The automotive industry is a major contributor to carbon emissions, posing a threat to the environment and human health. This research explores the potential of machinelearning for predicting vehicle CO2 emissions. I...
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Given the rising global energy costs and strengthened environmental protection requirements, the optimization of motor system efficiency has become a crucial topic in industrial automation and transportation fields. T...
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This study focuses on developing a robust flight delay prediction model by analyzing historical flight data, incorporating factors such as departure and arrival times, weather conditions, aircraft type, and airline de...
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Hybrid forecasting systems that combine inputs from humans and artificial intelligence/machinelearning (AI/ML) systems can be leveraged to forecast needs and analyze gaps for both commercial and military uses. Hybrid...
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ISBN:
(纸本)9798350365924;9798350365917
Hybrid forecasting systems that combine inputs from humans and artificial intelligence/machinelearning (AI/ML) systems can be leveraged to forecast needs and analyze gaps for both commercial and military uses. Hybrid systems have demonstrated the ability to produce results that are more accurate than inputs from humans or AI/ML systems, separately. By using hybrid forecasting systems, organizations could potentially identify their needs and fill their gaps more quickly than by using a human-only or an AI/ML-only system. As companies identify the gaps they have and the ways they want to fill them, technologies could be created or developed as solutions, based on results from hybrid systems. When organizations identify needs quickly, everyone benefits. Those organizations can grow and make advancements in areas that could then help other people or organizations. In turn, when those individuals and entities are more successful, the positive second- and third-order effects continue to expand. Thus, by combining human insight and intuition with AI/ML, some concerns of forecasting and analysis could be alleviated. As a result, leveraging the power of hybrid systems could give strategic leaders the advantages they need over commercial competitors or military near-peers.
This paper presents an coordinates system tackling machinelearning (ML) strategies to improve rural efficiency and maintainability. It presents a novel Edit Choice Strategy (CSM) pointed at maximizing regular trim ab...
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This study aims to develop a reliable, non-invasive method for predicting early health issues through iridology by leveraging computer vision and deep learning techniques. Iridology, which involves the analysis of iri...
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Nowadays, with the rapid development of mobile Internet and smartphones, credit card fraud has become more and more serious, causing significant credit and financial damage to cardholders and economics. Many previous ...
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machinelearning techniques can potentially revolutionise healthcare;training such models demands significant expertise. We aim to assess the effectiveness of automated machinelearning modules in empowering healthcar...
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The proceedings contain 206 papers. The topics discussed include: synergy of human language processing and artificial intelligence;utilizing ANN in an advanced machinelearning framework;CNN and random forest fusion f...
ISBN:
(纸本)9798350371567
The proceedings contain 206 papers. The topics discussed include: synergy of human language processing and artificial intelligence;utilizing ANN in an advanced machinelearning framework;CNN and random forest fusion for enhanced steel defect classification;exploring the feasibility of forward algorithm in neural networks;big data: an essential route for creating new business prospects;automated detection and classification of cotton leaf diseases: a computer vision approach;advancements in digital signal processing: design and implementation of IIR bandpass optical filter using Optisystem;an analysis of artificial intelligence and bigdata cyber security its applications;advancing multiclass emotion recognition with CNN-RNN architecture and illuminating module for real-time precision using facial expressions;and Parkinson’s disease diagnosis using neutral matrix and machinelearning.
Reinforcement learning (RL) has emerged as a vital component in the development of autonomous systems. However, several challenges, such as high computational demands, limited generalization in dynamic environments, a...
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