Abstract

We investigate an unexplored channel—loss given default (LGD)—through which accounting information can shape the design of debt contracts. Using a sample of defaulted bonds, we find that borrower accounting information available at contract initiation possesses significant power for predicting realized LGD at the subsequent default date. We then use this model to construct an accounting-based measure of expected LGD at the contracting date for a large sample of bond issuances. We find that this measure is positively associated with issuance date interest spread and covenant use, and document that these relations are not artifacts of an association between LGD and probability of default. We then show that accounting-based expected LGD has a stronger association with issuance date spread when the borrower’s underlying accounting is more conservative and when the accounting-based LGD predictors are more persistent. Our results increase our understanding of both the informational role and contracting role of accounting information.

Introduction

Financial reports are an important source of frm-specifc information available to lenders at the contracting date and thus may afect lenders’ behavior in the design of debt contracts. Whereas prior literature focuses on the usefulness of accounting information for probability of default assessment (e.g., Beaver 1966; Altman 1968; Shumway 2001), this study suggests that accounting information may also shape contracts by facilitating lenders’ assessment of loss given default. Loss given default, defned as the percentage loss that lenders experience from $1 of outstanding principal in a case of default, is a critical component of credit risk and debt contracting theories.1 Despite the theoretical importance of loss given default, there is little empirical evidence regarding how accounting information available to lenders at the contracting date is associated with their expectations about loss given default. This study is the frst to provide evidence that accounting information at the contracting date is a useful predictor of realized loss given default and that lenders behave as if they use accounting information about loss given default to design debt contracts.

Conclusion

A substantial body of research focuses on the informativeness of accounting information with respect to probability of default. However, there is very little research concerning the informativeness of accounting information with respect to loss given default, particularly at the contracting date. There exists some ancillary evidence in extant literature regarding the informativeness of accounting data about loss given default at the time of default. However, given the relatively long time between debt issuance and eventual default, this evidence says little about whether accounting information is useful to lenders in assessing expected loss given default at the contracting date—when lenders need information the most.

This study contributes to the literature along several dimensions. First, using a sample of defaulted bonds, we show that accounting measures available to lenders at the contracting date are informative about future loss given default. To the best of our knowledge, this study is the frst to do so. This fnding complements the literature which shows that accounting measures are informative about probability of default and therefore enhances our understanding of the informational role of accounting. Second, we construct an intuitive measure of accounting-based expected loss given default at the time of bond contract initiation, which could be of use in future research. Third, we show that lenders behave as if their accounting-based expectations about loss given default signifcantly afect price and non-price terms of bond contracts. We provide evidence that the association is stronger when the accounting-based predictors are more persistent, consistent with the intuition that accounting information with higher persistence is more reliable for predicting future outcomes. This fnding contributes to the literature on the role of accounting information in debt contracting by showing a specifc channel through which accounting information may be useful in lending decisions. Finally, our results provide important evidence that establishes a direct link between accounting-based LGD estimation and lenders’ demand for fnancial reporting conservatism.

Abstract

     Controlling the frequency response of an engineering component or structure is important in the aerospace and automotive sectors and is a key consideration when seeking a new and more efficient design for a given component. In this contribution, the standard truss layout optimization procedure is modified to incorporate semidefinite constraints to limit the minimum value of the first natural frequency. Since this increases the computational expense and reduces the scale of the problem that can be solved, a bespoke algorithm incorporating an adaptive ‘member adding’ procedure is proposed and applied to a number of benchmark example problems. It is demonstrated that this allows problems to be solved with relatively fine numerical discretization, allowing modified structures with an acceptable minimum first natural frequency response to be successfully identified.

