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80 articles for “Forest”
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Evaluation of Floral Diversity of BSI Dehradun and its Ecological Assessment
Abstract: The association of plants and animals with the environment and interaction within and among themselves constitutes biodiversity. The present requirement is to save and conserve our biodiversity with sustainable management of our resources such as land, water, air, forest, fossil fuels and many others. The present research work explains the true meaning of biodiversity along with sustainable management of biodiversity for betterment of ecological assessment and biogeochemical cycles of the …
Published in International Journal of Plant Biotechnology · Vol. 2, Issue 2, 2016 · pp. 1–13 Read article
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Heavy metal concentrations in two mangrove species grown in the UAE region and use of phytoremediation technology
Abstract: The mangrove vegetation of Eastern region of UAE, rhizosphere root, leaf, stem and soil sediments of two mangrove types studied for the levels of Cr, Cu, Cd, Mn, Ni, Zn and Pb in the eastern Emirates region. The analysis was carried out with FAAS spectroscopy using microwave digestion technique. The two mangrove plants are most abundant in this region. the bioconcentration factor (BCF) > 1 metal content was quantized. It …
Published in International Journal of Green Chemistry · Vol. 5, Issue 2, 2019 · pp. 1–8 Read article
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Brain Tumor Detection using Machine Learning Methods-KNN, SVM, DT, RF
Abstract: Brain tumor is a major contributor to illness and death worldwide, and timely diagnosis is essential for successful treatment and improved outcomes Recent research has looked into the possibility of machine learning algorithms for detecting and classifying brain tumours in medical pictures. In this study, four commonly used machine learning algorithms (K-Nearest Neighbours, Decision Trees, Support Vector Machines, and Random Forests) were evaluated for their effectiveness in brain tumor detection. …
Published in International Journal of Chemical Engineering and Processing · Vol. 9, Issue 2, 2023 · pp. 48–59 Read article
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Soft Computing Based Prediction of Ultimate Bearing Capacity of Footings Resting on Rock Masses
Abstract: The present study attempts to predict the ultimate bearing capacity of shallow foundations on rock masses using five different soft computing techniques namely i) random forest regression approach, ii) artificial neural networks, iii) support vector machine with polykernel, iv) support vector machine with (radial basis function) RBF kernel and v) M5P model tree. The ultimate bearing capacity was assumed to be dependent rock mass rating, unconfined compressive strength of rock, …
Published in International Journal of Geological and Geotechnical Engineering · Vol. 5, Issue 2, 2019 · pp. 1–14 Read article
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Health Care System Analysis Using Different Datasets
Abstract: In this research work, we have discussed data science and various machine learning algorithms like KNN, Support Vector Machine, Artificial neural network, Random forest etc. and the results obtained from these algorithms which can be used for the prediction of heart disease, cancer and in many other fields of medical science. Requirement of data cleaning along with various methods of data cleaning are mentioned here, along with these various issues …
Published in International Journal of Distributed Computing and Technology · Vol. 8, Issue 2, 2022 · pp. 29–37 Read article
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Discovery of Materials through Applied Machine Learning and General Machine Learning Architecture
Abstract: Advances in artificial intelligence technologies, especially machine learning, have generated possibilities in the material sciences to facilitate material exploration and to achieve a profound understanding of the relationship between the constituent elements of the material and the properties represented by the material. The application of machine learning to the exploration of experimental materials is sluggish due to the monetary and temporal costs of experimental data, however parallel methods, such as …
Published in International Journal of Analog Integrated Circuits · Vol. 6, Issue 2, 2020 · pp. 48–57 Read article
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Autonomous Multi-Agent Sensor Network for Distributed Environmental Instrumentation
Abstract: Environmental monitoring requires intelligent, scalable, and autonomous instrumentation systems capable of continuously sensing, processing, and communicating heterogeneous data under dynamic conditions. This study proposes an Autonomous Multi-Agent Sensor Network for Distributed Environmental Instrumentation, integrating distributed sensor nodes, edge computing, multi-agent collaboration, adaptive data fusion, and intelligent decision-making to achieve reliable real-time monitoring of environmental parameters. Each sensor agent autonomously performs local sensing, anomaly detection, energy-aware communication, and cooperative information exchange …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 12, Issue 2, 2026 Read article
