scholarly journals Design of a Fracture Detection System based on Deep Program in a Convolutional Neural Network

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 509-518
Author(s):  
Payman Hussein Hussan ◽  
Syefy Mohammed Mangj Al-Razoky ◽  
Hasanain Mohammed Manji Al-Rzoky
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Bone Fractures ◽  
Detection System ◽  
Training Data ◽  
Learning Models ◽  
Fracture Detection ◽  
Data Set ◽  
Final Fracture

This paper presents an efficient method for finding fractures in bones. For this purpose, the pre-processing set includes increasing the quality of images, removing additional objects, removing noise and rotating images. The input images then enter the machine learning phase to detect the final fracture. At this stage, a Convolutional Neural Networks is created by Genetic Programming (GP). In this way, learning models are implemented in the form of GP programs. And evolve during the evolution of this program. Then finally the best program for classifying incoming images is selected. The data set in this work is divided into training and test friends who have nothing in common. The ratio of training data to test is equal to 80 to 20. Finally, experimental results show good results for the proposed method for bone fractures.

scholarly journals Lightweight Convolutional Neural Network Based Intrusion Detection System

2020 ◽  
pp. 808-817
Author(s):  
Vinh Pham ◽  
◽  
Eunil Seo ◽  
Tai-Myoung Chung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Network Traffic ◽  
Detection System ◽  
Image Data ◽  
Training Data ◽  
Deep Packet Inspection ◽  
Detection Model

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

Building-damage detection method based on machine learning utilizing aerial photographs of the Kumamoto earthquake

2020 ◽  
Vol 36 (3) ◽  
pp. 1166-1187 ◽  
Author(s):  
Shohei Naito ◽  
Hiromitsu Tomozawa ◽  
Yuji Mori ◽  
Takeshi Nagata ◽  
Naokazu Monma ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Training Data ◽  
Aerial Photographs ◽  
Learning Models ◽  
Visual Interpretation ◽  
Damage Classification ◽  
Kumamoto Earthquake ◽  
Machine Learning Models

This article presents a method for detecting damaged buildings in the event of an earthquake using machine learning models and aerial photographs. We initially created training data for machine learning models using aerial photographs captured around the town of Mashiki immediately after the main shock of the 2016 Kumamoto earthquake. All buildings are classified into one of the four damage levels by visual interpretation. Subsequently, two damage discrimination models are developed: a bag-of-visual-words model and a model based on a convolutional neural network. Results are compared and validated in terms of accuracy, revealing that the latter model is preferable. Moreover, for the convolutional neural network model, the target areas are expanded and the recalls of damage classification at the four levels range approximately from 66% to 81%.

scholarly journals Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

2018 ◽  
Vol 8 (12) ◽  
pp. 2663 ◽  
Author(s):  
Davy Preuveneers ◽  
Vera Rimmer ◽  
Ilias Tsingenopoulos ◽  
Jan Spooren ◽  
Wouter Joosen ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Training Data ◽  
Learning Models ◽  
Traditional System ◽  
Blockchain Technology ◽  
Malicious Behavior ◽  
Machine Learning Models

The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases.

scholarly journals Fall detection system based on BiLSTM neural network

2019 ◽  
Vol 7 (5) ◽  
pp. 01-12
Author(s):  
Biao YE ◽  
Lasheng Yu
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Detection System ◽  
Input Sequence ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Training Data ◽  
Sensor Data ◽  
Data Set ◽  
Sequence Modeling

The purpose of this article is to analyze the characteristics of human fall behavior to design a fall detection system. The existing fall detection algorithms have problems such as poor adaptability, single function and difficulty in processing large data and strong randomness. Therefore, a long-term and short-term memory recurrent neural network is used to improve the effect of falling behavior detection by exploring the internal correlation between sensor data. Firstly, the serialization representation method of sensor data, training data and detection input data is designed. The BiLSTM network has the characteristics of strong ability to sequence modeling and it is used to reduce the dimension of the data required by the fall detection model. then, the BiLSTM training algorithm for fall detection and the BiLSTM-based fall detection algorithm convert the fall detection into the classification problem of the input sequence; finally, the BiLSTM-based fall detection system was implemented on the TensorFlow platform. The detection and analysis of system were carried out using a bionic experiment data set which mimics a fall. The experimental results verify that the system can effectively improve the accuracy of fall detection to 90.47%. At the same time, it can effectively detect the behavior of Near-falling, and help to take corresponding protective measures.

