This research paper proposes a curriculum for a five-year academic program with a bachelor's degree (honors) in intelligent systemsengineering and software engineering and a master's degree in intelligent sys...
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ISBN:
(纸本)9798350361513;9798350372304
This research paper proposes a curriculum for a five-year academic program with a bachelor's degree (honors) in intelligent systemsengineering and software engineering and a master's degree in intelligent systemsengineering and software engineering. This program includes courses in data structures, compiler design, operating system design, firmware design, database systems, computer graphics and virtual reality design, static and dynamic website design, development of chatbots and voice assistants, software engineering methodology, knowledge-based systems, fuzzy logic, neural networks, evolutionary computation, evolutionary multiojective optimization, machinelearning, image processing, computer vision, pattern recognition, voice recognition, natural language processing, data science, control systems, intelligent control systems, robotics, digital signal processing, mathematics, engineering physics, biology, etc. These degrees will allow graduates to have a good understanding of all of the main branches of intelligent systemsengineering and software engineering as well as other relevant subjects in electrical and computer engineering.
Intrusion detection system is highly effective and easy to understand in the situation of ubiquitous cyber threats. The trustworthiness and interpretability of traditional intrusion detection systems are balanced due ...
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In the largely expanding landscape of cloud service providers, the selection of an optimal cloud platform has emerged as a critical consideration. It would offer potential cost saving and operational efficiency enhanc...
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Accurately segmenting brain tumors from MRI scans is important for developing effective treatment plans and improving patient outcomes. This study introduces a new implementation of the Columbia-University-Net (CU-Net...
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ISBN:
(纸本)9798350375084;9798350375077
Accurately segmenting brain tumors from MRI scans is important for developing effective treatment plans and improving patient outcomes. This study introduces a new implementation of the Columbia-University-Net (CU-Net) architecture for brain tumor segmentation using the BraTS 2019 dataset. The CU-Net model has a symmetrical U-shaped structure and uses convolutional layers, max pooling, and upsampling operations to achieve high- resolution segmentation. Our CU-Net model achieved a Dice score of 82.41%, surpassing two other state-of-the-art models. This improvement in segmentation accuracy highlights the robustness and effectiveness of the model, which helps to accurately delineate tumor boundaries, which is crucial for surgical planning and radiation therapy, and ultimately has the potential to improve patient outcomes.
Diabetes is a prevalent circumstance that is also the core cause of numerous serious health issues, such as neurological disorders, heart attack, and stroke. So that it is placing a growing burden in the global health...
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Electromyography study focuses on the classification of electromyography (EMG) signals using machinelearning (ML) techniques. The classification of EMG signals with ML techniques improves the response and accuracy of...
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This study assesses advanced machinelearning algorithms, namely XGboost and Gradient Boosting machine (GBM), for fraud detection in social media profiles and payment systems. It focuses on two very different types of...
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As simulation technology is increasingly integrated into medical practice, the idea of diagnosis and treatments to individual patients through Digital Twin technology is gaining prominence in the field of precision me...
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ISBN:
(数字)9798350375077
ISBN:
(纸本)9798350375084;9798350375077
As simulation technology is increasingly integrated into medical practice, the idea of diagnosis and treatments to individual patients through Digital Twin technology is gaining prominence in the field of precision medicine. This paper aims to develop a digital twin model of the human heart for simulating its function and predicting abnormalities through the application of machinelearning techniques. Our method expands this frame-work to concentrate specifically on digital twin-based monitoring and abnormality detection of cardiac health by utilizing the readily accessible data from smartwatches. This method uses digital twin technology to create personalized real-time virtual heart models, detecting abnormalities and monitoring cardiac health using machinelearning. We explore the use of a digital twin model enhanced with machinelearning to forecast heart function abnormalities, relying solely on age as the input. We validate these forecasts through a brief two-minute assessment, covering three distinct heart conditions, including scenarios involving resting, walking, and normal heart rates. Furthermore, we examine the connection between normal heart rate and step count, aiming to identify any possible relationships. The findings illustrate its effectiveness in accurate diagnosis and tailored treatment. The incorporation of Digital Twins into healthcare holds the potential to revolutionize medical practice, facilitating precise detection tailored to individual patients.
Blockchain technology offers transformative potential for enhancing the security of online banking systems from the intruders. This paper investigates the application of blockchain in secure banking, focusing on the i...
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Autopilot perception technology is experiencing rapid development. LiDAR provides critical information for the autopilot system by perceiving objects, roads, and behaviors in the surrounding environment of the vehicle...
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ISBN:
(纸本)9798350375084;9798350375077
Autopilot perception technology is experiencing rapid development. LiDAR provides critical information for the autopilot system by perceiving objects, roads, and behaviors in the surrounding environment of the vehicle. Aiming at the problems of low recognition accuracy of target objects and prone to missed and false detections by the three-dimensional target detection algorithm in complex scenarios, a three-dimensional target detection algorithm based on the improved PointPillars is proposed. It adopts Distance-based sampling to reduce the influence of point cloud feature loss;and uses the 3D CIoU loss function to improve the accuracy of the PointPillars algorithm. Compared with the original Point-Pillars network, the average accuracy of the improved algorithm on the categories of cars, pedestrians, and cyclists has increased by 3.7%, 5.9%, and 5.7% respectively, demon-strating the effectiveness of the proposed method.
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