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Prediction of Flight Areas using Machine Learning Algorithm
Get PDFAnyone who often uses the airways wants to predict when it will be best to purchase a ticket in order to get the best possible value. Aircraft firms continuously adjust ticket prices in an effort to maximize profits. When it\u27s anticipated that demand for more income will grow, aircraft manufacturers may raise flying prices. Information analysis for a given air route, comprising the features like take-off time, entrance time, and airways during a specified period, has been gathered in order to decrease costs. To use the machine learning models, qualities are arranged based on the information that has been gathered. The machine learning approach to determine costs based on attributes is presented in the paper below
Improving Cleaning of Solar Systems through Machine Learning Algorithms
Get PDF The study focuses on the importance of maintaining photovoltaic (PV) systems for optimal performance in sustainable energy generation. It highlights the impact of dust accumulation on reducing system efficiency and proposes a method to predict system performance, aiding in scheduling cleaning activities effectively. Two prediction models are developed: one using time-series prediction techniques (LSTM, ARIMA, SARIMAX) to forecast Performance Ratio (PR), and another employing ensemble voting classifiers (RF, Log, GBM) to predict the need for cleaning. The SARIMAX model performs best, achieving high accuracy in PR prediction (R2 = 92.12%), while the classification model accurately predicts cleaning needs (91%). The research provides valuable insights for improving maintenance strategies and enhancing the efficiency and sustainability of PV systems
Digital skills in the use of artificial intelligence tools for the formulation of formative research projects from the TECSIS Research Seminar.
Get PDFArtificial intelligence (AI) has promoted a change in the way research and innovation (R&D) is performed bringing new perspectives, automating time and routine tasks contributing to the generation of knowledge. To take full advantage of the potential of AI, it is proposed to develop strategic digital skills for the use of artificial intelligence tools in the formulation of formative research projects from the TECSIS Research Seminar. The methodology corresponds to a qualitative research with a descriptive analytical approach with a cross-sectional approach in four stages: analysis, design, evaluation and dissemination. The population under study includes the students of the special programs of Computer Engineering and Systems Technology of the University of Caldas. The expected result is the characterization of strategic digital skills of use for the formulation of formative research projects. This project will contribute in the development of a methodological route that exposes in a practical and applied way the strategic digital skills of use of AI tools for the formulation of formative research projects for the special programs of the University
Potential of artificial intelligence in textual cohesion, grammatical precision, and clarity in scientific writing
Get PDFIntroduction: the use of artificial intelligence (AI) tools in writing has significantly increased in recent years, promising improvements in textual coherence, grammatical precision, and clarity of ideas. This study focused on evaluating the long-term impact of AI usage on these aspects of academic writing.Objective: Identify the long-term effects of AI on cohesion, grammatical precision, and clarity in academic writing, while also exploring its ethical implications.Methods: a qualitative systematic review was conducted using the SALSA method, analyzing recent studies that address the influence of AI on writing quality. The databases used included Scopus, Web of Science, SciELO, and Latindex, with results restricted to publications since 2023.Results: the findings indicate that AI can enhance cohesion, precision, and clarity in texts, especially when used as a support tool. However, the effectiveness of these improvements depends on the context of use and the appropriate integration of human intervention.Conclusions: although AI offers clear benefits in improving academic writing, its use raises ethical and legal challenges that must be addressed. It is crucial to continue researching to optimize these tools and ensure responsible use in educational setting
Linking New Information Technologies to Agricultural Economics: The Role of Artificial Intelligence Integration
Get PDFArtificial Intelligence (AI) is revolutionizing agricultural economics by optimizing productivity, reducing costs, and enhancing decision-making processes. This paper explores the integration of AI technologies—such as machine learning, predictive analytics, and automation—into agricultural economic frameworks. AI-driven innovations, including precision farming, yield forecasting, and supply chain management, are reshaping agricultural practices by improving efficiency and sustainability. Furthermore, AI facilitates data-driven policymaking, enabling governments and stakeholders to address food security, market fluctuations, and resource allocation more effectively. Despite its benefits, AI adoption in agriculture faces challenges, including high implementation costs, data privacy concerns, and the digital divide between developed and developing regions. The study highlights case studies and real-world applications demonstrating AI’s impact on economic growth and sustainable agricultural development. The findings suggest that strategic investment in AI infrastructure, combined with supportive policies and education, can accelerate its adoption and maximize its economic benefits. Ultimately, AI integration holds the potential to transform agricultural economies by fostering innovation, resilience, and sustainability
Navigating the Paradox: Climate Change, Cutting-Edge Technologies, and Groundwater Sustainability
Get PDFThis article explores the paradoxical relationship between climate change, advanced technologies, and groundwater sustainability. It highlights how emerging technologies like artificial intelligence, blockchain, and the Internet of Things (IoT) offer innovative solutions for optimizing groundwater management while addressing climate change impacts. However, the chapter also warns of the environmental risks associated with these technologies, particularly their energy consumption and e-waste generation, which can further exacerbate climate challenges. The chapter examines practical applications such as desalination, precision farming, and water harvesting, evaluating their contributions to groundwater management and their environmental footprints. It argues that the net impact of these technologies depends largely on their design, implementation, and governance frameworks. The research identifies best practices to maximize benefits while minimizing negative environmental consequences. This work addresses key issues of water scarcity and the need for sustainable water supplies in a changing climate. It underscores the importance of fresh water for essential industries, including agriculture, energy production, and mineral processing, while acknowledging the profound effects of climate change and societal shifts on traditional water sources. The chapter also discusses the risks associated with technological investments in water management, such as toxic waste emissions, geopolitical tensions, and corruption. It emphasizes that emissions from these processes contribute significantly to rising atmospheric temperatures and water vapor levels, intensifying climate change. The chapter concludes by advocating for a holistic approach to water management, balancing the costs, benefits, and risks of emerging technologies. It highlights the potential of green engineering advancements and efficient water treatment methods, such as desalination and cleaner urban designs, to sustainably provide fresh groundwater for various uses. The chapter integrates data analytics from engineering and public health performance metrics to establish safe industry targets and calls for responsible governance to ensure technologies contribute positively to both groundwater sustainability and climate change mitigation