scholarly journals Deep Neural Network for Multi-Class Prediction of Student Performance in Educational Data

2019 ◽  
Vol 8 (2) ◽  
pp. 5073-5081
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Student Performance ◽  
Activation Function ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Fine Tuning ◽  
Academic Excellence ◽  
Data Set

Prediction of student performance is the significant part in processing the educational data. Machine learning algorithms are leading the role in this process. Deep learning is one of the important concepts of machine learning algorithm. In this paper, we applied the deep learning technique for prediction of the academic excellence of the students using R Programming. Keras and Tensorflow libraries utilized for making the model using neural network on the Kaggle dataset. The data is separated into testing data training data set. Plot the neural network model using neuralnet method and created the Deep Learning model using two hidden layers using ReLu activation function and one output layer using softmax activation function. After fine tuning process until the stable changes; this model produced accuracy as 85%.

scholarly journals Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Thomas Martynec ◽  
Christos Karapanagiotis ◽  
Sabine H. L. Klapp ◽  
Stefan Kowarik
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Monte Carlo ◽  
Kinetic Monte Carlo ◽  
Diffusion Barriers ◽  
Binding Energies ◽  
Atomic Scale ◽  
Training Data ◽  
Data Set ◽  
Wide Range

AbstractMachine learning is playing an increasing role in the discovery of new materials and may also facilitate the search for optimum growth conditions for crystals and thin films. Here, we perform kinetic Monte-Carlo simulations of sub-monolayer growth. We consider a generic homoepitaxial growth scenario that covers a wide range of conditions with different diffusion barriers (0.4–0.55 eV) and lateral binding energies (0.1–0.4 eV). These simulations are used as a training data set for a convolutional neural network that can predict diffusion barriers and binding energies. Specifically, a single Monte-Carlo image of the morphology is sufficient to determine the energy barriers with an accuracy of approximately 10 meV and the neural network is tolerant to images with noise and lower than atomic-scale resolution. We believe this new machine learning method will be useful for fundamental studies of growth kinetics and growth optimization through better knowledge of microscopic parameters.

scholarly journals Survey for the Prediction of Chronic Kidney Disease using Machine Learning

2019 ◽  
pp. 154-158
Author(s):  
Pooja Sharma ◽  
Saket J Swarndeep
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Diseases ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Network System ◽  
Data Set ◽  
Neural Network System

According the 2010 global burden of disease study, Chronic Kidney Diseases (CKD) was ranked 18th in the list of causes of total no. of deaths worldwide. 10% of the population worldwide is affected by CKD. The prediction of CKD can become a boon for the population to predict the health. Various method and techniques are undergoing the research phase for developing the most accurate CKD prediction system. Using Machine Learning techniques is the most promising one in this area due to its computing function and Machine Learning rules. Existing Systems are working well in predicting the accurate result but still more attributes of data and complicity of health parameter make the root layer for the innovation of new approaches. This study focuses on a novel approach for improving the prediction of CKD. In recent time Neural network system has discovered its use in disease diagnoses, which is depended upon prediction from symptoms data set. Chronic kidney disease detection system using neural network is shown here. This system of neural network accepts disease-symptoms as input and it is trained according to various training algorithms. After neural network is trained using back propagation algorithms, this trained neural network system is used for detection of kidney disease in the human body.

scholarly journals Thinking Globally, Acting Locally: On the Issue of Training Set Imbalance and the Case for Local Machine Learning Models in Chemistry

2019 ◽  
Author(s):  
Mojtaba Haghighatlari ◽  
Ching-Yen Shih ◽  
Johannes Hachmann
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Imbalanced Data ◽  
Clean Energy ◽  
Training Data ◽  
Learning Models ◽  
Data Set ◽  
Global Models ◽  
Machine Learning Models ◽  
Training Sets

