienced medical personnel and equipment. The proposed system comprises IoT sensors, including Electrocardiogram (ECG), body temperature, and heart rate sensors, connected to an ESP32 microcontroller for data acquisition and transmission. A predictive system built using Random Forest and Support Vector Machine (SVM) classifiers categorises pregnancy risk into low, medium, and high. A Flask-based web application for real-time data visualization and diagnosis was developed to display the collected data and visually represent the risk level diagnosis. The performances of the predictive models, Random Forest and Support Vector Machine (SVM), were evaluated using accuracy, precision, recall, and F1-score. Random Forest achieved an accuracy surpassing SVMs by a margin of 5.28%. Random Forest and SVM precision were then compared and there was an improvement of 6.49%. In addition, Random Forest had a higher recall than SVM by 6.58%, and also had a performance increase of 6.49% on F1-score as compared to SVM. The comparative analysis shows that the Random Forest model works better than SVM in all the main measures. In this project, the Random Forest model was better than the SVM because it uses ensemble learning to manage the non-linear relationship, imbalance data and noise better to achieve superior accuracy, recall, and the F1 Scores. It was also more reliable in categorizing risks in pregnancy, as it was interpretable, which was also strong and guaranteed the timely and suitable intervention of health care.
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An Internet of Thing based Agribot (IOT- Agribot) for Precision Agriculture and Farm Monitoring
Статья научная
Developing nations like India have a huge potential for agricultural business and better cultivation. Because of the large size of cultivation land, improper water supply systems and lack of technology-based agricultural practices, there is a huge gap among expected and actual quantity and quality of agricultural products. Hence there is a need for significant revival in agribusiness using emerging technologies. The article proposes an intelligent water framework device called Agribot designed for the agricultural industry to minimize the water wastage and a better supply of cultivating materials using the Internet of Things (IoT). Our proposed IOT- Agribot will energize the water framework, improve the cost-effective water usage and reduce the labor force to achieve precision agriculture. The proposed IOT- Agribot has performed well for variable weather conditions, soli type, moisture content and crops.
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An Investigation on the Metric Threshold for Fault- Proneness
Статья научная
The software quality can be enhanced with the awareness and compassionate about the software faults. We acknowledge the impact of threshold of the object-oriented metrics on fault-proneness. The prediction of fault-prone classes in early stage of the life-cycle assures you to allocate the resources effectively. In this paper, we proposed the logistic regression based statistical method and metric threshold to reduce the false alarm for projects that fall outside the risk range. We presented the threshold effects on public datasets collected from the NASA repository and validated the use of threshold on ivy and jedit datasets. The results concluded that proposed methodology achieves the speculative results with projects having similar characteristic.
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Статья научная
Modern technologies like 5G, the Internet of Things (IoT), and Artificial Intelligence (AI) have just come together, creating previously unheard-of chances for creative solutions. As a result, several IoT use cases have come to fruition, particularly in the healthcare industry, enabling the creation of eHealth and mHealth applications for ambient assisted living (AAL). However, there are practical issues with the current healthcare system, such as service delays and exorbitant expenses, which have had serious repercussions, such the untimely passing of famous people from heart attacks. Real-time patient monitoring and therapy with few delays are necessary to solve these pressing challenges. IoT has changed the game in this area by making it easier to establish Remote Patient Monitoring (RPM) systems. Vital indicators can be sent in real time to clinicians using IoT-enabled wearable devices (biosensors), enabling quick intervention and the start of treatment. This article gives an overview of the state-of-the-art in RPM using IoT, highlighting its potential to save time, lower healthcare expenses, and considerably raise patient quality of life and the caliber of healthcare services. It also identifies research holes and ways to use RPM systems, laying the groundwork for further development in this area.
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An approach for software development for the management of an assembly line
Статья научная
The article presents an approach for software development for the management of an assembly line. Basic software tools and hardware solutions that are needed for the development of the software are described. Design of specific software for the management of an assembly line for bottling liquid food products is presented. A specially developed algorithm for the management of the assembly line is described. The realization of the management of the assembly line in the programming environment Simatic Manager Step 7 and the developed user interface in the graphical environment Simatic WinCC Flexible are also presented. The design of software for the management of an assembly line for bottling liquid food products is extremely important for the development of automated manufacturing in Bulgaria. After detailed testing of a trial version of this software, it will be used in Bulgarian company for the management of an assembly line for bottling liquid food products. The developed software is an open system that can be continuously updated and improved. This software is applicable in all manufacturing plants for bottling liquid products from canning, pharmaceutical, cosmetic industries and many others.
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An approach to represent social graph as multi-layer graph using graph mining techniques
Статья научная
In Social Graph, a set of entities or nodes or vertices interact with each other in a complicated manner that can form multiple types of relationships that depend on time and types of complications. Such graphs include multiple subsystems and layers of connectivity. So it is important to take such multi-layer features into account to make easier of understanding of such complex systems. In this paper, the author focuses on a Social Graph to represent in a multi-layer graph based on its characteristics lies in each node or vertex or entity. For this, the author proposes a general model related to Social Graph. For this model, the author proposes an algorithm, SoGraM for representation of Social Graph with multi-layer features using Graph Mining Techniques. Further, the author tries to prove the proposed algorithm with three examples of Social Graph namely Author Graph, Email Graph, and Telephone Graph.
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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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Статья научная
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
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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