Procedia Computer Science最新文献

There are several system modeling notations based on system thinking. So far, these notations have not been compared. In this paper, we propose the GPDAC (Goal, Process, Data, Actor, Control) as a framework for creating new systems engineering knowledge by recombining knowledge from different academic fields. Moreover, a comparative study reveals the relationship among system modeling notations such as Systemigram, OPM (Object Process Methodology), and ArchiMate.

In the Philippines, healthcare providers, government agencies, and research institutions use data from patient prescriptions to generate reports for health planning and decision-making. However, current e-prescription systems have vulnerabilities, including erroneous information, hacking attempts, a single point of failure, and medical fraud. In addition to affecting the quality of data reporting, these issues violate a patient’s rights to data privacy. One promising solution is a blockchain-based prescription system. Blockchain’s immutable ledger accurately traces medical fraud and erroneous information, while its decentralized nature reduces the impact of failures. Performance is an important consideration, as healthcare systems need to be scalable and time-sensitive. This study aims to understand the performance and security of blockchain-based prescription systems. It focuses on evaluating system performance and scalability when using different encryption algorithms. The study found that using the most secure technology had only a small performance impact for all prototype features except key generation and report viewing. In addition, the results suggest that the proposed system’s scalability is sufficient to service the entire Philippines. This knowledge will help improve prescription systems to protect patients’ rights and improve report reliability.

Daytrading has been showing a growing popularity in the world due to easy access via technology, the possibility of additional earnings and a large increase in courses and several mentors available on social networks. This scenario causes many people to be unprepared to enter this market that has a high risk and that end up causing many people to lose their savings. Considering this situation, this study proposes the analysis of the data of a daytrade strategy, applying a machine learning model to help the investor make better decisions. Data from November 2020 to July 2023 was used within the US market based on the company [AMD]. The method used was the supervised machine learning technique known as the decision tree model, which seeks to identify the probability of event and non-event within the scenarios proposed in this work. The results were analyzed using the confusion matrix, gauging the accuracy in the training and test base, applying several decision tree models in order to find the best model and accuracy in the test base. In this sense, an improvement in the assertiveness rate was observed with the application of the supervised machine learning model based on a decision tree.

This paper examines the pervasive barriers hindering the widespread adoption of energy efficiency measures across various sectors. It categorizes these barriers into economic barriers, high upfront costs, organizational challenges, such as technical expertise and behavioral barriers, namely risk aversion and framing. To address these barriers, a comprehensive review of energy efficiency policies is conducted. These policies include incentives, coercive instruments, award systems, university industry collaboration, and technical support. To review energy efficiency barriers and policies, a methodological approach integrating non-structured snowball sampling with targeted literature review techniques was devised. Finally, the paper underlines that the success of these policies is contingent upon the active involvement, the collaboration, and the feedback of businesses, ensuring the feasibility of these policies. Their proactive engagement is indispensable in tackling energy efficiency barriers and driving the implementation of energy-efficient technologies, thus paving the way for a more sustainable and competitive businesses.

Smart grids are two-way communications grids that converge Information Technology (IT) and Operational Technology (OT) to transfer energy-related information between different industry components within the grid. Smart grids have changed the energy sector by increasing sustainability, efficiency and integrating renewable energy sources. However, smart grids are vulnerable to IT-related attacks because they rely on Information and Communication Technology (ICT). By surveying relevant papers and evaluating accessible statistics, this study explores cybersecurity in smart grids by examining current communication protocols and standards. We carefully compile various datasets with general information about four of the most smart grid-related datasets. Our study and conclusions address the key components of a smart grid and offer information that can help create cybersecurity plans specifically for smart grids. This research contributes to the discourse on smart grid security, which is important for preserving the stability of contemporary energy systems.

Strategic management is a systemic, logical, and objective process that provides a guiding framework for all plans and actions of a company. The beginning of this logical process is in the external analysis of the organization. This dissertation analyzes the external situation faced by a micro-company providing digital advertising services in Barranquilla. The method used for the case study included technological development, innovation, and projective research with documentary and field observation. The results show the speed with which information technologies advance and, with them, societies' technological and environmental demands and interests. It is concluded that the commercial practices developed must be responsible for the environment and the direct impact generated on its customers. It will probably achieve sustainability over time by aligning these elements to its raison d'être.

Healthcare is a global pillar, with a surge in the adoption of information technology, particularly in hospital information systems (HIS). However, global protocols are needed to meet the growing demand for data interchange, practical implementations for sharing healthcare data among facilities, and a pressing need for processing and storage infrastructure to handle the escalating volume of healthcare data. This study proposes a solution for efficient data transmission using electronic health records (EHR) and Platform-as-a-Service (PaaS) to leverage cloud computing resources. This framework's architecture boasts robustness and adaptability, providing all registered software programs access to data interchange services. Through a comprehensive examination of the framework's structure, the essay also explores the most effective data-sharing methods. It identifies the healthcare system's optimal EHR data model. According to multiple healthcare experts, the operational building of this framework is expected to catalyze the growth of healthcare institutions both nationally and within specific industries.

Clinical trials are crucial in experimental medicine as they assess the safety and efficiency of new treatments. Due to its unstructured and plain language nature, clinical text data often presents challenges in understanding the relationships between various elements like disease, symptoms, diagnosis, and treatment. This task is challenging as the Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT) requires intricate reasoning involving textual and numerical elements. It involves integrating information from one or two Clinical Trial Reports (CTRs) to validate hypotheses, demanding a multi-faceted approach. To address these problems, we use BERT-base models’ ability to predict entailment or contradiction labels and compare the use of transformer-based feature extraction and pre-trained models. We utilize seven pre-trained models, including six BERT-based and one T5-based model: BERT-base uncased, BioBERT-base-cased-v1.1-mnli, DeBERTa-v3-base-mnli-fever-anli, DeBERTa-v3-base-mnli-fever-docnli-ling-2c, DeBERTa-large-mnli, BioLinkBERT-base, and Flan-T5-base. We achieve an F1-score of 61% on both DeBERTa-v3-base-mnli-fever-anli and DeBERTa-large-mnli models and 95% faithfulness on the BioLinkBERT-base model.

The growing prevalence of poor nutrition is a major public health concern, as it fuels the rise of various diseases. Obesity, a silent and rapidly growing threat linked to unhealthy eating, is a prime example. Despite the abundance of information on diets and recipes, finding a personalized approach to healthy eating can be a challenge. Recommendation systems can filter from a food logging dataset the information that best suits the nutrition profile of a given user. A powerful tool to use in food recommendation systems is neural networks. However, the user's available data are often limited, which compromises the performance of neural-based food recommendation models. To enhance user trust in food recommendations, this paper proposes a method using a secondary model to predict the errors of the primary neural network, especially when dealing with limited data.