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1
Gan, Jianbang, H. Michael Rauscher, C. T. Smith, et al. "The Southern US Forest Bioenergy Encyclopedia: Making Scientific Knowledge More Accessible." Southern Journal of Applied Forestry 32, no. 1 (2008): 28–32. http://dx.doi.org/10.1093/sjaf/32.1.28.
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Abstract Production of forest biomass in sustainably managed forests in the southern United States has great potential to improve forest health, make energy supplies more secure, and increase the social and economic welfare of rural communities. The awareness and access of landowners and forestry and natural resource professionals to new knowledge and market development opportunities must be increased, and new technological advances in knowledge dissemination systems can be very useful in this connection. The Encyclopedia of Southern Bioenergy was developed within The Forest Encyclopedia Network (www.forestencyclopedia.net/) to facilitate the transfer of useable knowledge from scientific experts in bioenergy and bio-based products to natural resource professionals, landowners, and the general public. Using the encyclopedia as a base, a team of bioenergy and extension education experts has developed several new educational products. These materials are designed as components of an overall biomass training program for the South that is being disseminated through the Southern Regional Extension Forest network.
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Rusana, Neema Eliya, Felister Mombo, Sayuni Mariki, and Johanna Bergman Lodin. "Assessment of Decentralized Decision-Making on Livestock Management in Miombo Woodlands of Tanga and Morogoro regions, Tanzania: Bridging Acts and Practice." East African Journal of Environment and Natural Resources 7, no. 2 (2024): 1–16. http://dx.doi.org/10.37284/eajenr.7.2.2266.
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Despite legal reforms such as the Local Government (District Authorities) Act of 1982 and the Forest Act of 2002, which decentralize forest management in Tanzania’s villages, the persistent degradation of Miombo woodland forests, primarily due to livestock activities, raises concerns about the effectiveness of these reforms. This study compares the provisions of these acts with on-ground realities through data from 27 Focus Group Discussions (FGDs) and 45 Key Informant Interviews (KIIs). Thematic analysis using NVIVO 12 software identified four key discrepancies: decision-making structures, decision-making processes, gender involvement, and village collaboration. Findings show that villagers often unknowingly delegate legislative power to leaders, decision-making is politicized with minimal stakeholder engagement, gender inclusivity is minimal, and villages manage forests independently rather than collaboratively. These gaps have led to biased decisions, conflicts among user groups, forest encroachment, and the neglect of women's needs, exacerbating forest degradation. To address these issues, the study recommends capacity building through leadership and technical training for village councils, community education on legal rights, and improved transparency via public forums and accessible reporting. These initiatives aim to empower local communities and foster sustainable management of Miombo woodland forests
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Wioletta, Kacprzyk. "The coherence of tourism facilities with the forest landscape." Geography and Tourism 6, no. 2 (2018): 109–17. https://doi.org/10.5281/zenodo.2144112.
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Organizing tourism and recreation in forested areas requires appropriate facilities to handle tourism flows. It is vital that the facilities be comfortable, fit the needs of tourists but also that they be coherent with the area’s landscape of their location. The objective of this article is to present the issue of matching of not so much the function but the external looks of the facilities to a given place, including their construction, design, colours as well as the material from which they are made. The author’s considerations are based on the analysis of literature and experts’ opinions conducted at the State Forest Enterprise – State Forests (Państwowe Gospodarstwo Leśne Lasy Państwowe, PGL LP). These considerations concern the facilities located in Polish forests, especially the town of Zakopane and its area, and the presented examples refer to the touristically attractive (due to its natural values) area. The conclusion drawn out of the conducted analyses is not optimistic. The facilities are generally not consistent with the local landscape.
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Pokorny, Benno, Juan Carlos Montero Terrazas, James Johnson, Karen Mendoza Ortega, Walter Cano Cardona, and Wil de Jong. "Making Timber Accessible to Forest Communities: A Study on Locally Adapted, Motor–Manual Forest Management Schemes in the Eastern Lowlands of Bolivia." Forests 16, no. 3 (2025): 496. https://doi.org/10.3390/f16030496.
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Forest communities around the world have great difficulties in utilizing the economic potential of their forests, especially timber, under current technical requirements and legal frameworks. The present study examines the feasibility of motor–manual timber management among indigenous Chiquitano communities in Bolivia’s Eastern Lowlands. It evaluates local practices, tests technical optimization options, and assesses their technical, financial, and environmental impacts. Findings reveal that traditional motor–manual timber production is scarcely profitable, exacerbated by burdensome legal frameworks and limited market access. However, motor–manual forest management remains an essential source of income for communities, and it constitutes an important option for rural development. Field tests demonstrate that, with the use of better equipment such as quality chainsaws, and improved maintenance and workflows, productivity and profitability of local logging can be enhanced. Despite a low environmental impact, optimized motor–manual timber management continues to be constrained by governance challenges, logistical limitations, and limited markets for locally produced timber. The study recommends optimizing these aspects, including targeted technical support, market development, simplified legal frameworks, and the setting up of robust local governance structures to replace ineffective centralized command and control approaches. These improvements would enable communities to sustainably use timber from their forests while addressing their socio-economic needs. The findings underscore the potential of logging by local communities as an alternative to large-scale mechanized logging, for Bolivia and in other tropical forest countries.
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Dewnath, N., P. Bholanath, R. Rivas Palma, B. Freeman, and P. Watt. "USING GUYANA’S MONITORING REPORTING & VERIFICATION SYSTEM TO GUIDE NATIONAL FOREST MANAGEMENT & DECISION MAKING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W11 (February 14, 2020): 43–50. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w11-43-2020.
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Abstract. The Guyana Forestry Commission’s (GFC) Monitoring, Reporting and Verification System (MRVS) is a combined Geographic Information System (GIS) and field-based monitoring system, which has underpinned the conducting of a historical assessment of forest cover as well as eight national assessments of forest area change to date. The System seeks to provide the basis for measuring verifiable changes in Guyana’s forest cover and resultant carbon emissions from Guyana’s forests, which will provide the basis for results-based REDD+ compensation in the long-term. With the continuous compilation, analysis and dissemination of MRVS results on a typically annual basis, the GFC envisioned a larger role for this data, in informing national processes such as natural resources policy and management. This resulted in a significant broadening of the application of the MRVS data and products for purposes that are aligned or complementary to national REDD+ objectives and forest policy and management. These broader applications have allowed for a beneficial shift towards the increased use of remote sensing data and scientific reporting to inform forest management, governance and decision making on natural resource management across forested land. This has resulted in a transformation in the nature of data available to inform decision making on forest management and governance, and overall environmental oversight, from predominantly social science data and factors to now incorporating remote sensing and scientific observations and reporting. Primary decision makers are turning to scientific based reporting to determine best approaches for developmental initiatives in Guyana. This study shows how Guyana has demonstrated significant progress in making remote sensing products accessible and useful to policy makers in Guyana.
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Nastran, Mojca. "Visiting the Forest with Kindergarten Children: Forest Suitability." Forests 11, no. 6 (2020): 696. http://dx.doi.org/10.3390/f11060696.
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By providing ecosystem services, urban forests contribute significantly to the well-being of urban populations. Urban forests, along with other urban green spaces, are often the closest natural environment in the city where a child can play. The majority of pre-school children spend a large part of the day in kindergarten, which means that forest visits should have a prominent place in the kindergarten curriculum. Therefore, this study focuses on making the forest more suitable and thus more accessible for visits with children. The first goal of the research is to identify teachers’ preferences for the forest environment they visit with a group of pre-school children. The second goal is to present a forest suitability model for a visit with kindergarten children based on the teachers’ preferences. Based on the research survey conducted among the teachers in Slovenian public kindergartens, we formed and evaluated the criteria for the construction of a model of forest suitability for a visit with children. As the most important requirement for visiting a forest, the teachers note its proximity. They prefer a mature, mixed forest, with a bit of undergrowth, dead wood, and a presence of water and a meadow. Based on the identified criteria, we used the multi-criteria evaluation method in the GIS-environment in order to build a model of urban forest suitability for a visit with kindergarten groups of children in the study area of the City of Ljubljana, Slovenia. The results are useful in urban forest planning and management to ensure better forest suitability and accessibility for visits by children. Suitability maps can be used as one of the spatial foundations necessary for an integrated urban forest planning with emphasis on social functions. The model can be adapted beyond Slovenia to different spatial and social requirements and contexts.
