In the past decade, Deep Neural Networks (DNNs) achieved state-of-the-art performance in a broad range of problems, spanning from object classification and action recognition to smart building and healthcare. The flex...
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The proposed work explains the comparative detailed study of multispectral image enhancement by means of contrast and decorrelation stretching. It is challenging to extract all necessary information from multispectral...
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The global challenge of diabetes demands innovative approaches for early diagnosis and effective management. This paper investigates the integration of advanced dimensionality reduction and feature selection technique...
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ISBN:
(数字)9798331532215
ISBN:
(纸本)9798331532222
The global challenge of diabetes demands innovative approaches for early diagnosis and effective management. This paper investigates the integration of advanced dimensionality reduction and feature selection techniques to improve diabetes prediction models. Utilizing Principal Component Analysis (PCA) and the Artificial Bee Colony (ABC) algorithm, the study focuses on optimizing feature selection to enhance model accuracy, computational efficiency, and interpretability. Employing two diverse datasets—the Behavioral Risk Factor Surveillance System (BRFSS) and the Pima Indians Diabetes Dataset—the research evaluates the performance of Random Forest and Logistic Regression models. Results reveal significant improvements in predictive accuracy and computational efficiency, particularly with ABC-selected features, reducing feature dimensionality while maintaining high accuracy. This study underscores the potential of combining PCA and ABC to revolutionize predictive healthcare analytics, offering robust tools for diabetes management and prevention.
Neighbor management in vehicular networks comes with the risk of unnecessarily overloading the wireless channel, particularly when two-hop neighbor information is required. A possible solution to this challenge is the...
Neighbor management in vehicular networks comes with the risk of unnecessarily overloading the wireless channel, particularly when two-hop neighbor information is required. A possible solution to this challenge is the use of probabilistic data structures. In our previous work, we explored the benefits of using Bloom filters for maintaining this neighbor information showing promising results. In this paper, we now evaluate the usage of a additional probabilistic data structure, the Cuckoo Filter, which is advertised as a superior alternative to Bloom filter. We assess the performance of the Cuckoo approach in a vehicular networking scenario and find that it does not meet these expectations. In fact, it may lead to worse performance in specific configurations.
Deep Learning is nowadays one of the powerful approaches of the Artificial Intelligence field in object detection and recognition. It also has gained reputation for improvement in the analysis of medical images. Early...
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ISBN:
(数字)9798331543891
ISBN:
(纸本)9798331543907
Deep Learning is nowadays one of the powerful approaches of the Artificial Intelligence field in object detection and recognition. It also has gained reputation for improvement in the analysis of medical images. Early diagnosis and accuracy in prediction can be obtained by finding various normal and abnormal patterns in complex datasets by using different deep learning algorithms. Once deep learning models have undergone extensive training on enormous datasets, it can learn distinguishing between tissues that are either cancerous or not. Moreover, it improves precision in diagnosis and also enables better results in the outcomes of patients. This paper elaborates various deep learning architectures employed in investigating the state of the colon in diagnosing cancer. Deep learning algorithms which detect colon cancer help to identify various significant performance parameters to measure their effectiveness. The metrics include accuracy which helps to make the correct predictions, sensitivity metric to correctly identify the patients suffering from the disease and specificity metric to identify the people without the disease accurately. This paper will present reviews of different research papers that outline the overall framework. This paper will be beneficial for those researchers who are keen to know about different deep learning techniques for diagnosing the cancer in colon.
Image recognition is one of the world's fastest growing technologies is Image recognition. Its use in security, monitoring systems has made it the matter of talk in the recent times. There are plentiful of imaging...
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With the advancement of artificial intelligence (AI), indoor positioning systems have become increasingly important for various applications, leading to the development of diverse indoor positioning methods. Among var...
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ISBN:
(数字)9798331517786
ISBN:
(纸本)9798331517793
With the advancement of artificial intelligence (AI), indoor positioning systems have become increasingly important for various applications, leading to the development of diverse indoor positioning methods. Among various approaches, image-based indoor positioning methods have demonstrated relatively good positioning accuracy. However, maintaining user privacy continues to be a significant challenge. To tackle this issue, we propose a Federated Learning (FL) approach and develop an FL-based indoor image recognition positioning system. We conducted experiments to validate our method using two real-world datasets and compare the results to a Non-FL approach. Furthermore, we evaluated the accuracy of our method across various communication rounds, the number of client devices, and the amount of data per client device. Our experimental results indicate that the proposed method effectively preserves client privacy while achieving accuracy similar to the Non-FL approach. By employing FedAvg and FedOpt algorithms with the MobileNet model, our system ultimately reaches 94% accuracy.
Unlike opaque object, novel view synthesis of transparent object is a challenging task, because transparent object refracts light of background causing visual distortions on the transparent object surface along the vi...
Unlike opaque object, novel view synthesis of transparent object is a challenging task, because transparent object refracts light of background causing visual distortions on the transparent object surface along the viewpoint change. Recently introduced Neural Radiance Fields (NeRF) is a view synthesis method. Thanks to its remarkable performance improvement, lots of following applications based on NeRF in various topics have been developed. However, if an object with a different refractive index is included in a scene such as transparent object, NeRF shows limited performance because refracted light ray at the surface of the transparent object is not appropriately considered. To resolve the problem, we propose a method based on NeRF, mathematical equations for the description of light refraction, and visual hull. Experimental evaluation results demonstrate that our method addresses the limitation of conventional NeRF with transparent objects.
Ethereum has recently switched to a Proof of Stake consensus protocol called Gasper. We analyze Gasper using PRISM+, an extension of the probabilistic model checker PRISM with primitives for modelling blockchain data ...
Ethereum has recently switched to a Proof of Stake consensus protocol called Gasper. We analyze Gasper using PRISM+, an extension of the probabilistic model checker PRISM with primitives for modelling blockchain data types. PRISM+ is therefore used to rapidly and automatically analyze the robustness of Gasper when tuning, up or down, several basic parameters of the protocol, such as network latencies and number of validators. We also study the effectiveness of Gasper in updating stakes and its resilience to three attacks: the balance, bouncing and time attacks.
The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy genera...
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