scholarly journals Intelligent Detection Methods of Electrical Connection Faults in RF Circuits

2021 ◽  
Vol 11 (21) ◽  
pp. 9973
Author(s):  
Ziren Wang ◽  
Jiaqi Li ◽  
George T. Flowers ◽  
Jinchun Gao ◽  
Kaixuan Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Integrated Circuit ◽  
Printed Circuit Boards ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Electrical Connection ◽  
Transmission Parameters ◽  
Bonding Wires ◽  
Solder Balls ◽  
Intelligent Detection

Printed circuit boards (PCBs) have a large number of electrical connection nodes. Exposure to harsh environments may lead to connection faults in these nodes. In the present work, intelligent detection methods for electrical connection faults were studied. Specifically, the fault characteristics of connectors, bonding wires and solder balls in the frequency domain were analyzed. The reflection and transmission parameters of an example filter circuit with electrical connection faults were calculated using the Simulation Program with Integrated Circuit Emphasis (SPICE). With these obtained electrical parameters, three machine learning algorithms were used to detect example electrical connection faults for the example circuit. Based upon the performance evaluations of the three algorithms, one can conclude that machine-learning-based intelligent fault detection is a promising technique in diagnosing circuit faults due to electrical connection issues with high accuracy and lower time cost as compared to current manual processes.

scholarly journals Analysis of Residual Current Flows in Inverter Based Energy Systems Using Machine Learning Approaches

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 582
Author(s):  
Holger Behrends ◽  
Dietmar Millinger ◽  
Werner Weihs-Sedivy ◽  
Anže Javornik ◽  
Gerold Roolfs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Residual Current ◽  
Operating Conditions ◽  
Machine Learning Algorithms ◽  
Photovoltaic System ◽  
Detection Methods ◽  
Learning Approaches ◽  
Residual Currents ◽  
Current Flows

Faults and unintended conditions in grid-connected photovoltaic systems often cause a change of the residual current. This article describes a novel machine learning based approach to detecting anomalies in the residual current of a photovoltaic system. It can be used to detect faults or critical states at an early stage and extends conventional threshold-based detection methods. For this study, a power-hardware-in-the-loop approach was carried out, in which typical faults have been injected under ideal and realistic operating conditions. The investigation shows that faults in a photovoltaic converter system cause a unique behaviour of the residual current and fault patterns can be detected and identified by using pattern recognition and variational autoencoder machine learning algorithms. In this context, it was found that the residual current is not only affected by malfunctions of the system, but also by volatile external influences. One of the main challenges here is to separate the regular residual currents caused by the interferences from those caused by faults. Compared to conventional methods, which respond to absolute changes in residual current, the two machine learning models detect faults that do not affect the absolute value of the residual current.

scholarly journals A Review on Advances in Automated Plant Disease Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Range Images

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

Intelligent Malware Detection Using Deep Dilated Residual Networks for Cyber Security

2021 ◽  
pp. 1085-1099
Author(s):  
S. Abijah Roseline ◽  
S. Geetha
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Security Threat ◽  
Signature Detection ◽  
Learning Techniques ◽  
Feature Based

Malware is the most serious security threat, which possibly targets billions of devices like personal computers, smartphones, etc. across the world. Malware classification and detection is a challenging task due to the targeted, zero-day, and stealthy nature of advanced and new malwares. The traditional signature detection methods like antivirus software were effective for detecting known malwares. At present, there are various solutions for detection of such unknown malwares employing feature-based machine learning algorithms. Machine learning techniques detect known malwares effectively but are not optimal and show a low accuracy rate for unknown malwares. This chapter explores a novel deep learning model called deep dilated residual network model for malware image classification. The proposed model showed a higher accuracy of 98.50% and 99.14% on Kaggle Malimg and BIG 2015 datasets, respectively. The new malwares can be handled in real-time with minimal human interaction using the proposed deep residual model.

scholarly journals Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Biosensors ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 193
Author(s):  
Alanna V. Zubler ◽  
Jeong-Yeol Yoon
Keyword(s):  
Machine Learning ◽  
Optical Sensors ◽  
Near Infrared ◽  
Plant Stress ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Stress Detection ◽  
Recent Advances ◽  
Wide Range

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

scholarly journals MALGRA: Machine Learning and N-Gram Malware Feature Extraction and Detection System

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1777
Author(s):  
Muhammad Ali ◽  
Stavros Shiaeles ◽  
Gueltoum Bendiab ◽  
Bogdan Ghita
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Detection Methods ◽  
Normal Operation ◽  
Analysis Technique ◽  
N Gram