<div><div><div><p>The appropriate sampling of training data out of a potentially imbalanced data set is of critical importance for the development of robust and accurate machine learning models. A challenge that underpins this task is the partitioning of the data into groups of similar instances, and the analysis of the group populations. In molecular data sets, different groups of molecules may be hard to identify. However, if the distribution of a given data set is ignored then some of these groups may remain under-represented and the sampling biased, even if the size of data is large. In this study, we use the example of the Harvard Clean Energy Project (CEP) data set to assess the challenges posed by imbalanced data and the impact that accounting for different groups during the selection of training data has on the quality of the resulting machine learning models. We employ a partitioning criterion based on the underlying rules for the CEP molecular library generation to identify groups of structurally similar compounds. First, we evaluate the performance of regression models that are trained globally (i.e., by randomly sampling the entire data set for training data). This traditional approach serves as the benchmark reference. We compare its results with those of models that are trained locally, i.e., within each of the identified molecular domains. We demonstrate that local models outperform the best reported global models by considerable margins and are more efficient in their training data needs. We propose a strategy to redesign training sets for the development of improved global models. While the resulting uniform training sets can successfully yield robust global models, we identify the distribution mismatch between feature representations of different molecular domains as a critical limitation for any further improvement. We take advantage of the discovered distribution shift and propose an ensemble of classification and regression models to achieve a generalized and reliable model that outperforms the state-of-the-art model, trained on the CEP data set. Moreover, this study provides a benchmark for the development of future methodologies concerned with imbalanced chemical data.</p></div></div></div>

scholarly journals Sequential learning to accelerate discovery of alkali-activated binders

2021 ◽  
Author(s):  
Christoph Völker ◽  
Rafia Firdous ◽  
Dietmar Stephan ◽  
Sabine Kruschwitz
Keyword(s):  
Machine Learning ◽  
Training Data ◽  
Sequential Learning ◽  
Learning Models ◽  
Data Set ◽  
Optimal Mix ◽  
Order Of Magnitude ◽  
Alkali Activated Binders ◽  
Alkali Activated ◽  
Machine Learning Models

AbstractAlkali-activated binders (AAB) can provide a clean alternative to conventional cement in terms of CO2 emissions. However, as yet there are no sufficiently accurate material models to effectively predict the AAB properties, thus making optimal mix design highly costly and reducing the attractiveness of such binders. This work adopts sequential learning (SL) in high-dimensional material spaces (consisting of composition and processing data) to find AABs that exhibit desired properties. The SL approach combines machine learning models and feedback from real experiments. For this purpose, 131 data points were collected from different publications. The data sources are described in detail, and the differences between the binders are discussed. The sought-after target property is the compressive strength of the binders after 28 days. The success is benchmarked in terms of the number of experiments required to find materials with the desired strength. The influence of some constraints was systematically analyzed, e.g., the possibility to parallelize the experiments, the influence of the chosen algorithm and the size of the training data set. The results show the advantage of SL, i.e., the amount of data required can potentially be reduced by at least one order of magnitude compared to traditional machine learning models, while at the same time exploiting highly complex information. This brings applications in laboratory practice within reach.

scholarly journals Machine Learning Model for IRIS Flower Classification using Tensor Flow and PyTorch

2021 ◽  
pp. 15-23
Author(s):  
Dr. Kalaivazhi Vijayaragavan ◽  
S. Prakathi ◽  
S. Rajalakshmi ◽  
M Sandhiya
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classification Accuracy ◽  
Learning Model ◽  
Training Data ◽  
Data Set ◽  
Machine Learning Model ◽  
Learning Frameworks ◽  
Learning Concept ◽  
Iris Data

Machine learning is a subfield of artificial intelligence, which is learning algorithms to make decision-based on data and try to behave like a human being. Classification is one of the most fundamental concepts in machine learning. It is a process of recognizing, understanding, and grouping ideas and objects into pre-set categories or sub-populations. Using precategorized training datasets, machine learning concept use variety of algorithms to classify the future datasets into categories. Classification algorithms use input training data in machine learning to predict the subsequent data that fall into one of the predetermined categories. To improve the classification accuracy design of neural network is regarded as effective model to obtain better accuracy. However, design of neural network is usually consider scaling layer, perceptron layers and probabilistic layer. In this paper, an enhanced model selection can be evaluated with training and testing strategy. Further, the classification accuracy can be predicted. Finally by using two popular machine learning frameworks: PyTorch and Tensor Flow the prediction of classification accuracy is compared. Results demonstrate that the proposed method can predict with more accuracy. After the deployment of our machine learning model the performance of the model has been evaluated with the help of iris data set.

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