CSsEv: Modelling QoS Metrics in Tree Soft Toward Cloud Services Evaluator based on Uncertainty Environment

Mona; Florentin; Mona

doi:https://doi.org/10.54216/IJNS.230204

Fusion: Practice and Applications | 2024

Enhancing Security in IoMT: A Blockchain-Based Cybersecurity Framework for Machine Learning-Driven ECG Signal Classification

The Internet of Medical Things (IoMT) revolutionizes healthcare, enhances patient care, and optimizes workflows. However, the integration of IoMT introduces concerns related to privacy and security. In addressing these issues and aiming to bolster privacy and data security, this study presents a novel cybersecurity framework based on blockchain (BC) technology. The primary goal is to ensure secure communication among IoMT devices, preventing unauthorized access and tampering with sensitive data. The proposed framework is implemented in a model designed for classifying electrocardiogram (ECG) signals, utilizing two datasets: a Medical Technology Database (MTDB) with a limited sample size and the Massachusetts Institute of Technology–Beth Israel Hospital (MITBIH) dataset with a more extensive sample size. The datasets are subsequently partitioned into training and testing data. Feature extraction and selection are performed using the Pan-Tomkins and genetic algorithms. To enhance security, BC technology is employed to encrypt the test data. Finally, signal classification is performed using the support vector machine (SVM) classifier. Thus, the model trained on the MITBIH dataset outperforms its small data counterpart, achieving an impressive accuracy rate of 99.9%. Additionally, the model exhibits a true positive rate (TPR) and true negative rate (TNR) of 100%, an F-score of 100%, and a positive predictive value (PPV) of 100%.

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Aya Hamid Ameen mail -

Mazin Abed Mohammed mail -

Ahmed Noori Rashid mail

link https://doi.org/10.54216/FPA.140117

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Predictive Modeling Through Fusion of Passengers Information Transferred to Alternate Dimensions

This research focuses on the identification of passengers, in dimensions using information fusion as a tool. We recognize the challenges involved in identifying individuals who have been transferred to alternate dimensions and in this study we make use of CatBoost, an open source machine learning algorithm to address this problem. Our approach includes a preprocessing strategy that involves filling in missing values using techniques like priori distribution terms, which helps ensure the reliability of our dataset. By leveraging CatBoosts ability to handle variables and prevent overfitting we achieve results in accurately predicting passenger movement across dimensions. Our analysis highlights CatBoosts effectiveness in identifying patterns within data leading to more precise predictions for interdimensional passenger transportation. Additionally we incorporate techniques, like Greedy TS augmentation to enhance the adaptability of the algorithm and improve precision while reducing bias in modeling. Proof-of-concept experiments demonstrate that the proposed fusion system not only advances predictive modeling in niche domains but also paves the way for broader applications of machine learning in deciphering complex phenomena beyond traditional realms, marking a significant stride in understanding and addressing unconventional challenges.

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Fabricio Lozada Torres mail -

Sharon Álvarez Gómez mail -

Diego Palma Rivero mail -

Christian F. Tantaleán Odar mail -

Sayfuddinov Shukhrat mail

link https://doi.org/10.54216/FPA.140118

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Survival Analysis Based on Fusion of Decisions from Multiple Tree Structure: A Cutting-Edge Approach

Survival analysis remains an important area in predictive modeling, especially in cases where event timing information is critical.  This work presents a research effort to investigate the application of LightGBM, a high-performance high-throughput model, to conduct an improved fusion of decisions from multiple trees to reach survival analysis. Our objective is to address the challenge of developing correct predictive models while advancing computational effectiveness.  Based on a case study of live disaster scenarios, the proposed approach applies and compares LightGBM with traditional prediction methods, which involve careful design engineering, and model training with LightGBM tree structure refinement. The results obtained from fair experimentation and comprehensive predictive performance evaluation demonstrate the robustness of LightGBM in increasing the accuracy of relevant classification tasks toward survival analysis. Furthermore, the findings highlighted that the combination of excellent tree depth for cutting and multi-thread optimization promotes efficient computational complexity and prediction accuracy.

