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Su Yu's Projects

acor icon acor

Estimate the autocorrelation time of time-series data very quickly

agv icon agv

Design and Analysis of Automated Guided Vehicle

android-architecture icon android-architecture

A collection of samples to discuss and showcase different architectural tools and patterns for Android apps.

biobss icon biobss

A package for processing signals recorded using wearable sensors, such as Electrocardiogram (ECG), Photoplethysmogram (PPG), Electrodermal activity (EDA) and 3-axis acceleration (ACC).

build-your-own-x icon build-your-own-x

Master programming by recreating your favorite technologies from scratch.

csapp icon csapp

Notes and Codes on Computer Systems A Programmer’s Perspective(CASPP)

cv icon cv

[LaTeX] My academic CV

dedup icon dedup

Deduplicate files from your computer

detecting-respiratory-diseases-from-recorded-lung-sounds-by-2d-cnn icon detecting-respiratory-diseases-from-recorded-lung-sounds-by-2d-cnn

Respiratory disease is among the leading causes of deaths around the world. A large amount of population is being affected regularly with some kinds of lung function disorders which eventually lead to respiratory diseases. Prevention and early detection are essential steps in managing respiratory diseases. To decrease the fatality, an efficient detection model is needed. In this paper, 2D convolutional neural network (CNN) is used to detect respiratory diseases from the recorded lung sounds at early stages. The proposed method can detect respiratory diseases like bronchiectasis, pneumonia, bronchiolitis, chronic obstructive pulmonary disease, upper respiratory tract infection, and healthy by using Mel-frequency cepstral co-efficients (MFCC). In the proposed scheme, a data frame is recorded and after extracting the statistical features from the audio clips, the data is loaded in the data frame where further classification is done using 2D CNN. The model is based on 2D CNN architecture where the number of layers is reduced to a certain extent to achieve more accuracy. The proposed model has only 13 CNN layers where each convolution layer is being associated with a pooling layer of max-pooling 2D type. The final convolution layer has a global-average pooling 2D layer. The proposed method obtained an accuracy of over 92.39%.

flask icon flask

The Python micro framework for building web applications.

funnlp icon funnlp

中英文敏感词、语言检测、中外手机/电话归属地/运营商查询、名字推断性别、手机号抽取、身份证抽取、邮箱抽取、中日文人名库、中文缩写库、拆字词典、词汇情感值、停用词、反动词表、暴恐词表、繁简体转换、英文模拟中文发音、汪峰歌词生成器、职业名称词库、同义词库、反义词库、否定词库、汽车品牌词库、汽车零件词库、连续英文切割、各种中文词向量、公司名字大全、古诗词库、IT词库、财经词库、成语词库、地名词库、历史名人词库、诗词词库、医学词库、饮食词库、法律词库、汽车词库、动物词库、中文聊天语料、中文谣言数据、百度中文问答数据集、句子相似度匹配算法集合、bert资源、文本生成&摘要相关工具、cocoNLP信息抽取工具、国内电话号码正则匹配、清华大学XLORE:中英文跨语言百科知识图谱、清华大学人工智能技术系列报

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