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Chaves, Marcelo Vitor Gualberto Santos, Marcelo Lourençoni Pauletti, Samuel José Silva Soares da Rocha, Lucas Rezende Gomide, and Carolina Souza Jarochinski e. Silva. "Edentree: A web application for optimal rotation age analysis." Revista Árvore 49, no. 1 (2025): 1–18. https://doi.org/10.53661/1806-9088202549263865.
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The R/Shiny package is a tool that allows the creation of interactive web applications, transforming complex analyses into accessible interfaces. In the forestry sector, Shiny’s potential is still little explored, despite its applications in areas such as forest inventory, fire monitoring, LiDAR data analysis, and biomass and carbon estimates in Brazilian forests. This tool has been adopted by researchers and companies for its ability to generate interactive statistical reports and dashboards, contributing to data visualization and supporting forestry decision-making. From this, the objective of this research was to develop a web application in R/Shiny for the forestry field that, through the calculation of biological and financial indicators, would assist the user in making decisions regarding their forestry project. The main packages used to create the web application interface were: Shiny, bs4Dash, and golem, which offer pre-built functionalities and allow the application to be developed with less effort. For validation, data from a Eucalyptus urograndis forest plantation in Alagoinhas, Bahia, collected in 2021, were used, along with Microsoft Excel and Planin® software. As a result, a functional and online web application was obtained for the forestry field, with satisfactory performance in the validation stages, meeting the established criteria. Thus, the developed tool can contribute to support decision-making during the planning and management of forest plantations.
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Aahna, Bandula. "Satellite based estimation of forest biomass for structural resource planning using gaussian processes and sentinel-2 imagery." i-manager's Journal on Structural Engineering 13, no. 3 (2024): 34. https://doi.org/10.26634/jste.13.3.21857.
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This study presents a replicable, cost-efficient method for estimating forest biomass critical for sustainable structural material sourcing using Sentinel-2 satellite imagery and Gaussian Process Regression. A simplified inventory method, coupled with spectral data in the visible to mid-infrared bands, enables accurate biomass quantification across diverse forest structures in Mediterranean climates. Compared to traditional LiDAR-based techniques, this approach offers faster, lower-cost deployment without significant trade-off in accuracy, making it suitable for applications in construction timber forecasting, infrastructure planning, and environmental assessments. The method has been validated across several Mediterranean forest types and is packaged in a freely accessible programming tool for direct integration into engineering planning workflows.
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Jain, Pinkal, and Vikas Thada. "Securing the Data Using an Efficient Machine Learning Technique." International Journal of Experimental Research and Review 40, Spl Volume (2024): 217–26. http://dx.doi.org/10.52756/ijerr.2024.v40spl.018.
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More accessible data and the rise of advanced data analysis contribute to using complex models in decision-making across various fields. Nevertheless, protecting people’s privacy is vital. Medical predictions often employ decision trees due to their simplicity; however, they may also be a source of privacy violations. We will apply differential privacy to this end, a mathematical framework that adds random values to the data to provide secure confidentiality while maintaining accuracy. Our novel method Dual Noise Integrated Privacy Preservation (DNIPP) focuses on building decision forests to achieve privacy. DNIPP provides more protection against breaches in deep sections of the tree, thereby reducing noise in final predictions. We combine multiple trees into one forest using a method that considers each tree’s accuracy. Furthermore, we expedite this procedure by employing an iterative approach. Experiments demonstrate that DNIPP outperforms other approaches on real datasets. This means that DNIPP offers a promising approach to reconciling accuracy and privacy during sensitive tasks. In DNIPP, the strategic allocation of privacy budgets provides a beneficial compromise between privacy and utility. DNIPP protects privacy by prioritizing privacy concerns at lower, more vulnerable nodes, resulting in accurate and private decision forests. Furthermore, the selective aggregation technique guarantees the privacy of a forest by combining multiple data points. DNIPP provides a robust structure for decision-making in delicate situations, ensuring the model's effectiveness while safeguarding personal privacy.
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Li, Yang. "Random Forest model-based risk prediction of COVID-19 regional infection." Applied and Computational Engineering 53, no. 1 (2024): 1–8. http://dx.doi.org/10.54254/2755-2721/53/20241126.
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The current prevalence of the COVID-19 pandemic worldwide has posed numerous challenges and questions. To assist governments, medical institutions, and the public in making informed decisions and minimize the risk of further spread of COVID-19, this paper employs the Random Forest model to predict the infection risk within certain regions. The dataset utilized underwent data cleaning and feature engineering, allowing predictions to be made using publicly accessible data such as local basic climate conditions. After conducting performance comparisons with other common machine learning models, including Linear Regression and Decision Tree Regressor, it was found that the Random Forest Regressor model exhibited superior performance across all evaluation metrics, with all error values below 0.05. Notably, the MAE for the Random Forest model was only 0.001089. This strongly suggests that the Random Forest model outperforms the other models used in this task.
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Mercan, Gamze, and Zümrüt Varol Selçuk. "Systematic review of research on reality technology-based forest education." SHS Web of Conferences 206 (2024): 01002. https://doi.org/10.1051/shsconf/202420601002.
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This systematic review explores the effectiveness of reality technology programs, such as augmented reality (AR) and virtual reality (VR), in enhancing forest education. By evaluating 13 selected studies from various academic databases, this research examines both the cognitive and affective outcomes of incorporating reality technologies into forest-related educational programs. The findings reveal that AR and VR significantly improve learning experiences by fostering deeper cognitive understanding and emotional engagement with forest environments and ecological concepts. Specifically, augmented reality was found to be highly effective in facilitating interactive, two-way communication, making learning more accessible and engaging for students. Additionally, virtual reality programs offered immersive experiences that enhanced participants’ emotional connection to the subject matter. Both technologies contributed to overcoming traditional barriers in forest education, such as geographic limitations and resource constraints. The cognitive effects were reported in 90% of the studies, while affective benefits were evident in 100% of the cases. This review underscores the transformative potential of reality technology in making forest education more dynamic, inclusive, and effective, providing valuable insights for educators and researchers looking to further explore the integration of AR and VR in educational settings. These findings offer a solid foundation for future research and the practical application of reality technologies in environmental education.
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Marano, Langella, Basile, et al. "A Geospatial Decision Support System Tool for Supporting Integrated Forest Knowledge at the Landscape Scale." Forests 10, no. 8 (2019): 690. http://dx.doi.org/10.3390/f10080690.
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Forests are part of a complex landscape mosaic and play a crucial role for people living both in rural and urbanized spaces. Recent progresses in modelling and Decision Support System (DSS) applied to the forestry sector promise to improve public participative forest management and decision-making in planning and conservation issues. However, most DSS are not open-source systems, being in many cases software designed for site-specific applications in forest ecosystems. Furthermore, some of these systems often miss challenging the integration of other land uses within the landscape matrix, which is a key issue in modern forestry planning aiming at linking recent developments in open-source Spatial-DSS systems to sectorial forest knowledge. This paper aims at demonstrating that a new type of S-DSS, developed within the Life+ project SOILCONSWEB over an open-source Geospatial Cyber-Infrastructure (GCI) platform, can provide a strategic web-based operational tool for forest resources management and multi-purpose planning. In order to perform simulation modelling, all accessible via the Web, the GCI platform supports acquisition and processing of both static and dynamic data (e.g., spatial distribution of soil and forest types, growing stock and yield), data visualization and computer on-the-fly applications. The DSS forestry tool has been applied to a forest area of 5,574 ha in the southern Apennines of Peninsular Italy, and it has been designed to address forest knowledge and management providing operational support to private forest owners and decision-makers involved in management of forest landscape at different levels. Such a geospatial S-DSS tool for supporting integrated forest knowledge at landscape represents a promising tool to implement sustainable forest management and planning. Results and output of the platform will be shown through a short selection of practical case studies.
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Romaszewski, Michał, Przemysław Sekuła, Przemysław Głomb, Michał Cholewa, and Katarzyna Kołodziej. "Through the Thicket: A Study of Number-Oriented LLMS Derived from Random Forest Models." Journal of Artificial Intelligence and Soft Computing Research 15, no. 3 (2025): 279–98. https://doi.org/10.2478/jaiscr-2025-0014.
