Assessment of the Educational Live Action in Uncertainty Environment under Single-Valued Neutrosophic Sets

Gustavo Alvarez; Corona Gómez; Ariel Romero

doi:https://doi.org/10.54216/IJNS.230115

Journal of Artificial Intelligence and Metaheuristics | 2023

Develop application for prediction COVID-19 using artificial intelligence

The subset of manufactured insights (AI) known as machine learning starts in design acknowledgment, where information can be organized for human comprehension. For a long time, various applications utilizing machine learning have been created in healthcare, fund, military gear, and space investigation; presently, machine learning is a zone that's extending and progressing quickly. It utilizes information to optimize computer execution. AI is vital in combating modern coronaviruses in 2019 (COVID-19) -related matters and is used additionally in computer-assisted blend-making plans. Computer programs' settings are optimized based on preparing information or past encounters. It can moreover make future forecasts utilizing the information. With the assistance of machine learning, we are creating a numerical demonstration based on the data's measurements. Numerous illustrations outline the viability of machine learning and counterfeit insights in this field. Counterfeit insights strategies can improve the consistency of forecasts and choices by making valuable calculations. AI is useful not for foreseeing people with COVID-19 but for assessing general wellbeing. It can screen the COVID-19 episode at different levels; in our paper, we use three machine learning calculations to analyze and predict. The leading precision was in XGP= 99%, but SVM and RF gave great precision at 98%.

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Noor abdulmuttaleb jaafar mail -

Noor Razzaq Abbas mail -

Ammar Kadi mail -

Abdelhameed Ibrahim mail -

Abdelaziz A. Abdelhamid mail

link https://doi.org/10.54216/JAIM.060103

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

An Optimized Architecture for COVID‑19 Prediction Using Chest X‑Ray Images

In modern times, a disease known as COVID-19 that is highly contagious is continuing to have a profoundly negative influence on the people of the entire world. The fundamental purpose of the model that has been proposed is to improve its predictive capabilities while also providing an effective model for predicting COVID-19 that has a robust diagnostic. Image scaling and noise reduction are two examples of the types of pre-processing techniques that are used at the very first step. The adoption of picture scaling and median filtering techniques, both of which work to enhance the quality of the input data in preparation for further processing steps, allows this goal to be accomplished. Several distinct data augmentation strategies, like flipping and rotation, are utilized to improve the model's performance on a limited dataset and assist it in better comprehending the differences present in the training data. In this article, we will provide a unique Optimized Architecture for COVID-19 Prediction (OACP) model to classify COVID-19 situations as either positive or negative effectively. Using CXR pictures, this novel method, based on a tunable deep learning technique called DenseNet, may predict the presence of COVID-19-positive patients. Based on the findings, it was determined that the proposed model utilized achieved better outcomes, with an accuracy of 98%.

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Yasser Fouad mail -

Ahmed M. Osman mail -

Ibrahim E. Abdelmaged mail -

Ahmed Mohamed Zaki mail -

Ahmed M. Elshewey mail

link https://doi.org/10.54216/JAIM.060104

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

CNN-Based Multiclass Classification for COVID-19 in Chest X-ray Images

Managing the increasing number of patients requiring first screening can be significantly aided by real-time automated detection of COVID-19. It's feasible that Deep CNN models that have been trained on sufficiently large datasets will emerge as the most promising options for achieving the goal. This study aims to automatically detect and classify COVID-19 and viral pneumonia infections in chest X-ray images using a deep CNN model. Our proposed model relies on multiclass labeling to accomplish our aims. Design and Organization: Using the ImageNet pre-trained weights, the proposed model is built on top of the VGG16 framework. Additional custom layers were used to fine-tune the model and produce a better overall performance that is more specific to the goal. In terms of its subjects and methods, this study uses 15,153 samples in total. There are X-rays of the lungs from patients with COVID-19, those with viral pneumonia, and healthy volunteers. The entire dataset was split into an 80:20 split for the train and test sets before the model was trained. Image preprocessing and augmentation were used to enhance crucial parts of the photos before they were sent to the model in batches. We measure the model's efficacy with accuracy, precision, recall, and the F1 score. The analysis that was performed statistically was. The model's output is compared to the results of other recent research that have set the standard in the field. The proposed model has a 98% accuracy in multiclass classification on the test dataset, as measured by 98% precision, 96% recall, and 97% F1 score. Receiver operating characteristic curve area scores of 0.99 were achieved in all three multiclass classification situations. Finally, the proposed categorization model may show to be highly useful in the first diagnosis of COVID-19 and viral pneumonia patients, especially when dealing with heavy workloads and large volumes.