Smart Tutors: improving the quality of higher education through AI
Get PDFIntelligent Tutoring Systems (ITS) are revolutionizing higher education through artificial intelligence (AI), offering personalized and adaptive learning experiences. In this sense, the study aimed to analyze the impact of ITS on the quality of higher education based on AI. For this purpose, a bibliographic review was carried out that explored the main trends around the current topic. Among the findings, it was recognized that ITS use advanced algorithms, such as data mining and Bayesian networks, which allow educational content to be dynamically adjusted to meet the individual needs of students, improving learning effectiveness and keeping students more engaged and motivated. . This integration was shown to significantly improve knowledge retention and reduce dropout rates through real-time, personalized interventions. In addition, a focus on the sustainability and scalability of these systems was evident, integrating sustainable design principles. These developments made it possible to ensure that intelligent tutors can be widely implemented in various educational institutions without losing their effectiveness, thus improving the quality of higher education in a sustainable and expansive manner
Machine Learning-Based and AI Powered Satellite Imagery Processing for Global Air Traffic Surveillance Systems
Get PDFThe unprecedented growth of global air traffic has put immense pressure on the air traffic management systems. In light of that, global air traffic situational awareness and surveillance are indispensable, especially for satellite-based aircraft tracking systems. There has been some crucial development in the field; however, every major player in this arena relies on a single proprietary, non-transparent data feed. This is where this chapter differentiates itself. AIS data has been gaining traction recently for the same purpose and has matured considerably over the past decade; however, satellite-based communication service providers have failed to instrument significant portions of the world’s oceans. This study proposes a multimodal artificial intelligence-powered algorithm to boost the estimates of global air traffic situational awareness using the Global Air Traffic Visualization dataset. Two multimodal artificial intelligence agents categorically detect air traffic streaks in a huge collection of satellite images and notify the geospatial temporal statistical agent whenever both modalities are in concordance. A user can fine-tune the multimodal threshold hyperparameter based on the installed detection rate of datasets to get the best satellite-derived air traffic estimates.
Enhancing Wetland Restoration through Machine Learning-Based Decision Support Systems
Get PDFResearchers are increasingly employing Machine Learning (ML) and Deep Learning (DL) algorithms to address complex geo-environmental challenges, particularly in predicting risk, susceptibility, and vulnerability to environmental changes. These advanced computational models have shown significant promise in various applications, ranging from natural disaster prediction to environmental monitoring. Despite their growing usage, very few studies have leveraged Machine Learning-Based Decision Support Systems (MLBDSS) to restore the health status of wetland habitats. To our knowledge, there are no comparative analyses between Machine Learning models and traditional Decision Support Systems (DSS) in this specific context. Wetlands play a crucial role in supporting biodiversity, including fish and wildlife populations, while also contributing to improved water quality and providing essential ecosystem services to nearby communities. These services include flood control, carbon sequestration, and water filtration, which are vital for both ecological and human well-being. However, over the past decades, wetland areas, particularly in coastal regions, have faced significant degradation due to anthropogenic pressures, resulting in a substantial reduction of these critical benefits. This ongoing loss poses serious ecological and socio-economic challenges that require immediate and effective intervention. Current wetland assessment and mitigation frameworks often encounter limitations in their practical implementation, despite regulatory advancements aimed at promoting wetland conservation. These shortcomings can lead to delayed project approvals, increased costs, and further loss of valuable ecosystem services. Integrating ML and DSS models into wetland management strategies could provide innovative solutions to overcome these challenges by improving predictive accuracy, optimizing restoration efforts, and enhancing decision-making processes. The development of hybrid models combining ML and DSS approaches may offer a more holistic framework for addressing wetland loss, ultimately contributing to sustainable habitat restoration and conservation efforts
Application of Artificial Intelligence in Tree Care in Sub-Saharan Africa
Get PDFArtificial intelligence (AI) has emerged as a transformative tool in various industries, including environmental conservation and tree care. In Sub-Saharan Africa, where deforestation, climate change, and inadequate tree management pose significant challenges, AI presents opportunities for improving tree care practices. This study explores the application of AI technologies in tree monitoring, disease detection, and sustainable management strategies within the region. Utilizing a combination of literature review and case study analysis, the research evaluates AI-driven approaches such as remote sensing, machine learning models, and automated data collection for assessing tree care and forest dynamicos. The findings indicate that AI enhances early disease detection, optimizes resource allocation, and supports decision-making for conservation efforts. However, challenges such as limited technological infrastructure, high implementation costs, and the need for specialized expertise hinder widespread adoption. The study concludes that while AI holds significant potential for revolutionizing tree care in Sub-Saharan Africa, strategic investments in digital infrastructure, policy support, and capacity building are essential for its successful integration into forestry and environmental management practices