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Luz M. Aguirre Paz mail -

Jorge Viteri Moya mail -

Rita Azucena D. Vásquez mail -

Darvin M. Ramírez Guerra mail -

Dekhkonov Burkhon mail

link https://doi.org/10.54216/FPA.140119

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

A Data Fusion Approach for Accurate Diagnosis of Parkinson's Disease

Diagnosing Parkinson's Disease (PD) can be quite challenging as it presents with symptoms and lacks biomarkers. Nevertheless, the use of data fusion, which combines types of data using machine learning techniques holds promise, for the timely detection of the disease. In this study, we explore the application of data fusion by employing Principal Component Analysis (PCA) as a step to reduce complexity. We then utilize the K Nearest Neighbors (KNN) classification to improve the accuracy of PD diagnosis. By analyzing nonlinear features associated with PD from a dataset PCA helps us extract attributes while maintaining important variations in the data. Subsequently, KNN is employed to identify patterns in this reduced feature space and effectively distinguish between individuals with PD and those who are healthy. Our results show improvements when using the KNN classifier compared to state-of-the-art approaches. This demonstrates its effectiveness in detecting PD leading to promising outcomes and providing a framework for precise PD diagnosis.

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Fredy Canizares Galarza mail -

Luis Freire Lescano mail -

Lina Espinoza Neri mail -

Dilafruz Nabieva mail

link https://doi.org/10.54216/FPA.140120

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

An Information Fusion Technique for Prognosticating Future Air Passenger Trends

The aviation industry is constantly changing and to keep up with the trends of air passengers we need predictive models. In this paper, we explore the use of Information Fusion methodologies and classical time series techniques to forecast how many passengers will be traveling by air. Predicting passenger demands is a task, due to various factors that influence travel patterns. The existing models often struggle to capture the dynamics in this field so it's crucial to develop accurate forecasting methods. By leveraging information fusion techniques like smoothing and Autoregressive Integrated Moving Average (ARIMA) our research creates models based on historical data of air passenger volumes. These techniques combine machine learning algorithms and time series analysis to identify dependencies and patterns in the dataset. Through evaluations and comparative analyses, our proposed models demonstrate promising capabilities in forecasting future air passenger volumes. Proof-of-concept experiments based on 5-fold cross-validation demonstrate the efficacy of the proposed approach in capturing underlying trends and seasonality within the dataset.

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Luis A. Zambrano mail -

Luis llerena Ocana mail -

Tannia Cristina P. Morales mail -

Vladimir Vega Falcón mail -

Mirzaliev Sanjar mail

link https://doi.org/10.54216/FPA.140121

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Early Diagnosis of Oral Cancer Using Image Processing and Artificial Intelligence

There has yet to be a comprehensive investigation on enhancing the diagnostic accuracy of oral disease using handheld smartphone photographic photos. To overcome the difficulties associated with the automatic detection of oral illnesses, we describe an approach based on smartphone image diagnosis powered by a deep learning algorithm. The centered rule method of image capture was offered as a quick and easy way to get high-quality pictures of the mouth. A resampling method was proposed to mitigate the influence of image variability from handheld smartphone cameras, and a medium-sized oral dataset with five types of disorders was developed based on this approach. Finally, we introduce a recently developed deep-learning network to assess oral cancer diagnosis. On 455 test images, the proposed technique showed an impressive 83.0% sensitivity, 96.6% specificity, 84.3% accuracy, and 83.6% F1. The proposed "center positioning" method was about 8% higher than a simulated "random positioning" method; the resampling process had an additional 6% performance improvement. The performance of a deep learning algorithm for detecting oral cancer can be enhanced by capturing oral photos centered on the lesion. Primary oral cancer diagnosis using smartphone-based images with deep learning offers promising potential.

