Multi-Level Fusion for Facial Expression Recognition in Human Behavior Identification

Aqeel; Ibraheem H.; Sarah Ali; Ryam Ali; Noor

doi:https://doi.org/10.54216/FPA.100210

Journal of Artificial Intelligence and Metaheuristics | 2022

Weather Forecasting over Iraq Using Machine Learning

The weather generally comprises various factors, such as wind speed, precipitation, and rainfall. Environmental weather forecasting is a demanding task for researchers, and in recent years it has attracted much study attention. Our assessment considers a wide range of weather conditions across Iraq utilizing information gathered from NASA's estimate of the world's energy resources for the years 1981 to 2021. Therefore, the correct forecast of meteorological parameters is a difficult challenge due to their changing environmental conditions. Random forest, decision tree, and GBR algorithms are used for weather forecasting.  A comparison among used methods is performed and the RF is achieved the best results with accuracy, MAE, MSE, R2 of 92%, 0.5, 2.45, and 0.92, respectively.

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Israa Jasim Mohammed mail -

Bashar Talib Al-Nuaimi mail -

Ther Intisar Baker mail

link https://doi.org/10.54216/JAIM.020204

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2023

Impact of Mobile Applications on Customer Service for the Tourism Sector: A Systematic Review and Neutrosophic Dematel

This study aims to systematically review mobile applications and their impact on customer service in the tourism sector from 2017 to 2021. For this, the use of the DEMATEL method in its neutrosophic variant is proposed. The search strategy identified 257,399 articles from digital libraries such as Scopus, IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Microsoft Academic, EBSCOhost, ProQuest, ScienceDirect, and ARDI. Likewise, only 70 articles based on exclusion criteria were considered using the PRISMA Flowchart. The results of the systematic review have focused on recent studies of mobile applications and their impact on customer service in tourism and also provide a mapping of the extracted studies, metrics, trends, and validation methods to compare relevance to their settings and situations. The applicability and importance of multiple decision-making methods for solving complex problems were demonstrated. In addition, the effectiveness of using neutrosophy to reach valid conclusions when faced with real-life problems was manifested.

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Julio Oncebay-López mail -

Javier Gamboa-Cruzado mail -

Augusto Hidalgo Sánchez mail -

Violeta Benites Tirado mail

link https://doi.org/10.54216/IJNS.200411

Volume & Issue

Vol. Volume 20 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2023

Analysis of Neutrosophic Elements in the Determination of Bankruptcies in SMEs Using Machine Learning

Nowadays, Machine Learning techniques stand out, especially in the business sector, in predicting bankruptcies in small and medium-sized enterprises (SMEs). This reduces the probability of making bad investments when creating SMEs. Therefore, a systematic review of Machine Learning for predicting bankruptcies in SMEs was conducted to identify ideal articles. The search was conducted on Taylor & Francis Online, IEEE Xplore, ARDI, ScienceDirect, ACM Digital Library, Google Scholar, and ProQuest. As a result, information was collected from 84 definitive studies on determining bankruptcies in SMEs using Machine Learning. Therefore, this study aims to determine the state-of-the-art regarding determining bankruptcies in SMEs using Machine Learning. To obtain the results, the Saaty Neutrosophic AHP method was used to identify the most applied business sector and predict possible bankruptcy due to its broad nature of indeterminacy in that subset. The systematic review results have allowed for determining essential details regarding the state-of-the-art of determining bankruptcies in SMEs using Machine Learning.

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J. Ramón R. de Vega mail -

A. G. Ruiz Conejo mail -

Carlos C. Carranza mail -

Vladimir R. Cairo mail

link https://doi.org/10.54216/IJNS.200412

Volume & Issue

Vol. Volume 20 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2023

Systematic Review Using Neutrosophic Torgerson and Neutrosophic Vader to Determine the Impact of Mobile Applications in the Labor Integration of Disabled People

People with a disability are the most likely to end up unemployed. Knowing and finding new ways to integrate them into society is urgent in an increasingly dynamic and versatile world. The systematic Review of the literature (SRL) was developed covering the issue of labor integration of people with disabilities.. The main objective of the research was to determine the state of the art of research on Mobile Applications and their impact on the Labor Integration of People with Disabilities. The use of relationship coefficients between two neutrosophic numbers, through the Torgerson method, Allowed the evaluation of the applications by the experts. The results obtained highlight the importance of mobile technology in the process of ensuring that people with disabilities find the desired job, in addition, accessibility must be met for the correct development of the application for the various existing disabilities. By utilizing Neutro-VADER and Neutrosophy, the precision and efficiency of sentiment analysis are enhanced, especially when handling uncertain or unclear text and consulting with experts. By implementing a more intricate and refined sentiment analysis method, these tools can generate more practical and valuable perspectives regarding the sentiment of written or spoken language.

