Vehicular ad hoc networks (VANETs) have emerged as a key area of interest in the research community due to their wide range of applications. As the number of vehicles increases, VANETs encounter challenges with access...
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Many Next-Generation consumer electronic devices would be distributed hybrid electronic systems, such as UAVs (Unmanned Aerial Vehicles) and smart electronic cars. The safety and risk control are the key issues for th...
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Air pollution is a significant environmental hazard in modern society because of its serious impact on human health and the environment. In point of fact, there has been a substantial rise in the levels of pollution i...
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Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced...
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In this paper,Modified Multi-scale Segmentation Network(MMU-SNet)method is proposed for Tamil text *** texts from digi-tal writing pad notes are used for text *** words recognition for texts written from digital writi...
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In this paper,Modified Multi-scale Segmentation Network(MMU-SNet)method is proposed for Tamil text *** texts from digi-tal writing pad notes are used for text *** words recognition for texts written from digital writing pad through text file conversion are challen-ging due to stylus pressure,writing on glass frictionless surfaces,and being less skilled in short writing,alphabet size,style,carved symbols,and orientation angle *** pressure on the pad changes the words in the Tamil language alphabet because the Tamil alphabets have a smaller number of lines,angles,curves,and *** small change in dots,curves,and bends in the Tamil alphabet leads to error in recognition and changes the meaning of the words because of wrong alphabet ***,handwritten English word recognition and conversion of text files from a digital writing pad are performed through various algorithms such as Support Vector Machine(SVM),Kohonen Neural Network(KNN),and Convolutional Neural Network(CNN)for offline and online alphabet *** proposed algorithms are compared with above algorithms for Tamil word *** proposed MMU-SNet method has achieved good accuracy in predicting text,about 96.8%compared to other traditional CNN algorithms.
In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only address...
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In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.
We focus on the medication recommendation problem aiming to recommend accurate medications for a patient's current visit. Most existing methods for this problem utilize the patient's current health status, med...
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Under the wave of digital transformation, remote collaboration has become the new normal in the daily operation of enterprises. But at the same time, the risk of sensitive data leakage and the insufficiency of protect...
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Stroke is a leading cause of global population mortality and disability, imposing burdens on patients and caregivers, and significantly affecting the quality of life of patients. Therefore, in this study, we aimed to ...
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Deep neural networks (DNNs) having multiple hidden layers are very efficient to learn large volume datasets and applied in a wide range of applications. The DNNs are trained on these datasets using learning algorithms...
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Deep neural networks (DNNs) having multiple hidden layers are very efficient to learn large volume datasets and applied in a wide range of applications. The DNNs are trained on these datasets using learning algorithms to learn the relationships among different variables. The base method that makes DNNs successful is stochastic gradient descent (SGD). The gradient reveals the way that a function’s steepest rate of alteration is occurring. No matter how the gradient behaves, the key issue with basic SGD is that all parameters must adjust in equal-sized increments. Consequently, creating adaptable step sizes for every parameter is an effective method of deep model optimization. Gradient-based adaptive techniques utilize local changes in gradients or the square roots of exponential moving averages of squared previous gradients. However, current optimizers continue to struggle with effectively utilizing optimization curved knowledge. The novel emapDiffP optimizer suggested in this study utilizes the prior two parameters to generate a non-periodic and non-negative function, and the upgrade parameter makes use of a partly adaptive value to account for learning rate adjustability. Thus, the optimization steps become smoother with a more accurate step size for the immediate past parameter, a partial adapting value, and the largest two momentum values as the denominator of parameter updating. The rigorous tests on benchmark datasets show that the presented emapDiffP performs significantly better than its counterparts. In terms of classification accuracy, the emapDiffP algorithm gives the best classification accuracy on CIFAR10, MNIST, and Mini-ImageNet datasets for all examined networks and on the CIFAR100 dataset for most of the networks examined. It offers the best classification accuracy on the ImageNet dataset with the ResNet18 model. For image classification tasks on various datasets, the suggested emapDiffP technique offers outstanding training speed. With MNIST, CIFAR1
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