scholarly journals Steel Bar Counting from Images with Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 402
Author(s):  
Ana Caren Hernández-Ruiz ◽  
Javier Alejandro Martínez-Nieto ◽  
Julio David Buldain-Pérez
Keyword(s):  
Machine Learning ◽  
Steel Industry ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Network Distance ◽  
Learning Techniques ◽  
Steel Bar ◽  
Steel Bars ◽  
Counting Time ◽  
Human Labour

Counting has become a fundamental task for data processing in areas such as microbiology, medicine, agriculture and astrophysics. The proposed SA-CNN-DC (Scale Adaptive—Convolutional Neural Network—Distance Clustering) methodology in this paper is designed for automated counting of steel bars from images. Its design consists of two Machine Learning techniques: Neural Networks and Clustering. The system has been trained to count round and squared steel bars, obtaining an average detection accuracy of 98.81% and 98.57%, respectively. In the steel industry, counting steel bars is a time consuming task which highly relies on human labour and is prone to errors. Reduction of counting time and resources, safety and productivity of employees and high confidence of the inventory are some of the advantages of the proposed methodology in a steel warehouse.

scholarly journals A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ali Soleymani ◽  
Fatemeh Arabgol
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Text Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Detection Accuracy ◽  
Domain Name ◽  
Botnet Detection ◽  
Learning Techniques

In today’s security landscape, advanced threats are becoming increasingly difficult to detect as the pattern of attacks expands. Classical approaches that rely heavily on static matching, such as blacklisting or regular expression patterns, may be limited in flexibility or uncertainty in detecting malicious data in system data. This is where machine learning techniques can show their value and provide new insights and higher detection rates. The behavior of botnets that use domain-flux techniques to hide command and control channels was investigated in this research. The machine learning algorithm and text mining used to analyze the network DNS protocol and identify botnets were also described. For this purpose, extracted and labeled domain name datasets containing healthy and infected DGA botnet data were used. Data preprocessing techniques based on a text-mining approach were applied to explore domain name strings with n-gram analysis and PCA. Its performance is improved by extracting statistical features by principal component analysis. The performance of the proposed model has been evaluated using different classifiers of machine learning algorithms such as decision tree, support vector machine, random forest, and logistic regression. Experimental results show that the random forest algorithm can be used effectively in botnet detection and has the best botnet detection accuracy.

scholarly journals Parallel processing using big data and machine learning techniques for intrusion detection

2020 ◽  
Vol 9 (3) ◽  
pp. 553
Author(s):  
Alaeddine Boukhalfa ◽  
Nabil Hmina ◽  
Habiba Chaoni
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Processing Time ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
New Approach ◽  
Learning Techniques ◽  
Proposed Model ◽  
Multiple Devices

Currently, information technology is used in all the life domains, multiple devices produce data and transfer them across the network, these transfers are not always secured, they can contain new menaces invisible by the current security devices. Moreover, the large amount and variety of the exchanged data cause difficulties related to the detection time. To solve these issues, we suggest in this paper, a new approach based on storing the large amount and variety of network traffic data employing Big Data techniques, and analyzing these data with Machine Learning algorithms, in a distributed and parallel way, in order to detect new hidden intrusions with less processing time. According to the results of the experiments, the detection accuracy of the Machine Learning methods reaches 99.9 %, and their processing time has been reduced considerably by applying them in a parallel and distributed way, which proves that our proposed model is effective for the detection of new intrusions.

scholarly journals Machine Learning Based Effective Classification of Distributed Denial of Service Attacks

2019 ◽  
Vol 9 (2) ◽  
pp. 1060-1064
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Learning Approach ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Learning Techniques ◽  
Machine Learning Approach

Distributed Denial of Service Attack (DDoS) is a deadliest weapon which overwhelm the server or network by sending flood of packets towards it. The attack disrupts the services running on the target thereby blocking the legitimate traffic accessing its services. Various advanced machine learning techniques have been applied for detection of different types of DDoS attacks but still the attack remains a potential threat to the world. There are mainly two broad categories of machine learning techniques: supervised machine learning approach and unsupervised machine learning approach. Supervised machine learning approach requires labelled attack traffic datasets whereas unsupervised machine learning approach analyses incoming network traffic and then categorizes it. In this paper we have attempted to apply four different classifiers for the detection of DDoS attacks. The four classifiers applied are Logistic Regression, Naïve Bayes, K- Nearest Neighbor and Artificial Neural Network. The chosen classifiers provide stable results when there is a large dataset. We compared their detection accuracy on KDD dataset which is a benchmark dataset in the field of network security. This paper is novel as it explains each pre-processing step with python conversion functions and explained in detail all the classifiers and detection accuracy with their functions in python as well.

