Enhancing Inventory Management through Advanced Technologies and Mathematical Methods: Utilizing Neutrosophic Fuzzy Logic

C. Balakrishna Moorthy; D. Rajani; A. P. Pushpalatha; S. Ramya; A. Selvaraj; Mohit Tiwari

doi:https://doi.org/10.54216/IJNS.240403

Journal of Cybersecurity and Information Management | 2024

Security Validation in OpenStack: A Comprehensive Evaluation

The study delves into the security architecture of OpenStack, an open-source cloud platform that is increasingly prevalent in modern computing environments. Its primary goal is to rigorously assess and confirm hypotheses about OpenStack's security infrastructure while identifying vulnerabilities and potential threats using a comprehensive security evaluation framework. The study utilizes a multifaceted security assessment methodology to analyze both private and public cloud deployments of OpenStack. This methodology involves various techniques, including vulnerability scanning, penetration testing, and analysis of security policies and configurations. Benchmarking against industry standards and previous studies further strengthens the analytical framework, ensuring a thorough exploration of various dimensions of OpenStack security. The assessment revealed that OpenStack has a robust security posture, with vulnerabilities detected in only 2% of cases across both private and public cloud deployments. The study also found a resilience rate of 95% against common security challenges. The comprehensive analysis covered various dimensions of OpenStack security, providing valuable insights into the platform's security resilience and vulnerabilities, thereby significantly contributing to the body of knowledge in cloud security research. The research underscores the importance of implementing robust security protocols in OpenStack environments to ensure the reliability of cloud infrastructure. Regular security updates and adherence to best practices can strengthen the security posture of OpenStack deployments. The insights from this study can inform the development of guidelines and policies aimed at enhancing security practices in cloud computing environments. Overall, the study evaluates the security framework of OpenStack and emphasizes the significance of implementing robust security measures to ensure the dependability of cloud infrastructure, guiding the creation of recommendations and superior practices for strengthening security in cloud computing environments.

groups

Mohammed Saffran mail -

Shailendra Mishra mail

link https://doi.org/10.54216/JCIM.140106

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Technological Tools before and after COVID-19 in Ecuador

The COVID-19-induced state of emergency in Ecuador necessitated compulsory isolation for most of the people. During this period, there was a rise in the utilization of technical equipment as individuals had to perform their tasks remotely from their homes. This study sought to assess the utilization of technology resources during the period of quarantine, necessitating the creation of a survey. Specific indicators were considered and standardized for processing. The data processing techniques employed were the Hierarchical Analytical Process and Logic Scoring of Preference. The key findings indicate that the indicators "Modes of Use," "Use Preferences," "Daily Usage Frequency," and "Monthly Expenditure" are crucial for measuring the composite indicator "use of technological tools." A survey was created to contribute to the research.

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Manuel A. Abarca-Zaquinaula mail -

Matius R.Mendoza-Poma mail -

Freddy A. Álvarez-Lema mail -

Milton A. Sampedro-Arrieta mail

link https://doi.org/10.54216/FPA.160117

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Fusion: Practice and Applications | 2024

Climate Optimization in Greenhouses Using the NARMA-L2 Model: An Advanced Integration of Environmental Variables

Agricultural systems, such as greenhouses, can be used to control environmental factors, such as temperature and humidity, to increase output by employing traditional automation techniques. The advancement of science has resulted in the utilization of mathematical models to understand the behavior of data by analyzing its variability. The objective of this project is to validate a method for controlling temperature and humidity in controlled experimental environments using artificial intelligence and Neutrosophy. The transfer functions obtained from temperature and humidity readings gathered via a SCADA system are utilized. Neutrosophic numbers are used to adjust the temperature and humidity values based on the experimental conditions of the greenhouse, indicating the optimal, important, and sensitive ranges. The control system being investigated employs NARMA-L2 neural networks that belong to the multilayer perception category. This facilitates efficient system administration and showcases outstanding performance in simulations conducted across several temperature and humidity scenarios. The observed errors consistently remain below 5% and any instances of exceeding this threshold are insignificant.

