Towards retention in airline industry using neutrosophic DEMATEL method: Does social media marketing activities affect passengers’ retention

Reneh; Ashraf

doi:https://doi.org/10.54216/IJNS.210214

Journal of Artificial Intelligence and Metaheuristics | 2023

A Data-Driven Approach for Obesity Classification using Machine Learning

Obesity is a global health concern with significant impacts on individuals and society. Accurate and timely classification of obesity levels can help in the development of personalized interventions and targeted healthcare strategies. In this paper, we propose a data-driven approach for obesity classification utilizing machine learning techniques. Our study leverages a comprehensive dataset consisting of anthropometric measurements, lifestyle factors, and demographic information of a large cohort of individuals. We explore the effectiveness of various machine learning algorithms, including decision trees, support vector machines, and neural networks, for obesity classification. Feature selection and preprocessing techniques are employed to enhance the performance of the models. Through extensive experimentation and cross-validation, we evaluate the predictive accuracy, sensitivity, specificity, and overall performance of the developed classifiers. Our results demonstrate the efficacy of our data-driven approach, achieving high accuracy in obesity classification. Furthermore, we conduct a comparative analysis of the different algorithms to identify the most suitable model for this task. The proposed framework has the potential to assist healthcare professionals in identifying and classifying obesity levels accurately, contributing to the development of personalized interventions and improving public health outcomes.

groups

Nima Khodadadi mail -

Mohamed Saber mail -

Mostafa Abotaleb mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

A Deep Learning Approach Visual Recognition of Bird Species in Noisy Environments

In this paper, we propose a deep learning approach for visual recognition of bird species in noisy environments. Bird species recognition has been a challenging task due to the high variation in bird appearances and the presence of noise and clutter in natural environments. Our approach utilizes a deep convolutional neural network (CNN) to learn discriminative features from bird images and classify them into different species. We also incorporate data augmentation techniques to increase the diversity of the training data and improve the robustness of the model. To address the issue of noisy environments, we introduce a novel noise-robust loss function that penalizes the model for incorrect predictions caused by noise. We evaluate our approach on a dataset of bird images collected from diverse environments and compare it with state-of-the-art methods. Our results demonstrate that our approach achieves superior performance in both clean and noisy environments, highlighting the effectiveness of our noise-robust loss function. Our approach has the potential to be applied in real-world scenarios for bird species recognition and conservation.

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P. K. Duta mail -

Nader Behdad mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

Automated Detection and Segmentation of COVID-19 Infection using Machine Learning

The accurate and timely segmentation of COVID-19 infection areas from CT scans is crucial for effective diagnosis and treatment planning. In this paper, we propose an automated approach utilizing machine learning techniques for COVID-19 infection segmentation. The proposed framework utilizes a convolutional neural network (CNN) architecture to extract informative features from CT scan images. These features are then fed into a segmentation model, which employs a combination of U-Net and attention mechanisms for accurate delineation of infection regions. To enhance the model's performance, we employ a transfer learning strategy by pretraining the CNN on a large dataset of general medical images. To evaluate the effectiveness of our approach, we conducted experiments on a diverse dataset consisting of CT scans from COVID-19 patients. The results demonstrate the superiority of our method in accurately segmenting infection areas, achieving an average Dice coefficient of 0.92 and a Jaccard index of 0.88. The proposed automated segmentation method offers significant potential for aiding radiologists and clinicians in identifying COVID-19 infection regions from CT scans rapidly and accurately. It can contribute to improved diagnosis, patient management, and treatment planning in the fight against the ongoing pandemic.

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S. K. Towfek mail -

Ehsaneh khodadadi mail -

Fatma M. Talaat mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2023

Neutrosophic Nonlinear Models

Nonlinear programming is an important and essential part of operations research and is more comprehensive than linear programming, its applications have spread in all branches of science, engineering, physics, chemistry, management, economic and military fields, etc.  Nonlinear programming can also be used in  forecasting, estimation, applied statistics and determining the costs resulting from the production, purchase and storage of goods, the mathematical model is a nonlinear model if any of the vehicles of the target or constraints are nonlinear statements and may be nonlinear statements In both, after neutrosophic science has achieved a  great development in most fields of science, it was necessary  to reformulate  nonlinear models according to its concepts, we present in this research a study of neutrosophic nonlinear dependencies and neutrosophic nonlinear models as a prelude to future research through which we study some methods of solving neutrosophic nonlinear models. 