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Abstract This paper introduces a novel approach to training Large Language Models (LLMs) using knowledge transfer from a Random Forest (RF) ensemble. By converting RF decision paths into natural language, this method enhances both the classification accuracy and explanation capabilities of LLMs. Our approach integrates three preprocessing techniques: Relation Encoding, Integer Normalisation, and Verbal Description of Values, tailored for numerical data, improving the model’s ability to interpret structured inputs effectively. Leveraging RF’s ensemble properties, we generate rule-based explanations that can be objectively validated, offering a cost-effective alternative to human evaluations. Experiments on well-known datasets demonstrate high classification accuracy highlighting the potential of our framework for numerical and structured data applications. This study also contributes to Explainable Artificial Intelligence (XAI) by providing LLMs with structured, objectively verifiable explanations, making them more accessible and interpretable for real-world decision-making tasks.
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PM, Harshitha. "Crisis Connect: Real-time Emergency Response System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 1301–6. https://doi.org/10.22214/ijraset.2025.67069.
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The "Crisis-Connect" project addresses the urgent need for effective disaster management solutions in the face of increasing natural and man-made disasters. By leveraging an extensive sensor network, the project collects real-time data on environmental parameters such as humidity, temperature, and ground movement. This data is analyzed using advanced machine learning models, including Random Forest and Support Vector Machines (SVMs), to accurately predict the likelihood of disasters like floods, forest fires, and earthquakes. The early detection and prediction capabilities of "Crisis-Connect" enable proactive emergency responses, facilitating timely evacuations and resource deployment to high-risk areas. In addition to predictive analytics, "Crisis-Connect" enhances coordination and information sharing during rescue operations, a critical need in the chaotic aftermath of disasters. Sensor data and victim locations are securely stored on cloud-based platforms, making them readily accessible to emergency teams. This system allows for informed decision-making and efficient management of rescue efforts.
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Aikpon, Gorgias, Kourouma Koura, and Jean C. Ganglo. "The main forest species encountered in southern and central Benin, West Africa." Biodiversity Data Journal 12 (September 5, 2024): e129134. https://doi.org/10.3897/BDJ.12.e129134.
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The south of Benin, a country in West Africa, is still home to remnants of dense forests that benefit from a particularly rainy sub-equatorial climate, with annual rainfall of up to 1,200 mm. These forest ecosystems are an integral part of the West African forest block, which stretches from Liberia to Togo. However, despite their richness and ecological importance, these forests are unfortunately subject to strong human pressures, particularly from slash-and-burn agriculture, intensive logging and the growing urbanisation of coastal areas. Preserving these forests is crucial, however, as they are home to remarkable plant and animal biodiversity, with many endemic species. What's more, these forests play an essential role in regulating the local climate, protecting soil and water resources, as well as providing local populations with a vital source of energy wood, non-timber forest products and support for their traditional agricultural practices. Faced with these conservation challenges, identifying and characterising the main tree species found in the forests of southern and central Benin, forest species and their ecology is an essential prerequisite for implementing sustainable management and restoration strategies for these threatened forest ecosystems in southern Benin. This work aims to identify and draw attention to the different forest species, specially tree forest species present in southern and central Benin.The dataset provides information on forest species found in southern and central Benin, West Africa. This dataset is extremely useful for forestry research, as it focuses mainly on the various forest species of major importance. It can be used as a basis for characterising individuals or populations of species, based on their abundance in relation to anthropogenic pressure and changes in environmental conditions.These species are characteristic of forests and, above all, are of particular interest both to populations and to managers of protected areas. Field collections were initiated in 2007 as part of natural forest inventory work. The data collected have been completed by various field works that followed this work on forest species in southern Benin. The latest version of the dataset is publicly and freely accessible on the GBIF website at the address https://www.gbif.org/dataset/aff3a10a-a86b-4eff-98e4-d63f92fd6f7e.It should be noted that the fact that the collection and monitoring were carried out in southern Benin, a region known for its great diversity of species, over a period of 10 years making these data particularly relevant information to study the effects of climate change and human pressure on ecosystems in this area.
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Hewson, Jennifer, Stefano Crema, Mariano González-Roglich, Karyn Tabor, and Celia Harvey. "New 1 km Resolution Datasets of Global and Regional Risks of Tree Cover Loss." Land 8, no. 1 (2019): 14. http://dx.doi.org/10.3390/land8010014.
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Despite global recognition of the social, economic and ecological impacts of deforestation, the world is losing forests at an alarming rate. Global and regional efforts by policymakers and donors to reduce deforestation need science-driven information on where forest loss is happening, and where it may happen in the future. We used spatially-explicit globally-consistent variables and global historical tree cover and loss to analyze how global- and regional-scale variables contributed to historical tree cover loss and to model future risks of tree cover loss, based on a business-as-usual scenario. Our results show that (1) some biomes have higher risk of tree cover loss than others; (2) variables related to tree cover loss at the global scale differ from those at the regional scale; and (3) variables related to tree cover loss vary by continent. By mapping both tree cover loss risk and potential future tree cover loss, we aim to provide decision makers and donors with multiple outputs to improve targeting of forest conservation investments. By making the outputs readily accessible, we anticipate they will be used in other modeling analyses, conservation planning exercises, and prioritization activities aimed at conserving forests to meet national and global climate mitigation targets and biodiversity goals.
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Altarriba Bertran, Ferran, Jordi Márquez Puig, Maria Llop Cirera, et al. "The Wild Probes: Towards a Collection of Hybrid Tools for Situated, Caring & Playful Co-design within the Forest." Temes de Disseny, no. 39 (July 27, 2023): 158–75. http://dx.doi.org/10.46467/tdd39.2023.158-175.
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The Wild Probes (WPs) are a set of hybrid tools for designers and researchers to facilitate multi-stakeholder co-design engagements within the forest. They support situated forestry future-making by helping the participants of a co-design process pay attention to, reflect on, ideate around, and document their forestry experiences in ways that can inspire contextually grounded forest-related ideation. Here we present the design and early use of the first iteration of the WPs. 
 The WPs extend existing tools available to designers by adapting their underlying mechanisms to the idiosyncratic character of the forest. We designed them building on recent research on the methodological underpinnings of (co-)designing for and from the forest. The WPs run on affordable, widely accessible electronics and can easily be built with basic DIY skills and equipment. We thus invite others to replicate, enhance, and repurpose them. Overall, here we contribute a first step towards creating a collection of tools to support co-design that is situated in the forest. We hope other designers will find our proposals useful and contribute to growing the collection by creating new WPs of their own.
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Mustafa, Mutya Qurratu'ayuni, Muhammad Riza Iqbal Latief, and Dewi Febriani. "Financing-Limit Prediction Classifier in Islamic Bank Using Tree-Based Algorithms." Journal of Islamic Contemporary Accounting and Business 3, no. 1 (2025): 22–39. https://doi.org/10.30993/jicab.v3i1.520.
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Islamic banks are one of the financial institutions that has been proven to be the catalyst to end extreme poverty in the world. However, amid the massive development of Industry 5.0, research about technology adaptation in Islamic banks is still considered rare. The aim of this study is to develop a technology that will help Islamic banks in making their financing decision more efficient. By using the current outstanding financing data in an Islamic bank, this study proposes a machine learning algorithm that could predict a financing limit based on customer classification. The tree-based learning algorithms used to build the algorithm have shown impressive results. The results show that the basic algorithm which is the Decision Tree gives 86% prediction accuracy. The algorithm is then improved by using the Random Forest algorithm. The Random Forest algorithm gives 91% prediction accuracy which significantly improves the base learning algorithm. Future research in this area is needed as the need to implement sophisticated technology is prominent in making Islamic banking more accessible across the globe.
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Needham, Jessica, Cory Merow, Chia-Hao Chang-Yang, Hal Caswell, and Sean M. McMahon. "Inferring forest fate from demographic data: from vital rates to population dynamic models." Proceedings of the Royal Society B: Biological Sciences 285, no. 1874 (2018): 20172050. http://dx.doi.org/10.1098/rspb.2017.2050.
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As population-level patterns of interest in forests emerge from individual vital rates, modelling forest dynamics requires making the link between the scales at which data are collected (individual stems) and the scales at which questions are asked (e.g. populations and communities). Structured population models (e.g. integral projection models (IPMs)) are useful tools for linking vital rates to population dynamics. However, the application of such models to forest trees remains challenging owing to features of tree life cycles, such as slow growth, long lifespan and lack of data on crucial ontogenic stages. We developed a survival model that accounts for size-dependent mortality and a growth model that characterizes individual heterogeneity. We integrated vital rate models into two types of population model; an analytically tractable form of IPM and an individual-based model (IBM) that is applied with stochastic simulations. We calculated longevities, passage times to, and occupancy time in, different life cycle stages, important metrics for understanding how demographic rates translate into patterns of forest turnover and carbon residence times. Here, we illustrate the methods for three tropical forest species with varying life-forms. Population dynamics from IPMs and IBMs matched a 34 year time series of data (albeit a snapshot of the life cycle for canopy trees) and highlight differences in life-history strategies between species. Specifically, the greater variation in growth rates within the two canopy species suggests an ability to respond to available resources, which in turn manifests as faster passage times and greater occupancy times in larger size classes. The framework presented here offers a novel and accessible approach to modelling the population dynamics of forest trees.
