Predicting Future Products Rate using Machine Learning Algorithms

Opinion mining in social networks data is considered as one of most important research areas because a large number of users interact with different topics on it. This paper discusses the problem of predicting future products rate according to users’ comments. Researchers interacted with this problem by using machine learning algorithms (e.g. Logistic Regression, Random Forest Regression, Support Vector Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression and Decision Tree). However, the accuracy of these techniques still needs to be improved. In this study, we introduce an approach for predicting future products rate using LR, RFR, and SVR. Our data set consists of tweets and its rate from 1:5. The main goal of our approach is improving the prediction accuracy about existing techniques. SVR can predict future product rate with a Mean Squared Error (MSE) of 0.4122, Linear Regression model predict with a Mean Squared Error of 0.4986 and Random Forest Regression can predict with a Mean Squared Error of 0.4770. This is better than the existing approaches accuracy.

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Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse

Recycling ◽

10.3390/recycling6040065 ◽

2021 ◽

Vol 6 (4) ◽

pp. 65

Author(s):

Ali Hewiagh ◽

Kannan Ramakrishnan ◽

Timothy Tzen Vun Yap ◽

Ching Seong Tan

Keyword(s):

Machine Learning ◽

Random Forest ◽

Waste Management ◽

Management System ◽

Learning Algorithms ◽

Fraud Detection ◽

Machine Learning Algorithms ◽

Support Vector ◽

Data Set ◽

Waste Management System

Online frauds have pernicious impacts on different system domains, including waste management systems. Fraudsters illegally obtain rewards for their recycling activities or avoid penalties for those who are required to recycle their own waste. Although some approaches have been introduced to prevent such fraudulent activities, the fraudsters continuously seek new ways to commit illegal actions. Machine learning technology has shown significant and impressive results in identifying new online fraud patterns in different system domains such as e-commerce, insurance, and banking. The purpose of this paper, therefore, is to analyze a waste management system and develop a machine learning model to detect fraud in the system. The intended system allows consumers, individuals, and organizations to track, monitor, and update their performance in their recycling activities. The data set provided by a waste management organization is used for the analysis and the model training. This data set contains transactions of users’ recycling activities and behaviors. Three machine learning algorithms, random forest, support vector machine, and multi-layer perceptron are used in the experiments and the best detection model is selected based on the model’s performance. Results show that each of these algorithms can be used for fraud detection in waste managements with high accuracy. The random forest algorithm produces the optimal model with an accuracy of 96.33%, F1-score of 95.20%, and ROC of 98.92%.

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Amino Acid Composition and Charge Based Prediction of Antisepsis Peptides by Random Forest Machine Learning Algorithm

10.1101/2021.09.26.461860 ◽

2021 ◽

Author(s):

Aayushi Rathore ◽

Anu Saini ◽

Navjot Kaur ◽

Aparna Singh ◽

Ojasvi Dutta ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Learning Algorithm ◽

Learning Algorithms ◽

Multiple Organ ◽

The Body ◽

Machine Learning Algorithms ◽

Support Vector ◽

Data Set ◽

Organ Systems

ABSTRACTSepsis is a severe infectious disease with high mortality, and it occurs when chemicals released in the bloodstream to fight an infection trigger inflammation throughout the body and it can cause a cascade of changes that damage multiple organ systems, leading them to fail, even resulting in death. In order to reduce the possibility of sepsis or infection antiseptics are used and process is known as antisepsis. Antiseptic peptides (ASPs) show properties similar to antigram-negative peptides, antigram-positive peptides and many more. Machine learning algorithms are useful in screening and identification of therapeutic peptides and thus provide initial filters or built confidence before using time consuming and laborious experimental approaches. In this study, various machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbour (KNN) and Logistic Regression (LR) were evaluated for prediction of ASPs. Moreover, the characteristics physicochemical features of ASPs were also explored to use them in machine learning. Both manual and automatic feature selection methodology was employed to achieve best performance of machine learning algorithms. A 5-fold cross validation and independent data set validation proved RF as the best model for prediction of ASPs. Our RF model showed an accuracy of 97%, Matthew’s Correlation Coefficient (MCC) of 0.93, which are indication of a robust and good model. To our knowledge this is the first attempt to build a machine learning classifier for prediction of ASPs.

