scholarly journals Classification and Prediction of Software Incidents Using Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Sikandar Ali ◽  
Muhammad Adeel ◽  
Sumaira Johar ◽  
Muhammad Zeeshan ◽  
Samad Baseer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Operational Environment ◽  
Target Class ◽  
Software Failure ◽  
Log Files ◽  
And Performance

An incident, in the perception of information technology, is an event that is not part of a normal process and disrupts operational procedure. This research work particularly focuses on software failure incidents. In any operational environment, software failure can put the quality and performance of services at risk. Many efforts are made to overcome this incident of software failure and to restore normal service as soon as possible. The main contribution of this study is software failure incidents classification and prediction using machine learning. In this study, an active learning approach is used to selectively label those data which is considered to be more informative to build models. Firstly, the sample with the highest randomness (entropy) is selected for labeling. Secondly, to classify the labeled observation into either failure or no failure classes, a binary classifier is used that predicts the target class label as failure or not. For classification, Support Vector Machine is used as a main classifier to classify the data. We derived our prediction models from the failure log files collected from the ECLIPSE software repository.

scholarly journals Machine Learning Frameworks in Cancer Detection

2021 ◽  
Vol 297 ◽  
pp. 01073
Author(s):  
Sabyasachi Pramanik ◽  
K. Martin Sagayam ◽  
Om Prakash Jena
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Cancer Development ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Fact Finding ◽  
Risk Of Cancer

Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer.

Survey of Breast Cancer Detection Using Machine Learning Techniques in Big Data

2019 ◽  
Vol 21 (3) ◽  
pp. 80-92
Author(s):  
Madhuri Gupta ◽  
Bharat Gupta
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Research Work ◽  
Cancer Recurrence ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Common Disease ◽  
Learning Techniques ◽  
Learning Frameworks ◽  
Processing Engine

Cancer is a disease in which cells in body grow and divide beyond the control. Breast cancer is the second most common disease after lung cancer in women. Incredible advances in health sciences and biotechnology have prompted a huge amount of gene expression and clinical data. Machine learning techniques are improving the prior detection of breast cancer from this data. The research work carried out focuses on the application of machine learning methods, data analytic techniques, tools, and frameworks in the field of breast cancer research with respect to cancer survivability, cancer recurrence, cancer prediction and detection. Some of the widely used machine learning techniques used for detection of breast cancer are support vector machine and artificial neural network. Apache Spark data processing engine is found to be compatible with most of the machine learning frameworks.

scholarly journals Machine Learning for Predictions in Academics

2020 ◽  
Vol 8 (5) ◽  
pp. 4624-4627
Keyword(s):  
Machine Learning ◽  
Career Path ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Bayes Classifier ◽  
Logistics Regression ◽  
Learning Techniques

In recent years, a lot of data has been generated about students, which can be utilized for deciding the career path of the student. This paper discusses some of the machine learning techniques which can be used to predict the performance of a student and help to decide his/her career path. Some of the key Machine Learning (ML) algorithms applied in our research work are Linear Regression, Logistics Regression, Support Vector machine, Naïve Bayes Classifier and K- means Clustering. The aim of this paper is to predict the student career path using Machine Learning algorithms. We compare the efficiencies of different ML classification algorithms on a real dataset obtained from University students.

scholarly journals Developing a Hyperparameter Tuning Based Machine Learning Approach of Heart Disease Prediction

2020 ◽  
Vol 7 (2) ◽  
pp. 631-647
Author(s):  
Emrana Kabir Hashi ◽  
Md. Shahid Uz Zaman
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Traditional Approach ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Traditional System ◽  
Prediction Approach

