scholarly journals Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Vladimir S. Kublanov ◽  
Anton Yu. Dolganov ◽  
David Belo ◽  
Hugo Gamboa
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Arterial Hypertension ◽  
Cardiac Activity ◽  
Support Vector ◽  
Learning Approaches ◽  
Short Term ◽  
Data Set ◽  
Suggested Approach ◽  
Machine Learning Approach

The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components.

Evaluation of Machine Learning Approaches for Automated Diagnosis of COVID-19 using X-Ray images (Preprint)

2020 ◽  
Author(s):  
Mazin Mohammed ◽  
Karrar Hameed Abdulkareem ◽  
Mashael S. Maashi ◽  
Salama A. Mostafa A. Mostafa ◽  
Abdullah Baz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Method ◽  
Learning Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
X Ray ◽  
Wide Range ◽  
Artificial Neural Network Ann

BACKGROUND In most recent times, global concern has been caused by a coronavirus (COVID19), which is considered a global health threat due to its rapid spread across the globe. Machine learning (ML) is a computational method that can be used to automatically learn from experience and improve the accuracy of predictions. OBJECTIVE In this study, the use of machine learning has been applied to Coronavirus dataset of 50 X-ray images to enable the development of directions and detection modalities with risk causes.The dataset contains a wide range of samples of COVID-19 cases alongside SARS, MERS, and ARDS. The experiment was carried out using a total of 50 X-ray images, out of which 25 images were that of positive COVIDE-19 cases, while the other 25 were normal cases. METHODS An orange tool has been used for data manipulation. To be able to classify patients as carriers of Coronavirus and non-Coronavirus carriers, this tool has been employed in developing and analysing seven types of predictive models. Models such as , artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbour (k-NN), Decision Tree (DT), and CN2 rule inducer were used in this study.Furthermore, the standard InceptionV3 model has been used for feature extraction target. RESULTS The various machine learning techniques that have been trained on coronavirus disease 2019 (COVID-19) dataset with improved ML techniques parameters. The data set was divided into two parts, which are training and testing. The model was trained using 70% of the dataset, while the remaining 30% was used to test the model. The results show that the improved SVM achieved a F1 of 97% and an accuracy of 98%. CONCLUSIONS :. In this study, seven models have been developed to aid the detection of coronavirus. In such cases, the learning performance can be improved through knowledge transfer, whereby time-consuming data labelling efforts are not required.the evaluations of all the models are done in terms of different parameters. it can be concluded that all the models performed well, but the SVM demonstrated the best result for accuracy metric. Future work will compare classical approaches with deep learning ones and try to obtain better results. CLINICALTRIAL None

scholarly journals An architectural framework for information integration using machine learning approaches for smart city security profiling

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096547
Author(s):  
Adnan Abid ◽  
Ansar Abbas ◽  
Adel Khelifi ◽  
Muhammad Shoaib Farooq ◽  
Razi Iqbal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information System ◽  
Information Integration ◽  
Geographical Information ◽  
Support Vector ◽  
Learning Approaches ◽  
Law And Order ◽  
Data Set ◽  
Architectural Framework ◽  
Data Ingestion

In the past few decades, the whole world has been badly affected by terrorism and other law-and-order situations. The newspapers have been covering terrorism and other law-and-order issues with relevant details. However, to the best of our knowledge, there is no existing information system that is capable of accumulating and analyzing these events to help in devising strategies to avoid and minimize such incidents in future. This research aims to provide a generic architectural framework to semi-automatically accumulate law-and-order-related news through different news portals and classify them using machine learning approaches. The proposed architectural framework discusses all the important components that include data ingestion, preprocessor, reporting and visualization, and pattern recognition. The information extractor and news classifier have been implemented, whereby the classification sub-component employs widely used text classifiers for a news data set comprising almost 5000 news manually compiled for this purpose. The results reveal that both support vector machine and multinomial Naïve Bayes classifiers exhibit almost 90% accuracy. Finally, a generic method for calculating security profile of a city or a region has been developed, which is augmented by visualization and reporting components that maps this information onto maps using geographical information system.

scholarly journals Comparative Study of Machine Learning Approaches for Predicting Creep Behavior of Polyurethane Elastomer

Polymers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1768
Author(s):  
Chunhao Yang ◽  
Wuning Ma ◽  
Jianlin Zhong ◽  
Zhendong Zhang
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Polyurethane Elastomer ◽  
Learning Approaches ◽  
Creep Stress ◽  
Machine Learning Approach ◽  
Creep Time ◽  
Multilayer Perceptron Network ◽  
Testing Set

The long-term mechanical properties of viscoelastic polymers are among their most important aspects. In the present research, a machine learning approach was proposed for creep properties’ prediction of polyurethane elastomer considering the effect of creep time, creep temperature, creep stress and the hardness of the material. The approaches are based on multilayer perceptron network, random forest and support vector machine regression, respectively. While the genetic algorithm and k-fold cross-validation were used to tune the hyper-parameters. The results showed that the three models all proposed excellent fitting ability for the training set. Moreover, the three models had different prediction capabilities for the testing set by focusing on various changing factors. The correlation coefficient values between the predicted and experimental strains were larger than 0.913 (mostly larger than 0.998) on the testing set when choosing the reasonable model.

