COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach—parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.

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MUFFIN: multi-scale feature fusion for drug–drug interaction prediction

Bioinformatics ◽

10.1093/bioinformatics/btab169 ◽

2021 ◽

Author(s):

Yujie Chen ◽

Tengfei Ma ◽

Xixi Yang ◽

Jianmin Wang ◽

Bosheng Song ◽

...

Keyword(s):

Molecular Structure ◽

Deep Learning ◽

Medical Information ◽

Feature Fusion ◽

Molecular Graph ◽

Knowledge Graph ◽

Sequence Information ◽

Learning Models ◽

Scale Feature ◽

Multi Scale

Abstract Motivation Adverse drug–drug interactions (DDIs) are crucial for drug research and mainly cause morbidity and mortality. Thus, the identification of potential DDIs is essential for doctors, patients and the society. Existing traditional machine learning models rely heavily on handcraft features and lack generalization. Recently, the deep learning approaches that can automatically learn drug features from the molecular graph or drug-related network have improved the ability of computational models to predict unknown DDIs. However, previous works utilized large labeled data and merely considered the structure or sequence information of drugs without considering the relations or topological information between drug and other biomedical objects (e.g. gene, disease and pathway), or considered knowledge graph (KG) without considering the information from the drug molecular structure. Results Accordingly, to effectively explore the joint effect of drug molecular structure and semantic information of drugs in knowledge graph for DDI prediction, we propose a multi-scale feature fusion deep learning model named MUFFIN. MUFFIN can jointly learn the drug representation based on both the drug-self structure information and the KG with rich bio-medical information. In MUFFIN, we designed a bi-level cross strategy that includes cross- and scalar-level components to fuse multi-modal features well. MUFFIN can alleviate the restriction of limited labeled data on deep learning models by crossing the features learned from large-scale KG and drug molecular graph. We evaluated our approach on three datasets and three different tasks including binary-class, multi-class and multi-label DDI prediction tasks. The results showed that MUFFIN outperformed other state-of-the-art baselines. Availability and implementation The source code and data are available at https://github.com/xzenglab/MUFFIN.

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Product Review Ranking in e-Commerce using Urgency Level Classification Approach

Jurnal Online Informatika ◽

10.15575/join.v5i2.612 ◽

2020 ◽

Vol 5 (2) ◽

pp. 212

Author(s):

Hamdi Ahmad Zuhri ◽

Nur Ulfa Maulidevi

Keyword(s):

Deep Learning ◽

Classification Model ◽

Support Vector ◽

Learning Models ◽

Classification Approach ◽

Value Range ◽

High Bias ◽

Product Domains ◽

Urgency Level ◽

Bayesian Support

Review ranking is useful to give users a better experience. Review ranking studies commonly use upvote value, which does not represent urgency, and it causes problems in prediction. In contrast, manual labeling as wide as the upvote value range provides a high bias and inconsistency. The proposed solution is to use a classification approach to rank the review where the labels are ordinal urgency class. The experiment involved shallow learning models (Logistic Regression, Naïve Bayesian, Support Vector Machine, and Random Forest), and deep learning models (LSTM and CNN). In constructing a classification model, the problem is broken down into several binary classifications that predict tendencies of urgency depending on the separation of classes. The result shows that deep learning models outperform other models in classification dan ranking evaluation. In addition, the review data used tend to contain vocabulary of certain product domains, so further research is needed on data with more diverse vocabulary.

