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The effectiveness of risk management will depend on its integration into the governance of the organization, including decision-making. This requires support from stakeholders, particularly top management. Framework development encompasses integrating, designing, implementing, evaluating and improving risk management across the organization.
Managing risks at all levels is an active process involving continuous planning, analysis, response, and monitoring and control. The execution of response strategies should be anticipatory and implemented by trigger events that launch response actions before the risk materializes so that opportunities (positive risks) may be enhanced or threats (negative risks) may be diminished. Even within an active process of continuous risk identification, risk management at the program, project, and operations areas are traditionally approached from a prescriptive, process-based perspective. However, within complex systems such as portfolios, risks may not be managed in the traditional or simple sense. Complexity requires a less prescriptive approach. In many cases, the execution of risk response strategies at the portfolio level involves the establishment of projects within the portfolio’s component programs or as part of continuing operations to address specific opportunities or threats (positive or negative risks) that have either materialized or have had a significant increase in the probability of occurrence as indicated by a trigger event occurring. There is an important distinction between portfolio risk management and risk management at the program or project level. In many cases, the portfolio manager should delegate risk response measures to subordinate programs or projects within the portfolio. A desired outcome from portfolio risk management is to utilize a structured risk planning and response effort in order to reduce management inaction and decision delay. Risk identification analysis and response planning acknowledge the limits of data and the lack of clear, unambiguous, and actionable information concerning many management factors at the portfolio level. Various possible risk scenarios are studied and response plans developed to limit the impact of the data and information disconnect described above. Through portfolio risk management, senior leadership and portfolio management staff are provided with courses of action or management options that assist in making decisions involving risk with incomplete information.
The importance of an appropriate selection of one project due to the combination of the selected projects for successful portfolio management is inevitable. Many companies try to implement a group of relevant projects as a portfolio to satisfy their synergy and economize their cost through efficient project management. Furthermore, it is needed to manage the risks of each project through the standard risk management process after the creation of the appropriate portfolio. The portfolio has an important role in managing a group of relevant projects so that they bring benefits and values. In the portfolio level, risk management requires a balanced attitude and management judgment exercises in two stages: the first stage is associated with the portfolio creation phase and the second one is allocated to the implementation phase of portfolio projects. We only benefit from the synergy and saving resulted from the portfolios projects management in the case of active risk management. A risk strategy response (RSS) is one of the most important processes of risk management. Therefore, selecting the appropriate projects and managing project risks are simultaneously two appropriate approaches to increase both revenue and profits of project-based organizations. In this research, the main aim is to choose an optimum portfolio of project investment considering its risk response cost and multi-term planning. Project portfolio selection observes the organization’s objectives in a planning horizon without outpacing available resources. Schniederjans and Santhanam [1] classified the system’s objectives and preferences as financial benefits, intangible benefits, availability of resources, and risk level of the project portfolio, so project risk assessment was a key element in their study [2].
Badri et al. [3] presented a binary goal programming model for the project selection of an information system. Wei and Chang [4] presented a portfolio choice model based on enterprise strategy considering customer’s resource and capability, project performance and project delivery, and project risk constraints. Project risks are categorized into three types: market risk, technical risk, financial risk. In any aspect of a project, risk can emerge. The nature of risk is uncertainty. For each project, risks should be identified and analyzed, and to cope with these risks, proper RRSs must be employed [5, 6, 7, 8, 9, 10, 11]. Tang et al. [9] developed a new solution method to the lean 6-sigma portfolio management as a binary quadratic programming problem. Muriana and Vizzini [12] presented a certain method to determine the risk of the Work Progress Status for assessing and preventing project risk.
On the other hand, Rahimi et al. [13] proposed a mathematical model, in which different risks are considered for activities so that different responses can be selected for each risk. Also, the risk responses are not considered as independent, and responses are associated with each other. Indeed, choosing the responses, which overlap each other, can affect their results, time, cost, and quality of the project. The objective function used different evaluation criteria and tried to choose the optimum responses, which maximizes these evaluation criteria. Ben-David and Raz [14] considered the cost of implementing strategies and incorporated them into an RRS selection problem. Ben-David et al. [15] extended their previous work by providing a mathematical model that facilitates computer implementation of the model. Because of the risk abatement actions, a selection problem is a complex one. Therefore, they proposed a branch-and-bound algorithm and two heuristic algorithms [16, 17]. Zhang and Fan [18] integrated all three key elements in project management (i.e., project expenditure, project planning horizon, and project quality). They proposed a new efficient solution for the mathematical model of the RRS.
