International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1160
Analysis of Large Set of Images Using MapReduce Framework
Статья научная
Due to the limitations of a physical memory, it is quite difficult to analyze and process big datasets. The Hadoop MapReduce algorithm has been widely used to process and mine such large sets of data using the Map and Reduce functions. The main contribution of this paper is to implement MapReduce programming algorithm to analyze large set of fingerprint images which cannot be normally processed due to a limited physical memory in order to find the features of these images at once. At first, the images are maintained in an image data store in order to be preprocessed and to extract the features for the biometric trait of each user, and then store them in a database. The algorithm preprocesses and extracts the features (ridges and bifurcation) from multiple fingerprint images at the same time. The extracted points are detected using the Crossing Number (CN) concept based on the proposed algorithm. It is validated using data taken from the National Institute of Standards and Technology’s (NIST) Special Database 4. The data consist of fingerprint images for many users. Our experiments on these large set of fingerprint images shows a significant reducing in the processing time to a nearly half when extracting the features of these images using our proposed MapReduce approach.
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Analysis of PV-FC Hybrid System Operation Considering Sale Electricity
Статья научная
This paper presents a hybrid power generation system modeling and simulation with the objective of electricity sale to distribution network (DN) which consists of photovoltaic (PV) module, proton exchange membrane (PEM) fuel cell (FC), hydrogen storage tank (HST) and electrolyzer (EL).Since last researches in optimal FC and PV application aimed in power electronic approach, In this paper the application between FC and PV is considered with the aim of maximizing profit gained due to electricity sale revenue to DN. The revenue from electricity sale to DN considering electricity price in low load, shoulder load and peak load hours is considered as the system profit. Also in a sensitivity analysis the impact of technical parameters of hybrid system components is investigated on system profit. The results showed that the system saves the electricity by hydrogen storage in HST in low load hours and sale it with more prices in shoulder load hours to DN. Also the obtained results show that several technical parameters of PV and PEM FC have considerable impact on system operation and profit.
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Analysis of Students' Performance by Using Different Data Mining Classifiers
Статья научная
Data mining is the analysis of a large dataset to discover patterns and use those patterns to predict the likelihood of the future events. Data mining is becoming a very important field in educational sectors and it holds great potential for the schools and universities. There are many data mining classification techniques with different levels of accuracy. The objective of this paper is to analyze and evaluate the university students' performance by applying different data mining classification techniques by using WEKA tool. The highest accuracy of classifier algorithms depends on the size and nature of the data. Five classifiers are used NaiveBayes, Bayesian Network, ID3, J48 and Neural Network Different performance measures are used to compare the results between these classifiers. The results shows that Bayesian Network classifier has the highest accuracy among the other classifiers.
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Статья научная
The foundational tenet of any nation's prosperity, character, and progress is education. Thus, a lot of emphasis is laid on quality of education and education delivery system in India with current financial year (2022-23) education budget outlay of Rs. 1,04,277.72 crores. This research contributes in analyzing how students perform in academics depending upon the time spent on their extracurricular activities with the help of three Machine Learning prediction algorithms namely Decision Tree, Random Forest and KNN. Additionally, in order to comprehend the underlying causes of the shortcomings in each machine learning technique, comparisons of the prediction outcomes obtained by these various techniques are made. On our dataset, the Decision Tree outscored all other algorithms, achieving F1 84 and an accuracy of 85%. The research, which is at an introductory level, is meant to open the door for more complexes, specialised, and in-depth studies in the area of predicting the performance in academics.
