The brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models. However, due to bottlenecks in data, computation, a...
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Cloud computing"is a computer model that provides end users with quantifiable, scalable, and on-demand services. These days, almost every organization uses computer technology extensively for infrastructure, cost...
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The development of industrial robots, as a carrier of artificial intelligence, has played an important role in promoting the popularisation of artificial intelligence super automation technology. The paper introduces ...
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Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offe...
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Executive managers are not all equipped with the cyber security expertise necessary to enable them to make business decisions that accurately represent the status and needs of the cyber security side of the business. ...
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ISBN:
(纸本)9781450394758
Executive managers are not all equipped with the cyber security expertise necessary to enable them to make business decisions that accurately represent the status and needs of the cyber security side of the business. Unfortunately, the lack of understanding between the business and cyber security domains contribute to structurally endorsed vulnerabilities within a business context, where either the business needs were considered without understanding the impact on cyber security, or alternatively, the cyber security needs were considered without fully understanding the impact this would have on the business strategy and financial stability. To combat this dilemma, a gamified approach to cyber security training for executives is proposed as a solution to not only minimise the realisation of cyber vulnerabilities within a business context, but also to improve business outcomes that are supported by cyber security measures. We developed a serious game software platform, Aurelius, to simulate an executive decision maker’s role in managing the everyday cyber security investment decisions, and linking that to business metrics to incorporate the business and cyber security understanding. Our game includes simulated cyber security attacks that would require the executive decision maker (the player) to respond appropriately. The algorithms underpinning our simulated cyber security game are a product of a complex systems approach, as this most accurately models an executive’s experience. In our design, we set up Aurelius to fulfil eight of the nine criteria specified for a state of the art serious game in the cyber security domain.
In numerous industries, weather forecasting is essential for making informed decisions and mitigating the effects of extreme weather events. The complexity and chaos of weather systems, however, place restrictions on ...
In numerous industries, weather forecasting is essential for making informed decisions and mitigating the effects of extreme weather events. The complexity and chaos of weather systems, however, place restrictions on standard procedures, leading to errors and significant threats. We suggest the Quantum Improved Weather Forecast framework, which combines quantum machine learning methods with conventional methodologies, to solve these issues. By using quantum algorithms including quantum support vector machines, quantum neural networks, and quantum clustering, the QWF framework seeks to improve accuracy. Despite a few drawbacks, the QWF framework provides a method to revolutionize weather forecasting by enhancing prediction accuracy and facilitating improved catastrophe preparedness. It may prevent fatalities, safeguard critical infrastructure, and promote sustainable growth.
INTRODUCTION: Tuberculosis (TB) remains a significant global health threat, demanding trustworthy and effective detection techniques. This study investigates the utilization of deep learning models, specifically ResNe...
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INTRODUCTION: Tuberculosis (TB) remains a significant global health threat, demanding trustworthy and effective detection techniques. This study investigates the utilization of deep learning models, specifically ResNet50, InceptionV3, AlexNet, DenseNet121, and Inception3, for diagnosing tuberculosis from chest X-ray images. With a substantial dataset comprising 4,000 chest X-ray images, sourced from seven different nations and categorized as TB-infected or normal, this research aims to evaluate the performance of various deep learning architectures in accurately distinguishing TB instances. OBJECTIVES: The primary objective of this study is to assess the efficacy of different deep learning models in differentiating TB instances from chest X-ray images. By employing segmentation, data augmentation, and image pre-processing techniques, the research aims to enhance model performance and reliability in TB diagnosis. METHODS: The chest X-ray image dataset, scaled to 224x224 pixels, underwent segmentation, data augmentation, and pre-processing before being fed into the deep learning models. The dataset was divided into 80% for model training and 20% for testing, utilizing a five-fold cross-validation technique. Performance evaluation metrics including accuracy, precision, recall, and F1-score were employed to assess the models' effectiveness in TB identification. RESULTS: The findings indicate that ResNet50 and InceptionV3 models achieved near-perfect accuracy, precision, recall, and F1-scores, demonstrating their potential as reliable methods for TB identification. Despite exhibiting lower accuracy for the TB class, AlexNet also displayed good performance. However, DenseNet121 and Inception3 models showed room for improvement, particularly in precision and recall for the TB class. CONCLUSION: This study underscores the potential of deep learning models in enhancing TB identification in chest X-ray images. It highlights the importance of segmentation, data augmentation, a
We study a family of distributed stochastic optimization algorithms where gradients are sampled by a token traversing a network of agents in random-walk fashion. Typically, these random-walks are chosen to be Markov c...
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Hypergraphs provide a superior modeling framework for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditional...
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The localization of wireless sensor network (WSN) is an increasingly promi-nent problem. The goal of this problem is to use the anchor nodes in WSN to esti-mate the geographical location of the unknown nodes. This pap...
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