Soil Organic Transformations in Urban Agricultural Systems: Application of a Neutrosophic Multicriteria Approach for Comprehensive Evaluation

Paolo Chasi

doi:https://doi.org/10.54216/IJNS.250130

International Journal of Advances in Applied Computational Intelligence | 2024

Computational Intelligence Methodology based on Neutrosophic Set with Multi-Criteria Decision Making for Evaluating Natural Gas Automobiles

This study presents a comprehensive evaluation of natural gas automobiles, focusing on their performance, environmental impact, economic viability, and potential as an alternative fuel for transportation. Natural gas vehicles (NGVs) have gained attention as an alternative to conventional gasoline or diesel vehicles due to their lower emissions profile and potential for reducing greenhouse gas emissions. The assessment encompasses a comparative analysis of NGVs against traditional internal combustion engine vehicles, evaluating factors such as vehicle efficiency, fuel availability, infrastructure, emissions, and cost-effectiveness. Findings reveal that NGVs exhibit lower emissions of pollutants like nitrogen oxides and particulate matter than their gasoline or diesel counterparts. However, challenges persist regarding limited refueling infrastructure, reduced driving range, and upfront vehicle conversion or purchase costs. Economic evaluations highlight the potential cost savings associated with natural gas as a fuel, particularly in regions with favorable pricing and infrastructure. Despite these benefits, scalability and widespread adoption of NGVs face barriers related to infrastructure development, technological advancements, and market incentives. This evaluation provides insights into the opportunities and challenges of natural gas automobiles, emphasizing the need for a balanced approach encompassing technological innovation, infrastructure investment, and supportive policies to unlock their full potential as a viable alternative in the transportation sector. We used multi-criteria decision-making (MCDM) to deal with various criteria of natural gas automobiles. The Range of Value Technique (ROV) method ranks the alternatives. The ROV is integrated with the neutrosophic set to deal with uncertainty information. The neutrosophic set is extension of fuzzy set to overcome the vague and incomplete information.  The sensitivity analysis is conducted to check the stability of the results.  

groups

Warshine Barry mail -

Josef Al Jumayel mail

link https://doi.org/10.54216/IJAACI.050102

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Farmland Fertility Optimization with Deep Learning based COVID-19 Detection for Healthcare Decision Making

Machine Learning (ML) and Artificial Intelligence (AI) are being employed in the fight against COVID19 by supporting the analysis of medical images, like X-rays and CT scans, to find characteristic paradigms linked with the virus. AI methods can evaluate huge volumes of data, which includes imaging data and patient medical records, for enriching the speed and precision of COVID19 diagnosis. Also, the use of ML and AI in medical imaging can aid in detecting new variants of viruses and forecasting their spread. The integration of ML and AI in COVID19 healthcare has greater potential to enhance the efficiency and accuracy of diagnoses along with that informing public health decision-making. Thus, the study proposes a Farmland Fertility Optimization Algorithm with Deep Learning based Healthcare Decision Making (FFOADL-HDM) approach for the detection of COVID19. The presented FFOADL-HDM approach emphasises the identification and classification of COVID19 using a CT scan. To achieve this, the FFOADL-HDM method exploits a modified SqueezeNet model for the generation of feature vector. Also, the hyperparameters of the modified SqueezeNet model can be selected by the use of FFOA. At last, the COVID-19 detection procedure is executed by the use of Adamax optimizer with (CFNN). The stimulation analysis of the FFOADL-HDM algorithm is studied on the SARS-CoV-2 CT image dataset from the Kaggle repository. The results highlighted the improved detection rate of the FFOADL-HDM technique over recent state of art approaches  

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Ahmed Hatip mail -

Necati Olgun mail -

Sandy Montajab Hazzouri mail

link https://doi.org/10.54216/IJAACI.050103

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Intelligent Data Processing and Mining of Histopathological Images using Improved Tunicate Swarm Algorithm with Deep Learning