Reviewing the aforementioned discussions and literature, we understand that there are gaps in (1) selecting the best projects portfolio that the effect of risk in selected projects is controlled [18], and (2) selecting projects to check the balance between the total cost of the selected projects and the profit of the selected projects, and all the predicted risk response effects. Furthermore, some of the parameters in the real-world are uncertain and can cause a high degree of uncertainty on a designed network [19].
To overcome and fulfill these gaps, for the first time, we develop a mathematical model for selecting the best projects and control risks of each selected projects under uncertainty. In this research, we investigate the trade-off between the total cost of the selected projects including all three types of resources (e.g., human, machine, raw materials) and implanting proper risk responses-and the net profit of the selected projects, and all the approximated risk response effects. The important items which this research contributes are as follows:
Presenting a new two-objective binary mathematical model to choose an optimum portfolio and control risks of the selected projects.
Introducing a new objective function for selecting projects with the maximum net profit and all the estimated risk response effects for each project.
Developing a new multi-period, multi-project, and multi-resource model to control risks of the selected projects.
We present a new model to select an optimum project portfolio tacking into account many constraints in the multi-period planning horizon. Also, this model can be used to select the RRSs. The portfolio selection problem of the project RRSs is combined with four basic concepts (i.e., project opportunity, work breakdown structure, risk event, and risk responses) as well as three key elements (i.e., schedule, quality, and cost) are considered in these concepts. These concepts are described as project scope, work breakdown structure, risk event, risk response. There is a strategy to respond r risk events. On the other hand, N project should be evaluated with their risk responses’ effects to select an optimum portfolio. The optimal portfolio will be top j projects. All parameters of the mathematical model change dynamically. In this model, an optimum portfolio is selected considering its risk response expenditure. The most enticing RRSs can be acquired by solving the mathematical model. Figure 1 depicts the process of portfolio RRSs.
Process of portfolio RRSs.
In this section, we present notations and mathematical modeling in Sections 2.1 and 2.2, respectively.
It should also be mentioned that the definition of parameters of
s.t.;
Objective function value (OFV) (1) maximizes the NP of the selected portfolio and effects on all RRSa for each project of the selected portfolio. Objective function value (2) is minimizing the total cost of the chosen projects consisting of four terms. These terms are the human resource expenditure, the machine resource expenditure, the raw materials resource cost, and implementing the RRSs, respectively.
Constraint (3) ensures that each project selection will happen only one time on the planning horizon. Constraint (4) states that the completion time of each selected project is less than the planning horizon plus the upper bound for project delivery delay. Constraints (5)–(7) define the maximum limits of all three resources. Constraint (5) states that the number of human resources of all types needed for projects during selection cannot exceed the maximum available human resources for all types and all planning terms. Constraint (6) ensures that all machine-hour resources of all types needed for projects during selection do not exceed the maximum available machine-hour resources for all types and all planning terms. Constraint (7) ensures that all raw materials resources of all types needed for projects during selection do not exceed the maximum available raw materials resources of all types and for all planning terms. Constraint (8) certifies that the total cost of each selected project is less than its net profit for all planning terms. Constraint (9) certifies that the total cost of a selected project including human resource expenditure, machine resource expenditure, raw material cost, and implementing the RRSs, is less than its budget for all projects and all planning terms.