Introduction

     In the design of modern engineering components, many considerations need to be taken into account including safety, cost, weight and manufacturability. The most prominent of these is safety, taking account of the regime of applied stresses to be sustained over the life of the component. Safety is influenced by the properties of the material employed, which may change as the design evolves. Additionally, when considering structures that include slender elements in compression, it is necessary to check for buckling instability to ensure safety is maintained. Another key parameter in the aerospace sector is the harmonic frequency of a structure. This should lie outside the frequency bands of surrounding components. Should a fundamental frequency of one component (e.g. a bracket) overlap with those of its attached neighbours, then resonance in the component may occur, also referred to as forced vibration. Forced vibration and resonance can then lead to High Cycle Fatigue (HCF) in the component, affecting its serviceable life and reducing its time to failure. It should be noted that a component may exhibit multiple resonant frequencies, each corresponding to a mode of vibration; repeated exposure to these frequencies may reduce the life of the component. However, this phenomenon is beyond the scope of the current contribution. Considering component manufacture, it is important to note that traditional manufacturing methods may limit the design freedom available; however, in the present contribution, it is assumed that Additive Layer Manufacturing (ALM) methods are available. The use of ALM means that complex truss forms can potentially be fabricated, beyond the scope of traditional subtractive manufacturing methods.

Conclusions

     Numerical layout optimization provides an efficient means of generating optimal truss structures for a given set of design requirements. However, traditional linear programming-based formulations are limited, and cannot for example accommodate frequency constraints. In this contribution, extended semi-definite programming-based formulations are considered that allow the minimum first natural frequency of a structure to be specified. The main conclusions are as follows.

     The use of a two phase approach, in which the traditional LP layout optimization formulation is used in the first phase and an SDP size optimization is used in the second phase, provides a computationally efficient means of generating solutions satisfying a specified frequency constraint. However, the solutions obtained are likely to be sub-optimal, with the resulting structures having higher than necessary volume.

     Alternatively, a constraint on frequency can be introduced in the optimization directly, furnishing layouts that satisfy both structural performance and first natural frequency requirements. However, when using a fully connected ground structure and a standard SDP solver, the computational cost and memory requirements have been found to be high, severely limiting the scale of problem that can be tackled.

Abstract

     Considering environmental factors such as temperature in structural health monitoring progress has been a consensus. However, the uncertainty of monitoring data usually makes it difficult. In this paper, the uncertainty factor has been introduced into the anomaly diagnosis process, a Markov chain-Monta Carlo (MCMC) anomaly diagnosis method based on temperature-induced response has been proposed. First, a novel diagnosis index has been developed based on the temperature data and static strain response data collected by the SHM system, the MCMC process is used to analyze the diagnosis index, and the posterior frequency distribution histogram of the actual diagnosis index is obtained. Finally, by analyzing the histogram of an unknown state and the initial state (baseline state) of the structure, the anomaly probability of the unknown condition is obtained, which can be used for anomaly probability diagnosis of components. The availability of the method is evaluated by a laboratory truss structure test under a series of working conditions and is verified by field monitoring data of a hanger roof structure. The results show that the method can make better use of the temperature effect of the structure for anomaly diagnosis, and the uncertainty is well considered.

Introduction

     Large building structures, including truss structures, are often used in airports, stations, factories, stadiums, and other important civil infrastructures [1], such structures often encounter sudden load changes (snow loads, high wind loads), changes in restraint conditions, member damage due to material degradation, and other structural anomalies during construction or service. Monitoring and diagnosing these structural anomalies using sensors placed on the surface of the structure is an efective means to ensure the safety of the structure throughout its life, and in recent years, with the development of sensor technology and intelligent algorithms, structural anomaly diagnosis (SAD) technique is becoming an increasingly important area.

     The vibration-based method is one of the most widely used SAD methods, which refects the abnormal state of the structure by monitoring the changes of vibration features [2–4]. However, the structural vibration features (e.g., natural frequencies) are not only related to the state of the structure itself, but also to environmental factors such as temperature, which will have an impact on the accuracy of SAD [5–7]. Although diferent solutions have been proposed for vibration-based SAD under environmental changes, these methods are still greatly limited by other defects in long-term practical monitoring, such as low sensitivity of vibration features to small local damage of the structure, the complicated method of sensor arrangement, and the large data transmission and storage caused by the high sampling frequency of the monitoring process, etc. [8].

