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Exploration of regularities in bipartite graphs using GEOGEBRA software
Get PDFA classroom proposal is presented to integrate contents of Graph Theory and Linear Algebra in complete bipartite graphs, linking adjacency and Laplacian matrices, the eigenvalues of graphs will be determined, applicable to connectivity concepts. Students will be given exploration activities working with GeoGebra software, starting from several particular cases, with table works and questionnaires to be completed, in order to determine patterns on the eigenvalues of adjacency and Laplacian matrices of complete bipartite graphs. The work with patterns will lead to the generalization process, to abstract properties from observation and experimentation on examples. This learning experience builds bridges between the concrete and the symbolic, and the student is initiated in researc
Assessing the Impact of Erratic Governance on Local and International NGOs in Zambia: An Exploratory Study Using Machine Learning and Artificial Intelligence
Get PDFThis study explores the impact of erratic governance on local and international NGOs in Zambia, using a mixed-methods approach that combines survey data, in-depth interviews, and machine learning (ML) and artificial intelligence (AI) techniques. The study finds that erratic governance practices, including funding constraints, operational challenges, and limited access to services, significantly affect the operations and effectiveness of NGOs in Zambia. Weak institutional frameworks, corruption, lack of transparency and accountability, political instability, and limited civic engagement are identified as key factors contributing to erratic governance. The study demonstrates the potential of ML and AI in analyzing and predicting the impact of erratic governance on NGOs, including predictive modeling, risk analysis, data visualization, automated reporting, and decision support systems. The findings of this study have implications for policymakers, NGO managers, and development practitioners seeking to promote more effective and sustainable development outcomes in Zambia
Use of artificial intelligence in nursing
Get PDFIntroduction: Artificial Intelligence (AI) encompasses technologies such as machine learning and neural networks, with applications across various fields. The World Health Organization recognizes its potential to enhance healthcare, yet emphasizes the need to address ethical considerations in its implementation. In nursing, AI has the potential to increase autonomy and efficiency in care, though its use remains limited and poorly understood within the profession.Objective: To analyze the use of AI in nursing by evaluating its impact on care functions, administrative tasks, educational activities, and research.Methods: A literature review was conducted, including original articles, reviews, and bibliometric studies. The research focused on AI applications across the four primary functions of nursing.Results: AI has demonstrated benefits in predictive analytics and improving patient care efficiency, as well as in administrative management and patient classification. In education, generative AI facilitates the development of educational materials, although it presents risks of bias. In research, AI serves as an assistant in data search and analysis, despite facing ethical and methodological challenges.Conclusions: AI has the potential to significantly transform nursing practice, enhancing both the quality and efficiency of care. However, its integration necessitates careful management to address its limitations and ensure a positive impact in the field
Color in images: a machine vision approach to the measurement of CIEL*a*b* coordinates in bovine loins
Get PDFElectronic machine vision systems bring together a set of technologies and techniques used to capture, process and analyze images to perform a specific task, such as object or measurement pattern recognition. These systems rely on image processing and machine learning algorithms to interpret visual information. Therefore, the objective of this research was the construction of an electronic machine vision system (SVA) for color analysis in bovine (longisimus dorsi) loins based on the CIEL*a*b* color space. The VAS implementation was carried out using the programming language Python3.9 programming language and the color parameters obtained were compared with those obtained on a Minolta CR-400 colorimeter (CM). Both systems were synchronized to provide the user with information about the color coordinates in the samples of loins stored for 6 days at 4°C. The results obtained showed no significant differences. The results obtained showed no significant differences in the values of the L* parameter, while b* and a* showed significant differences during the storage time of the loins. These results are attributed to the oxidation process of the myoglobin and to factors such as breed, feeding and slaughtering process of the cattle, which affect the color of the samples. The results obtained indicate that VAS could be used for the determination of color during the storage of beef loins in real time, offering a non-invasive and low-cost solution to the actors in the meat chain.Keywords: image analysis, beef, colorimeter, artificial vision system.
