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Simple Summary: Accurately grading gliomas, which are the most common and aggressive malignant brain tumors in adults, poses a significant challenge for radiologists. This study explores the application of Deep Learning techniques in assisting tumor grading using Magnetic Resonance Images (MRIs). By analyzing a glioma database sourced from multiple public datasets and comparing different settings, the aim of this study is to develop a robust and reliable grading system. The study demonstrates that by focusing on the tumor region of interest and augmenting the available data, there is a significant improvement in both the accuracy and confidence of tumor grade classifications. While successful in differentiating low-grade gliomas from high-grade gliomas, the accurate classification of grades 2, 3, and 4 remains challenging. The research findings have significant implications for advancing the development of a non-invasive, robust, and trustworthy data-driven system to support clinicians in the diagnosis and therapy planning of glioma patients. Glioma is the most common type of tumor in humans originating in the brain. According to the World Health Organization, gliomas can be graded on a four-stage scale, ranging from the most benign to the most malignant. The grading of these tumors from image information is a far from trivial task for radiologists and one in which they could be assisted by machine-learning-based decision support. However, the machine learning analytical pipeline is also fraught with perils stemming from different sources, such as inadvertent data leakage, adequacy of 2D image sampling, or classifier assessment biases. In this paper, we analyze a glioma database sourced from multiple datasets using a simple classifier, aiming to obtain a reliable tumor grading and, on the way, we provide a few guidelines to ensure such reliability. Our results reveal that by focusing on the tumor region of interest and using data augmentation techniques we significantly enhanced the accuracy and confidence in tumor classifications. Evaluation on an independent test set resulted in an AUC-ROC of 0.932 in the discrimination of low-grade gliomas from high-grade gliomas, and an AUC-ROC of 0.893 in the classification of grades 2, 3, and 4. The study also highlights the importance of providing, beyond generic classification performance, measures of how reliable and trustworthy the model's output is, thus assessing the model's certainty and robustness. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to identify the factors affecting the intention to use peer-to-peer (P2P) mobile payment. Although mobile technology has become part of everyday life, certain actions and services, such as mobile payments, are still used relatively infrequently. In this paper, we analyse consumers' adoption of P2P mobile payment services. Following a review of previous literature in this field, we identify the main factors that determine the adoption of mobile payments, and then perform a logistic regression (LR) analysis and propose a neural network to predict this adoption. From the logistic regression results obtained we conclude that six variables significantly influence intentions to use P2P payment: ease of use, perceived risk, personal innovativeness, perceived usefulness, subjective norms and perceived enjoyment. With respect to the nonparametric technique, we find that the multilayer perceptrons (MLP) prediction model for the use of P2P payment obtains higher AUC values, and thus is more accurate, than the LR model. This paper is a pioneer study of intention to use with mobile payment using these methodologies. The outcome of this research has important implications for the theory and practice of the adoption of P2P mobile payment services. [ABSTRACT FROM AUTHOR]

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods. [ABSTRACT FROM AUTHOR]

The sudden rise in the ability of machine learning methodology, such as deep neural networks, to identify and predict with great accuracy instances of malignant cell growth from radiological images has led prominent developers of this technology, such as Geoffrey Hinton, to hold the view that "[...] we should stop training radiologists." Similar views exist in other contexts regarding the replacement of humans with artificial intelligence (AI) technologies. The assumption in these kinds of views is that deep neural networks are better than human radiologists in that they are more accurate, less costly, and have more predictive power than their human counterparts. In this paper, I argue that these considerations, even if true, are simply inadequate as reasons for us to allocate the kind of trust suggested by Hinton and others to these sorts of artifacts. In particular, I show that if the same considerations were true of something other than an AI device, say a pigeon, we would not have sufficient reason to trust them in the same way as suggested of deep neural networks in a medical setting. If this is the case then these considerations are also insufficient to trust AI enough to replace radiologists. Furthermore, I argue that the reliability of AI methodologies such as deep neural networks—which are at the center of this argument—is something that has not yet been established, and doing so faces fundamental challenges. Because of these challenges, it is not possible to ascribe the level of reliability expected from the deployment of a medical device. So, not only are the reasons cited in favor of the deployment of AI technologies in medical settings not sufficient/adequate even if they are true, but knowing whether they are true or not faces non‐trivial epistemic challenges. If this is so, then we have no good reasons to advocate replacing radiologists with AI methodologies such as deep neural networks. [ABSTRACT FROM AUTHOR]

