In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative ne...
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Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning...
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Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.
Data exfiltration of Advanced Persistent Threats (APTs) is a critical concern for high-value entities such as governments, large enterprises, and critical infrastructures, as attackers deploy increasingly sophisticate...
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Session-based recommendation aims to predict the next item based on a user’s limited interactions within a short *** approaches use mainly recurrent neural networks(RNNs)or graph neural networks(GNNs)to model the seq...
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Session-based recommendation aims to predict the next item based on a user’s limited interactions within a short *** approaches use mainly recurrent neural networks(RNNs)or graph neural networks(GNNs)to model the sequential patterns or the transition relationships between ***,such models either ignore the over-smoothing issue of GNNs,or directly use cross-entropy loss with a softmax layer for model optimization,which easily results in the over-fitting *** tackle the above issues,we propose a self-supervised graph learning with target-adaptive masking(SGL-TM)***,we first construct a global graph based on all involved sessions and subsequently capture the self-supervised signals from the global connections between items,which helps supervise the model in generating accurate representations of items in the ongoing *** that,we calculate the main supervised loss by comparing the ground truth with the predicted scores of items adjusted by our designed target-adaptive masking ***,we combine the main supervised component with the auxiliary self-supervision module to obtain the final loss for optimizing the model *** experimental results from two benchmark datasets,Gowalla and Diginetica,indicate that SGL-TM can outperform state-of-the-art baselines in terms of Recall@20 and MRR@20,especially in short sessions.
1 Introduction Key-value data,as NoSQL data widely used in recent years,has been frequently collected and used for analysis in major websites and mobile *** frequency distribution estimation on the key domain,as well ...
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1 Introduction Key-value data,as NoSQL data widely used in recent years,has been frequently collected and used for analysis in major websites and mobile *** frequency distribution estimation on the key domain,as well as the mean estimation of values associated with the same key,are crucial and worth billion dollars in the ***,in real applications,the key-value records are submitted by terminal devices of different *** collecting the information from users may pose serious risks on personal *** the other hand,the emerging shuffled differential privacy(SDP)model[1]is proposed to collect sensitive data from users.
In the determination of the Earth gravity field in satellite geodesy, the inclination functions represent the projection of data observed along the orbital plane of a satellite orbit into the sphere in the terrestial ...
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In the determination of the Earth gravity field in satellite geodesy, the inclination functions represent the projection of data observed along the orbital plane of a satellite orbit into the sphere in the terrestial reference frame. The inclination functions in this work is studied from a group theoretical perspective. The inclination functions are proved to generate a representation of the SO(3) group. An orthogonal relation of the inclination functions is derived and some recurrence relations for the inclination functions are given, based on which an algorithm to calculate the inclination functions is proposed.
Medical image super-resolution is a fundamental challenge due to absorption and scattering in *** challenges are increasing the interest in the quality of medical *** research has proven that the rapid progress in con...
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Medical image super-resolution is a fundamental challenge due to absorption and scattering in *** challenges are increasing the interest in the quality of medical *** research has proven that the rapid progress in convolutional neural networks(CNNs)has achieved superior performance in the area of medical image ***,the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance(MR)images,adding extra noise in the models and more memory ***,conventional deep CNN approaches used layers in series-wise connection to create the deeper mode,because this later end layer cannot receive complete information and work as a dead *** this paper,we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as *** our proposed approach,a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling ***,a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input *** and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.
In recent years, the application of artificial intelligence has revolutionized the field of lip reading by enabling the development of sophisticated models capable of accurately interpreting lip movements from video d...
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The task of deducing the causal network from time series data and identifying relationships among multiple series is increasingly vital across various sectors such as industry, medicine, and finance. Despite numerous ...
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In a number of industries, including computer graphics, robotics, and medical imaging, three-dimensional reconstruction is essential. In this research, a CNN-based Multi-output and Multi-Task Regressor with deep learn...
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