International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1096
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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Artificial Neural Network Training Criterion Formulation Using Error Continuous Domain
Статья научная
One of the trends in information technologies is implementing neural networks in modern software packages [1]. The fact that neural networks cannot be directly programmed (but trained) is their distinctive feature. In this regard, the urgent task is to ensure sufficient speed and quality of neural network training procedures. The process of neural network training can differ significantly depending on the problem. There are verification methods that correspond to the task’s constraints; they are used to assess the training results. Verification methods provide an estimate of the entire cardinal set of examples but do not allow to estimate which subset of those causes a significant error. This fact leads to neural networks’ failure to perform with the given set of hyperparameters, making training a new one time-consuming. On the other hand, existing empirical assessment methods of neural networks training use discrete sets of examples. With this approach, it is impossible to say that the network is suitable for classification on the whole cardinal set of examples. This paper proposes a criterion for assessing the quality of classification results. The criterion is formed by describing the training states of the neural network. Each state is specified by the correspondence of the set of errors to the function range representing a cardinal set of test examples. The criterion usage allows tracking the network’s classification defects and marking them as safe or unsafe. As a result, it is possible to formally assess how the training and validation data sets must be altered to improve the network’s performance, while existing verification methods do not provide any information on which part of the dataset causes the network to underperform.
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Artificial Neural Network in Prognosticating Human Personality from Social Networks
Статья научная
The analysis of text in the form of tweets, chat or posts can be an interesting as well as challenging area of research. In this paper, such an analysis provides information about the human behavior as positive, negative or neutral. For simplicity, tweets from social networking site, Twitter, are extracted for analyzing human personality. Various concepts from natural language processing, text mining and neural networks are used to establish the final outcome of the application. For analyzing text, Neural Networks are implemented which are so modeled that they predict the Human behavior as positive, negative or neutral based on extracted and preprocessed data. Using Neural Networks, the particular pattern is identified and weights are provided to words based on the extracted pattern.Neural networks have an added advantage of adaptive learning. This application can be immensely useful for politics, medical science, sports, matrimonial purposes etc.The results so obtained are quite promising.
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Aspectual Analysis of Legacy Systems: Code Smells and Transformations in C
Статья научная
This paper explores the various code smells or the so called bad code symptoms present in procedural C software. The code smells are analyzed in the light of aspect oriented programming. The intention is to handle the code smells with aspect oriented constructs as it offers more versatile decomposition techniques than the traditional modularization techniques, for software evolution and understandability. The code smells are described at the function and program level. The code smells are followed by the aspect oriented transformations that may be required in order to improve the code quality.
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Assessing Student Academic Performance with Fuzzy Expert System
Статья научная
Nowadays, higher education institutions and universities are facing a competitive environment for enhancing the quality of students to achieve extensive knowledge with critical thinking skills and a good personality for better employment in the industry. Universities and other higher education establishments ensure that students overcome the obstacles in these cutthroat environments. In order to do this, it is necessary to analyze the academic performance of each student by determining their strengths and weaknesses. A fuzzy expert system (FES) is used in this study to evaluate student’s academic performance. This FES uses fuzzy logic to classify each student’s performance based on a variety of linguistic factors. It classifies each student’s performance by considering various linguistic factors using fuzzy logic. For this purpose, seven significant input factors have been taken into account which is Stress, Motivation, Confidence, Parent’s support & Availability, Self study hours, Punctuality, and Friend circle. Several defuzzification techniques are applied in order to examine student performance using the FES & generate more precise and measurable results. These findings could aid colleges and other educational establishments in determining the right variables that influence student’s academic performance. Additionally, a comparison of various Mamdani fuzzy defuzzification techniques, including the centroid, bisector, and mean of maxima methods, is provided in this study. After comparing all three techniques by taking different scenarios of all the external factors, it has been concluded that all of them are performing equally.
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Assessment and Feedback as Predictors for Student Satisfaction in UK Higher Education
Статья научная
Assessment and feedback mechanisms are essential components towards effective teaching in higher education and are continuously monitored. The annual student satisfaction survey in UK higher education collects students’ perception on those dimensions and issues results to assist institutions identify their weaknesses and amend their strategies and improve their teaching effectiveness. This study explores assessment and feedback as predictors for overall student satisfaction. It focuses on business schools mainly and uses the officially published dataset. Following a regression analysis approach, it can be concluded that there is evidence to support the claim that assessment and marking can be used as predictors for overall student satisfaction in this subdomain. The significance of the study lies in the fact that universities consider assessment and feedback as of key importance for improving student experience. It is thus critical for the institutions to gain a better understanding on whether those factors can be safely used as predictors of overall student satisfaction, something that is related to university ranking tables. Results in the study, demonstrate some important aspects of this and indicate that improved quality in marking and feedback can have a positive effect in student satisfaction. A more comprehensive study can unfold additional dimensions of the survey and shed light on how students perceive marking, assessment and feedback in higher education in general.
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Assessment and attainment of program educational objectives for post graduate courses
Статья научная
As per the guidelines issued by NBA (National Board of Accreditation) of All India Council of Technical Education (AICTE) Outcome Based Education is implemented in engineering colleges of India. The outcome based evaluation model measures the performance of UG and PG programs. The performance is based on calculating attainments of Program Educational Objectives (PEOs) and Program Outcomes (PO).In this paper we will discuss the process for the attainments of POs and PEOs for Post Graduate program approved by AICTE, India. The attainments are calculated by applying direct and indirect tools. The attainments summaries are generated Batch wise and a comparison of different Batches were made. The attained PEOs and POs would help in accomplishing Vision and Mission of the department.