Constraint (10) certifies that, in each project, each work packages (except the last one) is completed in the due date, otherwise (if it takes more), it does not affect the schedule of its successors’ start times. Constraint (11) ensures that, in each project, each work packages (except the last one) maintain a certain level of quality. Constraint (12) indicates that, in each project, the last work package must be finished in the project deadline. Constraint (13) indicates that in each project, the last work packages must conform to project quality standards. Constraint (14) ensures if a project is selected, it is attractive and that means the internal RoR of the chosen projects should be greater than or equal to the MARR. Constraint (15) indicates that in each period, projects can be chosen. Constraints (16)–(18) are about strategies. Constraint (16) ensures that strategies
Uncertainty in data can be grouped into two categories: randomness and fuzziness. Randomness originates from the random nature of data and Fuzziness refers to the vague parameters Infected with epistemic uncertainty-ambiguity of these parameters stems from the lack of knowledge regarding the exact value of these parameters. The proposed model for this problem is a fuzzy multi-objective non-linear programming (FMONLP). There are a number of adopted methods to transform this model into its equivalent crisp match, from which a two-phase approach is offered [13, 14, 15, 16, 17, 18, 19, 20]. Firstly, using an efficient method introduced by Jimenez et al., [21], the basic model is transformed into an equivalent auxiliary crisp multi-objective model. Secondly, the fuzzy aggregation function, developed by [20], is used to solve the crisp multi-objective mode. To do this, a single-objective parametric model to find the final preferred compromise solution replaces the crisp multi-objective model.
Several methods have been proposed to convert a probabilistic model into an equivalent non-probabilistic one. Probabilistic constraints transform into non-probabilistic ones using fuzzy measures, which was introduced, in the literature review section. The possibility (Pos) and necessity (Nec) are the general fuzzy measures respectively showing the optimistic and pessimistic attitudes of the decision maker. The Pos measure shows the possibility degree of occurrence of a probabilistic event, and the Nec measure indicates the minimum possibility degree of occurrence of a probabilistic event. Certainty degree of occurrence of an uncertain event is measured by credibility (Cr), which equals the average of the Pos and Nec measures [22]. New fuzzy measure Me, which is a developed Cr measure is presented by [23]. The main advantage of this measure is its flexibility to avoid excessive views. In the following, the three measures of a fuzzy event, including possibility, necessity and credibility, are described. Variable ξ is determined as a fuzzy variable on probabilistic space
Set A is in
More details and descriptions of the fuzzy theory are explained in [22]. In this research, the Me-based probabilistic programming method is selected to deal with the uncertain parameters of the presented model. The fuzzy measure Me is defined, according to [22], as follows:
Where ε as a parameter shows the optimistic-pessimistic attitude of a decision maker. Mathematical programming problem (25) with fuzzy parameters is as follows:
Subjected to
In this notation
To deal with the probabilistic objective functions and constraints, the expected value and chance-constrained operators based on the Me measure in this method are used. Accordingly, we can rewrite this model (26) as below:
Subjected to
In this notation, E is the expected value operator, α and β are respectively the decision maker’s minimum confidence level for satisfaction of probabilistic constraints. Jiménez et al. [21] defined the expected value operator based on Me measure as follows:
According to [22] we can transform the aforementioned model (26) into two approximation models including the upper approximation model (UAM) and the lower approximation model (LAM). These models are presented as follows:
Where ε is the optimistic-pessimistic parameter. Solving the LAM and UAM models provides the decision maker with the lower and upper bound of the optimal decision respectively. In this research, we use UAM models to solve problem. Accordingly, the auxiliary crisp equivalent of the presented model with triangular fuzzy parameters is presented as follows:
UAM:
Subjected to
Other Constraints
Subjected to
Other Constraints
The select Portfolio RRSs proposed in this study is a mixed-integer linear programming model. It worth noting that the general algebraic modeling system (GAMS) software is used to solve the mathematical model. In this section, a P.G. company (One of the huge companies in the field of construction) in Iran is investigated as a real-case study to validate the performance of the proposed select Portfolio RRSs model.
An efficient multi-objective method can be done as an efficient method for obtaining the satisfaction level for each OFVs according to the decision maker’s preferences. For further explanations, the interested reader can refer to TH [20]. Two parameters in this method are very critical: relative importance of OFVs (i.e., weight factor) and coefficient of compensation. Details of the distribution functions of the parameters and the size of test problems are listed in Table 1. After that, the results on test problems for diverse values of
Parameters | Values | ||
---|---|---|---|
First Problem | Second Problem | Third Problem | |
J | 3 | 3 | 3 |
I | 20 | 20 | 20 |
K | 3 | 3 | 3 |
O | 2 | 2 | 2 |
T | 5 | 5 | 5 |
W | 12 | 12 | 12 |
R | 12 | 12 | 12 |
A | 8 | 8 | 8 |
(2 × 104,3.5 × 104) | (4 × 104,6 × 104) | (6 × 104,9 × 104) | |
(5 × 103,104) | (8 × 103,2 × 104) | (1.8 × 104,5 × 104) | |
(103,5 × 103) | (104,2 × 104) | (3 × 104,5 × 104) | |
(5 × 103,1.5 × 104) | (3 × 104,5 × 104) | (5 × 104,7.5 × 104) | |
(500,700) | (800,1000) | (1500,2000) | |
(800,1500) | (2000,3000) | (4000,8000) | |
(1000,2000) | (1000,2000) | (1000,2000) |
Amount of the parameters by random generation.