Intelligent data processing and mining of histopathological images involve the application of advanced techniques and algorithms to analyze and extract meaningful information from digital pathology images. Osteosarcoma is a general malignant bone cancer generally established in teenagers and children. Manual diagnoses of osteosarcoma is a laborious task and needs skilled professionals. The mortality rate can be minimalized only if it is identified on time. Automatic detection systems and new technologies were utilized to classify and analyze medical images that, minimalize the dependency on specialists and result in fast processing. Recently, a lot of Computer-Aided Diagnosis (CAD) systems were proposed by research workers to diagnose and segment osteosarcoma from medical images. Deep learning (DL) algorithms are employed for the automated recognition and identification of osteosarcoma on histopathological images (HSI). The study proposes an Improved Tunicate Swarm Algorithm with Deep Learning for Osteosarcoma Detection and Classification (ITSA-DLODC) approach on pathological imageries. The proposed ITSA-DLODC method mainly enhances the recognition and classification of osteosarcoma on HSI. To attain this, the presented ITSA-DLODC method performs feature extraction using ShuffleNet convolutional neural network model. Besides, the ITSA-based hyperparameter optimizer is exploited to finetune the hyperparameters of the ShuffleNet model. Moreover, the salp swarm algorithm (SSA) with convolutional autoencoder (CAE) approach was utilized for the recognition and identification of osteosarcoma. A wide range of analyses can be applied to exemplify the higher performance of the ITSA-DLODC methodology. The simulation study demonstrated the development of the ITSA-DLODC methodology over other present models  

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Rama Asad Nadweh mail -

Arwa Hajjari mail

link https://doi.org/10.54216/IJAACI.050104

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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International Journal of Advances in Applied Computational Intelligence | 2024

Design of Long Short Term Memory Based Deep Learning Model for Customer Churn Prediction in Business Intelligence

Innovations in business intelligence are crucial in the digital era to staying popular and competitive across the increasing business trends. Businesses have started scrutinizing the next level of data analytics and business intelligence solutions. Customer Churn Prediction (CCP), on the other hand, a crucial for making business decisions, which correctly recognizes the churn customers and acts appropriately for customer retention. Customer churn is an unavoidable consequence when the user is not satisfied with the company’s service for a longer period. Service unsubscription by the user does not emerge unexpectedly; instead, it comes from the customer as a vigorous act owing to its accumulation of long-term disappointment. Thus, there is a need for the service provider to find and address their challenges related to customer satisfaction and service for retaining irate customers. The possibilities to predict customer churn have dramatically increased with the advances in artificial intelligence (AI) and machine learning (ML) algorithms. Therefore, this study introduces an Optimal Long Short Term Memory Based Customer Churn Prediction for Business Intelligence (OLSTM-CCPBI) method. The proposed OLSTM-CCPBI method incorporates many innovative components, such as Min-Max scaling for normalization, LSTM networks for temporal sequence modelling, and Adam optimization for hyperparameter tuning. The OLSTM-CCPBI method effectively captures temporal dependency in sequential customer data by leveraging the dynamic nature of the LSTM network, which enables correct prediction of churn events. Through detailed investigations on real-time customer churn datasets, OLSTM-CCPBI achieves better predictive capabilities than classical approaches, showcasing its promising solution to aid businesses in preemptively addressing customer attrition and considerably enhancing churn prediction accuracy.

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Zahraa Hasan mail -

Dasha Stablichenkova mail

link https://doi.org/10.54216/IJAACI.050105

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Leveraging Stochastic Gradient Descent with Deep Learning Model for Financial Distress Prediction

