Autism, a neurological disorder, manifests uniquely in areas such as verbal and nonverbal communication, social interactions, behavioral adaptability, and specific interests. The results collected indicate that health...
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The training of large language models (LLMs) necessitates substantial data and computational resources, and updating outdated LLMs entails significant efforts and resources. While numerous model editing techniques (ME...
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This study develops an advanced automated prediction system using Machine Learning (ML) techniques to identify diabetes early. The research employs the WBSMOTE method for data preprocessing, addresses class imbalances...
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In recent times,Internet of Things(IoT)and Deep Learning(DL)mod-els have revolutionized the diagnostic procedures of Diabetic Retinopathy(DR)in its early stages that can save the patient from vision *** the same time,...
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In recent times,Internet of Things(IoT)and Deep Learning(DL)mod-els have revolutionized the diagnostic procedures of Diabetic Retinopathy(DR)in its early stages that can save the patient from vision *** the same time,the recent advancements made in Machine Learning(ML)and DL models help in developing computer Aided Diagnosis(CAD)models for DR recognition and *** this background,the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network(ODBN)model i.e.,NS-ODBN model for diagnosis of *** presented model involves Interval Neutrosophic Set(INS)technique to dis-tinguish the diseased areas in fundus *** addition,three feature extraction techniques such as histogram features,texture features,and wavelet features are used in this ***,Optimal Deep Belief Network(ODBN)model is utilized as a classification model for *** model involves Shuffled Shepherd Optimization(SSO)algorithm to regulate the hyperparameters of DBN technique in an optimal *** utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model *** presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation *** resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.
In agriculture, proper water management is important for crop growth and resource conservation. This brief describes a smart water system designed to optimize water use by integrating dual-axis solar trackers. The sys...
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Test case generation is a complex problem when dealing with multiple objectives, which hinders search efficiency with algorithms like DynaMOSA in the EvoSuite framework. To address this issue, we propose SVD-DynaMOSA,...
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Solar hydrogen production using a photoelectrochemical anion exchange membrane reactor is vital to reduce our dependence on fossil fuels. Anion exchange membranes use electrocatalysts based on low-cost earth metals. T...
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Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects children from childhood to adulthood. The symptoms of ADHD include attention deficit, impulsive behaviour, hyperactivity, a...
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Missing data pose significant obstacles in data analysis. Many imputation methods, operating under the assumption that similar instances exhibit similar feature values, often overlook the essential role of the feature...
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Chronic Kidney Disease (CKD), a significant global health issue and has a major impact on a vast number of people. In the early stages, it is impossible to identify the disease's symptoms. Very few people are awar...
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ISBN:
(纸本)9798350393569
Chronic Kidney Disease (CKD), a significant global health issue and has a major impact on a vast number of people. In the early stages, it is impossible to identify the disease's symptoms. Very few people are aware of this illness and are able to foresee the symptoms earlier. The disorder is considered as potential risk factor. As a result, a potent deep learning framework is used in this paper to reduce the risk of acquiring these diseases as a way to prevent them. The machine learning algorithms used in early disease prediction, has been found to be computationally expensive, frequently overfit, and underperform in terms of accuracy since they must examine the large amount of clinical data until the model converges. Therefore, in this paper three novel work has been proposed an efficient novel hybrid feature selection strategy RFITLO algorithm is used to find the optimal features that gives the major contribution is classifying the CKD disease. Then two proposed classification algorithms namely Enhanced Multi-Layer Perceptron (HW-MLP) and Optimized Multi-Layer Perceptron (PKD-OMLP) are used in prediction model to capture the complex patterns and optimize the learning algorithm to predict the CKD at prior stage from the data gathered in Kaggle, Real Time and UCI Machine Learning Repository Dataset. In order to measure the classifications of disease, performance measures including accuracy, precision and recall are analyzed. The experimental findings show that the PKD-OMLP strategy produces better results than the proposed HW-MLP and other conventional approaches like Support Vector Machine (SVM), Linear Regression (LR) and Multi-Layer Perceptron (MLP). Among the preceding four models PKD-OMLP renders the best outcome as per its performance level producing a high accuracy of 94.89% on testing Real Time (RT) CKD dataset comparatively with other datasets such as Kaggle and UCI repository. Therefore, these proposed algorithms can support clinicians to enable secured an
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