Leveraging Social Media Data Fusion for Enhanced Student Evolution in Media Studies using Machine Learning

Najla M. Alnaqbi; Walaa Fouda; Muhammad Eid Balbaa

doi:https://doi.org/10.54216/FPA.120215

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.

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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

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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.

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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

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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

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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

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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.

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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.

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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

Journal of Artificial Intelligence and Metaheuristics | 2027

Smart Home Energy Consumption Forecasting: An Engineering Approach with IoT and Al-Biruni Earth Radius Optimized LSTM Networks

With the growing complexity of energy consumption patterns, accurate forecasting is required to maximize the benefits of demand-side management, which relies on precise forecasting and optimization. To respond to this challenge, this research suggests a new forecasting method by integrating the Al-Biruni Earth Radius (BER) optimization algorithm with the Long Short-Term Memory (LSTM) network within an Internet of Things (IoT)-based framework. The BER-optimized LSTM model processes real-time IoT sensor data, such as energy usage patterns and weather conditions, to enhance energy usage predictions. Time-series clustering addresses data uncertainty and noise, providing a probabilistic load forecast at both the household and aggregated levels. Extensive evaluation of the model’s performance demonstrates substantial predictive accuracy, achieving a root mean squared error (RMSE) of 0.007146, surpassing conventional forecasting methods. These findings corroborate that BER optimization, coupled with the use of LSTM networks, provides a suitable tool for smart home energy forecasting, which, in turn, can facilitate improved energy management, demand-side optimization, and innovative grid applications in the evolving energy market.

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M. El-Said mail

link https://doi.org/10.54216/JAIM.120105

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

A Review of Artificial Intelligence-Based Diabetes Prediction Using Machine Learning, Deep Learning, and Optimizer-Assisted Decision Support

Diabetes prediction has become an important direction in clinical artificial intelligence because early identification of high-risk individuals can support preventive intervention, personalized monitoring, and improved long-term healthcare outcomes. This review examines recent machine learning, deep learning, and optimizer-assisted methodologies for diabetes prediction and related diagnostic decision support. It discusses how clinical variables, physiological measurements, biomedical signals, retinal images, population-specific attributes, and healthcare monitoring data can be transformed into predictive evidence through preprocessing, feature engineering, feature selection, model training, hyperparameter optimization, and risk interpretation. The review also emphasizes major methodological concerns, including class imbalance, model robustness, interpretability, subgroup variation, complication-oriented prediction, and deployment within connected healthcare environments. Overall, the reviewed literature indicates that effective diabetes prediction should be developed as an integrated clinical decision-support pipeline rather than as a single classifier comparison, with careful attention to data quality, feature relevance, model transparency, and practical healthcare usability.

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Ancy Cheriyan mail

link https://doi.org/10.54216/MOR.070201

Volume & Issue

Vol. Volume 7 / 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.

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Aldo Tenis mail -

Santhosh R. mail

link https://doi.org/10.54216/FPA.120213

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

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Fusion: Practice and Applications | 2023

Clinical Fusion for Real-Time Complex QRS Pattern Detection in Wearable ECG Using the Pan-Tompkins Algorithm

This scientific paper presents a novel approach of real-time signal analysis in electrocardiogram (ECG) monitoring systems, focusing on the integration of device design,algorithm implementation for accurate measurement and interpretation of heart activity. The proposed system leverages a low-cost framework, employing a microcontroller and Arduino programming language for raw ECG data acquisition, while utilizing the AD8232 sensor and ESP8266 Node MCU for continuous patient monitoring. The acquired data is processed, stored, and analyzed using the Pan-Tompkins algorithm, which effectively filters and analyzes heart signals, including noise reduction and QRS complex detection. Two case studies involving a healthy individual and a patient with Myocarditis were conducted to demonstrate the effectiveness of the system. The integration of device design and algorithm development in ECG analysis is emphasized, highlighting the affordability, wearability, and potential for continuous monitoring and early detection of heart conditions. By successfully mitigating noise-related challenges, the implementation of the Pan algorithm enables accurate signal analysis. This interdisciplinary research contributes to the advancement of ECG interpretation and underscores the significance of clinical fusion between designed systems and applied algorithms on real cases. The performance of two Pan-Tompkins based QRS complex detection algorithms was systematically analyzed, offering valuable insights for their reasonable utilization.

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Entisar Y. Abd Al-Jabbar mail -

Marwa M. Mohamedsheet Al-Hatab mail -

Maysaloon abed qasim mail -

Wameedh Raad Fathel mail -

Maan Ahmed Fadhil mail

link https://doi.org/10.54216/FPA.120214

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2023

Leveraging Social Media Data Fusion for Enhanced Student Evolution in Media Studies using Machine Learning

 In the realm of media studies, understanding student evolution is a crucial aspect for educators and researchers. However, traditional research methods often struggle to capture the dynamic nature of media consumption and the intricate interactions between individuals and media content. To address this challenge, this paper focuses on leveraging social media data fusion and machine learning techniques to enhance the comprehension of student evolution. By integrating data from diverse social media sources and employing the CATBoost algorithm with the Greedy Target-based Statistics (Greedy TBS) technique, we aim to predict student outcomes based on a comprehensive set of attributes. The results showcase the superior performance of CATBoost in accurately capturing the complexities of student evolution, surpassing other machine learning algorithms. The findings hold immense significance for educators, empowering them with valuable insights into students' behaviors, preferences, and performance.

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Najla M. Alnaqbi mail -

Walaa Fouda mail -

Muhammad Eid Balbaa mail

link https://doi.org/10.54216/FPA.120215

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new