A COVID-19 Detection Algorithm Using Deep Features and Discrete Social Learning Particle Swarm Optimization for Edge Computing Devices

2022 ◽  
Vol 22 (3) ◽  
pp. 1-17
Author(s):  
Chaonan Shen ◽  
Kai Zhang ◽  
Jinshan Tang

COVID-19 has been spread around the world and has caused a huge number of deaths. Early detection of this disease is the most efficient way to prevent its rapid spread. Due to the development of internet technology and edge intelligence, developing an early detection system for COVID-19 in the medical environment of the Internet of Things (IoT) can effectively alleviate the spread of the disease. In this paper, a detection algorithm is developed, which can detect COVID-19 effectively by utilizing the features from Chest X-ray (CXR) images. First, a pre-trained model (ResNet18) is adopted for feature extraction. Then, a discrete social learning particle swarm optimization algorithm (DSLPSO) is proposed for feature selection. By filtering redundant and irrelevant features, the dimensionality of the feature vector is reduced. Finally, the images are classified by a Support Vector Machine (SVM) for COVID-19 detection. Experimental results show that the proposed algorithm can achieve competitive performance with fewer features, which is suitable for edge computing devices with lower computation power.

2021 ◽  
Vol 11 (12) ◽  
pp. 2897-2906
Author(s):  
K. Sangeetha ◽  
S. Prakash

The demand in breast cancer’s early detection and diagnosis over the last few decade has given a new research avenues. For an individual who is suffered from breast cancer, a successful treatment plan can be specified if early stage diagnosis of non-communicable disease is done as stated by world health organization (WHO). Around the world, mortality can be reduced by cure disease’s early diagnosis. For breast cancer’s early detection and to detect other abnormalities of human breast tissue, digital mammogram is used as a most popular screening method. Early detection is assisted by periodic clinical check-ups and self-tests and survival chance is significantly enhanced by it. For mammograms (MGs), deep learning (DL) methods are investigated by researchers due to traditional computer-aided detection (CAD) systems limitations and breast cancer’s early detection’s extreme importance and patients false diagnosis high impact. So, there is need to have a noninvasive cancer detection system which is efficient, accurate, fast and robust. There are two process in proposed work, Histogram Rehabilitated Local Contrast Enhancement (HRLCE) technique is used in initial process for contrast enhancement with two processing stages. Contrast enhancements potentiality is enhanced while preserving image’s local details by this technique. So, for cancer classification, Particle Swarm Optimization (PSO) and stacked auto encoders (SAE) combined with framework based on DNN called SAE-PSO-DNN Model is used. The SAE-DNN parameters with two hidden layers are tuned using PSO and Limited-memory BFGS (LBFGS) is used as a technique for reducing features. Specificity, sensitivity, normalized root mean square erro (NRMSE), accuracy parameters are used for evaluating SAE-PSO-DNN models results. Around 92% of accurate results are produced by SAE-PSO-DNN model as shown in experimentation results, which is far better than Convolutional Neural Network (CNN) as well as Support Vector Machine (SVM) techniques.


2011 ◽  
Vol 130-134 ◽  
pp. 3821-3825 ◽  
Author(s):  
Long Zhao ◽  
Xue Mei Sun ◽  
Ming Wei Zhang

Shot boundary detection (SBD) is the first step which segments video data into elementary shots for content-based video retrieval. In this paper, a shot boundary detection algorithm based on support vector machine (SVM) and particle swarm optimization (PSO) is proposed. First of all, the extracted features of pixel domain and compressed domain are combined to form a multi-dimension feature vector by using the scheme of sliding window. Next, particle swarm optimization with global search capacity is adopted to seek the approximately optimal parameters of radial basis function of SVM. Finally the model trained by the parameters obtained is applied to judge and categorize the frames into cut transitions, gradual transitions and non-transitions. The experimental results on the TREC video set 2001 demonstrate our algorithm is efficient and robust, and it solves the difficulty in parameter selection of SVM well.


2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.


Author(s):  
Midde Venkateswarlu Naik ◽  
D. Vasumathi ◽  
A.P. Siva Kumar

Aims: The proposed research work is on an evolutionary enhanced method for sentiment or emotion classification on unstructured review text in the big data field. The sentiment analysis plays a vital role for current generation of people for extracting valid decision points about any aspect such as movie ratings, education institute or politics ratings, etc. The proposed hybrid approach combined the optimal feature selection using Particle Swarm Optimization (PSO) and sentiment classification through Support Vector Machine (SVM). The current approach performance is evaluated with statistical measures, such as precision, recall, sensitivity, specificity, and was compared with the existing approaches. The earlier authors have achieved an accuracy of sentiment classifier in the English text up to 94% as of now. In the proposed scheme, an average accuracy of sentiment classifier on distinguishing datasets outperformed as 99% by tuning various parameters of SVM, such as constant c value and kernel gamma value in association with PSO optimization technique. The proposed method utilized three datasets, such as airline sentiment data, weather, and global warming datasets, that are publically available. The current experiment produced results that are trained and tested based on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy. Background: The sentiment analysis plays a vital role for current generation people for extracting valid decisions about any aspect such as movie rating, education institute or even politics ratings, etc. Sentiment Analysis (SA) or opinion mining has become fascinated scientifically as a research domain for the present environment. The key area is sentiment classification on semi-structured or unstructured data in distinguish languages, which has become a major research aspect. User-Generated Content [UGC] from distinguishing sources has been hiked significantly with rapid growth in a web environment. The huge user-generated data over social media provides substantial value for discovering hidden knowledge or correlations, patterns, and trends or sentiment extraction about any specific entity. SA is a computational analysis to determine the actual opinion of an entity which is expressed in terms of text. SA is also called as computation of emotional polarity expressed over social media as natural text in miscellaneous languages. Usually, the automatic superlative sentiment classifier model depends on feature selection and classification algorithms. Methods: The proposed work used Support vector machine as classification technique and particle swarm optimization technique as feature selection purpose. In this methodology, we tune various permutations and combination parameters in order to obtain expected desired results with kernel and without kernel technique for sentiment classification on three datasets, including airline, global warming, weather sentiment datasets, that are freely hosted for research practices. Results: In the proposed scheme, The proposed method has outperformed with 99.2% of average accuracy to classify the sentiment on different datasets, among other machine learning techniques. The attained high accuracy in classifying sentiment or opinion about review text proves superior effectiveness over existing sentiment classifiers. The current experiment produced results that are trained and tested based on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy. Conclusion: The objective of the research issue sentiment classifier accuracy has been hiked with the help of Kernel-based Support Vector Machine (SVM) based on parameter optimization. The optimal feature selection to classify sentiment or opinion towards review documents has been determined with the help of a particle swarm optimization approach. The proposed method utilized three datasets to simulate the results, such as airline sentiment data, weather sentiment data, and global warming data that are freely available datasets.


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