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Статья научная
The study aimed to assess whether the students from mathematical science-based undergraduate degree programmes in Kaduna State University perform academically better when either the Computer-Based Test (CBT) or the Paper-Pencil Test (PPT) is used to write the Unified Tertiary Matriculation Examination (UTME), which is conducted annually by the Joint Admissions Matriculation Board (JAMB). The study adopted a quantitative approach to research. A purposive sample of one thousand and twenty-three (1023) first-year students constituted the population for the study. This population was drawn from Computer Science, Mathematics and Physics undergraduate degree programmes in the Kaduna State University who were admitted from the 2010/2011 to 2012/2013 and 2015/2016 to 2016/2017 academic sessions respectively. The instruments used for data collection were the UTME scores and the academic standing of first-year Cumulative Grade Point Average (CGPA) results, which were coded and analysed with the aid of Computational Statistical Package for Social Sciences (SPSS) version 23. Descriptive statistics and Analysis of Variance (ANOVA) were the statistical tools used to answer the four (4) research questions raised. The results revealed a majority of the students who performed academically better were those who used the PPT as their test medium in writing the UTME. It concluded that the majority of the students who wrote the UTME using PPT performed better in their academics. The study thereby recommended that there is a need for the Joint Admissions Matriculation Board (JAMB) to review its examination policies in mathematics-based subjects to enable students to pass such subjects with flying colours, thereby encouraging them to perform better academically in the undergraduate studies.
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Статья научная
Various agile software development methodologies, since their commencement, encouraged the development of high quality software product. Quality of a product is the compelling trait that plays a vital role in any product's success. Usability engineering and User centered design are user-centered approaches, covering the customer's concerns. The way these approaches are understood and carried out with agile practices is not properly understood and adopted till now. For software applications to be usable and valuable it is necessary to understand the correct user requirements in order to develop the interface that is usable and valuable to the customer. In this research work, we are discussing the scrum approach of agile development and integrate this with the usability engineering and user centered design approaches which helps the agile development team to understand usability demand of users and develop a product according to their expectations.
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Automated Cardiac Beat Classification Using RBF Neural Networks
Статья научная
This paper proposes a four stage, denoising, feature extraction, optimization and classification method for detection of premature ventricular contractions. In the first stage, we investigate the application of wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In this stage, the Stationary Wavelet Transform is used. Feature extraction module extracts ten ECG morphological features and one timing interval feature. Then a number of radial basis function (RBF) neural networks with different value of spread parameter are designed and compared their ability for classification of three different classes of ECG signals. Genetic Algorithm is used to find best value of RBF parameters. A classification accuracy of 100% for training dataset and 95.66% for testing dataset and an overall accuracy of detection of 95.83% were achieved over seven files from the MIT/BIH arrhythmia database.
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Статья научная
This paper deals with one of our research directions on software tools enhancing self-learning in computer science disciplines. In this study, we discuss an experiment on relational data bases learning using a tool for the edition and automated evaluation of learners’ solutions given as relational algebra trees. Indeed, in addition to the interest of the graphic languages for any training, the evaluation of our precedent works on modeling and evaluating solutions as algebraic expressions showed us some problems: first, there are various languages for the algebraic expressions. Second, among the detected errors by the prototype, developed in our precedent works for algebraic expressions, the form errors about the algebraic language have to be corrected before starting the semantic analysis. Third, in some cases, errors in the form have led to other non-committed errors which can cause inconsistencies in the errors’ diagnosis process. Starting from these problems, the two principal objectives of the work presented in this article concern the algebraic trees construction and the evaluation assisted by a graphic tool which essentially consists in a semantic analysis as recommended in ODALA (ontology driven auto-evaluation learning approach) that we have already proposed. The tool was evaluated by a set of tests and experimented with second year LMD license students. These experiments results were interesting and showed that the tool is particularly helpful for novice students and their teachers.
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Automatic Cyberstalking Detection on Twitter in Real-Time using Hybrid Approach
Статья научная
Many people are using Twitter for thought expression and information sharing in real-time. Twitter is one of the trendiest social media applications that cybercriminals also widely use to harass the victim in the form of cyberstalking. Cyberstalkers target the victim through sexism, racism, offensive language, hate language, trolling, and fake accounts on Twitter. This paper proposed a framework for automatic cyberstalking detection on Twitter in real-time using the hybrid approach. Initially, experimental works were performed on recent unlabeled tweets collected through Twitter API using three different methods: lexicon-based, machine learning, and hybrid approach. The TF-IDF feature extraction method was used with all the applied methods to obtain the feature vectors from the tweets. The lexicon-based process produced maximum accuracy of 91.1%, and the machine learning approach achieved maximum accuracy of 92.4%. In comparison, the hybrid approach achieved the highest accuracy of 95.8% for classifying unlabeled tweets fetched through Twitter API. The machine learning approach performed better than the lexicon-based, while the performance of the proposed hybrid approach was outstanding. The hybrid method with a different approach was again applied to classify and label the live tweets collected by Twitter Streaming in real-time. Once again, the hybrid approach provided the outstanding result as expected, with an accuracy of 94.2%, recall of 94.1%, the precision of 94.6%, f-score of 94.1%, and the best AUC of 98%. The performance of machine learning classifiers was measured in each dataset labeled by all three methods. Experimental results in this study show that the proposed hybrid approach performed better than other implemented approaches in both recent and live tweets classification. The performance of SVM was better than other machine learning algorithms with all applied approaches.
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