Problem No. | ||||||
---|---|---|---|---|---|---|
1 | 0.6 | 0.3,0.7 | 33218.2 | 0.924 | 781.08 | 0.973 |
0.6 | 0.5,0.5 | 32039.2 | 0.958 | 817.20 | 0.930 | |
0.6 | 0.7,0.3 | 31287.4 | 0.981 | 826.98 | 0.919 | |
0.4 | 0.3,0.7 | 34838.8 | 0.881 | 772.35 | 0.984 | |
0.4 | 0.5,0.5 | 32791.6 | 0.936 | 805.93 | 0.943 | |
0.4 | 0.7,0.3 | 30909.3 | 0.994 | 867.57 | 0.876 | |
2 | 0.6 | 0.3,0.7 | 50448.4 | 0.892 | 1317.2 | 0.911 |
0.6 | 0.5,0.5 | 48966.2 | 0.919 | 1345.3 | 0.890 | |
0.6 | 0.7,0.3 | 46632.1 | 0.965 | 1415.0 | 0.848 | |
0.4 | 0.3,0.7 | 53507.7 | 0.841 | 1248.6 | 0.961 | |
0.4 | 0.5,0.5 | 51903.1 | 0.867 | 1295.8 | 0.926 | |
0.4 | 0.7,0.3 | 47418.3 | 0.949 | 1393.7 | 0.861 | |
3 | 0.6 | 0.3,0.7 | 70806.1 | 0.918 | 2098.6 | 0.953 |
0.6 | 0.5,0.5 | 69370.3 | 0.937 | 2164.5 | 0.924 | |
0.6 | 0.7,0.3 | 67427.3 | 0.964 | 2171.5 | 0.921 | |
0.4 | 0.3,0.7 | 71982.2 | 0.903 | 2044.9 | 0.978 | |
0.4 | 0.5,0.5 | 69817.4 | 0.931 | 2148.2 | 0.931 | |
0.4 | 0.7,0.3 | 66598.3 | 0.976 | 2229.6 | 0.897 |
Results of test problem 1 (
According to Table 2, the values of objective functions change based on the value of
Based on the acquired results and considering the budget and time limitations, the most appropriate strategy for responding to the risk work packages is provided in Table 3. In this test problem project 8 and 3 are selected. Appendix A. shows the amount of maximum allowed time reduction (day) and the quality of each activity (in percentage). The obtained quality of each activity under acceptable and ideal condition is assumed 90% and 99% respectively (
Optimal allocation in project 3 | Risks | Work Packages (WP) |
---|---|---|
RR 27 | R1 | WP 1- WP 10 |
RR 17 | R 5 | WP 5- WP 10 |
RR 21 | R 8 | WP 5- WP 10 |
RR 10 | R 9 | WP 3- WP 4 |
RR 12 | R 12 | WP 1, WP 9, WP 10 |
RR 7 | R 24 | WP 2- WP 8 |
RR 22 | R 25 | WP 4- WP 6 |
RR 1 | R 26 | WP 6, WP 7, WP 9 |
Optimal allocation in project 3 | Risks | Work packages |
RRS 27 | R 1 | WP 1- WP 12 |
RRS 13 | R 2 | WP 1, WP 3- WP 10 |
RRS 14 | R 4 | WP 2- WP 12 |
RRS 11 | R 7 | WP 3- WP 12 |
RRS 21 | R 8 | WP 5- WP 12 |
RRS 10 | R 9 | WP 5- WP 12 |
RRS 30 | R 10 | WP 8, WP 10, WP 11 |
RRS 16 | R 11 | WP 3- WP 12 |
Solution allocation of RRs for projects 8 and 3.