Summary and conclusions

     In this paper, a temperature-based anomaly diagnosis of truss structure system using Markov chain-Monte Carlo method is proposed, which not only introduces the uncertainty method into a SAD method based on static response monitoring, but also actively uses the temperature efect of the structure for SAD. In this method, a novel diagnosis index based on T-stress-induced strain that takes uncertainties into account is proposed, which is obtained from the stress-induced strain and temperature measured by sensors arranged in the focus area of the structure. Then, the diagnosis index measurement data set is processed by MCMC to obtain the posterior relative frequency distribution histogram of the actual diagnosis index, and the histogram of the baseline state and an unknown state are analyzed to obtain the structural anomaly probability of the members under this unknown state. As two aspects of anomaly diagnosis, the structural damage diagnosis and state change diagnosis were verifed by a truss test and feld monitoring data of the BDIA hanger roof structure respectively, and the following conclusions were obtained:

Abstract

     Depression is a serious mental health condition that may lead to poor mental and emotional functioning at work, at school and in the family causing the mental imbalance. In worst scenarios, depression may lead to severe anxiety or suicide. Hence, it is necessary to diagnose depression at early stages. This paper elaborates the development of a novel approach for a convolutional neural network model that can examine facial images from the recorded interview sessions to discover facial patterns that could indicate depression level. The user-generated data helps to distinguish between different depressive groups with depression symptoms that can manifest people with various mental illnesses in different ways. In particular, we want to automatically predict the depression scale and differentiate depression from other mental disorders using the patient’s psychiatric illness history and dynamic textual descriptions extracted from the user inputs. We apply the k-nearest neighbour algorithm on the dynamic textual descriptors to make a linguistic analysis for classifying mental illness into different classes. We apply dimensionality reduction and regression using the Random Forest algorithm to predict the depression scale. The proposed framework is an extension to pre-existing frameworks, replacing the handcrafted feature extraction technique with the deep feature extraction. The model performs 2.7% better than existing frameworks in facial detection and feature extraction.

Introduction

     In India, depression is common in adults between the ages of 16–25, considered to be a leading cause of disability. For years, researchers have to identify and map the relationship between brain function and structure using neuroimaging data. The researchers at the University of Texas have identified a unique technique to categorize people susceptible to developing depression and anxiety using deep learning with a supercomputer. Now they are using the Stampede supercomputer at the Texas advanced computing centre to train deep learning algorithms that can identify similarities among hundreds of patients using magnetic resonance imaging genomics data and other factors to predict patients at risk of depression and psychological disorders (Valstar et al., 2013). In the past, the researchers have worked on the development of a model that takes raw text and audio segments as input and analyzes the wave-forms that predict depression. The work provides a method for deep learning-based segmentation to detect depression, as well as irregular segmentation and masks used for Gabor wavelength detection (Long et al, 2014). A Gabor filter, named after Dennis Gabor, is a linear local texture filter in image processing. It analyses if the model includes any specific frequency content in specific directions in a localized region around the point or region of analysis. The researchers trained the deep learning algorithm using extracted audio and textual features from clinical patients with suicide attempts and higher rate of mood swings (Ma et al, 2016). Extreme Gradient boosting technique is used for identifying and categorizing the important parameters of depression and predicting depression cases by re-sampling methods using different balanced samples but the researchers were not able to precisely predict the depression scale. Also differentiating the mental disorders is a tedious task due to similarities in symptoms (Marcus et al., 2012).

Conclusion and future work

     Here, we have developed an AI-based automated system for predicting the depression scale. The model is purely based on recognizing facial expressions from recorded video sessions of depressed patients. We have used deep learning algorithms for extracting dynamic facial features and recognizing expressions producing the feature dynamic history histogram applied to feature dynamic vector sequences applied to the video sample. After calculating the feature dynamic history histogram, we apply regression techniques to correlate between feature and depression scales. The experimental results showed that the model had performed well on the test video dataset. The deep feature extraction technique significantly performs much better than the handcrafted feature extraction technique. The feature attributes are directly extracted from the responses provided by the convolutional layers perform best rather the performance of the neural networks are closely connected giving benchmarking results.

