Artificial intelligence continues to evolve, particularly in the realms of natural language processing (LLMs), image generation, and task automation. Despite these advancements, multi-musical instrument recognition re...
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Convolutional neural network (CNN) is widely used for analyzing time series data as it allows for the rapid learning of inherent characteristics in the series with a small number of parameters through filter operation...
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Cardiovascular diseases, such as heart attacks, are a significant global health concern, responsible for a great deal of annual mortality. The introduction of wearable edge devices with advanced sensors has enabled co...
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Fetal health status classification is essential for monitoring and confirming the well-being of the fetus through pregnancy. This study evaluates various Machine Learning techniques for classifying fetal health status...
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Rapid advancements in technology has aided in early prediction of Breast cancer which is a high mortality rate characterized condition. Fuzzy and Neural network-based models have been effective in prediction of early ...
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The objective of the paper is to propose an approach for a real-time accurate Automatic Number Plate Recognition(ANPR) system, which recognizes vehicle license plates. The system is based on a common vision concept th...
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ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
The objective of the paper is to propose an approach for a real-time accurate Automatic Number Plate Recognition(ANPR) system, which recognizes vehicle license plates. The system is based on a common vision concept that combines Optical character recognition (OCR) and YOLOv9 deep learning algorithm. Intended to overcome challenges including poor light, partial obstruction, and different angles for high-speed vehicle plate recognition over multiple regions. This means that not only is the architecture future-friendly for license plate designs, but it will also work with existing infrastructure so this can be deployed in traffic management, law enforcement, and security use cases.
Weeds are unwanted plants in agriculture that compete with crops for nutrients, water, and space, resulting in reduced crop production. Farmers often use pesticides to control weeds, but some pesticides may remain on ...
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According to statistics, 8% of the population cannot interpret sign language during conversations with deaf individuals. This creates a pressing need for sign language communication with many finding it challenging to...
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ISBN:
(纸本)9798350372748
According to statistics, 8% of the population cannot interpret sign language during conversations with deaf individuals. This creates a pressing need for sign language communication with many finding it challenging to communicate effectively without the assistance of an interpreter. The goal is to recognize a wide range of sign language variations and produce corresponding voice outputs facilitating seamless communication for deaf and hard-of-hearing people. This research work deals with the basic understanding of the problem statement and analyses the various techniques present in the existing era of sign language translation. Our objective is to recognize a diverse array of sign language variations and generate corresponding voice outputs in real-time, thereby fostering effective communication for the deaf and hard-of-hearing community. Through a comprehensive analysis of existing techniques in sign language translation, the necessity for advanced computational solutions capable of accommodating the intricacies of sign language gestures and expressions has been determined. The proposed methodology utilizes R-CNN, a state-of-the-art object detection framework, which is known for its accuracy in identifying objects present within images. By integrating R-CNN to recognize and interpret sign language gestures, this study intends to achieve robust performance across a spectrum of sign language variations. A significant aspect of this research study lies in its delineation of the computing environment requisite for implementing real-time sign language translation. By synthesizing theoretical frameworks with practical considerations, this study offers a comprehensive analysis for developing and deploying real-time sign language translation solutions. The proposed implementation has resulted in an accuracy of 92.25%. This research study significantly describes the computing environment and factors to be included to implement the same as a real-time application for transla
Efficient removal of marine debris is imperative for safeguarding public health and the environment, particularly marine ecosystems. This research addresses the limitations of conventional methods, such as image detec...
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An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this techniq...
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