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Javier Gamboa Cruzado mail -

Julio Canales Parraga mail -

Santiago M. Benites mail -

María León Morales mail -

Liset S. Rodríguez-Baca mail

link https://doi.org/10.54216/IJNS.200312

Volume & Issue

Vol. Volume 20 / Iss. Issue 3

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International Journal of Neutrosophic Science | 2023

Practical Validation in a Neutrosophic Environment of the NEBS Methodology for the Optimization of SME Financing through Machine Learning

Micro and small enterprises (MSEs) have generated great opportunities for the growth of countries in the Latin American region. Unfortunately, as a result of the global crisis caused by the Sar-Cov-2, MSEs were severely affected. The main objective of this investigation is to validate in a practical way in a neutrosophic environment the use of a predictive Machine Learning technique that demonstrates the probability of the return on investment that a candidate investor will obtain with respect to a given business plan. With them it is expected that the investor can make the decision to finance a MSE, with the positive decision will close gaps in the growth of micro and small enterprises in Peru. The research is descriptive and predictive, with a research design of post-test only and control group. Neutrosophic TOPSIS was used as a technique. NEBS turns out to be efficient for the applicability of Machine Learning by obtaining statistical evidence to accept the hypotheses proposed for the finance sector in micro and small en-terprises in Peru. The results showed that the use of Machine Learning is validated, and its implementation increases the amount of financing obtained, decreases the evaluation time of requirements, reduces the number of complaints, and increases the number of formal sources used. Machine Learning research should be continued due to the complexity of this technology, which is con-stantly evolving.…

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Julia Juro-Barrios mail -

Javier Gamboa-Cruzado mail -

Alfonso Romero Baylon mail -

C. del Valle Jurado mail

link https://doi.org/10.54216/IJNS.200313

Volume & Issue

Vol. Volume 20 / Iss. Issue 3

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American Journal of Business and Operations Research | 2023

Business Process Management and Process Mining Technologies: The progress of a discipline

A wide variety of approaches, strategies, and tools for designing, implementing, managing, and analyzing functional business processes have emerged from studies in business process management (BPM). It is the goal of the emerging topic of research known as "process mining" (PM) to improve the analysis of business process models by gleaning actionable insights from massive quantities of event logs. The purpose of this study is to research business process management and process mining by surveying the state-of-the-art methods and tools in each area and highlighting the most recent developments. This study concludes with a discussion of BPM and PM, in which PM acts as a bridge between BPM and data science to enhance business processes (BPs).

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Samah Ibrahim Abdelaal mail

link https://doi.org/10.54216/AJBOR.100105

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

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Fusion: Practice and Applications | 2023

Accurate Recognition of Natural language Using Machine Learning and Feature Fusion Processing

To enhance the performance of Chinese language pronunciation evaluation and speech recognition systems, researchers are focusing on developing intelligent techniques for multilevel fusion processing of data, features, and decisions using deep learning-based computer-aided systems. With a combination of score level, rank level, and hybrid level fusion, as well as fusion optimization and fusion score improvement, these systems can effectively combine multiple models and sensors to improve the accuracy of information fusion. Additionally, intelligent systems for information fusion, including those used in robotics and decision-making, can benefit from techniques such as multimedia data fusion and machine learning for data fusion. Furthermore, optimization algorithms and fuzzy approaches can be applied to data fusion applications in cloud environments and e-systems, while spatial data fusion can be used to enhance the quality of image and feature data In this paper, a new approach has been presented to identify the tonal language in continuous speech. This study proposes the Machine learning-assisted automatic speech recognition framework (ML-ASRF) for Chinese character and language prediction. Our focus is on extracting highly robust features and combining various speech signal sequences of deep models. The experimental results demonstrated that the machine learning neural network recognition rate is considerably higher than that of the conventional speech recognition algorithm, which performs more accurate human-computer interaction and increases the efficiency of determining Chinese language pronunciation accuracy.

