**Part 3**

**Clinical Applications**

90 Medical Informatics

Zhang, S.Q.; Zhou, X.; Wang, H.; Suffredini, A.; Gonzales, D.; Ching, W.K.; Ng, M. & Wong

Research, 7, pp. 222-231.

S. (2007). Peak detection with chemical noise removal using Short-Time FFT for a kind of MALDI Data, *Proceedings of OSB 2007*, Lecture Notes in Operations

**5**

*Brazil*

**Using Brazilian Digital TV to**

 and Vicente Ferreira De Lucena Jr. *Federal University of Amazonas, UFAM,*

**Integrate Health Care Services**

Vandermi Silva, Ricardo Erikson Veras De Sena Rosa

**Embedded in Medical Commercial Devices**

The Digital TV (DTV) System is getting a stimulus within industry and in Brazil that stimulus is based on the belief that this new system will be successful. Several proposals for extending the potential of interactivity, for providing innovative services are available today. In this context, health care is a concept being studied. The health care concept is relative to care about health, in preventing diseases, quality of life and with applications to hospitals, elderly care centres and so on. Integrating health care technology with home devices is a new trend. Thus this chapter shows how the software programmer can

Our solution is a software platform based on the home gateway, which is in charge of all medical devices interconnecting with the DTV. As proof of the concept, we will build two implementation scenarios. One for measuring heart beat frequency and the other for measuring blood pressure. All the procedures related to the integration are detailed. We believe this work will be useful in this new field, bringing alternative ways for using

The chapter is divided into sections containing foundations, the DTV architecture of health care, construction on a prototype of health care and tests and results, to present the Brazilian standard for digital TV applications and the concepts of health. An overview of the integrated system for medical devices in the architecture section will show an architecture based on filters through all the details and technologies used. In the section on building the prototype we will present how to configure a development environment and how to implement a simple example using the Java programming language and the language Lua. Finally, in the tests and results section illustrations of a system running on a TV will be

The technology improvements in wireless communications and the increasing usage of commercial medical devices to monitor patients have contributed to the development of

integrating devices and sensors with the architecture based in filters presented here.

common devices to provide quality of life to the home user.

presented with the Brazilian digital TV standard installed.

**1. Introduction**

**1.1 Foundations**


Biometric-Based Human Recognition Systems: An Overview

*David Palma and Pier Luca Montessoro*

### **Abstract**

With the proliferation of automated systems for reliable and highly secure human authentication and identification, the importance of technological solutions in biometrics is growing along with security awareness. Indeed, conventional authentication methodologies, consisting of knowledge-based systems that make use of something you know (e.g., username and password) and token-based systems that make use of something you have (e.g., identification card), are not able to meet the strict requirements of reliable security applications. Conversely, biometric systems make use of behavioral (extrinsic) and/or physiological (intrinsic) human characteristics, overcoming the security issues affecting the conventional methods for personal authentication. This book chapter provides an overview of the most commonly used biometric traits along with their properties, the various biometric system operating modalities as well as various security aspects related to these systems. In particular, it will be discussed the different stages involved in a biometric recognition process and further discuss various threats that can be exploited to compromise the security of a biometric system. Finally, in order to evaluate the systems' performance, metrics must be adopted. The most widely used metrics are, therefore, discussed in relation to the provided system accuracy and security, and applicability in real-world deployments.

**Keywords:** biometrics, authentication, identification, human traits, evaluation criteria, pattern recognition system, security, vulnerabilities

### **1. Introduction**

This chapter stands as an introduction to the field of biometrics which is rising as an advanced layer to many user- and enterprise-centric security systems. In fact, conventional authentication methods, such as traditional passwords, have long been a weak point for security systems. Biometrics aims to answer this issue by linking proofof-identity to our physiological traits and behavioral patterns. It is therefore important to present the concepts and primitives of performance metrics due to their impact on secure biometric systems. Thus, a brief overview is given to describe the main biometric traits along with their properties as well as the various biometric system operating modalities and the relatively known vulnerabilities. Finally, the criteria for performance evaluation have been defined to determine the system accuracy and security which are related to the applicability in real-world deployments.

### **2. Biometric traits**

Various biometric modalities have been developed over the years making the biometric technology landscape very vibrant. Prominent examples of physiological/ biological and behavioral biometric characteristics, which have been the purpose of major real-world applications, are illustrated in **Figure 1**.

### **2.1 Physiological/biological (intrinsic) human characteristics**

Biological biometrics make use of traits at a genetic and molecular level which may include features like DNA or blood, whilst physiological biometrics involve the individual physical traits like a fingerprint, iris, or the shape of the face. On the other hand, behavioral biometrics are based on patterns unique to each person, for example, how an individual walks, speaks, or even types on a keyboard. Some examples of biometric traits are briefly described below.

Fingerprint: Fingerprint recognition, which measures a finger's unique pattern, is one of the oldest forms of biometric identification. This trait appears as a series of dark lines and white spaces when captured from the device and it consists of a set of ridges and valleys located on the surface tips of a human finger to uniquely distinguish individuals from each other. The fingerprint features are generally categorized into— (i) macroscopic ridge flow patterns (core and delta points), (ii) minutia features (which consists of the ridge bifurcations/trifurcation and the ridge endings), and (iii) pores and ridge contour attributes (incipient ridges, pore, shape, and width). Fingerprints of identical twins are different and so are the prints on each finger of the same person [1].

Face: Facial features use the location and shape (geometry) of the face, including the distance between the eyes, the distance from the chin to the forehead, or other measures that involve eyebrows, nose, lips, and jawline [2]. This kind of recognition is

**Figure 1.**

*Examples of physiological/biological and behavioral traits applied in biometric recognition applications.*

a nonintrusive method with reasonable authentication performance in commercially available systems. However, several constraints may be imposed by the systems on how the facial images are obtained to work properly, for example, controlled illumination and background. Moreover, its susceptibility to change due to factors such as aging or expression may present a challenge [3].

Hand geometry: This trait is based on the geometric characteristics of the hand such as the length and width of fingers, their curvature, and their relative position to other features of the hand. Though once a dominant method of biometric measurement due to the requirement of the low complexity in feature extraction and low-cost imaging, modern advances in biometrics have replaced its relevance in most applications [4]. Furthermore, such a biometric trait is not known to be very distinctive and hand geometry-based recognition systems cannot be scaled up for systems requiring the identification of an individual from a large population. In addition, handgeometry features from both hands are expected to be similar, as their anatomy is quite similar [5].

Iris: Systems based on this trait are among the most accurate biometric systems available. This human characteristic refers to the colored part in the eye that consists of thick, thread-like muscles characterized by unique folds and patterns that can be used to identify and verify the identity of humans. Furthermore, this biometric trait is stable because iris patterns do not vary during the course of a person's life and are not susceptible to loss, manipulation, or theft, making an iris recognition system robust to spoofing attacks. One interesting point worth noting is that even the two eyes in the same person have different patterns [6].

