Facial Emotion Recognition (FER) is an important field inc omputer vision that h as significant im plications for human-computer interaction, healthcare, and education. This study aims to tackle the particular difficu...
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This research has been aimed at analyzing the efficiency of utilizing the YOLO models for determining and classifying human activities, primarily YOLOv9. The comparison of YOLO models from v1 to v9 indicates the subst...
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Platooning has been researched for decades but debate about its lasting impact is still ongoing. Meanwhile, adaptive cruise control (ACC) became de facto standard for all new cars as well as for automated driving on t...
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Cloud service providers require accurate prediction of cloud workloads to promptly determine resource allocation strategies and enhance resource utilization while meeting Service-Level Agreements. However, existing wo...
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For maximum crop productivity, leaf disease classification and early identification are essential. Conventional methods for identifying illness need a lot of time and money. Promising results have been observed in the...
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Air quality monitoring is gaining increasing importance as awareness about the health impacts of air pollution continues to grow. These monitors typically track air pollutants to give users a clearer picture of their ...
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The paper has focussed on the global landcover for the identification of cropland *** growth and rapid industrialization are somehow disturbing the agricultural lands and eventually the food production needed for huma...
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The paper has focussed on the global landcover for the identification of cropland *** growth and rapid industrialization are somehow disturbing the agricultural lands and eventually the food production needed for human *** agricultural land monitoring requires proper management of land *** paper has proposed a method for cropland mapping by semantic segmentation of landcover to identify the cropland boundaries and estimate the cropland areas using machine learning *** process has initially applied various filters to identify the features responsible for detecting the land boundaries through the edge detection *** images are masked or annotated to produce the ground truth for the label identification of croplands,rivers,buildings,and *** selected features are transferred to a machine learning model for the semantic segmentation *** methodology has applied Random Forest,which has compared to two other techniques,Support Vector Machine and Multilayer perceptron,for the semantic segmentation *** dataset is composed of satellite images collected from the QGIS *** paper has derived the conclusion that Random forest has given the best result for segmenting the image into different regions with 99%training accuracy and 90%test *** results are cross-validated by computing the Mean loU and kappa coefficient that shows 93%and 69%score value respectively for Random Forest,found maximum among *** paper has also calculated the area covered under the different segmented ***,Random Forest has produced promising results for semantic segmentation of landcover for cropland mapping.
Agriculture has long been the backbone of the Indian economy, defining the country's social and cultural milieu. Some of the most common difficulties that farmers face includes selecting suitable crops for their a...
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Seas and oceans, vital sources of food and critical reservoirs of sustenance and ecological balance, present formidable challenges to exploration due to their profound depths. Addressing this, the integration of deep ...
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Corn is a crucial staple crop globally, with its production facing challenges due to the prevalence of pests and diseases. This paper presents the use of YOLOv8 (You Only Look Once version 8) algorithm, an advanced ob...
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