ANOVA and the 2-Tuple Neutrosophic linguistic method: A case study to analyze the interaction between elements

Luis Alonso Chicaiza Sánchez; Patricia Marcela Andrade Aulestia; Dildora Abduturapova

doi:https://doi.org/10.54216/IJNS.250141

Journal of Intelligent Systems and Internet of Things | 2024

Brain Tumor Semantic Segmentation using U-Net and Moth Flame Optimization

Brain tumor is an abnormal development of brain cells that, if left untreated, can have severe consequences. Brain tumour semantic segmentation is the process of determining and distinguishing the impacted brain regions, which is essential for accurate diagnosis, treatment planning, as well as surveillance of the tumor's development over time. This paper presents a model for identifying and segmenting brain tumor using Unet architecture with the optimization of hyper parameters using the Moth Flame Optimization (MFO) algorithm. Due to its capacity to collect spatial information, the Unit architecture is a common choice for picture segmentation tasks. The MFO algorithm is an optimization technique that draws inspiration and replicates from the behavior of moths. Both techniques are developed to improve efficiency. The performance of the model has increased using the MFO method, which led to improved segmentation results. Based on comparative analysis report, the proposed model shows a percentage improvement of approximately 65.16% in MSE, 28.87% in PSNR, and 40.30% in Tversky compared to the Unet and Unet++ models. This method has demonstrated good results in identifying and segmenting brain tumors, which can be helpful in the early identification and treatment of brain tumor.

groups

B. Tapasvi mail -

E. Gnanamanoharan mail -

N. Udaya Kumar mail

link https://doi.org/10.54216/JISIoT.130226

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Enhancing Tomato Leaf Disease Detection through Generative Adversarial Networks and Genetic Algorithm based Convolutional Neural Network

In the agricultural sector, tomato leaf diseases signify a lot because they result in a lower crop yield and quality. Timely detection and classification of diseases help to ensure early interventions and effective treatment solutions. Nonetheless, the existing methods are confined by the dataset imbalance which affects class distribution negatively and thus results in poor models, especially for rare diseases. The research is designed to improve the capability of tomato leaf disease identification by investing a new deep-learning method beyond the challenge of imbalanced class distribution. By balancing the dataset, we aim to improve classification accuracy as we pay more attention to the under-represented classes. The proposed GAN-based method that combines the Weighted Loss Function to produce tomato leaf disease synthetic images is underrepresented. They improve the quality of the entire dataset, and the images from every class are now in a more balanced proportion. A CNN, which is the convolutional neural network, is trained for the classifier, with the weighted loss function as a part of the model. We used Genetic Algorithm (GA) for hyperparameter optimization of the CNN. It helps in emphasizing the learning process from the under-represented class. The suggested one will not only decrease the accuracy of tomato leaf disease detection but also increase it. Therefore, the synthetic images created by GAN enhance the dataset since the class distribution is brought to equilibrium. The incorporation of the weighted loss function into the model’s training process makes it very effective in handling with the class instability problem and consequently, the model can identify both common and rare diseases. From the outcomes of this study, it can be concluded that it is feasible to employ GAN and one loser weights function to solve the problem of class imbalance in tomato leaf disease recognition. A suggested approach that increases the model’s accuracy and reliability could be a good move to enhancing a reliable method of disease detection in the agricultural sector.

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Vasima Khan mail -

Seema Sharma mail -

Janjhyam Venkata Naga Ramesh mail -

Piyush Kumar Pareek mail -

Prashant Kumar Shukla mail -

Shraddha V. Pandit mail

link https://doi.org/10.54216/FPA.160210

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Systematic Analysis of threats, Machine Learning solutions and Challenges for Securing IoT environment

The Internet of Things (IoT) has revolutionized our daily lives, impacting everything from healthcare to transportation and even home automation and industrial control systems. However, as the number of connected devices continues to rise, so do the security risks. In this review, we explore the different types of attacks that target various layers of IoT infrastructure. To counter these threats, researchers have proposed using machine learning (ML) and deep learning (DL) techniques for detecting different types of attacks. However, our examination of existing literature reveals that the effectiveness of these techniques can vary greatly depending on factors like the dataset used, the features considered, and the evaluation methods employed. Finally, we delve into the current challenges facing Intrusion Detection Systems (IDS) in their mission to protect IoT environments from evolving threats.

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Bharti Yadav mail -

Deepak Dasaratha Rao mail -

Yasaswini Mandiga mail -

Nasib Singh Gill mail -

Preeti Gulia mail -

Piyush Kumar Pareek mail

link https://doi.org/10.54216/JCIM.140227

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Neutrosophic and Information Fusion | 2024

Finding the Complete List of Different K-Brackets for the Projective Plane PG (2,8)

A k-arc in a plane PG(2,q) is a set of k points such that every line in the plane intersects it in at most two points and there is a line intersecting it in exactly two points. A k-arc is complete if there is no (k + 1)-arc containing it. This study is concerned with k-arcs, k = 4,5,...,10, and with the classification of projectively distinct k-arcs and distinct arcs under collineation. Using a computer program, we prove that complete k-arcs occur only for k = 6 and k = 10.

