Using a Rough-Set-based approach, this study investigates the connection between financial performance, Environmental, Social, and Governance (ESG) factors, and stock returns. It analyzes data from various companies and industries to examine how ESG metrics interact with financial indicators to influence stock prices. Those findings underscore the importance of considering financial and ESG factors when evaluating a company's performance and market valuation. The Rough-Set-based bipolar model comprises eight decision rules, and the DEMATEL (Decision-Making Trial and Evaluation Laboratory) analysis reveals the influence relationship among the core attributes. Furthermore, the ranking result, based on the bipolar model, is consistent with the market returns, which suggests the validity of this study.

The proposed study introduces innovative Maclaurin symmetrical mean aggregation operators (MSMAO) in picture fuzzy multiple-attribute group decision-making. These operators are built upon a newly developed Frank t-norm (FTN) and t-conorm (TCN). Utilizing these novel operators aims to enhance the effectiveness of decision-making processes in scenarios characterized by picture-fuzzy information. The study explores these aggregation operators' potential applications and advantages within the context of multiple-attribute group decision-making under uncertainty. As a novel method to handle MAGDM difficulties, we build these aggregation operators (AOs) on PFs in this paper by utilizing the FTN and TCN under PFs information. In addition, PF values (PFVs) and their fundamental operations are created and shown. Picture fuzzy Frank weighted Maclaurin symmetrical mean (MFFWMSM) and Picture fuzzy Frank Maclaurin symmetrical mean (MFFMSM) operators are two AOs introduced and studied based on these operations. The induction approach theoretically and quantitatively verifies the newly created AOs' accuracy and dependability. The problem of project evaluation utilizing these proposed operators is thoroughly examined to provide further applications and investigate the sensitivity of these PS Frank (PFF) operators. The outcomes of employing these PFF operators are contrasted with a few AOs of PFVs that were previously in existence. The suggested strategy is dependable and effective based on comparison outcomes.

A dashboard is a steering tool for data-driven decision-making and a lever for innovation in the pharmaceutical industry. It makes it possible to integrate relevant performance measurement indicators, chosen according to the objectives to be achieved and according to specific criteria. In this context, our work is inscribed, which aims to develop a dashboard for the sales department of a pharmaceutical company. The choice of this tool is motivated by its importance and the strategic challenge it presents for companies today. The implementation of this dashboard will be broken down into four phases. First, we collect sales department sales and develop the data marts. Then, to generate the data model, we have to import the database into Power BI (Microsoft Power Business Intelligent). We select the adequate KPIs (Key Performance Indicators) to create the interactive sales dashboard. This dashboard allows the decision makers to analyze and visualize data, moving from traditional sales data storage to big data analytics. Based on this visualization tool, the sales manager can propose potential improvements and essential investments and develop the management review to be presented as an annual report. This report is important for managing and monitoring pharmaceutical activities. Existing data are used and visualized to target the goals of decision-makers.

The study introduces the Brain Tumor Detection Transfer Learning Algorithm (BTDTLA), a novel model that employs transfer learning and a comprehensive dataset of brain images. The algorithm makes a significant breakthrough in the precise detection of brain tumors, particularly critical for cases requiring swift intervention. Development and testing of BTDTLA are conducted on MATLAB 2018. The evaluation metrics, including sensitivity, specificity, precision, accuracy, and the Matthews correlation coefficient, highlight the robust performance of BTDTLA, positioning it as a valuable tool for medical practitioners. This underscores the algorithm's potential to advance practices for early and accurate brain tumor detection. The study emphasizes BTDTLA's pivotal role in contributing to the field, underscoring its significance in enhancing medical practices related to brain tumor diagnosis.

