Pancreatic cancer is one of the most fatal cancers, and medical doctors basically face major problems in its early diagnosis. This article throws light on the work involving the application of Kolmogorov-Arnold Networ...
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Cardiovascular diseases are still among the leading causes of mortality, which require fast and reliable classification methods for diagnosing and preventing the disease. This work thus presents an elaborate framework...
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Dyslexia, recognized as a common learning disability, adversely affects reading, spelling, and word decoding capabilities, frequently resulting in considerable academic and social obstacles if not identified at an ear...
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Growing plants in nutrition solution with any growing medium or roots dipped in distilled water is Hydroponics. Hydroponics, the practice of growing plants in a nutrient-rich solution without soil, offers significant ...
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It is quite challenging to monitor goods quality or security issues due to the intricacy of a tracking system, particularly for the fundamental farming food supply chains that contribute to making up everyday feeds of...
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This study focuses on creating an accurate reflection prediction model that will guide the design of filters with multilayer Anti-Reflection Coating (ARC) to optimize the thickness parameters using Machine Learning (M...
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This study focuses on creating an accurate reflection prediction model that will guide the design of filters with multilayer Anti-Reflection Coating (ARC) to optimize the thickness parameters using Machine Learning (ML) and Deep Learning (DL) techniques. This model aims to shed light on the design process of a multilayer optical filter, making it more cost-effective by providing faster and more precise production. In creating this model, a dataset containing data obtained from 3000 (1500 Ge–Al2O3, 1500 Ge–SiO2) simulations previously performed on a computer based on the thicknesses of multilayer structural materials was used. The data are generated using Computational Electromagnetic simulation software based on the Finite-Difference Time-Domain method. To understand the mechanism of the proposed model, two different two-layer coating simulations were studied. While Ge was used as the substrate in both coatings, Al2O3 and SiO2 were used as the second layers. The data set consists of the 3–5 µm and 8–12 µm bands typical for the mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands and includes reflectance values for wavelengths ranging between these spectra. In the specified 2-layer data set, the average reflectance was obtained with a minimum of 0.36 at 515 nm Ge and 910 nm SiO2 thicknesses. This value can be increased by adapting the proposed model to more than 2 layers. Six ML algorithms and a DL model, including artificial neural networks and convolutional neural networks, are evaluated to determine the most effective approach for predicting reflectance properties. Furthermore, in the proposed model, a hyperparameter tuning phase is used in the study to compare the efficiency of ML and DL methods to generate dual-band ARC and maximize the prediction accuracy of the DL algorithm. To our knowledge, this is the first time this has been implemented in this field. The results show that ML models, particularly decision tree (MSE: 0.00000069, RMSE: 0.00083), rand
Skin cancer is characterized by the uncontrolled proliferation of abnormal cells in the outermost skin layer, the epidermis, due to unrepaired DNA damage leading to mutations. These mutations cause rapid multiplicatio...
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ISBN:
(纸本)9789819764648
Skin cancer is characterized by the uncontrolled proliferation of abnormal cells in the outermost skin layer, the epidermis, due to unrepaired DNA damage leading to mutations. These mutations cause rapid multiplication of skin cells, forming malignant tumors. The primary types of skin cancer include basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma, and Merkel cell carcinoma (MCC). Melanoma of the skin ranks as the 17th most common cancer worldwide, with more than 150,000 new cases reported in 2020. Early detection and treatment of melanoma can significantly impact patient outcomes. The present work aims to detect melanoma skin cancer in its early stages using image processing through computer Vision and deep learning methodologies. The culmination of this effort is an Android application designed to facilitate self-diagnosis for users, offering timely alerts on when to consult a medical professional. Hospitals can also utilize the application to prioritize patient care based on their risk percentages, benefiting both patients and healthcare providers. The study delves into relevant research papers published in esteemed journals related to skin cancer diagnosis. Deep learning methods are proposed to assist dermatologists in achieving early and accurate diagnoses. While specialists can provide accurate diagnoses, the development of automated systems becomes crucial to efficiently diagnose diseases, saving lives and reducing healthcare and financial burdens. Machine learning (ML) emerges as a valuable tool in this context. The article focuses on the fundamentals of ML and its potential in aiding skin cancer diagnosis. The objective is to conduct a comparative study between the DenseNet-121, ResNet-50, and CNN-RF models. The study reveals that DenseNet-121 outperformed with a testing accuracy of 83%, surpassing ResNet- 50, which achieved 81% testing accuracy. This comparative analysis contributes to the ongoing research and development in the field of
This paper centers on leveraging Convolution-Augmented Transformer Models originally designed for Automatic Speech Recognition (ASR) in the realm of Sign Language—specifically, American Sign Language Fingerspelling R...
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Internet of Thing based healthcare ecosystems are extremely popular as they are built on a network of devices that are connected directly to one another in order to collect, share, and use important contextual informa...
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Heart disease is considered as one of the leading causes of death worldwide. Predicting heart diseases from retinal fundus images is a promising approach in the early detection and monitoring of cardiovascular health ...
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