scholarly journals Development of an IoT Architecture Based on a Deep Neural Network against Cyber Attacks for Automated Guided Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8467
Author(s):  
Mahmoud Elsisi ◽  
Minh-Quang Tran
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Online Monitoring ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Automated Guided Vehicles ◽  
Gradient Boosting ◽  
Machine Model ◽  
Extreme Gradient Boosting

This paper introduces an integrated IoT architecture to handle the problem of cyber attacks based on a developed deep neural network (DNN) with a rectified linear unit in order to provide reliable and secure online monitoring for automated guided vehicles (AGVs). The developed IoT architecture based on a DNN introduces a new approach for the online monitoring of AGVs against cyber attacks with a cheap and easy implementation instead of the traditional cyber attack detection schemes in the literature. The proposed DNN is trained based on experimental AGV data that represent the real state of the AGV and different types of cyber attacks including a random attack, ramp attack, pulse attack, and sinusoidal attack that is injected by the attacker into the internet network. The proposed DNN is compared with different deep learning and machine learning algorithms such as a one dimension convolutional neural network (1D-CNN), a supported vector machine model (SVM), random forest, extreme gradient boosting (XGBoost), and a decision tree for greater validation. Furthermore, the proposed IoT architecture based on a DNN can provide an effective detection for the AGV status with an excellent accuracy of 96.77% that is significantly greater than the accuracy based on the traditional schemes. The AGV status based on the proposed IoT architecture with a DNN is visualized by an advanced IoT platform named CONTACT Elements for IoT. Different test scenarios with a practical setup of an AGV with IoT are carried out to emphasize the performance of the suggested IoT architecture based on a DNN. The results approve the usefulness of the proposed IoT to provide effective cybersecurity for data visualization and tracking of the AGV status that enhances decision-making and improves industrial productivity.

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 Research on Accurate Prediction of the Container Ship Resistance by RBFNN and Other Machine Learning Algorithms

2021 ◽  
Vol 9 (4) ◽  
pp. 376 ◽  
Author(s):  
Yunfei Yang ◽  
Haiwen Tu ◽  
Lei Song ◽  
Lin Chen ◽  
De Xie ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Prediction Method ◽  
Resistance Coefficient ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Container Ship ◽  
Extreme Gradient Boosting ◽  
Better Than

Resistance is one of the important performance indicators of ships. In this paper, a prediction method based on the Radial Basis Function neural network (RBFNN) is proposed to predict the resistance of a 13500 transmission extension unit (13500TEU) container ship at different drafts. The predicted draft state in the known range is called interpolation prediction; otherwise, it is extrapolation prediction. First, ship features are extracted to make the resistance Rt prediction. The resistance prediction results show that the performance of the RBFNN is significantly better than the other four machine learning models, backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Then, the ship data is processed in a dimensionless manner, and the models mentioned above are used to predict the total resistance coefficient Ct of the container ship. The prediction results show that the RBFNN prediction model still performs well. Good results can be obtained by RBFNN in interpolation prediction, even when using part of dimensionless features. Finally, the accuracy of the prediction method based on RBFNN is greatly improved compared with the modified admiralty coefficient.

scholarly journals Multi-scale Two-way Deep Neural Network for Stock Trend Prediction

2020 ◽  
Author(s):  
Guang Liu ◽  
Yuzhao Mao ◽  
Qi Sun ◽  
Hailong Huang ◽  
Weiguo Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Gradient Boosting ◽  
Trend Prediction ◽  
Future Studies ◽  
Multi Scale ◽  
Single Scale ◽  
Extreme Gradient Boosting ◽  
Scale Behavior

Stock Trend Prediction(STP) has drawn wide attention from various fields, especially Artificial Intelligence. Most previous studies are single-scale oriented which results in information loss from a multi-scale perspective. In fact, multi-scale behavior is vital for making intelligent investment decisions. A mature investor will thoroughly investigate the state of a stock market at various time scales. To automatically learn the multi-scale information in stock data, we propose a Multi-scale Two-way Deep Neural Network. It learns multi-scale patterns from two types of scale-information, wavelet-based and downsampling-based, by eXtreme Gradient Boosting and Recurrent Convolutional Neural Network, respectively. After combining the learned patterns from the two-way, our model achieves state-of-the-art performance on FI-2010 and CSI-2016, where the latter is our published long-range stock dataset to help future studies for STP task. Extensive experimental results on the two datasets indicate that multi-scale information can significantly improve the STP performance and our model is superior in capturing such information.