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S.K.Towfek mail

link https://doi.org/10.54216/JAIM.060105

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2023

Certain Determinants for New Subclasses of μ-Fold bi-Univalent Functions

This paper introduces and investigate a new subclasses of the class  of analytic functions that both are -fold symmetric -univalent functions in the open unit disk  and get the estimates of the initial coefficients  for functions in each of these new subclasses. After this, the work will be discussing the Hankel determinant and a Fekete-Szegö functional.

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Aqeel K. AL-khafaji mail -

Heyam K. Alkhayyat mail -

M. Abdul-Jabbar Albayati mail

link https://doi.org/10.54216/GJMSA.090102

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Integrated Digital Signature Based Watermarking Technology for Securing Online Electronic Documents

Even though the transmission and processing speeds of electronic documents have been vastly enhanced, electronic document information may be revealed, counterfeited, tampered with, or otherwise compromised. To maintain corporate success in the marketplace, network security should be essential to the protection of electronic documents. As a result, there is a rising demand for authentication and verification procedures for a variety of important documents, including those used in banking, government, and other transactions as well as certificates and other academic credentials. In recent years, there has been a fast growth of digital watermarking technology, which involves embedding invisible or hidden digital signatures into data without compromising the data's authenticity. Hence, in this paper, we utilize the watermarking technology in the encrypted data using dynamic wavelet transform algorithm to make a document more protected. Now the protected data is sent to cloud database for storage. Integrated digital signature algorithm (SHA-256 + DSA) is proposed in this research to generate digital signature for each document. When recipients download the data, the data is verified for its integrity after extracting the digital signature and encrypted data. This strategy improves record security. We also compare the suggested technique to standard practices and assess its performance based on a variety of indicators to demonstrate its effectiveness.  

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Sinan Q. Salih mail -

Ravi Sekhar mail -

Jamal Fadhil Tawfeq mail -

Amer Ibrahim mail -

Pritesh Shah mail -

Ahmed Dheyaa Radhi mail

link https://doi.org/10.54216/FPA.140111

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

A Fusion of Multi-Criteria Decision-Making for Select Recharge Structure

Groundwater recharge is essential in establishing reliable groundwater supplies in a region. Groundwater is a vital natural water resource, but its quantity and quality may vary significantly from one area to another. Growing urbanization and population increase have put a significant demand on groundwater supplies. Using Multi-Criteria Decision-Making (MCDM), several studies have identified good areas for recharging groundwater supplies. To help choose between several types of artificial recharge (AR) structures, we have developed an MCDM approach for this research. We used an MCDM fusion methodology to combine various AR criteria with the alternatives. This study collected eight criteria and eight alternatives. We used the average method to compute the weights of the criteria. Then, we used the COCOSO method as an MCDM fusion method to rank the alternatives. The results show that hydrological conditions are the best criteria, and stakeholder engagement is the lowest weight. The sensitivity analysis is performed to show the stability of the results in this study. 

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Walter Culque Toapanta mail -

Fausto Vizcaino Naranjo mail -

Antonio Castillo Medina mail

link https://doi.org/10.54216/FPA.140110

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

A Fusion Selection Approach of the Best Plan for Energy Remodeling Hospital Wards Using a Multi-Criteria Decision-Making

Energy policy implementation relies heavily on assessing savings from retrofitting for energy efficiency. Because of their unique purpose, hospitals need energy-efficient renovations to improve indoor air quality and create a pleasant space for staff and visitors. Because of this crucial distinction, investors' preferences must be considered when deciding on refurbishment plans. Considering elements including energy savings, financial viability, and thermal comfort, this research provides a multi-criteria decision-making (MCDM) approach to guide investors in choosing the most effective remodeling plan for hospital wards. We used the MABAC method as an MCDM fusion method to combine the various criteria and alternatives to select the best one. We used ten criteria and ten alternatives in this study. We compute the weights of criteria to rank the criteria. Then, we used the MABAC fusion to rank the alternatives. The results show the financial viability has the least weight and the building envelope has the highest. We conducted a sensitivity analysis to show the stability of the results in this study.

