International Journal of Education and Management Engineering @ijeme
Journal articles - International Journal of Education and Management Engineering
All articles: 685
A Decision Tree Approach for Predicting Students Academic Performance
Scientific article
This research is on the use of a decision tree approach for predicting students' academic performance. Education is the platform on which a society improves the quality of its citizens. To improve on the quality of education, there is a need to be able to predict academic performance of the students. The IBM Statistical Package for Social Studies (SPSS) is used to apply the Chi-Square Automatic Interaction Detection (CHAID) in producing the decision tree structure. Factors such as the financial status of the students, motivation to learn, gender were discovered to affect the performance of the students. 66.8% of the students were predicted to have passed while 33.2% were predicted to fail. It is observed that much larger percentage of the students were likely to pass and there is also a higher likely of male students passing than female students.
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A Dynamic Scheme Based on Clustering for Wireless Sensor Networks MAC Protocol
Scientific article
Wireless Sensor Networks(WSNs), as one of the most important techniques in the 21st century, receives auniversal attention to the research on its key techniques.A solution is urgently expected to meet thehigher-quality service requirements of various applications on the limited bandwidth of wireless networks.Based on the ideas of Priority-based polling transmission with high-efficiency, and dynamic self-adaptation to loading, this paper presents a new analysis model of MAC Scheme of wireless sensor networks, which has made possible the Cluster Header capable of differentiating the nodes of different priority levels under mixed services policy and which has improved the flexibility and adaptability of the system via adjustment of the switch-in service in three time of load change input. The theoretical model of this protocol is established with Markov chain and probability generating function. Mathematical analysis is made on the mean queue length and the mean inquiry cyclic time of the common queue and the key node. It turns out that the findings from theoretical analysis correspond well with those from simulated experiments.
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A Fake Product Identification and Prevention System Using Blockchain Technology
Scientific article
Blockchain technology can revolutionize product authenticity verification by utilizing decentralized networks to collect and retain product data. This generates an irrevocable record of a product's path from manufacture to sale, making it possible to detect phony or counterfeit goods, a problem that plagues many different industries. A common problem that compromises consumer confidence and harms brand integrity in a variety of businesses is counterfeiting. Our strategy involves establishing a blockchain-based product registry, allowing various supply chain nodes, including production and store shipping, to update information such as origin, materials, and certificates. Smart contracts also provide various applications for detecting fake goods, thus preventing the introduction of counterfeit goods into the supply chain. Based on predefined standards, these self-executing contracts validate the product's legitimacy. Blockchain technology makes it possible to verify things accurately, ensuring that customers receive the real goods they pay for. Our system enables accurate verification, ensuring that customers receive genuine goods. by scanning a QR code that connects to the blockchain record, customers can instantly authenticate the legitimacy and history of a product, ensuring its authenticity. By offering a clear picture of the product's route, this approach boosts consumer trust by ensuring a high level of security and traceability. This method offers a safe and effective solution to the age-old issue of product authenticity because of its decentralized structure and immutable record-keeping. This is a breakthrough in fighting counterfeiting and maintaining product integrity by empowering customers, defending brands, and encouraging a more trustworthy marketplace.
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Scientific article
Employing Computerized Adaptive Testing (CAT) to evaluate verbal ability symptoms proves advantageous over traditional tests by delivering heightened measurement precision and reducing the testing burden. The CAT-Verbal Ability, developed from a large sample of 2689 participants in Gulf countries, underwent meticulous item bank development, ensuring unidimensionality, local independence, and investigating differential item functioning (DIF). The CAT-Verbal Ability item bank has high content validity, is unidimensional, locally independent, and does not have DIF; these outstanding psychometric qualities were confirmed by CAT simulations that were based on real data. With just 14 items needed, CAT simulations showed a high degree of measurement accuracy (r=0.73). In addition to being a psychometrically sound instrument, the proposed CAT-Verbal Ability demonstrated acceptable marginal reliability, criterion-related validity, sensitivity, and specificity. This makes it an efficient assessment method that reduces testing burden while maintaining information integrity, and it also saves time.