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Статья научная
The study was aimed at analyzing the effectiveness of blended education for economics students using information and communication technology (ICT). The research methods consisted of literature analysis, case method, comparative analysis, mathematical statistics, and statistical experiment. The article describes the following results. Three Russian universities (Vladivostok State University of Economics and Service [VSUES], Kemerovo State University [KemSU], and Ryazan State Radio Engineering University [RSREU]) have introduced ICT to implement a blended model for teaching economic disciplines. This made it possible to use the strengths of traditional classroom and distance electronic education, as well as to quickly correct the problems that arise at the initial stage of ICT implementation, especially when training systems are integrated into international educational projects. The field study enrolled 236 economics students from the above-mentioned universities. The obtained empirical data confirmed some hypotheses regarding the effectiveness of ICT in teaching economics students. The practical significance of the article lies in the possibilities of applying leading ICT technologies to improve the professional competences of future businesspeople in blended economic learning. The results obtained can help universities to shape a rational economic blended learning course to maximize the business impact for future careers in this field. Future researchers may pay attention to the effectiveness of using Massive Open Online Courses (MOOCs) in the context of improving the economic education of future entrepreneurs with the possibility of involving real business cases in their educational process.
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Статья научная
Pedagogical scientists often need to process the results of a pedagogical experiment. However, not every scientist (especially in humanitarianism) has appropriate mathematical training, so statistical data processing is a problem for him. Scientists-pedagogues in Ukraine use various statistical methods to process the results of a pedagogical experiment and face the problem of cumbersome calculations and the accuracy of assessments. Therefore, we developed a method that is based on the correct mathematical apparatus, simplifies the processing of empirical data, and allows us to draw qualitative conclusions without the explicit use of mathematical apparatus. To simplify the statistical analysis of the results of the pedagogical experiment and the interpretation of the obtained data, the authors suggest using a spreadsheet and analyzing the data according to Student's and Fisher's criteria (comparing the average sample and its variance) and controlling intermediate indicators of the results of the pedagogical experiment. The method developed by the authors has an advantage compared to other methods: it is enough to analyze the pair "mean and variance" for the sample to conclude the significance of the differences in the control and experimental groups. The method has a simple implementation since almost every researcher has a spreadsheet processor on his computer. The method does not require a thorough knowledge of the statistics course. The method guarantees more reasonable conclusions (two criteria are used at once), which is important when conducting a pedagogical experiment.
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Analysis on Energy Optimized Data Collection in Tree Based Ad-Hoc Sensor Network
Статья
Fast and energy efficient data collection in an energy constraint ad-hoc sensor network is always a challenging issue. The network topology and interferences causes significant effects on data collection and hence on sensors’ energy usage. Various approaches using single channel, multichannel and convergecasting had already been proposed. Here in this paper we have shown data collection performance using multi-frequency in channel assignment, and effect of network topology, for moderate size networks of about 50-100 nodes. For the study we have used some realistic simulation models under many-to-one communication paradigm called convergecast, a single frequency channel and TDMA technique to have minimum time slots for convergecasting.
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Analyzing Sentiments on Twitter Using Deep Learning Techniques
Статья научная
In today’s digital age dominated by social media, understanding public sentiment through Twitter analysis has become imperative. With a staggering 100 million active users on platforms like Twitter and an influx of 572,000 new accounts daily, the vast reservoir of user-generated content underscores the necessity for advanced sentiment analysis tools. This study delves into the realm of sentiment analysis techniques on Twitter, with a particular emphasis on employing Machine Learning (ML) methods. The proposed framework harnesses the power of Natural Language Processing (NLP) and Deep Learning architectures, specifically advocating for a synergistic blend of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Additionally, it explores the efficacy of traditional ML algorithms such as Support Vector Machines (SVM), Random Forest, and Multi-Layer Perceptron (MLP) in this context. The study’s findings illuminate diverse performance metrics across the employed models. While SVM exhibits moderate accuracy, it grapples with challenges in recall and F1-score for sentiment class 1. Conversely, the CNN-LSTM model emerges as a standout performer, boasting impressive accuracy rates of 97% and 98% respectively. Notably, this model excels in sentiment classification across all classes, underscoring its efficacy in discerning nuanced sentiment nuances within tweets. Furthermore, the study underscores the critical importance of judiciously selecting ML algorithms tailored to the intricacies of Twitter sentiment analysis. By leveraging advanced NLP techniques and deep learning architectures, researchers and practitioners can glean deeper insights into the dynamic landscape of public sentiment on social media platforms like Twitter. Such insights hold significant implications for diverse domains, including marketing, brand management, and public opinion analysis.
