*3.3. Economic Analysis*

Finally, the economic analysis is carried out for two scenarios, namely, without and with potable water production, based on the particular characteristics designed in the previous items. The economic viability is based on two analyses.


**1. Introduction**

The topic presented in this chapter serves to give novice risk readers an idea of how institutions quantify their operational risks using parametric loss distributions. For various financial institutions, risk is classified into different components. Firstly though, risk is defined as the probability of an event and the potential loss. Put differently, [1] defined risk as a condition in which there is a possibility of an adverse deviation from the desired outcome that is expected or hoped for. Secondly, [2] provides an excellent account of four main categories of financial risks (more applicable in the banking sector), i.e. credit risk, market risk, operational risk, and others. For more discussion on some of the latter mentioned risks, see [3]'s Chapter 7 and the corresponding tools and techniques discussed in [3]'s Chapter 8. In this chapter, we are focusing mainly on the quantification of operational risk.

There have been multiple instances in the previous century of many big multinational firms experiencing total collapse due to a lack of risk control. For instance, employees may embezzle funds from the firm, rogue employees may make unauthorized deals, etc. For best examples of the latter, see Chapter 1 of [2] and Chapter 20 of [3]. Note though, in the South African context, the best example for poor operational risk management are Steinhoff, Hullett, Venda Building Society (VBS) mutual bank, Eskom, South African Airways (SAA) and more recently the Capitec bank computer systems failure during a peak period of the month in 2022. It is worth mentioning that a variety of sources have indicated that, in most instances, losses incurred due to operational risk normally would originate from poor management practices, outsourcing nonstrategic activities, or external factors.

In this chapter, a study on operational loss data will be conducted. We hope to determine the loss distribution that best fits the data by performing goodness-of-fit tests to the proposed models and estimating the parameters using appropriate statistical methods so that it can be possible to forecast or quantify the loss to be anticipated.

To date, financial institutions are making it a norm to manage their exposure to different types of risks, see [4]. Quantification of risk is of great importance, a proper evaluation of risk in any financial institution is an uncertainty problem that may easily lead to the bankruptcy of that firm and would consequently become a major concern for national and international financial regulatory bodies. This research work is compiled to contribute to the improvement of the quantification of operational risk using the loss distribution approach (LDA). According to [5], operational risk is the probability of loss resulting from insufficient or unsuccessful internal processes, people, and systems or from external events. Consequently, in the next section, we review the five most common parametric loss distributions namely: Pareto, Burr, gamma, Weibull, and log-normal distributions. These loss distributions are reviewed mainly in the aspect of quantification of operational risk.

This topic is applicable to a wide variety of fields as all institutions face some certain type of risk which if left unnoticed and unmanaged, could lead to total collapse of the firm or the worldwide economy (as seen in the last two global financial crises the domino effect). Operational risk is quantified in several institutions; according to [6], this is done because we cannot predict the future for certain, but we can prepare and anticipate it. Risk quantification gives us an insight into what we can anticipate. Quantification of risk is done in several financial institutions, e.g. banks, universities, insurance companies, etc. The limitation of our research is as follows: it is applicable in scenarios when the underlying operational loss data fits (or almost fits) the loss distributions considered here (i.e. Pareto, Burr, gamma, Weibull, and log-normal distributions). In the event of the data not passing the goodness-of-fit tests for any of the latter distributions, then in the concluding section (i.e. Section 4), we shall list different alternatives approaches that the readers need to consider.

Note that the field of risk identification and quantification has become more important as globalization is expanding. To date, different financial institutions are realizing the importance of quantifying risk to avoid huge losses that may even result in bankruptcy. The aspect of risk quantification is pivotal in making the best business decisions.

Therefore, the rest of the chapter is structured as follows: in Section 2, we review several publications that have covered operational risk using different loss distributions. Moreover, we take note of various approaches that were used to quantify risk exposure. Next, in Section 3, we use a dataset to illustrate quantification of risk using loss distributions. Given that this research work is a continuation of previous literature studies, in Section 4, we provide some concluding remarks and offer several possible future research topics.


