International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
One of the major lifestyle disorders brought on by unwholesome daily routines and inherited ailments is obesity and overweight. And this illness is a risk factor for a wide range of chronic illnesses, such as cancer, diabetes, metabolic syndrome, and cardiovascular conditions. Additionally, according to the World Health Organization (WHO), 30% of deaths worldwide will be caused by lifestyle illnesses by 2030. These deaths can be prevented by appropriately identifying and treating risk factors that relate to these diseases as well as by implementing behavioral engagement policies. Thence, the study is leveraging machine learning (ML) techniques for analyzing data and discovering new patterns for predicting body fat. The problem of predicting fat classifies as a regression, hence, we are deploying two regression techniques to deal with the regression dataset. These techniques are used linear regression (LR) and k nearest neighbors (KNN) which fall under umbral of ML. The two techniques are applied on real datasets. The dataset has 252 records. The results showed the LR has the highest score than the KNN model.
Read MoreDoi: https://doi.org/10.54216/IJAACI.030101
Vol. 3 Issue. 1 PP. 08-18, (2023)
Rice is one of the most important staple crops worldwide, and rice plant diseases are a significant threat to global food security. Early detection and accurate classification of these diseases are crucial for effective disease management and prevention of crop losses. In this paper, we propose a novel computational intelligence-based technique for rice disease detection and classification. Our proposed method is composed of a residual network-based feature extractor followed by a Light Gradient Boosting Machine (LGBM) classifier. We use a publicly available rice leaf dataset to evaluate the performance of our proposed method. The results demonstrate that our proposed method achieves high accuracy, sensitivity, and specificity in identifying diseased rice plants, outperforming existing state-of-the-art methods. We also compare our proposed method against other methods using different performance metrics, showing its superior performance. The proposed method provides a promising approach to enhance rice crop health management and can be adapted and customized for other crops and agricultural settings. The proposed computational intelligence-based technique for rice disease detection and classification has significant implications for improving crop productivity and ensuring food security.
Read MoreDoi: https://doi.org/10.54216/IJAACI.030102
Vol. 3 Issue. 1 PP. 19-26, (2023)
Reasons for Human Error, the term "risk" is used to describe the many potential causes of human mistakes. Capabilities, organizational culture, job complexity, and environmental variables are just a few of the many aspects that fall under this category. Accidents, improvements in safety, and gains in productivity may all benefit from a better understanding of and approach to minimizing human error. This paper highlights the necessity for comprehensive methods and actions to limit the effect of human error by providing an overview of the primary human error components and their implications for risk management. Due to various criteria, the concept of multi-criteria decision-making (MCDM) is used to deal with various criteria. This paper used the MCDM tools to rank and evaluate the risks of human error factors. The DEMATEL method is a MCDM tool is used to compute the weights of these factors and rank the risks. The DEMATEL method is integrated with the neutrosophic set to deal with uncertain information. This paper used the single-valued neutrosophic set with three values (truth, indeterminacy, and falsity) values. The twenty risks are identified in this paper and ranked.
Read MoreDoi: https://doi.org/10.54216/IJAACI.030103
Vol. 3 Issue. 1 PP. 27-40, (2023)
This study investigates the feasibility of using wearable technologies in education to improve safety. This article explores how wearables may be used to improve school safety and wellness, as well as their advantages, disadvantages, and future potential. The article covers a wide range of wearable gadgets and their respective safety-related features, from smartwatches to location trackers to panic buttons and biometric sensors. Privacy issues, data security, user acceptability, and ethical considerations are only some of the problems and hazards discussed in this research on wearables in education. This study the neutrosophic set to deal with uncertain data. The neutrosophic set is integrated with the multi-criteria decision-making (MCDM) CRITIC method. The CRITIC method is used to compute the weights of factors and rank it. There are 15 factors used in this study. The case study is applied in the education field. Educators, technologists, and legislators all need to work together to guarantee the safe and effective use of wearable devices in schools, as shown by the study's findings. The article reiterates the importance of wearables and their potential to enhance safety measures in education before making the case for more studies, pilot programmers, and policy development to fully realize their promise.
Read MoreDoi: https://doi.org/10.54216/IJAACI.030104
Vol. 3 Issue. 1 PP. 41-52, (2023)
This paper aims to explore the impact of Smart Blockchain-based Letters of Credit (BTLOC) on business transactions in the realm of trade finance. The involvement of a third party in business transactions often leads to complications such as process heterogeneity, increased complexity, information security risks, and higher costs. To address these challenges, this research proposes an innovative solution for activities dependent on third-party participation, specifically in the context of global trading. To provide a comprehensive understanding of this solution, the study employs business process modeling in a transaction scenario, offering a deeper insight into its mechanics. The implementation of platforms built upon blockchain technology (BT), and smart contracts has the potential to significantly reshape and streamline business procedures, thereby benefiting participants engaged in global trade. This research primarily focuses on investigating the theoretical aspects and feasibility of incorporating BT into global trade, considering a paradigm shift in the field. A novel BTLOC is introduced as a key element of the research, enabling the examination of its practicality. Additionally, we explore the applications of BTLOC in real case study of international Trading and explore its potential integration into trade finance processes. Through a multi-case analysis, this research contributes to the understanding of the paradigm shift facilitated by BT. The findings shed light on the future potential applications of blockchain in finance and serve as an illustrative example of the extended capabilities associated with financial processes.
Read MoreDoi: https://doi.org/10.54216/IJAACI.030105
Vol. 3 Issue. 1 PP. 53-63, (2023)