scholarly journals A Smart Machine Learning Model for the Detection of Brain Hemorrhage Diagnosis Based Internet of Things in Smart Cities

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hang Chen ◽  
Sulaiman Khan ◽  
Bo Kou ◽  
Shah Nazir ◽  
Wei Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Internet Of Things ◽  
Learning Algorithms ◽  
Feedforward Neural Network ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Brain Hemorrhage ◽  
Smart Machine

Generally, the emergence of Internet of Things enabled applications inspired the world during the last few years, providing state-of-the-art and novel-based solutions for different problems. This evolutionary field is mainly lead by wireless sensor network, radio frequency identification, and smart mobile technologies. Among others, the IoT plays a key role in the form of smart medical devices and wearables, with the ability to collect varied and longitudinal patient-generated health data, and at the same time also offering preliminary diagnosis options. In terms of efforts made for helping the patients using IoT-based solutions, experts exploit capabilities of the machine learning algorithms to provide efficient solutions in hemorrhage diagnosis. To reduce the death rates and propose accurate treatment, this paper presents a smart IoT-based application using machine learning algorithms for the human brain hemorrhage diagnosis. Based on the computerized tomography scan images for intracranial dataset, the support vector machine and feedforward neural network have been applied for the classification purposes. Overall, classification results of 80.67% and 86.7% are calculated for the support vector machine and feedforward neural network, respectively. It is concluded from the resultant analysis that the feedforward neural network outperforms in classifying intracranial images. The output generated from the classification tool gives information about the type of brain hemorrhage that ultimately helps in validating expert’s diagnosis and is treated as a learning tool for trainee radiologists to minimize the errors in the available systems.

Survey of Machine Learning Algorithms to Detect Malware in Consumer Internet of Things Devices

2021 ◽  
Vol 30 (04) ◽  
pp. 2150020
Author(s):  
Luke Holbrook ◽  
Miltiadis Alamaniotis
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Internet Of Things ◽  
Deep Neural Network ◽  
Learning Algorithms ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Iot Devices

With the increase of cyber-attacks on millions of Internet of Things (IoT) devices, the poor network security measures on those devices are the main source of the problem. This article aims to study a number of these machine learning algorithms available for their effectiveness in detecting malware in consumer internet of things devices. In particular, the Support Vector Machines (SVM), Random Forest, and Deep Neural Network (DNN) algorithms are utilized for a benchmark with a set of test data and compared as tools in safeguarding the deployment for IoT security. Test results on a set of 4 IoT devices exhibited that all three tested algorithms presented here detect the network anomalies with high accuracy. However, the deep neural network provides the highest coefficient of determination R2, and hence, it is identified as the most precise among the tested algorithms concerning the security of IoT devices based on the data sets we have undertaken.

scholarly journals A new ML-based approach to enhance student engagement in online environment

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258788
Author(s):  
Sarra Ayouni ◽  
Fahima Hajjej ◽  
Mohamed Maddeh ◽  
Shaha Al-Otaibi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Student Engagement ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Online Environment

The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting.

scholarly journals Predicting rural patients� use of eHealth through supervised machine learning algorithms: A study on Portable Health Clinic in Bangladesh (Preprint)

2018 ◽  
Author(s):  
Nazmul Hossain ◽  
Fumihiko Yokota ◽  
Akira Fukuda ◽  
Ashir Ahmed
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Rural Patients

BACKGROUND Predictive analytics through machine learning has been extensively using across industries including eHealth and mHealth for analyzing patient’s health data, predicting diseases, enhancing the productivity of technology or devices used for providing healthcare services and so on. However, not enough studies were conducted to predict the usage of eHealth by rural patients in developing countries. OBJECTIVE The objective of this study is to predict rural patients’ use of eHealth through supervised machine learning algorithms and propose the best-fitted model after evaluating their performances in terms of predictive accuracy. METHODS Data were collected between June and July 2016 through a field survey with structured questionnaire form 292 randomly selected rural patients in a remote North-Western sub-district of Bangladesh. Four supervised machine learning algorithms namely logistic regression, boosted decision tree, support vector machine, and artificial neural network were chosen for this experiment. A ‘correlation-based feature selection’ technique was applied to include the most relevant but not redundant features into the model. A 10-fold cross-validation technique was applied to reduce bias and over-fitting of the data. RESULTS Logistic regression outperformed other three algorithms with 85.9% predictive accuracy, 86.4% precision, 90.5% recall, 88.1% F-score, and AUC of 91.5% followed by neural network, decision tree and support vector machine with the accuracy rate of 84.2%, 82.9 %, and 80.4% respectively. CONCLUSIONS The findings of this study are expected to be helpful for eHealth practitioners in selecting appropriate areas to serve and dealing with both under-capacity and over-capacity by predicting the patients’ response in advance with a certain level of accuracy and precision.