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Scientific article
Fire is a threat to lives and properties when no proactive measure is engaged towards curbing the occurrence. Fire occurs whenever there is a complete reaction of combustible fuel, heat and oxygen. The students require technological skills for digitally-based society. This paper presented an automated flaming ions–fire detection and notification system that detected a combustible flaming ion before fire occurs, alerted the relevant authority using Short Message Service (SMS) through Global System for Mobile Communication (GSM) modem for urgent attention and imparted students with relevant skills in designing and implementation of automated systems. The developed system was continuously scanning for detection of combustible flaming ions by the sensors in its vicinity. The relevant authority not available within the surroundings as well as people within the neighbourhood was alerted for fast response before the fire incidence. The developed system had a response mean-time of 3.55s in detecting flaming ions before the occurrence of fire. The flaming ions were detected at the mean-time of 3.55seconds prior to the fire occurrence from the tested materials. It was remarked by the users that a prompt detection of flaming ions could prompt response from fire fighters and prevent any damages from fire incidence and principally, the students in the field of Computer Science were imparted with computational skills in designing and implementing a detection and notification system.
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A Framework of Network Virtual Experiments for Control Courses
Scientific article
With the development of computer science and Internet, using the virtual experiments to help improvement of teaching effects is becoming more and more popular. This paper describes a framework of virtual experiments for control courses in college. It includes three layers. The user interface layer bases on Flash technology, the data exchange layer bases on Webservice technology, the calculation module in data process layer bases on Modelica language. This framework provides a friendly user interface and solved the problem that the control experiments need multifarious calculation when simulation the real phenomenon. An example of virtual experiment is also be shown in the final of this paper. This framework can also be used for virtual experiments of other courses in college education.
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A Fuzzy Delphi and Fuzzy AHP Application for Evaluating Online Game Selection
Scientific article
The trend of playing online games as an online entertainment has rapidly proliferated worldwide. This paper examines critical factors related to the selection of online games. Taking content design and operations management into account, we identified 15 constructs in the literature survey. The fuzzy Delphi Method is used to evaluate the primary criteria collected from 20 heavy players in Taiwan and the Fuzzy Analytic Hierarchy Process employed to calculate the weights of these criteria so as to establish a fuzzy multi-criteria model of online game selection. The results indicate the greatest weight being placed on the dimension of online game selection, and the 11 critical criteria related to online game selection were: (1) scenario, (2) graphics, (3) sound/light effects, (4) interface, (5) access, (6) interactivity, (7) security, (8) reliability, (9) responsiveness, (10) assurance, and (11) empathy. This study may have a contribution to make in terms of understanding how online game companies can reinforce content design and operation quality in an attempt to achieve competitiveness.
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A Hybrid Approach to Sentiment Analysis of Technical Article Reviews
Scientific article
Sentiment analysis is similar to opinion mining, which is a popular research problem to search out in the field of NLP. Sentiment analysis determines the perspective of the author and identifies the positive, negative and neutral reviews. It provides the reviews or opinions of people's on text, article and product which can be positive, negative or neutral. Reviews on the different websites, social networking sites is an important source to collect the information regarding various brands of product and new features in technology (e.g. Windows, Mobiles). During the sentiment analysis various classification tools within the NLP are used to find out the positivity and negativity of reviews or comments. The paper presents a length aware hybrid approach to analyses the reviews either as positive or negative and present approach is tested on SuperFetch data set. The present approach is a combination of both supervised machine learning techniques that are Support Vector Machine and K-Nearest Neighbor in which SVM is working great for large size review and KNN is working best for small size review.
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Scientific article
PerformanceX is a proposed system that combines Educational Data Mining (EDM) techniques to enhance student performance and reduce dropout rates. It employs a hybrid feature selection approach to identify the most significant attributes from student academic datasets, eliminating unnecessary features that are not crucial for predicting performance. The selectX algorithm, a critical component of PerformanceX, selects a limited number of high-performing features to optimize student learning effectiveness and prediction accuracy. The system applies various machine learning classifiers, including a fusion Voting Classifier, to different subsets of features, ultimately determining the best combination. The study achieved an impressive accuracy rate of 99.41%, with the selectX approach utilizing 10 features in conjunction with a random forest (RF) classifier offering the highest accuracy. These findings underscore the importance of categorizing student performance based on a concise yet meaningful set of features, leading to improved student quality and career progression. The research value of PerformanceX lies in the development of a performance forecasting system that eliminates irrelevant information and provides precise predictions for student performance. Its efficacy and efficiency make it an invaluable tool for educators and educational institutions. By assisting students in selecting appropriate courses to enhance their performance and advance their careers, PerformanceX contributes to diminishing dropout rates while fostering positive student outcomes.