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Hybrid Rule-Based Solution for Phishing URL Detection Using Convolutional Neural Network

Wireless Communications and Mobile Computing ◽

10.1155/2021/8241104 ◽

2021 ◽

Vol 2021 ◽

pp. 1-24

Author(s):

Youness Mourtaji ◽

Mohammed Bouhorma ◽

Daniyal Alghazzawi ◽

Ghadah Aldabbagh ◽

Abdullah Alghamdi

Keyword(s):

Deep Learning ◽

High Efficiency ◽

Personal Information ◽

Support Vector ◽

Learning Models ◽

Rule Based ◽

Common Purpose ◽

Behavioral Method ◽

And Control ◽

Similarity Method

The phenomenon of phishing has now been a common threat, since many individuals and webpages have been observed to be attacked by phishers. The common purpose of phishing activities is to obtain user’s personal information for illegitimate usage. Considering the growing intensity of the issue, this study is aimed at developing a new hybrid rule-based solution by incorporating six different algorithm models that may efficiently detect and control the phishing issue. The study incorporates 37 features extracted from six different methods including the black listed method, lexical and host method, content method, identity method, identity similarity method, visual similarity method, and behavioral method. Furthermore, comparative analysis was undertaken between different machine learning and deep learning models which includes CART (decision trees), SVM (support vector machines), or KNN ( K -nearest neighbors) and deep learning models such as MLP (multilayer perceptron) and CNN (convolutional neural networks). Findings of the study indicated that the method was effective in analysing the URL stress through different viewpoints, leading towards the validity of the model. However, the highest accuracy level was obtained for deep learning with the given values of 97.945 for the CNN model and 93.216 for the MLP model, respectively. The study therefore concludes that the new hybrid solution must be implemented at a practical level to reduce phishing activities, due to its high efficiency and accuracy.

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Performance Prediction by an SVM with a Firefly Optimization Method

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539321500170 ◽

2020 ◽

pp. 2150017

Author(s):

Yongquan Yan

Keyword(s):

Support Vector Machine ◽

Economic Loss ◽

Optimization Method ◽

Parameter Selection ◽

Support Vector ◽

Software Aging ◽

Memory Prediction ◽

Firefly Optimization ◽

Aging Phenomena ◽

Selection Of

Since software system is becoming more and more complex than before, performance degradation and even abrupt download, which are called software aging phenomena, bring about a great deal of economic loss. To counter these problems, some methods are used. Support vector machine is an effective method to tackle software aging problems, but its performance is influenced by the selection of hyper-parameters. A method is proposed to optimize the hyper-parameter selection of support vector machine in this work. The proposed method which is used as a training algorithm to optimize the parameter selection of support vector machine, utilizes the global exploration power of firefly method to achieve faster convergence and also a better accuracy. In the experiment, we use two metrics to test the effect of the proposed method. The results indicate that the presented method owns the highest accuracy in both the available memory prediction and heap memory prediction of Web server for software aging predictions.

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Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach

Applied Sciences ◽

10.3390/app10238400 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8400 ◽

Cited By ~ 1

Author(s):

Abdelkader Dairi ◽

Fouzi Harrou ◽

Ying Sun ◽

Sofiane Khadraoui

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Solar Power ◽

Power Production ◽

Superior Performance ◽

Support Vector ◽

Learning Models ◽

Short Term ◽

Learning Methods ◽

Short Term Forecasting

The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar power production using Variational AutoEncoder (VAE) model. Adopting the VAE-driven deep learning model is expected to improve forecasting accuracy because of its suitable performance in time-series modeling and flexible nonlinear approximation. Both single- and multi-step-ahead forecasts are investigated in this work. Data from two grid-connected plants (a 243 kW parking lot canopy array in the US and a 9 MW PV system in Algeria) are employed to show the investigated deep learning models’ performance. Specifically, the forecasting outputs of the proposed VAE-based forecasting method have been compared with seven deep learning methods, namely recurrent neural network, Long short-term memory (LSTM), Bidirectional LSTM, Convolutional LSTM network, Gated recurrent units, stacked autoencoder, and restricted Boltzmann machine, and two commonly used machine learning methods, namely logistic regression and support vector regression. The results of this investigation demonstrate the satisfying performance of deep learning techniques to forecast solar power and point out that the VAE consistently performed better than the other methods. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models.

