The Beneficial Techniques in Preprocessing Step of Skin Cancer Detection System Comparing

AbstractAutomatic diagnostics of skin cancer is one of the most challenging problems in medical image processing. It helps physicians to decide whether a skin melanoma is benign or malignant. So, determining the more efficient methods of detection to reduce the rate of errors is a vital issue among researchers. Preprocessing is the first stage of detection to improve the quality of images, removing the irrelevant noises and unwanted parts in the background of the skin images. The purpose of this paper is to gather the preprocessing approaches can be used in skin cancer images. This paper provides good starting for researchers in their automatic skin cancer detections

Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges

Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks like, node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network (GCN), graph attention network (GAT), graph autoencoder (GAE), and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field

Graph learning for anomaly analytics : algorithms, applications, and challenges

Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network, graph attention network, graph autoencoder, and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field. © 2023 Association for Computing Machinery

Algorithm development for the non-destructive testing of structural damage

Monitoring of structures to identify types of damages that occur under loading is essential in practical applications of civil infrastructure. In this paper, we detect and visualize damage based on several non-destructive testing (NDT) methods. A machine learning (ML) approach based on the Support Vector Machine (SVM) method is developed to prevent misdirection of the event interpretation of what is happening in the material. The objective is to identify cracks in the early stages, to reduce the risk of failure in structures. Theoretical and experimental analyses are derived by computing the performance indicators on the smart aggregate (SA)-based sensor data for concrete and reinforced-concrete (RC) beams. Validity assessment of the proposed indices was addressed through a comparative analysis with traditional SVM. The developed ML algorithms are shown to recognize cracks with a higher accuracy than the traditional SVM. Additionally, we propose different algorithms for microwave- or millimeter-wave imaging of steel plates, composite materials, and metal plates, to identify and visualize cracks. The proposed algorithm for steel plates is based on the gradient magnitude in four directions of an image, and is followed by the edge detection technique. Three algorithms were proposed for each of composite materials and metal plates, and are based on 2D fast Fourier transform (FFT) and hybrid fuzzy c-mean techniques, respectively. The proposed algorithms were able to recognize and visualize the cracking incurred in the structure more efficiently than the traditional techniques. The reported results are expected to be beneficial for NDT-based applications, particularly in civil engineering

Computational intelligence for structural health monitoring

Reliable quantification of the health state of critical infrastructure (such as power plants, high-rise buildings, long-span bridges, dams, airports, tunnels and railway tracks) is a fundamental concern in civil engineering as it can directly affect the national assets and public safety of countries. Concerning this matter, structural health monitoring (SHM) can potentially provide effective solutions to continuously assessing the health state of infrastructure, as it can reduce asset management costs, prolong the structures' operational lifetime and ensure public safety. Therefore, getting access to a robust paradigm to deal with SHM concerns has always been a high priority. The automated condition assessment systems are one of the technologies that have received significant attention in the area of advanced monitoring systems. These systems can interpret large volumes of inspection data to detect and prevent structural failure in early stages by minimising errors to ensure effective risk management while reducing the asset management costs. Although there are various research activities reported in this field, only a few robust methods can determine the adverse condition of a structure effectively, which is the motivation of this research work. Therefore, the main objective of this study is to propose a more robust scheme for automated damage detection systems that can monitor and evaluate the health state of a structure. A non-destructive testing (NDT) method, that has applications in SHM for characterising and assessing the materials and structures, is used to characterise the concrete members (beams). However, after a comprehensive literature review, the mounted Smart Aggregates (SA) based approach is used to monitor cracks in simple concrete and reinforced concrete beams under loading. The collected experimental data is analysed through different proposed algorithms based on signal processing, image processing and artificial intelligence (AI) techniques. These analyses are categorised into four steps of pre-processing and feature extraction, feature selection, classification and visualisation. In the feature extraction step of this detection system, seven sets of features are proposed through statistical parameters, time-frequency analysis, and utilising dynamic and spectral features. However, the time-frequency algorithm which has been proposed based on Hilbert-Huang transform and Wavelet transform is used to perform both feature extraction and crack localisation in structures. Additionally, this thesis proposes two damage indexes (DI), Entropy-based Dispersion (ED) and Entropy-based Beta (EB) for feature extraction, localisation and crack severity purposes. In addition to comparing the damage indexes with the benchmark, experimental investigations are carried out against a conventional load cell system to determine the effectiveness of the proposed DIs. The proposed damage indexes could determine, localise and estimate the cracks' severity status even earlier than the load cell system. However, these damage indexes have also been considered as feature sets which will be used with machine learning approaches. Among the seven investigated feature sets, the hybrid ED-EB could provide the best accuracy and F1-score in determining the damage occurrence through AI techniques. The next step of this system is the feature selection in which the algorithm is proposed based on the Neighbourhood Component Analysis (NCA), Particle Swarm Optimization (PSO) and Support Vector Machine (SVM). The algorithm showed better performance when compared with the traditional approach. The classification step of this research consisted of developing four SVM based algorithms to classify and detect a failure in the structural components. The algorithms are based on the misclassified data, hybrid kernels and hybrid classifiers, respectively. The algorithms outperformed the traditional classifier, and the SVM based on misclassified data could obtain the highest accuracy. The last step of this system is the microwave imaging approach, which is used to perform local visualisation of the damage. This step uses the image processing approaches to enhance the quality of obtained images. To evaluate the performance of these approaches, the original obtained images are compared with the enhanced ones. The experimental results showed that the algorithm could accomplish and enhance the crack visualisation. This thesis provided different algorithms for developing an NDT based damage detection system to detect and localise flexural cracks based on soft computing approaches