Large construction companies mainly use the project to carry out their activities. Due to the limited resources of these companies, which can be considered project-based organizations, they have to decide on selecting, stopping projects and allocating resources, and have using portfolio management tools, consequently. Portfolio Risk Management is one of the common knowledge scopes in portfolio management with project portfolio decisions application. The primary purpose of risk management is to protect the organization against damages and to prepare the organization for possible future damage. Therefore, the risks should be met with proper risk responses. Risk management at the portfolio level supports the aforementioned goals in different ways.
Firstly, enables the portfolio manager to compare the risks of single projects in terms of risk feature reduction actions. This comparison allows to make difference between options and the single risk levels are clarified and the results of risk responses actions are reflected and facilitate the transfer of experiences between the projects. Secondly, the comparison of the public risks of the portfolio and its trend according to the life cycle of the project has been revealed. Clarity growth leads to preventing other project risks or increasing focus on risks that are prevalent in most of the projects. Thirdly, risk management reduces uncertainty by providing enough information to make decisions. As a result, estimations are more accurate, reliable, and reduce the chance of surprise and the rate of failures. Therefore, risk management should increase information clarity, detecting and clarifying problems, risk response capacity, and depth of information for decision making.
In this research, a linear mixed-integer model was proposed to solve a project selection problem and provide RRSs. According to objective functions, this model firstly aims to select projects with the highest net profit and risk response effects. Secondly, these projects should be carried out with minimum resource and implanting risk responses costs. The model is solved to select the most desirable projects and risk response strategies to deal with risk events. The main contribution of this research is combining of project selection from a portfolio and calculation of risk response effect. In addition, because of environmental effects, some parameters (including the cost of human, machine, material, risk response effect, etc.) were considered as fuzzy numbers. Fuzzy Me measure is considered to deal with the uncertain parameters of the proposed model. To solve the model ten building project were studied, and Net profit and resources costs were considered as objective functions. Finally, optimal allocation of risk response strategies was determined. TH method was used to solve the model, which was coded in GAMS. Results showed that increasing budgets in sample problem, led to higher net profit and less projects costs. The sensitivity analysis of the case study showed the necessity of the trade-offs between maximizing profit and minimizing projects cost. At last, Pareto frontier was analyzed. Results indicate that this model can act as a powerful criterion and help project managers to increase desirable impacts of a solution before implementing the project. Also, uncertain parameters like robust programming can be determined to cover the limitations of the designed model. Moreover, since the presented model is categorized as an NP-hard problem, meta-heuristic algorithms may be utilized to solve the model.
Project 1 | Project 2 | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
W1 | W2 | W3 | W4 | W5 | W6 | W7 | W8 | W9 | W1 | W2 | W3 | W4 | W5 | W6 | W7 | |
10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | |
14 | 8 | 10 | 15 | 10 | 8 | 14 | 14 | 12 | 14 | 11 | 14 | 11 | 11 | 11 | 8 | |
