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Fusion: Practice and Applications | 2023

Analysis of Fusion of Machine Learning Tools in Education

In this modern world, artificial intelligence has revolutionized human life in multiple ways. Like other fields, education industry is also transforming with the influence of AI with its smart learning platform and automation of tasks. The introduction of fusion of machine learning tools (FMLT) in the field of education helps to predict learning outcomes and identify challenges in learning. The objective of this paper is to study the fusion of application of machine learning tools in education. This paper highlights the role of data driven FMLT in teaching and learning and also analyzes students and teachers’ experiences as well as challenges faced during the implementation of FMLT system. This article discusses various machine learning tools that can be fused into academics. The experiment is conducted on students at graduate level and the results reveal an increase of 88% in terms of learning efficiency for the proposed FMLT system compared to traditional methods, which reflects high positive impact of the contributions of FMLT in academics. Results of the findings also reveal that FMLT applications facilitate thinking, creativity, class engagement and quality teaching inside and outside classrooms. The feedback findings express mixed attitudes concerning the use of machine learning tools in classrooms.

groups
Anita Venugopal mail -
Mukesh Madanan mail -
Thangadurai kadarkarai mail
link https://doi.org/10.54216/FPA.120207

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new
Fusion: Practice and Applications | 2023

Potential Pitfalls of Data Fusion Digitalization in Microfinance Context

The current era of socio-economic development is featured by rapidly increasing fusion of technology and rapid data fusion digitalization of human activities. While most of these advancements are promising to bring various positive aspects, such as reductions in cost and time, there are potential pitfalls that should be considered. In this paper, we aim to measure potential challenges that data fusion data fusion digitalization could bring in the microfinance context. First, we provide the set of stylized facts and trends in data fusion digitalization using three measures: mobile cell users, internet provision, and mobile-money-service provision. The trends in data fusion digitalization are provided across groups of countries by different income levels. Secondly, using pair-wise correlations we analyze how the set of five microfinance financial and five social indicators are correlated with the digital ranking of countries. Our results demonstrate that while there are increasing patterns in data fusion digitalization in most of the developing countries, negative correlations are observed. We explain the negative implications of data fusion digitalization due to increasing moral hazard and asymmetric information. Our findings call for further research on the long-term implications of digitalization in other areas of social sciences so that to better cope with potential challenges.  

groups
Maxbuba Ismailova mail -
Nargiza Alimukhamedova mail
link https://doi.org/10.54216/FPA.120208

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new
Fusion: Practice and Applications | 2023

Enhancing Fusion Teaching based Research from the Student Perspective

There has been a growing need for fusion based research studies on the relations between university teaching and research. The higher educational universities of the current generation believe in and focus on the teaching-research nexus in academics. To improve teaching methodology there is more emphasis given to the fusion teaching by enabling the information fusing concept through pedagogy. Many discussions have been carried out to evaluate the contribution of teaching–research nexus from the student's point of view. The current study highlights teaching through research where the learners are taught with a special focus on research activities at the same time. The objective of this work is to promote fusion of research related activities into the teaching and learning process to achieve positive impact on enhancing students’ interest towards learning. In view of this and to support this, various opportunities were provided for students and teachers to conduct research and a strategic approach was implemented to achieve this objective. We explore a) student involvement in different scholarly activities b) analyse research skill acquisition of the experimental group c) collect feedback to find students satisfaction level on FTBR approach.The findings demonstrate (mean score 3.09) the positive contribution of the fusion research-teaching towards achieving academic excellence.The strategies discussed in the methodology and results of the study may be used to broaden the fusion teaching-research at the graduate and undergraduate levels.

groups
Anita Venugopal mail -
Aditi Sharma mail -
F. Abdul Munaim Al Rawas mail -
Rama Devi S. mail
link https://doi.org/10.54216/FPA.120209

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new
Journal of Neutrosophic and Fuzzy Systems | 2023

Interval – Valued Pythagorean Fuzzy Soft Graphs

Interval-valued Pythagorean fuzzy soft sets and graph theory are combined in this essay. Then, we present notations for Pythagorean fuzzy soft graphs with interval values. On interval-valued Pythagorean fuzzy soft graphs, we also provide a variety of operations, such as Cartesian product and composition, and we look at some of their characteristics. Finally, we consider the application of I-VPFSGs for the selection  of suitable houses and got the appropriate result by using score function.

groups
R. Sivasamy mail -
M. Mohammed Jabarulla mail -
broumi said mail
link https://doi.org/10.54216/JNFS.070101

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new
Fusion: Practice and Applications | 2023

Improving Link Prediction in Network Representation Learning with Feature Fusion and Local Outlier Factor