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Surový, Peter, and Karel Kuželka. "Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review." Forests 10, no. 3 (2019): 273. http://dx.doi.org/10.3390/f10030273.
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In recent decades, remote sensing techniques and the associated hardware and software have made substantial improvements. With satellite images that can obtain sub-meter spatial resolution, and new hardware, particularly unmanned aerial vehicles and systems, there are many emerging opportunities for improved data acquisition, including variable temporal and spectral resolutions. Combined with the evolution of techniques for aerial remote sensing, such as full wave laser scanners, hyperspectral scanners, and aerial radar sensors, the potential to incorporate this new data in forest management is enormous. Here we provide an overview of the current state-of-the-art remote sensing techniques for large forest areas thousands or tens of thousands of hectares. We examined modern remote sensing techniques used to obtain forest data that are directly applicable to decision making issues, and we provided a general overview of the types of data that can be obtained using remote sensing. The most easily accessible forest variable described in many works is stand or tree height, followed by other inventory variables like basal area, tree number, diameters, and volume, which are crucial in decision making process, especially for thinning and harvest planning, and timber transport optimization. Information about zonation and species composition are often described as more difficult to assess; however, this information usually is not required on annual basis. Counts of studies on forest health show an increasing trend in the last years, mostly in context of availability of new sensors as well as increased forest vulnerability caused by climate change; by virtue to modern sensors interesting methods were developed for detection of stressed or damaged trees. Unexpectedly few works focus on regeneration and seedlings evaluation; though regenerated stands should be regularly monitored in order to maintain forest cover sustainability.
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Begum, M. Fathima, Lekha Sree C, and Manasa P. "Enhancement of Web Application Security using SQLMap and Machine Learning." International Research Journal of Innovations in Engineering and Technology 09, Special Issue (2025): 267–72. https://doi.org/10.47001/irjiet/2025.inspire43.
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SQL Injection (SQLi) is a critical vulnerability that allows attackers to manipulate databases through malicious queries. To detect such vulnerabilities in web applications, we integrated SQLMAP, a penetration testing tool, with a Random Forest machine learning model. SQLMAP automates vulnerability detection, and its commands are further automated to enable users to perform tests using simple numerical inputs, improving usability and efficiency. Data collected through SQLMAP is analyzed by the Random Forest classifier, trained on labeled datasets of malicious and benign queries, to predict vulnerabilities with high accuracy. Automation streamlines the process, making penetration testing faster and accessible even to non-technical users. This scalable approach can be expanded to detect other vulnerabilities like Cross-Site Scripting or Remote Code Execution, providing an efficient and user-friendly solution that enhances web application security while contributing to broader cyber security advancements.
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McEwan, Kirsten, Kari S. Krogh, Kim Dunlop, Mahnoor Khan, and Alyssa Krogh. "Virtual Forest Bathing Programming as Experienced by Disabled Adults with Mobility Impairments and/or Low Energy: A Qualitative Study." Forests 14, no. 5 (2023): 1033. http://dx.doi.org/10.3390/f14051033.
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Background: Although access to nature is demonstrated to benefit health and wellbeing, adults with mobility impairments and/or low energy often face barriers in accessing nature environments and nature-based programs. This study aimed to examine the experiences and impacts of virtual forest bathing by capturing the perspectives of disabled adults with mobility impairments and/or low energy. Methods: A total of 26 adults with mobility impairments provided written and spoken qualitative feedback during and following virtual forest bathing programs and 23 participants provided feedback at a one month follow-up. Virtual programs were presented online, using an accessible format, 2D videos, and images of nature accompanied by guidance led by a certified forest bathing guide and mindfulness teacher. The programs involved disabled facilitators and participants, which created a social environment of peer support. Results: Qualitative thematic analysis revealed 10 themes comprising intervention themes (virtual delivery and soothing facilitation); process themes (nature connection, relaxation, embodiment, and memories with complex emotions); and outcome themes (happiness, agency, metaphor making, and belonging). Conclusions: Virtual forest bathing may offer an effective adjunct to improve wellbeing and provide peer support for disabled adults with mobility impairments and/or low energy.
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Singh, Chandrakant, Ruud van der Ent, Ingo Fetzer, and Lan Wang-Erlandsson. "Multi-fold increase in rainforest tipping risk beyond 1.5–2 °C warming." Earth System Dynamics 15, no. 6 (2024): 1543–65. https://doi.org/10.5194/esd-15-1543-2024.
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Abstract. Tropical rainforests rely on their root systems to access moisture stored in soil during wet periods for use during dry periods. When this root zone soil moisture is inadequate to sustain a forest ecosystem, they transition to a savanna-like state, losing their native structure and functions. Yet the influence of climate change on ecosystem's root zone soil moisture storage and the impact on rainforest ecosystems remain uncertain. This study assesses the future state of rainforests and the risk of forest-to-savanna transitions in South America and Africa under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Using a mass-balance-based empirical understanding of root zone storage capacity (Sr), defined as the maximum volume of root zone soil moisture per unit area accessible to vegetation's roots for transpiration, we project how rainforest ecosystems will respond to future climate changes. We find that under the end-of-the-21st-century climate, nearly one-third of the total forest area will be influenced by climate change. As the climate warms, forests will require a larger Sr than they do under the current climate to sustain their ecosystem structure and functions, making them more susceptible to water limitations. Furthermore, warming beyond 1.5–2 °C will significantly elevate the risk of a forest–savanna transition. In the Amazon, the forest area at risk of such a transition grows by about 1.7–5.8 times in size compared to the immediate lower-warming scenario (e.g. SSP2-4.5 compared to SSP1-2.6). In contrast, the risk growth in the Congo is less substantial, ranging from 0.7–1.7 times. These insights underscore the urgent need to limit the rise in global surface temperature below the Paris Agreement to conserve rainforest ecosystems and associated ecosystem services.
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Anitha Bujunuru, Nanam Shiva Kumar, Pedimalla Nishwanth, and Mylaram Manoj Kumar. "Real-Time Zigbee Sensor Network for Forest Monitoring and Wildlife Conservation." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 5 (2025): 423–26. https://doi.org/10.51583/ijltemas.2025.140500043.
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Abstract Tree smuggling, especially of high-value species like sandalwood and teak, poses a significant threat to biodiversity and forest ecosystems. These trees are highly sought after for their commercial value, making them frequent targets of illegal logging operations. This illicit activity not only depletes valuable natural resources but also contributes to deforestation and environmental degradation. In response to this growing concern, an IoT-based monitoring system has been developed to detect and prevent such activities. At the core of the system is a Node MCU microcontroller, integrated with multiple sensors such as accelerometers to detect tree tilting, fire sensors for identifying potential fire hazards, ultrasonic sensors for motion detection, and pH sensors to monitor soil health. These sensors provide real-time data, accessible via an Android smartphone application, enabling immediate awareness and action. Supporting components like buzzers, water pumps, and relays offer responsive measures such as alarms and fire suppression. This innovative solution aims to safeguard endangered tree species, mitigate illegal activities, and promote ecological sustainability through smart, technology-driven forest management.
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García, Oscar. "Plasticity as a Link Between Spatially Explicit, Distance-Independent, and Whole-Stand Forest Growth Models." Forest Science 68, no. 1 (2021): 1–7. http://dx.doi.org/10.1093/forsci/fxab043.
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Abstract Models at various levels of resolution are commonly used for both forest management and ecological research. They all have comparative advantages and disadvantages, making desirable a better understanding of the relationships between various approaches. Accounting for crown and root morphological plasticity in the limit where equilibrium among neighbors is reached (perfect plasticity) transforms spatial models into nonspatial, distance-independent versions. The links between spatial and nonspatial models obtained through a perfect plasticity assumption are more realistic than ignoring spatial structure by a mean field approximation. This article also reviews the connection between distance-independent models and size distributions and how distributions evolve over time and relate to whole-stand descriptions. In addition, some ways in which stand-level knowledge feeds back into detailed individual-tree formulations are demonstrated. This presentation is intended to be accessible to nonspecialists.