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An Integrated Machine Learning and IoT-Based Framework for Predictive Urban Water Demand Management and Distribution Optimization
Abstract: Abstract Urban water resource management has become increasingly complex due to rapid urbanization, population growth, climate variability, and aging infrastructure. Conventional water management approaches are often reactive, inefficient, and incapable of handling real-time fluctuations in demand and supply. This study proposes an integrated framework that combines machine learning (ML) techniques with Internet of Things (IoT)-based real-time monitoring to enhance water demand prediction and optimize distribution systems in urban environments. The …
Published in International Journal of Water Resources Engineering · Vol. 12, Issue 1, 2026 Read article
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Predicting Traffic Congestion in Mumbai Using Artificial Intelligence
Abstract: Abstract — Mumbai's ongoing traffic problems are brought on by the city's fast urbanization, population expansion, and growing reliance on private automobiles. The majority of traffic management technologies in use today are reactive, frequently reacting only after congestion has already arisen. Using information from a variety of sources, such as commuter surveys, weather reports, and traffic incident logs, this study explores the application of artificial intelligence (AI) and machine learning …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 12, Issue 2, 2026 Read article
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AI-Driven Predictive Maintenance in Smart Homes: Enhancing Efficiency and Reliability
Abstract: The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed smart homes into intelligent, self-regulating environments. Despite these advancements, ensuring the long-term reliability and efficiency of interconnected devices remains a complex challenge due to device heterogeneity, dynamic usage patterns, and continuous data streams. This study investigates AI-driven predictive maintenance (PdM) as a proactive strategy for detecting early signs of equipment degradation in residential settings. The proposed …
Published in International Journal of Distributed Computing and Technology · Vol. 12, Issue 1, 2026 · pp. 1–6 Read article
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A Study on Machine-Learning-Driven Insight into theThermal Behavior of Polymers and Composite Materials
Abstract: The thermal performance of polymeric and composite systems underpins their suitability for aerospace, electronics, and energy‑storage applications, yet reliable prediction of temperature‑dependent properties remains a formidable challenge. In this work we present a data‑driven framework that harnesses modern machine‑learning (ML) techniques to forecast key thermal metrics – glass‑transition temperature (Tg), melting point (Tm), thermal conductivity (k), and decomposition onset (Td) – from a chemically informed descriptor space. A curated database …
Published in International Journal of Polymer Science and Engineering · Vol. 12, Issue 1, 2026 · pp. 1–11 Read article
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Transportable Water and Air Quality Monitoring Equipment Karishma
Abstract: The purpose of this article is to provide a broad summary of recent discoveries, identify important constraints, and suggest future lines of inquiry. In recent years, pollution has surpassed the environment's bearing capacity. There hasn't been much progress despite the installation of multiple pollution-control initiatives. Natural resources are occasionally exploited by unavoidable waste discharges, despite our government's best attempts to safeguard them. For instance, the water-intensive and polluting sectors of …
Published in International Journal of Environmental Chemistry · Vol. 12, Issue 1, 2026 Read article
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Data Science for Environmental Modelling
Abstract: Abstract: Soil moisture is a critical Earth system variable linking atmospheric, hydrological, and ecological processes. However, observations are sparse and fragmented: in situ gauges provide point data, satellite missions (SMAP, SMOS, Sentinel-1) offer broad coverage at coarse resolutionQ, and land‐surface models (GLDAS, ERA5, etc.) yield estimates with their own biase. Here we propose a conceptual framework for integrated environmental data modelling, combining rainfall, temperature, and soil moisture information with machine …
Published in International Journal of Water Resources Engineering · Vol. 12, Issue 1, 2026 · pp. 33–40 Read article
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Data Science for Environmental Modelling