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A Lexical Approach for Opinion Mining in Twitter
Scientific article
The blossoming of a significant number of social networking sites, blogs, and microblogs has given a podium for general masses to voice their opinion regarding social topics, economic issues, political matters, market trends etc. However, this sudden eruption of review data had opened floodgates to unmanageable records as it is almost impossible for any individual or organization to manually extract any useful information from it. Opinion mining or sentimental analysis is a natural language processing which can obtain the opinion or feeling of people about any particular product or subject. The main focus of this paper is to find a method to perform sentiment analysis of Twitter which is one of the most prevalent microblogging sites. The lexical method proposed in this paper classifies the tweets as positive, negative or neutral depending on the polarity of the words in it. Also, the role of negation words has been investigated.
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A Low Power Consumption Architecture Model for Multiple Processors on Optical Printed Circuit Board
Scientific article
Traditional single-core processors and multiprocessors architectures encounter some problems which are high heat generation, high cost to produce and high power consumption. In this paper, a model to achieve low power consumption by using several processors managed separately with useful energy saving strategies including switching the mode of processors, assign tasks to different CPUs, using optical fiber as transmitting media etc. is proposed. In this way, the architecture manages to disperse the energy dissipation. Some evaluations on power consumption, cost and performance are made which proves that it is an effective method to reduce the power consumption.
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A MDA-based campus data analysis and visualization framework
Scientific article
As the rapid development of high education informatization, it will be a new important research topic to con-duct the data mining and visual analytics to the existing data in the separated information systems. The existing campus information system is the integration of some business systems, so the system has some problems, such as it stores the data separately and it has poor ability to analyze data. In order to solve these problems, we pro-pose Model Driven Architecture (MDA) based campus data analysis and visualization framework. The frame-work is composed of multi-dimension data modeling, data extraction, visualization-based data exhibition, and some other modules. Data extraction solves the problem caused by separately stored data and heterogeneous data. Multi-dimension data modeling analysis and visualization enhances the analysis ability of existing system. Based on the idea of MDA modeling analysis, we provide a rapid develop platform of campus business for both business analyzers and developers.
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A Method to Detect Breast Cancer Based on Morphological Operation
Scientific article
Breast cancer is one of the most common cancer in women worldwide. Early detection of breast cancer can lead to better treatment and decrease in mortality. Mammogram image in medical technology, made it easier to analyze breast cancer. Mammography exam is a specialized imaging technique in medical to scan breast which results in mammogram image. Detecting breast cancer earlier, a patient can have several treatment options and also can save live. Early detection of breast cancer can leads to survive 93 percent or greater in the initial five years. This paper proposes a brseast cancer detection method from mammogram image sample by applying morphological operation on gray image rather than binary. Firstly, image is sent for gamma correction. Then it is converted to gray and applied morphological dilation. Again morphological opening operation is formed on the dilated image. Output of dilated and opening operation is then binarized. An AND operation is performed between both binary images. Some post processing like- small area filtering and hole filling task is took place. Then common unwanted object is removed. Finally rest of the region is the desired cancer infected region. Achieved performance is acceptable and satisfactory through the proposed method.
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A Metrics-Driven Approach to Agile Success in Complex and Distributed Software Projects
Scientific article
This paper aims at describing the difficulties of using Agile methodologies in large scale and distributed projects and then it presents a solution to such problems. The approach presented is the continuation of the development of the original Agile approach to incorporate working in the environment that involves multiple teams and remain Agile in concepts and values. Some of these are: broadening the concept of success criteria from being simply measured in time, cost and scope to including qualities such as team morale and customer satisfaction. To address geographical and organizational separations, integrated project management systems and various instant collaboration methods are endorsed. The use of the phased implementation strategy entails an analysis of the literature, a cyclical design approach, and the pilot project validation to implement and adopt the best strategies within context. Through this solution, there are insights into how the Agile approach can integrate and work cohesively, creating harmonious interdependent working systems, engaging stakeholders, and ensuring performance delivery that comes close to the Agile concept while addressing the complexities of the large project environment.
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A Model Driven Framework for Portable Cloud Services: Proof of Concept Implementation
Scientific article
Rapid development of Cloud Computing and its increasing popularity in recent years has driven many commercial cloud providers in the market. Cloud service providers have a lot of heterogeneity in the resources they use. They have their own servers, different cloud infrastructures and APIs and methods to access the cloud resources. Lack of standards has caused the collaboration and portability of cloud services a very complex task. In this paper we have identified the challenges involved in portability of cloud apps and analyzed the existing techniques for portability at platform level. In this paper, we propose an approach using Model Driven Engineering to develop SaaS applications in a cloud-agnostic way. We introduce DSkyL, an eclipse plugin for cloud application development using feature models and domain model analysis, which would support construction, customization, development and deployment of cloud application components across multiple clouds. It also reduces the application development time drastically. This paper aims to sketch the architecture of DSkyL and the major steps involved in the process.