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Analyzing Student Evaluations of Teaching in a Completely Online Environment
Статья научная
Almost all educational institutions have shifted their academic activities to digital platforms due to the recent COVID-19 epidemic. Because of this, it is very important to assess how well teachers are performing with this new way of online teaching. Educational Data Mining (EDM) is a new field that emerged from using data mining techniques to analyze educational data and making decision based on findings. EDM can be utilized to gain better understanding about students and their learning processes, assist teachers do their academic tasks, and make judgments about how to manage educational system. The primary objective of this study is to uncover the key factors that influence the quality of teaching in a virtual classroom environment. Data is gathered from the students’ evaluation of teaching from computer science students of three online semesters at X University. In total, 27622 students participated in these survey. Weka, sentimental analysis, and word cloud generator are applied in the process of carrying out the research. The decision tree classifies the factors affecting the performance of the teachers, and we find that student-faculty relation is the most prominent factor for improving the teaching quality. The sentimental analysis reveals that around 78% of opinions are positive and “good” is the most frequently used word in the opinions. If the education system is moved online in the future, this research will help figure out what needs to be changed to improve teachers’ overall performance and the quality of their teaching.
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Analyzing Students’ Performance Using Fuzzy Logic and Hierarchical Linear Regression
Статья научная
Due to the COVID-19 situation, all activities, including education, were shifted to online platforms. Consequently, instructors encountered increased challenges in evaluating students. In traditional assessment methods, instructors often face ambiguous cases when evaluating students’ competencies. Recent research has focused on the effectiveness of fuzzy logic in assessing students’ competencies, considering the presence of uncertain factors or multiple variables. Additionally, demographic characteristics, which can potentially influence students’ performance, are not typically utilized as inputs in the fuzzy logic method. Therefore, analyzing students’ performance by incorporating these factors is crucial in suggesting adjustments to teaching and learning strategies. In this study, we employ a combination of fuzzy logic and hierarchical linear regression to analyze students’ performance. The experiment involved 318 students from various programs and showed that the hybrid approach assessed students’ performance with greater nuance and adaptability when compared to a traditional method. Moreover, the findings in this study revealed the following: 1) There are differences in students’ performance between traditional and fuzzy evaluation methods; 2) The learning method is an impact on students’ fuzzy grades; 3) Students studying online do not perform better than those studying onsite. These findings suggest that instructors and educators should explore effective strategies being fair and suitable in assessment and learning.
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Статья научная
A well designed website using various search engine optimization (SEO) techniques can help to survive in the competition. Thus, for the students who are likely to be web developer in future; learning the theoretical concept of SEO is not enough. The way in which SEO strategy is being drafted varies as per the purpose of website. Hence along with the concept assimilation, instructor needs to make the student think critically to identify the problem and solve it in best possible way. Hence to explore the board panorama of SEO techniques, experiential and collaborative leaning techniques are used. The main objective of the study is to analyses the impact of these modern techniques on depth of concept assimilation by students. To ascertain the effect of these learning techniques, analytical data of the entire website is analyzed. Also feedback is taken from student to know their perception about the whole process. It has been found that students enjoyed the whole learning process. The analytical data proves that the website performed really well which in turn proves that student got in depth understanding of the concept and they were able to implement it commendably in real world scenario.
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Analyzing the Influencing Factors of Group Learning: A Mixed Approach
Статья научная
The purpose of this paper is to explore which factors influence group learning content, and content analysis is chosen as the research method. The sample for this study is the literature of group learning. 35 books and 1 paper was examined. The coding system for the content analysis is an opened and a self-expanded system in this study, which means that the original coding system can be updated if the new coding item is developed during the data collection. A total of 62 influencing factors are identified in terms of the content analysis. In order to organise them systematically, we categorised them into four aggregations according to one model of the group learning processes:planning, organising, learning process, and evaluation. The result of this study may be used to design a questionnaire and to model group learning process in our further research.