groups

Maissam Jdid mail

link https://doi.org/10.54216/PAMDA.020104

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2023

Blockchain based Certificate Validation

Certificate management is a tedious task for any university or any other organization. These schemes impose problems in Public Key Infrastructure (PKI). Checking the validity and preserving the security of these documents is of utmost importance. In this work, we have devised a blockchain-based solution for preventing malfunctioning in certificate validation which is an important step for any university. Each certificate is uploaded in its hash format and is stored using blockchain. The hashes are stored in unique transactions in nodes, which are deployed on a private network. Using the SHA-256 hashing algorithm, the certificates are uploaded into the system and can be viewed by anyone with the right credentials. Due to the usage of blockchain technology, the certificates are stored in a decentralized manner, which ensures there is no central point of failure. Any changes in the uploaded document need to be validated by other nodes. This paper also improvises that when certificate uploading is required new nodes are added, instead of modifying the past blocks. This work provides a very user-friendly app where any user with the right credentials can upload documents. In this work, digitized documents are stored using Inter Planetary File System (IPFS) which is distributed method of storage. Our theoretical analysis proves that it is a user-friendly application with the security of blockchain technology in partnership with IPFS. Only the issuer can upload documents and others can only view them. Using our proposed solution, problem of malicious certificates can be tackled with E-certification. The proposed method solves all the issues of storing, validating, and sharing documents. Chaotic Map technique is used in hash generation which is quite simple to implement. The proposed approach Chaotic Key based Certificate validation (CK-Cert) provides a hassle-free solution for certificate managements since it better manages the block size as compared to previously proposed techniques (PBCert and CertChain) as discussed with the help of graphs.

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Rachna Jain mail -

Geetika Dhand mail -

Kavita Sheoran mail -

Shaily Malik mail -

Nishtha Jatana mail

link https://doi.org/10.54216/FPA.120204

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023

Neutrosophic Framework to Analysis Factors in Leadership and Policy Undergraduate Students: A Case Study

Based on evidence choices on the transition from in-person to digital delivery of leadership and policy classes to learners in the aftermath of the COVID-19 pandemic are crucial given the significance of establishing leadership abilities throughout school. The purpose of this brief study is to locate useful instructional methods for analyzing factors of undergraduates' digital leadership and policy classes. This paper used the concept of multi-criteria decision-making (MCDM) due to various factors. The concept of a neutrosophic set is used to deal with uncertain data. Effective and adaptable tools, MCDM may be used for a wide range of environmental issues. The essay suggests an innovative approach to the problem of factors of leadership and policy in education. The neutrosophic step-wise weight assessment ratio analysis (SWARA) approach is used to analyze the factors of leadership and policy in education (FLPE).

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Nouran Ajabnoor mail

link https://doi.org/10.54216/IJNS.210309

Volume & Issue

Vol. Volume 21 / Iss. Issue 3

Details open_in_new

International Journal of BIM and Engineering Science | 2023

Quality Assurance of Construction Design and Contractual Phases in Syria Within BIM Environment: A Case study

Globally, the Quality Management System Research in the construction sector has not received sufficient attention compared to the industrial sector. The modern construction industry is known as complex due to magnitude of the projects on one hand, and the diversity of the works included in the project, on the other hand. The establishment of projects is no longer limited to the implementation of a few types of work or activities in the project. However, it is increasing in response to the overall technical and urban development of life, and to the increasing requirements of the beneficiaries of these projects. Projects nowadays are integrated and complex systems of different works and contracts, which are connected with each other to perform the overall function of the project, which, in return, has evolved as well as its qualitative content. This research aims to improve the quality of design, and contracting phase of the State Company for Engineering Studies in Damascus’s projects, by examining the current reality of practices and methods in quality management during the designing stages and pointing out its weaknesses. Determining the factors affecting the quality of the contractual phase of the company, through a survey published online. Twenty-two engineers working in the company have responded. It was estimated that the company's realities and the extent to which the employees understood the importance of quality.  Through this research, a system of procedures was proposed to ensure the quality of design and contractual phases with ISO 9001: 2015 Integrate it with building information modelling technology that addresses or overcomes current problems. Using BIM provides an effective means to increase the overall quality of the company's projects.

