With the progress of intelligent computers, the auto speech recognition function has developed rapidly. However, at present, the automatic recognition efficiency of on-board speech interaction is low and the recogniti...
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A crucial concern in biomedical research revolves around forecasting genome disorders, which contribute to diverse and severe ailments such as cancer, dementia, diabetes, cystic fibrosis, and leigh syndrome, resulting...
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Image classification is the process of identification and classification of an input image or visual from a predetermined set of labeled images. This work comes under computer vision and machinelearning. This work is...
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The Power electronic system plays a significant role in versatile applications. The power electronic converters are largely used in energy conversion mechanisms. A fault is defined as the abnormal condition of the sys...
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Heart disease is one of the key causes of death worldwide, and it is important to detect in its early stage which helps in halting its progression. Because of its superiority in pattern identification and categorizati...
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In the field of computer network communication, there are various types of data, and these data have strong commercial value. Therefore, it is particularly important to apply them to relevant security monitoring. This...
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With the continuous expansion of power grid scale and the improvement of power grid informatization level, China has accumulated a certain amount of wire wind vibration data in years of transmission line operation, in...
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The proceedings contain 85 papers. The special focus in this conference is on data Science, machinelearning and Applications. The topics include: Design of QCA-Based XOR/XNOR Structures;Design of QCA-Based 1-Bit Magn...
ISBN:
(纸本)9789811959356
The proceedings contain 85 papers. The special focus in this conference is on data Science, machinelearning and Applications. The topics include: Design of QCA-Based XOR/XNOR Structures;Design of QCA-Based 1-Bit Magnitude Comparator;a Novel Multimodal Anatomical Medical Image Fusion Using Structure Extraction;Parametric Analysis for Channel Estimation in Massive MIMO Systems with 1-Bits ADCs;skin Cancer Classification Using Deep learning;image Dehazing Using Improved Dark Channel and Vanherk Model;A Novel Bayesian Fusion Model for IR and Visible Images;Retinal Boundary Segmentation in OCT Images Using Active Contour Model;Spectral Efficiency for Multi-bit and Blind Medium Estimation of DCO-OFDM Used Vehicular Visible Light Communication;An Efficient Retinal Layer Segmentation Based on Deep learning Regression Technique for Early Diagnosis of Retinal Diseases in OCT and FUNDUS Images;Design of QCA-Based BCD Adder;crop Yield Prediction Using Deep learning;preface;road Accident Detection and Indication System;real-Time Tweets Streaming and Comparison Using Naïve Bayes Classifier;smart Shopping Trolley for Billing System;a Survey on IoT Protocol in Real-Time Applications and Its Architectures;safe Characteristic Signature Systems with Different Jurisdiction Using Blockchain in E-Health Records;web-Based Trash Segregation Using Deep learning Algorithm;home Automation Using Face recognition for Wireless Security;Hybrid-Network Intrusion Detection (H-NID) Model Using machinelearning Techniques (MLTs);impact of Using Partial Gait Energy Images for Human recognition by Gait Analysis;Several Routing Protocols, Features and Limitations for Wireless Mesh Network (WMN): A Review;a Deep Meta-model for Environmental Sound recognition;design of Progressive Monitoring Overhead Water Tank.
Mobility patternrecognition is a central aspect of transportation and datamining research. Despite the development of various machinelearning techniques for this problem, most existing methods face challenges such ...
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ISBN:
(纸本)9783031398308;9783031398315
Mobility patternrecognition is a central aspect of transportation and datamining research. Despite the development of various machinelearning techniques for this problem, most existing methods face challenges such as reliance on handcrafted features (e.g., user has to specify a feature such as "travel time") or issues with data imbalance (e.g., fewer older travelers than commuters). In this paper, we introduce a novel data Balancing Generative Adversarial Network (DBGAN), which is a specifically designed attention mechanism-based GAN model to address these challenges in mobility patternrecognition. DBGAN captures both static (e.g., travel locations) and dynamic (e.g., travel times) features of different passenger groups, and avoids using handcrafted features that may result in information loss, based on a sequence-to-image embedding method. Our model is then applied to overcome the data imbalance issue and perform mobility patternrecognition. We evaluate the proposed method on real-world public transportation smart carddata from Suzhou, China, and focus on recognizing two different passenger groups: older people and students. The results of our experiments demonstrate that DBGAN is able to accurately identify the different passenger groups in the data, with the detected mobility patterns being consistent with the ground truth. These results highlight the effectiveness of DBGAN in overcoming data imbalance in mobility patternrecognition, and demonstrate its potential for wider use in transportation and datamining applications.
Most of the traditional GNSS interference detection algorithms have disadvantages such as low detection accuracy and complicated algorithms when detecting interference signals. In this regard, a GNSS interference sign...
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