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Study of Dementia Severity Prognosis in Mr Images Using Grey Wolf Optimization Based Dual Deep Learning Technique

10.21203/rs.3.rs-412358/v1 ◽

2021 ◽

Author(s):

Ahana priynaka ◽

Kavitha Ganesan

Keyword(s):

Deep Learning ◽

Symmetry Analysis ◽

Support Vector ◽

Grey Wolf ◽

Mr Images ◽

Grey Wolf Optimization ◽

Feature Vectors ◽

Dementia Severity ◽

Improved Performance ◽

Multiclass Svm

Abstract Prognosis of in a dementia disorder is a tedious task in preclinical stage. Ventricle pathology changes in dementia appear to be overlapped for neuro degeneration in brain. Identification of these overlaps among the groups severity helps to understand the pathogenesis of this disorder. In this work impact of changes in ventricle region on severity stages of dementia is observed using dual deep learning techniques (DDLT). Alzheimer's Disease Neuroimaging Initiative (ADNI) database that contains 1169 MR images are used in this study. Segmentation of ventricle region is carried out using multilevel threshold based Grey Wolf Optimization (GWO) technique. The feature vectors obtained from combined AlexNet and ResNet are analysed. The fused feature vectors are given to support vector machine (SVM) to observe the severity changes. Consequently, symmetry analysis of ventricle is carried out to perceive the distinctive changes in progression. The obtained results show that ventricle region is accurately delineated from other region with optimized thresholds. The segmented ventricle shows better correlation for all considered classes (> 0.9). It is observed that DDLT with multiclass SVM provides an improved accuracy of about 79.87% compared to individual transfer learning such as AlexNet (74%) and ResNet (76.53%). Further, symmetry analysis shows that left side ventricle with DDLT features shows an improved performance than right side for onset stages. Further, clinical correlation of left ventricle seems to be statically significant (p<0.0001) which prominently differentiate dementia severity variations. This framework is more prominent and clinically useful to identify the distinct ventricle region variation in dementia.

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The Unreasonable Effectiveness of the Baseline: Discussing SVMs in Legal Text Classification

10.3233/faia210317 ◽

2021 ◽

Author(s):

Benjamin Clavié ◽

Marc Alphonsus

Keyword(s):

Deep Learning ◽

Language Processing ◽

Text Classification ◽

Traditional Approach ◽

Error Reduction ◽

Support Vector ◽

Learning Models ◽

Legal Text ◽

Classification Tasks ◽

Legal Domain

We aim to highlight an interesting trend to contribute to the ongoing debate around advances within legal Natural Language Processing. Recently, the focus for most legal text classification tasks has shifted towards large pre-trained deep learning models such as BERT. In this paper, we show that a more traditional approach based on Support Vector Machine classifiers reaches competitive performance with deep learning models. We also highlight that error reduction obtained by using specialised BERT-based models over baselines is noticeably smaller in the legal domain when compared to general language tasks. We discuss some hypotheses for these results to support future discussions.

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Differential Evolution and Multiclass Support Vector Machine for Alzheimer’s Classification

Security and Communication Networks ◽

10.1155/2022/7275433 ◽

2022 ◽

Vol 2022 ◽

pp. 1-13

Author(s):

Jhansi Rani Kaka ◽

K. Satya Prasad

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Differential Evolution ◽

Optimization Method ◽

Support Vector ◽

Local Optima ◽

Whale Optimization ◽

Multiclass Support Vector Machine ◽

Poor Convergence

Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image normalization method is applied to enhance the quality of the image and represent the features effectively. The AlexNet model is applied to the normalized images to extract the features and also applied for feature selection. The Differential Evolution method applies Pareto Optimal Front for nondominated feature selection. This helps to select the feature that represents the characteristics of the input images. The selected features are applied in the MSVM method to represent in high dimension and classify Alzheimer’s. The DE-MSVM method has accuracy of 98.13% in the axial slice, and the existing whale optimization with MSVM has 95.23% accuracy.