Complex networks are a diverse set of networks found in various fields, such as social, technological, and biological networks. One important task in complex network analysis is link prediction, which involves detecting missing links or predicting future link formation. Many methods based on network structure analysis have been developed for link prediction, including network representation learning (NRL) models that represent nodes in a low-dimensional space. Fusion-based attributed NRL methods are particularly effective, as they capture both content and structure information. However, NRL models for link prediction are binary classification models, which face challenges in identifying negative links and prioritizing predicted links. To address these challenges, we propose a novel approach that treats link prediction as a novelty detection problem. Our approach uses the Local Outlier Factor (LOF) algorithm to quantify the novelty of non-existent links based on the representations of existing links. Our experimental results show that our proposed approach outperforms existing methods, particularly when used with fusion-based attributed NRL models

groups
Amr Al-Furas mail -
Mohammed F. Alrahmawy mail -
Waleed Mohamed Al-Adrousy mail -
Samir Elmougy mail
link https://doi.org/10.54216/FPA.120210

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new
Fusion: Practice and Applications | 2023

Randomized Vector Network Model for Thyroid Prediction Using Relief And Lasso Feature Selection Approaches

The studies’ primary aim is to help the research scholars as a source who would like to research in the thyroid disease detection region. UC Irvin knowledge discovery provides databases files for the machine learning archives' thyroid dataset. Here, a random vector network model (RVNM) is proposed to perform classification tasks. The proposed model integrates the prior dataset information regarding the samples to train the more effective classifier. This cascaded random vector network model helps in thyroid disease prediction. The evaluation process is performed to predict and determine the respective performance concerning accuracy. The intuition is provided in this research, like forecasting the thyroid disease; it also calls attention to the process of using a Randomized Vector Network Model (RVNM) as a medium for classification. The simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing approaches. The model gives a prediction accuracy of 96.1% accuracy compared to other models and shows a better trade than others.

groups
Maruthi Prasad mail -
Santhosh R. mail
link https://doi.org/10.54216/FPA.120211

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new
Fusion: Practice and Applications | 2023

A Learning Model for Acute Myeloid Leukemia Prediction Using Dense Polynomial Dimensionality-Based Predictor

Analysis of microarray data is extremely complex and considered as a hot topic in recent research. Acute Myeloid Leukemia (AML) prediction based on machine learning shows huge impact on prediction which automatically diagnoses the disease severity and any malfunctions. It is important to design the relevant classifier that processes the large data volume with large data size. Deep learning is an updated machine learning approach for mitigating these issues. It is easy to handle the huge volume of data because of the large number of hidden layers. The proposed classification methodology is used for understanding the training of the proposed Dense Polynomial Dimensionality based Predictor Model (). The hidden neuron numbers are large in a sufficient way where the proposed  is elaborated to predict AML. AML and ALL samples are classified using five layers in the deep network model. The data is partitioned as 20% data and 80% data testing and training in the network. Compared with other classifiers, the satisfying outcome from the proposed  is higher and fulfilling. The validation is done in three datasets: Kaggle, Gene expression and Bio GPS and it gives 96% accuracy, 94% precision, 96% recall, 96% F1-score, and 98% AUROC while executing with Kaggle; then, 95.50% accuracy, 94% precision, 95% recall, 96% F1-score, and 96% AUROC is achieved while executing with Gene expression and finally 98% accuracy, 94.5% precision, 98.5% recall, 96% F1-score, and 94% AUROC is achieved while executing with Bio GPS. Based on this analysis, it is proven that the model works well with the proposed  and establishes a better trade-off.

groups
K. Venkatesh mail -
S. Pasupathy mail -
S. P. Raja mail
link https://doi.org/10.54216/FPA.120212

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new
Fusion: Practice and Applications | 2023

Modelling of an Adaptive Network Model for Phishing Website Detection Using Learning Approaches

Phishing links are spread via text messages, social media platforms, and email by phishing attackers. Social engineering skills are used to visit phishing websites to trick the users and enter critical information related to personal data. The confidential data is stolen to defraud legitimate financial institutions or general websites for illegally attaining the benefits. Many machine learning-based solutions are in the enhancements and the technology of machine learning applications to detect the suggested phishing. The rules are used for a solution which depends on the extracted features, and few features require to lies on the services of third-party that, creating time-consuming and instability in the service of prediction. A deep learning-based framework is suggested to detect website of phishing. A framework is established to determine if there is a risk of phishing in real-time during the web page is visited by the user to give a message of warming by the browser plug-in. The prediction service in real-time merges the various techniques for enhancing the accuracy to lower the fake alarm rates and the time of computation which has the filtering whitelist, interception of the blacklist, and prediction of deep learning (DL). Various models of deep learning are compared using the different datasets in the module of machine learning prediction. The greatest accuracy is obtained as 99.18% by the adaptive Recurrent Neural Networks (a−RNN) model from the results of experiments to demonstrate the suggested feasibility solution.

groups
Aldo Tenis mail -
Santhosh R. mail
link https://doi.org/10.54216/FPA.120213

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new