The malware detection methods are classified into two categories, namely, dynamic analysis (active analysis) and static analysis (passive analysis). These methods undergo unusual obstruction, and challenges that are process complexity, limitation over detection accuracy. The static method serves to discover malicious applications using various parameters like permission analysis, signature verification. It can be regularly obfuscated. Dynamic techniques entail investigating the performance of an application by administering it in a restricted environment. The complex version of a portable executable often emerges with an intervention by hardening the dynamic analysis centric malware detection methods. The various constraints of these dynamic and static models contribute to this manuscript represents a Multi-Level Malware detection using Triad Scale (MLMTS) built on regression coefficients. The proposed method MLMTS spans into three levels, such that the first and second level performs static analysis, and the third level performs the dynamic analysis. The second and third levels of the hierarchy invoke upon the ambiguous decision of their respective predecessor level. The proposed work is based on the Machine Learning (ML) model that determines the triad scale by applying linear regression for each level of malware detection. The call sequences of the portable executable, arguments passed to these call sequences and their fallouts (resultant values) in respective order of three levels of the MLMTS method. The experimental study manifests the significance of the proposal compared to the other recent malware detection methods.

1. Introduction

The malicious software that is often termed as malware intends to infiltrate, infect, or intrude the cryptographic verification of the owner in the computer system. According to contemporary statistics [1], an average of 400 million malware models is recognized per annum. Currently, the malware family has boosted through the software modules engineered by incredible software skills [2]. The attacks can anonymize the source of the attack [2] and considerably succeeds in hacking the potential industrial structures known as the Stuxnet [3]. Anonymized sources are significant challenges to contemporary malware detection strategies. Extensive utilization of computer-networks is vulnerable to potential malware attacks. The dynamic network connectivity exposes the vulnerabilities of the corresponding network, which entertains the attackers to exploit these vulnerabilities to inject the malware into the respective system. The Intel organization has estimated the impact of malware in terms of loss of revenue as above 400 billion (USD 400 * 109 ) dollars worldwide per annum [1]. These statistics concreting the need for potential malware detect and defense mechanisms.

1- Introduction

2- Proposed approach

3- Techniques

4- Results

5- Conclusion and future scope

References

Abstract

Electroencephalography (EEG) is a non-invasive method for the analysis of neurological disorders. Epilepsy is one of the most widespread neurological disorders and often characterized by repeated seizures. This paper intends to conduct an iterative filtering based decomposition of EEG signals to improve upon the accuracy of seizure detection. The proposed approach is evaluated using All India Institute of Medical Science (AIIMS) Patna EEG database and online CHB-MIT surface EEG database. The iterative filtering decomposition technique is applied to extract sub-components from the EEG signal. The feature set obtained from each segmented intrinsic mode function consists of 2-D power spectral density and time-domain features dynamic mode decomposition power, variance, and Katz fractal dimension. The Hidden Markov Model (HMM) based probabilistic model has been designed using the above-stated features representing the seizure and non-seizure EEG events. The EEG signal is classified based on the maximum score obtained from the individual feature-based classifiers. The maximum score derived from each HMM classifier gives the final class information. The proposed decomposition of EEG signals achieved 99.60% and 99.74% accuracy in seizure detection for the online CHB-MIT surface EEG database and AIIMS Patna EEG database, respectively.

