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

Using vector agents to implement an unsupervised image classification algorithm

Unsupervised image classification methods conventionally use the spatial information of pixels to reduce the effect of speckled noise in the classified map. To extract this spatial information, they employ a predefined geometry, i.e., a fixed-size window or segmentation map. However, this coding of geometry lacks the necessary complexity to accurately reflect the spatial connectivity within objects in a scene. Additionally, there is no unique mathematical formula to determine the shape and scale applied to the geometry, being parameters that are usually estimated by expert users. In this paper, a novel geometry-led approach using Vector Agents (VAs) is proposed to address the above drawbacks in unsupervised classification algorithms. Our proposed method has two primary steps: (1) creating reliable training samples and (2) constructing the VA model. In the first step, the method applies the statistical information of a classified image by k-means to select a set of reliable training samples. Then, in the second step, the VAs are trained and constructed to classify the image. The model is tested for classification on three high spatial resolution images. The results show the enhanced capability of the VA model to reduce noise in images that have complex features, e.g., streets, buildings. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

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

An optimal scheduling method in iot-fog-cloud network using combination of aquila optimizer and african vultures optimization

Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks. © 2023 by the authors

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

Pre-processing of automatic skin cancer detection system: Comparative study

Skin cancer is increasing and effect many people in different part of the world. Malignant melanoma as the deadliest type of skin cancer can be treated successfully if it detected early. Automatic detection is one of the most challenging research areas that can be used for early detection of such vital cancer. Over the last few years, many automatic diagnosis systems been suggested by different researchers targeting increasing of the diagnosis accuracy. This paper presents a quick review on the design of whole system and focus in preprocessing step of the automatic system. Preprocessing as the basis of automation system plays a vital role for accurate detection. This paper implements three techniques of contrast enhancement in the framework of three methodologies to find out the most effective one for further processing. The quality of resulted images in each methodology has been found based on testing the skin cancer images database using three image quality measurements

Machine learning based biosignals mental stress detection

Mental Stress can be defined as a normal physiological and biological reaction to an incident or a situation that makes a person feel challenged, troubled, or helpless. While dealing with stress, some changes occur in the biological function of a person, which results in a considerable change in some bio-signals such as, Electrocardiogram (ECG), Electromyography (EMG), Electrodermal Activity (EDA), respiratory rate. This paper aims to review the effect of mental stress on mental condition and health, the changes in biosignals as an indicator of the stress response and train a model to detect stressed states using the biosignals. This paper delivers a brief review of mental stress and biosignals correlation. It represents four Support Vector Machine (SVM) models trained with ECG and EMG features from an open access database based on task related stress. After performing comparative analysis on the four types of trained SVM models with chosen features, Gaussian Kernel SVM is selected as the best SVM model to detect mental stress which can predict the mental condition of a subject for a stressed and relaxed condition having an accuracy of 93.7%. These models can be investigated further with more biosignals and applied in practice, which will be beneficial for the physician. © 2021, Springer Nature Singapore Pte Ltd