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463 articles for “forests”
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Enhancing Maintenance Decision-Making in Thermal Power Plants Using Generative AI-Based Fault Diagnosis
Abstract: The growing complexity of operation and power consumption of thermal power stations involve the need to have intelligent fault diagnosis systems that can be used to guarantee reliability and safety in operation. In this research, a Generative AI (GenAI)-based hybrid architecture of early fault detection and predictive maintenance is proposed to improve the decision-making process of the maintenance team. The data-driven analytic approach combines methods of data-driven analytics, Generative AI …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 25–33 Read article
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A Review on Artificial Intelligence Techniques for Analyzing Deforestation and Illegal Logging Using Satellite Imagery
Abstract: Deforestation and illegal logging remain critical environmental threats, driving biodiversity loss, climate change, and socio-economic disruption. Conventional monitoring techniques frequently do not yield real-time, large-scale insights. Recent developments in Artificial Intelligence (AI), especially in deep learning and computer vision, have revolutionized the ability to analyze high-resolution satellite images for detecting deforestation and monitoring illegal logging. This review synthesizes recent developments in AI-driven approaches, highlighting convolutional neural networks (CNNs), anomaly detection …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 1–9 Read article
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Analyzing and Predicting Academic Behavior from Peer Pressure Indicators Using Machine Learning
Abstract: The academic achievement of a student is determined by their capability, but also by the companions with whom they associate. Friends can have a positive impact on students' motivation for school, and at times friends are distractions leading to a lack of attention on their school assignments. This particular study focuses on the number and quality of companions students associate with and to what extent that could be used as …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 · pp. 1–7 Read article
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Predicting Student Placement Readiness: A Machine Learning Approach Using Coding Activities and Multi-Dimensional Performance Indicators
Abstract: In the modern information-driven academic world, identifying student employability and placement preparedness has predicted. be made a part and parcel of academic planning and career. development. This study provides a machine learning-based. structure to evaluate and forecast student placement pre-paredness by combining various performance aspects-academic achieve- ment, coding activity, aptitude and behavioral engage-ment metrics. Multi-source was gathered and preprocessed in the study. student information, such as student records (CGPA, attendance), …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 Read article
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Smart Education through Machine Learning: A Review of Trends, Benefits, and Risks
Abstract: Machine learning (ML) is transforming the contemporary education by transforming it into smarter, data-driven and personalised learning. This review examines the key tendencies, advantages, and possible threats of applying ML in intelligent education. ML promotes adaptive learning, automatization of assessments, and student engagement, which is highly beneficial both to learners and educators. Nonetheless, issues like data privacy, algorithmic bias or unequal access are also a significant concern. The article emphasises …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 · pp. 24–28 Read article
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Prediction of Compressive Strength of Concrete Using Machine Learning Techniques
Abstract: Compressive strength of concrete is an important parameter for designing any concrete structure. Compressive strength of concrete is a complex nonlinear function of its ingredients. Prediction of Concrete compressive strength plays a vital role in pre design phases of the structure and quality control of construction. The conventional methods of compressive strength determination are time consuming, so the use of data mining methods to predict the strength beforehand is helpful. …
Published in Journal of Construction Engineering, Technology & Management · Vol. 5, Issue 3, 2015 · pp. 34–41 Read article
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Finding Churn Trend Scenario Using ML Techniques
Abstract: Abstract Media transmission and in addition the network access suppliers assumes an essential part in the mobile communications and the data society. Telecom segment is consistently developing with its progression in highlights and innovation. Consumer loyalty is an imperative factor which chooses the execution of firms. Customer churn expectation is an essential technique in CRM (Customer Relationship Management), knowing the churn propensity appeared by the customers helps to take better …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 6, Issue 1, 2019 · pp. 1–8 Read article
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Human Activity Recognition Using For Smartphone Sensors To Predict The Best Accuracy Based On Machine Learning Algorithms
Abstract: Human activity recognition requires predicting the action of a person based on sensor-generated data. Due to the enormous number of applications possible by modern ubiquitous computing devices, it has sparked a lot of attention in recent years. It categorizes data into actions such as walking, sitting, standing, and lying. The accelerometer and gyroscope were used to generate the sensor data, and the sensor signals were pre-processed with noise filters. The …
Published in Recent Trends in Sensor Research & Technology · Vol. 9, Issue 1, 2022 · pp. 12–23 Read article
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Machine Learning Approach to Predict the Mental Health Issues of Employees in Tech Companies
Abstract: Mental disorders are conditions that have an impact on your emotions, thoughts, and behaviour. It may happen infrequently or have a persistent effect. The topic of mental health has been major and challenging, especially for working professionals. Over time, the urbanized living and workload put a strain on people, making them more vulnerable to mental disorders like anxiety and mental disorders. Working professionals therefore seem to be at an elevated …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 3, 2022 · pp. 1–10 Read article
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Ethnomedicine and Tribes: A Case Study of the Baiga’s Traditional Treatment
Abstract: This article describes the role of Baiga tribes who have conserved the biodiversity in and around localities of their natural habitat. Tribes derive both, directly and indirectly, a substantial amount of their livelihood from the forests. They are also talented and expertise in ethnomedicine and the priest's practices (faith-healing) which have particular importance, with the help of local raw materials which they procure from their forest environment. They also obtain …