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María F. Molina mail -

Secundino Marrero mail

link https://doi.org/10.54216/FPA.160118

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Integrative Analysis of Diesel-Kerosene Blends on Engine Performance and Emissions

Combines diesel fuel with cheese to enhance engine efficiency and mitigate detrimental pollutants. Analyzed using a meticulous approach derived from the ISO 8178 standard, combinations containing different ratios of cheese are investigated. The aim of the research is to conduct a multivariate analysis that provides insights into the rheology of diesel and kerosene mixes, thereby enhancing our understanding of the fuel's properties and performance. The researchers conducted experimental trials utilizing diesel blends with varying proportions of cheese, including 5%, 10%, 15%, 20%, 25%, and 30%. A descriptive and multivariate analysis was conducted to measure parameters such as opacity, NOx, CO, HC emissions, and fuel efficiency under different load circumstances. The study identified key elements that determine gasoline characteristics and emissions, including density, viscosity, calorific value, and sulfur content. It emphasized that the addition of cheese had a significant impact on these crucial factors. Two separate categories were created based on the composition of fuel. Blends containing a lower amount of cheesesine (up to 20%) formed a cluster that exhibited an ideal equilibrium in terms of both performance and emissions. The groupings of factors are interconnected, with substantial correlations shown between the physical qualities of the fuel and emissions. This highlights the direct impact of the fuel composition on the engine's environmental performance.

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Cristian I. Eugenio-Pilliza mail -

Francisco J. Montalvo-Marquez mail -

Ángel Portilla mail

link https://doi.org/10.54216/FPA.160119

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Fusion: Practice and Applications | 2024

Fusion Data Management and Modeling Techniques in Power Quality Compensation Using SAPF

The development of transportation today encompasses a broad range of technological applications that occasionally present new challenges arising from difficulties that require solutions. The article analyzes the difficulties in electric trains concerning the compensation of electric power quality in a traction system using a parallel active power filter (SAPF). From the literature review of several studies, the test distribution system in a distribution network for an electric train system is analyzed, with a variable load and harmonic content. The estimation and control technique used in the SAPF to compensate for the harmonic content and reduce the reactive power at the output of a traction substation is described. A data fusion management strategy is employed in the analyses, demonstrating the system's effectiveness.

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Jessica N. Castillo mail -

Guido G. Carrillo mail -

Luigi O. Freire mail -

Javier Culqui mail

link https://doi.org/10.54216/FPA.160201

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Integrating Machine Learning Models for Enhanced Soil Organic Carbon Estimation: A Multi-Model Fusion Approach

Machine learning approaches are utilized to identify patterns in behavior and generate predictions across various applications. The objective of this work is to create a highly efficient model for accurately measuring and analyzing the levels of soil organic carbon (SOC) in the Chambo river sub-basin, which is situated in the province of Chimborazo. The model evaluation entails the application of diverse machine learning algorithms and approaches to determine the most efficient regression model. Regression models are improved using techniques such as Artificial Neural Networks, Support Vector Machines, and Decision Trees. The Resilient Backpropagation method yields the most precise model, as it accounts for a greater proportion of the variability in SOC content for the test data. This aligns with the findings from the training data, demonstrating a relatively low mean absolute error and a processing time that is approximately 400 times faster than that of the Multilayer Perceptron algorithm. The evaluation of estimating models is an objective procedure that considers not only the findings and precise metrics derived from the model's design, but also other relevant elements. The effectiveness of the Random Forest approach, specifically the quantile regression forests technique, has been established for estimating SOC contents in the Chambo river sub-basin data.

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Bryan Barragán-Pazmiño mail -

Angel Ordóñez Echeverría mail -

Magdy Echeverría Guadalupe mail -

Theofilos Toulkeridis mail

link https://doi.org/10.54216/FPA.160202

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Fusion of Forensic Analysis of Mobile Devices: Integrating Multi-Criteria Decision Methods and Case Study Insights

This study employed a Multi-Criteria Decision Analysis (MCDM) approach, utilizing the DEMATEL and TOPSIS methodologies, to assess the effectiveness of forensic tools designed for mobile devices, with a specific emphasis on Android and iOS platforms. The investigation evaluated technologies used for collecting, retrieving, and validating data in the Cyber Forensic Field Triage paradigm, with a focus on rapidly identifying and interpreting digital evidence. The study incorporated several factors and expert preferences, concluding that the Android Triage and Andriller tools were the most efficient.

