scholarly journals Block chain based Malware Detection using Machine Learning Algorithms for IoT enabled E-Health Applications

2021 ◽  
pp. 84-92
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Wireless Communication ◽  
Optimization Technique ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Swarm Optimization ◽  
Block Chain ◽  
Iot Devices ◽  
Health Applications

IoT devices are playing a greater role in business specially in wireless communication. IoT devices are achieving higher maturity as seen in smartdust. The aim of this research is to study the functionality of MOTES in smartdust to integrate with IoT architecture and infrastructure for optimization of wireless communication specially linked with 2.4Ghz and 5Ghz band. MOTES are being modeled in MALTAB using Artificial Neural Network integrated with optimization for speed, power and frequency linked with IoT architecture. The result proves that smartdust architecture if utilized in IoT architecture, the over all performances result of IoT devices is increased specially in bandwidth and power consumption. All the modeling result were compared for general sensor data bandwidth in ESP8266 for 2.4 Ghz, and mathematical model are presented for 5Ghz using smartdust MOTES. It is been proposed that using AI optimization technique like Ant Colonization Optimization or Particle Swarm Optimization we can mathematically model smartdust Architecture.

scholarly journals Block chain based Malware Detection using Machine Learning Algorithms for IoT enabled E-Health Applications

2021 ◽  
pp. 73-82
Keyword(s):  
Machine Learning ◽  
New Technologies ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Digital Age ◽  
Machine Learning Algorithms ◽  
Private Cloud ◽  
Block Chain ◽  
Iot Devices ◽  
Health Applications

Due to increasing digitalization and the development of new technologies such as the IoT, the application of machine learning (ML) algorithms is rapidly expanding (IoT). ML algorithms are being used in healthcare, IoT, engineering, finance, and other fields in today's digital age. However, in order to predict/solve a specific issue, all of these algorithms must be taught. There's a good chance that the training datasets have been tampered with, resulting in skewed findings. As a result, we have suggested a blockchain-based approach to protect datasets produced by IoT devices for E-Health applications in this paper. To address the aforementioned problem, the suggested blockchain-based system makes use of a private cloud. For assessment, we created a mechanism that dataset owners may use to protect their data.

scholarly journals A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1623 ◽  
Author(s):  
Huibing Zhang ◽  
Tong Li ◽  
Lihua Yin ◽  
Dingke Liu ◽  
Ya Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Decision Tree ◽  
Particle Swarm ◽  
Sensor Data ◽  
Gradient Boosting ◽  
Integrated Navigation ◽  
Swarm Optimization ◽  
Positioning Accuracy

The fusion of multi-source sensor data is an effective method for improving the accuracy of vehicle navigation. The generalization abilities of neural-network-based inertial devices and GPS integrated navigation systems weaken as the nonlinearity in the system increases, resulting in decreased positioning accuracy. Therefore, a KF-GDBT-PSO (Kalman Filter-Gradient Boosting Decision Tree-Particle Swarm Optimization, KGP) data fusion method was proposed in this work. This method establishes an Inertial Navigation System (INS) error compensation model by integrating Kalman Filter (KF) and Gradient Boosting Decision Tree (GBDT). To improve the prediction accuracy of the GBDT, we optimized the learning algorithm and the fitness parameter using Particle Swarm Optimization (PSO). When the GPS signal was stable, the KGP method was used to solve the nonlinearity issue between the vehicle feature and positioning data. When the GPS signal was unstable, the training model was used to correct the positioning error for the INS, thereby improving the positioning accuracy and continuity. The experimental results show that our method increased the positioning accuracy by 28.20–59.89% compared with the multi-layer perceptual neural network and random forest regression.

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.

scholarly journals An improved LogNNet classifier for IoT applications

2021 ◽  
Vol 2094 (3) ◽  
pp. 032015
Author(s):  
H Heidari ◽  
A A Velichko
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Chaotic Map ◽  
Network Architectures ◽  
Direct Relation ◽  
Swarm Optimization ◽  
Computing Power ◽  
Iot Applications ◽  
Special Matrix ◽  
Iot Devices

Abstract In the age of neural networks and Internet of Things (IoT), the search for new neural network architectures capable of operating on devices with limited computing power and small memory size is becoming an urgent agenda. Designing suitable algorithms for IoT applications is an important task. The paper proposes a feed forward LogNNet neural network, which uses a semi-linear Henon type discrete chaotic map to classify MNIST-10 dataset. The model is composed of reservoir part and trainable classifier. The aim of the reservoir part is transforming the inputs to maximize the classification accuracy using a special matrix filing method and a time series generated by the chaotic map. The parameters of the chaotic map are optimized using particle swarm optimization with random immigrants. As a result, the proposed LogNNet/Henon classifier has higher accuracy and the same RAM usage, compared to the original version of LogNNet, and offers promising opportunities for implementation in IoT devices. In addition, a direct relation between the value of entropy and accuracy of the classification is demonstrated.

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.

Prediction Length of Stay with Neural Network Trained by Particle Swarm Optimization

2017 ◽  
Vol 2 (2) ◽  
pp. 21-38
Author(s):  
Azadeh Oliyaei ◽  
Zahra Aghababaee
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Length Of Stay ◽  
Missing Values ◽  
Negative Impact ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Real World Data ◽  
Swarm Optimization ◽  
Percent Accuracy

This article describes how the prediction of the length of stay demonstrates the severity of the disease as well as the practice patterns of hospitals. Also, it helps the hospital resources management provide better services for inpatients and increase inpatients' satisfaction. In this article, an efficient model based on neural network algorithms is trained by a stochastic optimization technique called particle swarm optimization is proposed to predict the length of stay for coronary artery diseases. Real world data is used to generate the model. According to the number of missing values, some policies are considered. Since the outlier data has negative impact on the prediction model, it would be eliminated. The parameters of the proposed model are adjusted by Taguchi method. The applied algorithm evaluation result on the test data indicates that the model has the capability to predict the length of stay with 90 percent accuracy.

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