This paper examines the use of chaos theory towards the ensuring the securitoy of nonlinear signal communication networks. Chaos encryption is based on the given properties of the chaos: the minimized differences betw...
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作者:
Tian, YePan, JingwenYang, ShangshangZhang, XingyiHe, ShupingJin, YaochuAnhui University
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center
Institute of Artificial Intelligence Hefei230088 China Anhui University
School of Computer Science and Technology Hefei230601 China Anhui University
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University
Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University
Faculty of Technology Bielefeld33619 Germany
The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision s...
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Supervisory control and data acquisition(SCADA)systems are computer systems that gather and analyze real-time data,distributed control systems are specially designed automated control system that consists of geographi...
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Supervisory control and data acquisition(SCADA)systems are computer systems that gather and analyze real-time data,distributed control systems are specially designed automated control system that consists of geographically distributed control elements,and other smaller control systems such as programmable logic controllers are industrial solid-state computers that monitor inputs and outputs and make logic-based *** recent years,there has been a lot of focus on the security of industrial control *** to the advancement in information technologies,the risk of cyberattacks on industrial control system has been drastically *** they are so inextricably tied to human life,any damage to them might have devastating *** provide an efficient solution to such problems,this paper proposes a new approach to intrusion ***,the important features in the dataset are determined by the difference between the distribution of unlabeled and positive data which is deployed for the learning ***,a prior estimation of the class is proposed based on a support vector *** results show that the proposed approach has better anomaly detection performance than existing algorithms.
IoT devices, constrained by limited resources and weak security measures, are highly vulnerable to malware at- tacks. This review examines malware detection methods using textual, visual, and network traffic features,...
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ISBN:
(数字)9798331510299
ISBN:
(纸本)9798331510305
IoT devices, constrained by limited resources and weak security measures, are highly vulnerable to malware at- tacks. This review examines malware detection methods using textual, visual, and network traffic features, with experiments fo- cused on network traffic data. Feature extraction techniques such as correlation analysis, eXtreme Gradient Boosting(XGBoost), and the Firefly algorithm were applied to machine learning models, including logistic regression, random forest, Adaptive Boosting(AdaBoost), and perceptron. Results highlight the poten- tial of these approaches for resource-constrained environments.
In this research, we analyze the implementation of three different clustering algorithms to sort individuals over five common mental health conditions: Schizophrenia, Depression, Anxiety, Bipolar Disorder and Eating D...
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ISBN:
(数字)9798331536381
ISBN:
(纸本)9798331536398
In this research, we analyze the implementation of three different clustering algorithms to sort individuals over five common mental health conditions: Schizophrenia, Depression, Anxiety, Bipolar Disorder and Eating Disorders. The clustering methods evaluated include Spectral clustering, K-Means and Agglomerative Clustering. We carry out a detailed performance analysis to assess the effectiveness of each algorithm at recognizing patterns in the mental health dataset. The results derived from our research are for better comprehension of cluster methods with respect to the identification of mental health conditions and further serve the purpose of more accurate classifications and assisting in early detection. These results have got the capabilities of strengths and weaknesses of each algorithm in the classification of the selected mental health disorders, providing clarity to future research and real-time applications in the area of mental health.
Collection of waste is one of the important goals of Waste Management Unit (WMU), where collecting waste decreases the amount of time, expenses, and the impact of waste collectors on the environment. The work is to cr...
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ISBN:
(数字)9798331538538
ISBN:
(纸本)9798331538545
Collection of waste is one of the important goals of Waste Management Unit (WMU), where collecting waste decreases the amount of time, expenses, and the impact of waste collectors on the environment. The work is to create a set of routes for waste collection that will take minimal time to get through several collection centers given the capabilities of the collection vehicles, and road systems and traffic conditions. For waste collection points, ten major stations in Bengaluru were selected along with the disposal point. For this purpose, several algorithms such as Dijkstra's Algorithm, Bellman-Ford Algorithm, Floyd-Warshall Algorithm, Johnson's Algorithm, Nearest Neighbour Algorithm, and Ant Colony Optimization (ACO) were designed and evaluated. Algorithms were measured based on parameters such as the time taken, the fuel consumed, the CO2 produced, the effectiveness of the trip that was made. The theoretical and applied objectives of the project correlate with the goals of the integration of improved optimization possibilities with operational issues with the purpose of defining such effective methods in relation to the containment of fuel consumption, the decrease in emissions and the waste management optimization in the urban environment.
The success and safety of block cipher systems heavily depend on how efficient and secure their Key Schedule Algorithms (KSAs) are, especially when fighting against cryptanalytic attacks. This paper proposes a novel K...
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Cervical cytology image segmentation is a crucial component in the automated analysis of cervical cytology screening. This research investigates the efficacy of federated learning against traditional learning methods ...
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Recognizing architectural styles is essential for preserving & understanding cultural heritage, as it helps categorize & document diverse structures, highlighting their historical, cultural & artistic valu...
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ISBN:
(数字)9798331523893
ISBN:
(纸本)9798331523909
Recognizing architectural styles is essential for preserving & understanding cultural heritage, as it helps categorize & document diverse structures, highlighting their historical, cultural & artistic value. The study addresses the gap between technology & heritage by developing a framework for classifying 8 architectural styles: Buddhist, Indo Islamic, Rajput, Dravidian, Hindu, Sikh, British & Modern. Using the HOG method for feature extraction, the models capture intricate architectural details. Classification is done using machine learning models like Logistic Regression, Random Forest & XG-Boost, as well as advanced deep learning models such as DenseNet & InceptionV3. DenseNet achieves the highest accuracy of 86%, followed by InceptionV3 at 79%, showing the capability of deep learning in handling complex visual data. The study not only compares model performance but also provides a scalable method for architectural style classification that contributes to the heritage preservation.
This research focuses on generating image captions using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased comput...
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ISBN:
(数字)9798331509828
ISBN:
(纸本)9798331509835
This research focuses on generating image captions using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased computing power make it more feasible to build models capable of creating captions for images. This paper uses CNN and RNN models in Python to achieve this. Image captioning combines image recognition and Natural Language Processing (NLP) to interpret image context and express it in English, drawing on core computer vision principles. This study reviews important concepts in image captioning, including the applications of Keras, NumPy, and Jupyter notebook for development. This paper also explores the use of the Flickr dataset and CNN for image classification. The BLEU score of the proposed models is found to be close to 60%. On further enhancement to the model, it could be achieved to get a better score.
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