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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Gynecologic malignancies, including cervical cancer, endometrial cancer, and ovarian cancer, represent a significant threat to women's health, and their diagnosis and treatment are crucial aspects of global public...
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Traffic sign recognition can quickly extract and identify road perception information, making it one of the key technologies in the field of autonomous driving. the reliable recognition of traffic signs is the primary...
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ISBN:
(纸本)9789819603534;9789819603541
Traffic sign recognition can quickly extract and identify road perception information, making it one of the key technologies in the field of autonomous driving. the reliable recognition of traffic signs is the primary task in achieving safe autonomous driving. However, current machinelearning-based traffic sign recognition technologies still suffer from low interpretability and an excessive dependence on the sample space. therefore, this paper proposes a data and Knowledge Dual-Driven Traffic Sign recognition Algorithm that leverages the convenience of data-driven methods and the reliability of knowledge-driven approaches. the algorithm integrates data-driven methods such as color recognition, shape recognition, and convolutional neural networks, along with knowledge-driven methods that involve reasoning based on traffic sign knowledge base, to accomplish the task of traffic sign recognition. the experimental results demonstrate that compared to other methods, the dual-drive approach proposed in this paper can identify traffic signs with greater accuracy and reliability.
this paper offers a novel deep learning (DL)-powered Tiny machinelearning (TinyML) model meticulously customized to recognize Arabic hand gestures (AHGs) executed in mid-air. the principal focus of this study lies in...
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ISBN:
(纸本)9783031686740;9783031686757
this paper offers a novel deep learning (DL)-powered Tiny machinelearning (TinyML) model meticulously customized to recognize Arabic hand gestures (AHGs) executed in mid-air. the principal focus of this study lies in the intricate task of precisely classifying Arabic letters through these gestures. the paper provides a comprehensive exposition of the complex dataflow architecture, encompassing the processing of gyroscope and accelerometer data to derive precise 2D gesture coordinates. the pivotal role of convolutional neural networks in the DL model is elucidated, emphasizing their outstanding performance in reaching a level of accuracy of 94.2% in classifying diverse AHDs. this accuracy showcases the model's efficacy and robustness, underscoring its potential for real-time, practical deployments in scenarios of gesture recognition. the implications of this research extend beyond the immediate domain of Arabic letter recognition, contributing to the progress of TinyML applications in real-world gesture recognition apps.
Detection of fraud still remains one of the most important challenges that continue to stir up research and following implementation of advanced technologies in the field of financial transactions, like machine learni...
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