Ear acoustic: The main purpose of this kind of recognition system is to map one aspect within acoustic ear recognition, namely the performance of the ear characteristics bands and peaks. An ear signature is generated by probing the ear with inaudible sound waves which are reflected bouncing in different directions and picked up by a small microphone. The shape of the ear canal determines the acoustic transfer function which forms the basis of the signature. The recognition process is also possible, whilst the subject is on the move and caters to the protection of secrecy, which expands the applicability of this technology [7].

Vascular patterns: This biometric trait has been largely investigated for its advantages over other features. In fact, the vascular pattern of the human body is unique to every individual, even between identical twins [8], remains steady during the course of a person's life, and lies underneath the human skin ensuring confidentiality and robustness to counterfeiting, as opposed to other intrinsic and extrinsic biometric traits that are more vulnerable to spoofing, thus leading to important security and privacy concerns [9]. To acquire the network structure of blood vessels underneath the human skin, a vascularbased recognition system uses near-infrared light to reflect or transmit images of blood vessels, since they are almost invisible in normal lighting conditions [10]. The most commonly used vascular biometric solutions use hand-oriented modalities, such as finger vein, palm vein, hand dorsal vein, and wrist vein recognition, as well as eye-oriented modalities, such as retina and sclera recognition [11].

Electrocardiogram (ECG): This trait considers the human heart and body anatomic features form the shape of the ECG signal typically acquired using a few electrodes, amplifiers, filters, and a data acquisition module, and which reports the strength and timing of the electrical activity of the heart [12]. However, scientific findings to date throw doubt on the specificities of real-world application scenarios and acceptability by the potential end users, which pose several constraints and questions.

Deoxyribonucleic acid (DNA): DNA matching is based on a common molecular biology method named short tandem repeat (STR)1 analysis, which is used to compare allele repeats at specific locations on a chromosome in DNA between two or more samples [14, 15]. DNA-based biometric recognition has been widely used in forensic science and scientific investigation due to its very high accuracy, despite the fact that identifications require tangible physical samples and cannot be done in real time.

#### **2.2 Behavioral (extrinsic) human characteristics**

Keystrokes, handwriting, gait, how a person uses a mouse, and other movements are some of the behavioral traits that a biometric system may analyze to assess the individual's identity.

Gait: This characteristic may be changeable over a large time span due to various reasons, such as weight gain [16]. Thus, it can be used in low-security applications for massive crowd surveillance as it can quickly identify people from afar based on their walking style, even harnessing the potential of a large number of surveillance cameras installed in public locations into a biometric system. In fact, such a system does not require the individuals to be cooperative, nor that they wear any special device or equipment to be recognized [17].

Mobile interactions: It is based on the unique ways in which users swipe, tap, pinchzoom, type, or apply pressure on the touchscreen of mobile devices like tablets and phones, thus providing characteristic patterns that may be used to identify people, even considering further features deriving from on-board sensors such as GPS, gyroscope, and accelerometers [18], which can also be configured to collect data in passive mode. Therefore, mobile interactions-based biometrics focuses not so much on the outcome of the user's actions but rather on the way a user performs those actions.

Signature: Signature recognition is the most widely accepted method for documents authentication and it makes use of shorter handwriting probes compared to text-independent writer recognition methods, but it requires to write the same sign every time. A signature authentication scheme can be categorized into two methods— (i) off-line or static (the signature is digitized after the writing process) and (ii) online or dynamic (the signature is digitized during the writing process). Signature biometric features are extracted by analyzing curves, edges, spatial coordinates, inclination, the center of gravity, pen pressure, and pen stroke of the signature samples in both offline and online applications. However, dynamic information like writing speed and stroke order is available only in online signatures [19].

Mouse dynamics: It makes use of patterns in mouse or trackpad cursor movement including clicks, trajectories, direction changes, tracking speed, and the relationships between them. Mouse-generated movement features are relatively stable for the same individual and different compared to other users, as such can be used to authenticate individuals [20]. These methods are most often used to continuously verify the user's identity.

Keystrokes: Keystroke dynamics (also known as typing biometrics) include the tracking of the rhythm used to type on a keyboard.  The first one occurs when an individual presses a key, whilst the second one is associated with the event that occurs when the pressed key is

1 STR is the DNA sequence of the short repeat region of the sequence in the noncoding region of the human genome [13].

released. Making use of these events, a set of inter-key and intra-key features known as delay times, hold times, and key down-key downtimes can be extracted. In general, keystroke recognition will work on the computer or virtual keyboards, mobile phones, smartwatches, and touchscreen panels, providing a low-cost authentication method that can be easily deployed in a variety of scenarios [21].

Voice: Voice recognition technology falls under both the physiological and behavioral biometric categories. Voice biometric recognition allows to distinguish among humans' voice for personal authentication as voice features include physical characteristics such as vocal tracts, nasal cavities, mouth, and larynx [22]. Behaviorally, the way a person speaks or says something, for example, tone, movement variations, accent, pace, and so on, is also considered unique to each individual. Using data from both physiological and behavioral biometrics creates, therefore, a precise vocal signature, though mismatches may occur due to illness or other factors.

### **2.3 Properties of biometric traits**

The main requirements that should be satisfied before a trait can be characterized as suitable for its applicability in a biometric recognition system, are briefly discussed as follows [23].

Even though any human characteristic can be used as a biometric trait as long as the previous requirements are satisfied, in real-world biometric recognition applications there are a number of other issues that should be considered, such as:

### **Table 1.**

*Comparison study of the most common traits based on the characteristics of biometric entities.*

situation caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [24], raising questions about how safe using touch-based biometric systems really is as touching the sensors can potentially spread viruses. As a consequence, lessconstrained biometrics will likely be the preferred modality, whilst there may be less demand for other solutions that rely on physical contact with a reader.

• Circumvention: This property reflects how easily the system can be deceived through potential spoofing attacks. It refers to the ways in which an attacker can endeavor to bypass a biometric system and finally attack the weak spot of such a system in order to gain unauthorized access.

Real-life biometric recognition systems ought to meet the requirements of accuracy, speed, and resource constraints, be harmless to the users, be accepted by the intended population as well as sufficiently robust to various fraudulent methods and attacks to the system [25].

**Table 1** is reported a comparison study of the most popular traits based on the characteristics of biometric entities [26].

### **3. Biometric system operating modes**

A biometric system can provide two kinds of operating modes (identity management functionalities), namely, *verification* and *identification*. Biometric systems can indeed automatically authenticate2 or identify subjects in a reliable and fast way and

2 Throughout this book chapter, the term authentication will be used as a synonym for verification.

*Biometric-Based Human Recognition Systems: An Overview DOI: http://dx.doi.org/10.5772/intechopen.101686*

**Figure 2.** *Basic building blocks of a generic biometric recognition system.*

are, therefore, suitable to be used in a wide range of applications to face the risks of unauthorized logical or physical access and identity theft, as well as new threats such as terrorism or cybercrime [27]. **Figure 2** provides a high-level view of a generic biometric recognition system as well as all its basic building blocks, whilst **Figure 3** depicts the enrollment and the biometric recognition schemes of the authentication and identification modalities.

