scholarly journals QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1734
Author(s):  
Cosmin Alexandru Bugeac ◽  
Robert Ancuceanu ◽  
Mihaela Dinu
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Logistic Regression ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
Disk Diffusion ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Disk Diffusion Test ◽  
Tree Classifier

Pseudomonas aeruginosa is a Gram-negative bacillus included among the six “ESKAPE” microbial species with an outstanding ability to “escape” currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (support vector classifier, K nearest neighbors, random forest classifier, decision tree classifier, AdaBoost classifier, logistic regression and naïve Bayes classifier). We used four sets of molecular descriptors and fingerprints and three different methods of data balancing, together with the “native” data set. In total, 32 models were built for each set of descriptors or fingerprint and balancing method, of which 28 were selected and stacked to create meta-models. In terms of balanced accuracy, the best performance was provided by KNN, logistic regression and decision tree classifier, but the ensemble method had slightly superior results in nested cross-validation.

scholarly journals QSAR Models for Active Substances Against Pseudomonas aeruginosa Using Disk-diffusion Test Data

2021 ◽  
Author(s):  
Cosmin Alexandru Bugeac ◽  
Robert Ancuceanu ◽  
Mihaela Dinu
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Model Development ◽  
Qsar Model ◽  
Machine Learning Algorithms ◽  
Disk Diffusion ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
K Nearest Neighbors ◽  
Disk Diffusion Test ◽  
Tree Classifier

Pseudomonas aeruginosa is a Gram-negative bacillus included among the six "ESKAPE" microbial species with an outstanding ability to "escape" currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature, and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (Support vector classifier, K Nearest Neighbors, Random Forest Classifier, Decision Tree Classifier, AdaBoost Classifier, Logistic Regression, and Naive Bayes Classifier). The main descriptors used in building the models belonged to the families of adjacency matrix, constitutional descriptors, first highest eigenvalue of Burden matrix, centered Moreau-Broto autocorrelation, and averaged and centered Moreau-Broto autocorrelation descriptors. A total of 32 models were built, of which 28 were selected and stacked to create a meta-model. In terms of balanced accuracy, the best performance was provided by KNN, SVM and AdaBoost algorithms, but the ensemble method had slightly superior results in nested cross-validation.

scholarly journals Sarcasm Detection From Twitter Database Using Text Mining Algorithms

2021 ◽  
Vol 12 (11) ◽  
pp. 1916-1924
Author(s):  
Tamanna Siddiqui, Et. al.
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Support Vector Machines ◽  
Decision Tree ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Text Data ◽  
Data Set ◽  
Tree Classifier ◽  
Vector Machines

Sarcasm is well-defined as a cutting, frequently sarcastic remark intended to fast ridicule or dislike. Irony detection is the assignment of fittingly labeling the text as’ Sarcasm’ or ’non- Sarcasm.’ There is a challenging task owing to the deficiency of facial expressions and intonation in the text. Social media and micro-blogging websites are extensively explored for getting the information to extract the opinion of the target because a huge of text data existence is put out into the open field into social media like Twitter. Such large, openly available text data could be utilized for a variety of researches. Here we applied text data set for classifying Sarcasm and experiments have been made from the textual data extracted from the Twitter data set. Text data set downloaded from Kaggle, including 1984 tweets that collected from Twitter. These data already have labels here. In this paper, we apply these data to train our model Classifiers for different algorithms to see the ability of model machine learning to recognize sarcasm and non-sarcasm through a set of the process start by text pre-processing feature extraction (TF-IDF) and apply different classification algorithms, such as Decision Tree classifier, Multinomial Naïve Bayes Classifier, Support vector machines, and Logistic Regression classifier. Then tuning a model fitting the best results, we get in (TF-IDF) we achieve 0.94% in Multinomial NB, Decision Tree Classifier we achieve 0.93%, Logistic Regression we achieve 0.97%, and Support vector machines (SVM) we achieve 0.42%. All these result models were improved, except the SVM model has the lowest accuracy. The results were extracted, and the evaluation of the results has been proved above to be good in accuracy for identifying sarcastic impressions of people.

