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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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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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.
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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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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This paper presents an advanced approach in skin disease classification using a modified ResNet-50 architecture, applied to a specific subset from the ISIC 2019 Dataset focusing on Benign Keratosis, Basal Cell Carcino...
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The growing volume of healthcare data necessitates advanced data mining techniques to extract meaningful patterns and insights. This paper introduces 'RX Assist,' an Intelligent Disease Prediction and Drug Rec...
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E-learning is one of the educational alternatives available to students who need assistance during an emergency. (e.g., Covid-19 pandemic, bad climate, etc.). Most educational institutions are moving a significant por...
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ISBN:
(纸本)9783031519789;9783031519796
E-learning is one of the educational alternatives available to students who need assistance during an emergency. (e.g., Covid-19 pandemic, bad climate, etc.). Most educational institutions are moving a significant portion of their curriculum toward an online learning paradigm to reduce the amount of face-to-face interaction between students and faculty members during times of emergency (e.g., in the case of Covid-19 pandemic). The success of E-learning is conditional on a wide range of aspects, such as students' and teachers' levels of self-efficacy, attitudes toward, and confidence in making use of the relevant technology;the instructional approaches that are utilized;the capacity to monitor and evaluate educational outcomes;and students' levels of motivation. The performance and circumstances of students who are engaged in e-learning are analyzed in this paper. The research investigates and evaluates predictions made by a model that is based onmachinelearning techniques. Predicting the degree to which students are delighted with the online mode of instruction by considering several parameters, including internet capability, and involvement in the online mode of instruction.
Medicinal plant identification has shown great benefit from deep learning, especially when CNNs are employed. CNNs are ideally suited for this task since they can extract complex features from photos. The promise of d...
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