### **3.1 Authentication**

In the authentication mode, the purpose of the biometric system is to verify whether an individual's claimed identity is genuine or not (binary classification). Thus, the captured biometric data (query) is compared only with the biometric template(s) stored in the system database and corresponding to the claimed identity (one-to-one or one-to-few comparison). Given a claimed identity *I* and a query feature set *xQ* , the biometric system has to be categorized *I*, *xQ* � � into "genuine" or "impostor" class. Let *xE I* be the stored biometric template corresponding to the identity *I* (i.e., the enrolled user with identity *I*). The similarity measure between *xQ* and *xE I* gives, as a result, a matching score. Hence, the biometric system applies the decision rule given by

$$\mathfrak{s}(I, \mathfrak{x}^{Q}) \in \begin{cases} \text{genuine}, & \text{if } \mathfrak{s}(\mathfrak{x}^{Q}, \mathfrak{x}\_{I}^{E}) \ge \xi, \\\\ \text{impostor}, & \text{otherwise}, \end{cases} \tag{1}$$

where *S* represents a similarity function and *ξ* represents a pre-defined threshold at which the system is intended to operate. The authentication mode is typically employed for positive recognition, where the aim is to prevent multiple people from using the same identity [28].

**Figure 3.**

*Different operating modes of a biometric system—(a) enrollment mode, (b) authentication mode (the dashed line is an optional operation aimed at updating a specific user's template), and (c) identification mode.*

### **3.2 Identification**

In the identification mode, the purpose of the biometric system is to recognize an individual's identity by searching the templates of all the enrolled individuals in the system database for a match (one-to-many comparison) without the subject having to claim an identity.

*Biometric-Based Human Recognition Systems: An Overview DOI: http://dx.doi.org/10.5772/intechopen.101686*

This operating mode can be further split into negative and positive identification—in the negative identification (also known as *screening*), the user is considered to be hiding her/his true identity from the biometric system, whilst in the positive identification, the user tries to positively identify herself/himself to the system without explicitly claiming an identity. Given a query feature set *xQ*, the biometric system has to determine the identity *Ik* ∀*k*∈ f g 1, 2, … , *n*, *n* þ 1 where f g *I*1,*I*2, … ,*In* are identities of the enrolled users in the system, whilst *In*þ1 represents the failure case where no identity can be assigned for the given query (*open-set identification*). Hence, assuming that *xE Ik* is the stored template corresponding to the identity *Ik*, the biometric system applies the decision rule given by

$$\mathbf{x}^{Q} \in \begin{cases} I\_k, & \text{if } \max\_k \left\{ \boldsymbol{\delta} \left( \mathbf{x}^{Q} \mathbf{x}\_{I\_k}^{E} \right) \right\} \ge \xi, \\\ I\_{n+1}, & \text{otherwise}, \end{cases} \tag{2}$$

where *s* represents a similarity function and *ξ* represents a pre-defined threshold at which the system is intended to operate.

The identification mode is typically employed for screening3 , where the aim is to prevent a single person from using multiple identities [28].

### **4. Vulnerabilities**

Biometric-based cybersecurity solutions ensuring tight access control are essential in preventing intrusions and unauthorized accesses. However, even though a biometric system enhances user convenience and security, does not necessarily mean that it is also exempt from security and privacy issues. Many security measures in biometric systems are designed to protect one or more facets of the CIA triad, which is a common framework that refers to confidentiality, integrity, and availability [31].

3 In some real scenario, such as latent palmprint matching [29], it is preferable to use a semi-automated approach aimed at providing the top *n* identities that best match to the given template for further analysis by a human expert. Alternatively, it is possible to consider all the identities whose corresponding match scores exceed the threshold *ξ* that leads to a challenging task in a quite large database (e.g., FBI's next generation identification (NGI) system, which provides the world's largest repository of biometric and criminal history information [30]).

**Figure 4.** *Attack points of a general biometric system.*

• Availability of information refers to ensuring that only legitimate and authorized parties are able to access the information when needed. Problems affecting the information system could make it impossible to access information, thereby making the information unavailable. Some types of security attacks attempt to deny access to the appropriate user, either for the sake of inconveniencing them, or because there is some secondary effect.

Biometric recognition systems implicitly (and effectively) address the authentication problem included in the last issue of the CIA triad, which consists in guaranteeing access to data only to authorized users. The reason for this is because biometric traits are (generally) not susceptible to loss, manipulation, or theft, and therefore overcome the security issues affecting the conventional methods for personal authentication, such as knowledge-based and token-based systems. However, it must be kept in mind that a biometric-based security solution is composed of several different components and the recognition module, which is only capable of addressing the authentication aspect, is just one of them. Thus, a logical structure-based approach of biometric systems is used to describe the eight points of attacks illustrated in **Figure 4**.

4 Since biometric recognition algorithms are likely susceptible to reverse engineering techniques, it is possible to conduct off-line experiments on a copy of the biometric software to be hacked in order to achieve the objective [32].

may be used to forge features in presented samples to cause computation of incorrect features. To achieve this, an attacker can replace the feature extractor with a Trojan horse program that produces the desired feature sets.

### **5. Criteria for performance evaluation**

The reliability and validity of a biometric scheme as well as the selection of a certain biometric trait for an application are determined by specific measures that are used to evaluate the recognition accuracy and effectiveness as addressed in ISO/IEC Standards [36]. Accordingly, to evaluate the accuracy of the proposed method based on a single-sample approach for unimodal biometric systems, each sample in the database should undergo a one-to-one matching test against every single stored sample. Hence, a comparison between a subject with a real identity *Ir* and a subject with claimed identity *Ic* is aimed at testing the hypothesis:

$$H\_0: \{I\_r = I\_\mathfrak{c}\} \text{ versus } H\_1: \{I\_r \neq I\_\mathfrak{c}\} \tag{3}$$

where *H*0 is the null hypothesis that the user is who s/he claims to be (genuine or intra-class matching), whilst *H*1 is the alternative hypothesis that the user is not who s/he claims to be (impostor or inter-class matching). To test the hypothesis in (3), it is required to compute a similarity measure, *s Q*ð Þ , *T* where large (respectively, small)

values of *s* indicate that the template *T* of the claimed identity *Ic* in the database and the biometric query *Q* of a real user *Ir* are close to (far from) each other. Formally, the verification problem consists of determining if a claimed identity *I* with biometric data *Q* belongs to the class *H*0 or not:

$$(I, Q) = \begin{cases} H\_0, & \text{if } s(Q, T) \ge \xi, \\ H\_1, & \text{otherwise.} \end{cases} \tag{4}$$

Precisely, given a threshold *ξ*, all matching values *s* lower (respectively, greater) than *ξ* lead to the rejection (acceptance) of the null hypothesis [37]. Therefore, whether the hypothesis is accepted or not, the test is prone to two kinds of error:

Let *H*0 and *H*1 be the labels that denote the genuine and impostor classes, respectively. Assume also that the *p s*ð Þ j*H*0 and *p s*ð Þ j*H*1 represent the probability density functions of the genuine and impostor scores, respectively. Then the FAR and FRR distributions are given by:

$$FAR(\xi) = p(\mathfrak{s} \ge \xi | H\_1) = \int\_{\xi}^{+\infty} p(\mathfrak{s} | H\_1) d\mathfrak{s},\tag{5}$$

$$FRR(\xi) = p(s < \xi | H\_0) = \int\_{-\infty}^{\xi} p(s | H\_0) ds. \tag{6}$$

The false acceptance and false rejection rates are functions of the system threshold *ξ* and are closely related because the increase of one implies the decrease of the other. Hence, for a given biometric system, it is not possible to decrease both these errors at the same time by varying the threshold *ξ* [25]. The separation between the two distributions (or classes) indicates the ability of the system to distinguish the genuine user samples from those of the impostors. Indeed, the separation also provides a hint on the threshold point that maximizes the variance between the two classes in order to correctly mark a user sample image as authentic or impostor [23].