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Khaled Moaz mail

link https://doi.org/10.54216/NIF.030201

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

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Neutrosophic and Information Fusion | 2024

On the Effect of the Layers’ Number of Deep Neural Network for Improving the Reward of a Reinforcement Learning Robot

The Q-learning algorithm in reinforcement learning is one of the algorithms that allows the robot to learn the surrounding environment without the need for prior training samples, using the principle of reward and punishment through interaction with the environment. Increasing the number of hidden layers of the deep neural network and adjusting some of its hyperparameters can increase the robot’s reward and thus obtain the best path to achieve the goal.

groups

Talal Markabi mail -

Bahaa Mansoura mail

link https://doi.org/10.54216/NIF.030202

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Neutrosophic and Information Fusion | 2024

On the Stability Analysis of the Fisher Equation Based on Some Numerical Galerkin Techniques

We studied the stability of the steady-state solutions for the Fisher equation in two cases. In the first case, with constant amplitude, we show that the steady-state solution u1 = 1 is always stable under any condition, whereas the other two solutions, u1 = 0 and u1(x) = Acos(nπX), are conditionally stable. In the second case, we studied the steady-state solutions for variable amplitude using two methods. The first is an analytical direct method, and the second is a numerical method using the Galerkin technique. Both methods show the same results: the steady-state solution u1 = 1 is always stable under any conditions, whereas the other two solutions, u1 = 0 and u1(x) = Acos(nπX), are conditionally stable.

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Murat Ozcek mail

link https://doi.org/10.54216/NIF.030203

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

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Neutrosophic and Information Fusion | 2024

On a Novel Simulation of a Control Technique for Power Oscillation Damping and Applications

This research presents a novel simulation model of adaptive control to implement a control process using the MIT-rule adjustment mechanism for power oscillation damping in the single-machine infinite-bus (SMIB) system, and to measure its possible effects on the response of the damper by changing its parameters according to an external disturbance using Simulink. The results show that the use of the model-reference adaptive control (MRAC) technique maintains the response of the damper when the transfer function changes because of an external disturbance.

groups

Talal Markabi mail

link https://doi.org/10.54216/NIF.030204

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Neutrosophic Analytical Hierarchy Process (NAHP) for Addressing Cyber violence

To address the complex challenges of cyberviolence and gender-based violence among young students, it is crucial to employ analytical approaches that consider the multifaceted nature of these phenomena. The Neutrosophic Analytical Hierarchy (NAHP) method is presented as an innovative tool that allows us to unravel the different layers of influences and factors involved in these behaviors. This approach not only recognizes the diversity of perspectives and experiences that contribute to online and gender-based violence, but also offers a structured framework to assess and prioritize these factors holistically. By applying the NAHP, not only the visible and direct aspects of cyberviolence and gender violence are explored, but also the more subtle and underlying aspects that may go unnoticed in conventional analyses. This method allows us to capture the dynamic complexity of how individual perceptions, social norms, and power dynamics interact to perpetuate these problems in student environments. Thus, a deeper and more nuanced understanding of the triggering and contributing factors is fostered, facilitating the formulation of more effective interventions and policies that are sensitive to the specific needs of affected young people.

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Patricia Estefanía Rodríguez Palomo mail -

Sandra Giuliana Suárez Peña mail -

Paola Estefanía Salinas Aguilar mail -

Sanjar Mirzaliev mail

link https://doi.org/10.54216/IJNS.250139

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

Application of Multi-Criteria Methods and Neutrosophic Logic for the Analysis of Productive Factors

This article explores the innovative application of multi-criteria methods and neutrosophic logic in the analysis of productive factors, highlighting how these approaches can offer a more nuanced and comprehensive view of industrial and business dynamics  ̣ Multicriteria methods allow different aspects to be evaluated simultaneously, considering complex variables that affect productivity and efficiency in various sectors  ̣ On the other hand, neutrosophic logic introduces a theoretical framework that manages the uncertainty and imprecision inherent in many business decisions, offering tools to better interpret and manage the variabilities and ambiguities that influence productive results  ̣ This integrative approach not only seeks to improve accuracy in the evaluation of critical factors such as cost, quality and time, but also to promote more informed and strategic decision making in competitive and changing environments  ̣ By combining rigorous analysis with interpretive flexibility, the door is opened to new methodologies that can effectively adapt to the complexities of the globalized market and the dynamic demands of consumers  ̣ This article examines case studies and practical examples to illustrate how these methods can be successfully applied in the optimization of production processes and in the formulation of business strategies that seek not only to remain competitive, but also to anticipate and proactively respond to emerging challenges  ̣

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Franklin A. Molina-Borja mail -

Wendy Maribel-Molina mail -

Wilmer L. Toul Ayala mail -

Freddy X. Guamangate-Chiguano mail -

Sanjar Mirzaliev mail

link https://doi.org/10.54216/IJNS.250140

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

ANOVA and the 2-Tuple Neutrosophic linguistic method: A case study to analyze the interaction between elements

In this article, an innovative approach is presented that combines analysis of variance (ANOVA) with the Neutrosophic 2-Tuple linguistic method to explore and analyze the complex interactions between elements in various contexts. ANOVA, known for its ability to decompose variance and detect significant differences between groups, is here merged with the Neutrosophic method, which provides tools to handle the uncertainty and linguistic ambiguity present in many real data sets. This methodological synergy not only expands analytical possibilities, but also allows for a more nuanced and profound interpretation of the relationships between variables, overcoming the limitations of traditional approaches that assume absolute certainty in the data. Through detailed case studies and practical examples, it is demonstrated how this hybrid model can be effectively applied in fields as diverse as scientific research, business management, and public policy evaluation. The results obtained illustrate how the combination of ANOVA and 2-Tuple Neutrosophic not only improves the precision of statistical analysis, but also enriches the understanding of complex phenomena by considering and modeling uncertainty in a more realistic and adaptable way to different contexts and scenarios.

groups

Luis Alonso Chicaiza Sánchez mail -

Patricia Marcela Andrade Aulestia mail -

Dildora Abduturapova mail

link https://doi.org/10.54216/IJNS.250141

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new