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A Recommender System for Predicting Students' Admission to a Graduate Program using Machine Learning Algorithms

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v17i02.20049 ◽

2021 ◽

Vol 17 (02) ◽

pp. 135

Author(s):

Inssaf El Guabassi ◽

Zakaria Bousalem ◽

Rim Marah ◽

Aimad Qazdar

Keyword(s):

Machine Learning ◽

Random Forest ◽

Recommender System ◽

Learning Algorithms ◽

Educational Process ◽

Machine Learning Algorithms ◽

Support Vector ◽

Random Forest Regression ◽

Grade Point ◽

University Admission

<p>In the 21st century, University educations are becoming a key pillar of social and economic life. It plays a major role not only in the educational process but also in the ensuring of two important things which are a prosperous career and financial security. However, predicting university admission can be especially difficult because the students are not aware of admission requirements. For that reason, the main purpose of this research work is to provide a recommender system for early predicting university admission based on four Machine Learning algorithms namely Linear Regression, Decision Tree, Support Vector Regression, and Random Forest Regression. The experimental results showed that the Random Forest Regression is the most suitable Machine Learning algorithm for predicting university admission. Also, the Cumulative Grade Point Average (CGPA) is the most important parameter that influences the chance of admission.</p>

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Prediction of Breast Cancer Disease using Machine Learning Algorithms

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d1866.029420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 2868-2878

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Random Forest ◽

Missing Values ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Cancer Disease ◽

Data Set ◽

Cancer Data

Background/Aim: Breast Cancer is the most often identified cancer among women and major reason for increasing mortality rate among women. The early strategies for estimating the breast cancer sicknesses helped in settling on choices about the progressions to have happened in high-chance patients which brought about the decrease of their dangers. Methods: In the proposed research, we have considered breast cancer data set from kaggle and we have done pre-processing tasks for missing values .We have no missing data values from the considered data set .The performance of the diagnosis model is obtained by using methods like classification, accuracy, sensitivity and specificity analysis. This paper proposes a prediction model to predict whether a people have a breast cancer disease or not and to provide an awareness or diagnosis on that. This is done by comparing the accuracies of applying rules to the individual results of Support Vector Machine, Random forest, Naive Bayes classifier and logistic regression on the dataset taken in a region to present an accurate model of predicting breast cancer disease. Results: The machine learning algorithms under study were able to predict breast cancer disease in patients with accuracy between 52.63% and 98.24%. Conclusions: It was shown that Random Forest has better Accuracy (98.24 %) when compared to different Machine-learning Algorithms.

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Influenza Prediction: Analyzing Machine Learning Algorithms

Asian Journal of Computer Science and Technology ◽

10.51983/ajcst-2020.9.1.2155 ◽

2020 ◽

Vol 9 (1) ◽

pp. 14-18

Author(s):

Sapna Yadav ◽

Pankaj Agarwal

Keyword(s):

Machine Learning ◽

Random Forest ◽

Linear Regression ◽

Random Forests ◽

Ridge Regression ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Digital Data ◽

Support Vector ◽

Lasso Regression

Analyzing online or digital data for detecting epidemics is one of the hot areas of research and now becomes more relevant during the present outbreak of Covid-19. There are several different types of the influenza virus and moreover they keep evolving constantly in the same manner the COVID-19 virus has done. As a result, they pose a greater challenge when it comes to analyzing them, predicting when, where and at what degree of severity it will outbreak during the flu season across the world. There is need for greater surveillance to both seasonal and pandemic influenza to ensure the health and safety of the mankind. The objective of work is to apply machine learning algorithms for building predictive models that can predict where the occurrence, peak and severity of influenza in each season. For this work we have considered a freely available dataset of Ireland which is recorded for the duration of 2005 to 2016. Specifically, we have tested three ML Algorithms namely Linear Regression, Support Vector Regression and Random Forests. We found Random Forests is giving better predictive results. We also conducted experiment through weka tool and tested Zero R, Linear Regression, Lazy Kstar, Random Forest, REP Tree, Multilayer Perceptron models. We again found the Random Forest is performing better in comparison to all other models. We also evaluated other regression models including Ridge Regression, modified Ridge regression, Lasso Regression, K Neighbor Regression and evaluated the mean absolute errors. We found that modified Ridge regression is producing minimum error. The proposed work is inclined towards finding the suitability & appropriate ML algorithm for solving this problem on Flu.

