the proceedings contain 300 papers. the topics discussed include: generative AI in focus : a comprehensive review of leading models across modalities;improving performance of supervised machine learning algorithms on ...
ISBN:
(纸本)9798331529635
the proceedings contain 300 papers. the topics discussed include: generative AI in focus : a comprehensive review of leading models across modalities;improving performance of supervised machine learning algorithms on small datasets;automated detection of tight junction damage in corneal endothelium using machine learning;smart waste management system using deep learning;gesture recognition technology in smart gloves enhanced by machine learning;design and implementation of a forest flame identification system;exploring advanced approaches: a comprehensive analysis of machine learning and deep neural networks in spectrum sensing applications;leveraging deep quantum convolutional neural networks for student facial expression identification and mode assessments;and real-time vehicle detection and road condition prediction for smart urban areas.
In the intelligent road traffic system, the sensor module is required to quickly distinguish each traffic target, but the traditional camera-based or millimeter-wave radar-based detection system is difficult to meet t...
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ISBN:
(纸本)9798350375084;9798350375077
In the intelligent road traffic system, the sensor module is required to quickly distinguish each traffic target, but the traditional camera-based or millimeter-wave radar-based detection system is difficult to meet this requirement. Aiming at the problem that the limitation of a single sensor makes the longitudinal traffic target indistinct and the detection speed slow, a method for target detection is developed by combining 4D millimeter wave radar with a camera. the ROI of the target detection region of interest is constructed by using the characteristics of 4D millimeter wave radar with stronger perception ability and faster data processing, which reduces the computing power for the subsequent visual traffic target detection. the test results of the proposed detection algorithm in the experimental field show that the detection method is suitable for practical road traffic target detection applications.
Parking guidance technology through real-time monitoring of the use of parking spaces, to provide drivers with real-time free parking information, quickly guide them to the destination, so as to effectively alleviate ...
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ISBN:
(纸本)9798400710353
Parking guidance technology through real-time monitoring of the use of parking spaces, to provide drivers with real-time free parking information, quickly guide them to the destination, so as to effectively alleviate the parking problem in the city. this study is devoted to analyzing the key technologies, application status, challenges faced by parking guidance technology in the field of smart parking, as well as its future development direction, and introduces the significance of resource construction of smart parking system in the level of smart city. the research results show that the future evolution of parking space guidance technology will tend to a higher level of intelligence, integration and networking. However, in this process, the challenges brought by multiple factors such as technology, economy and society cannot be ignored.
the RB211 gas turbine, extensively utilized in the oil and gas industries, generates significant CO2 emissions during its combustion process. Optimizing turbine operation and reducing emissions can be achieved through...
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there are hundreds of millions of natural proteins in nature, and these natural proteins can play different roles due to their special structure. the first field of AI for Science is the prediction and design of prote...
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ISBN:
(纸本)9798400716645
there are hundreds of millions of natural proteins in nature, and these natural proteins can play different roles due to their special structure. the first field of AI for Science is the prediction and design of protein structure, and simulating the structure of certain natural proteins has become a popular topic in biopharmaceutical research. the generative adversarial network model in deep learning and a target sequence method were used in the paper to mimic the special structure of natural proteins. We successfully synthesized the ProteinA-like peptide sequence which had a high structural similarity withthe natural proteins in its tertiary *** synthetic peptide sequences by computer will lay the foundation for the later transcription to become mRNA and to synthesize protein in the laboratory
In an era where information technology is increasingly prevalent, music score recognition, as a vital branch of intelligent audio processing, has garnered significant attention. It not only enhances the listening expe...
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ISBN:
(纸本)9798400711848
In an era where information technology is increasingly prevalent, music score recognition, as a vital branch of intelligent audio processing, has garnered significant attention. It not only enhances the listening experience for users but also holds substantial importance in realms such as music copyright protection, automatic scoring, and intelligent editing. the advent of deep learning technologies in computing resembles a revolution, transforming traditional audio analysis methods and rendering the processing of complex audio data more efficient and precise. By leveraging advanced deep learning models like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deep Belief Networks (DBN), researchers can extract valuable insights from vast troves of musical data, gradually uncovering the underlying patterns hidden within notes and melodies. Particularly when confronted with an array of diverse forms and styles in musical compositions, deep learning techniques significantly enhance the accuracy and speed of score recognition, revealing extraordinary potential in applications such as automatic tagging, recommendation systems, and real-time audio processing.
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 withthe 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.
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.
Falls in the elderly are a major public health problem. In this paper, an effective fall detection method based on millimeter wave radar system is proposed. Using the time-frequency characteristics of radar echoes, th...
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ISBN:
(纸本)9798331530372;9798331530365
Falls in the elderly are a major public health problem. In this paper, an effective fall detection method based on millimeter wave radar system is proposed. Using the time-frequency characteristics of radar echoes, the signal-to-noise ratio is significantly improved by least squares adaptive filtering to eliminate interference, and classification is performed by combining short-time Fourier transform (STFT) and machine learning algorithms KNN (Nearest Neighbor), Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Decision Tree (DT). By collecting data from two idle bathrooms, data from four actions were collected and the effectiveness of the machine learning SVM, KNN and MLP algorithms were verified by the measured data, in which the classification performance of SVM was better than the other three algorithms.
the burgeoning demand for sustainable energy sources has catalyzed interest in Waste-to-Energy solutions offering both environmental benefits and energy generation potential. this paper explores the integration of mac...
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ISBN:
(纸本)9783031837951;9783031837968
the burgeoning demand for sustainable energy sources has catalyzed interest in Waste-to-Energy solutions offering both environmental benefits and energy generation potential. this paper explores the integration of machine learning (ML) techniques to optimize and predict key aspects of the Waste-to-Energy process. the study leverages historical data from waste facilities incorporating variables such as waste composition, operational parameters, and environmental conditions. In this paper, machine learning models, including regression, classification, and ensemble methods, are employed to optimize combustion efficiency, predict energy output, and enhance the overall operational performance of Waste-to-Energy conversion process. Furthermore, the predictive analytics are employed to anticipate maintenance needs for mitigating the downtime and optimizing the resource allocation. the findings contribute to the growing field of the sustainable energy by showcasing the efficacy of machine learning in Waste-to-Energy systems providing a scalable and adaptive solution for the challenges inherent in this dynamic and complex process.
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