Climate sustainability plays a very crucial role in environmental sustainability and by considerable use of natural resources, and reducing pollution, we can make our planet beautiful and sustaining for the life of fu...
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Optimizing renewable energy systems in healthcare facilities via use of modern ML algorithms to improve energy efficiency along with sustainability is goal of this research. Integrating machine learning provides a pot...
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Withthe continuous improvement of big data and computing power, deep learning models have achieved remarkable results in the field of image recognition, but building and training a deep neural network from scratch of...
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Withthe growing popularity of the Internet and digital technology, network security threats are increasing, and people's demand for advanced security defense means is rising. By combining advanced deep learning a...
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Software-defined networking (SDN) has transformed the landscape of network communication. SDN separates the control plane from the data plane, offering a centralized management system and dynamic resource allocation. ...
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ISBN:
(纸本)9798350375084;9798350375077
Software-defined networking (SDN) has transformed the landscape of network communication. SDN separates the control plane from the data plane, offering a centralized management system and dynamic resource allocation. Nevertheless, SDN is susceptible to security risks, necessitating the deployment of sophisticated Intrusion Detection Systems (IDS). Several researchers have recently employed machine learning and other cutting-edge technologies to analyze and identify rapidly growing attacks and anomalies. However, the majority of these techniques exhibit low accuracy and poor scalability. In response to this challenge, this paper proposes an Intrusion Detection System (IDS) framework based on the Convolutional Neural Network-Gated Recurrent Unit (CNN- GRU) network. this framework leverages Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to identify real-time network intrusions. the framework was trained and evaluated on the UNSW-NB15 and InSDN datasets using Bayesian optimization (BO), achieving exceptional accuracy and F1 scores exceeding 99.93% on the UNSW-NB15 dataset. Similarly, on the InSDN dataset, the framework achieved an accuracy of 99.93%, with precision, recall, and F1 score values of 99.89%, 99.97%, and 99.93%, respectively. these demonstrate the framework's effectiveness in discerning between normal and malicious network behavior.
Evolutionary computation is a collection of algorithms based on the evolution of a population toward a solution to a certain problem. these algorithms have demonstrated their effectiveness in various optimization task...
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the deepening application and development of automated stereoscopic warehouses not only improve the efficiency of goods entering and exiting the warehouse, but also face the problem of dynamic storage space allocation...
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Artificial Intelligence applications become one of the most important tools that help to increase the profits of ecommerce stores. It is expected that AI methods will push this aspiration to an even higher level. this...
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ISBN:
(纸本)9798350354140;9798350354133
Artificial Intelligence applications become one of the most important tools that help to increase the profits of ecommerce stores. It is expected that AI methods will push this aspiration to an even higher level. this study aims at classifying images of clothing products using deep learning (DL) techniques while embedding them in an e-commerce web application. We utilize deep learning techniques to determine the type of clothing products in the image, such as shirts, dresses, pants, shoes, etc. this study performs several steps, including requirements collecting and modeling, DL model training using two deep learning models, and then testing the models and the system's accuracy on a set of images. We have used two deep learning models improved from classic Convolutional Neural Networks (CNN). the Convolutional Neural Network models are achieving high accuracy on image classification tasks. therefore, the literature proposes many suggested improvements to CNN architecture, such as Xception and VGG-19 architectures. In this study, we have selected a VGG-19-based clothing image classification. We found out that the VGG-19 model outperforms the Xception model. therefore, the trained VGG-19 model is incorporated into a clothing store web application for classifying clothing image products. Testing accuracy is found, and then a manual test of the system accuracy is achieved using in-lab sample images.
this paper proposes a frequent pattern data mining algorithm based on support vector machine (SVM), aiming to solve the performance bottleneck of traditional frequent pattern mining algorithms in high-dimensional and ...
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Library user behavior was investigated using artificial intelligence (AI) technology to propose corresponding service optimization strategies. through data collection and analysis, the behavioral characteristics of us...
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