The selection of the best location for a facility is generally a complex process especially when considering multiple criteria in the selection process. The costs associated with purchasing the land and related constructions makes the location selection problem a long-term investment decision. Hence, the problem should be analysed carefully with a reliable approach to avoid the consequences that follows awkward decisions. Many multiple criteria decision-making (MCDM) methods are developed recently which made the selection of a suitable MCDM method has become a confusing decision. In this study, measurement alternatives and ranking according to compromise solution (MARCOS) method is used to solve a location selection problem with conflicting criteria. The ranking produced by MARCOS method is analysed by a comparison with other common MCDM methods and a sensitivity analysis. The aim of the comparison to show that the method agreed the choice of the best alternative while the sensitivity analysis is employed to check the robustness and efficiency of this method. The sensitivity analysis includes the sensitivity of weight test and rank reversal (RR) test. The comparison of ranking with different methods showed that MARCOS method has strong ranking correlation with other methods. Moreover, the sensitivity analysis showed the reliability and robustness of MARCOS method as it produced high consistency of ranking through the tests. Thus, this study validates the applicability of MARCOS method to handle the presence of conflicting criteria in location selection problems.

To handle problematic and ambiguous data, Schweizer and Sklar added a parameter p in 1960, which helped to develop the theory of SS t-norm (SSTN) and t-conorm (SSTCN). The parameter p=-1.1 can be used to easily derive the information of the Hamacher and Lukasiewicz t-norms. Furthermore, prioritized aggregation operators (PAOs) choose which data will be collected into a singleton set. The main contribution of this work is the construction of new aggregation operators for T-spherical fuzzy (T-SF) information based on SS t-norm and t-conorm. Moreover, the fundamental characteristics of the operators are identified. Further, we developed MADM (Multi-Attribute Decision-Making) models and deduced several useful properties from the operators T-SFSSPA, T-SFSSWPA, T-SFSSPG, and T-SFSSWPG. Finally, using an actual case study, we were able to draw the conclusion that, in comparison to the ground-breaking and current methods to enhance the value and capability of the diagnosed operators, the proposed MADM algorithm performs noticeably better than the operators in place for resolving the water recycling problem in a way that is easy to understand.

This research presents an innovative application of the YOLOv4 object detection model for the identification and classification of marine objects within a dataset encompassing seven distinct classes. The study focuses on enhancing the robustness and accuracy of object detection in challenging marine environments, leveraging the unique capabilities of YOLOv4. Pre-processing steps involve resizing raw images, applying data augmentations, and normalizing pixel values to ensure optimal model training. Specifically tailored for underwater scenarios, additional color space transformations address variations in lighting conditions. The model is trained to detect marine objects such as fish, corals, and underwater structures, contributing to advancements in underwater exploration, environmental monitoring, and marine resource management. Experimental results demonstrate the effectiveness of the proposed approach, showcasing YOLOv4's ability to accurately identify and classify marine objects across the specified seven classes. This research not only expands the applicability of YOLOv4 in the marine domain but also provides valuable insights for the development of intelligent systems for underwater object detection.

The development of electronic commerce has resulted in an increase in the number of shipments in their transfer systems. Delivery companies strive to respond to emerging situations, meet customer needs, and achieve profit through the optimization of their operations. The imperative is to establish a delivery system that is in line with the principles of sustainable development. This paper defines the task of choosing a sustainable model for shipment delivery and proposes the AROMAN method for its resolution. The applicability of the proposed method is demonstrated by solving the defined task in the territory of the city of Belgrade.

Detection of bone fractures using modern technology has significant implications in medical analysis and artificial intelligence. This importance is especially pronounced in the realm of deep learning. Deep learning techniques find extensive application in the field of medicine and disease classification. The early identification of bone fractures is crucial for efficient treatment planning and patient care. Our research proposes a transfer learning-based model for predicting bone fractures using a dataset of bone X-ray images. These images will be classified into two categories: normal and bone fracture, based on extracted features. Our proposed model, the Bone Fracture Detection Transfer Learning Algorithm (BFDTLA), achieved an average accuracy of 97% on the dataset. The BFDTLA model demonstrated superior performance when compared to previous quantitative and qualitative research studies. This research focuses on the early detection of bone fractures using transfer learning algorithms, emphasizing the significance of accurate and timely diagnosis.