Introduction

EEG has a wide application in the diagnosis of neurological diseases such as dementia, migraine, and epilepsy [1]. Epilepsy is a non-communicable disease that can affect people of different ages. Nearly 70 million people are affected by epilepsy all over the world, and in India alone, 12 million people are suffering from epilepsy [2]. The unexpected simultaneous activity of groups of neurons is called a seizure. The duration of seizure in continuous EEG signals varies from 1 to 10 seconds. Depending on the seizure condition, the recording duration varies from minutes to hours. At present, the seizure detection is manual, and accuracy depends largely on the doctor’s experience. There is an urgent requirement to develop an efficient algorithm which can detect seizure efficiently from continuous EEG signal. There are different state-of-the-art methods proposed for seizure detection. Discrete wavelet transform (DWT) and empirical mode decomposition (EMD) are widely used techniques for data decomposition. EMD time-domain features and frequency domain features with a linear discriminant analysis classifier are used for seizure classification [3]. Multivariate empirical mode decomposition, instantaneous rate, instantaneous amplitude, and artificial neural network are efficient in seizure detection [4]. EMD intrinsic mode function (IMF) and Higher-order moment features are found to be effective in seizure detection [5]. DWT coefficients are found to be efficient in seizure and non-seizure classification [6]. Energy ratio from wavelet coefficients and ant colony classifier achieved good classification accuracy [7]. Stationary wavelet transform has a good application in seizure detection.

This work studies the parameter identification problem for the Markov chain choice model of Blanchet, Gallego, and Goyal used in assortment planning. In this model, the product selected by a customer is determined by a Markov chain over the products, where the products in the offered assortment are absorbing states. The underlying parameters of the model were previously shown to be identifiable from the choice probabilities for the all-products assortment, together with choice probabilities for assortments of all-but-one products. Obtaining and estimating choice probabilities for such large assortments is not desirable in many settings. The main result of this work is that the parameters may be identified from assortments of sizes two and three, regardless of the total number of products. The result is obtained via a simple and efficient parameter recovery algorithm.

Introduction

In assortment planning, the seller’s goal is to select a subset of products (called an assortment) to offer to a customer so as to maximize the expected revenue. This task can be formulated as an optimization problem given the revenue generated from selling each product, along with a probabilistic model of the customer’s preferences for the products. Such a discrete choice model must capture the customer’s substitution behavior when, for instance, the offered assortment does not contain the customer’s most preferred product. Our focus in this paper is the Markov chain choice model (MCCM) proposed by Blanchet et al. [2]. This MCCM chooses to model the product selected by the customer as a Markov chain over products where the products in the offered assortment are absorbing states. The current state represents the desired product; if that product is not offered, the customer transitions to another product according to the Markov chain probabilities, and the process continues until the desired product is offered or the customer leaves. MCCM generalizes widely-used discrete choice models such as the multinomial logit model [6,8], as well as other generalized attraction models [5]; it also well-approximates other random utility models found in the literature such as mixed multinomial logit models [7]. At the same time, the MCCM permits computationally efficient unconstrained assortment optimization as well as efficient approximation algorithms in the constrained case [2,4]; this stands in contrast to some richer models such as mixed multinomial logit models [9] and the nested logit model [3] for which assortment optimization is generally intractable. This combination of expressiveness and computational tractability makes MCCM very attractive for use in assortment planning. A crucial step in this overall enterprise—e.g., before assortment optimization may take place—is the estimation of the choice model’s parameters from observational data. Parameter estimation for MCCM is only briefly considered in the original work of Blanchet et al. [2].

Abstract

In this paper, a hidden Markov model with auto-correlated observations (HMM-AO) is developed to handle the degradation modeling of manufacturing systems. Unlike the standard hidden Markov models (HMMs), the current observation in the HMM-AO model not only depends on the corresponding hidden system state, but also on the previous observations. A novel algorithm using the expectation maximum is presented to estimate the unknown parameters. Furthermore, missing data and noise that accumulate over time are also considered by modifying the proposed model. Then two remaining useful life prediction methods based on the HMM-AO model are developed. Predictive values of more accuracy can be obtained, since the autocorrelation of observations has been considered and the temporal evolution of degradation processes has been described properly. A case study is illustrated to highlight the advantages of HMM-AO and demonstrate the accuracy and efficiency of the prediction methods. Furthermore, an improved maintenance policy is developed based on the results of remaining useful life prediction. Finally, a comparison with a conventional condition-based maintenance policy is provided to prove the performance of this proposed policy.

