Machine Learning in Healthcare: A Comprehensive Review of Predictive Models for COVID-19 Transmission among Vaccinated Individuals

Ali; Mohd Khanapi Abd

doi:https://doi.org/10.54216/FPA.170220

Pure Mathematics for Theoretical Computer Science | 2024

Accurate Numerical Method Using Exponential Spline for solving boundary Value Problems

This study introduces a precise numerical technique employing exponential splines for singly perturbed singularity boundary values problems. A numerical scheme is devised to address issues encountered in diverse scientific and engineering domains. The framework consists of a triad of nonlinear equations. The approach is employed in several test cases to demonstrate accuracy and implementation.

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Ahmed R Khlefha mail

link https://doi.org/10.54216/PMTCS.040204

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Enhancing Convolutional Neural Network for Image Retrieval

With the continuous progress of image retrieval technology, the speed of searching for the required image from a large amount of image data has become an important issue. Convolutional neural networks (CNNs) have been used in image retrieval. However, many image retrieval systems based on CNNs have poor ability to express image features. Content-based Image Retrieval (CBIR) is a method of finding desired images from image databases. However, CBIR suffers from lower accuracy in retrieving images from large-scale image databases. In this paper, the proposed system is an improvement of the convolutional neural network for greater accuracy and a machine learning tool that can be used for automatic image retrieval. It includes two phases; the first phase (offline processing) consist of two stages; stage1 for CNN model classification while stage 2 for extracts high-level features directly from CNN by a flattening layer, which will be stored into a vector. In the second phase (online processing), the retrieval depends on query by image (QBI) from the system, which relies on the online CNN model stage to extract the features of the transmitted image. Afterward, an evaluation is conducted between the extracted features and the features that were previously stored by employing the Hamming distance to return all similar images. Last, it retrieves all the images and sends them to the system. Classification for images was achieved with 97.94% deep learning results, while for retrieved images, the deep learning was 98.94%. For this paper, work done on COREL image dataset. The images in the dataset used for training are more difficult than image classification due to the need for more computational resources. In the experimental part, training images using CNN achieved high accuracy, proving that the model has high accuracy in image retrieval.

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Zena M. Saadi mail -

Ahmed T. Sadiq mail -

Omar Z. Akif mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/JISIoT.140212

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Journal of Neutrosophic and Fuzzy Systems | 2024

Turiyam a Co-ordinate Free Geometry and its exploration

Recent time data representation and visualization is considered as one of the major issues. It become more crucial when the data sets exists beyond the non-euclidean geometry and its hybridization also. There are several examples given by non-euclidean geometry by Lobachevsky, Bolyai, Riemannian which contains failure of Euclid postulates V and II, respectively. The problem arises when none of the Euclid Postulates exists. It might possible that the data sets contains unknown or co-ordinate free geometry. In this case the data can be explored based on a defined vector space rather than available co-ordinate geometry. It require human Turiyam consciousness to explore these types of unknown, undefined, co-ordinate free data. To understand this problem current paper explores the Turiyam geometry and its basic for exploring the unknown or undefined data with an example.

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Prem Kumar Singh mail

link https://doi.org/10.54216/JNFS.090106

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

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Galoitica: Journal of Mathematical Structures and Applications | 2024

On The Diagonalization Problem of Weak Fuzzy Complex Matrices Based On a Special Isomorphism

In this paper, we study the diagonalization problem of weak fuzzy complex matrices. To solve this problem we build a special algebraic isomorphism between the ring of weak fuzzy complex matrices and the direct product of the classical ring of real-entries matrices with itself, then we use it to solve the diagonalization problem by using the classical diagonalization problem for real matrices with the inverse isomorphism formula. Also, we illustrate many examples to explain the validity of our method.

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Maretta Sarkis mail

link https://doi.org/10.54216/GJMSA.0110203

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

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Galoitica: Journal of Mathematical Structures and Applications | 2024

On The Algebraic Classification of the 4-Cyclic Refined Neutrosophic Real Roots of Unity Group

This paper is dedicated to finding all 4-cyclic refined neutrosophic real solutions of the equation 𝑋𝑛=1 which are called 4-cyclic refined real roots of unity. Also, we classify the algebraic group of these solutions as a direct product of some familiar finite cyclic groups. On the other hand, we illustrate many examples to clarify the validity of our work.

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Agnes Osagie mail

link https://doi.org/10.54216/GJMSA.0110204

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

IoT Innovations for Transforming the Future of Tourism Industry: Towards Smart Tourism Systems

The Internet of Things (IoT) has significantly transformed the tourism industry, reshaping travel design, supply, and experiences. This paper reviews the key developments in tourism IoT from the mid-2010s, highlighting technological, economic, and socio-cultural impacts. It explores the adoption of IoT technologies –such as smart wearables, intelligent transportation systems, and augmented reality –across tourism sectors, emphasizing their effects on tourist behaviour and sustainable tourism development. A mixed-method approach, including literature reviews and expert interviews, is used to analyse these trends. Findings reveal that IoT enhances personalization, immersion, and sustainability in travel experiences, though privacy, security, and ethical issues pose challenges. Strategic planning and collaboration are necessary to leverage IoT innovations for sustainable tourism growth.