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

Tremor Identification Using Machine Learning in Parkinson's Disease

2019 ◽  
pp. 128-151
Author(s):  
Angana Saikia ◽  
Vinayak Majhi ◽  
Masaraf Hussain ◽  
Sudip Paul ◽  
Amitava Datta
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Discriminant Analysis ◽  
Learning Algorithms ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

Introduction and Implementation of Machine Learning Algorithms in R

2019 ◽  
pp. 126-147
Author(s):  
S. R. Mani Sekhar ◽  
G. M. Siddesh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Computer Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Linear Discriminant ◽  
The Given

Machine learning is one of the important areas in the field of computer science. It helps to provide an optimized solution for the real-world problems by using past knowledge or previous experience data. There are different types of machine learning algorithms present in computer science. This chapter provides the overview of some selected machine learning algorithms such as linear regression, linear discriminant analysis, support vector machine, naive Bayes classifier, neural networks, and decision trees. Each of these methods is illustrated in detail with an example and R code, which in turn assists the reader to generate their own solutions for the given problems.

A comparison of machine learning algorithms for assessment of delamination in fiber-reinforced polymer composite beams

2020 ◽  
pp. 147592172096715
Author(s):  
Mengyue He ◽  
Yishou Wang ◽  
Karthik Ram Ramakrishnan ◽  
Zhifang Zhang
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Polymer Composites ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Fiber Reinforced Polymer ◽  
Support Vector ◽  
Fiber Reinforced ◽  
Reinforced Polymer ◽  
Fiber Reinforced Polymer Composites

Structural health monitoring techniques based on vibration parameters have been used to assess the internal delamination damage of fiber-reinforced polymer composites. Recently, machine learning algorithms have been adopted to solve the inverse problem of predicting delamination parameters of the delamination from natural frequency shifts. In this article, a delamination detection methodology is proposed based on the changes in multiple modes of frequencies to assess the interface, location, and size of delamination in fiber-reinforced polymer composites. Three types of machine learning algorithms including back propagation neural network, extreme learning machine, and support vector machine algorithm were adopted as inverse algorithms for assessment of the delamination parameters, with a special focus on the interface prediction. A theoretical model of fiber-reinforced polymer beam with delamination under vibration was constructed to learn how the frequencies are affected by the delaminations (“forward problem”) and to generate a database of “frequency shifts versus delamination parameters” to be used in machine learning algorithms for delamination prediction (“inverse problem”). Multiple carbon/epoxy fiber-reinforced polymer beam specimens were manufactured and measured by a laser scanning Doppler vibrometer to extract the modal frequencies. Numerical and experimental verification results have shown that support vector machine has the best prediction performance among the three machine learning algorithms, with high prediction accuracy and only requiring a small number of samples. For predicting the interface of delamination which is a discrete variable, support vector machine classification has observed better prediction accuracy and requiring less running time than regression. This study is one of the first to prove the applicability of support vector machine for structural health monitoring of delamination damage in fiber-reinforced polymer composites and has the potential to improve the prediction capability of machine learning algorithms. Another significant outcome of the study is that the interface of delamination has been predicted accurately with support vector machine.

scholarly journals Encrypted DNP3 Traffic Classification Using Supervised Machine Learning Algorithms

2019 ◽  
Vol 1 (1) ◽  
pp. 384-399 ◽  
Author(s):  
Thais de Toledo ◽  
Nunzio Torrisi
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Smart Grids ◽  
Learning Algorithms ◽  
Electric Utility ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Communication Link

The Distributed Network Protocol (DNP3) is predominately used by the electric utility industry and, consequently, in smart grids. The Peekaboo attack was created to compromise DNP3 traffic, in which a man-in-the-middle on a communication link can capture and drop selected encrypted DNP3 messages by using support vector machine learning algorithms. The communication networks of smart grids are a important part of their infrastructure, so it is of critical importance to keep this communication secure and reliable. The main contribution of this paper is to compare the use of machine learning techniques to classify messages of the same protocol exchanged in encrypted tunnels. The study considers four simulated cases of encrypted DNP3 traffic scenarios and four different supervised machine learning algorithms: Decision tree, nearest-neighbor, support vector machine, and naive Bayes. The results obtained show that it is possible to extend a Peekaboo attack over multiple substations, using a decision tree learning algorithm, and to gather significant information from a system that communicates using encrypted DNP3 traffic.

scholarly journals Classification of Children’s Sitting Postures Using Machine Learning Algorithms

2018 ◽  
Vol 8 (8) ◽  
pp. 1280 ◽  
Author(s):  
Yong Kim ◽  
Youngdoo Son ◽  
Wonjoon Kim ◽  
Byungki Jin ◽  
Myung Yun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Monitoring System ◽  
Multinomial Logistic Regression ◽  
Learning Algorithms ◽  
Feedback System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Future Research ◽  
Support Vector

Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children’s sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children’s chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

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