Analysis of failure in concrete and reinforced-concrete beams for the smart aggregate–based monitoring system

Monitoring of structures and defining the severity of damages that occur under loading are essential in practical applications of civil infrastructure. In this article, we analyze failure using a smart aggregate sensor–based approach. The signals captured by smart aggregate sensors mounted on the structure under loading are de-noised using wavelet de-noising technique to prevent misdirection of the event interpretation of what is happening in the material. The performance of different mother wavelets on the de-noising process was investigated and analyzed. The objective is to identify the optimal mother wavelet for assessing and potentially reducing the effects of existing noise on signal properties for structural damage detection. In addition, we propose two innovative damage indices, entropy-based dispersion and entropy-based beta, for diagnostic purposes. The proposed entropy-based dispersion damage index is based on the modified wavelet packet tree and root mean square deviation, whereas the entropy-based beta damage index is based on the modified wavelet packet tree and slope of linear regression (beta). In both damage indices, the modified wavelet packet tree uses entropy as a high-level feature. Theoretical and experimental analyses are derived by computing indices on smart aggregate–based sensor data for concrete and reinforced-concrete beams. Validity assessment of the proposed indices was addressed through a comparative analysis with root mean square deviation damage index (benchmark) and the loading history. The proposed indices recognized the cracks faster than other measures and well before major cracking incurs in the structure. This article is expected to be beneficial for smart aggregate–based structural health monitoring applications particularly when damages occurred under loading

Proposed machine learning techniques for bridge structural health monitoring : a laboratory study

Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms. © 2023 by the authors

Microwave imaging of composite materials using image processing

This paper presents the results of application of a relatively simple microwave continuous wave reflectometer with an open-ended waveguide antenna for the purpose of nondestructive testing and evaluation of composite materials using their images. It is shown that the resulting original images could not reveal a desired amount of information about the interior of the sample under investigation and the proposed image processing techniques can improve the results in particular as it relates to detecting the targets located at different depths. This paper presents the results of this investigation and a discussion of these results

Structural damage identification using millimeter wave imaging and image processing

In the past decades, structural health monitoring (SHM) has received wide attention in preventing the sudden failure of structural components by identifying the damages in its early stages. Basically, to ensure the safety and reducing the serviceabili Otsu's thresholding ty of civil infrastructure it is important to inspect and assess the physical and functional condition of structures. Currently, manual inspection is the main form of assessing the conditions to ensure structure still meets the safety requirements. However, there are still several accidents that are reported as a result of insufficient inspection and conditional assessment of structures. In order to prevent further incidents, it is necessary to continuously inspect and assess the condition of structures with appropriate techniques. This is why the development and application of efficient non-destructive testing and computer vision methods for infrastructure health monitoring are in demand. This paper present the developed smart damage detection system for a local infrastructure health monitoring which complement the image-based damage detection methods in hazardous scenarios. The system is based on a relatively simple millimeter wave continuous wave reflectometer with an open-ended waveguide antenna for the purpose of automatic imaging of flaws such as cracks in a steel plate at different standoff distances, monitoring the crack to avoid further deformation through a sequence of inspections at intervals. However, in some cases the original images based on measured data do not provide desired information, leading to developing the image processing algorithm to enhance the imaging result. The proposed algorithm is based on Otsu's thresholding method and Prewitt approximation in six directions in an image which has been created from the measured data to facilitate the structural damage identification. The proposed algorithm can successfully enhance the quality of images and visualize the crack in a steel plate under dielectric coating at different standoff distances

Statistical features and traditional SA-SVM classification algorithm for crack detection

In recent years, the interest in damage identification of structural components through innovative techniques has grown significantly. Damage identification has always been a crucial concern in quality assessment and load capacity rating of infrastructure. In this regard, researchers focus on proposing efficient tools to identify the damages in early stages to prevent the sudden failure in structural components, ensuring the public safety and reducing the asset management costs. The sensing technologies along with the data analysis through various techniques and machine learning approaches have been the area of interest for these innovative techniques. The purpose of this research is to develop a robust method for automatic condition assessment of real-life concrete structures for the detection of relatively small cracks at early stages. A damage identification algorithm is proposed using the hybrid approaches to analyze the sensors data. The data obtained from transducers mounted on concrete beams under static loading in laboratory. These data are used as the input parameters. The method relies only on the measured time responses. After filtering and normalization of the data, the damage sensitive statistical features are extracted from the signals and used as the inputs of Self-Advising Support Vector Machine (SA-SVM) for the classification purpose in civil Engineering area. Finally, the results are compared with traditional methods to investigate the feasibility of the hybrid proposed algorithm. It is demonstrated that the presented method can reliably detect the crack in the structure and thereby enable the real-time infrastructure health monitoring