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Mostafa, Nihal N., and Ibrahim Elhenawy. "Neutrosophic AHP Method with Machine Learning Algorithms to The Priority of Maintenance in the Facility of Healthcare." Journal of Neutrosophic and Fuzzy Systems 1, no. 2 (2021): 114–23. http://dx.doi.org/10.54216/jnfs.010208.
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The creation of decision-support techniques that can be used in the planned preservation and recertification ordering of healthcare facility investments is regarded as an assignment of extremely high difficulty due to the multitude of ambiguity and levels of individuality that is accessible in a decision-making procedure of this nature. This research employs a mixture of Neutrosophic logic and the Analytical Hierarchical Process (AHP) to generate a trustworthy score of hospital structure facilities depending on their varying levels of evaluation and achievement deficiencies. This is done to reduce the partiality that is related to expert-driven choices and to make the rankings more objective. This is additionally merged with the innovative use of machine learning techniques in this field, specifically: Random Forest, and Naive Bayes, to automate the process of setting priorities and making it reproducible, thereby reducing the essential for extra professional decisions.
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TAN, MING KAI, RAZY JAPIR, and ARTHUR Y. C. CHUNG. "Uncovering the Grylloidea and Tettigonioidea (Orthoptera: Ensifera) in the Forest Research Center (Sepilok) Entomological Collection." Zootaxa 4701, no. 4 (2019): 301–49. http://dx.doi.org/10.11646/zootaxa.4701.4.1.
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Type specimens carry valuable information that can facilitate biodiversity research, especially in an era of mass extinction and unprecedent anthropogenic climate change. For Orthoptera, a few initiatives, including the Orthoptera Species File and digitization of collections by numerous museums, have helped to make images and information about the type specimens available on the World Wide Web. However, many local collections, especially those from the poorly studied regions of Southeast Asia, are still not available to the public. The collection of Grylloidea and Tettigonioidea gathered at the Forest Research Center, Sepilok in Sandakan, Sabah (Borneo) is one such example. We examined, identified, and imaged 12 and 45 species of Grylloidea and Tettigonioidea respectively from the collection (deposited as of October 2019) to facilitate taxonomic research by making the species in the collection accessible for everyone.
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Nagesh, V. "CROP RECOMMENDATION SYSTEM USING KNN ALGORITHM AND RANDOM FOREST." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27660.
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In agriculture, the integration of machine learning has been a long-standing aspiration, resulting in significant advancements. While machine learning models have been developed for crop and yield predictions, traditional algorithms like decision trees often fall short of delivering the desired accuracy. This paper introduces an accessible and user-friendly solution for crop recommendations and yield predictions. Users provide inputs such as temperature, humidity, soil pH, and rainfall. To enhance accuracy, a hybrid approach using K-nearest neighbor (KNN) and Random Forest (RF) algorithms is employed. The K-nearest neighbor (KNN) algorithm achieves an impressive accuracy rate of 98%. Additionally, the Random Forest (RF) algorithm attains a commendable 96% accuracy by aggregating multiple decision trees. These high accuracy rates signify the system's potential to empower farmers with data-driven insights for crop selection and yield projections. Furthermore, the user-friendly interface promises broader adoption within the agriculture sector, catering to users with varying levels of technical proficiency. To strengthen the system's credibility, transparency regarding data sources and quality is imperative. Utilizing accurate and relevant data for reliable predictions. In summary, this paper presents a promising solution for informed decision-making in agriculture, combining crop recommendations and yield predictions. Acknowledging the limitations of traditional approaches, it capitalizes on the strengths of K-nearest neighbor and Random Forest algorithms. Keywords: Crop recommendation, Yield prediction, Machine learning, KNN, Random Forest
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Potić, Ivan, Zoran Srdić, Boris Vakanjac, et al. "Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia." Applied Sciences 13, no. 14 (2023): 8289. http://dx.doi.org/10.3390/app13148289.
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Vegetation plays an active role in ecosystem dynamics, and monitoring its patterns and changes is vital for effective environmental resource management. This study explores the possibility of machine learning techniques and remote sensing data to improve the accuracy of forest detection. The research focuses on the southeastern part of the Republic of Serbia as a case study area, using Sentinel-2 multispectral bands. The study employs publicly accessible satellite data and incorporates different vegetation indices to improve classification accuracy. The main objective is to examine the practicability of expanding the input parameters for forest detection using a machine learning approach. The classification process is performed by employing support vector machines (SVM) algorithm and utilising the SVM module in the scikit-learn package. The results demonstrate that including vegetation indices alongside the multispectral bands significantly improves the accuracy of vegetation detection. A comprehensive assessment reveals an overall classification accuracy of up to 99.01% when the selected vegetation indices (MCARI, RENDVI, NDI45, GNDVI, NDII) are combined with the Sentinel-2 bands. This research highlights the potential of machine learning and remote sensing in forest detection and monitoring. The findings underscore the importance of incorporating vegetation indices to enhance classification accuracy using the Python programming language. The study’s outcomes provide valuable insights for environmental resource management and decision-making processes, particularly in regions with diverse forest ecosystems.
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Belousova, A. P. "Landscape Features affecting the dynamics of forest growth on agricultural lands of the Perm Territory." Лесоведение, no. 1 (August 8, 2024): 43–51. http://dx.doi.org/10.31857/s0024114824010052.
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The dynamics of forest growth on agricultural lands has been studied since the 1990s, which was the starting point of a massive reduction in their cultivation intensity. The total area of the overgrown agricultural land in the Perm Territory reached 58.8% of the total area of agricultural land as of 1985. The boundaries of the forested lands were recorded using remote sensing methods and the archival satellite images of medium spatial resolution. The dynamics analysis was carried out within the flat part of the Perm Territory, taking into account the soil regions’ boundaries. It has been established that the main natural factors of lands differentiation in terms of the scale and rate of their withdrawal from agricultural use are the small scale or uneven terrain of separate agricultural lands, making it less accessible for the agricultural machines, and differences in soil fertility. The least fertile soils, as well as the most difficult to cultivate, were the first to become susceptible to forest overgrowth. The more fertile soils were reforested later, as agricultural activity declined.
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Srbek-Araujo, Ana Carolina, Bárbara Victor Sonegheti, and Luiza de Carvalho Alzuguir. "Predation of a Red-browed Amazon, Amazona rhodocorytha (Salvadori, 1890), in Captivity by a Wild Margay, Leopardus wiedii (Schinz, 1821)." International Journal of Zoology and Animal Biology 8, no. 1 (2025): 1–4. https://doi.org/10.23880/izab-16000643.
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Leopardus wiedii is a small neotropical felid whose diet primarily consists of small mammals, although birds may constitute significant prey in certain regions. Although birds are part of the margay’s diet, no studies have documented psittacid predation in the wild. The only reported case of psittacid predation by the margay occurred in a scientific aviary in southern Brazil, within enclosures located in a forest remnant. The present study reports the predation of an Amazona rhodocorytha in captivity by a wild margay in southeastern Brazil. The predation likely occurred at night when the felid entered in the enclosure situated approximately 10 m from the forest edge. The parrot was partially consumed, and the margay was found trapped inside the enclosure the following morning. We suggest that psittacid predation, if it occurs in the wild, is opportunistic and sporadic. This rarity may be due to the social behavior of psittacids, which helps detect potential threats, and their use of high tree branches, making them less accessible to non-flying predators. These behaviors may result in a low predation rate, hindering detection in margay diet studies.
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Sugi Ardana, I. Made, and Yan Mitha Djaksana. "Perancangan Basis Data Kawasan Suci Danau Tamblingan dengan Menerapkan Model Data Relasional." Jurnal Syntax Admiration 4, no. 10 (2023): 1598–612. http://dx.doi.org/10.46799/jsa.v4i10.725.
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The indigenous community of Tamblingan highly reveres the forest as the true source of life (Merta Jati). Within the forest area, there are temples and shrines that are interconnected. Information about these sacred temples and shrines is currently limited to a few individuals who possess specific manuscripts, making it not easily accessible. A database is a crucial component of information technology implementation, as it facilitates faster information retrieval, enhances data security, and reduces data storage redundancy. The Database Lifecycle (DBLC) is a method that outlines the life cycle of a database. In this study, the design of a database for the sacred area of Lake Tamblingan is carried out, starting from conceptual database design to physical database design using a relational model. The design is created using the Power Designer tool. Data collection is conducted through literature review and interviews, while data processing is performed through descriptive analysis. This research aims to produce a database design for the sacred area of Lake Tamblingan that can be utilized for further studies, such as developing an information system for the sacred area of Lake Tamblingan.