Introduction

Maintenance policies play important roles in improving the effectiveness of production systems’ operation. In recent years, maintenance strategies combining data-driven reliability models with observed data of condition monitoring has gained more and more attention [1–3]. Diagnosis and prognosis are two important aspects in the framework of maintenance and have been largely researched [4]. The former focuses on the fault detection [5], while the latter devotes to evaluate the current health state of the system as well as to predict its remaining useful life (RUL). An accurate prediction of the RUL could help develop a more economical and effective maintenance policy.

abstract

The evaluation of subjective data is a very demanding task. The classification of the information gathered from human evaluators and the possible high noise levels introduced are ones of the most difficult issues to deal with. This situation leads to adopt individuals who can be considered as experts in the specific application domain. Thus, the development of Expert Systems (ES) that consider the opinion of these individuals have been appeared to mitigate the problem. In this work an original methodology for the selection of subjective sequential data for the training of ES is presented. The system is based on the arrangement of knowledge acquired from a group of human experts. An original similarity measure between the subjective evaluations is proposed. Homogeneous groups of experts are produced using this similarity through a clustering algorithm. The methodology was applied to a practical case of the Intelligent Transportation Systems (ITS) domain for the training of ES for driving risk prediction. The results confirm the relevance of selecting homogeneous information (grouping similar opinions) when generating a ground truth (a reliable signal) for the training of ES. Further, the results show the need of considering subjective sequential data when working with phenomena where a set of rules could not be easily learned from human experts, such as risk assessment.

Introduction

The practice of Knowledge Engineering (Van Do, Le Thi, & Nguyen, 2018) has become a very useful approach to solve complex problems that require a high level of human expertise. This discipline involves integrating knowledge into computer systems which emulates the decision-making ability of a human expert in a specific domain. The systems in charge of achieving these tasks are the Expert Systems (ES) (Agarwal & Goel, 2014). The building, maintaining and development of ES (Djamal et al., 2017) are mainly based on the interaction between the knowledge engineer and the domain expert (Yau & Sattar, 1994). The devel- opment of a reliable ES requires a deep understanding and a good representation of the knowledge of the domain expert. In most of the cases, the knowledge representation is based on a set of rules (a production system) that ease the explanation of the decision-making made by the inference engine (Wick & Slagle, 1989). These rules are build from the knowledge acquired from human experts with the application of Machine Learning techniques (such as Neural Networks (Lin & Zhang, 2012), Deep Learning (Wei, He, Chen, Zhou, & Tang, 2017), Decision Trees (Sriram & Yuan, 2012), Fuzzy Logic (Wang, Lee, & Ho, 2007), Bayesian methods (WenBin, XiaoLing, YiJun, & Yu, 2010), Genetic Algorithms (Daza et al., 2011), among others). Knowledge acquisition is a process which aims to extract knowledge, experience and problem-solving procedures from one or more domain experts. Several techniques have been proposed for a correct knowledge acquisition (see Hua, 2008 for a complete review). Nevertheless, there are several problems that must be considered when acquiring knowledge from human experts (Gaines, 1987): • Experts may not be able to express their knowledge in a structured way • Experts may not be aware of the significance of the knowledge they have used. • The expressed knowledge may be irrelevant, incomplete or not understandable. In some cases, depending on the field of application, it may be easier to extract the knowledge from human experts through a continuous scale. This is the case of the risk assessment, where the knowledge could be acquired in a predefined scale (e.g. from 0, no risk, to 100, maximum risk). Here, the knowledge of the experts is gathered in form of subjective sequential data (Prelec, 2004) and could be treated as time series for its study and integration (see, for instance, de Diego, Crespo, Siordia, Conde, & Cabello, 2011; de Diego, Siordia, Conde, & Cabello, 2011; Siordia, de Diego, Conde, & Cabello, 2011a). However, the integration of several opinions into a unique ground truth (i.e. a reliable signal) is a hard-to-achieve task (Liou & Nunamaker, 1990). Two different scenarios appear. The consideration of knowledge from too few experts could provide a ground truth with insufficient information. In contrast, the consideration of knowledge from too many experts could generate a noisy ground truth due to the appearance of possible contradictions between their evaluations (Turban, 1991). Different statistical approaches have been proposed in the past (see, for instance, meta-analysis methods in Brockwell & Gordon (2001)).