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Esteban López E. mail -

Silvio Machuca Vivar mail -

Luis Molina Chalacan mail

link https://doi.org/10.54216/FPA.140112

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Information Fusion from Multimodal Clinical Sensors for Effective Supplier Decision-Making in Healthcare

Effective procurement of clinical devices in healthcare demands a sophisticated decision-making approach integrating diverse data sources from multiple devices, brands, and suppliers, particularly within the context of information fusion. This study addresses this challenge by proposing an improved best-worst method harmonized with information fusion techniques and multi-criteria decision-making methodologies. The background emphasizes the dynamic nature of healthcare procurement, necessitating systematic strategies for navigating the complexities of device selection and integration. Recognizing the intricacies inherent in this challenge, the problem statement revolves around enhancing the best-worst method to amalgamate data from clinical devices while concurrently evaluating brands and suppliers. This aims to optimize performance and minimize costs within the information fusion paradigm. Our proposed methodology introduces an augmented best-worst approach, encompassing weighted criteria assessment for clinical devices, brands, and suppliers, providing a more adaptable and nuanced decision-making framework tailored to the information fusion landscape. The results showcase a structured evaluation matrix derived from refined weighted criteria, elucidating the relative performance and strengths across various entities within the healthcare procurement ecosystem. Emphasizing reliability, compatibility, innovation, and quality assurance, this process highlights pivotal factors influencing procurement decisions within the realm of information fusion.

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Fredy Canizares Galarza mail -

Becker Neto Mullo mail -

Miguel Ramos Argilagos mail

link https://doi.org/10.54216/FPA.140113

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Integrative Multi-Information Fusion for Enhanced Risk Assessment: A Multi-Criteria Decision-Making Framework

This study addresses the burgeoning challenges in autonomous Maritime navigation by employing information fusion methodologies to assess and manage multifaceted risks. The proliferation of autonomous maritime systems has led to a complex interplay among maritime-related, shore-based remote control, environmental, and emergency management factors, necessitating a comprehensive risk evaluation framework. Leveraging a multi-criteria decision-making approach and employing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), this research presents a methodical analysis of the coupling coordination degree among these risk variables. Through a meticulous examination of historical accident data and information fusion techniques, our study reveals dynamic trends in the comprehensive risk evaluation index, showcasing the evolving nature of risks inherent in autonomous Maritime navigation. The predictive insights gleaned from these analyses forecast an initial increase followed by a peak in accidents, underscoring the urgency for proactive risk mitigation strategies. This study's conclusions emphasize the pivotal role of information fusion methodologies in comprehensively assessing, understanding, and managing risks within autonomous Maritime navigation.

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Luis Albarracin Zambrano mail -

Bolivar Villalta Jadan mail

link https://doi.org/10.54216/FPA.140114

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Assessment of the Educational Live Action in Uncertainty Environment under Single-Valued Neutrosophic Sets

Health professional educators are increasingly using escape rooms as a teaching tool. Given the fast development in their use, investigators have chosen varied assessment approaches to evaluate the instructional rooms. Considering educational escape rooms is a multi-attribute decision-making (MADM) process based on various criteria. This study proposed a MADM model to assess the educational escape rooms. This study used the VIKOR MADM method to evaluate the criteria and alternatives. This evaluation is made under a neutrosophic set to overcome the uncertainty information. We collected fourteen criteria and ten alternatives in this study. We employed a sensitivity analysis to show the proposed model's effectiveness and the results' stability. The analysis shows the results are stable.

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Gustavo Alvarez Gómez mail -

Corona Gómez Armijos mail -

Ariel Romero Fernández mail

link https://doi.org/10.54216/IJNS.230115

Volume & Issue

Vol. Volume 23 / Iss. Issue 1

Details open_in_new