CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose ***(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information....
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CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose ***(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance ***,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution ***(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between *** problem can be overcome by the use of Wrappers as they select better features by accounting for test and train *** aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between *** proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)*** methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.
The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver...
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Huge amounts of genes in single-cell RNA sequencing (scRNA-seq) data may influence the performance of data clustering. To obtain high-quality genes for data clustering, the study proposes a novel gene selection algori...
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As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy conce...
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As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
Lakes in cold and arid regions play an important role in the daily lives of people. However, the health of the lake ecosystem is seriously affected by climate change, especially human activities. Remote sensing satell...
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In the field of computer-aided robotics, trajectory scheduling is crucial for multi-robot systems operating in complex, dynamic, and different kinds of environments. This paper introduces a novel trajectory optimizati...
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This paper introduces a novel methodology for designing secure hardware accelerator tailored for convolutional neural network (CNN) applications, leveraging security-aware high-level synthesis (HLS). The methodology o...
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In the task of public opinion sentiment analysis, short-text sentiment analysis and public opinion network sentiment evolution models play a crucial role. This paper provides an in-depth discussion of public opinion s...
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In recent times, the swift evolution of mobile internet technology and the ubiquitous presence of diverse applications have made e-commerce and social media platforms integral to everyday life. These platforms offer a...
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With the rapid development of autonomous Vehicls technology, the detection of surrounding pedestrians, vehicles, Cyclists and other targets by autonomous vehicles is an indispensable technology, especially for pedestr...
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ISBN:
(纸本)9798350363043
With the rapid development of autonomous Vehicls technology, the detection of surrounding pedestrians, vehicles, Cyclists and other targets by autonomous vehicles is an indispensable technology, especially for pedestrian detection. Therefore, there aremore and more related algorithmms and network models based on target recognition. In recent years, many scholars have stagnated in the process of discovering new algorithms and models and have rarely improved (such as SECOND, PointRCNN, PointPillars, etc.) These classic models, due to the different configuration environments of these model codes and the need to redownload each time you want to run a new model, are very time consuming and energy consuming. In order to solve these difficulties, we chose to use the OpenPCDet target detection framework to improve these models. This framework integrates all the above original object detection models to facilitate us to improve and compare the indicators between the models, and in the comparison of the results of the original model built in the OpenPCDet framework, it is found that the PointPillars modelusing 3D single-stage object detection is the most suitable for autonomous Vehicles. The recognition speed of the original PointPillars for vehicles, pedestrians and other objects can fully meet the use of autonomous Vehicles technology, but the accuracy of object recognition, especially in pedestrian detection, needs to be improved. In this regard, we propose a SelfAttention-PointPillars model. Based on the architecture of the PointPillars model and the idea of self-attention, we use our own pillar amount and modify the original backbone structure into our own self-attention network to improve the accuracy of identifying target pedestrians. We also improved the original L1 loss function into a faster weighted L2 function and we also replaced the activation function with the more efficient LeakyRelu function. Therefore, this paper mainly introduces the OpenPCDet target detect
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