scholarly journals Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest, Extreme Gradient Boosting, and Deep Neural Network

2019 ◽  
Vol 11 (15) ◽  
pp. 1741 ◽  
Author(s):  
Yeonjin Lee ◽  
Daehyeon Han ◽  
Myoung-Hwan Ahn ◽  
Jungho Im ◽  
Su Jeong Lee
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
High Resolution ◽  
Random Forest ◽  
Correlation Coefficient ◽  
Deep Neural Network ◽  
Precipitable Water ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Resolution Data

Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides information on the spatial distribution of moisture. The high-resolution TPW, together with atmospheric stability indices such as convective available potential energy (CAPE), is an effective indicator of severe weather phenomena in the pre-convective atmospheric condition. With the advent of high performing imaging instrument onboard geostationary satellites such as Advanced Himawari Imager (AHI) onboard Himawari-8 of Japan and Advanced Meteorological Imager (AMI) onboard GeoKompsat-2A of Korea, it is expected that unprecedented spatiotemporal resolution data (e.g., AMI plans to provide 2 km resolution data at every 2 min over the northeast part of East Asia) will be provided. To derive TPW from such high-resolution data in a timely fashion, an efficient algorithm is highly required. Here, machine learning approaches—random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN)—are assessed for the TPW retrieved from AHI over the clear sky in Northeast Asia area. For the training dataset, the nine infrared brightness temperatures (BT) of AHI (BT8 to 16 centered at 6.2, 6.9, 7.3, 8.6, 9.6, 10.4, 11.2, 12.4, and 13.3 μ m , respectively), six dual channel differences and observation conditions such as time, latitude, longitude, and satellite zenith angle for two years (September 2016 to August 2018) are used. The corresponding TPW is prepared by integrating the water vapor profiles from InterimEuropean Centre for Medium-Range Weather Forecasts Re-Analysis data (ERA-Interim). The algorithm performances are assessed using the ERA-Interim and radiosonde observations (RAOB) as the reference data. The results show that the DNN model performs better than RF and XGB with a correlation coefficient of 0.96, a mean bias of 0.90 mm, and a root mean square error (RMSE) of 4.65 mm when compared to the ERA-Interim. Similarly, DNN results in a correlation coefficient of 0.95, a mean bias of 1.25 mm, and an RMSE of 5.03 mm when compared to RAOB. Contributing variables to retrieve the TPW in each model and the spatial and temporal analysis of the retrieved TPW are carefully examined and discussed.

Using Artificial Neural Network and Machine Learning Algorithms to Scrutinize Liver Diseases 

2021 ◽  
Author(s):  
Tanu Sharma ◽  
Gaurav Kumawat ◽  
Prasun Chakrabarti ◽  
Sandeep Poddar ◽  
Tulika Chakrabarti ◽  
...  
Keyword(s):  
Neural Network ◽  
Liver Disorder ◽  
Machine Learning Algorithms ◽  
Random Trees ◽  
Gradient Boosting ◽  
Support Vector ◽  
Second Stage ◽  
The Neural Network ◽  
Extreme Gradient Boosting ◽  
Third Stage

Abstract In this recommendation, liver incurable record is investigated for structure gathering replica to foresee liver disorder. This proposal actualized a component replica development and near investigation for refining the forecast exactness of Indian liver incurable in three stages. In the primary stage, the min-max standardization calculation is put into the unique liver incurable record accumulated from the UCI document. In liver incurable conjecture the second stage, by the use of PSO characteristic decision, a portion of the liver incurable record from the sum normalized liver incurable record is gained which includes simply enormous impute. In the third stage, portrayal figuring’s are applied to the educational list. In this paper, an introduction evaluation between various estimation: Random Trees, Neural Network, eXtreme Gradient Boosting, Support Vector Machine (SVM), C5.0. The rule objective is to survey the rightness in social affair information concerning the benefit and plausibility of every calculation with respect to the accuracy, precision, affectability, and unequivocally. Exploratory outcomes show that the Neural Network gives the most fundamental accuracy (93.48 %.) with the least blunder rate. All assessments are executed inside a redirection environment and drove in SPSS information mining device.

scholarly journals Deep Learning for Stock Market Prediction

2020 ◽  
Author(s):  
Mojtaba Nabipour ◽  
Pooyan Nayyeri ◽  
Hamed Jabani ◽  
Shahab Shamshirband ◽  
Amir Mosavi
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Models ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Model Fitting ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting

Prediction of stock groups values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree_based models, there is often an intense competition between Adaboost, Gradient Boosting and XGBoost.

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