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Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Sensors ◽

10.3390/s18103532 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3532 ◽

Cited By ~ 16

Author(s):

Nicola Mansbridge ◽

Jurgen Mitsch ◽

Nicola Bollard ◽

Keith Ellis ◽

Giuliana Miguel-Pacheco ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Time Budget ◽

Learning Algorithms ◽

Eating Behaviour ◽

Machine Learning Algorithms ◽

Support Vector ◽

Optimum Number ◽

Eating Behaviours ◽

Adaptive Boosting

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

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Techniques for Detecting Malware Traffic: A Comprehensive Approach to Feature Selection and Classification

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39088 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1-10

Author(s):

Harsha A K

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Random Forest ◽

Learning Algorithms ◽

Malware Detection ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Steady Increase ◽

Extreme Gradient Boosting

Abstract: Since the advent of encryption, there has been a steady increase in malware being transmitted over encrypted networks. Traditional approaches to detect malware like packet content analysis are inefficient in dealing with encrypted data. In the absence of actual packet contents, we can make use of other features like packet size, arrival time, source and destination addresses and other such metadata to detect malware. Such information can be used to train machine learning classifiers in order to classify malicious and benign packets. In this paper, we offer an efficient malware detection approach using classification algorithms in machine learning such as support vector machine, random forest and extreme gradient boosting. We employ an extensive feature selection process to reduce the dimensionality of the chosen dataset. The dataset is then split into training and testing sets. Machine learning algorithms are trained using the training set. These models are then evaluated against the testing set in order to assess their respective performances. We further attempt to tune the hyper parameters of the algorithms, in order to achieve better results. Random forest and extreme gradient boosting algorithms performed exceptionally well in our experiments, resulting in area under the curve values of 0.9928 and 0.9998 respectively. Our work demonstrates that malware traffic can be effectively classified using conventional machine learning algorithms and also shows the importance of dimensionality reduction in such classification problems. Keywords: Malware Detection, Extreme Gradient Boosting, Random Forest, Feature Selection.

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Abstract 15895: Machine Learning Algorithms to Predict Major Adverse Cardiovascular Events in Patients Undergoing Orthotopic Liver Transplantation: A Retrospective Cohort Study

Circulation ◽

10.1161/circ.142.suppl_3.15895 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

vardhmaan jain ◽

Vikram Sharma ◽

Agam Bansal ◽

Cerise Kleb ◽

Chirag Sheth ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Cardiovascular Events ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Major Adverse Cardiovascular Events ◽

Support Vector ◽

Post Transplant ◽

Extreme Gradient Boosting ◽

All Cause Mortality

Background: Post-transplant major adverse cardiovascular events (MACE) are amongst the leading cause of death amongst orthotopic liver transplant(OLT) recipients. Despite years of guideline directed therapy, there are limited data on predictors of post-OLT MACE. We assessed if machine learning algorithms (MLA) can predict MACE and all-cause mortality in patients undergoing OLT. Methods: We tested three MLA: support vector machine, extreme gradient boosting(XG-Boost) and random forest with traditional logistic regression for prediction of MACE and all-cause mortality on a cohort of consecutive patients undergoing OLT at our center between 2008-2019. The cohort was randomly split into a training (80%) and testing (20%) cohort. Model performance was assessed using c-statistic or AUC. Results: We included 1,459 consecutive patients with mean ± SD age 54.2 ± 13.8 years, 32% female who underwent OLT. There were 199 (13.6%) MACE and 289 (20%) deaths at a mean follow up of 4.56 ± 3.3 years. The random forest MLA was the best performing model for predicting MACE [AUC:0.78, 95% CI: 0.70-0.85] as well as mortality [AUC:0.69, 95% CI: 0.61-0.76], with all models performing better when predicting MACE vs mortality. See Table and Figure. Conclusion: Random forest machine learning algorithms were more predictive and discriminative than traditional regression models for predicting major adverse cardiovascular events and all-cause mortality in patients undergoing OLT. Validation and subsequent incorporation of MLA in clinical decision making for OLT candidacy could help risk stratify patients for post-transplant adverse cardiovascular events.