scholarly journals Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning

2020 ◽  
Vol 19 ◽  
pp. 153303382090982
Author(s):  
Melek Akcay ◽  
Durmus Etiz ◽  
Ozer Celik ◽  
Alaattin Ozen
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Naive Bayes ◽  
Nasopharyngeal Cancer ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tumor Diameter ◽  
Survival Prognosis ◽  
Data Set

Background and Aim: Although the prognosis of nasopharyngeal cancer largely depends on a classification based on the tumor-lymph node metastasis staging system, patients at the same stage may have different clinical outcomes. This study aimed to evaluate the survival prognosis of nasopharyngeal cancer using machine learning. Settings and Design: Original, retrospective. Materials and Methods: A total of 72 patients with a diagnosis of nasopharyngeal cancer who received radiotherapy ± chemotherapy were included in the study. The contribution of patient, tumor, and treatment characteristics to the survival prognosis was evaluated by machine learning using the following techniques: logistic regression, artificial neural network, XGBoost, support-vector clustering, random forest, and Gaussian Naive Bayes. Results: In the analysis of the data set, correlation analysis, and binary logistic regression analyses were applied. Of the 18 independent variables, 10 were found to be effective in predicting nasopharyngeal cancer-related mortality: age, weight loss, initial neutrophil/lymphocyte ratio, initial lactate dehydrogenase, initial hemoglobin, radiotherapy duration, tumor diameter, number of concurrent chemotherapy cycles, and T and N stages. Gaussian Naive Bayes was determined as the best algorithm to evaluate the prognosis of machine learning techniques (accuracy rate: 88%, area under the curve score: 0.91, confidence interval: 0.68-1, sensitivity: 75%, specificity: 100%). Conclusion: Many factors affect prognosis in cancer, and machine learning algorithms can be used to determine which factors have a greater effect on survival prognosis, which then allows further research into these factors. In the current study, Gaussian Naive Bayes was identified as the best algorithm for the evaluation of prognosis of nasopharyngeal cancer.

scholarly journals Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran

2020 ◽  
Vol 10 (15) ◽  
pp. 5047 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Danesh Zandi ◽  
Himan Shahabi ◽  
Kamran Chapi ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Shallow Landslide ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Algorithm ◽  
Alternating Decision Tree ◽  
Bayesian Logistic Regression

This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future.

scholarly journals Future Prediction of Diabetics using XG Booster Classifiers

2020 ◽  
Vol 9 (3) ◽  
pp. 2128-2132
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Common Disease ◽  
Data Set ◽  
Glucose Content

Diabetes is a most common disease that occurs to most of the humans now a day. The predictions for this disease are proposed through machine learning techniques. Through this method the risk factors of this disease are identified and can be prevented from increasing. Early prediction in such disease can be controlled and save human’s life. For the early predictions of this disease we collect data set having 8 attributes diabetic of 200 patients. The patients’ sugar level in the body is tested by the features of patient’s glucose content in the body and according to the age. The main Machine learning algorithms are Support vector machine (SVM), naive bayes (NB), K nearest neighbor (KNN) and Decision Tree (DT). In the exiting the Naive Bayes the accuracy levels are 66% but in the Decision tree the accuracy levels are 70 to 71%. The accuracy levels of the patients are not proper in range. But in XG boost classifiers even after the Naïve Bayes 74 Percentage and in Decision tree the accuracy levels are 89 to 90%. In the proposed system the accuracy ranges are shown properly and this is only used mostly. A dataset of 729 patients can be stored in Mongo DB and in that 129 patients repots are taken for the prediction purpose and the remaining are used for training. The training datasets are used for the prediction purposes.

scholarly journals Ensemble-Based Machine Learning for Predicting Sudden Human Fall Using Health Data

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Utkarsh Saxena ◽  
Soumen Moulik ◽  
Soumya Ranjan Nayak ◽  
Thomas Hanne ◽  
Diptendu Sinha Roy
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Majority Voting ◽  
Support Vector ◽  
Human Beings ◽  
Medical Terminology ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Health Parameters