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Ahamed, Mohammad Tanvir, Anna Danielsson, Szilárd Nemes, and Helena Carén. "MethPed: an R package for the identification of pediatric brain tumor subtypes." BMC Bioinformatics 17, no. 1 (2016): 262. https://doi.org/10.1186/s12859-016-1144-0.
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<strong>Background: </strong>DNA methylation profiling of pediatric brain tumors offers a new way of diagnosing and subgrouping these tumors which improves current clinical diagnostics based on histopathology. We have therefore developed the MethPed classifier, which is a multiclass random forest algorithm, based on DNA methylation profiles from many subgroups of pediatric brain tumors.<strong>Results: </strong>We developed an R package that implements the MethPed classifier, making it easily available and accessible. The package can be used for estimating the probability that an unknown sample belongs to each of nine pediatric brain tumor diagnoses/subgroups.<strong>Conclusions: </strong>The MethPed R package efficiently classifies pediatric brain tumors using the developed MethPed classifier. MethPed is available via Bioconductor: http://bioconductor.org/packages/MethPed/
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Paluba, Daniel, Josef Laštovička, Antonios Mouratidis, and Přemysl Štych. "Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems." Remote Sensing 13, no. 9 (2021): 1743. http://dx.doi.org/10.3390/rs13091743.
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This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.’s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.’s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range >50° and LIA interquartile range (IQR) >12°, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community.
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Peterson, Chris J. "The Silver Lining: The Benefits of Natural Disasters." Forest Science 48, no. 3 (2002): 623. http://dx.doi.org/10.1093/forestscience/48.3.623.
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Abstract In this welcome and accessible volume, Seth Reice, a stream ecologist who specializes in the study of disturbance effects on community structure, explains to the informed nonspecialist how important are disturbances to the health and diversity of ecosystems. It is important to note that this book is intended to enlighten a lay audience; it is not intended strictly or even primarily for forest scientists or ecologists. And therein lies its potential value: by making the benefits of natural disasters clear to the nonspecialist, Reice communicates how, by embracing the inevitability of natural disturbances and recognizing their benefits for natural systems, we can adopt more sustainable policies, behaviors, and attitudes. I wholeheartedly applaud this goal, and believe as a whole that Reice's book is an excellent step in that direction.
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Giri, Aviskar, Vasit Sagan, Haireti Alifu, et al. "A Wavelet Decomposition Method for Estimating Soybean Seed Composition with Hyperspectral Data." Remote Sensing 16, no. 23 (2024): 4594. https://doi.org/10.3390/rs16234594.
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Soybean seed composition, particularly protein and oil content, plays a critical role in agricultural practices, influencing crop value, nutritional quality, and marketability. Accurate and efficient methods for predicting seed composition are essential for optimizing crop management and breeding strategies. This study assesses the effectiveness of combining handheld spectroradiometers with the Mexican Hat wavelet transformation to predict soybean seed composition at both seed and canopy levels. Initial analyses using raw spectral data from these devices showed limited predictive accuracy. However, by using the Mexican Hat wavelet transformation, meaningful features were extracted from the spectral data, significantly enhancing prediction performance. Results showed improvements: for seed-level data, Partial Least Squares Regression (PLSR), a method used to reduce spectral data complexity while retaining critical information, showed R2 values increasing from 0.57 to 0.61 for protein content and from 0.58 to 0.74 for oil content post-transformation. Canopy-level data analyzed with Random Forest Regression (RFR), an ensemble method designed to capture non-linear relationships, also demonstrated substantial improvements, with R2 increasing from 0.07 to 0.44 for protein and from 0.02 to 0.39 for oil content post-transformation. These findings demonstrate that integrating handheld spectroradiometer data with wavelet transformation bridges the gap between high-end spectral imaging and practical, accessible solutions for field applications. This approach not only improves the accuracy of seed composition prediction at both seed and canopy levels but also supports more informed decision-making in crop management. This work represents a significant step towards making advanced crop assessment tools more accessible, potentially improving crop management strategies and yield optimization across various farming scales.
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P M, Pragathi, Suma H M, Rohini N, and Sampige B S. "Edge AI-based Bone Fracture Detection using TFlite." International Journal of Innovative Research in Advanced Engineering 12, no. 04 (2025): 138–48. https://doi.org/10.26562/ijirae.2025.v1204.04.
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Bone fractures are among the most common injuries requiring prompt and accurate diagnosis to ensure effective treatment. Traditional manual interpretation of X-ray images is often time-consuming and prone to human error, highlighting the need for automated solutions. This project presents an Edge AI-based system designed for detecting and classifying bone fractures using TensorFlow Lite. By leveraging lightweight deep learning models such as Convolutional Neural Networks (CNN) and MobileNet, and introducing a hybrid approach combining MobileNet with a Random Forest classifier, the system achieves high accuracy while maintaining computational efficiency. Developed using Python, the solution is optimized for real-time diagnosis on portable devices, making it highly accessible in both clinical and remote healthcare settings. Experimental results demonstrate improved fracture detection accuracy, supporting the potential of Edge AI to enhance diagnostic reliability and reduce the workload on medical professionals.
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Powers, Ryan P., Nicholas C. Coops, Jessica L. Morgan, et al. "A remote sensing approach to biodiversity assessment and regionalization of the Canadian boreal forest." Progress in Physical Geography: Earth and Environment 37, no. 1 (2012): 36–62. http://dx.doi.org/10.1177/0309133312457405.
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Successful conservation planning for the Canadian boreal forest requires biodiversity data that are both accessible and reliable. Spatially exhaustive data is required to inform on conditions, trends and context, with context enabling consideration of conservation opportunities and related trade-offs. However, conventional methods for measuring biodiversity, while useful, are spatially constrained, making it difficult to apply over wide geographic regions. Increasingly, remotely sensed imagery and methods are seen as a viable approach for acquiring explicit, repeatable and multi-scale biodiversity data over large areas. To identify relevant remotely derived environmental indicators specific to biodiversity within the Canadian boreal forest, we assessed indicators of the physical environment such as seasonal snow cover, topography and vegetation production. Specifically, we determined if the indicators provided distinct information and whether they were useful predictors of species richness (tree, mammal, bird and butterfly species). Using cluster analysis, we also assessed the applicability of these indicators for broad ecosystem classification of the Canadian boreal forest and the subsequent attribution of these stratified regions (i.e. clusters). Our results reveal that the indicators used in the cluster creation provided unique information and explained much of the variance in tree (92.6%), bird (84.07%), butterfly (61.4%) and mammal (22.6%) species richness. Spring snow cover explained the most variance in species richness. Results further show that the 15 clusters produced using cluster analysis were principally stratified along a latitudinal gradient and, while varied in size, captured a range of different environmental conditions across the Canadian boreal forest. The most important indicators for discriminating between the different cluster groups were seasonal greenness, a multipart measure of climate, topography and land use, and wetland cover, a measure of the percentage of wetland within a 1 km2 cell.
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Campbell, Gene E., and David C. White. "Interpretations of Illinois Stumpage Price Trends." Northern Journal of Applied Forestry 6, no. 3 (1989): 115–20. http://dx.doi.org/10.1093/njaf/6.3.115.
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Abstract To provide insight into the timber market accessible to owners of non-industrial private forestland in the central hardwoods region, we examine Illinois statewide average stumpage prices for 16 hardwood species and 2 species composites. Over the study period, upland hardwood stumpage prices have risen faster than inflation. Swings in stumpage prices during this same period have been frequent and significant, e.g., woods-run stumpage prices for upland hardwoods have changed as much as 41% within 1 year and 75% within 2 years. The frequency of price changes suggests a predominantly buyer controlled market. Illinois forest land-owners can increase their return on investment from forestland through judicious market timing when making timber sales, and by requiring competitive bids. When Illinois timber supply data are considered with the general price trends, a picture emerges of an ongoing market advantage for the production of high-value intolerant species, such as the oaks. North. J. Appl. For. 68:115-120, September 1989.
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Lackowska, Marta, Barbara Nowicka, Marta Bałandin, and Mirosław Grochowski. "Lakes sensitivity to climatic stress – a sociological assessment." Miscellanea Geographica 20, no. 4 (2016): 38–47. http://dx.doi.org/10.1515/mgrsd-2016-0025.