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Dalia Yasser Rudwan mail -

Rana Maya mail -

Natalija Lepkova mail

link https://doi.org/10.54216/IJBES.060204

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023

The Integrated Novel Framework: Linguistic Variables in Pythagorean Neutrosophic Set with DEMATEL for Enhanced Decision Support

This study proposes a methodology that integrates the Pythagorean Neutrosophic Set (PNS) with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. This integration is intended to effectively handle the challenges of uncertainty and linguistic vagueness that are commonly encountered in multi-criteria decision-making scenarios. The PNS-DEMATEL framework comprises eight distinct steps, wherein the fundamental divergence lies in the creation of a linguistic variable within the PNS-DEMATEL framework. The linguistic variable has been formulated utilizing the PNS methodology, thereby enabling a more all-encompassing depiction of the viewpoints of experts. The application of the 7-point linguistic scale is utilized to acquire evaluations from experts, resulting in heightened precision and discernment. The method that has been put forth aims to improve the representation and management of linguistic variables, thereby enhancing the usability and validity of the model. The verification of the linguistic variable's validity is achieved by ensuring that the conditions outlined for the PNS are met. The integration of PNS with DEMATEL can facilitate the exploration of causal relationships in the barriers to halal certification. This approach can provide decision-makers with a more comprehensive and accurate representation of their opinions and judgements, leading to improved effectiveness in decision-making across various application domains.

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Jamiatun Nadwa Ismail mail -

Zahari Rodzi mail -

Faisal Al-Sharqi mail -

Hazwani Hashim mail -

Nor Hashimah Sulaiman mail

link https://doi.org/10.54216/IJNS.210212

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023

Neutrosophic-based Machine Learning Techniques in the Context of Supply Chain Management: A Survey

Supply Chain Management (SCM) plays a critical role in the success of any business organization. Individuals involved in business activities often have to make decisions regarding different aspects of the supply chain, including planning, procurement, production, inventory management, transportation, distribution, and customer relationship management. The combination of neutrosophic logic and machine learning has gained significant attention in the field of SCM as a means to tackle uncertainties and improve decision-making. This paper highlights the potential benefits and applications of integrating neutrosophic reasoning and machine learning in SCM. Neutrosophic reasoning provides a framework for handling imprecise and uncertain information, while machine learning techniques offer powerful tools for data analysis, pattern recognition, and predictive modeling. By leveraging machine learning algorithms within the context of neutrosophic logic, SCM practitioners can enhance demand forecasting accuracy, optimize inventory management, improve transportation and routing decisions, and strengthen supply chain collaboration. The integration of neutrosophic logic and machine learning enables the handling of complex supply chain data, accommodates dynamic and uncertain environments, and supports proactive decision-making. Furthermore, the combination of these approaches can contribute to improved supply chain resilience, sustainability, and customer satisfaction. This paper applied the neutrosophic AHP method as a feature section to select the highest importance criteria as an input to machine learning. Then we applied two machine learning models named random forest and decision. The results show the random forest has the highest accuracy followed by a decision tree.

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Esraa Kamal mail -

Amal F. Abdel-Gawad mail -

Shereen Zaki mail

link https://doi.org/10.54216/IJNS.210213

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023

Towards retention in airline industry using neutrosophic DEMATEL method: Does social media marketing activities affect passengers’ retention

This study examines the effect of social media marketing activities on passenger retention through brand image in the airline industry in the case of Fly Emirates. The survey included a sample of his 758 passengers traveling to Dubai from destinations around the world. The questionnaire was randomly distributed, and data was collected and returned from his 455 passengers. The purpose of this study is to examine the effect of social media marketing activities (trendiness, entertainment, interaction, word of mouth, and customization) on passenger retention through mediated brand image. This study applies structural equation modeling (PLS-SEM) to investigate the relationship among the constructs in the proposed model. These aspects are modeled using the neutrosophic decision-making trial and evaluation laboratory (DEMATEL) approach. The DEMATEL models the fuzziness related to domain specialists generating judgments inside the DEMATEL as single-valued neutrosophic numbers, while also handling the causal linkages among elements of social media marketing activities. The study concludes that all dimensions of social media marketing activities (trendiness, entertainment, interaction, word of mouth, and customization) have a positive effect on brand image. The findings also confirmed that brand image plays a mediation role between social media and passenger retention, with a positive brand image being associated with increased passenger retention. The study found that without effective social media marketing, Fly Emirates loses passengers, and brand image is a key driver of passenger retention. The study recommends that Fly Emirates, looking to improve passenger retention, invest heavily in attractive social media marketing activities in the marketing innovation segment.

groups

Reneh Abokhoza mail -

Ashraf Jahmani mail

link https://doi.org/10.54216/IJNS.210214

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new