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Eman Shawky Mira mail -

Ahmed M. Saaduddin Sapri mail -

Rowaa F. Aljehanı mail -

Bayan S. Jambı mail -

Taseer Bashir mail -

El-Sayed M. El-Kenawy mail -

Mohamed Saber mail

link https://doi.org/10.54216/FPA.140122

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

On n-Refined Neutrosophic Vector Spaces For Some Special Values 3≤ n ≤6

This work is dedicated to study some different types of n-refined neutrosophic vector spaces for different values of n between 3 and 6. Where we present some related algebraic concepts such as 3-refined neutrosophic homomorphism, 4-refined neutrosophic homomorphism, 5-refined neutrosophic homomorphism, and 6-refined neutrosophic homomorphism. Also, we provide some theorems to clarify the algebraic behaviour of 3-refined, 4-refined, 5-refined, and 6-refined neutrosophic subspaces.

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Nabil Khuder Salman mail -

Bolivar Villalta Jadan mail -

Marcos Lalama Flores mail

link https://doi.org/10.54216/IJNS.230201

Volume & Issue

Vol. Volume 23 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

A Study of Systems of Neutrosophic Linear Equations

Operations research methods are among the modern scientific methods that have occupied a prominent place among the mathematical methods used in planning and managing various economic and military activities. They have been able to help specialists in developing ideal plans in terms of costs, production, storage, or investment of human energies. One of its most important methods is the method Linear programming, which was built based on the sets of linear equations that represent the constraints for any linear model. Based on the methods for solving the systems of linear equations, researchers were able to prepare algorithms for solving linear models, such as the direct Simplex algorithm and its modifications. After the emergence of neutrosophic science, we found that research methods had to be reformulated. Operations using the concepts of this science, and as a basis and foundation for neutrosophic linear programming. In this research, we will reformulate the systems of linear equations and some methods for solving them using the concepts of neutrosophic to be a basis for any study presented in the field of neutrosophic linear programming.

groups

Maissam Jdid mail -

Florentin Smarandache mail

link https://doi.org/10.54216/IJNS.230202

Volume & Issue

Vol. Volume 23 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

On The Classification of 3-Cyclic/4-Cyclic Refined Neutrosophic Real and Rational Von Shtawzen's Group

This paper aims to solve the group of units problem for 3-cyclic real and rational refined neutrosophic rings and 4-cyclic real and rational refined neutrosophic rings, where ring isomorphisms between the real/rational 3-cyclic and 4-cyclic refined neutrosophic rings and the direct product of some field extensions of Q and R. These isomorphisms will help in classifying the group of units of each studied ring in terms of direct products of classical well-known abelian groups. Also, we use the classification isomorphisms to determine all ideals in these classes of algebraic rings.

groups

Narek Badjajian mail -

Maikel Leyva Vazquez mail -

Batista Hernández Noel mail

link https://doi.org/10.54216/IJNS.230203

Volume & Issue

Vol. Volume 23 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

CSsEv: Modelling QoS Metrics in Tree Soft Toward Cloud Services Evaluator based on Uncertainty Environment

Cloud computing (ClC) has become a more popular computer paradigm in the preceding few years. Quality of Service (QoS) is becoming a crucial issue in service alteration because of the rapid growth in the number of cloud services. When evaluating cloud service functioning using several performance measures, the issue becomes more complex and non-trivial. It is therefore quite difficult and crucial for consumers to choose the best cloud service. The user's choices are provided in a quantifiable manner in the current methods for choosing cloud services. Hence, this study attempts to achieve this objective through construction. decision-making framework so-called cloud services evaluator (CSsEv). The main indicator and its sub-indicators are formed as nodes at levels(n) in tree soft sets (TSSs). Thereafter Single Value Neutrosophic Sets (SVNSs) as branch of neutrosophic sets which conjunction with the Multi-Criteria Decision Making (MCDM) technique to facilitate analysis and evaluation process for the available Cloud services providers. Hence, entropy is employed to obtain indicators and sub_indicators’ weights and Complex Proportional Assessment utilizes these weights to facilitate the decision process of selecting optimal ClSPs.

groups

Mona Gharib mail -

Florentin Smarandache mail -

Mona Mohamed mail

link https://doi.org/10.54216/IJNS.230204

Volume & Issue

Vol. Volume 23 / Iss. Issue 2

Details open_in_new