The extreme learning machine is a fast neural network with outstanding performance. However, the selection of an appropriate number of hidden nodes is time-consuming, because training must be run for several values, a...
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Medical image classification tasks frequently encounter challenges associated with class imbalance, resulting in biased model training and suboptimal classification performance. To address this issue, the combination ...
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This study scrutinizes five years of Sarajevo's Air Quality Index (AQI) data using diverse machine learning models - Fourier autoregressive integrated moving average (Fourier ARIMA), Prophet, and Long short-term m...
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The main task of this work is to establish the consumption of energy from renewable sources such as solar and wind energy for the production of such a combustible substance as hydrogen, and further optimal calculation...
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Advanced Metering Infrastructure (AMI) is a system consisting of smart meters, communications networks, and data management systems that enable two-way communication between utilities and customers. This paper aims to...
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Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater *** machine learning(ML)approaches have been enhanced to improve streamflow *** techniques have been viewed as a vi...
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Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater *** machine learning(ML)approaches have been enhanced to improve streamflow *** techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone *** researchers have also emphasised using hybrid models to improve forecast ***,this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years,summarising data preprocessing,univariate machine learning modelling strategy,advantages and disadvantages of standalone ML techniques,hybrid models,and performance *** study focuses on two types of hybrid models:parameter optimisation-based hybrid models(OBH)and hybridisation of parameter optimisation-based and preprocessing-based hybridmodels(HOPH).Overall,this research supports the idea thatmeta-heuristic approaches precisely improveML ***’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches(classified into four primary classes)hybridised with ML *** study revealed that previous research applied swarm,evolutionary,physics,and hybrid metaheuristics with 77%,61%,12%,and 12%,***,there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.
Children growth and development can be supported by children stores that specialize in toys and clothing. Not only do these stores stock a wide variety of goods, but they also have a huge influence on the way children...
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This study focuses on using the Weka toolkit's machine learning algorithms to forecast weather conditions based on a dataset that includes columns for precipitation, temperature_max, temperature_min, and wind. For...
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Prior study has developed the RouteSegmentation algorithm to identify the perimeter area surrounding a route. In this study, a comparative experiment was carried out to investigate the performance of the RouteSegmenta...
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The utilization of haul trucks within the mining industry presents a myriad of safety challenges, primarily due to their considerable size. Haul trucks serve as indispensable assets in large-scale construction project...
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ISBN:
(纸本)9798331520861
The utilization of haul trucks within the mining industry presents a myriad of safety challenges, primarily due to their considerable size. Haul trucks serve as indispensable assets in large-scale construction projects, owing to their unparalleled capacity for transporting heavy loads. Tailored specifically for such rigorous tasks, these trucks are restricted to traversing non-crowded roads to uphold safety standards. Nonetheless, their sheer size and design contribute to numerous blind spots, rendering them challenging to operate. In this study, we address the issue of blind spots in haul trucks by proposing a cutting-edge machine vision system. Our innovative approach integrates machine vision technology with advanced Deep Neural Network (DNN) algorithms to detect objects within the truck's blind spots and promptly alert the driver in real time. Leveraging a comprehensive dataset of images, the DNN is trained to identify objects of varying sizes and shapes accurately. To facilitate seamless monitoring, our system incorporates a visual display unit installed in the truck's cab, offering the driver a real-time panoramic view of the surroundings. Initial testing conducted in controlled laboratory settings yielded promising results, with the DNN algorithm demonstrating high accuracy in object detection. To validate our concept, we opted to employ a standard car as a surrogate for the haul truck. Equipped with four strategically positioned cameras providing a 360 -degree field of view, we scaled down the truck's dimensions to match those of a car. Utilizing the D455 Intel RealSense camera, we precisely measure the distances of detected objects and issue alerts to the driver within a 4 -meter range via a built-in buzzer. Our system is further fortified with the inclusion of the Jetson Orin Nano developer kit. Real-world testing conducted in a moving vehicle showcased the effectiveness of our system, garnering favorable results. Our endeavor underscores a significant ste
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