Machine learning techniques are widely used in healthcare sectors to predict fatal diseases. The objective of this research was to develop and compare the performance of the traditional system with the proposed system that predicts the heart disease implementing the Logistic regression, K-nearest neighbor, Support vector machine, Decision tree, and Random Forest classification models. The proposed system helped to tune the hyperparameters using the grid search approach to the five mentioned classification algorithms. The performance of the heart disease prediction system is the major research issue. With the hyperparameter tuning model, it can be used to enhance the performance of the prediction models. The achievement of the traditional and proposed system was evaluated and compared in terms of accuracy, precision, recall, and F1 score. As the traditional system achieved accuracies between 81.97% and 90.16%., the proposed hyperparameter tuning model achieved accuracies in the range increased between 85.25% and 91.80%. These evaluations demonstrated that the proposed prediction approach is capable of achieving more accurate results compared with the traditional approach in predicting heart disease with the acquisition of feasible performance.

scholarly journals Highly accurate and efficient two phase-intrusion detection system (TP-IDS) using distributed processing of HADOOP and machine learning techniques

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Abhijit Dnyaneshwar Jadhav ◽  
Vidyullatha Pellakuri
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Phase Ii ◽  
Intrusion Detection System ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Network Connections ◽  
Learning Techniques

AbstractNetwork security and data security are the biggest concerns now a days. Every organization decides their future business process based on the past and day to day transactional data. This data may consist of consumer’s confidential data, which needs to be kept secure. Also, the network connections when established with the external communication devices or entities, a care should be taken to authenticate these and block the unwanted access. This consists of identification of the malicious connection nodes and identification of normal connection nodes. For that, we use a continuous monitoring of the network input traffic to recognize the malicious connection request called as intrusion and this type of monitoring system is called as an Intrusion detection system (IDS). IDS helps us to protect our network and data from insecure and malicious network connections. Many such systems exists in the real time scenario, but they have critical issues of performance like accuracy and efficiency. These issues are addressed as a part of this research work of IDS using machine learning techniques and HDFS. The TP-IDS is designed in two phases for increasing accuracy. In phase I of TP-IDS, Support Vector Machine (SVM) and k Nearest Neighbor (kNN) are used. In phase II of TP-IDS, Decision Tree (DT) and Naïve Bayes (NB) are used, where phase II is the validation phase of the system for increasing accuracy. Also, both the phases are having Hadoop distributed file system underlying data storage and processing architecture, which allows parallel processing to increase the speed of the system and hence achieve the efficiency in TP-IDS.

A Novel Approach of Weighted Support Vector Machine with Applied Chance Theory for Forecasting Air Pollution Phenomenon in Egypt

2018 ◽  
Vol 17 (01) ◽  
pp. 1850001 ◽  
Author(s):  
Nabil Mohamed Eldakhly ◽  
Magdy Aboul-Ela ◽  
Areeg Abdalla
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Management Control ◽  
Air Pollutant ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Chance Theory

The particulate matter air pollutant of diameter less than 10 micrometers (PM[Formula: see text]), a category of pollutants including solid and liquid particles, can be a health hazard for several reasons: it can harm lung tissues and throat, aggravate asthma and increase respiratory illness. Accurate prediction models of PM[Formula: see text] concentrations are essential for proper management, control, and making public warning strategies. Therefore, machine learning techniques have the capability to develop methods or tools that can be used to discover unseen patterns from given data to solve a particular task or problem. The chance theory has advanced concepts pertinent to treat cases where both randomness and fuzziness play simultaneous roles at one time. The main objective is to study the modification of a single machine learning algorithm — support vector machine (SVM) — applying the chance weight of the target variable, based on the chance theory, to the corresponding dataset point to be superior to the ensemble machine learning algorithms. The results of this study are outperforming of the SVM algorithms when modifying and combining with the right theory/technique, especially the chance theory over other modern ensemble learning algorithms.

scholarly journals Capacity Control in Indoor Spaces Using Machine Learning Techniques Together with BLE Technology

2021 ◽  
Vol 10 (2) ◽  
pp. 35
Author(s):  
M. Encarnación Beato Gutiérrez ◽  
Montserrat Mateos Sánchez ◽  
Roberto Berjón Gallinas ◽  
Ana M. Fermoso García
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Prediction Models ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Capacity Control ◽  
The People ◽  
Learning Techniques ◽  
Enclosed Space ◽  
Indoor Spaces

