the emergence of artificial intelligence (AI) in educational contexts presents transformative potential for higher engineering education. this paper conducts a comprehensive literature review to explore the role of AI...
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Nowadays, the images recognition has become a fundamental component in computer vision and assist to identify the objectives in the complex images. Withthe development of images processing methods, the image-level ob...
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the latest research results show that activity recognition based on frequent patternmining is the main method. Its advantages are that the activity patterns obtained through learningmining are comprehensive and accu...
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the proceedings contain 43 papers. the topics discussed include: deep learning based Marathi sentence recognition using Devanagari character identification;deep learning based Marathi sentence recognition using Devana...
ISBN:
(纸本)9781665459877
the proceedings contain 43 papers. the topics discussed include: deep learning based Marathi sentence recognition using Devanagari character identification;deep learning based Marathi sentence recognition using Devanagari character identification;efficient detection of small and complex objects for autonomous driving using deep learning;implementation of exploratory data analysis on weather data;deep learning model for simulating self-driving car;summarization of video clips using subtitles;reliability stripe coagulation in two failure tolerant storage arrays;efficient video anomaly detection using residual variational autoencoder;situational portfolio forecasting and allocation with deep-learning approach;and recognition of emotions based on facial expressions using bidirectional long-short-term memory and machinelearning techniques.
Globally, the incidence of Alzheimer's disease is increasing. Although there are many diagnostic methods, traditional methods often rely on professional knowledge and consume much time. therefore, there is an urge...
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this paper employs the Center Symmetric Local Binary pattern (CSLBP) algorithm for extracting fine features in facial expression recognition. Additionally, the Rotation Invariant Local Phase Quantization (RILPQ) algor...
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In medical decision making, the efficacy of classification, a vital component of decision support systems, can be significantly compromised by imbalanced class distributions within training data. this discrepancy is e...
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ISBN:
(纸本)9783031821523;9783031821530
In medical decision making, the efficacy of classification, a vital component of decision support systems, can be significantly compromised by imbalanced class distributions within training data. this discrepancy is especially critical due to the heightened consequences of misclassification, particularly when a minority class is mistakenly classified as a majority class. Diabetes, posing a substantial public health challenge, underscores the urgency of addressing classification issues. the World Health Organization reports that 30% to 40% of diabetes cases remain undiagnosed, contributing to a 3% increase in diabetes-related mortality rates from 2000 to 2019. In 2019 alone, approximately 2 million deaths were attributed to diabetes and related kidney diseases. Consequently, effective intervention strategies are imperative. Managing imbalanced datasets in medical contexts presents significant obstacles to accurate classification, with profound implications for patient outcomes. To address this challenge, this research proposes leveraging machinelearning and artificial intelligence techniques. the research comprises two main components: applying boosting and unbalanced bagging to a dataset containing diabetes-related information. Initial findings indicate that boosting yields promising results in classification accuracy. Building upon this success, we compare boosting withthree additional technique Synthetic Minority Over-sampling Technique (SMOTE), Edited Nearest Neighbors (ENN), and Tomek links each sequentially applied to the dataset. through rigorous experimentation and analysis, we aim to identify the most effective strategy for handling imbalanced datasets in diabetes classification. By enhancing our understanding of classification methods and their impact on imbalanced datasets, this research aims to contribute to improved decision-making processes in healthcare settings, ultimately leading to better outcomes for patients affected by diabetes.
the past decade has witnessed the role that artificial intelligence (AI) has in university research and development (R&D) processes emerge as a very potent catalyst for accelerating scientific discovery. It thus s...
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Due to the design requirements for miniaturization and high-density placement of silicon photonics (SiPh) components in integrated circuit (IC) packaging, the system in package (SiP) process and equipment have become ...
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this article focuses on the problem of fault monitoring and diagnosis in small array systems. We analyzed the composition of the small array system and BIT settings, and proposed a modeling sensitive parameter set for...
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