The genuine acceptance rate (GAR) is instead the probability of accepting the null hypothesis *H*0 when input is valid, hence it can be used as an alternative to FRR:

$$GAR(\xi) = p(s \ge \xi | H\_0) = \mathbf{1} - FRR(\xi). \tag{7}$$

Depending on the security level required by the final application (i.e., forensics, surveillance and homeland security, civilian, or high-security applications), the same biometric system may operate at different threshold values (*ξ*), as illustrated in **Figure 5**.

Hence, in order to evaluate the biometric system performance as a function of the threshold *ξ*, the following curves can be considered:

*Biometric-Based Human Recognition Systems: An Overview DOI: http://dx.doi.org/10.5772/intechopen.101686*

#### **Figure 5.**

*Examples of biometric system error rates: (a) FAR and FRR for a given threshold ξ are displayed over the genuine and impostor score distributions and (b) typical operating points of different biometric applications are displayed on a DET curve aimed at relating FAR and FRR at different threshold values.*

Furthermore, the above-mentioned ROC and DET curves are thresholdindependent, allowing performance comparison of different biometric systems under similar conditions [23], as illustrated in **Figure 6**. Given a set of thresholds f g *ξi* ∣ *s*min ≤*ξi* ≤*s*max ∀*i* ∈f g 1, 2, … , *n* where *s*min and *s*max are the minimum and maximum scores, respectively, in a given set of match scores f g*si* ∣ 0≤ *si* ≤1 ∀*i* ∈f g 1, 2, … , *n* . Then, it is possible to generate a ROC curve computing the overall false acceptance and false rejection rates for each threshold value *ξ* as follows:

$$FAR = \frac{1}{N} \sum\_{k=1}^{N} FAR(\xi),\tag{8}$$

$$FRR = \frac{1}{N} \sum\_{k=1}^{N} FRR(\xi),\tag{9}$$

5 In the normal deviate scale, the threshold values *ξ* correspond to linear multiples of standard deviation *σ* of a Gaussian distribution. Thus, if the FAR and FRR distributions are Gaussian, the corresponding DET curve would be linear [25].

#### **Figure 6.**

*Example of vascular-based biometric systems performance comparison [4]. Comparative graph of—(a) DET curves generated by plotting FRR against FAR and (b) ROC curves generated by plotting GAR against FAR.*

where *N* represents all identities being evaluated by the system and

$$FAR(\xi) = \frac{\text{no.of ARs}}{\text{no.of impostor access}} \tag{10}$$

$$FRR(\xi) = \frac{\text{no.of FRRs}}{\text{no.of genuine access}}.\tag{11}$$

Since biometric systems cannot jointly provide a false acceptance rate equal to zero and a perfect verification/identification rate, the system threshold must be adjusted for the given application considering the trade-off between accuracy and false positives. Once the threshold has been set, the system can be evaluated by means of common measures that are used to assess the classification accuracy and effectiveness. In this context, we are interested in confirming or denying the identity of a subject leading thus to a dichotomous binary classification problem, where the labels are *P* (genuine) and *N* (impostor) and the predictions of the classifier are summarized in a 2 � 2 contingency table known as confusion matrix [38] (expanded in **Table 2**):

$$\mathbf{M} = \begin{bmatrix} TP & FN \\ FP & TN \end{bmatrix} \tag{12}$$

**Table 2.**

*Example of confusion matrix for a dichotomous binary classification problem.*

which completely describes the outcome of the classification task. This contingency table may be expressed using raw counts of the number of records from class times each predicted label is associated with each actual class. As illustrated in **Table 2**, the confusion matrix reports:

Based on the entries in the confusion matrix, the total number of correct predictions carried out by the model is *TP* þ *TN*, whilst the number of incorrect predictions is *FP* þ *FN* [39]. Therefore, if.

$$\mathbf{M} = \begin{bmatrix} n^+ & \mathbf{0} \\ \mathbf{0} & n^- \end{bmatrix} \tag{13}$$

where obviously *n*þ ¼ *TP* þ *FN* and *n*� ¼ *FP* þ *TN*, then the classification has been perfectly done. Conversely, if the confusion matrix is as follows

$$\mathbf{M} = \begin{bmatrix} \mathbf{0} & n^+ \\ n^- & \mathbf{0} \end{bmatrix} \tag{14}$$

it represents the worst case (perfect misclassification).

Several measures have been defined to assess the quality of a prediction [40], aimed at conveying into a single figure the structure of *M*. The most used functions are briefly described as follows.

**Precision** also known as positive predictive value (PPV) counts the true positives, how many samples are properly classified within the same cluster (closeness of the measurements to each other)

$$PPV = \frac{TP}{TP + FP}.\tag{15}$$

**Sensitivity** also known as recall or true positive rate (TPR) refers to the proportion of the samples properly classified as true positives out of the actual number of true positives

$$TPR = \frac{TP}{TP + FN}.\tag{16}$$

**F-measure** combines precision and recall in a single metric, indeed, it is the harmonic mean of precision and sensitivity and as a function of *M*, has the following form:

$$F\_1 = 2\frac{\text{PPV} \cdot \text{TPR}}{\text{PPV} + \text{TPR}} = \frac{\text{TP}}{\text{TP} + \frac{1}{2}(\text{FN} + \text{FP})} \tag{17}$$

where the worst case (*F*1 ¼ 0) is achieved for *TP* ¼ 0, whilst the best case (*F*1 ¼ 1) is reached for *FN* ¼ *FP* ¼ 0.

**Accuracy** represents the ratio between the correctly predicted instances and all the instances in the dataset, whose range is between 0 (worst case) and 1 (best case):

$$\text{ACC} = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{TN} + \text{FP} + \text{FN}}.\tag{18}$$

**Matthews correlation coefficient** is the measure of the quality of binary (twoclass) classifications:

$$\text{MCC} = \frac{\text{TP} \cdot \text{TN} - \text{FP} \cdot \text{FN}}{\sqrt{(\text{TP} + \text{FP})(\text{TP} + \text{FN})(\text{TN} + \text{FP})(\text{TN} + \text{FN})}} \tag{19}$$

it is a correlation coefficient between the actual and predicted binary classifications and it returns a value between �1 (worst case) and 1 (best case).

