Ocular diseases represent a significant public health concern worldwide, which needs accurate and efficient diagnostic methods for timely intervention and management. Fundus imaging, a foundation of ophthalmic diagnos...
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ISBN:
(数字)9798331519094
ISBN:
(纸本)9798331519100
Ocular diseases represent a significant public health concern worldwide, which needs accurate and efficient diagnostic methods for timely intervention and management. Fundus imaging, a foundation of ophthalmic diagnostics, offers a non-invasive yet comprehensive view of the retina, insights into disease severity and early warnings, aiding in the identification and characterization of various ocular pathologies. However, the manual classification of these images by ophthalmologists may require substantial time investment and is subject to inter-observer variability. The advancement of artificial intelligence (AI) and machine learning algorithms has revolutionized medical imaging analysis, presenting an opportunity to enhance the diagnostic process. Deep learning, renowned for its excellence in tasks related to computer vision, forms the foundation of our strategy. In this study, we analyzed the efficiency of our newly developed architecture VisionAiNet in detecting ocular diseases, namely cataract, diabetic retinopathy, glaucoma, and normal eye conditions. The study reveals that while pretrained models offer competitive performance, a scratch CNN (VisionAiNet) architecture consistently outperforms in disease detection with the accuracy of 94.83%, highlighting the importance of tailored model development, contributing to the advancement of automated diagnostic systems in ophthalmology.
Text categorization is a task for text mining that involves pattern classification and is essential for the effective management of textual information systems (TIS). Each document is automatically assigned one or mor...
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This study aims to optimize injection and ignition timings in hydrogen internal combustion engines through the Engine Control Module (ECM). Optimization in terms of computational time is essential for seamless engine ...
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The proliferation of electric vehicles (EVs) and EV charging infrastructure along the U.S. Interstate Highway system poses significant challenges to the power grid. To address these challenges, it is necessary to deve...
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Given how unpredictable the stock market has recently become, it is now crucial to be able to accurately predict the future trend of equities. Because of the financial crisis, it is now essential to estimate stock val...
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The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have ...
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The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%.
In this paper, a new tool for system identification and model predictive control (MPC) has been developed. The mathematical approximation of the model identification was derived using the neural network theory. The em...
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This research focuses on the development of OpenCV-enabled assistance system for Alzheimer's patients using embedded technology. The system was designed to enhance user safety and independence through real-time mo...
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ISBN:
(数字)9798331501488
ISBN:
(纸本)9798331501495
This research focuses on the development of OpenCV-enabled assistance system for Alzheimer's patients using embedded technology. The system was designed to enhance user safety and independence through real-time monitoring and alert mechanisms. It integrates key components such as a GPS module for location tracking, a GSM module for sending emergency alerts, and an intuitive interface comprising an LCD display and a push-button. The system operates by prompting users to confirm their safety through the push-button interface, with a countdown timer displayed on the screen. If the user fails to respond within the allocated time, an automated SMS alert containing the user’s real-time GPS location is sent to registered caregivers. This ensures that assistance is provided promptly in emergency situations. The hardware was selected and optimized for low power consumption and reliability, ensuring continuous operation. The results demonstrated efficient performance in message transmission, precise location tracking, and user interaction, making the system highly effective for its intended purpose. Limitations, such as dependency on network availability and potential issues in GPS signal reception in certain environments, were identified, suggesting areas for future enhancement. The proposed system addresses critical challenges faced by Alzheimer’s patients, such as the risk of wandering or becoming lost, by leveraging embedded technology to provide timely support. This work establishes a practical, cost-effective solution to improve the safety of Alzheimer's patients, while also offering a foundation for further advancements in assistive technologies.
The woman safety is utmost priority at any situation. However, the woman harassment is common irrespective of places. It is necessary to improve the safety measures everywhere such as workplace, travel. There are numb...
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This paper describes the validation of a Field Programmable Gate Array (FPGA)-based controller for Brushless Direct Current (BLDC) motor drive using the hardware co-simulation feature enabled in the Xilinx system Gene...
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