This paper presents an advanced version of the failure mode effects and criticality analysis (FMECA), whose capabilities are enhanced; in that the criticality assessment takes into account possible interactions among the principal causes of failure. This is obtained by integrating FMECA and Analytic Network Process, a multi-criteria decision making technique. Severity, Occurrence and Detectability are split into sub-criteria and arranged in a hybrid (hierarchy/network) decision-structure that, at the lowest level, contains the causes of failure. Starting from this decision-structure, the Risk Priority Number is computed making pairwise comparisons, so that qualitative judgements and reliable quantitative data can be easily included in the analysis, without using vague and unreliable linguistic conversion tables. Pairwise comparison also facilitates the effort of the design/maintenance team, since it is easier to place comparative rather than absolute judgments, to quantify the importance of the causes of failure. In order to clarify and to make evident the rational of the final results, a graphical tool, similar to the House of Quality, is also presented. At the end of the paper, a case study, which confirms the quality of the approach and shows its capability to perform robust and comprehensive criticality analyses, is reported. Copyright © 2011 John Wiley and Sons Ltd. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to present a novel method to achieve maximum reliability for fault tolerant optimal network design when network has variable size. Reliability calculation is most important and critical component when fault tolerant optimal network design is required. A network must be supplied with certain parameters that guarantee proper functionality and maintainability under worse situations. Many alternative methods for measuring reliability have been stated in literature for optimal network design. Most of these methods mentioned in literature for evaluating reliability may be analytical and simulation based. These methods provide significant way to compute reliability when network has limited size. Also, significant computational effort is required for growing variable sized networks. Therefore, a novel neural network method is presented to achieve significant high reliability for fault tolerant optimal network design in highly growing variable networks. This paper computes reliability with improved learning rate gradient descent based neural network method. The result shows that improved optimal network design with maximum reliability is achievable by novel neural network at manageable computational cost. [Copyright &y& Elsevier]

This paper presents an application of artificial neural network (ANN) technique for conducting the reliability analysis of Boeing 737 tires. For this purpose, an ANN model utilizing the feed-forward back-propagation algorithm as a learning rule is developed. The inputs to the neural network are the flight operational time and the number of landings as independent variables and the output is the failure rate of the tires. Two years of data are used for failure rate prediction model and validation. Model validation, which reflects the suitability of the model for future predictions, is performed by comparing the predictions of the model with that of Weibull regression model. The results show that the failure rate predicted by the ANN is closer in agreement with the actual data than the failure rate predicted by the Weibull model. The present work also identifies some of the common tire failures and presents representative results based on the established model for the most frequently occurring tire failure. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection of process faults. Even though fault detection algorithms are designed and implemented for quickly detecting incidents, most these algorithms do not have an optimal property in terms of detection delay with respect to false alarm rate. Based on this optimization property, a real-time system for detecting changes in dynamic systems is designed in this paper. This work is motivated by combining two fault detection (FD) methods; a simplified procedure of the incident detection problem is formulated by using the combination of the Artificial Neural Networks (ANN) and the cumulative sum (CUSUM) or Page-Hinkley test. It is intended to reveal any drift from the normal behavior of the process. The process behavior under its normal operating conditions is established by a reliable model. In order to obtain this reliable model for the process dynamics, the black-box identification by means of a NARX (Nonlinear Auto-Regressive with eXogenous input) model is chosen in this study. The purpose is to develop and test the fault detection method on a real incident data, to detect the change presence, and pinpoint the moment it occurred. The experimental results demonstrate the robustness of the FD method. [ABSTRACT FROM AUTHOR]