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Jorge B. Rubio Peñaherrera mail -

Kevin Mauricio T. Diaz mail -

Adam Marks mail

link https://doi.org/10.54216/FPA.160203

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

Numerical Solutions for Fractional Multi-Group Neutron Diffusion System of Equations

This paper addresses fractional-order versions of multi-group neutron diffusion systems of equations, focusing on two numerical solutions. First, it employs the Laplace transform method to solve the classical version of multi-group neutron diffusion equations. Subsequently, it transforms these equations into their corresponding fractional-order versions using the Caputo differentiator. To handle the resultant fractional-order system, a novel approach is introduced to reduce it from a system of 2α-order to a system of α-order. This converted system is then solved using the so-called Modified Fractional Euler Method (MFEM). As far as we know, this is the first time that such numerical schemes have been used to deal with the systems at hand. The paper covers the multi-group neutron diffusion equations in spherical, cylindrical, and slab reactors, all solved and converted for verification purposes.

groups

Mohammed Shqair mail -

Iqbal M. Batiha mail -

Mohammed H. E. Abu-Sei’leek mail -

Shameseddin Alshorm mail -

Amira Abdelnebi mail -

Iqbal H. Jebril mail -

S. A. Abd El-Azeem mail

link https://doi.org/10.54216/IJNS.240401

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

Harnessing Dimensionality Reduction with Neutrosophic Net-RBF Neural Networks for Financial Distress Prediction

Neutrosophy is the study of neutralities and extends the discussion of the truth of opinions. Neutrosophic logic may be employed in any domain, for providing the solution for the ambiguity problems. Several real-time data experience problems such as indeterminacy, incompleteness, and inconsistency. A fuzzy set provides an uncertain solution, and intuitionistic fuzzy set handles incomplete data, but both fail to manage uncertain data. Before bankruptcy, financial distress is the early stage. Bankruptcies caused by financial problems can be seen in the financial statement of the company. The capability to predict financial problems became a crucial area of research since it provides earlier warning for the company. Moreover, predicting financial problems is advantageous for creditors and investors. In this article, we develop a new Dimensionality Reduction with Neutrosophic Net-RBF Neural Networks (DR-NSRBFNN) technique for FCP process. The DR-NSRBFNN technique concentrates on the predictive modelling of financial distress. In the DR-NSRBFNN technique, two major stages are involved. In the preliminary phase, the high dimensionality features can be reduced by the use of arithmetic optimization algorithm (AOA). In the second phase, the DR-NSRBFNN technique applies the NSRBFNN model to predict financial distress. The performance evaluation of the DR-NSRBFNN technique can be examined using distinct aspects. The widespread study stated the improved performance of the DR-NSRBFNN technique compared to other systems

groups

Tawfiq Hasanin mail

link https://doi.org/10.54216/IJNS.240402

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

Enhancing Inventory Management through Advanced Technologies and Mathematical Methods: Utilizing Neutrosophic Fuzzy Logic

Optimal inventory management is one of the most critical components for companies to thrive in the competitive market while meeting their customers’ demands, reducing costs, and developing their operations. In this paper, the utilization of different technologies and instruments ranging from the most modern ones to mathematical ones was analyzed to demonstrate how the system can function successfully. It is expected that Neutrosophic fuzzy logic is one of the most complicated approaches that allow for proper uncertainty management, forecasting, and inventory control improvements. Fundamentally, the process could be that much more insightful due to the availability of mathematical modelling and on-the-go support systems. Through the use of dynamic programming with the help of Python tools to process these models, Full optimization under fuzzy demand is possible to achieve. Therefore, one could conclude that companies have many opportunities to develop their operations, reduce costs, and keep their customers happy even in a highly dynamic and uncertain business environment.

groups

C. Balakrishna Moorthy mail -

D. Rajani mail -

A. P. Pushpalatha mail -

S. Ramya mail -

A. Selvaraj mail -

Mohit Tiwari mail

link https://doi.org/10.54216/IJNS.240403

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new