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AbstractOne of the conditions for effective water resources management in protected areas is local decision makers’ knowledge about potential threats caused by climate changes. Our study, conducted in the UNESCO Biosphere Reserve of Tuchola Forest in Poland, analyses the perception of threats by local stakeholders. Their assessments of the sensitivity of four lakes to the extreme weather events are compared with hydrological studies. The survey shows that the lakes’ varying responses to extreme weather conditions is rarely noticed by ordinary observers. Their perception is usually far from the hydrological facts, which indicates a lack of relevant information or a failure in making it widely accessible and understandable. Moreover, it is rather the human impact, not climate change, which is seen as the biggest threat to the lakes. Insufficient environmental knowledge may hinder the effective protection and management of natural resources, due to bad decisions and lack of the local communities’ support for adaptation and mitigation policies.
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Meidelfi, Dwiny, Hendrick, Fanni Sukma, and Srintika Yunni Kharisma. "Analysis of Eye Disease Classification by Comparison of the Random Forest Method and K-Nearest Neighbor Method." International Journal of Advanced Science Computing and Engineering 5, no. 2 (2023): 136–45. http://dx.doi.org/10.62527/ijasce.5.2.151.
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Eye disease is a serious issue all over the world, and image-based classification systems play an important role in the early detection and management of eye disease. This research compares the performance between Random Forest (RF) and K-Nearest Neighbor (KNN) classification models in identifying eye disorders using image datasets divided into four classes: "normal," "glaucoma," "cataract," and "diabetic retinopathy."Â Â The dataset is converted into a feature vector and then divided into training data and test data subsets. The analysis results show that the RF model achieved an accuracy level of 80%, whereas the KNN model achieved 70%. Based on these findings, it is possible to conclude that the RF model outperforms the other models in categorizing the types of eye illnesses in the dataset. A Python-based website was also built utilizing the Flask framework to build an interactive and real-time eye illness diagnosis system. Users can upload photos of their retinas to this website and quickly receive eye disease detection results. The adoption of this technology has a tremendous impact, making eye disease detection solutions more accessible. Furthermore, this solution plays an important role in the early detection and effective management of eye health cases.
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Yoo, Gilsang, Sungdae Hong, and Hyeocheol Kim. "Emotion Recognition and Multi-class Classification in Music with MFCC and Machine Learning." International Journal on Advanced Science, Engineering and Information Technology 14, no. 3 (2024): 818–25. http://dx.doi.org/10.18517/ijaseit.14.3.18671.
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Background music in OTT services significantly enhances narratives and conveys emotions, yet users with hearing impairments might not fully experience this emotional context. This paper illuminates the pivotal role of background music in user engagement on OTT platforms. It introduces a novel system designed to mitigate the challenges the hearing-impaired face in appreciating the emotional nuances of music. This system adeptly identifies the mood of background music and translates it into textual subtitles, making emotional content accessible to all users. The proposed method extracts key audio features, including Mel Frequency Cepstral Coefficients (MFCC), Root Mean Square (RMS), and MEL Spectrograms. It then harnesses the power of leading machine learning algorithms Logistic Regression, Random Forest, AdaBoost, and Support Vector Classification (SVC) to analyze the emotional traits embedded in the music and accurately identify its sentiment. Among these, the Random Forest algorithm, applied to MFCC features, demonstrated exceptional accuracy, reaching 94.8% in our tests. The significance of this technology extends beyond mere feature identification; it promises to revolutionize the accessibility of multimedia content. By automatically generating emotionally resonant subtitles, this system can enrich the viewing experience for all, particularly those with hearing impairments. This advancement not only underscores the critical role of music in storytelling and emotional engagement but also highlights the vast potential of machine learning in enhancing the inclusivity and enjoyment of digital entertainment across diverse audiences.
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Li, Xinze, Jiaxin Liu, Lan Mi, et al. "Prognosis Prediction and Treatment Recommendation for Follicular Lymphoma: Enhancing Decision-Making." Blood 144, Supplement 1 (2024): 6353. https://doi.org/10.1182/blood-2024-212407.
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Backgroud:Despite the abundance of therapeutic options, advanced-stage follicular lymphoma remains incurable. Randomized trials of rituximab maintenance (MR) have shown improved progression-free survival (PFS) in patients with follicular lymphoma, but its effect on overall survival remains inconclusive. Data on R-maintenance in the Chinese population has always been lacking, and there have been few reports on the duration of maintenance cycles and safety.To evaluate the impact of MR on overall survival based on patient and disease characteristics, and to explore certain adverse events, we utilized real-world patient electronic health records (EHR). Our objective was to develop a lymphoma disease model that aligns with EHR data characteristics and meets precision medicine needs. This involved constructing a knowledge analysis and treatment recommendation system to achieve precise risk stratification and diagnosis for lymphoma patients, and to enhance the effectiveness and sequencing of treatment plans. Methods: This study retrospectively included clinical data from 444 patients diagnosed with FL grades 1-3A, treated between November 1, 2001, and December 31, 2019, at four centers: Peking University Cancer Hospital, Peking University International Hospital, Inner Mongolia Cancer Hospital, and Tangshan Workers' Hospital. Data preprocessing and screening included handling missing values and performing basic statistical observations, such as plotting curves of progression time with or without R maintenance.The primary endpoint was POD24, while secondary endpoints included progression-free survival (PFS), overall survival (OS), and safety. We used a combination of random forest, uplift model, and catboost methods to predict the risk of tumor progression within 24 months. Features highly correlated with tumor progression were extracted based on model feature importance. Results: We conducted an evaluation using ten-fold cross-validation, covering baseline static information, laboratory tests, imaging, and pathological examination features from 444 patients. The model's AUROC for predicting POD24 was 0.859, AUPRC was 0.780, and accuracy was 0.768. Using feature selection via random forest and ranking by Qini importance, we identified that highly important features include PET-CT lesion length, SUVmax, lactate dehydrogenase, and β2-microglobulin, all of which are clinically accessible indicators. Regarding the model for R maintenance therapy for each patient, we utilized individual treatment effect (ITE) estimation. It was found that patients with SUVmax &gt; 17 had the lowest mean uplift, indicating significant benefit from R maintenance therapy when SUVmax is very high. The youngest patients (&lt;24 years old) had the lowest benefit level, while the oldest patients (≥65 years old) had the highest benefit level, suggesting that R maintenance therapy is more recommended for older patients. Conclusion：Our model offers evidence-based recommendations and highlights potential risks for FL patients, aiding healthcare professionals in real-world decision-making. This support can enhance patient adherence and improve overall outcomes.
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Mondal, Pinki, Xue Liu, Temilola E. Fatoyinbo, and David Lagomasino. "Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa." Remote Sensing 11, no. 24 (2019): 2928. http://dx.doi.org/10.3390/rs11242928.
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Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now more accessible to conduct such large-scale monitoring and assessment. Yet, few studies examine the reproducibility of such mangrove monitoring frameworks, especially in terms of generating consistent spatial extent. Our objective was to evaluate a combination of image processing approaches to classify mangrove forests along the coast of Senegal and The Gambia. We used freely available global satellite data (Sentinel-2), and cloud computing platform (Google Earth Engine) to run two machine learning algorithms, random forest (RF), and classification and regression trees (CART). We calibrated and validated the algorithms using 800 reference points collected using high-resolution images. We further re-ran 10 iterations for each algorithm, utilizing unique subsets of the initial training data. While all iterations resulted in thematic mangrove maps with over 90% accuracy, the mangrove extent ranges between 827–2807 km2 for Senegal and 245–1271 km2 for The Gambia with one outlier for each country. We further report “Places of Agreement” (PoA) to identify areas where all iterations for both methods agree (506.6 km2 and 129.6 km2 for Senegal and The Gambia, respectively), thus have a high confidence in predicting mangrove extent. While we acknowledge the time- and cost-effectiveness of such methods for the landscape managers, we recommend utilizing them with utmost caution, as well as post-classification on-the-ground checks, especially for decision making.
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Yudantha, Georgius Erlangga Mahendra, and Tranggono Tranggono. "Usability Analysis of the Gudang Madu Sumatera Website for Individual Customers using Nielsen's Attributes of Usability." Journal of Applied Science, Engineering, Technology, and Education 6, no. 1 (2024): 34–42. https://doi.org/10.35877/454ri.asci2734.