Background. Research on intelligent tongue diagnosis is a main direction in the modernization of tongue diagnosis technology. Identification of tongue shape and texture features is a difficult task for tongue diagnosis in traditional Chinese medicine (TCM). This study aimed to explore the application of deep learning techniques in tongue image analyses. Methods. A total of 8676 tongue images were annotated by clinical experts, into seven categories, including the fissured tongue, tooth-marked tongue, stasis tongue, spotted tongue, greasy coating, peeled coating, and rotten coating. Based on the labeled tongue images, the deep learning model faster region-based convolutional neural networks (Faster R-CNN) was utilized to classify tongue images. Four performance indices, i.e., accuracy, recall, precision, and F1-score, were selected to evaluate the model. Also, we applied it to analyze tongue image features of 3601 medical checkup participants in order to explore gender and age factors and the correlations among tongue features in diseases through complex networks. Results. The average accuracy, recall, precision, and F1-score of our model achieved 90.67%, 91.25%, 99.28%, and 95.00%, respectively. Over the tongue images from the medical checkup population, the model Faster R-CNN detected 41.49% fissured tongue images, 37.16% tooth-marked tongue images, 29.66% greasy coating images, 18.66% spotted tongue images, 9.97% stasis tongue images, 3.97% peeled coating images, and 1.22% rotten coating images. There were significant differences in the incidence of the fissured tongue, tooth-marked tongue, spotted tongue, and greasy coating among age and gender. Complex networks revealed that fissured tongue and tooth-marked were closely related to hypertension, dyslipidemia, overweight and nonalcoholic fatty liver disease (NAFLD), and a greasy coating tongue was associated with hypertension and overweight. Conclusion. The model Faster R-CNN shows good performance in the tongue image classification. And we have preliminarily revealed the relationship between tongue features and gender, age, and metabolic diseases in a medical checkup population. [ABSTRACT FROM AUTHOR]

Uncertainty is usually modeled using random variable with certain probability distribution. However, the probability distributions of many random variables are often truncated in engineering applications. In the procedure of reliability based design optimization for structural systems with truncated random variables, repeated function evaluations are required for different design points where the computational costs are extremely huge. In this paper, an efficient as well as novel reliability method is proposed for structural systems with truncated random variables which does not require repeated function evaluations for the different design points. Uniformly distributed samples are generated for truncated random variables in the supported intervals and design variables in the specified intervals to approximate cover the entire uncertain space fully. In order to avoid repeated function evaluations and improve computational efficiency, a surrogate model is established using back-propagation (BP) neural networks which can approximate the relationships between the inputs and system responses properly in almost entire uncertain space using the proposed given available data. The main advantages of the proposed method are high accuracy and effectiveness in estimating the probability of failure under different design points which requires neither large samples nor the repeated function evaluations when compared to the existing reliability methods. Four numerical examples are investigated to demonstrate the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Methods of artificial management represent a new approach to problem management in numerous applications in technical systems. The aim of paper is to demonstrate the application of fuzzy logic in the process of management and control of technical systems (Zimmermann, 2001). Program tool "Fuzzy Logic Toolbox" is used in this work. Its functions are called within software package Matlab. Fuzzy logic at the controlled engine speed is applied, input and output variables are selected, fuzzy sets are specified, and their shape is defined through membership function. The value that reflects the linguistic meaning of each fuzzy set is defined. Results enable the value estimation of the output parameters. This leads to the solution of the identified problem, with the help of base of fuzzy rules for observed characteristics which are stored within the model. If the number of input and output variables increases, this influences inference mechanism and solution reliability, through the use of artificial intelligence methods (expert systems, neural network, fuzzy logic, fuzzy, genetic algorithm). [ABSTRACT FROM AUTHOR]

