Breast cancer is a hazardous disease that should be seriously tackled to reduce its danger in all aspects of the world. Therefore, several imaging ways to detect this disease were considered, but the produced images need to be accurately processed to effectively detect it. Image segmentation is an indispensable step in image processing to segment the homogenous regions that have similar features such as brightness, color, texture, contrast, form, and size. Several techniques like region-based, threshold-based, edge-based, and feature-based clustering have been developed for image segmentation; however, thresholding, which is divided into two classes: bilevel and multilevel, won the highest attention by the researchers due to its simplicity, ease of use and accuracy. The multilevel thresholding-based image segmentation is difficult to be tackled using traditional techniques, especially with increasing the threshold level; therefore, the researchers pay attention to the metaheuristic algorithms which could overcome several hard problems in a reasonable time. In this paper, a new hybrid metaheuristic algorithm based on integrating the jellyfish search algorithm with an effective improvement method is proposed for segmenting the color images of breast cancer, namely the hybrid jellyfish search algorithm HJSO. Experiments are extensively performed to appear the superiority of the proposed algorithm, including validating its performance using various breast cancer images and conducting an extensive comparison with several rival algorithms to explore its effectiveness. The experimental findings, including various performance metrics like fitness values, CPU time, Peak signal-to-noise ratio (PSNR), standard deviation, Features similarity index (FSIM), and Structural similarity index (SSIM), totally show the efficiency of HJSO.
This paper presents a case study on the implementation of business intelligence (BI) in a retail company with the main aim to analyze the benefits of BI implementation and the confronts encountered during the process. The case study involves a large retail company that operates in multiple countries and offers a wide range of products. The implementation of BI was driven by the need to improve decision-making processes, increase operational efficiency, and enhance customer satisfaction. We also cover the different phases of BI implementation, including planning, data integration, data modeling, and dashboard development. The results of the study indicate that the implementation of BI has led to significant improvements in the company's performance, such as increased revenue, improved inventory management, and better customer segmentation. We investigate how artificial intelligence can provide great support for improving and automating the implementation of BI in retail companies. However, we also highlight some challenges encountered during the implementation process, such as data quality issues and resistance to change. The paper concludes by emphasizing the importance of careful planning, stakeholder engagement, and ongoing evaluation in ensuring the success of BI implementation in retail companies.
The blockchain as a distributed ledger with flourishing blocks are secured and linked with cryptographic hashes. The blockchain is a type of distributed database that is used in many vital business transactions of replication, sharing, tracking, synchronization data among various sites. Recently, the global technological and industrial revolution is accelerating, the bitcoin extends the industrial revolution to become a lot of interest from both the business world and academic circles. This paper aims to take the advantages of blockchain concepts to be applied in Enterprise Banking Systems (EBS). The EBS depend on smart contract and blockchain technologies for trust only the installation of a blockchain platform with a solid design and a proven user base. Unfortunately, only a few blockchain platforms (BP) have achieved stable design and confident implementation. The selection of appropriate BP is leading step for decision makers that pretended to be a real challenge. Therefore, any digital transformation project that makes use of blockchain must contend with the difficulty of selecting a BP that is suited to the requirements of EBS. In this study, a hybrid approach of a neutrosophic theory for uncertainty conditions in a multi-criteria decision-making problem with the use wise weight assessment ratio analysis (SWARA) and Weighted Sum Method (WSM) to select the appropriate and efficient BP. A case study is applied on EBS, as an uncertain environment, to show the efficiency for the proposed model in aiding decision makers to achieve to ideal BP according to challenges to achieve sustainability.
Applications that are enabled by blockchain technology have been infused with a decentralized system without the need for intermediate entities. Blockchain technology indicates opportunities with various technologies and applications. Recently, a meteoric rise in the amount of interest has been indicated by academics in blockchain technology. Nevertheless, the acceptance of this blockchain technology paradigm in corporate distributed systems is not exactly promising. Executives and technocrats in a business are required to engage in multiple-criteria decision-making (MCDM) with operating uncertainty factors for the acceptance of new technologies. The proposed model aims to develop a model to identify and keep track of major elements that contribute to the sluggish pace for blockchain technology to be adopted by the general public. The study applied the Evaluation Based on the Distance from Average Solution (EDAS) approach to its interval-valued neutrosophic variant, which has the benefit of concurrently with the consideration of a decision maker's truthiness, falsity, and indeterminacy. The EDAS considers the distances of alternatives from the actual solutions considered by each criterion. In addition, the proposed model illustrated the use of neutrosophic theory with the EDAS method to rank blockchain technology in enterprise-distributed applications in uncertain conditions to aid decision-makers in optimal solutions. A numerical case study is illustrated to show the effectiveness of the proposed model in aiding decision-makers to achieve optimal solutions in uncertain conditions.
In today's highly competitive business environment, companies are increasingly focusing on enhancing customer satisfaction to improve customer loyalty and drive business growth. In this context, the use of data-driven approaches can provide valuable insights for companies to improve their service quality and customer experience. This paper presents a case study in service operations management, where a data-driven approach is used to enhance customer satisfaction. We employ a dataset of customer feedback from a service company and proposes a deep learning (DL) algorithm learn to identify the factors that affect customer satisfaction. The results show that the proposed data-driven approach is effective in identifying the key drivers of customer satisfaction and in providing actionable insights for service improvement. We highlight the potential of our DL approach for enhancing customer satisfaction and provides insights for service companies to improve their customer experience based on the analysis of customer feedback.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.