Deep learning is having a particularly revolutionary effect in the field of artificial intelligence, namely in computer vision, where machines are equipped with the capacity to comprehend and evaluate visual informati...
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Smartphone app expansion needs strict security measures to avoid fraud and danger. This study overcomes this issue by identifying apps differently. This new solution uses convolutional neural network (CNN), natural la...
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Smartphone app expansion needs strict security measures to avoid fraud and danger. This study overcomes this issue by identifying apps differently. This new solution uses convolutional neural network (CNN), natural language processing (NLP), and the strong AppAuthentix Recommender algorithm to secure app stores and boost customer confidence in the digital marketplace. Since the app ecosystem has grown, counterfeit and harmful applications have risen, threatening consumers and app merchants. These risks need advanced technology that can distinguish malware from legitimate apps. A complex prediction model using CNNs for image analysis, NLP for text feature extraction, and the novel AppAuthentix Recommender algorithm to properly identify legitimate and counterfeit mobile applications is the goal of this research. The whole strategy secures app stores and authenticates apps. The urgent need to safeguard app markets and users against unauthorized and hazardous programs sparked this study. Our cutting-edge solutions make mobile app consumers' digital lives safer and app marketplaces more trustworthy. CNN, NLP, and AppAuthentix Recommender yielded amazing results in this investigation. Mobile app authenticity may be estimated with 98.25% accuracy. This technology greatly improves app store security and enables mobile app verification. In conclusion, our work offers a novel way to app identification at a time of rapid mobile app development. CNN, NLP, and AppAuthentix Recommender have dramatically enhanced app store security. These new solutions may boost mobile app security and consumer confidence.
Liquid crystal image classification is a vital part of modern biosensors. Recent studies have shown that this problem can be solved using computationally intensive intelligent analysis tools, such as convolutional neu...
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Liquid crystal image classification is a vital part of modern biosensors. Recent studies have shown that this problem can be solved using computationally intensive intelligent analysis tools, such as convolutional neuralnetworks (CNNs) and support vector machines (SVMs). This article proposes a new method of microscopic image classification for liquid crystals-based biosensors with fast response. This method is based on topological analysis and provides 95% accuracy. Moreover, on the same hardware, it reaches eightfold performance compared to CNNs, which are usually used in similar applications. Finally, it has only nine parameters. Most of those parameters are independent and can be easily tuned based on the properties of the liquid crystals suspension and the microscope. This is a significant benefit compared to machine learning approaches that require large training datasets. The proposed solution can be considered a new step toward the creation of fully automatic biosensors for industrial water quality assessment systems.
In recent years, the application of artificial intelligence (AI) techniques for fire detection has gained significant attention due to its potential for enhancing early fire detection systems. This study aims to compa...
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In this paper, a novel remote sensing image object detection algorithm is proposed by combining constraint energy minimization (CEM) and noise tolerance zeroing neural network (NTZNN). This algorithm combines traditio...
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ISBN:
(纸本)9789819607945;9789819607952
In this paper, a novel remote sensing image object detection algorithm is proposed by combining constraint energy minimization (CEM) and noise tolerance zeroing neural network (NTZNN). This algorithm combines traditional imageprocessing methods with recursive neural network (RNN) and proposes a multi scene usable NTZNN-CEM object detection model for hyperspectral and RGB remote sensing images. Finally, through numerical simulation experiments and remote sensing image object detection experiments, it has been proven that the NTZNN-CEM algorithm has advantages such as fast detection speed and strong robustness, providing a new visual approach for improving the advanced perception of robots in complex environments.
Recently, applications using artificial intelligence (AI) technique in mobile devices such as augmented reality have been extensively pervasive. The hardware specifications of mobile devices, dynamic service demands, ...
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Recently, applications using artificial intelligence (AI) technique in mobile devices such as augmented reality have been extensively pervasive. The hardware specifications of mobile devices, dynamic service demands, stochastic network states, and characteristics of DNN (Deep neural Network) models affect the quality of experience (QoE) of such applications. In this paper, we propose CutEdge , that leverages a virtual queue-based Lyapunov optimization framework to jointly optimize DNN model partitioning between a mobile device and a mobile edge computing (MEC) server and processing/networking resources in a mobile device with respect to internal/external system dynamics. Specifically, CutEdge makes decisions of (i) the partition point of DNN model between the mobile device and MEC server, (ii) GPU clock frequency, and (iii) transmission rates in a mobile device, simultaneously. Then, we theoretically show the optimal trade-off curves among energy consumption, throughput, and end-to-end latency yielded by CutEdge where such QoE metrics have not been jointly addressed in the previous studies. Moreover, we show the impact of joint optimization of three control parameters on the performances via real trace-driven simulations. Finally, we show the superiority of CutEdge over the existing algorithms by experiment on top of implemented testbed using an embedded AI device and an MEC server.
Among various data processing tools, neuralnetworks stand out prominently in the fields of computer vision, intelligent control, and brain-like computing. They emulate the operational processes of the human brain, co...
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The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different...
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Using machine vision and imageprocessing methods has an important role in the identification of defects of agricultural products, especially potatoes. The applications of imageprocessing and artificial intelligence ...
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Using machine vision and imageprocessing methods has an important role in the identification of defects of agricultural products, especially potatoes. The applications of imageprocessing and artificial intelligence in agriculture in identifying and classifying pests and diseases of plants and fruits have increased and research in this field is ongoing. In this paper, we use the convolution neural network (CNN) methods, also, we examined 5 classes of potato diseases with the names: Healthy, Black Scurf, Common Scab, Black Leg, Pink Rot. We used a database of 5000 potato images. We compared the results of potato defect classification our methods with other methods such as Alexnet, Googlenet, VGG, R-CNN, Transfer Learning. The results show that the accuracy of the deep learning proposed method is higher than other existing works. We get 100% and 99% accuracy in some of the classes, respectively.
Quality assessment is a key problem to be resolved in imageprocessing. Few research works have been designed to analyze the quality of images using different techniques. However, the accuracy involved during the proc...
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