scholarly journals Classification of adult autistic spectrum disorder using machine learning approach

2021 ◽  
Vol 10 (3) ◽  
pp. 743
Author(s):  
Nurul Amirah Mashudi ◽  
Norulhusna Ahmad ◽  
Norliza Mohd Noor
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Autism Spectrum ◽  
Naïve Bayes ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Learning Approaches ◽  
Related Disorder ◽  
Machine Learning Approach ◽  
Linear Svm

Autism spectrum disorder (ASD) is a neurological-related disorder. Patients with ASD have poor social interaction and lack of communication that lead to restricted activities. Thus, early diagnosis with a reliable system is crucial as the symptoms may affect the patient’s entire lifetime. Machine learning approaches are an effective and efficient method for the prediction of ASD disease. The study mainly aims to achieve the accuracy of ASD classification using a variety of machine learning approaches. The dataset comprises 16 selected attributes that are inclusive of 703 patients and non-patients. The experiments are performed within the simulation environment and analyzed using the Waikato environment for knowledge analysis (WEKA) platform. Linear support vector machine (SVM), k-nearest neighbours (k-NN), J48, Bagging, Stacking, AdaBoost, and naïve bayes are the methods used to compute the prediction of ASD status on the subject using 3, 5, and 10-folds cross validation. The analysis is then computed to evaluate the accuracy, sensitivity, and specificity of the proposed methods. The comparative result between the machine learning approaches has shown that linear SVM, J48, Bagging, Stacking, and naïve bayes produce the highest accuracy at 100% with the lowest error rate.

scholarly journals Open-source QSAR models for pKa prediction using multiple machine learning approaches

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Kamel Mansouri ◽  
Neal F. Cariello ◽  
Alexandru Korotcov ◽  
Valery Tkachenko ◽  
Chris M. Grulke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Acid Dissociation ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
Pka Prediction ◽  
Chemical Structures ◽  
Extreme Gradient Boosting ◽  
Qsar Models

Abstract Background The logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affects chemical absorption, distribution, metabolism, excretion, and toxicity properties. Multiple proprietary software packages exist for the prediction of pKa, but to the best of our knowledge no free and open-source programs exist for this purpose. Using a freely available data set and three machine learning approaches, we developed open-source models for pKa prediction. Methods The experimental strongest acidic and strongest basic pKa values in water for 7912 chemicals were obtained from DataWarrior, a freely available software package. Chemical structures were curated and standardized for quantitative structure–activity relationship (QSAR) modeling using KNIME, and a subset comprising 79% of the initial set was used for modeling. To evaluate different approaches to modeling, several datasets were constructed based on different processing of chemical structures with acidic and/or basic pKas. Continuous molecular descriptors, binary fingerprints, and fragment counts were generated using PaDEL, and pKa prediction models were created using three machine learning methods, (1) support vector machines (SVM) combined with k-nearest neighbors (kNN), (2) extreme gradient boosting (XGB) and (3) deep neural networks (DNN). Results The three methods delivered comparable performances on the training and test sets with a root-mean-squared error (RMSE) around 1.5 and a coefficient of determination (R2) around 0.80. Two commercial pKa predictors from ACD/Labs and ChemAxon were used to benchmark the three best models developed in this work, and performance of our models compared favorably to the commercial products. Conclusions This work provides multiple QSAR models to predict the strongest acidic and strongest basic pKas of chemicals, built using publicly available data, and provided as free and open-source software on GitHub.

scholarly journals Various Classifiers Performance Based Machine Learning Methods

2019 ◽  
Vol 8 (3S3) ◽  
pp. 305-310 ◽  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Research Work ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Vector Machines ◽  
Machine Learning Approach ◽  
Future Work ◽  
Neighbor Classifier

Classification is a form of data mining (regarding machine learning) approach that is helpful in the prediction of group membership for data instances, where the data input is used by the computer program for learning and thereafter this learning is used for classifying the fresh observation made. This data set might just be bi-class or it can be multi-class also. Few instances of the problems in classification include: speech identification, handwriting identification, bio metric detection, document classification etc. Many classification methods exist, which can be utilized for classification. In this research work, the fundamental classification approaches and few important kinds of classification approaches that include decision tree induction, Bayesian networks,k-nearest neighbor classifier and Support Vector Machines (SVM) and fuzzy learning classifiers with their merits, drawbacks, probable applications and challenges faced with the solution available. There are different problems that have an effect on the classification and prediction. The objective of this research work is to render an extensive review of various classification approaches in machine learning. At last, the future work intended on the best classification techniques for the input data are discussed.

scholarly journals Wavelet Scattering Network-Based Machine Learning for Ground Penetrating Radar Imaging: Application in Pipeline Identification