Accuracy and F-score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets [40]. Hence, among all the parameters described above, the Matthews correlation coefficient (MCC) is the only one that takes into account true and false positives and negatives and is generally regarded as a balanced measure that can be used even if the classes are of very different sizes [41].

### **6. Conclusions**

Biometric-based technologies make use of unique behavioral (extrinsic) and/or physiological/biological (intrinsic) attributes to overcome the security issues affecting the conventional methods for identity authentication. Even though biometrics has been in use for decades, the advent of technology has expanded its application from primarily criminal identification to a wide range of everyday tasks, becoming a regular security process of our nowadays life. Accurate authentication or identification is fundamental to physical security, cyber security, military applications (e.g., biometric-driven lethal autonomous weapon systems), financial transactions, contracts and employment, public services, criminal justice, national security, and more. The approaches that have been proposed in literature depend on the type and the number of the underlying biometric traits, which, in general, cannot be easily transferred between people, and thereby represents a highly secure unique identifier. As a matter of fact, various biometric modalities have been developed over the years making the biometric technology landscape very vibrant. In this book chapter, we have provided an overview of the most commonly used biometric traits along with their properties, the various biometric system operating modalities as well as various limitations and weaknesses related to these systems. Indeed, biometric technologies have a number of vulnerabilities that underscore the concerns over their employment and may result in the failure of the technology to perform as anticipated. We have also discussed how the system threshold must be adjusted for the given application considering the trade-off between accuracy and false positives since biometric systems cannot jointly provide a FAR equal to zero and a perfect recognition rate. Finally, the criteria for performance evaluation have been defined to determine the system's

*Biometric-Based Human Recognition Systems: An Overview DOI: http://dx.doi.org/10.5772/intechopen.101686*

accuracy and security which are related to the applicability in real-world deployments, even though the existing evaluation metrics are more related to the data quality than the security aspects of the overall system. However, despite the risks, biometrics provide very compelling security solutions remaining a growing way to verify identity offering tons of promise for the future of cybersecurity.

### **Conflict of interest**

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

### **Abbreviations**

*Recent Advances in Biometrics*

### **Author details**

David Palma\*† and Pier Luca Montessoro† Polytechnic Department of Engineering and Architecture, University of Udine, Udine, Italy

\*Address all correspondence to<EMAIL_ADDRESS>
† D.P. and P.L.-M. designed the research; D.P. performed the research and wrote the paper. The results and the paper were analysed and reviewed by P.L.-M. All authors have read and agreed to the published version of the manuscript.

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Biometric-Based Human Recognition Systems: An Overview DOI: http://dx.doi.org/10.5772/intechopen.101686*

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### **Chapter 3**


**Abstract**

This chapter presents a comprehensive framework for measuring and managing service quality of mobile smart devices across seven key dimensions: interactions, usability, efficiency, information quality, availability, security, and reliability. Grounded in established models like SERVQUAL and E-S-QUAL, the framework identifies specific metrics based on user perceptions for evaluating expertise, accuracy, responsiveness, and customization. The SERVQUAL model outlines five service quality dimensions: tangibles, reliability, responsiveness, assurance, and empathy. E-S-QUAL adapts these to the digital environment. The proposed framework draws on these seminal models while incorporating new factors relevant to interconnected, artificial intelligence (AI)-enabled mobile platforms and devices. The framework is designed to enable proactive service quality management throughout the customer journey with mobile smart devices—from product design and testing to postsales support. A continuous measurement, analysis, and improvement process is outlined involving quantitative and qualitative techniques. An implementation road map covers considerations for organizing, change management, and training to integrate the methodology into product development processes. The goal is to provide technology companies with actionable and evidence-based guidance for optimizing satisfaction and loyalty among mobile smart device consumers. As these devices continue proliferating, managing user expectations through integrated hardware, software, and service experiences will be key to competitive positioning in mobile ecosystems.

**Keywords:** service quality, mobile devices, user experience, satisfaction, loyalty, SERVQUAL, E-S-QUAL


*Mixed 2D-3D Halide Perovskite Solar Cells DOI: http://dx.doi.org/10.5772/intechopen.97684*

*Solar Cells - Theory, Materials and Recent Advances*

carrier traps, which favors the charge transport.

pinhole density with rising temperature, resulting in high-efficiency devices. The inorganic layers in 2D-perovskite films prepared by the hot-cast method have a preferential orientation vertical to the substrate with excellent crystallinity and few

*(a) The (BA)2(MA)3Pb4I13 films cast from room temperature (R.T.) to 150°C, (b) GIXRD spectra, (c, d) AFM images, and (e, f) SEM images of films prepared by traditional room-temperature-cast method. Reprinted*

*Another solution vapor annealing method* was used to prepare high-quality (PEA)2PbBr4 nanosheets at room temperature as an emitting layer for LEDs [79]. Using a precursor solution of DMF comprising PEABr and PbBr2 (2/1) was spincoated on the top of the ITO/PEDOT:PSS substrate and the productive sample was placed face down on the edge of a glass dish without contacting it. In the following step, the dish was transferred into a lidded beaker filled with DMF to form a closed space with DMF vapor at 30°C for several minutes before the DMF vapor diffused under the (PEA)2PbI4 film and contacted with it. The film was removed out into open air rapidly and heated at 100°C for 10 min while turning into purple. By using DMF vapor annealing, the small and compact (PEA)2PbI4 perovskite grains was recrystallized into sized nanosheets equally distributed on the substrate, which has larger grain size, higher crystallinity, and higher P.L. intensity (higher photoluminescence quantum yield (PLQY) due to the quantum confinement), comparing with unprocessed films. The nanosheet-LEDs showed a longer P.L. lifetime (much longer than the 3D-perovskite MAPbI3). They exhibited a small leak of current and low turn-on voltage, the external quantum efficiency (EQE) was 20 times higher

*Sequential deposition method* The sequential deposition is used to prepare a sequential dipping process and sequential spin-coating of 2D-perovskite films [80]. The dipping process was used to fabricate quasi-2D and quasi 3D-perovskite films with spin-coating (IC2H4NH3)2PbI4 layers on mp-TiO2 substrates [81]. It is observed, immersing the films into MAI solution with a specific concentration for different times (1–5 min), transferring the films into cleaning fluid (2 mL isopropanol mixed with 10 mL methylbenzene) to remove the MAI residual. With increasing

increased inorganic PbI4 layers' thickness. Then, the nanoparticles regularly grew up and interconnected with each other in film morphology (uniform films without

cations in solution passed through (IC2H4NH3)2PbI4 films and

**172**

than poly-LEDs.