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Gudang Madu Sumatera (GMS) is a sustainable integrated beekeeping company established with the aim of empowering communities in the forest areas of Indonesia. To market its products more effectively, GMS designed a website to provide information about the company and its products, making it easier and more accessible to a wider audience. Therefore, a website with an effective design and functionality is needed to facilitate both customers and the company. Ensuring good usability is crucial for the website’s sustainability.Usability testing is a method used to ensure the quality of an application. The usability testing to be conducted will focus on usability aspects based on ISO 9126, which serves as a benchmark for determining the website's success. The usability evaluation will be conducted using the usability testing method, interpreting each usability factor measurement based on ISO 9126 and the Nielsen Model, which will result in a usability framework hypothesis test. By conducting the usability evaluation, the expected outcome is to understand how customers assess the system implemented on the Gudang Madu Sumatra website.
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Chaitrali B. Kamble and Kishor T. Mane. "A Review on Handwritten Recognition System Using Machine Learning Techniques." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 06 (2024): 1590–99. http://dx.doi.org/10.47392/irjaeh.2024.0218.
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Marathi language is the most widely spoken language in India, and its script is unique and complex Handwriting recognition of the Marathi language poses a significant challenge due to the variety in writing styles and the script's complexity. Machine learning techniques can help in building Marathi handwriting recognition systems that can accurately recognize handwritten Marathi text. The Devanagari script is the source of Marathi, the official language of Maharashtra. Devanagari script is used for the Marathi language and it has 12 vowels and 36 consonants. Handwritten character recognition in any script is a challenging task for researchers. Nowadays, handwritten Marathi character identification is the hardest problem. Sharing physical documents is a laborious and time-consuming task. Because of the structure, shape, various strokes, and writing styles, handwritten Marathi characters are more difficult to read as well as understand. Marathi handwritten recognition system is very essential in various aspects as further described. Preservation of cultural heritage. The mechanism of recognition facilitates accessibility by making Marathi information more easily accessible to people who are visually impaired or have difficulty with traditional text input techniques. The paper focuses on a review of methods used for the development of handwritten character recognition systems using machine learning approaches, including Sanskrit, Hindi, Marathi, and Maithili languages. Different machine learning classifiers such as Decision Tree, Nearest Centroid, KNN, Extra Trees, and Random Forest were implemented and compared for their performance. Extra Trees and Random Forest showed better accuracy.
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Sharma, Harish. "Disease Prediction and Medication Recommendation Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46458.
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Abstract—Advancements in healthcare have greatly benefited from machine learning, especially in disease prediction and medication recommendations. This project introduces a Python-based machine learning model designed to predict potential diseases based on user-reported symptoms and suggest suitable medications. We implemented and evaluated four machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Naïve Bayes—to determine the most accurate model for deployment, ensuring reliable predictions. To make the system user-friendly, we developed an interactive graphical interface using Tkinter. This allows users to easily input their symptoms and receive potential diagnoses, along with relevant precautions and medication suggestions. The recommendation system identifies appropriate pharmaceutical salts for the diagnosed condition, improving medication accuracy and guidance. By comparing multiple machine learning models, our approach ensures that the most accurate algorithm is selected, enhancing the reliability of disease predictions. The intuitive GUI makes healthcare support more accessible, even for individuals without medical expertise, bridging the gap between users and early healthcare assessments. Future improvements will focus on expanding the dataset, integrating deep learning techniques for better accuracy, and connecting with real-time medical databases to provide up-to-date medication recommendations. This project highlights the potential of artificial intelligence in transforming early disease detection and medication guidance, ultimately improving healthcare accessibility and decision-making. Keywords—Machine Learning, Disease Prediction, Medication Recommendation, Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Naïve Bayes, Graphical User Interface (GUI), Healthcare Accessibility, Artificial Intelligence.
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Gupta,, Sanskriti. "HEALTHCURE-Disease Diagnosis using NLP." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35455.
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HealthCure is an innovative medical project designed to provide an all-in-one solution for the detection of seven critical diseases using advanced machine learning, computer vision, and deep learning technologies. The project aims to revolutionize healthcare by enabling users to obtain immediate diagnostic results from the comfort of their homes, making medical testing more accessible and efficient. The backend of HealthCure is powered by Flask, a lightweight and flexible web framework that facilitates rapid development and seamless integration of various machine learning models. The core of the diagnostic capabilities relies on Convolutional Neural Networks (CNNs), particularly for image-based disease detection tasks. Custom CNN architectures and pre-trained models like VGG-16 are utilized to achieve high accuracy in detecting conditions such as Covid-19, brain tumors, and pneumonia. For numerical data-based diseases, such as diabetes and heart disease, algorithms like Random Forest and XGBoost are implemented, leveraging their robustness and precision in handling structured data. Data storage and management are efficiently handled using SQLite, ensuring quick access and retrieval of user data and diagnostic results. The backend also employs RESTful API design, enabling smooth and efficient communication between the front-end and back-end, providing real-time data processing and immediate feedback. On the front-end, technologies like HTML, CSS, and JavaScript are used to create a responsive and user-friendly interface. Frameworks such as React.js and Bootstrap ensure a dynamic, interactive, and visually appealing user experience. Users can easily input data and receive diagnostic results with just a few clicks, making the application highly accessible across various devices. HealthCure also emphasizes future scalability and improvement. As more data becomes available, the models will be continually refined to enhance accuracy and include additional disease detection capabilities. Plans for integrating features such as personalized health advice and real-time monitoring are also underway. This comprehensive approach makes HealthCure a powerful, reliable, and scalable platform for real-time medical diagnostics, significantly contributing to improved health outcomes and patient care.
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Gibbons, Don L., and Margaret Kielian. "Molecular Dissection of the Semliki Forest Virus Homotrimer Reveals Two Functionally Distinct Regions of the Fusion Protein." Journal of Virology 76, no. 3 (2002): 1194–205. http://dx.doi.org/10.1128/jvi.76.3.1194-1205.2002.
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ABSTRACT Semliki Forest virus (SFV) is an enveloped alphavirus that infects cells via a membrane fusion reaction triggered by the acidic pH of endosomes. In response to low pH, the E1 proteins on the virus membrane undergo a series of conformational changes, resulting in the formation of a stable E1 homotrimer. Little is known about the structural basis of either the E1 conformational changes or the resulting homotrimer or about the mechanism of action of the homotrimer in fusion. Here, the E1 homotrimer was formed in vitro from either virus or soluble E1 ectodomain and then probed by various perturbants, proteases, or glycosidase. The preformed homotrimer was extremely stable to moderately harsh conditions and proteases. By contrast, mild reducing conditions selectively disrupted the N-terminal region of trimeric E1, making it accessible to proteolytic cleavage and producing E1 fragments that retained trimer interactions. Trypsin digestion produced a fragment missing a portion of the N terminus just proximal to the putative fusion peptide. Digestion with elastase produced several fragments with cleavage sites between residues 78 and 102, resulting in the loss of the putative fusion peptide and the release of membrane-bound E1 ectodomain as a soluble trimer. Elastase also cleaved the homotrimer within an E1 loop located near the fusion peptide in the native E1 structure. Mass spectrometry was used to map the C termini of several differentially produced and fully functional E1 ectodomains. Together, our data identify two separate regions of the SFV E1 ectodomain, one responsible for target membrane association and one necessary for trimer interactions.
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Ma, Runmei, Jie Ban, Qing Wang, et al. "Full-coverage 1 km daily ambient PM<sub>2.5</sub> and O<sub>3</sub> concentrations of China in 2005–2017 based on a multi-variable random forest model." Earth System Science Data 14, no. 2 (2022): 943–54. http://dx.doi.org/10.5194/essd-14-943-2022.
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Abstract. The health risks of fine particulate matter (PM2.5) and ambient ozone (O3) have been widely recognized in recent years. An accurate estimate of PM2.5 and O3 exposures is important for supporting health risk analysis and environmental policy-making. The aim of our study was to construct random forest models with high-performance and estimate daily average PM2.5 concentration and O3 daily maximum of 8 h average concentration (O3-8 hmax) of China in 2005–2017 at a spatial resolution of 1 km × 1 km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on 10-fold cross-validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly estimations of PM2.5 from test datasets gave average model-fitting R2 values of 0.85, 0.88 and 0.90, respectively; these R2 values were 0.77, 0.77 and 0.69 for O3-8 hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM2.5 and O3-8 hmax estimations. During 2005–2017, PM2.5 concentration exhibited an overall downward trend, while ambient O3 concentration experienced an upward trend. Whilst the spatial patterns of PM2.5 and O3-8 hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristics. The dataset is accessible to the public at https://doi.org/10.5281/zenodo.4009308 (Ma et al., 2021a), and the shared dataset of Chinese Environmental Public Health Tracking (CEPHT, 2022) is available at https://cepht.niehs.cn:8282/developSDS3.html.