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Hayder Mahmood Salman mail -

Vian S. Al-Doori mail -

Hayder sharif mail -

Wasfi Hameed4 mail -

Rusul S. Bader mail

link https://doi.org/10.54216/FPA.100108

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

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Fusion: Practice and Applications | 2023

Fusion Processing Techniques and Bio-inspired Algorithm for E-Communication and Knowledge Transfer

This study suggests employing a dynamic natural and bio-inspired algorithm (DNBIA) to strengthen the confidentiality, integrity, and availability of digital information exchanges. You may think of the suggested method as a clever approach to Fusion Processing. Fusion Processing is the practice of combining and analyzing information from many databases. The efficiency and reaction time of e-communication systems may be increased by the use of the suggested DNBIA algorithm, which processes and integrates data from different sources. It is also possible to see the multi-objective optimization study presented in this work as a type of Fusion Processing. Cyberattacks and other types of computer security risks are the focus of this study, which seeks to optimize numerous objectives concurrently in order to eliminate them. The study can give a complete solution to improve the security of e-communication systems by combining different goals. The suggested method of enhancing e-communication and information transmission using DNBIA and multi-objective optimization analysis can be seen as a type of Fusion Processing. Efficient e-communication systems may be achieved by collecting data from a variety of sources and analyzing the results.

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Omar Saad Ahmed mail -

Fay Fadhil mail -

Laith H. Jasim Alzubaidi mail -

Riyadh Al-Obaidi mail

link https://doi.org/10.54216/FPA.100109

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

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Fusion: Practice and Applications | 2023

Text and Social Analytics with Fusion Techniques Enhance Hospital Health Management

the impact of social analytics on hospital health management: a multilevel fusion approach for data-driven decision-making and brand improvement. The hospital health management center should use feature extraction techniques to learn more about customers' feelings towards their services and optimize their business strategies and promotions accordingly. The proposed multi-level/hybrid level fusion system architectures can effectively integrate data/images from multiple sources, including social networks, to collect and process essential data for score level and rank level decision-making. This approach leverages intelligent techniques, such as deep learning models, fuzzy logic, and optimization algorithms, to improve fusion scores and achieve optimal fusion performance. The proposed framework can also be extended to various applications, including multimedia data fusion, e-systems data fusion, and spatial data fusion, to enable intelligent systems for information fusion and decision-making in diverse domains. Therefore, this paper proposes Improved Customer Relation and Business Operations (ICR-BO) to enhance customer relationships in business development using text and social analytics. A case study is carried out to explore the online debate of computer brands operated in hospital environments and Twitter suppliers. The authors used text-mining strategies and social analytics to analyze business operations. Social Media uses data sets to view important observations and trends to identify consumer awareness after collecting critical tweets using Twitter search. The experimental results show that ICR-BO achieves the highest customer relation compared to other existing methods.

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Rana K. A. Ahmed mail -

Ryham Ali Zubaid mail -

Fay Fadhil mail -

Israa Habeeb Naser mail

link https://doi.org/10.54216/FPA.100209

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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Fusion: Practice and Applications | 2023

Multi-Level Fusion for Facial Expression Recognition in Human Behavior Identification

In this study, we present a multi-level fusion of deep learning technique for facial expression identification, with applications spanning the fields of cognitive science, personality development, and the detection and diagnosis of mental health disorders in humans. The suggested approach, named Deep Learning aided Hybridized Face Expression Recognition system (DLFERS), classifies human behavior from a single image frame through the use of feature extraction and a support vector machine. An information classification algorithm is incorporated into the methodology to generate a new fused image consisting of two integrated blocks of eyes and mouth, which are very sensitive to changes in human expression and relevant for interpreting emotional expressions. The Transformation of Invariant Structural Features (TISF) and the Transformation of Invariant Powerful Movement (TIPM) are utilized to extract features in the suggested method's Storage Pack of Features (SPOF). Multiple datasets are used to compare the effectiveness of different neural network algorithms for learning facial expressions. The study's major findings show that the suggested DLFERS approach achieves an overall classification accuracy of 93.96 percent and successfully displays a user's genuine emotions during common computer-based tasks.

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Aqeel Hussein mail -

Ibraheem H. M. mail -

Sarah Ali Abdulkareem mail -

Ryam Ali Zubaid mail -

Noor Thamer mail

link https://doi.org/10.54216/FPA.100210

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new