**Figure 8.**

*with permission from Ref. [66].*

dipping time, MA+

residuals), leading to sharper diffraction peaks. In the previous studies, ITO/NiOx/ mixed-dimensional perovskite/PCBM/bis-C60/Ag devices were prepared using the sequential spin-coating method with mixed-cations perovskite FAxPEA1 − xPbI3 as the active layer [81]. With observing, a PbI2 layer was spin-coated on NiOx substrate, followed by the PEAxFA1 − xI solution loaded on it for 15 s before spin-coating. After thermal treatment, the mixed-cations FAxPEA1 − xPbI3 perovskite films were formed, with migrating PEA<sup>+</sup> cations to lattice surfaces and grain boundaries of 3D-perovskite FAPbI3 to form quasi 3D- rather than quasi 2D-perovskite at room temperature. This self-assembly organic shell could prevent perovskite crystals from ambient moisture and passivated the surface defects to enhance the device performance and quasi 3D-perovskite's stability (the transition energy transformed from black phase to yellow phase). In summary, the 2D-perovskite films were commonly fabricated using one-step spin-coating method (simple process and low cost), with small-n members contrary to their 3D-counterparts but increasing "n" showed pre- or post-treatment is necessary such as hot-cast, antisolvent or solution vapor annealing processes for better crystallization. On the other hand, sequential deposition was used for obtaining efficient charge collection and extraction, dense and uniform films (due to the superior PbI2 framework for crystal growth). In sequential spin-coating, the large cations were not likely to enter into the lattice but pack on the surface of 3D-perovskite grains, forming a quasi 3D-structure. For future large-scale fabrication, the solution process was not enough for high-quality. Still, doctor blading, pressure-processing method, and so on were more appropriate as long as high-quality crystal is needed [82]. Melt processing is another innovative technique with an excellent quality. It was implemented in 2017 to fabricate lead iodide based 2D-perovskites using PEA derivatives [83]. Although it had not been used for device fabrication yet, however it is a promising one that it exhibits high phase purity, crystallinity, and potential crystal orientation control under ambient air, but its disadvantage is the used toxic solvents in processing.


**1. Introduction**

The stochastic differential equation (SDE) has been used to model various phenomena and investigate their properties, such as the moments, variance and conditional moments, which are beneficial for estimating parameters that play significant roles in several practical applications. For example, financial derivative prices, such as moment swaps, can be obtained by calculating the conditional moments of their payoffs under the risk neutral measure; see for more concrete studies Araneda et al. [1], Cao et al. [2], He and Zhu [3] and Nonsoong et al. [4]. Actually, such moments can be directly computed by employing SDE's transition probability density function (PDF). However, the transition PDF is often unavailable in closed form; so is the formula for those conditional moments of the SDE. Investigating properties of those SDEs is still imperative and challenging.

There are several empirical studies confirming that a mean-reverting drift process, such as the Vašíˇcek, Ornstein–Uhlenbeck (OU) [5] and Cox–Ingersoll–Ross (CIR) [6] processes, should not necessarily be linear. Indeed, the behaviors and dynamics of interest rate and its derivatives prefer nonlinearity in the mean-reverting drift rather than linear drift processes; see for more details in [7–10]. In order to extend the OU process, a nonlinear diffusion process was introduced by Cox [11], namely, the constant elasticity of variance (CEV) process. The CEV process is useful and has many applications in various fields. However, the drift term of Cox's CEV process is still linear. For many reasons described in the existing literature [7,8], an extended case of Cox's CEV process was first studied by Marsh

**Citation:** Chumpong, K.; Tanadkithirun, R.; Tantiwattanapaibul, C. Simple Closed-Form Formulas for Conditional Moments of Inhomogeneous Nonlinear Drift Constant Elasticity of Variance Process. *Symmetry* **2022**, *14*, 1345. https://doi.org/10.3390/ sym14071345

Academic Editor: Zhivorad Tomovski

Received: 1 June 2022 Accepted: 24 June 2022 Published: 29 June 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and Rosenfeld [12]. The process is sometimes called the Marsh–Rosenfeld (MR) process, and its transition PDF that can be straightforwardly calculated by using Itô's lemma and the transition PDF of the CIR process are very complicated; the closed-form formula for conditional moments of the MR process is also complicated or unavailable in general; see for more details in [13]. It gets even more complicated for an inhomogeneous-time MR process that extends the MR process by replacing the constant parameters in the process with time-dependent functions. From now on, we subsequently call the inhomogeneous-time MR process in general an inhomogeneous nonlinear drift constant elasticity of variance (IND-CEV) process.

Conditional moments have been extensively used in modern financial markets. For example, they can be used to price moment swaps. Unfortunately, the conditional moments, which can be directly computed by applying the transition PDFs, are often unavailable in closed form because the transition PDFs are hardly known. The Feynman–Kac technique is used to overcome this problem for calculating the conditional moments of many stochastic processes. There has still been little research on the analytical formula for conditional moments regarding the IND-CEV process. In this work, a novel approach is developed based on the Feynman–Kac theorem, where the partial differential equation (PDE) is solved analytically, and some combinatorial techniques are used to simplify the system of recursive ordinary differential equations (ODEs) associated with the conditional moment.

The rest of the paper is organized as follows. Section 2 provides an overview of the IND-CEV process and sufficient conditions of the time-dependent parameter functions in the process. The key methodology and main results are given in Section 3. Section 4 proposes some essential properties such as conditional moments, conditional variance and central moments, conditional mixed moments, conditional covariance and correlation. Section 5 provides the formula of the unconditional moments of the IND-CEV process with constant parameters. Experimental validations for our results applied with Monte Carlo (MC) simulations are addressed in Section 6. Conclusions, limitations and future researches are discussed in Section 7.


*4.8. Sequence Alignments and Prediction of Transmembrane Domains of SlUGlcAE Family*

All nine SlUGlcAE and six AtUGlcAE protein sequences were aligned using the (version 5.0.6, North Carolina State University, Raleigh, NC, USA), and then the results were output by genedoc program. Next, the hydrophilicity of the SlUGlcAE protein sequences was predicted by the trans-membrane Hidden Markov model algorithm (available online: http://www.cbs.dtu.dk/ services/TMHMM/) [52].


*4.3. Comparative Analysis*

The comparison of the overall accuracy rate of classification between different methods of classification with previous research work is represented in Table 13.

vector quantization 96.23%

**Table 13.** Comparison of overall accuracy of classification with previous research work.

As shown in Table 13, the proposed classifier with Random subspace-SVM achieved an accuracy rate of 99.22% for 10-fold cross-validation and 98.74% for holdout method. These proposed classifiers show improvement of overall accuracy rate of classification on the same dataset (10,000 samples) compared to previous work done by Ranoa and Chao [21] that has achieved an overall accuracy rate of 93.18%, Anguita et al. [25] that obtained overall accuracy rate of 96.5%, Romero-Paredes et al. [24] that acquired an overall accuracy rate of 96.4% and Kastner et al. [23] that produced overall accuracy rate of 96.23%. This shows that the Random subspace-SVM ensemble method has the capability to produce higher accuracy due to its ability to find a hyper plane which separates positive and negative training observations, and maximizes the margin between these observations through this hyper plane compared to other methods such as Two stages of continuous Hidden Markov model, OVA MC-SVM-Gaussian kernel, OVO MC-SVM-Linear Kernel majority voting and Kernel generalized learning vector quantization.