scholarly journals Analysis and Prediction of Breast Cancer using Machine Learning Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 26-30
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Breast Cancer Dataset ◽  
Detection Accuracy ◽  
Vast Number ◽  
Learning Techniques ◽  
Number Of Patients

Rapid multiplication of cells in the human body leads to cancer. It is the foremost cause of death due to cancer in females, after lung cancer. As the breast cancer is one of the recurrent kinds of cancer, diagnosis of breast cancer recurring is extremelyessential to increase the survival rate of patient suffering from it. Although cancer is avertible and also treatable in primary/early stages yet a vast number of patients are diagnosed with cancer when it is very late. Almost 8% of females are detected with breast cancer. Its characteristics are mutation of genes, constant pain, changes in the size and redness of skin texture of breasts. With the development of technology and machine learning techniques, cancer diagnosis and detection accuracy has greatly improved. This paper presents an outline of evolved machine learning techniques in this medical field by applying machine learning algorithms on breast cancer dataset like Logistic regression, Random Forest, Decision Trees (DT) etc.

scholarly journals Machine Learning Techniques to Detecting and Preventing Jamming Attacks in Optical Networks

2019 ◽  
Author(s):  
Mounir Bensalem ◽  
Sandeep Kumar Singh ◽  
Admela Jukan
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Detection Accuracy ◽  
Resource Reallocation ◽  
Jamming Attacks ◽  
Learning Techniques ◽  
Artificial Neural

We study the effectiveness of various machine learning techniques, including artificial neural networks, support vector machine, logistic regression, K-nearest neighbors, decision tree and Naive Bayesian, for detecting and mitigating power jamming attacks in optical networks. Our study shows that artificial neural network is the most accurate in detecting out-of-band power jamming attacks in optical networks. To further mitigating the power jamming attacks, we apply a new resource reallocation scheme that utilizes the statistical information of attack detection accuracy, and propose a resource reallocation algorithm to lower the probability of successful jamming of lightpaths. Simulation results show that higher the accuracy of detection, lower is the likelihood of jamming a lightpath.

scholarly journals Improving the performance of Transposable Elements detection tools

2013 ◽  
Vol 10 (3) ◽  
pp. 40-50 ◽  
Author(s):  
Tiago Loureiro ◽  
Rui Camacho ◽  
Jorge Vieira ◽  
Nuno A. Fonseca
Keyword(s):  
Machine Learning ◽  
Transposable Elements ◽  
In Silico ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Learning Techniques ◽  
A Genome ◽  
Different Types ◽  
Species Evolution ◽  
Genome Alteration

Summary Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single tool achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an in silico evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning constructed classifiers.

scholarly journals Graph-based Feature Enrichment for Online Intrusion Detection in Virtual Networks

2019 ◽  
Author(s):  
Igor Jochem Sanz ◽  
Otto Carlos Muniz Bandeira Duarte
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Threat Detection ◽  
Distributed Network ◽  
Service Function ◽  
Learning Techniques

The ubiquitousness of Internet-of-Things devices paves the way for distributed network attacks at an unprecedented scale. Graph theory, strengthened by machine learning techniques, improves an automatic discovery of group behavior patterns of distributed network threats often omitted by traditional security systems. This dissertation proposes an intrusion detection system for online threat detection enriched by a graph-learning analysis. We develop a feature enrichment algorithm that infers metrics based on a graph analysis. By using different machine learning techniques, we evaluated our system for three network traffic datasets. Results show that the proposed enrichment improves the threat detection accuracy up to 15.7% and significantly reduces false-positive rate. Furthermore, we evaluate intrusion detection systems deployed as virtual network functions and propose SFCPerf, a framework for automating performance evaluation of service function chaining. To demonstrate SFCPerf functionality, we evaluate different NFV scenarios, including a real security service function chain prototype, composed of our intrusion detection system and a firewall.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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