Stock is a financial product considered by flexible trading, high risk, and high return that can preferred by several investors. Investors may get an abundance of returns through the accurate prediction of stock price trends. Nevertheless, the stock price can be influenced by certain factors including market conditions, companies’ managerial decisions, macroeconomic situation, and investors’ preferences for major economic and social events. Econometric and Statistical models are widely utilized in classical stock price prediction; however, these techniques could not handle the complex and dynamic environments of the stock market. Researchers have begun using deep learning (DL) and machine learning (ML) to estimate stock fluctuations and prices with the rapid evolution of artificial intelligence (AI), serving investors to define investment strategies to increase returns and decrease risk. Therefore, this manuscript presents a new dung beetle optimization with deep learning based stock price prediction (DBODL-SPP) methodology. The purpose of the DBODL-SPP algorithm is to predict the rise or fall of stock prices using the optimal DL model. In the DBODL-SPP technique, the min-max scalar can be deployed for pre-processing the input data. Besides, the DBODL-SPP approach applies the DBO algorithm for electing an optimal subset of features. The DBODL-SPP technique makes use of a multi-head attention long short-term memory (MHA-LSTM) model for the stock price prediction. Finally, by using the equilibrium optimizer (EO) algorithm, the parameter tuning of the MHA-LSTM algorithm can be carried out. A detailed set of experimentations has been applied to evaluate the enriched performance of the DBODL-SPP technique. The simulation values emphasized that the DBODL-SPP algorithm achieves better results than other techniques for stock price prediction

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Barbara Charchekhandra mail -

Rashel Abu Hakmeh mail -

Murat Ozcek mail

link https://doi.org/10.54216/IJAACI.050201

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

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International Journal of Advances in Applied Computational Intelligence | 2024

Whale Optimization Algorithm with Deep Learning based Indoor Monitoring of Elderly and Disabled People

Social isolation and loneliness are subjective measures related to the feeling of distress and discomfort for disabled and elderly people. Currently, computing platform offers a smart healthcare observing technique for earlier fall detection. Internet of Things (IoT) based health system had a crucial role in the healthcare service and assists in improving data processing and its prediction. Transmitting data or reports takes more energy and time, as well as causes energy issues and higher latency. These study concentrations on the development of Whale Optimization Algorithm with Deep Learning based Indoor Monitoring System (WOADL-IMS) for Elderly and Disabled People. The presented WOADL-IMS system purposes to identify the presence of indoor activity by elderly people. In the presented WOADL-IMS technique, NASNetMobile model is applied to produce feature vectors. In addition, the WOADL-IMS technique uses WOA based hyperparameter selection approach. Finally, triplet neural network (TNN) model can be employed for automated classification and recognition of indoor activity. The simulation result of the WOADL-IMS approach can be examined on indoor activity dataset. The outcomes of the experimentation highlighted that the WOADL-IMS technique reaches better results than other recent approaches  

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Taher Ahmed Jubbori mail -

Ahmad Khaldi mail -

Karla Zayood mail

link https://doi.org/10.54216/IJAACI.050202

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

A New Deneutrosophication Method Proposal for Use in Delphi Methods: Application in Ancestral Knowledge Analysis

This study explores the rich cultural heritage of indigenous peoples and communities, whose traditions and adaptability have intrigued scholars interested in understanding their relevance in modern times. The Neutrosophic Delphi Method emerges as a vital tool in this research, offering a dynamic and versatile approach to address the inherent complexity of indigenous activities. By investigating the uncertainty and ambiguity in decision-making, this method enables a thorough examination of cultural practices. The interdisciplinary methodology employed focuses on the interaction between traditional and modern aspects, examining the main activities that define the daily lives of indigenous communities. The use of the Neutrosophic Delphi Method is highlighted for its ability to handle diverse perspectives and complex data, and the deneutrosophication process to improve precision and clarity in the findings. This technique ensures an accurate and harmonized representation of indigenous knowledge with modern scientific research. This effort seeks not only to enhance the academic legacy but also to foster international dialogue, promoting the recognition and appreciation of cultural diversity. By empowering indigenous populations to contribute to the generation of knowledge about their experiences, the study advocates for a more inclusive and equitable approach in scientific inquiry, acknowledging the invaluable contributions of indigenous communities to the cultural richness of our world.