Abstract: Operating reserve (OR) is a major portion of ancillary services (AS) in a competitive electricity market and need to be procured by independent system operator (ISO), to achieve a high degree of power system reliability and security, following the major generation and transmission contingencies. Several ISOs have adopted deterministic methods to assess the OR requirements, however, such methods do not explicitly consider the unforeseen load swings and the probability of equipment outages. This paper proposes an adaptive wavelet neural network (AWNN) based two-stage approach to forecast OR requirements for both day-ahead and hour-ahead AS market in the California ISO (CAISO) controlled grid. The AWNN is a new class of feed-forward neural network with continuous wavelet function as the hidden layer node''s activation function. The forecasting results for winter and summer seasons of the year 2007 are presented and compared with those obtained by feed-forward multi-layer perceptron neural network (MLPNN). It is found that AWNN based proposed method outperforms the MLPNN model. [Copyright &y& Elsevier]

The paper considers the variant for problem solving of different object classes by neuron networks. Specifically, it was experimentally proven that a neuron network with high exactness and reliability can search for coils in image of circuit board, number plates, and eyes in image. [ABSTRACT FROM AUTHOR]

Abstract: The inverse first-order reliability method (FORM) is one of the most widely used methods in inverse reliability analysis. However, this method has two drawbacks in the solution of inverse reliability problems with implicit response functions. First, it requires the evaluation of the derivatives of the response functions with respect to the random variables. When these functions are implicit functions of the random variables, derivatives of these response functions are not readily available. Second, it usually involves repeated deterministic response analyses of complicated structures due to the variation of the basic variables, and therefore requires a relatively long computation time. To overcome these drawbacks of the inverse FORM, an artificial neural network (ANN)-based inverse FORM is proposed in this paper. In this method, an ANN model is used to approximate the structural response function so that the number of deterministic response analyses can be dramatically reduced. The explicit formulation of structural response is derived by using the parameters of the ANN model. After the explicit response function is determined, the inverse FORM is applied to solve the inverse reliability problem. The accuracy and efficiency of the proposed method is demonstrated through two numerical examples. Some important parameters in the proposed method are also discussed. [Copyright &y& Elsevier]

The objective of this study is to approximate the links between user satisfaction and its determinants without having the restrictions of common statistical procedures such as linearity, symmetry and normality. For this reason, artificial neural networks are utilised and trained with the observations of an extensive survey on user satisfaction with respect to website attributes. Each observation includes evaluations about the performance of 18 specific and 9 general website attributes as well as an evaluation about overall user satisfaction. The analysis results indicate that website attributes present different impacts on satisfaction whereas the relationships found feature both asymmetry and nonlinearity. Finally, function approximation using neural networks is found to be appropriate for estimating such kind of relationships providing valuable information about satisfaction's formation. [ABSTRACT FROM AUTHOR]

Presents an empirical comparison of the predictive performance of two different methods of software reliability predictions: Neural networks and recalibration for parametric models. Variable-term and end-point prediction; Feed-forward net; Kolmogorov distance.

In the recent past, Internet of Things (IoT) plays a significant role in different applications such as health care, industrial sector, defense and research etc.… It provides effective framework in maintaining the security, privacy and reliability of the information in internet environment. Among various applications as mentioned health care place a major role, because security, privacy and reliability of the medical information is maintained in an effective way. Even though, IoT provides the effective protocols for maintaining the information, several intermediate attacks and intruders trying to access the health information which in turn reduce the privacy, security and reliability of the entire health care system in internet environment. As a result and to solve the issues, in this research Learning based Deep-Q-Networks has been introduced for reducing the malware attacks while managing the health information. This method examines the medical information in different layers according to the Q-learning concept which helps to minimize the intermediate attacks with less complexity. The efficiency of the system has been evaluated with the help of experimental results and discussions. [ABSTRACT FROM AUTHOR]