Although RF classifiers have predictive performance comparable to that of the best performing alternatives such as SVMs for classification of HAR, nevertheless, this research shows that SVM has a slight edge over RF. From a small sample size (dataset 1) to a larger sample size (dataset 2), the accuracy significantly increased with RF. This is true for SVM as well. This shows that sample size has more impact on the classification accuracy of both the RF and SVM. This is consistent with the results reported by Shao and Lunetta [54], Thanh and Kappas [55], Hasan et al. [56], Solé et al. [57] and Sheshasaayee and Thailambal [58].

It can be established that for the proposed Random subspace-SVM ensemble classification method, the 10-fold Cross-validation evaluation method produced better results than the holdout evaluation method. This is supported by various researchers such as Bengio et al. [59], Kim [60] and Sakr et al. [61].

It can be deduced that the ensemble method gives instinctive, straightforward, well-designed, and strong resolutions for an assortment of machine learning issues. As pointed out by Polikar [16], this method was initially created to enhance classification accuracy by decreasing the modification in classifier outputs. Ensemble methods have since ended up being exceptionally powerful in various areas that are difficult to address utilizing a single model-based classifier. Generally, most ensemble methods are self-determined for the type of base classifier used to construct the ensemble. This is a significant advantage that permits developing a specific kind of classifier that might be known to be most appropriate for a certain application.


2.3.2. Technological Level

Two classification fields are included regarding to this aspect. Thus, assets can be identified as "pre-industrial" or "industrial". This classification can seem easy, but when you classify an element you are applying some criteria, and sometimes the decision about which are the appropriate criteria can be complicated and the subsequent application of the selected criteria can also be confusing.

The definition of industrial heritage that was proposed by the National Plan of Industrial Heritage of Spain in 2001 [2] identified the assets that compound this typology as the one result of the relation between a social model, capitalism, and the technological resources of the machining, which constrains these assets within the period between the second half of the 18th century and the last third of the 20th century. This temporal limitation is similar to the one proposed by The International Committee for the Conservation of the Industrial Heritage (TICCIH), considering the same start and an as of yet undefined end that goes on until today.

**Figure 5.** Graphical guidelines for the classification as a single asset or as a set of assets.

These general approaches define a limit between an industrial era and a pre-industrial era, and that idea was discarded in this work. The authors understand that the concept of industrial heritage must be able to cover and include any element—both movable and immovable assets—that are capable of helping the understanding of the operation of productive processes of interest, and also the life around these activities. The corresponding historical period is important information and in this study it is used as a classification criterion, but it is not a reason for the exclusion of a group of assets of interest, nor for the preference of some of them.

Thus, after the decision includes both possibilities in the catalogue, the guidelines for making the decision in each particular case must be established. The limits between industrial and pre-industrial assets might sound simple, but in many cases, it will not be so clear to decide in which group a particular asset must be included. The operation period of the asset is not a key aspect in this respect. Pre-industrial assets such as windmills were operating (and also were built) during the period defined by the National Plan and TICCIH as industrial. So, what criteria could be of help?

For that purpose, the authors established two main aspects that must be analyzed before the classification of each particular asset as pre-industrial or industrial [1]. On the one hand, there is the technological level of the asset, but this level is from a particular point of view that is focused on the power source that was employed. When this source is linked to the natural resources of the environment, the location of the asset is conditioned by the need of proximity to that resource, and this is understood as a pre-industrial attribute, in contrast to industrial situations in which steam engine or electric energy are the usual power sources. However, special situations can exist, so a second aspect is analyzed: the productive level or capacity. When the productive activity supplies the area near the asset, the productive level is understood as local, which means a pre-industrial attribute for the classification into the catalogue. When the asset is focused on exporting the products to other areas or regions, the productive volume is higher, and this is understood as an industrial attribute.

By combining the conclusions of both analyses, it is possible to select the industrial or pre-industrial nature of each particular asset. There will be situations in which these conclusions will point in opposite directions, and then the trend that is valued as the one of higher relevance will be one considered for the final classification of the asset.

#### 2.3.3. Relation with Local Population

As exposed before, one of the main functions of the developed catalogue is the support of other initiatives of study, promotion, or actuation. In that sense, the catalogue was initially integrated in a global methodology for the study and management of the industrial immovable assets that have been developed by some of the authors, concretely for the selection of the most compatible new uses for particular assets, in order to minimize as much as possible the impact of the adaptation to the new activity [19]. Precisely in that context, the interest of including some new criteria in the catalogue was identified. When one of these assets is recovered for a new activity, one of the most important aspects is the success of this activity. When the new use fails, two new scenarios emerge for the asset. On the one hand it can be again in a situation of abandonment for some time, which probably will deteriorate the asset in some way. On the other hand, it can be adapted again for a different new use, and that process will have some kind of new impact. Both situations are undesired, so making the right selection of the new use is a key aspect.

In that sense, the criteria structures of the decisions of the mentioned global methodology demand the inclusion of new criteria into the developed catalogue. The environment of the assets is very important for the success of the potential new uses. For example, some uses will benefit from highly populated environments, while other activities can be more appropriate for rural contexts. Thus, in addition to the location criteria that are initially included into the catalogue, criteria related to the rural or urban context of each asset considered, and the size of the surrounding villages and cities have been included in the catalogue. Thus, eight new categories were considered and included into the catalogue as new classification criteria. These new criteria were listed below in Table 1, which also shows the logic behind the new criteria incorporated into the catalogue, but not the needed application guidelines.

In the case of rural environments, two possible situations are considered. The classification as a "rural location" is used when the analyzed asset has no direct relation to any nearby village or town, either because a nearby village or town does not exist, or because there is not a clear connection with the analyzed asset, for example a road. Thus, this criterion of classification shows a certain grade of territorial isolation. The criterion called "semi-rural location" can represent assets out of the urban fabric, but with certain connection and relation to some nearby town or village, for example due to its close proximity or for the existence of a road, and also it can be used for assets associated to small groups of houses without their own real administrative or fabric structure. As it is possible to appreciate, assets classified with this criterion will be neither included in large urban structures nor connected to them, but they will not have the previous level of territorial isolation. If potential synergies with human settlements can be identified, then the potential demand of certain new uses can be taken into account.

On the other hand, four categories are defined for assets located in urban environments. The category called "town" is used for populations of less than 10,000 inhabitants. When the number of inhabitants is higher than 10,000 the term city is used, while "small city" is when the population is between 10,000–30,000 inhabitants, "medium city" is when the population is between 30,000–100,000 inhabitants, and "big city" is when the population is higher than 100,000 inhabitants. Figure 6 shows graphically the key aspects for decision making about the most appropriate category of classification in each case when these new sets of criteria are applied to each particular asset.

**Figure 6.** Guidelines for assets classification by criteria related to the grade of relation with human settlements.

In addition, the special role as "province capital" is also considered. When the population intervals were defined, the number of inhabitants of the main cities and towns of Andalusia was consulted in the database of the National Institute of Statistical of Spain [23]. Table 2 shows that, together with the eight province capitals, other cities have an important population in Andalusia, cities in which different industries are present, and contribute with some elements to the catalogue, so this additional classification criterion was considered of interest.

