In this paper, we introduce new discourse quality metrics and an evaluation method, and furthermore, we provide a pilot implementation and evaluate it in a specific use case – that of public speaking. Voice analysis ...
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This research focuses on the primary aspects that influence the usage of renewable energy sources in microgrids. These aspects include energy demand, the amount of solar irradiation, and the capacity of the batteries....
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In this paper, we present a new mission planning optimisation method for coverage missions involving Uncrewed Aerial systems (UAS) and Ground Vehicles (GV) to minimize the mission planning time and the UAS and GV rout...
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In this paper, we present a new mission planning optimisation method for coverage missions involving Uncrewed Aerial systems (UAS) and Ground Vehicles (GV) to minimize the mission planning time and the UAS and GV route length. Optimal planning of paths for the UAS and GV using the Mixed Integer Linear Program (MILP), often struggles with computational inefficiency and limited scalability in scenarios with a growing number of waypoints and vehicles. To overcome the MILP computational issues, we present a reinforcement learning technique based on rollout policy optimisation called as Multi-Agent Rollout Policy Optimisation (MARPO). Through simulations, we showcase MARPO’s ability to match the precision of conventional MILP formulation in small instances and excel in scalability and computational efficiency in larger cases. Additionally, MARPO is compared with a heuristic method, Multi-Agent Greedy Path-Finding Algorithm (MAGPA), and the superior performance of MARPO in terms of total path length and computational efficiency is demonstrated. Several simulations are presented to showcase the advantages of MARPO. In simulations with 1 UAS and 1 UGV, MARPO achieved path lengths up to 1.56% longer than MILP’s optimum for 9 to 25 waypoints, while significantly reducing computation time by up to 99.88%. In larger scenarios of 36 and 49 waypoints, where MILP was infeasible, MARPO provided convincing solutions with greatly enhanced computational efficiency, demonstrating its robust scalability and effectiveness. Authors
This work proposes simply wideband four-element L-shaped notch-patch (LNP) MIMO antenna for 5G new radio (NR) networks. The proposed LNP MIMO antenna comprised of four-port antenna elements. The single antenna scheme ...
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ISBN:
(数字)9798331543952
ISBN:
(纸本)9798331543969
This work proposes simply wideband four-element L-shaped notch-patch (LNP) MIMO antenna for 5G new radio (NR) networks. The proposed LNP MIMO antenna comprised of four-port antenna elements. The single antenna scheme contained L-shaped with notch microstrip patch antenna and modified ground plane. The proposed LNP MIMO antenna at 4.5 GHz (center frequency) can be provided impedance bandwidth (IBW) of 64% (3.57 – 6.45 GHz). The LNP MIMO antenna has been evaluated the MIMO performance metrics, including transmission coefficient (isolation), envelop correlation coefficient (ECC), diversity gain (DG), and mean effective gain (MEG). The simulated results of the LNP MIMO antenna achieved isolation of 20 dB, ECC of below 0.005, DG of above 9.975 dB, and MEG of approximately -3 dB. All simulated results are in good satisfactory, rendering the proposed LNP MIMO antenna suitable for 5G applications.
Recently,there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy(DR).DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged peopl...
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Recently,there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy(DR).DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed *** detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus *** article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy(DLLSHDM-DR)on Retinal Fundus *** proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making *** the DLLSHDM-DR technique,image preprocessing is initially performed to improve the quality of the fundus ***,the DLLSHDM-DR applies HybridNet for producing a collection of feature *** retinal image classification,the DLLSHDM-DR technique exploits the Emperor Penguin Optimizer(EPO)with a Deep Recurrent Neural Network(DRNN).The application of the EPO algorithm assists in the optimal adjustment of the hyperparameters related to the DRNN model for DR detection showing the novelty of our *** assuring the improved performance of the DLLSHDMDR model,a wide range of experiments was tested on the EyePACS *** comparison outcomes assured the better performance of the DLLSHDM-DR approach over other DL models.
one of the reasons of women and newborns death in developing countries is the complications during pregnancy and childbirth. An early diagnosis of pregnancy problems would help to alleviate some of these risks. In thi...
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This paper presents a series of approaches aimed at enhancing the performance of Aspect-Based Sentiment Analysis (ABSA) by utilizing extracted semantic information from a Semantic Role Labeling (SRL) model. We propose...
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The identification and classification of different kinds of parasite eggs in microscopic samples represent a critical challenge in the field of Soil-transmitted helminth infection diagnosis. Traditional methods are of...
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This article addresses a system designed for identifying and quantifying several species of nematodes, free-living and parasitic, placed on Petri dishes using modern image recognition methods. Traditional methods of c...
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ISBN:
(数字)9798350350708
ISBN:
(纸本)9798350350715
This article addresses a system designed for identifying and quantifying several species of nematodes, free-living and parasitic, placed on Petri dishes using modern image recognition methods. Traditional methods of counting nematodes are often time-consuming and prone to errors, leading to inaccurate biological and ecological study results. Our method employs advanced machine learning algorithms and computer vision to detect and count live nematodes automatically. The process utilizes algorithms to recognize specific larval characteristics, such as size and shape. This allows for high accuracy and efficiency while minimizing human intervention. The results demonstrate that our approach is robust and precise and can be widely utilized in biological research and applications.
Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations...
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