2020 ◽  
Vol 12 (21) ◽  
pp. 3655
Author(s):  
Yang Jin ◽  
Yunling Duan
Keyword(s):  
Machine Learning ◽  
Ground Penetrating Radar ◽  
Good Practice ◽  
Object Identification ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Framework ◽  
Imaging Application ◽  
Machine Learning Approach ◽  
Ground Penetrating

Automatic and efficient ground penetrating radar (GPR) data analysis remains a bottleneck, especially restricting applications in real-time monitoring systems. Deep learning approaches have good practice in automatic object identification, but their intensive data requirement has reduced their applicability. This paper developed a machine learning framework based on wavelet scattering networks to analyze GPR data for subsurface pipeline identification. Wavelet scattering network is functionally equivalent to convolutional neural networks, and its null-parameter property is intended for non-intensive datasets. A double-channel framework is designed with wavelet scattering networks followed by support vector machines to determine the existence of pipelines on vertical and horizontal traces separately. Classification accuracy rates arrive around 98% and 95% for datasets without and with noises, respectively, as well as 97% for considering surface roughness. Pipeline locations and diameters are convenient to determine from the reconstructed profiles of both simulated and practical GPR signals. However, the results of 5 cm pipelines are sensitive to noises. Nonetheless, the developed machine learning approach presents promising applicability in subsurface pipeline identification.

Scrutiny of Mental Depression through Smartphone Sensors Using Machine Learning Approaches

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Abdul Kadar Muhammad Masum ◽  
Erfanul Hoque Bahadur ◽  
Forhada Akther Ruhi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Strategies ◽  
Sensor Data ◽  
Support Vector ◽  
Learning Approaches ◽  
Validation Data ◽  
Data Set ◽  
Mental Depression ◽  
Depressed Patients

In addition to a variety of exceptional sensors, Smartphones now facilitates vigorous open entries in data mining and machine learning to scrutinize the Human Activity Recognition (HAR) system. The follow-up to the treatment of diseases, HAR monitoring system, can be used to recognize mental depression that until now has been overlooked for HAR applications. In this scrutinize, Smartphone sensor data were collected in the 1 Hz frequency from 20 data subjects of different ages. We drove the HAR by using basic machine learning strategies, namely Support Vector Machine, Random Forest, K-Nearest Neighbors, and Artificial Neural Network to recognize physical activities which are associated with mental depression. Random Forest outperformed to recognize daily patterns of activities with 99.80% accuracy of the validation data set. Along with, sensors data was amassed regarding the activities performed over the most recent 14 days continuously from target subjects’ Smartphone. This data was fed to the optimized Random Forest model and quantified the duration of each symptomatic activity of mental depression. Here, a push was connected to figure the risk factor for the probability that an individual has been encountering mental depression. So, a questionnaire was surveyed to collect data from 50 patients who were suffering from mental depression. The questionnaire enquires for the duration of activities related to mental depression. Then, the similarity of these experimental subjects’ activity pattern was measured with those of 50 depressed patients. Finally, data was collected from target subjects’ and applied similarity approach to induce the relation between the target subjects’ and depressed patients. Average similarity value of 90.94% for the depressing subject and 34.99% of the typical subject justifies that this robust system was able to achieve a good performance in terms of measurement of risk factors.

scholarly journals Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility

2020 ◽  
Vol 12 (17) ◽  
pp. 2833 ◽  
Author(s):  
Alireza Arabameri ◽  
Omid Asadi Nalivan ◽  
Subodh Chandra Pal ◽  
Rabin Chakrabortty ◽  
Asish Saha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Conservation ◽  
Learning Algorithm ◽  
Gully Erosion ◽  
Support Vector ◽  
Learning Approaches ◽  
Operating Characteristics ◽  
Validation Data ◽  
Data Set ◽  
Jackknife Test

The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help future researchers to select models from this perspective. This type of outcome should help planners and local stakeholders to implement appropriate land and water conservation measures.

scholarly journals Machine Learning Blunts the Needle of Advanced SQL Injections

MENDEL ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 23-30
Author(s):  
Marina Volkova ◽  
Petr Chmelar ◽  
Lukas Sobotka
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Detection System ◽  
Support Vector ◽  
Learning Approaches ◽  
Sensitive Data ◽  
Machine Learning Approach ◽  
Query String ◽  
Gated Recurrent Units ◽  
Sequential Models

SQL injection is one of the most popular and serious information security threats. By exploiting database vulnerabilities, attackers may get access to sensitive data or enable compromised computers to conduct further network attacks. Our research is focused on applying machine learning approaches for identication of injection characteristics in the HTTP query string. We compare results from Rule-based Intrusion Detection System, Support Vector Machines, Multilayer Perceptron, Neural Network with Dropout layers, and Deep Sequential Models (Long Short-Term Memory, and Gated Recurrent Units) using multiple string analysis, bag-of-word techniques, and word embedding for query string vectorization. Results proved benets of applying machine learning approach for detection malicious pattern in HTTP query string.

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