Many problems related to ITS (Intelligent Transportation systems) require the handling of huge GNSS (Global Navigation Satellite System) datasets that contain historical data on vehicles in transportation, and most of...
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TheGlobal Hunger Crisis has long been one of the most pressing problems of the modern world. Surveys have shown that globally, around 14 percent of food produced is wasted between harvest and retail. This project aims...
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ISBN:
(纸本)9783031310652;9783031310669
TheGlobal Hunger Crisis has long been one of the most pressing problems of the modern world. Surveys have shown that globally, around 14 percent of food produced is wasted between harvest and retail. This project aims to develop a mechanism that uses imageprocessing and deep learning to classify agricultural produce and perform anomaly detection. The system performs two kinds of evaluations;a mass-evaluation and a singular evaluation. The mass evaluation of produce is done by angling a camera at an angle theta (.), that is pre calculated through an optimal angle calculation algorithm. In addition, the system provides controls to a supervisor to specifically evaluate individual items based on the factor of "intuitive inquiry". In this process, a robotic arm picks the target item and takes it to the camera physically for end-to-end coverage. The data obtained from both mass analysis and individual analysis is fed into a program containing metrics for evaluation. Based on the degree of adherence/divergence from standards, the system also recommends a further progression by classifying the item into sets-i.e., if the item is anomaly-free, if it is fully defective and must be discarded, if it can be corrected through further processing, or if it has been under processed. With each iteration of item evaluation, the system intelligently learns from its decisions for improved accuracy and speed.
As a deep feed-forward neural network, the convolutional neural network (CNN) model has achieved major breakthroughs in scheme of image recognition. Compared with the traditional classification methods (KNN, SVM, PSO)...
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This paper introduces the completed project development of a cutting-edge vision Semantics image Captioner., a comprehensive platform aimed at generating contextually rich descriptions for images. Focused on leveragin...
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This research paper aims to address the critical need for efficient and accurate identification of chest diseases using chest X-rays through a combination of advanced imageprocessing techniques and machine learning a...
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ISBN:
(纸本)9798350375480;9798350375497
This research paper aims to address the critical need for efficient and accurate identification of chest diseases using chest X-rays through a combination of advanced imageprocessing techniques and machine learning algorithms. With the growing prevalence of respiratory and cardiovascular conditions worldwide, timely and precise diagnosis is paramount for effective patient care. The study begins with a comprehensive review of existing methodologies and technologies employed in the identification of chest diseases from X-ray images. It critically evaluates the strengths and limitations of current approaches, highlighting the challenges faced in achieving high accuracy, speed, and scalability. To address these issues, the project aims to develop an AI-powered system for medical image analysis. In response to these challenges, our research proposes a novel approach that integrates Inception V3 model and imagenet. We leverage a large dataset of annotated chest X-rays to train a deep neural network capable of recognizing subtle patterns indicative of various diseases, including pneumonia, pneumothorax, lung and cardiac abnormalities. The model is optimized to provide not only accurate diagnoses but also to minimize false positives and negatives. In conclusion, this research contributes to the ongoing efforts in utilizing chest X-ray images for disease identification, presenting a robust and efficient methodology that could revolutionize the current diagnostic landscape. The findings hold promise for the development of automated systems capable of assisting healthcare professionals in the accurate and timely detection of chest diseases, ultimately contributing to enhanced patient care and management.
With the rapid development of machine vision and embedded technology, real-time positioning technology is increasingly important in the field of automatic navigation. This study developed a real-time positioning and c...
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Breast cancer is a significant global health concern, with early detection being critical for successful treatment and improved patient outcomes. In recent years, machine learning-based imageprocessing techniques hav...
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ISBN:
(纸本)9798350313987
Breast cancer is a significant global health concern, with early detection being critical for successful treatment and improved patient outcomes. In recent years, machine learning-based imageprocessing techniques have emerged as powerful tools in the field of medical imaging, particularly in breast cancer detection and diagnosis. This research paper explores the application of machine learning algorithms to enhance the accuracy and efficiency of breast cancer detection using various medical imaging modalities, such as mammography, ultrasound, and magnetic resonance imaging (MRI). The study begins by reviewing the current state of breast cancer detection methodologies and highlighting their limitations. It then delves into the utilization of machine learning algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), and deep learning models, for the automated analysis and interpretation of breast cancer images. Various preprocessing steps, feature extraction techniques, and data augmentation methods are discussed to optimize the performance of these algorithms. Furthermore, the paper examines the integration of machine learning models with radiomics, genomics, and clinical data to create comprehensive breast cancer diagnostic systems. These integrated systems aim to provide more accurate risk assessment, personalized treatment recommendations, and improved patient management. The results of several case studies and clinical trials are presented to demonstrate the effectiveness of machine learning-based imageprocessing techniques in breast cancer detection. These studies illustrate how these techniques can improve sensitivity, specificity, and overall diagnostic accuracy compared to traditional methods. This research underscores the promising role of machine learning-based imageprocessing techniques in advancing breast cancer detection. It highlights the potential for early diagnosis and improved patient care, paving the way for mor
As the demand for high-precision underwater measurements of nuclear fuel assemblies (NFA) continues to rise, there is a concurrent improvement in underwater optical measurement technology. The sheet of light, an optic...
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In recent times, Internet of Things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & pr...
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ISBN:
(纸本)9781665477062
In recent times, Internet of Things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep Neural Network (DNN) models on them. To address this, we introduce Split Computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called Distributed Artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. The QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. The result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.
Stellar energy exchange, fundamental to the dynamics of celestial systems, remains a captivating yet challenging area of study in astronomy. This paper introduces an innovative methodology for quantifying stellar ener...
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