1- Introduction

2- Related study

3- Methodology

4- Experimental result

5- Discussion

6- Conclusion and future work

References

Abstract

Diabetes causes a large number of deaths each year and a large number of people living with the disease do not realize their health condition early enough. In this study, we propose a data mining based model for early diagnosis and prediction of diabetes using the Pima Indians Diabetes dataset. Although K-means is simple and can be used for a wide variety of data types, it is quite sensitive to initial positions of cluster centers which determine the final cluster result, which either provides a sufficient and efficiently clustered dataset for the logistic regression model, or gives a lesser amount of data as a result of incorrect clustering of the original dataset, thereby limiting the performance of the logistic regression model. Our main goal was to determine ways of improving the k-means clustering and logistic regression accuracy result. Our model comprises of PCA (principal component analysis), k-means and logistic regression algorithm. Experimental results show that PCA enhanced the k-means clustering algorithm and logistic regression classifier accuracy versus the result of other published studies, with a k-means output of 25 more correctly classified data, and a logistic regression accuracy of 1.98% higher. As such, the model is shown to be useful for automatically predicting diabetes using patient electronic health records data. A further experiment with a new dataset showed the applicability of our model for the predication of diabetes.

Introduction

Diabetes stands among the top 10 causes of death for 2016. Diabetes killed 1.6 million people in 2016, up from less than 1 million in 2000. With this figure diabetes replaced HIV/AIDS as the seventh top cause of death [1]. The number of people with diabetes has risen from 108 million in 1980 to 422 million in 2014, with the global prevalence of diabetes among adults over 18 years of age rising from 4.7% in 1980 to 8.5% in 2014 [2]. By 2040, 642 million adults (1 in 10 adults) are expected to have diabetes. Also, 46.5% of those with diabetes have not been diagnosed [3]. In order to reduce the number of deaths attributable to diabetes, it is essential that methods and techniques that will aid in early diagnosis of diabetes be devised, because a large number of deaths in diabetic patients are due to late diagnosis. In order to achieve cutting-edge techniques for the early diagnosis of diabetes, we need to utilize advanced information technology, and data mining is a suitable field for this. Data mining offers the ability to extract and discover previously unknown, hidden, but interesting patterns from a large database repository. These patterns can aid medical diagnosis and decision-making. Various techniques and algorithms have been designed for application in extracting knowledge and information in the diagnosis and treatment of disease from medical databases. PCA is a simple, nonparametric method for extracting relevant information from confusing data sets [4]. When a large dataset is to be clustered into a user specified number of clusters (k), which are represented by their centroids, k-means will cluster the data by minimizing the squared error function [5], and often misclassifies some data due to outliers; also the time complexity will be greater. To overcome these problems, principal components analysis (PCA) can be used to reduce the dataset to a lower dimension, while ensuring that the least information is lost, and providing a better centroid point for clustering. K-means clustering partitions a dataset into different groups of similar objects. Clusters that are highly dissimilar from the others are regarded as outliers and discarded. Logistic regression is an efficient regression predictive analysis algorithm. Its application is efficient when the dependent variable of a dataset is dichotomous (binary).

