Although deep neural networks exhibiting superior performance across numerous tasks, their application in high-risk domains is limited by a lack of interpretability and trustworthiness. In this paper, an interaction v...
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As sports grow in popularity need for effective online turf booking applications has also increased. Although some existing applications are faced with numerous challenges such as;restrictions on access, ineffectivene...
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There are several approaches to solving every coding algorithm in computerscience. To achieve the same result, these methods may use various techniques and reasoning. The difficulty is that as the number of inputs in...
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Wireless Sensor Networks (WSNs) are pivotal for various applications, but they face significant challenges, primarily due to the limited battery life of sensor nodes. The clustering of sensor nodes is a key research d...
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As the promise of advancements in efficiency and cost-effectiveness of healthcare sector, through artificial intelligence (AI), gains momentum globally, especially in resource-scarce regions like Nepal, potential chal...
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This paper provides an efficient and accurate sign language recognition system in real time that understands gestures employed using MediaPipe and Random Forest in American Sign Language (ASL). The system captures and...
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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.
As the world's biggest game, football enjoys the interest of billions across the globe. The prediction of football matches fascinates everyone, from the fans to the managers themselves. Football is an inherently c...
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Personalized Federated Learning (pFL) holds immense promise for tailoring machine learning models to individual users while preserving data privacy. However, achieving optimal performance in pFL often requires a caref...
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Software-defined vehicles (SDVs) are an emerging paradigm in the automotive industry where vehicles' functionality, performance, and safety can be enhanced and updated through software, even after production. Unli...
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