Cloud Data Security (CDS) is a set of strategies for securing data from security threats. However, the previous works ignored the prevention of multiple cloud security attacks. To tackle this problem, an efficient sec...
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Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix...
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The gait abnormality may be the cause of various diseases like foot drop, lower back trembling, and osteoarthritis in the human body. The causes may affect body performance. The problem may be solved if we notice it b...
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Efficient detection of defects in fabric plays a dominant role in automating quality control. It significantly enhances efficiency as well as accuracy of quality assurance in textile manufacturing. Skilled humans...
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Skin health is a critical concern for humans, especially in geographical areas where environmental conditions and lifestyle factors adversely affect their condition, leading to a prevalence of skin diseases. This issu...
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In recent days, a wireless body area network (WBAN) has been developed as part of the Internet of Things (IoT) with sensors and actuators in three different modes, building its network, i.e., in-body sensors, wearable...
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This paper proposes the Modified Light GBM to classify the Malicious Users (MUs) and legitimate Secondary Users (SUs) in the cognitive-radio network. The proposed method is to avoid the consequences of malicious users...
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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
Concerns related to the proliferation of automated face recognition technology with the intent of solving or preventing crimes continue to mount. The technology being implicated in wrongful arrest following 1-to-many ...
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The emergence of the Internet of Things (IoT) has enabled the proliferation of interconnected devices and sensors, generating vast amounts of often complex and unstructured data. Deep learning (DL), a subfield of mach...
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