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Analyzing the Performance of SVM for Polarity Detection with Different Datasets
Статья научная
Social media and micro-blogging websites have become the popular platforms where anyone can express his/her thoughts about any particular news, event or product etc. The problem of analyzing this massive amount of user-generated data is one of the hot topics today. The term sentiment analysis includes the classification of a particular text as positive, negative or neutral, is known as polarity detection. Support Vector Machine (SVM) is one of the widely used machine learning algorithms for sentiment analysis. In this research, we have proposed a Sentiment Analysis Framework and by using this framework, analyzed the performance of SVM for textual polarity detection. We have used three datasets for experiment, two from twitter and one from IMDB reviews. For performance evaluation of SVM, we have used three different ratios of training data and test data, 70:30, 50:50 and 30:70. Performance is measured in terms of precision, recall and f-measure for each dataset.
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Analyzing the performance of various clustering algorithms
Статья научная
Clustering is one of the extensively used techniques in data mining to analyze a large dataset in order to discover useful and interesting patterns. It partitions a dataset into mutually disjoint groups of data in such a manner that the data points belonging to the same cluster are highly similar and those lying in different clusters are very dissimilar. Furthermore, among a large number of clustering algorithms, it becomes difficult for researchers to select a suitable clustering algorithm for their purpose. Keeping this in mind, this paper aims to perform a comparative analysis of various clustering algorithms such as k-means, expectation maximization, hierarchical clustering and make density-based clustering with respect to different parameters such as time taken to build a model, use of different dataset, size of dataset, normalized and un-normalized data in order to find the suitability of one over other.
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Anatomy and Diseases of Human Biliary System: An Analysis by Mathematical Model
Статья научная
The objective of this paper is to develop an understanding of the diseases related with gallbladder, liver, and biliary tract. The study focuses on human biliary system that is how bile flows in the human body. This can be done by developing an understanding of gallbladder and bile flowing in the body and related organs very briefly. Gallstone is an important disease of gallbladder and is closely related to pressure drop. A small model for the human biliary system is also analyzed in this study. The cylindrical model of gallbladder and ducts in contraction and extension phase is used for the study. The amount of substances present in the organ varies in these cases. With the help of this study it is concluded that the flux decreases on increasing the radius and length of the cylinder. It is observed that the behavior of flow of bile in gallbladder is similar to the flow of bile in the ducts.
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Application of QFD on Planning courses of Industrial Engineering
Статья научная
Courses are the link between teachers and students. The matching degree of the curriculum and the students’ future development largely affects the future competitiveness of students and students’ satisfaction with the school specialty. Based on industrial engineering specialty of Nanchang University, this article applies fishbone diagram to analyze the direction of students’ future development, and uses questionnaire to collect students’ preference of the future development, and analyzes the collecting data by AHP (analytical hierarchy process). Combining with the weight of the direction of future development and the importance of courses, we find a new combination of curriculum and a plan of rearrangement. By comparing the results with the existing curriculum program, we ultimately find a more scientific way to rearrange the curriculum.
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Application of hybrid search based algorithms for software defect prediction
Статья научная
In software engineering software defect class prediction can help to take decision for proper allocation of resources in software testing phase. Identification of highly defect prone classes will get more attention from tester as well as security experts. In recent years various artificial techniques are used by researchers in different phases of SDLC. Main objective of the study is to compare the performances of Hybrid Search Based Algorithms in prediction of defect proneness of a class in software. Statistical test are used to compare the performances of developed prediction models, Validation of the models is performed with the different releases of datasets.