abstract

The moment-based method is a fundamental approach to the extraction of affine invariants. However, only integer-order traditional moments can be used to construct affine invariants. No invariants can be constructed by moments with an order lower than 2. Consequently, the obtained invariants are sensitive to noise. In this paper, the moment order is generalized from integer to non-integer. However, the moment order cannot simply be generalized from integer to non-integer to achieve affine invariance. The difficulty of this generalization lies in the fact that the angular factor owing to shearing in the affine transform can hardly be eliminated for non-integer order moments. In order to address this problem, the Mellin polar coordinate moment (MPCM) is proposed, which is directly defined by a repeated integral. The angular factor can easily be eliminated by appropriately selecting a repeated integral. A method is provided for constructing affine invariants by means of MPCMs. The traditional affine moment invariants (AMIs) can be derived in terms of the proposed MPCM. Furthermore, affine invariants constructed with real-order (lower than 2) MPCMs can be derived using the proposed method. These invariants may be more robust to noise than AMIs. Several experiments were conducted to evaluate the proposed method performance.

 Introduction

Images of an object captured from different viewpoints are often subject to perspective distortions [1–4]. If the object is small compared to the camera-to-scene distance, the perspective effect becomes negligible and the affine model provides a reasonable approximation of the projective model [2,5]. Therefore, the extraction of affine invariant features plays an important role in object recognition and registration [6–10]. This method has been used extensively in numerous fields, such as shape recognition and retrieval [11,12], watermarking [13], aircraft identification [14,15], texture classification [16], and image registration [17,18]. The moment-based method is the most commonly used technique for the extraction of affine invariant features. However, only integer-order moments can be used to construct affine invariants [4]. It has been reported that high-order moments are sensitive to noise [19]. Hence, in practice, a moment of the lowest possible order should be used [20]. For similarity transform (including only translation, scaling, and rotation), Fourier Mellin descriptors [21] can be viewed as the invariants constructed by complex number order moments. However, similarity transform is only a spe cial case of affine transform [4]. Thus far, an order of moments for constructing affine invariants can only be an integer. In this paper, we consider generalizing the moment order from integer to noninteger, and construct affine invariants by means of the proposed moment.

Statistics is a mathematical science for collecting, analyzing, interpreting and drawing conclusions from a set of data. The first instance of descriptive statistics was given by the “bills of mortality” collected by John Graunt’s in 1662. Around 1749, the meaning of statistics was limited to information about “states” to design the systematic collection of demographic and economic data. In the early 19th century, accumulation of information intensified, and the definition of statistics extended to include disciplines concerned with the biology and the biomedicine. The main important areas of statistics are the descriptive statistics and the inferential statistics. The first statistical method gives numerical and graphic procedures to summarise a collection of data in a clear and understandable way without assuming any underlying structure for such data, the second one provides procedures to draw inferences about a population on the basis of observations obtained from samples. Therefore, the use of descriptive and inferential methods enables researchers to summarize findings and conduct hypothesis testing. The descriptive statistics is the primary step in any applied scientific investigation to simplify large amounts of data in a sensible way. Indeed, the goal of descriptive statistics is to give a clear explanation and interpretation of the information collected during an experiment. For instance, in medicine and biology, the observations obtained by a phenomenon are large in number, dispersed, variable and heterogeneous preventing the researcher from directly understanding it. To have a full knowledge of the under investigated phenomenon, it is first necessary to arrange, describe, summarize and visualize the collected data (Spriestersbach et al., 2009). In this article, we present two statistical descriptive methods: graphical and numerical. The graphs and tables are used to organize and visualize the collected data. Numerical values are computed to summarize the data. Such numbers are called parameters if they describe population; they are called statistics if they describe a sample. The most useful numerical value or statistics for describing a set of observations are the measures of location, dispersion and symmetry. Generally, graphical methods are better suited than numerical methods for identifying patterns in the data, although the numerical approaches are more precise and objective. Since the numerical and graphical approaches complement each other, it is wise to use both. In the following sections, we first introduce the statistical data types (quantitative or qualitative), and then, the way to organize and visualize collected data using tables and graphs. Several kinds of statistics measures (location, dispersion and symmetry) are also discussed to provide a numerical summary of data (Manikandan, 2011a,b,c; Wilcox and Keselman, 2003). Finally, clinical data are elaborated and discussed as an illustrative example.