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Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

Diagnostics ◽

10.3390/diagnostics9030104 ◽

2019 ◽

Vol 9 (3) ◽

pp. 104 ◽

Cited By ~ 11

Author(s):

Ahmed ◽

Yigit ◽

Isik ◽

Alpkocak

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Nearest Neighbor ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

K Nearest Neighbor ◽

Data Set ◽

Leukemia Data

Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multiclass classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other wellknown machine learning algorithms.

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Prediction of novel mouse TLR9 agonists using a random forest approach

BMC Molecular and Cell Biology ◽

10.1186/s12860-019-0241-0 ◽

2019 ◽

Vol 20 (S2) ◽

Author(s):

Varun Khanna ◽

Lei Li ◽

Johnson Fung ◽

Shoba Ranganathan ◽

Nikolai Petrovsky

Keyword(s):

Machine Learning ◽

Random Forest ◽

Correlation Coefficient ◽

Matthews Correlation Coefficient ◽

Learning Algorithms ◽

Ensemble Classifier ◽

Innate Immune ◽

Machine Learning Algorithms ◽

Support Vector ◽

Random Forest Algorithm

Abstract Background Toll-like receptor 9 is a key innate immune receptor involved in detecting infectious diseases and cancer. TLR9 activates the innate immune system following the recognition of single-stranded DNA oligonucleotides (ODN) containing unmethylated cytosine-guanine (CpG) motifs. Due to the considerable number of rotatable bonds in ODNs, high-throughput in silico screening for potential TLR9 activity via traditional structure-based virtual screening approaches of CpG ODNs is challenging. In the current study, we present a machine learning based method for predicting novel mouse TLR9 (mTLR9) agonists based on features including count and position of motifs, the distance between the motifs and graphically derived features such as the radius of gyration and moment of Inertia. We employed an in-house experimentally validated dataset of 396 single-stranded synthetic ODNs, to compare the results of five machine learning algorithms. Since the dataset was highly imbalanced, we used an ensemble learning approach based on repeated random down-sampling. Results Using in-house experimental TLR9 activity data we found that random forest algorithm outperformed other algorithms for our dataset for TLR9 activity prediction. Therefore, we developed a cross-validated ensemble classifier of 20 random forest models. The average Matthews correlation coefficient and balanced accuracy of our ensemble classifier in test samples was 0.61 and 80.0%, respectively, with the maximum balanced accuracy and Matthews correlation coefficient of 87.0% and 0.75, respectively. We confirmed common sequence motifs including ‘CC’, ‘GG’,‘AG’, ‘CCCG’ and ‘CGGC’ were overrepresented in mTLR9 agonists. Predictions on 6000 randomly generated ODNs were ranked and the top 100 ODNs were synthesized and experimentally tested for activity in a mTLR9 reporter cell assay, with 91 of the 100 selected ODNs showing high activity, confirming the accuracy of the model in predicting mTLR9 activity. Conclusion We combined repeated random down-sampling with random forest to overcome the class imbalance problem and achieved promising results. Overall, we showed that the random forest algorithm outperformed other machine learning algorithms including support vector machines, shrinkage discriminant analysis, gradient boosting machine and neural networks. Due to its predictive performance and simplicity, the random forest technique is a useful method for prediction of mTLR9 ODN agonists.

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