Facial image captured under uncontrolled environment often degraded by blur, noise and low-light effect which significantly affect the performance of different real life applications. To solve this problem numerous fa...
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The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security...
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The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.
Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, ...
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The variety of crops, differences in climate, and the multiplicity of disease symptoms make early identification and evaluation of leaf diseases a challenging task. Although deep-learning methods have been created for...
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Heart disease increases the strain on the heart by reducing its ability to pump blood throughout the body, which can lead to heart attacks and strokes. Heart disease is becoming a global threat to the world due to peo...
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In the current digital era, video surveillance has become a part of daily life. The person re-identification(re-ID) task involves choosing a person as a target in one camera feed and recognizing that target in footage...
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ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and co...
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Plant diseases significantly threaten global food security and economic stability by reducing crop yields, increasing production costs, and exacerbating food shortages. Early and precise detection of plant diseases is...
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This work introduces novel architecture components and training procedures to create augmented neural networks with the ability to process data bidirectionally via an end-to-end approximate inverse. We develop pseudoi...
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Handwritten documents generated in our day-to-day office work, class room and other sectors of society carry vital information. Automatic processing of these documents is a pipeline of many challenging steps. The very...
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