Abstract: Abstract: Soil moisture is a critical Earth system variable linking atmospheric, hydrological, and ecological processes. However, observations are sparse and fragmented: in situ gauges provide point data, satellite missions (SMAP, SMOS, Sentinel-1) offer broad coverage at coarse resolutionQ, and land‐surface models (GLDAS, ERA5, etc.) yield estimates with their own biase. Here we propose a conceptual framework for integrated environmental data modelling, combining rainfall, temperature, and soil moisture information with machine …
Published in International Journal of Water Resources Engineering · Vol. 12, Issue 1, 2026 Read article
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AI-Integrated Precision Agriculture System for Fertilizer Optimization, Multimodal Crop Yield Prediction
Abstract: Agriculture is rapidly transforming into a data-driven ecosystem supported by advances in artificial intelligence (AI), Internet of Things (IoT) sensor networks, and satellite remote sensing technologies. These innovations enable precision-driven decision-making aimed at improving productivity while minimizing environmental impact. This paper presents an AI-integrated precision agriculture system designed to optimize fertilizer usage and enhance crop yield prediction accuracy through a robust multimodal deep learning framework. The proposed approach integrates heterogeneous …
Published in International Journal of Plant Biotechnology · Vol. 12, Issue 1, 2026 · pp. 11–17 Read article
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Review of Anomaly Detection Techniques in MANETs: AMachine Learning Approach to Intrusion Detection, Attack Prediction, and Routing Security
Abstract: Mobile Ad hoc Networks (MANETs) are decentralized, self-organizing networks that offer flexibility and scalability for a variety of applications. However, their dynamic topology and limited resources make them highly susceptible to security threats. Traditional security measures often struggle to address the complexities of evolving and sophisticated attacks within such networks. This paper reviews the role of machine learning (ML) techniques in enhancing anomaly detection capabilities in MANETs, focusing on their …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 2, 2026 · pp. 1–5 Read article
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Evaluation of Machine Learning and Deep Learning Models for Classification Tasks on Crime Data: A Comparative Study
Abstract: The classification of crime data has grown more and more important for public safety and law enforcement due to the increasing demand for precise and effective predictive models. To classify crime data, this study assesses and contrasts the effectiveness of several machine learning (ML) and deep learning (DL) models, such as decision trees, support vector machines, neural networks, and convolutional neural networks. The study evaluates the models' accuracy, efficiency, and …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 1, 2025 · pp. 1–15 Read article
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Decision Trees in Intrusion Detection: A Comparative Analysis of Machine Learning Techniques
Abstract: Recent malware sophistication poses immense challenges in intrusion detection systems, making it imperative to develop highly sophisticated methodologies for detection of fast-evolving cyber threats. Traditional IDS typically employs a signature-based detection approach and has limitations in identifying unknown and obfuscated attacks, particularly zero-day vulnerabilities. This paper discusses the existing limitations of IDS based on ML techniques to further improve the detection capability of such systems. Machine learning algorithms, particularly decision …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 1, 2025 Read article
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Decision Trees in Intrusion Detection: A Comparative Analysis of Machine Learning Techniques
Abstract: Recent malware sophistication poses immense challenges in intrusion detection systems, making it imperative to develop highly sophisticated methodologies for detection of fast-evolving cyber threats. Traditional IDS typically employs a signature-based detection approach and has limitations in identifying unknown and obfuscated attacks, particularly zero-day vulnerabilities. This paper discusses the existing limitations of IDS based on ML techniques to further improve the detection capability of such systems. Machine learning algorithms, particularly decision …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 1, 2025 Read article
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Predictive Models for Drinking Water Quality: Leveraging Machine Learning for Improved Safety and Efficiency
Abstract: Quality of drinking water is one of the critical determinants of public health, leading to severe environmental and health-related issues when poor. The paper examines the integration of machine learning techniques that could improve accuracy and efficiency in assessing drinking water quality. The traditional techniques of monitoring drinking water are resource-intensive yet lack timely insight into contamination events. In this scenario, an evaluation of the performance of ML models such …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 11, Issue 1, 2025 Read article