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Edwin Fabián Cerda Andino mail -

Gabriela Beatriz Arias Palma mail -

Sanjar Mirzaliev mail

link https://doi.org/10.54216/IJNS.250129

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Neutrosophic Approach to Increasing Production in Small Guinea Pig Breeding Systems: Exploring Tree Soft Set

The article examines the neutrosophic approach as an innovative tool to optimize production in small guinea pig farming systems. Through the exploration of bipolar sets and interval values, the application of this methodology in improving breeding processes is investigated, thus identifying areas of improvement and opportunities for economic and sustainable growth in the sector. The research highlights the importance of considering the uncertainty and imprecision inherent in these systems, proposing a flexible and adaptive framework that allows informed and strategic decision making to increase productivity and profitability. Likewise, the study highlights the need for a holistic and multidisciplinary understanding of the challenges and opportunities in guinea pig farming, recognizing the complexity of the social, economic, and environmental factors involved. Through an interdisciplinary approach, we seek to integrate traditional knowledge and practices with innovative approaches, thus promoting sustainability and the well-being of both producers and animals. Ultimately, this article offers a comprehensive and dynamic perspective on how the neutrosophic approach can significantly contribute to the development and optimization of guinea pig farming systems, thereby driving progress and prosperity in the agricultural sector.

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Luis A. Chicaiza Sánchez mail -

Patricia M. Andrade Aulestia mail -

César R. Delgado Acurio mail -

Rafael A. Garzón Jarrín mail -

Xavier C. Quishpe Mendoza mail

link https://doi.org/10.54216/IJNS.250132

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

An Innovative Approach to Financial Distress Prediction Using Relative Weighted Neutrosophic Valued Distances

The financial constraints of companies listed jeopardize the interests of employees and internal managers but also carries significant threats to outer investor and other stakeholders. Thus, there is need to create an effective financial distress predictive system.  The two most pressing issues in finance are assessing credit risk and predicting bankruptcies. Thus, credit scoring and financial distress prediction remain crucial areas of research in the financial industry. Previous research has aimed at the design of ML and statistical approaches to predict the financial distress of the company. Neutrosophic set may be utilized, which is a generality of classical, fuzzy, and intuitionistic fuzzy sets (IFS). They establish a foundation for addressing inconsistency, indeterminacy, and uncertainty associated with real-world challenges. This study presents an Innovative Approach to Financial Distress Prediction using Relative Weighted Neutrosophic Valued Distances (IAFDP-RWNVD) technique. The IAFDP-RWNVD technique intends to estimate the occurrence of financial distress in any firm or organization. In the IAFDP-RWNVD technique, two major processes are comprised. At the primary stage, the IAFDP-RWNVD technique applies RWNVD technique for the identification of financial distress. In the second stage, the IAFDP-RWNVD technique designs fish swarm algorithm (FSA) for finetuning the RWNVD model. The experimental outcomes of the IAFDP-RWNVD method is investigated using distinct aspects. The experimentation outcome shows the improvements of the IAFDP-RWNVD technique.

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Ilyоs Abdullayev mail -

Eduard Osadchy mail -

Natalya Shcherbakova mail -

Irina Kosorukova mail

link https://doi.org/10.54216/IJNS.250133

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Soil Organic Transformations in Urban Agricultural Systems: Application of a Neutrosophic Multicriteria Approach for Comprehensive Evaluation

This study highlights the importance of urban agriculture in ensuring food security and promoting sustainability in urban areas, using a neutrosophic multi-criteria approach to evaluate the impact of biostimulants and organic additives on soil quality, plant growth, and crop yields. The research demonstrates that biofertilizers such as Chromococcus and Azotobacter significantly improve nutrient availability and plant health, resulting in robust and high-quality harvests, while mineral additives like zeolites enhance soil fertility and moisture retention. Three scenarios were analyzed using neutrosophic logic to handle the inherent uncertainty in urban agricultural systems: the first scenario shows exceptional plant growth and yield with high sustainability (valued as "Very Very High" according to neutrosophic logic), the second scenario highlights challenges in vegetative growth and sustainability (valued as "Low"), and the third scenario combines good plant growth with high sustainability and significant contributions to climate change mitigation (valued as "Medium High"). In summary, integrating organic amendments and biofertilizers in urban agriculture, evaluated through neutrosophic methods, is essential for creating resilient and productive agricultural systems, benefiting soil health, biodiversity, resource conservation, and local economies.

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Paolo Chasi Vizuete mail

link https://doi.org/10.54216/IJNS.250130

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new