Detection and mitigation of modern malware are critical for the normal operation of an organisation. Traditional defence mechanisms are becoming increasingly ineffective due to the techniques used by attackers such as code obfuscation, metamorphism, and polymorphism, which strengthen the resilience of malware. In this context, the development of adaptive, more effective malware detection methods has been identified as an urgent requirement for protecting the IT infrastructure against such threats, and for ensuring security. In this paper, we investigate an alternative method for malware detection that is based on N-grams and machine learning. We use a dynamic analysis technique to extract an Indicator of Compromise (IOC) for malicious files, which are represented using N-grams. The paper also proposes TF-IDF as a novel alternative used to identify the most significant N-grams features for training a machine learning algorithm. Finally, the paper evaluates the proposed technique using various supervised machine-learning algorithms. The results show that Logistic Regression, with a score of 98.4%, provides the best classification accuracy when compared to the other classifiers used.

scholarly journals Mitigating Webshell Attacks through Machine Learning Techniques

2020 ◽  
Vol 12 (1) ◽  
pp. 12 ◽  
Author(s):  
You Guo ◽  
Hector Marco-Gisbert ◽  
Paul Keir
Keyword(s):  
Machine Learning ◽  
Feature Matching ◽  
Naive Bayes ◽  
Web Server ◽  
Naïve Bayes ◽  
Malicious Code ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Detection Model

A webshell is a command execution environment in the form of web pages. It is often used by attackers as a backdoor tool for web server operations. Accurately detecting webshells is of great significance to web server protection. Most security products detect webshells based on feature-matching methods—matching input scripts against pre-built malicious code collections. The feature-matching method has a low detection rate for obfuscated webshells. However, with the help of machine learning algorithms, webshells can be detected more efficiently and accurately. In this paper, we propose a new PHP webshell detection model, the NB-Opcode (naïve Bayes and opcode sequence) model, which is a combination of naïve Bayes classifiers and opcode sequences. Through experiments and analysis on a large number of samples, the experimental results show that the proposed method could effectively detect a range of webshells. Compared with the traditional webshell detection methods, this method improves the efficiency and accuracy of webshell detection.

scholarly journals Robust Malware Detection using Residual Attention Network

2020 ◽  
Author(s):  
Shamika Ganesan ◽  
vinayakumar R ◽  
Moez Krichen ◽  
Sowmya V ◽  
Roobaea Alroobaea ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Attention Network ◽  
Conventional Machine

In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%.

scholarly journals The role of technical elements of digitalization in the development of modern animal farming

2021 ◽  
Vol 7 (Special) ◽  
pp. 10-10
Author(s):  
Alexey Shemetov ◽  
◽  
Andrey Ivanov
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Large Scale ◽  
Sampling Rate ◽  
Digital Technologies ◽  
Animal Husbandry ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Animal Products ◽  
Technical Problems

It is estimated that farmers need to increase production by 70% over the next 50 years to meet the growing global demand for meat and animal products [1]. Since land and other natural resources are limited, more efficient ways of raising more animals per hectare of land will need to be found to meet this growing demand. Today, most animal husbandry methods require manual intervention at some level. This affects production productivity. Previously, digital technologies were expensive and could not be applied on a large scale. Today, sensors, big data, and machine learning algorithms have significant cost advantages over these older detection methods. Currently, the sensors offered by the market are significantly limited in reliable forecasting and disease management in animal husbandry due to continuous automated monitoring in real time. In addition, there are certain technical problems, such as the location of the sensors, what the sampling rate will be, and how the data will be transmitted. All of these considerations affect the accuracy of the algorithms, as well as the scalability and practicality of a solution that ultimately can be used on a livestock farm. In real-time systems, large feature sets can be problematic due to computational complexity and higher storage requirements. In light of the still existing pandemic, when restrictions prevent veterinarians and producers from visiting farms, cowsheds and feed mills (but there is a need for 24/7 information on activities, consumption and production of products in real time), then the current and practically only possible solution is the introduction of digital technologies. Keywords: LIVESTOCK, SMART FARMING, SENSORS, SENSORS, MACHINE LEARNING

Intelligent Malware Detection Using Deep Dilated Residual Networks for Cyber Security

2019 ◽  
pp. 211-229
Author(s):  
S. Abijah Roseline ◽  
S. Geetha
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Security Threat ◽  
Signature Detection ◽  
Learning Techniques ◽  
Feature Based

Malware is the most serious security threat, which possibly targets billions of devices like personal computers, smartphones, etc. across the world. Malware classification and detection is a challenging task due to the targeted, zero-day, and stealthy nature of advanced and new malwares. The traditional signature detection methods like antivirus software were effective for detecting known malwares. At present, there are various solutions for detection of such unknown malwares employing feature-based machine learning algorithms. Machine learning techniques detect known malwares effectively but are not optimal and show a low accuracy rate for unknown malwares. This chapter explores a novel deep learning model called deep dilated residual network model for malware image classification. The proposed model showed a higher accuracy of 98.50% and 99.14% on Kaggle Malimg and BIG 2015 datasets, respectively. The new malwares can be handled in real-time with minimal human interaction using the proposed deep residual model.

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