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Scientific article
Learning management systems (LMS) have become an important part of the modern higher education industry, and Moodle is one of the most popular open-source systems, which have become popular on an international level. Despite its extensive application, the majority of the existing evaluation tools are inclined to examine either performance, sustainability, or scalability separately and, thus, cannot be useful in long-term institutional planning. The current study proposes an assessment model that is a rational model of a Moodle-based LMS, as all three dimensions are included in the assessment model. The framework identifies the performance of the system, resource utilization and resource management, load-adaptive scalability metric, and an adaptable and predictable algorithmic-based assessment procedure in a variety of deployment environments. The framework applies Min–Max normalization and weighted aggregation to combine the three evaluation dimensions into a unified assessment score. To make the model more realistic, it was tested on publicly available data, for example, the Open University Learning Analytics Dataset (OULAD), which simulated actual interactions between LMS users and the utilization of cloud resource traces to analyze scalability and sustainability. Experimental evaluation using the OULAD and cloud resource datasets demonstrated approximately a 10% improvement in performance under medium workload conditions, an 18% reduction in sustainability due to increased resource utilization, and a 20% improvement in scalability as workload increased. These findings demonstrate that the proposed framework provides a systematic and data-driven approach for evaluating Moodle-based learning management systems and supports informed institutional decision-making.
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A Multi-factor based Sleep Quality Prediction System Using Machine Learning
Scientific article
Sleep is a critical biological process required for physical recovery, cognitive function, emotional regulation, and sound health. Conventional techniques for evaluating the quality of sleep are usually costly and intrusive, especially when they use sleep clinics and advanced sensors. Instead of using several factors to predict sleep quality, the majority of earlier studies only employed one factor and a short dataset. Their results were less accurate since they did not apply machine learning to look into the cause of poor sleep quality. This paper initiates a machine-learning (ML) based method for assessing and predicting sleep quality using a larger dataset and the Pittsburgh Sleep Quality Index (PSQI). To find the best machine learning model for predicting sleep quality, the proposed system tests eight classifiers. The results show that the Cat Boost classifier outperforms other models, with an accuracy value of 90.1%, precision value of 87%, recall value of 88%, and f1-score value of 87%. The proposed prediction model also outperformed previous works in terms of accuracy, precision, and recall by 12%, 8%, and 11%, respectively. This paper also describes a web application with features such as personalized sleep quality prediction, result checking, improvement suggestions, and doctor consultation services. According to the review results, up to 65 percent of users agreed that the proposed sleep quality assistance web application features were appropriate and necessary.
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A New Color Image Quantization Algorithm Based on Fuzzy Kernel Clustering
Scientific article
A new color image quantization algorithm based on fuzzy kernel clustering is studied in this paper. Firstly, the original image is quantized to 256 colors using the octree algorithm. Secondly, based on the quantitative relation of the NBS distance and the color difference of human vision, the initial clustering centers and number are determined automatically. Thirdly, the clustering center color values are modified by use of the fuzzy kernel clustering algorithm in the Munsell space. And then, a color image quantization effect is achieved. At last, simulations are performed on the presented algorithm, and the simulation result shows that the presented algorithm not only can solve the problem of giving the number of quantization in advance but also has better quantization effect than the octree algorithm and fuzzy c-means algorithm in the same quantization number.
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A New Color on Discrimination Based on Characteristics of the Vehicle License Plate Location
Scientific article
The license plate recognition system, the license plate location is one of the important technologies, can accurately complete the orientation will directly affect the follow-up of the character segmentation and recognition. This paper presents a new color on discrimination based on characteristics of the vehicle license plate location, Firstly, edge detection, image enhancement preprocessing, effectively highlighting the license plate features, And then by line scanning of the plate rough location, and color-based features to distinguish the license plate location method further extract the real license plate. Experiments show that with the car in a complex background, this method can accurately locate the license plate.
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Scientific article
The current state and problems of the graduate students’ education in China are set forth in the first. Then, the causes of problems are deeply analyzed. Some ideas and methods in engineering specialty graduate student’s education are offered with co-founding by colleges and enterprises, uniting of production, learning and researching. Some problems that should be noted are pointed out, finally.
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