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Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification

Diagnostics ◽

10.3390/diagnostics11112109 ◽

2021 ◽

Vol 11 (11) ◽

pp. 2109

Author(s):

Skandha S. Sanagala ◽

Andrew Nicolaides ◽

Suneet K. Gupta ◽

Vijaya K. Koppula ◽

Luca Saba ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Tissue Characterization ◽

Area Under The Curve ◽

Learning Models ◽

Non Invasive ◽

Lower Accuracy ◽

Improved Performance ◽

Higher Order Spectra

Background and Purpose: Only 1–2% of the internal carotid artery asymptomatic plaques are unstable as a result of >80% stenosis. Thus, unnecessary efforts can be saved if these plaques can be characterized and classified into symptomatic and asymptomatic using non-invasive B-mode ultrasound. Earlier plaque tissue characterization (PTC) methods were machine learning (ML)-based, which used hand-crafted features that yielded lower accuracy and unreliability. The proposed study shows the role of transfer learning (TL)-based deep learning models for PTC. Methods: As pertained weights were used in the supercomputer framework, we hypothesize that transfer learning (TL) provides improved performance compared with deep learning. We applied 11 kinds of artificial intelligence (AI) models, 10 of them were augmented and optimized using TL approaches—a class of Atheromatic™ 2.0 TL (AtheroPoint™, Roseville, CA, USA) that consisted of (i–ii) Visual Geometric Group-16, 19 (VGG16, 19); (iii) Inception V3 (IV3); (iv–v) DenseNet121, 169; (vi) XceptionNet; (vii) ResNet50; (viii) MobileNet; (ix) AlexNet; (x) SqueezeNet; and one DL-based (xi) SuriNet-derived from UNet. We benchmark 11 AI models against our earlier deep convolutional neural network (DCNN) model. Results: The best performing TL was MobileNet, with accuracy and area-under-the-curve (AUC) pairs of 96.10 ± 3% and 0.961 (p < 0.0001), respectively. In DL, DCNN was comparable to SuriNet, with an accuracy of 95.66% and 92.7 ± 5.66%, and an AUC of 0.956 (p < 0.0001) and 0.927 (p < 0.0001), respectively. We validated the performance of the AI architectures with established biomarkers such as greyscale median (GSM), fractal dimension (FD), higher-order spectra (HOS), and visual heatmaps. We benchmarked against previously developed Atheromatic™ 1.0 ML and showed an improvement of 12.9%. Conclusions: TL is a powerful AI tool for PTC into symptomatic and asymptomatic plaques.

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Evaluating Deep Learning models for predicting ALK-5 inhibition

PLoS ONE ◽

10.1371/journal.pone.0246126 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0246126

Author(s):

Gabriel Z. Espinoza ◽

Rafaela M. Angelo ◽

Patricia R. Oliveira ◽

Kathia M. Honorio

Keyword(s):

Neural Network ◽

Biological Activity ◽

Deep Learning ◽

Deep Neural Network ◽

External Validation ◽

Machine Learning Techniques ◽

Coefficient Of Determination ◽

Support Vector ◽

Learning Models ◽

Alk 5

Computational methods have been widely used in drug design. The recent developments in machine learning techniques and the ever-growing chemical and biological databases are fertile ground for discoveries in this area. In this study, we evaluated the performance of Deep Learning models in comparison to Random Forest, and Support Vector Regression for predicting the biological activity (pIC50) of ALK-5 inhibitors as candidates to treat cancer. The generalization power of the models was assessed by internal and external validation procedures. A deep neural network model obtained the best performance in this comparative study, achieving a coefficient of determination of 0.658 on the external validation set with mean square error and mean absolute error of 0.373 and 0.450, respectively. Additionally, the relevance of the chemical descriptors for the prediction of biological activity was estimated using Permutation Importance. We can conclude that the forecast model obtained by the deep neural network is suitable for the problem and can be employed to predict the biological activity of new ALK-5 inhibitors.

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