Every day in different countries, shopping is carried out by every category of persons. Currently, people going shopping have to push around trolleys loaded with a lot of merchandise and this is so stressful and diffi...
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The paper discusses the use of machine learning in recognizing handwritten digits and text, which has wide applications in areas such as surveillance, healthcare, and document analysis. The study focuses on evaluating...
The paper discusses the use of machine learning in recognizing handwritten digits and text, which has wide applications in areas such as surveillance, healthcare, and document analysis. The study focuses on evaluating the accuracy and variability of classifying handwritten digits with different numbers of hidden layers using the Modified National Institute of Standards and technology (MNIST) dataset, and compares the performance of common machine learning algorithms such as SVM, KNN, and RFC. The study notes that recognizing handwritten digits and text is challenging due to their dissimilarities in size, thickness, position, and orientation. The ability to accurately recognize handwritten digits is essential in various fields, including banking, post offices, and tax files. The paper demonstrates handwritten digit recognition (HDR) using the MNIST dataset and selected classification algorithms. Overall, handwriting recognition is a major area of development with many possibilities for applications.
Intelligent Traffic Management Systems (ITMS) play a crucial role in advancing smart city applications by addressing the rapid increase in road traffic due to the growing number of vehicles. Effective real-time traffi...
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The rapid expansion of Internet of Things (IoT) devices in smart homes has significantly improved the quality of life, offering enhanced convenience, automation, and energy efficiency. However, this proliferation of c...
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As the impact of climate change on the environment increases, the demand for new energy sources is growing. In the development of renewable energy, wind power generation is an important technology that can use wind en...
As the impact of climate change on the environment increases, the demand for new energy sources is growing. In the development of renewable energy, wind power generation is an important technology that can use wind energy to generate electricity. However, due to its unstable nature, the forecasting of wind power generation has become one of the most important problems in wind farm management. In this paper, historical wind power data is selected as input to establish a wind power prediction model based on Long Short Term Memory (LSTM). By training LSTM model, the prediction of wind power in next 12 hours is realized. Result shows that the model has certain feasibility and accuracy, can accurately predict the future short-term wind power generation, and has a wide range of application value.
A chatbot is an AI-powered software or application designed to communicate with people. This technology can perform a variety of tasks, including providing instant responses and answers to users, delivering informatio...
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Finding connected components in a graph is a fundamental problem in graph analysis. In this work, we present a novel minimum-mapping based Contour algorithm to efficiently solve the connectivity problem. We prove that...
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Although visual perception algorithms have made significant progress in most normal scenes, it is still challenging for autonomous driving systems to accurately perceive long-tail scenes that occur less frequently, wh...
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ISBN:
(数字)9798350349252
ISBN:
(纸本)9798350349269
Although visual perception algorithms have made significant progress in most normal scenes, it is still challenging for autonomous driving systems to accurately perceive long-tail scenes that occur less frequently, which can lead to serious traffic safety issues. However, existing open-source datasets do not systematically collect sufficient long-tail scenes. To fill this gap, we propose a pipeline for designing large-scale, diverse long-tail traffic scenes and generating virtual datasets based on the parallel vision approach. A virtual dataset named Vir-LTTS (virtual long-tail traffic scenes) is built, comprising various scenes such as extreme weather conditions, adverse lighting conditions, traffic accidents, unique forms of traffic objects, and blurry images caused by camera defects. We investigate the potential of training models using the Vir-LTTS dataset in long-tail traffic scenes. Experimental results show that pre-training with Vir-LTTS significantly improves the performance of visual models in long-tail traffic scenes.
There is a growing need for diverse, high-quality stuttered speech data, particularly in the context of Indian languages. This paper introduces Project Boli, a multi-lingual stuttered speech dataset designed to advanc...
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In Medical question-answering (QA) tasks, the need for effective systems is pivotal in delivering accurate responses to intricate medical queries. However, existing approaches often struggle to grasp the intricate log...
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