Comparative Study of AI Code Generation Tools: Quality Assessment and Performance Analysis
Get PDFArtificial intelligence (AI) code generation tools are crucial in software development, processing natural language to improve programming efficiency. Their increasing integration in various industries highlights their potential to transform the way programmers approach and execute software projects. The present research was conducted with the purpose of determining the accuracy and quality of code generated by artificial intelligence (AI) tools. The study began with a systematic mapping of the literature to identify applicable AI tools. Databases such as ACM, Engineering Source, Academic Search Ultimate, IEEE Xplore and Scopus were consulted, from which 621 papers were initially extracted. After applying inclusion criteria, such as English-language papers in computing areas published between 2020 and 2024, 113 resources were selected. A further screening process reduced this number to 44 papers, which identified 11 AI tools for code generation. The method used was a comparative study in which ten programming exercises of varying levels of difficulty were designed and the results obtained from 4 of them are presented. The identified tools generated code for these exercises in different programming languages. The quality of the generated code was evaluated using the SonarQube static analyzer, considering aspects such as safety, reliability and maintainability. The results showed significant variations in code quality among the AI tools. Bing as a code generation tool showed slightly superior performance compared to others, although none stood out as a noticeably superior AI. In conclusion, the research evidenced that, although AI tools for code generation are promising, they still require a pilot to reach their full potential, giving evidence that there is still a long way to go
Understanding AI\u27s Role in the Banking Industry: A Conceptual Review
No full textThis study delves into the shifting role of Artificial Intelligence (AI) within the banking industry, with a focus on its transformative effects on service quality, operational effectiveness, and customer interaction. The research underscores significant developments in AI and its integration, highlighting its pivotal role in updating traditional banking practices and tackling modern-day challenges. It offers a comprehensive analysis of the potential of AI to enhance banking services, while also addressing obstacles such as technical difficulties and regulatory concerns. The outlook section predicts ongoing AI expansion in the banking sector, particularly its capacity to further tailor banking services and improve risk management. The goal of this research is to provide a comprehensive understanding of AI\u27s integration into Indian banking, shedding light on the evolving relationship between technological innovation and the financial secto
Machine learning and AI for security mechanisms: A Systematic Literature Review Using a PRISMA Framework
Get PDFCyber threats are evolving rapidly, posing significant risks to individuals, organizations, and digital infrastructure. Traditional cybersecurity measures, which rely on predefined rules and static defence mechanisms, struggle to counter emerging threats such as zero-day attacks and advanced persistent threats (APTs). The integration of artificial intelligence (AI) and machine learning (ML) into cybersecurity presents a transformative approach, enhancing threat detection, anomaly identification, and automated response mechanisms. This study systematically reviews the role of ML and AI in cybersecurity defence using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. A comprehensive literature search was conducted across multiple academic databases, identifying and analyzing studies published within the last decade. The review focuses on AI-driven cybersecurity applications, including intrusion detection systems (IDS), malware analysis, and anomaly detection in cloud and IoT environments. Findings indicate that ML models, such as neural networks, support vector machines, and ensemble learning techniques, improve detection accuracy and adaptability to evolving threats. AI-driven automated response systems enhance incident mitigation, reducing reliance on human intervention. However, challenges such as adversarial attacks, data privacy concerns, and computational resource demands persist. The study concludes that AI and ML significantly enhance cybersecurity resilience but require continuous advancements in model robustness, interpretability, and ethical considerations. Future research should focus on refining AI-driven security mechanisms, addressing adversarial vulnerabilities, and improving regulatory frameworks to maximize AI’s potential in cybersecurity
Artificial Intelligence in Psychological Diagnosis and Intervention
Get PDFThe integration of artificial intelligence (AI) in the field of psychology is significantly transforming the diagnosis and intervention of mental disorders. Deep learning techniques enable the analysis of large volumes of data, such as neuroimages and electroencephalograms (EEG), to identify and diagnose psychiatric conditions with greater precision. These technologies also facilitate early detection of risk factors by analyzing data from social networks and electronic medical records, enabling personalized interventions. AI-based chatbots and mobile applications democratize access to psychological therapy, providing real-time support and improving the management of conditions such as anxiety and depression. Additionally, AI optimizes administrative tasks and enhances the training of new clinicians through learning platforms and virtual simulators, contributing to better preparedness and efficiency in the mental healthcare system. These innovations not only improve the quality of diagnosis and treatment but also enable more proactive and patient-centered car
Code optimization opportunities in the JavaScript ecosystem with Rust
Get PDFThis paper explores the potential of optimizing node.js applications by integrating rust. In particular, in processing cpu-intensive tasks where javascript faces performance limitations due to its single-threaded architecture. Rust\u27s memory safety and parallelism model, which eliminates the need for a garbage collector, makes it an attractive alternative to traditional c/c++ modules for extending the capabilities of node.js. This study explores the performance gains achieved by integrating rust, both through native bindings and WebAssembly, demonstrating significant improvements in computational efficiency, especially in parallel processing scenarios. Rust\u27s ability to efficiently handle computation-intensive workloads with work interception algorithms is emphasized as a key factor in overcoming javascript bottlenecks. The study includes a detailed performance evaluation that compares synchronous and asynchronous modules in node.js with rust implementations. Tests demonstrate how rust optimizations outperform javascript by up to ten times in certain computational tasks. The study also evaluates cross-compiled rust modules using WebAssembly in the browser environment, which once again illustrates the advantages of rust in providing near-native performance. The results emphasize the potential of rust to enhance node.js applications by making them more scalable, reliable, and efficient for high-performance web application
Leveraging Artificial Intelligence for Enhancing Wheat Yield Resilience Amidst Climate Change in Sub-Saharan Africa
Get PDFThe introduction of a deep learning-based method for non-destructive leaf area index (LAI) assessment has enhanced rapid estimation for wheat and similar crops, aiding crop growth monitoring, water, and nutrient management. Convolutional Neural Network (CNN)-based algorithms enable accurate, non-destructive quantification of seedling leaf areas and assess LAI across diverse genotypes and environments, demonstrating adaptability. Transfer learning, known for efficiency in plant phenotyping, was tested as a resource-saving approach for training the wheat LAI model. These advancements support wheat breeding, facilitate genotype selection for varied environments, accelerate genetic gains, and enhance genomic selection for LAI. By capturing diverse environments, this method can improve wheat resilience to climate change. Additionally, advances in machine learning and data science enable better prediction and distribution mapping of global wheat rust pathogens, a major agricultural challenge. Accurate risk identification allows for timely and effective control measures. Moreover, wheat lodging prediction models using CNNs can assess lodging-prone varieties, influencing selection decisions to improve yield stability. These artificial intelligence-driven techniques contribute to sustainable crop growth and yield enhancement, especially in the context of climate change and increasing global food demand