Physio-chemical characteristics, including heat, pH, EC, hardness, chlorine ions, alkalinity, phosphate, and sulfur of water samples from various sampling points are the main focus of the current study. The increase i...
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Numerous brands employ marketing strategies to persuade customers to buy their products while concealing the potentially dangerous side effects of some of the product's constituents, leaving the customer open to u...
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Progressions in machine learning and crop simulation techniques have created new opportunities for improving agro-based prediction. In crop yield analysis, machine learning is a rapidly expanding research area. Predic...
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The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle **...
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The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle ***,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of *** recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a *** this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is *** Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as ***,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)***,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading ***,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.
The internet has evolved a lot in the last 40 years and its early applications has become unrecognizable. Web 1.0 focused on serving static pages is considered as the read-only web, whereas web 2.0 made way for dynami...
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The maritime industry heavily relies on various fluids stored in ship tanks, encompassing fuel, oils, sludge, sewage, and water. Over time, these fluids solidify, forming layers that necessitate regular cleaning. Trad...
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In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,a...
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In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.
Multi-area Closed-Circuit Television (CCTV) networks and proximity-based police station mapping are both included into the Missing Persons Comprehensive Tracking System (MPCTS), which is designed to improve search and...
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Precision agriculture relies on crop disease detection to boost production and food security. This paper introduces Cognitive Computing-based wheat leaf disease detection. The approach uses machine learning, natural l...
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As automobiles are the primary cause of environmental pollution, an increase in automotive traffic results in an increase in air pollution. Oxides of nitrogen and carbon are among the pollutants that a vehicle emits a...
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