Published in Research and Reviews: A Journal of Health Professions · Vol. 8, Issue 2, 2018 · pp. 62–77 Read article
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A Review- Recent Outbreak of Zika Viruses in India, its Vectorology and Virology
Abstract: Zika virus is an arbovirus (arthropod-borne virus) which belongs to the family Flaviviridae that was first isolated in 1947 from blood of a febrile rhesus macaque monkey during a yellow fever study in the Zika forest of Uganda and later identified in Aedes africanus mosquito from the same forest. But in 2007, the first major outbreak of Zika virus infection occurred in Yap Island (Federated States of Micronesia), where, it …
Published in Research and Reviews: A Journal of Microbiology and Virology · Vol. 9, Issue 2, 2019 · pp. 25–33 Read article
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Semantic Similarity Framework for Automatic Hallucination Detection in Large Language Models
Abstract: Large Language Models can generate fluent, contextually appropriate text across a range of NLP tasks, but they frequently produce outputs that are factually wrong while sounding confident and plausible. This problem, referred to as hallucination, poses serious risks in domains where accuracy matters. We propose a post-processing framework that detects hallucinated responses by comparing them against verified reference text using sentence embeddings. The system computes cosine similarity between the response …
Published in Emerging Trends in Languages · Vol. 3, Issue 2, 2026 · pp. 16–22 Read article
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Machine Learning-Based Structure–Property Quantification of Advanced Polymer Composites
Abstract: Advanced polymer composites are widely used in high-performance engineering due to their superior mechanical and multifunctional properties. Accurate structure–property quantification is essential for efficient material design and reducing experimental costs. Existing Machine Learning (ML) approaches often exhibit limited predictive generalization due to inadequate feature discrimination and suboptimal hyperparameter tuning. To address these limitations, the proposed method enhances the ability to capture the complex nonlinear interactions among composite structural descriptors. The …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Harnessing Artificial Intelligence for Sustainable Development in Jharkhand: Environmental and Socio-Economic Perspectives
Abstract: Jharkhand is derived from Indo-Aryan languages, comprising two words, “Jhar” meaning “Forest” and “khand” meaning “Land”, meaning FOREST LAND. Geographically, Jharkhand is India’s 15th-largest state, and population-wise, it is the 14th-largest. Despite Jharkhand accounting for India’s 40% mineral production, 39.1% of its population lives below the poverty line, and 19.6% of its children below the age of five are malnourished. It is plagued by a lot of environmental and socioeconomic …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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A Comprehensive Method for Sentiment Analysis on Twitter Posts
Abstract: AbstractIn recent years, with ascent of users of social media, researchers get drawn to sentiment analysis. The challenge is that sentiment-dependent info from multiple sources does not seem to be thought of usually in existing sentiment classification techniques. Sentiment analysis is incredibly vital as a result of it helps folks and organizations in their decision-making process. There is vast explosion of “sentiments” on the market from social media like Twitter, …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 4, Issue 2, 2017 · pp. 1–5 Read article
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A Complete Study of Different Models for Software Fault Prediction
Abstract: Context- Software faults prediction improves software quality, software reliability, and software efficiency by early identification of faults. Numerous classification methods have been suggested for this task.Objective- The main objectives of the studies are (i) find the number of defects in AEEEM defect datasets, (ii) Challenges faced with Imbalanced Datasets.Method- We use AEEEM defect datasets for the prediction of faulty classes using four deferent machine learning technique. This particular paper present …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 5, Issue 2, 2018 · pp. 1–10 Read article
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Rainfall Forecasting Using Optimized Support Vector Regression Technique
Abstract: Abstract—Rainfall prediction has been an important area of research in the field of hydrology. In this article an attempt is made to apply Support vector regression (SVR) in annual rainfall forecasting and comparing its performance with other models like Random forest regression (RFR) and Artificial Neural Network (ANN) model. This study proposes an approach by combining grid search technique with SVR model. Grid search technique carefully tunes the SVR’s hyper …
Published in Journal of Web Engineering & Technology · Vol. 6, Issue 2, 2019 · pp. 29–33 Read article
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Analysis of Machine Learning Algorithms for the Detection of Mental Health Problems in Employees
Abstract: Mental health-related issues are more critical issues in the workplace. Mental health has been a major and challenging issue, especially for working professionals. A bad work environment can cause a variety of physical and physiological problems as well as decreased productivity. People are reluctant to ask for help from mental health specialists, according to studies. The fundamental reason for this is the stigma attached to mental illness. Working professionals therefore …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 9, Issue 3, 2022 · pp. 12–24 Read article
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User Engagement in Mobile Applications: Modeling and Predicting
Abstract: The mobile ecosystem is expanding rapidly, making app developers highly competitive. To keep users engaged, apps can anticipate when they might disengage, allowing developers to implement intervention tactics. This work demonstrates that it is possible to accurately forecast user disengagement using numerical models. The framework includes Cox proportional hazards, negative binomial, random forest, boosted-tree, and optimized agglomerative hierarchical clustering models. A 12-month observation dataset from a trash recycling app development …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 10, Issue 3, 2023 · pp. 1–7 Read article
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Monitoring Recent Vegetation Greenness Trend Impacted from Past Drought Events in Northern Arizona with AVIRIS and MODIS
Abstract: Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 Northern Arizona has experienced a severe drought with significant impacts on various ecosystems. There have been extensive mortality and bark beetle insect population in conjunction with the multi-year drought. We evaluated remotely-sensed drought indicator with Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) hyperspectral data flown across the C. Hart Merriam Transect in northern Arizona and MODIS multispectral sensor. Drought impacts on vegetation …
Published in Journal of Remote Sensing & GIS · Vol. 2, Issue 1, 2011 · pp. 23–33 Read article