**2. Optical transmitters**

One of the critical components in VLC and SSL to achieve high data rate data link and high CRI lighting is the optical transmitter. The modulated electrical signal is applied to the transmitter. The transmitter usually consists of LED and LD to convert the electrical signal to an optical signal. Further optical elements and color converter phosphor can be used to control the shape and color of the emitted light beam, respectively. The transmitter's capabilities are usually determined by its design. In VLC and SSL promising light source requires high energy conversion efficiency, large modulation bandwidth, high light output power, low operating voltage, small foot print and long lifetime. Practically, the low modulation response and low emitting intensity of LED are the drawbacks for the development of ultrahigh-speed VLC and high-quality SSL system. Blue gallium nitride (GaN) laser *Blue Laser Diode-Based Visible Light Communication and Solid-State Lighting DOI: http://dx.doi.org/10.5772/intechopen.100106*

## **Table 1.**

*Specification difference between LED and LD [11].*

diode (LD) and remote phosphor based generated white light can be used for both high-speed VLC and SSL simultaneously. The specification difference between LED and LD is shown in **Table 1**.


*2.4. Relative Chlorophyll Content*

Relative chlorophyll content was evaluated as SPAD value by placing the third expanded leaf of each plantlet, counted from top downwards, in a portable SPAD-502 chlorophyll meter (SPAD-502, Minolta Co., Ltd., Tokyo, Japan). Five random plantlets were selected per treatment.


4.3.2. Subjective Evaluation

To compare the subjective quality of the enhanced speech by baseline and proposed methods, we conducted two pairs of AB preference tests: AECNN versus the progressive generator and SERGAN versus the progressive generator with the multi-scale discriminator. Two speech in each pair were given in arbitrary order. For each listening test, 14 listeners participated, and 50 pairs of the speech were randomly selected. Listeners could listen to the speech pairs as many times as they wanted and were instructed to choose the speech with better perceptual quality. If the quality of the two samples was indistinguishable, listeners could select no preference.

#### **5. Experiments and Results**

In order to investigate the individual effect of the proposed generator and discriminator, we experimented on the progressive generator with and without the multi-scale discriminator. Furthermore, we plotted *L*1 losses at each layer *L*1(*Gn*) to show that the proposed model makes training fast and stable. Finally, the performance of the proposed model is compared with that of the other GAN-based speech enhancement techniques.

#### *5.1. Performance of Progressive Generator*


**6. References**

84 Recurrent Neural Networks and Soft Computing

The Neuro-Fuzzy Digital Filter time evolution responses was less that the reference process time state change proposed with a value of 0.08 sec, and is delimited by the processor,

Neural net in identification sense, considered the adaptation process requiring adjust the weights dynamically using the common proportional condition. But in many cases, these applications generate convergence problems because the gain in all cases increase the neural net weights positive or negatively without converge to desired value. In the black box traditional scheme the internal weights are known, but in real conditions it is impossible and only has a desired or objective answer. But, the neural nets help jumping weights estimation adjusting dynamically their internal weights, needing adaptation process with smooth movements as a function of identification error (function generated by the difference between the filter answer with respect to desired answer.). An option considered was the fuzzy logic in where its natural actions based on distribution function error allowing built the adjustable membership functions and mobile inference limits. Therefore, the neural weights are adjusted dynamically considering the fussy logic adaptable properties applied in the law actions, shown in figure 7. Stable weights conditions were exposed in section 3, with movements bounded in (8). In the results section, the figure 13, illustrated the Neuro-Fuzzy Digital Filter advantages without lost the stability with respect to desired system answer observed in distribution sense, observing the Hausdorff condition approximating the filter to desired system answer in

considered in (10). The convergence time is 0.0862 sec, described in (Medel, 2008).

Figure 14, shows the evolution functional error described by (5).

Fig. 14. Functional error considered in (5).

**5. Conclusion**

distribution sense.

Abraham K. (1991). *Fuzzy Expert Systems, Florida*, Ed. CRC Press, ISBN:<PHONE_NUMBER>974

**Part 2**

**Recurrent Neural Network**

Yamakawa F. (1989). *Fuzzy Neurons and Fuzzy Neural Networks*.

Zadeh L. (1965). Fuzzy Sets, *Information and control*, Vol. 8, pp. 338-353.

National security. (2008). Intelligence on the Brain, A New Dialogue on Neuro- Research and National Security

http://www.scienceprogress.org/2008/11/intelligence-on-the-brain/

**Part 2**

**Recurrent Neural Network**

86 Recurrent Neural Networks and Soft Computing

National security. (2008). Intelligence on the Brain, A New Dialogue on Neuro- Research

http://www.scienceprogress.org/2008/11/intelligence-on-the-brain/

Yamakawa F. (1989). *Fuzzy Neurons and Fuzzy Neural Networks*.

and National Security

Zadeh L. (1965). Fuzzy Sets, *Information and control*, Vol. 8, pp. 338-353.

