Generative artificial intelligence systems such as large language models (LLMs) exhibit powerful capabilities that many see as the kind of flexible and adaptive intelligence that previously only humans could exhibit. ...
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One crucial element of intelligent transportation systems (ITS) is the use of vehicle ad hoc networks, or VANETs. Among the many advantages, these networks provide are increased road safety and decreased traffic. Desp...
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During the COVID-19 pandemic, the healthcare sector faced unprecedented challenges in effectively managing hospital resources. A crucial aspect of resource planning and allocation is the ability to predict the expecte...
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In this modern era, the demand for efficient and automated cricket video summarization techniques is rapidly increasing. This paper introduces an innovative and advance neural network system that transforms the way cr...
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Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cemen...
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Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat
Some of the conventional approaches to task scheduling are discussed in this section. The scheduling of tasks in the Internet of Things (IoT) application is a complex job in cloud computing due to the heterogeneity ch...
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This work demonstrates the effectiveness of convolutional neural networks (CNNs) and proposes a fresh approach to prostate cancer diagnosis through the application of machine learning models. The research makes use of...
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In contemporary corporate settings, a candidate's personality is as crucial as their skill set. Achieving success, whether in personal or professional realms, heavily relies on one's personality traits. Theref...
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In recent years, deep learning and machine learning methods have been extensively employed in almost every field due to their capability of data processing and analysis. These are the subdomains of Artificial intellig...
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Autonomous vehicle (AV) systems can comprehend driving scenes has increased due to improvements in vision-based techniques. AV systems need to identify multiple detections like potholes, pedestrian, curb, road lanes, ...
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