Transformer and Convolutional Neural Network (CNN) are currently two important models in the field of deep learning. Among them, Transformer has strong global perception ability but weak local perception ability, and ...
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A travel itinerary is a complex problem that involves multiple objectives and constraints, such as cost, time, transportation modes, and comfort levels. This research study focuses on creating a real-time eco-friendly...
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In an era where security and surveillance are paramount, this paper presents a comprehensive framework for real-time face recognition and pedestrian trajectory prediction, leveraging the capabilities of MobileFaceNet ...
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This paper studies the artificial intelligence method based on the collaboration of edge and terminal, integrates the complementary advantages of the local computing and strong computing power of the two, realizes the...
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The proceedings contain 260 papers. The topics discussed include: a critical study on hybrid computing and its current influences and future challenges;daily updates on construction sites progress using artificial int...
ISBN:
(纸本)9798331515911
The proceedings contain 260 papers. The topics discussed include: a critical study on hybrid computing and its current influences and future challenges;daily updates on construction sites progress using artificial intelligence;green energy for smart cities: a review of research trends;smart ventilator for affordable health monitoring system;quantum dot-enhanced sodium-air batteries for unprecedented energy storage performance;intelligent question generator system using natural language processing;hybrid product recommendation system using popularity based and content-based filtering;high performance closed-loop control of a phase shifted full bridge converter for electric vehicle application;smart impact mitigation: intelligent speed and safety;face recognition attendance framework using quantum edge processing;and empowering communication: emergency hand recognition for the differently abled.
Trauma is a significant cause of mortality and disability, particularly among individuals under forty. Traditional diagnostic methods for traumatic injuries, such as X-rays, CT scans, and MRI, are often time-consuming...
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ISBN:
(纸本)9798350352634;9798350352627
Trauma is a significant cause of mortality and disability, particularly among individuals under forty. Traditional diagnostic methods for traumatic injuries, such as X-rays, CT scans, and MRI, are often time-consuming and dependent on medical expertise, which can delay critical interventions. This study explores the application of artificial intelligence (AI) and machine learning (ML) to improve the speed and accuracy of abdominal trauma diagnosis. We developed an advanced AI-based model combining 3D segmentation, 2D Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to enhance diagnostic performance. Our model processes abdominal CT scans to provide real-time, precise assessments, thereby improving clinical decision-making and patient outcomes. Comprehensive experiments demonstrated that our approach significantly outperforms traditional diagnostic methods, as evidenced by rigorous evaluation metrics. This research sets a new benchmark for automated trauma detection, leveraging the strengths of AI and ML to revolutionize trauma care.
Humans have longed for computers to take over and make the monotonous and tedious tasks obsolete. Artificial intelligence(AI) is just the right answers to this problem. There are not one but many sectors that are bein...
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After the advent of industry 4.0, technologies are comprehensively unveiled to the public. Because of this, the proliferation in demand for technology in every field has begun to soar. Today, in the modern society, th...
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Menopause, an inevitable milestone in a woman39;s journey, signifies not only the onset of physical transformations but also the potential emergence of mental health complexities. This study investigates the potenti...
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Artificial intelligence (AI) technology has recently shown promising results in detecting Autism Spectrum Disorder (ASD), but it faces significant challenges because it is still in early stages of development. This st...
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ISBN:
(纸本)9798350372977;9798350372984
Artificial intelligence (AI) technology has recently shown promising results in detecting Autism Spectrum Disorder (ASD), but it faces significant challenges because it is still in early stages of development. This study proposes to use linguistic features in the detection of ASD using artificial intelligence. In order to systematically understand different AI model performances, two types of AI models have been explored and evaluated. Five classical ML models were used: logistic regression, Gaussian naive Bayes, random forests, k-nearest neighbors (K-NN), and support vector machines (SVMs), and two deep learning models were used: multilayer perceptron (MLP) and a convolutional neural network (CNN). All model development work is based on analyzing speech transcripts of 64 children total aged 3 to 6 from two data banks, CHILDES and ASDBank, including 30 children with ASD and 34 typically developing (TD) controls. The first step was to examine the annotations of the transcriptions, then extract various linguistic features from the texts. AI models were trained to determine whether a child has ASD based on the characteristics of the transcripts analyzed. There are three major findings of this study. First, neural network models will outperform classical models in ASD detection by around 5%-10%. The models multilayer perceptron and convolutional neural networks achieved 83% and 84% accuracy, respectively. Second, among the classical machine learning models, logistic regression, SVMs, K-NN, and random forests all achieved accuracies in the range 77%-80%, while Gaussian naive Bayes performed the worst by around 10%. Third, the linguistic feature with the highest importance in ASD detection is MLU, and the features MLU, MLT, rUtts, and rTNW show significant distinctions between age and gender demographics in addition to having high importance in ASD detection. Overall, this research recommends using deep learning for clinical applications of ASD detection.
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