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289 articles for “Monitoring techniques”
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Performance of Artificial Neural Network for Tree Species Identification using Sentinel-2 Data
Abstract: Accurate land cover mapping, especially concerning vegetation, is crucial for effective land use policy planning and sustainable forest management. Hence, achieving accuracy in mapping requires a deep understanding of composition changes, vegetation conditions, and the spatial distribution of tree species. In the spatial context of tree species, it holds significant potential for applications including invasive species monitoring, delineating contaminated areas, and biodiversity conservation. However, traditional methods for tree species identification …
Published in Journal of Remote Sensing & GIS · Vol. 15, Issue 2, 2024 · pp. 12–21 Read article
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Utilizing Image Processing Methods for Infrared (IR) Intensity Measurement across Spatial and Temporal Domains
Abstract: The research outlined in this investigation centres on employing image processing techniques to measure infrared (IR) intensity in spatial and temporal domains. The primary objective is to ascertain intensity levels at various spatial points over time, utilizing thermal imaging technology. To record the burning flare and produce radiometric data, the experimental setup makes use of a thermal camera. Additionally, a spectroradiometer is utilized to gauge intensity. To provide a particular …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 1, 2024 · pp. 1–1O Read article
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Security Challenges and Solutions in Wireless Sensor Networks: a Case study of Afghanistan
Abstract: Due to their capability to collect and relay data from locations without supervision, Wireless Sensor Networks (WSNs) have become essential for numerous contemporary applications (such as environmental monitoring, smart cities, and healthcare). However, the open and resource-constrained nature of WSNs makes them particularly vulnerable to security threats. This paper reviews the key security Issue faced by WSNs and the solutions proposed in recent literature. We examine the unique constraints of …
Published in Recent Trends in Sensor Research & Technology · Vol. 13, Issue 1, 2026 Read article
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Explainable Machine Learning Integrated with Polymer-Based Diagnostic Technologies for Liver Health Classification
Abstract: Early and reliable assessment of liver health is essential for timely treatment, yet most machine-learning approaches face limitations such as class imbalance and low clinical interpretability. This study proposes a polymer-integrated, explainable machine-learning framework that combines SMOTE-based data balancing, Logistic Regression, and XAI techniques (SHAP and LIME) for transparent liver-health classification. In addition to ML modelling, the study emphasizes the emerging role of polymer-based biosensors, microfluidic polymer chips, polymer nanomaterials, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 631–643 Read article
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Animal Detection in Farms Using Opencv
Abstract: Agriculture plays a fundamental role in sustaining the Indian economy, providing employment and livelihood to a large portion of the population. Despite advancements in farming techniques, one of the persistent challenges faced by farmers is the intrusion of wild animals into agricultural fields. Such intrusions often lead to large-scale crop damage, financial loss, and emotional distress for farmers. Traditional animal deterrent methods, such as manual patrolling, fences, or scarecrows, have …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 13, Issue 2, 2025 Read article
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A Web Application for Predicting Diabetes Using Machine Learning Methods
Abstract: Diabetes is a long-term disease caused by high glucose quantity in the blood. It has the potential to result in serious health complications like heart disease, hypertension, and ocular damage. It is good to identify any health issues as early as possible to get the right medical treatment and make necessary lifestyle adjustments. One makes use of machine learning techniques to predict diabetes and develop treatment options using actual cases. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 92–102 Read article
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Thin Film Technology in Sensor Manufacturing – A Technical Discussion
Abstract: Thin‑film technology has become the cornerstone of modern sensor manufacturing, enabling the convergence of miniaturisation, multifunctionality, and cost‑effective mass production. This paper surveys the latest advances in deposition techniques—ranging from magnetron sputtering and chemical vapour deposition to atomic‑layer deposition (ALD) and ink‑jet‑printed sol‑gel processes—and examines how their unique material‑control capabilities translate into performance gains across the sensor spectrum (chemical, physical, and bio‑sensing). By integrating nanoscale thickness control (≤ 10 nm) …
Published in Journal of Thin Films, Coating Science Technology & Application · Vol. 13, Issue 1, 2026 · pp. 48–58 Read article
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A case study on the application of TPM techniques in the manufacturing industry
Abstract: The steel sector has developed dramatically during recent years, driven by technical breakthroughs, competitive challenges, and changing client requirements. Consumers now place increasing focus on cost effectiveness, shorter delivery lead times, and consistently excellent product quality, driving industrial organizations to continually strengthen their operational performance. Adopting structured quality and maintenance systems has become crucial for maintaining competitiveness and attaining operational excellence in response to these difficulties.With a focus on enhancing …
Published in Journal of Production Research & Management · Vol. 16, Issue 1, 2026 · pp. 1–9 Read article
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A Study on the Comparative Changes in Nutritional Status in Follow-Up of End Stage Renal Disease Patients
Abstract: Dialysis Disequilibrium Syndrome (DDS) is a rare but serious neurological complication associated with hemodialysis, particularly during the initiation phase or in patients with high metabolic derangements. It is characterized by a range of neurological symptoms resulting from rapid shifts in plasma osmolality, leading to cerebral edema. Despite advancements in dialysis techniques, DDS continues to pose a clinical challenge due to its unpredictable nature and potential for severe outcomes. The present …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 16, Issue 1, 2026 · pp. 9–13 Read article