**Table 2.** Cities with the highest population in Andalusia. Source: National Institute of Statistical of Spain [23].

The application of this group of criteria, as well as the other ones included in the catalogue, is supported by the geolocation information included for every asset of the catalogue. This aspect is exposed in the following section.


**5. Excess heat capacity at low temperatures of alkali borate glasses**

The excess heat capacity has been observed as the deviation from the Debye *T*3 law at low temperatures. The broad peak in a *Cp/T*3 vs. *T* plot is the thermal boson peak, where *C*p is the heat capacity at a constant pressure. It is related to the non-Debye excess heat capacity. The alkali content dependence of *Cp/T*3 in lithium borate glasses is shown in **Figure 12** [35]. In the pure borate glass, the peak of *Cp/T*3 was observed at the temperature,*T*m = 5.8 K [39]. As the lithium content increases, the peak value decreases and the peak temperature increases. Such a behavior of the peak value and the peak temperature of thermal boson peaks is similar to the peak intensity and peak frequency of boson peaks observed by Raman scattering and neutron inelastic scattering, respectively.

**Figure 12.** *Temperature dependence of* C*p/*T*3 of lithium borate glasses at low temperatures.*

#### **Figure 13.**

*(a) Temperature dependence of* C*p/*T*3 of alkali borate glasses normalized by Debye contribution at low temperatures, and (b) Scaled thermal boson peaks of alkali borate glasses.*

**Figure 14.** *Correlation between the peak temperature of* C*p/*T*3 , boson peak frequency, and shear modulus.*

The temperature dependence of *C*p/*T*3 of alkali borate glasses with *x*=0.22 below 50 K is shown in **Figure 13** [35]. The peak temperature of CsB glass is close to that of pure borate glass. However, as the ionic radius of alkali metal ions decreases, the peak temperature markedly increases. The peak temperature of LiB glass is *T*m=14.6 K, which is about three times of a pure borate glass.

For all the alkali borate glasses, the universal nature of the master plot in *Cp/T*3 vs. *T* is also observed as shown in **Figure 13b**. This universal nature is the same as that of boson peaks observed by Raman scattering and neutron inelastic scattering. This fact indicates that the distribution of the low-energy excess VDoS remains the same for all the alkali modification.

On the discussion on the origin of a boson peak, the correlation between the transverse acoustic mode and a boson peak is very interesting. According to the numerical simulation, the equality of the boson peak frequency to the Ioffe–Regel limit for "transverse" phonons was reported. The boson peak energy is proportional to the shear modulus [40]. Since the peak temperature of a thermal boson peak is proportional to the boson peak frequency, the correlation is examined between the peak temperature of *C*p/*T*3 , boson peak energy, and shear modulus. The good correlation is found between these quantities as shown in **Figure 14**. This fact indicates that the origin of a boson peak is closely related to the Ioffe-Regal limit for transverse acoustic waves.


**Abstract**

In the late summer of 2018, the Hambach Forest (North Rhine Westphalia/ Germany) appeared prevalently in the media due to massive protests against its clearance for lignite mining with for the power generation. Because coal power as a form of energy supply is extremely climate intensive, the Hambach Forest rapidly became a symbol of the fight against climate change and the ongoing destruction of nature and its resources for economic reasons. Due to the extra-ordinarily prominent role of the Hambach Forest in the public opinion across Germany, this research addresses values of the forest to the population in monetary terms as well as the underlying factors that determine those values. For the analysis, a contingent valuation survey was conducted in December 2019 in Germany. The proposed amounts for the preservation of the Hambach Forest are mostly in accordance with previous evaluation studies of woods and forests, although this time almost only passive-use values are decisive. Further, a conversion of the WTP values to the area of the Hambach Forest results in an extraordinarily high per-hectare value of about 3.6 million. Thus, the symbolic value of the forest is remarkable and should be considered in future political decisions.

**Keywords:** symbolic value, environmental valuation, climate movement


**4. Challenges: in management of diabetes in Sub-Saharan Africa**

Despite the improvement that is taking place, Africa still has a long way to go before realizing the standard of care. There are different challenges affecting the management of diabetes in Africa, including the following:


**2. Impact of climate change on Uganda**

A large population depends on agriculture for their food security and livelihood and eventually becomes susceptible to climate change related consequences [21]. It is likely that Climate change will have an adverse effect on African countries owing to the fact that it is dependent on rain-fed agriculture and there is a shortage of skilled human resources in the domain of disaster management besides limited financial resources and weak institutional capacity [22]. Other than this, poor condition of the soil in Sub-Saharan Africa, along with poor production techniques and lack of appropriate policies with respect to use of inputs (fertilizer) and access to credit only exacerbates the situation [23].

To top it all, Africa's warmer climate increases the chances of pandemic recurrence (e.g., HIV/AIDS), crop and animal pests and other such diseases [24]. The negligent governance of the alarming issue in these countries has widened the income disparity making more and more people economically weak [22].

In the eastern region, the agriculture production supply has started falling lower than the demand to a great extent, worsening the already delicate food security. This has also resulted in increasing vulnerability and rural poverty, further amplifying the impact of droughts that appear to have taken a grave turn in the recent years. [25].

**3. General climatic characteristics of the study site: Lwengo district, Uganda**

A discussion on hot and cold seasons (**Figure 1**) of Lwengo would not be meaningful as the variation in the temperature of the study area is too little. The type of climate found in Lwengo is moderately hot, humid and misty. The temperature alters over the year ranging from 58°F to 81°F and is seldom below 55°F or above 87°F. A steady fall of water from the sky in significant quantities happens during 31 days in and around April 17 with an average total accumulation of 5.6 inches (**Figure 2**). The lowest rain falls around the month of June 27 accumulating an average total of 1.0 inches [26].


Meet the editor

Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULP-GC, teaching from 2001 on subjects on signal processing and

learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011.

He has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)

He is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the "Catedra Telefonica" Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.

Contents

**Section 1**

**Section 2**

*by Norio Tagawa*

**Preface XI**

Data Analysis **1**

**Chapter 1 3**

**Chapter 2 25**

**Chapter 3 43**

Automatic Systems **59**

**Chapter 4 61**

**Chapter 5 77**

**Chapter 6 95**

**Chapter 7 109**

New Attributes Extraction System for Arabic Autograph as Genuine

Current State-of-the-Art of Clustering Methods for Gene Expression

Stereoscopic Calculation Model Based on Fixational Eye Movements

*by Nwagwu Honour Chika, Ukekwe Emmanuel, Ugwoke Celestine,*

*by Roi Santos Mateos, Xose M. Pardo and Xose R. Fdez-Vidal*

Retina Recognition Using Crossings and Bifurcations

Visual Identification of Inconsistency in Pattern

*Ndoumbe Dora and George Okereke*

Incomplete Data Analysis

Data with RNA-Seq

*by Bo-Wei Chen and Jia-Ching Wang*

*by Ismail Jamail and Ahmed Moussa*

*by Lukáš Semerád and Martin Drahanský*

and Forged through a Classification Technique *by Anwar Yahya Ebrahim and Hoshang Kolivand*

Build 3D Abstractions with Wireframes