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Статья научная
Secondary schools as part of the foundational level of education cannot afford to lag behind in using multimedia to improve the intellectual and creative capabilities among the teachers and students. This study investigated multimedia technologies as modern pedagogical tools for strategic improvement on the teaching and learning process. The study adopted a descriptive survey design. The population was the senior secondary school students and teachers in Kuje Area Council of Federal Capital Territory (FCT), Nigeria. A simple random sampling technique was used to select 250 students and 100 teachers. Cronbach's alpha statistical tool was used to obtain a reliability coefficient of 0.83 on validated questionnaires which were distributed to collect data from the respondents. Statistically, mean, standard deviation, percentage and partial correlation were used to answer the research questions while t-test and Chi-Square were used to test the postulated hypotheses at 0.05 level of significance. The findings showed that television sets, projectors and computers were the major multimedia facilities used for teaching and learning in the council, multimedia facilities had a high influence on teaching and learning. It was gathered that multimedia enriched teaching cognitive skills and psychomotor skills and developed concretization of abstraction on any subject matter. Some recommendations were made which included the provision of financial support to procure multimedia facilities to schools towards the attainment of educational objectives, provision of subsiding policies by the government on the importation of multimedia facilities, employment of competent and experienced technical staff should be employed to solve a series of technical problems in using multimedia facilities. Experimental research design on the effectiveness of multimedia facilities as modern pedagogical tools for strategic improvement on teaching and learning was proposed for future work.
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Appraisement of IEEE 802.11s based Mesh Networks with Mean Backoff Algorithm
Статья научная
Wireless mesh networks, consisting of mesh routers and mesh clients are very robust, reliable and easily maintained networks. The current work is based on IEEE 802.11 standard amendment IEEE 802.11s specific for mesh topology based networks to improve the connectivity and coverage. It aims to control the shared medium access among the stations using Distributed Coordination Function (DCF) MAC protocol for reducing collisions and delays, thereby increasing throughput of the network.. The actual Binary Exponential Backoff (BEB) algorithm as implemented in IEEE 802.11 resets the contention window to its least value after an acknowledged transmission. From the previous works it has been observed that this sudden reset to minimum value of contention window does not ensure a reduction in collisions. It may lead to more contention in the network thereby increasing delays and affecting its throughput. The proposed work presents a Mean Backoff Algorithm in order to solve this flaw of BEB algorithm. The proposed algorithm aims to bring the contention window to some appropriate value in order to cope up with the unfairness caused due to its minimum value on successful transmission.
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Статья научная
This paper proposes the hybrid framework of privacy preserving that combines the concept of federated learning and homomorphic encryption with differential privacy, to address the privacy issue of collaborative machine learning for healthcare application. The proposed approach makes three contributions: (1) multi-layered architecture using federated learning in combination homomorphic encryption (based on CKKS scheme) and differential privacy that offers defense against inference attacks at different layers, (2) the implementation which alleviates the computational overhead compared to homomorphic encryption only with optimised cryptographic parameters, and (3) the application of the Grasshopper-Black Hole Optimization (G-BHO) for the optimisation of privacy parameters (e, deltas, gradient clipping thresholds) in order to balance the privacy-utility trade-off. Cryptographic keys are produced using the principles of cryptographically secure random number generation. Experimental evaluation on two healthcare data sets (MIMIC-III and chest X rays of the patients of Covid-19) to compare the hybrid approach to the single technique baselines in four metrics: classification accuracy (93.0±1.2% vs. 89.0±1.5% for federated learning only), differential privacy guarantee (ε=0.7, δ=10⁻⁵), computational overhead (2.5x baseline vs. 8x for homomorphic encryption only) and the resistance to membership inference attacks (92% vs. 68%) The observed improvement in the accuracy is unexpected, and potentially a consequence of side-effects due to the effects of the regularization in the differential privacy noise; this finding needs to be further explored in theory. The evaluation is restricted to the tasks of healthcare classification, while generalization to other domains needs more validation. The main contribution is an empirical proof that by using a combination of several privacy mechanisms, it will be possible to achieve a stronger attack resistance with a lower computational overhead than by using homomorphic encryption alone.
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