scholarly journals Semi-supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

2020 ◽  
Author(s):  
Yutao Lu ◽  
Juan Wang ◽  
Miao Liu ◽  
Kaixuan Zhang ◽  
Guan Gui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Cellular Networks ◽  
Expert Knowledge ◽  
Learning Algorithm ◽  
Positive Sample ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Soft Decision ◽  
Decision Methods

The ever-increasing amount of data in cellular networks poses challenges for network operators to monitor the quality of experience (QoE). Traditional key quality indicators (KQIs)-based hard decision methods are difficult to undertake the task of QoE anomaly detection in the case of big data. To solve this problem, in this paper, we propose a KQIs-based QoE anomaly detection framework using semi-supervised machine learning algorithm, i.e., iterative positive sample aided one-class support vector machine (IPS-OCSVM). There are four steps for realizing the proposed method while the key step is combining machine learning with the network operator's expert knowledge using OCSVM. Our proposed IPS-OCSVM framework realizes QoE anomaly detection through soft decision and can easily fine-tune the anomaly detection ability on demand. Moreover, we prove that the fluctuation of KQIs thresholds based on expert knowledge has a limited impact on the result of anomaly detection. Finally, experiment results are given to confirm the proposed IPS-OCSVM framework for QoE anomaly detection in cellular networks.

scholarly journals Semi-supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

2020 ◽  
Author(s):  
Yutao Lu ◽  
Juan Wang ◽  
Miao Liu ◽  
Kaixuan Zhang ◽  
Guan Gui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Cellular Networks ◽  
Expert Knowledge ◽  
Learning Algorithm ◽  
Positive Sample ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Soft Decision ◽  
Decision Methods

The ever-increasing amount of data in cellular networks poses challenges for network operators to monitor the quality of experience (QoE). Traditional key quality indicators (KQIs)-based hard decision methods are difficult to undertake the task of QoE anomaly detection in the case of big data. To solve this problem, in this paper, we propose a KQIs-based QoE anomaly detection framework using semi-supervised machine learning algorithm, i.e., iterative positive sample aided one-class support vector machine (IPS-OCSVM). There are four steps for realizing the proposed method while the key step is combining machine learning with the network operator's expert knowledge using OCSVM. Our proposed IPS-OCSVM framework realizes QoE anomaly detection through soft decision and can easily fine-tune the anomaly detection ability on demand. Moreover, we prove that the fluctuation of KQIs thresholds based on expert knowledge has a limited impact on the result of anomaly detection. Finally, experiment results are given to confirm the proposed IPS-OCSVM framework for QoE anomaly detection in cellular networks.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals Classifying relevant video tutorials for the school’s learning management system using support vector machine algorithm

2020 ◽  
Author(s):  
Castro Mayleen Dorcas Bondoc ◽  
Tumibay Gilbert Malawit
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Video Tutorials ◽  
Learning Management ◽  
Face To Face Instruction

Today many schools, universities and institutions recognize the necessity and importance of using Learning Management Systems (LMS) as part of their educational services. This research work has applied LMS in the teaching and learning process of Bulacan State University (BulSU) Graduate School (GS) Program that enhances the face-to-face instruction with online components. The researchers uses an LMS that provides educators a platform that can motivate and engage students to new educational environment through manage online classes. The LMS allows educators to distribute information, manage learning materials, assignments, quizzes, and communications. Aside from the basic functions of the LMS, the researchers uses Machine Learning (ML) Algorithms applying Support Vector Machine (SVM) that will classify and identify the best related videos per topic. SVM is a supervised machine learning algorithm that analyzes data for classification and regression analysis by Maity [1]. The results of this study showed that integration of video tutorials in LMS can significantly contribute knowledge and skills in the learning process of the students.

Support Vector Machine

2009 ◽  
pp. 501-522 ◽  
Author(s):  
A. B.M. Shawkat Ali
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
System Analysis ◽  
Learning Algorithm ◽  
Mathematical Formulation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Support Vector ◽  
Analysis And Design ◽  
Simple Demonstration

From the beginning, machine learning methodology, which is the origin of artificial intelligence, has been rapidly spreading in the different research communities with successful outcomes. This chapter aims to introduce for system analysers and designers a comparatively new statistical supervised machine learning algorithm called support vector machine (SVM). We explain two useful areas of SVM, that is, classification and regression, with basic mathematical formulation and simple demonstration to make easy the understanding of SVM. Prospects and challenges of future research in this emerging area are also described. Future research of SVM will provide improved and quality access to the users. Therefore, developing an automated SVM system with state-of-the-art technologies is of paramount importance, and hence, this chapter will link up an important step in the system analysis and design perspective to this evolving research arena.

scholarly journals Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1557 ◽  
Author(s):  
Ilaria Conforti ◽  
Ilaria Mileti ◽  
Zaccaria Del Prete ◽  
Eduardo Palermo
Keyword(s):  
Machine Learning ◽  
Material Handling ◽  
Learning Algorithm ◽  
Occupational Injuries ◽  
Wearable Sensors ◽  
High Stress ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Kinematic Parameters

Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.

scholarly journals Attack Detection in IoT using Machine Learning

2021 ◽  
Vol 11 (3) ◽  
pp. 7273-7278
Author(s):  
M. Anwer ◽  
M. U. Farooq ◽  
S. M. Khan ◽  
W. Waseemullah
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Learning Algorithm ◽  
Attack Detection ◽  
Supervised Machine Learning ◽  
Detection Methods ◽  
Support Vector ◽  
Web Traffic ◽  
Local Networks ◽  
Iot Devices

Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

scholarly journals Effective and efficient network anomaly detection system using machine learning algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 46-51 ◽  
Author(s):  
Mukrimah Nawir ◽  
Amiza Amir ◽  
Naimah Yaakob ◽  
Ong Bi Lynn
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Processing Time ◽  
Learning Algorithm ◽  
Detection System ◽  
Malicious Code ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Network Anomaly Detection ◽  
Anomaly Detection System

Network anomaly detection system enables to monitor computer network that behaves differently from the network protocol and it is many implemented in various domains. Yet, the problem arises where different application domains have different defining anomalies in their environment. These make a difficulty to choose the best algorithms that suit and fulfill the requirements of certain domains and it is not straightforward. Additionally, the issue of centralization that cause fatal destruction of network system when powerful malicious code injects in the system. Therefore, in this paper we want to conduct experiment using supervised Machine Learning (ML) for network anomaly detection system that low communication cost and network bandwidth minimized by using UNSW-NB15 dataset to compare their performance in term of their accuracy (effective) and processing time (efficient) for a classifier to build a model. Supervised machine learning taking account the important features by labelling it from the datasets. The best machine learning algorithm for network dataset is AODE with a comparable accuracy is 97.26% and time taken approximately 7 seconds. Also, distributed algorithm solves the issue of centralization with the accuracy and processing time still a considerable compared to a centralized algorithm even though a little drop of the accuracy and a bit longer time needed.

scholarly journals An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Aurelle Tchagna Kouanou ◽  
Thomas Mih Attia ◽  
Cyrille Feudjio ◽  
Anges Fleurio Djeumo ◽  
Adèle Ngo Mouelas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
High Rate ◽  
Supervised Machine Learning ◽  
Polymerase Chain Reaction Test ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Test Results

Background and Objective. To mitigate the spread of the virus responsible for COVID-19, known as SARS-CoV-2, there is an urgent need for massive population testing. Due to the constant shortage of PCR (polymerase chain reaction) test reagents, which are the tests for COVID-19 by excellence, several medical centers have opted for immunological tests to look for the presence of antibodies produced against this virus. However, these tests have a high rate of false positives (positive but actually negative test results) and false negatives (negative but actually positive test results) and are therefore not always reliable. In this paper, we proposed a solution based on Data Analysis and Machine Learning to detect COVID-19 infections. Methods. Our analysis and machine learning algorithm is based on most cited two clinical datasets from the literature: one from San Raffaele Hospital Milan Italia and the other from Hospital Israelita Albert Einstein São Paulo Brasilia. The datasets were processed to select the best features that most influence the target, and it turned out that almost all of them are blood parameters. EDA (Exploratory Data Analysis) methods were applied to the datasets, and a comparative study of supervised machine learning models was done, after which the support vector machine (SVM) was selected as the one with the best performance. Results. SVM being the best performant is used as our proposed supervised machine learning algorithm. An accuracy of 99.29%, sensitivity of 92.79%, and specificity of 100% were obtained with the dataset from Kaggle (https://www.kaggle.com/einsteindata4u/covid19) after applying optimization to SVM. The same procedure and work were performed with the dataset taken from San Raffaele Hospital (https://zenodo.org/record/3886927#.YIluB5AzbMV). Once more, the SVM presented the best performance among other machine learning algorithms, and 92.86%, 93.55%, and 90.91% for accuracy, sensitivity, and specificity, respectively, were obtained. Conclusion. The obtained results, when compared with others from the literature based on these same datasets, are superior, leading us to conclude that our proposed solution is reliable for the COVID-19 diagnosis.

scholarly journals Semi-Supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

2020 ◽  
Vol 69 (8) ◽  
pp. 8459-8467 ◽  
Author(s):  
Yutao Lu ◽  
Juan Wang ◽  
Miao Liu ◽  
Kaixuan Zhang ◽  
Guan Gui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Cellular Networks ◽  
Detection Method ◽  
Supervised Machine Learning

Gaussian Distribution-Based Machine Learning Scheme for Anomaly Detection in Healthcare Sensor Cloud

2021 ◽  
Vol 11 (1) ◽  
pp. 52-72
Author(s):  
Rajendra Kumar Dwivedi ◽  
Rakesh Kumar ◽  
Rajkumar Buyya
Keyword(s):  
Machine Learning ◽  
Information Systems ◽  
Anomaly Detection ◽  
Gaussian Distribution ◽  
Supervised Machine Learning ◽  
Sensor Data ◽  
Support Vector ◽  
Learning Scheme ◽  
Healthcare Monitoring ◽  
Sensor Cloud

Smart information systems are based on sensors that generate a huge amount of data. This data can be stored in cloud for further processing and efficient utilization. Anomalous data might be present within the sensor data due to various reasons (e.g., malicious activities by intruders, low quality sensors, and node deployment in harsh environments). Anomaly detection is crucial in some applications such as healthcare monitoring systems, forest fire information systems, and other internet of things (IoT) systems. This paper proposes a Gaussian distribution-based supervised machine learning scheme of anomaly detection (GDA) for healthcare monitoring sensor cloud, which is an integration of various body sensors of different patients and cloud. This work is implemented in Python. Use of Gaussian statistical model in the proposed scheme improves precision, throughput, and efficiency. GDA provides 98% efficiency with 3% and 4% improvements as compared to the other supervised learning-based anomaly detection schemes (e.g., support vector machine [SVM] and self-organizing map [SOM], respectively).

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