We attempt to predict the accidental fall of human beings due to sudden abnormal changes in their health parameters such as blood pressure, heart rate, and sugar level. In medical terminology, this problem is known as Syncope. The primary motivation is to prevent such falls by predicting abnormal changes in these health parameters that might trigger a sudden fall. We apply various machine learning algorithms such as logistic regression, a decision tree classifier, a random forest classifier, K-Nearest Neighbours (KNN), a support vector machine, and a naive Bayes classifier on a relevant dataset and verify our results with the cross-validation method. We observe that the KNN algorithm provides the best accuracy in predicting such a fall. However, the accuracy results of some other algorithms are also very close. Thus, we move one step further and propose an ensemble model, Majority Voting, which aggregates the prediction results of multiple machine learning algorithms and finally indicates the probability of a fall that corresponds to a particular human being. The proposed ensemble algorithm yields 87.42% accuracy, which is greater than the accuracy provided by the KNN algorithm.

scholarly journals A Comparative Study to Evaluate the Performance of Classification Algorithms in Mammogram Analysis

2018 ◽  
Vol 7 (3.6) ◽  
pp. 154
Author(s):  
S K. Sajan ◽  
M Germanus Alex
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Decision Tree ◽  
Automated System ◽  
Support Vector ◽  
Classification Algorithms ◽  
Neural Network Classifier ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Mammogram Image

Breast cancer is a major threat humans are facing irrespective of geographical limits. The awareness about breast cancer has increased during the last decade and many preventive measures were in practice to detect the breast cancer before the symptoms were felt. Mammography is a screening methodology currently in practice. In this paper the mammogram image is analyzed using automated system. The automated system is designed to be capable of distinguishing the mammogram image into a normal or malignant. This process involves image enhancement and image segmentation at preprocessing level. Histogram equalization technique is used to transform low contrast region of the mammogram into region with higher contrast and Fuzzy C Means (FCM) algorithm is used to segment the mammogram image into regions suitable for further analysis. After enhancement and segmentation at preprocessing level the classification is done using three classification algorithms like decision tree classifier, Neural Network classifier and Support Vector Machine (SVM). The performance of the classification algorithms is evaluated using the following criteria like speed, flexibility, robustness, scalability, interpretability, Time complexity and also based on accuracy, sensitivity and specificity. The results obtained in classification are compared with other classification algorithms. It is found that the neural network classifier approach produces better results compared to other classifiers.The average accuracy in diagnosis by Neural Network approach classifier is around 91%.  Also it is found that the decision tree approach is much flexible and easy to use compared to other approaches.  

scholarly journals Prediction of Students’ Performance based on Academic, Behaviour, Extra and Co-Curricular Activities

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 01) ◽  
pp. 262-279
Author(s):  
T. Jenitha ◽  
S. Santhi ◽  
J. Monisha Privthy Jeba
Keyword(s):  
Extracurricular Activities ◽  
Family Background ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Academic Institutions ◽  
Physical And Mental Health ◽  
Decision Tree Classifier ◽  
Academic Scholarship ◽  
Tree Classifier ◽  
Training Programmes

Since Academic institutions contain huge volume of data regarding students such as academic scores, scores in co and extracurricular activities, family annual income, family background and other supporting documents, predicting individual students performance in all aspects manually is a difficult task. The proposed work uses data mining techniques to identify students who are eligible for scholarships and other benefits. Students are classified into different categories by means of academic, behavior, extra and co-curricular activities. Machine Learning algorithms such as Naive Bayes, Decision Tree Classifier and Support Vector Machine are used for predicting the performance of the student. With the help of this proposed model parents and instructors can monitor student’s performance and they can also provide essential technical and moral support. Also this helps in providing academic scholarship and training to the students to support them financially and to enrich their knowledge. It suggests the Academic Institutions to organize induction or training programmes at the beginning of the semester. Technical training, motivational talks, Yoga, etc are organized by the institutions by keeping in mind of students physical and mental health. Considering the e-learning platforms huge volumes of data and plethora of information are generated. In this work, various learning models are constructed and their accuracies are compared to analyse which algorithm out-performs.

scholarly journals A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6221
Author(s):  
Rahman Shafique ◽  
Hafeez-Ur-Rehman Siddiqui ◽  
Furqan Rustam ◽  
Saleem Ullah ◽  
Muhammad Abubakar Siddique ◽  
...  
Keyword(s):  
Human Error ◽  
Acoustic Analysis ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Railway Track ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Traditional Procedure ◽  
Novel Approach ◽  
Tree Classifier

Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%.

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