**5**

*Iraq*

**Recurrent Neural Network with Human**

During almost three decades, the study on theory and applications of artificial neural network has increased considerably, due partly to a number of significant breakthroughs in research on network types and operational characteristics, but also because of some distinct advances in the power of computer hardware which is readily available for net implementation. In the last few years, recurrent neural networks (RNNs), which are neural network with feedback (closed-loop) connects, have been an important focus of research and development. Examples include bidirectional associative memory (BAM), Hopfield, cellular neural network (CNN), Boltzmann machine, and recurrent back propagation nets, etc.. RNN techniques have been applied to a wide variety of problems due to their dynamics and parallel distributed property, such as identifying and controlling the real-time system,

RNNs are widely acknowledged as an effective tool that can be employed by a wide range of applications that store and process temporal sequences. The ability of RNNs to capture complex, nonlinear system dynamics has served as a driving motivation for their study. RNNs have the potential to be effectively used in modeling, system identification, and adaptive control applications, to name a few, where other techniques may fall short. Most of the proposed RNN learning algorithms rely on the calculation of error gradients with respect to the network weights. What distinguishes recurrent neural networks from static, or feedforward networks, is the fact that the gradients are time dependent or dynamic. This implies that the current error gradient does not only depend on the current input, output, and targets, but rather on its possibly infinite past. How to effectively train RNNs remains a

The learning problem consists of adjusting the parameters (weights) of the network, such that the trajectories have certain specified properties. Perhaps the most common online learning algorithm proposed for RNNs is the real-rime recurrent learning (RTRL), which calculates gradients at time (k) in terms of those at time instant (k-1). Once the gradients are evaluated, weight updates can be calculated in a straightf\_\_Gorward manner. The RTRL algorithm is very attractive in that it is applicable to real-time systems. However, the two main drawbacks of RTRL are the large computational complexity of O(N4) and, even more critical, the storage

requirements of O(N3), where N denotes the number of neurons in the network.

**1. Introduction**

neural computing, image processing and so on.

challenging and active research topic.

**Simulator Based Virtual Reality**

*Al Anbar University, Engineering College, Electrical Dept.,*

Yousif I. Al Mashhadany


*3.3. Effect of PD and History of FOG on GI*

We observed significant alterations in the GI execution in both PDNF and PDF patients, with the latter group showing overall more severely altered APA measurements (Table 3, Figure 2).

The CoP displacement and velocity during IMB showed a progressive and significant reduction from HCs to PDNF to PDF groups along both the mediolateral and anteroposterior axes.

The UNL and stepping phases were also altered in PDNF and PDF patients (Table 3, Figure 2). Of most relevance, in PDF patients, the anteroposterior displacement of the CoP during UNL was backwards in most of the trials.

PDF patients had a significantly reduced first step length and both PD groups had a lower first step average velocity compared with HCs.

The CoM forward propulsion (velocity and acceleration) progressively decreased from HCs to PDNF to PDF.

**Figure 2.** Two-dimensional center of pressure and center of mass trajectories during gait initiation.

ρ

**ρ**

Example of the pathway of the center of pressure (CoP, grey solid line) and center of mass (CoM, black dotted line) during a gait initiation trial of one healthy subject (left panel) and one Parkinsonian patient without (PDNF, central panel) and one patient with (PDF, right panel) a positive history of freezing of gait. We defined the imbalance (IMB) phase as the interval between the onset of the APAs (APAONSET) and the heel-off of the swing foot (HOSW), and the unloading phase (UNL) as the interval between the HOSW and the toe-off of the swing foot (TOSW). The black dashed line represents the CoP–CoM vector at the end of the unloading (UNL) phase. With respect to healthy controls, the CoP displacement during the IMB phase was reduced for both PD and PDF patients. The CoP displacement during the UNL phase was in most cases backwards for the PDF patients only. Please see Table 3 for further details. Abbreviations: APAs, anticipatory postural adjustments; AP, anterior–posterior; CoP, center of pressure; HC, healthy controls; HO, heel off; ML, mediolateral; PDF, Parkinson's disease with freezing of gait; PDNF, Parkinson's disease with no freezing of gait; TO, toe-off.