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Olim Astanakulov mail -

Muhammad Eid BALBA mail -

Khayitov Khushvakt mail -

Sokhibova Muslimakhon mail

link https://doi.org/10.54216/JISIoT.140213

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

On The Group of Units Problem of the Non-Commutative Logical Extension of the Rings 𝒁𝒑 and 𝒁𝟐𝒏

This paper is dedicated to studying the group of units problem of the non-commutative logical extension of two different rings 𝑍𝑝 and 𝑍2𝑛, where we classify the group of units of these rings as semi-direct products of well-known abelian groups as follows: 𝑈(𝑁𝐶𝑅)𝑍𝑝&cong;𝑍𝑃−1&prop;(𝑍𝑃&prop;𝑍𝑃−1) 𝑈(𝑁𝐶𝑅)2𝑛&cong;(𝑍2×𝑍2𝑛−2)⋉(𝑍2𝑛⋉(𝑍2×𝑍2𝑛−2)).

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Sandra Terazic mail -

Stipan Podobnic mail

link https://doi.org/10.54216/GJMSA.0110205

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Forecasting for Vaccinated COVID-19 Cases using Supervised Machine Learning in Healthcare Sector

Machine learning (ML)-based forecasting techniques have demonstrated significant value in predicting postoperative outcomes, aiding in improved decision-making for future tasks. ML algorithms have already been applied in various fields where identifying and ranking risk variables are essential. To address forecasting challenges, a wide range of predictive techniques is commonly employed. Research indicates that ML-based models can accurately predict the impact of COVID-19 on Jordan's healthcare system, a concern now recognized as a potential global health threat. Specifically, to determine COVID-19 risk classifications, this study utilized three widely adopted forecasting models: support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and linear regression (LR). The findings reveal that applying these techniques in the current COVID-19 outbreak scenario is a viable approach. Results indicate that LR outperforms all other models tested in accurately forecasting death rates, recovery rates, and newly reported cases, with LASSO following closely. However, based on the available data, SVM exhibits lower performance across all predictive scenarios.

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Ali Khraisat mail -

Mohd Khanapi Abd Ghani mail

link https://doi.org/10.54216/JISIoT.140214

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Fusion Model of Quantum Wavelet Transform and Neural Network for Video Coding on the Internet of Things Environment

Solving the video compression problem requires a multi-faceted approach, balancing quality, efficiency, and computational demands. By leveraging advancements in technology and adapting to the evolving needs of video applications, it is possible to develop compression methods that meet the challenges of the present and future digital landscape. To address these objectives, machine learning and AI approaches can be utilized to predict and remove redundancies more effectively, optimizing compression algorithms dynamically based on content. Still, state-of-the art neural network-based video compression models need large and diverse datasets to generalize well across different types of video content. Wavelets can provide both time (spatial) and frequency localization, making them highly effective for video compression. This dual localization allows wavelet transforms to handle both rapid changes in video content and slow-moving scenes efficiently, leading to better compression ratios. Yet, some wavelet coefficients may be more critical for maintaining visual quality than others. Inaccurate quantization can lead to noticeable degradation. For the first time, the suggested model combine Quantum Wavelet Transform (QWT) and Neural Networks (NN) for video compression. This fusion model aims to achieve higher compression ratios, maintain video quality, and reduce computational complexity by utilizing QWT’s efficient data representation and NN’s powerful pattern recognition and predictive capabilities. Quantum bits (qubits) can encode large amounts of information in their quantum states, enabling more efficient data representation. This is especially useful for encoding large video files. Furthermore, quantum entanglement allows for correlated data representation across qubits, which can be exploited to capture intricate details and redundancies in video data more effectively than classical methods. The experimental results reveal that QWT achieves a compression ratio of almost twice that of traditional WT for the same video, maintaining superior visual quality due to more efficient redundancy elimination.

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Iptehaj Alhakam mail -

Ali Abdullah Ali mail -

Oday Ali Hassen mail -

Saad M. Darwish mail -

Nur Azman Abu mail

link https://doi.org/10.54216/FPA.170219

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Machine Learning in Healthcare: A Comprehensive Review of Predictive Models for COVID-19 Transmission among Vaccinated Individuals

This review provides an in-depth exploration of machine learning (ML) applications in healthcare, focusing specifically on predictive models for COVID-19 transmission among vaccinated individuals. It underscores the pivotal role of ML in disease forecasting and prognosis, showcasing its potential to enhance healthcare outcomes in pandemic contexts. Key challenges of COVID-19, such as the high transmission rate of asymptomatic carriers and the effectiveness of containment strategies, are analyzed to highlight areas where ML can offer significant advantages. The study aims to develop an advanced forecasting model for COVID-19 transmission using diverse supervised ML regression techniques, including linear regression, LASSO, support vector machine, and exponential smoothing, applied to an extensive COVID-19 patient dataset. The insights generated from this review support efforts to combat COVID-19 and improve public health strategies, demonstrating ML's vital contribution to pandemic management and healthcare resilience.

groups

Ali Khraisat mail -

Mohd Khanapi Abd Ghani mail

link https://doi.org/10.54216/FPA.170220

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new