A key component of understanding the human brain's information processing mechanism is neural networking, which seeks to create a quantifiable connection between the stimuli and the triggered brain processes. Popu...
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The current voting systems used in political elections in India have appeared to be prone to security breaches, with individuals voting more than once to create biased results in favor of their favorite candidates whi...
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Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first prop...
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Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.
Skin lesion segmentation is a complex and severe project, which aims to accurately segment abnormal regions in skin lesion images. However, obtaining accurate segmentation results is difficult because of the great unc...
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Agriculture, a crucial foundation for human existence, exists at the intersection of conventional methods and modern innovations. Any country's primary issue in agriculture science is effective plant disease contr...
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ISBN:
(纸本)9798331519056
Agriculture, a crucial foundation for human existence, exists at the intersection of conventional methods and modern innovations. Any country's primary issue in agriculture science is effective plant disease control, driven by the growing need for food due to population growth. Moreover, developments in modern technology have greatly improved the precision and effectiveness of plant detection. A critical challenge in the agricultural sector is leaf diseases, significantly impacting crop production and economic profit. Integrating digitalization and technology in agriculture is imperative for augmenting productivity and ensuring food security. This research emphasizes the principal concerns and obstacles associated with classifying leaf diseases. By experimenting with different approaches, researchers have already achieved great strides in the identification and categorization of illnesses. But due to assessments, new information, and discussions, changes are required. The resilience of deep learning (DL) and machine learning (ML) techniques, such as feed-forward neural networks (FFNN), k-means clustering (KMC), fuzzy logic (FL), artificial neural networks (ANN), and genetic algorithms (GA),so forth, has been demonstrated by earlier research. Due to their inherent ability to automatically obtain relevant visual information and understand spatial hierarchies, CNNs are usually the preferred option for image identification and classification among the ML and DL methods addressed in this study. The selection between machine learning and deep learning is dependent upon numerous issues, the data that is available, and the amount of processing power that is available. Because of this, DL - primarily using CNNs are advised for many complicated image detection and classification applications. They also show outstanding results for classification and detection on their datasets but not on others. Lastly, by using a variety of methods for processing images in the field of artif
Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper *** performing this entire process,there is a high possibility for da...
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Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper *** performing this entire process,there is a high possibility for data corruption in the mid of *** the other hand,the network performance is also affected due to various *** address these issues,an efficient algorithm that jointly offers improved data storage and reliable routing is ***,after the deployment of sensor nodes,the election of the storage node is achieved based on a fuzzy expert *** Random Linear Network Coding(IRLNC)is used to create an encoded *** encoded packet from the source and neighboring nodes is transmitted to the storage ***,to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector(DSDV)*** analysis of the proposed work is carried out by evaluating some of the statistical *** residual energy,packet delivery ratio,compression ratio and storage time achieved for the proposed work are 8.8%,0.92%,0.82%,and 69 *** on this analysis,it is revealed that better data storage system and system reliability is attained using this proposed work.
In e-commerce, session-based recommendation systems (SBRS) have enticed a lot of scrutiny for improving item discovery and user experience. This paper provides an insight about the session-based recommendation system ...
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White matter microstructure plays a pivotal role in the diagnosis and study of brain disorders. Deep learning-based estimation of white matter microstructural indices from diffusion MRI (dMRI) data has gained increasi...
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A Real-Time Web Traffic Forecasting System Using Hybrid LSTM and CNN Paper Development of the real-time web traffic forecasting system uses a hybrid approach combining LSTM with Conventional Neural Networks (CNN) to f...
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This research introduces a novel method for traffic sign identification utilizing a variant 2-D convolutional neural network (CNN) architecture with a shift-invariant operation. Addressing challenges such as variation...
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