At present, capacity control in indoor spaces is critical in the current situation in which we are living in, due to the pandemic. In this work, we propose a new solution using machine learning techniques with BLE technology. This study presents a real experiment in a university environment and we study three different prediction models using machine learning techniques—specifically, logistic regression, decision trees and artificial neural networks. As a conclusion, the study shows that machine learning techniques, in particular decision trees, together with BLE technology, provide a solution to the problem. The contribution of this research work shows that the prediction model obtained is capable of detecting when the COVID capacity of an enclosed space is exceeded. In addition, it ensures that no false negatives are produced, i.e., all the people inside the laboratory will be correctly counted.

scholarly journals Change Detection using Deep Learning and Machine Learning Techniques for Multispectral Satellite Images

2019 ◽  
Vol 9 (1S) ◽  
pp. 90-93
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Change Detection ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Remote Sensing Images ◽  
Detection Analysis ◽  
Learning Techniques ◽  
And Performance

Change detection is used to find whether the changes happened or not between two different time periods using remote sensing images. We can use various machine learning techniques and deep learning techniques for the change detection analysis using remote sensing images. This paper mainly focused on computational and performance analysis of both techniques in the application of change detection .For each approach, we considered ten different kinds of algorithms and evaluated the performance. Moreover, in this research work, we have analyzed merits and demerits of each method which have used to change detection.

scholarly journals Analysis of Educational Robotics Activities Using a Machine Learning Approach

2021 ◽  
pp. 203-211
Author(s):  
Lorenzo Cesaretti ◽  
Laura Screpanti ◽  
David Scaradozzi ◽  
Eleni Mangina
Keyword(s):  
Machine Learning ◽  
Learning Styles ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Educational Robotics ◽  
School Students ◽  
K Nearest Neighbors ◽  
Log Files ◽  
Learning Techniques ◽  
Mixed Approach

AbstractThis paper presents the preliminary results of using machine learning techniques to analyze educational robotics activities. An experiment was conducted with 197 secondary school students in Italy: the authors updated Lego Mindstorms EV3 programming blocks to record log files with coding sequences students had designed in teams. The activities were part of a preliminary robotics exercise. We used four machine learning techniques—logistic regression, support-vector machine (SVM), K-nearest neighbors and random forests—to predict the students’ performance, comparing a supervised approach (using twelve indicators extracted from the log files as input for the algorithms) and a mixed approach (applying a k-means algorithm to calculate the machine learning features). The results showed that the mixed approach with SVM outperformed the other techniques, and that three predominant learning styles emerged from the data mining analysis.

Prediction of Severity of Non Proliferated Diabetic Retinopathy Using Machine Learning Techniques

2020 ◽  
Vol 17 (9) ◽  
pp. 4219-4222
Author(s):  
ManjulaSri Rayudu ◽  
Srujana Pendam ◽  
Srilaxmi Dasari
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Diversity Index ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Discriminant ◽  
Learning Techniques

All the patients of Type1 and more than 60% of Type2 Diabetes suffer from Diabetic Retinopathy (DR). Diabetic retinopathy causes damage to retina of eye and slowly leads to complete vision loss. The longer the patients are suffering from diabetes the probability of presence of DR is more. Hence diabetic retinopathy is to be identified in early stage to avoid blindness. The objective of this research work is to predict the severity of diabetic retinopathy (Non Proliferated) using machine learning techniques. Proliferated diabetic retinopathy (later stage) is characterized by neovasculature in the retinal veins and is the final stage. Non proliferated DR (earlier stage) is identified by any of the abnormalities out of microaneurysms, Hard exudates and hemorrhages. Then Machine learning techniques are employed to identify the class of DR. The following Classification and regression techniques are employed for categorizing the DR: Gini Diversity Index method, Linear discriminant analysis, Ensemble method with bagged and boosted trees, K-Nearest Neighbor, and Support Vector Machine classification methods. 89 images from DRIVE database (DiaRet DB1) are classified using the machine learning techniques cited above. It is observed the maximum accuracy is achieved as 88.8% with Linear SVM classifier.

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