다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

COVID-19 Dashboard

Due to digitization, a huge volume of data is being generated across several sectors such as healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms are used to uncover patterns among the attributes of this data. Hence, they can be used to make predictions that can be used by medical practitioners and people at managerial level to make executive decisions. Not all the attributes in the datasets generated are important for training the machine learning algorithms. Some attributes might be irrelevant and some might not affect the outcome of the prediction. Ignoring or removing these irrelevant or less important attributes reduces the burden on machine learning algorithms. In this work two of the prominent dimensionality reduction techniques, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are investigated on four popular Machine Learning (ML) algorithms, Decision Tree Induction, Support Vector Machine (SVM), Naive Bayes Classifier and Random Forest Classifier using publicly available Cardiotocography (CTG) dataset from University of California and Irvine Machine Learning Repository. The experimentation results prove that PCA outperforms LDA in all the measures. Also, the performance of the classifiers, Decision Tree, Random Forest examined is not affected much by using PCA and LDA.To further analyze the performance of PCA and LDA the eperimentation is carried out on Diabetic Retinopathy (DR) and Intrusion Detection System (IDS) datasets. Experimentation results prove that ML algorithms with PCA produce better results when dimensionality of the datasets is high. When dimensionality of datasets is low it is observed that the ML algorithms without dimensionality reduction yields better results.

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Analysis of Dimensionality Reduction Techniques on Big Data

Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city

Deep Learning and Medical Image Processing for Coronavirus (COVID-19) Pandemic: A Survey.

The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

/pdf/a-novel-pca-firefly-based-xgboost-classification-model-for-47dnj3dnwq.pdf

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

Recently, security surveillance applications exploited the computer vision based detection and tracking approaches to improve the safety and comfort of humans. A major concern in real time surveillance video tracking is the process of identifying the human crowd behavior and classifying them. It finds useful to alert the crow in case of any disasters and unpredicted events. The investigation of human behavior in crowded surveillance videos is an essential and crucial area of research. The recent advances in Artificial Intelligence (AI) and deep learning (DL) models can be employed for determining the crowd behavior analysis in surveillance videos. With this motivation, this article focuses on the design of intelligent deep learning enabled crowd behavior detection and classification (IDL-CBDC) model in real time surveillance videos. The goal of the IDL-CBDC technique is to detect the crowd and classify it into four classes namely marriage, political, school, and college. Primarily, the IDL-CBDC technique performs preprocessing in two levels namely adaptive median filtering (AMF) technique and contrast enhancement (CE) approach. Besides, a deep instance segmentation approach using PSPNet-101 model is used for the segmentation of input video frames into crowds. Moreover, the black widow optimization (BWO) with residual network (ResNet50) model is applied for the crowd detection and classification process. The design of BWO algorithm helps to properly adjust the hyperparameter values such as learning rate, batch size, number of epochs, and number of hidden layers. In order to ensure the improved performance of the IDL-CBDC technique, a set of simulations take place using an own dataset, gathered from public places. Extensive comparative result analysis reported the supremacy of the IDL-CBDC technique over the other techniques.

Siva Rama Krishnan S

Papers

Deep Learning and Medical Image Processing for Coronavirus (COVID-19) Pandemic: A Survey.

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

Intelligent deep learning enabled crowd detection and classification model in real time surveillance videos