22,000 (USD) | 25,000(USD) | |||||||||||||||
35,000 (USD) | 30,000(USD) | |||||||||||||||
30,000 (USD) | 38,000(USD) | |||||||||||||||
200 million (USD) | 250 million (USD) |
Project 3 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
W1 | W2 | W3 | W4 | W5 | W6 | W7 | W8 | W9 | W10 | W11 | W12 | |
10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | |
11 | 12 | 14 | 10 | 12 | 13 | 9 | 13 | 15 | 10 | 8 | 14 | |
20,000 (USD) | ||||||||||||
35,000 (USD) | ||||||||||||
28,000 (USD) | ||||||||||||
270 million (USD) |
Project 4 | Project 5 | Project 6 | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
W1 | W2 | W3 | W4 | W5 | W6 | W7 | W1 | W2 | W3 | W4 | W5 | W6 | W1 | W2 | W3 | W4 | W5 | |
10% | 10% | 10% | 11% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | |
10 | 10 | 12 | 12 | 13 | 9 | 10 | 12 | 13 | 9 | 13 | 14 | 10 | 10 | 12 | 12 | 14 | 9 | |
25,000 (USD) | 25,000 (USD) | 20,000 (USD) | ||||||||||||||||
30,000 (USD) | 35,000 (USD) | 35,000 (USD) | ||||||||||||||||
35,000 (USD) | 35,000 (USD) | 38,000 (USD) | ||||||||||||||||
200 million (USD) | 200 million (USD) | 250 million (USD) |
Project 7 | ||||||||
---|---|---|---|---|---|---|---|---|
W1 | W2 | W3 | W4 | W5 | W6 | W7 | W8 | |
10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | |
8 | 11 | 14 | 11 | 13 | 13 | 12 | 14 | |
25,000 (USD) | ||||||||
45,000 (USD) | ||||||||
28,000 (USD) | ||||||||
300 million (USD) |
Project 8 | Project 9 | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
W1 | W2 | W3 | W4 | W5 | W6 | W7 | W8 | W9 | W10 | W1 | W2 | W3 | W4 | W5 | W6 | W7 | |
10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 11% | 11% | 11% | 11% | 11% | 11% | 11% | |
14 | 15 | 14 | 16 | 14 | 11 | 12 | 9 | 14 | 14 | 10 | 12 | 13 | 9 | 13 | 15 | 9 | |
20,000(USD) | 20,000(USD) | ||||||||||||||||
35,000(USD) | 38,000(USD) | ||||||||||||||||
25,000(USD) | 40,000(USD) | ||||||||||||||||
200 million (USD) | 200 million (USD) |
Project 10 | |||||||||
---|---|---|---|---|---|---|---|---|---|
W1 | W2 | W3 | W4 | W5 | W6 | W7 | W8 | W9 | |
10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | 10% | |
11 | 11 | 8 | 11 | 12 | 14 | 10 | 12 | 13 | |
25,000(USD) | |||||||||
35,000(USD) | |||||||||
28,000(USD) | |||||||||
250 million (USD) |
Risks | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Risk 1 | Risk 2 | Risk 3 | Risk 4 | Risk 5 | Risk 6 | Risk 7 | Risk 8 | Risk 9 | Risk 10 | ||
Risk responses strategies | Risk Response 1 | 3320 | 7260 | — | — | — | — | — | — | — | — |
Risk Response 2 | — | — | 4530 | — | — | — | — | 7310 | — | — | |
Risk Response 3 | — | — | 9480 | 4790 | 5460 | — | 4450 | — | — | 10,020 | |
Risk Response 4 | — | 5230 | — | — | — | 5960 | — | — | — | — | |
Risk Response 5 | 3800 | — | — | — | — | — | 5010 | — | — | — | |
Risk Response 6 | — | — | — | — | 6140 | 5630 | — | 6730 | — | — | |
Risk Response 7 | 5840 | 5040 | — | — | — | — | — | — | 8300 | — | |
Risk Response 8 | — | — | — | 4800 | — | — | — | 7100 | — | — | |
Risk Response 9 | — | 7100 | — | — | 3960 | — | — | 6200 | — | — | |
Risk Response 10 | — | — | 7980 | — | — | 4560 | — | — | 9200 | — | |
Risk Response 11 | — | — | — | 4320 | 5070 | — | 8900 | 4070 | — | 8090 | |
Risk Response 12 | — | — | — | — | 9750 | 10,450 | — | 6470 | 5300 | — | |
Risk Response 13 | — | 7890 | 4600 | 4800 | — | — | 4500 | — | — | 4750 | |
Risk Response 14 | 4200 | 6540 | — | 9800 | 6540 | 4890 | — | — | 4750 | 9640 | |
Risk Response 15 | — | — | — | 6940 | — | — | 7310 | 4800 | — | — | |
Risk Response 16 | 5470 | — | — | 4580 | 6700 | 11,230 | 4500 | 4980 | — | — | |
Risk Response 17 | — | 4860 | — | — | 9870 | 7800 | 6940 | 7500 | — | — | |
Risk Response 18 | — | 7560 | — | — | 7460 | 8600 | 7120 | — | — | — | |
Risk Response 19 | — | 3980 | 4690 | — | — | — | 6400 | — | — | — | |
Risk Response 20 | — | — | 7310 | 4600 | — | 5600 | — | — | — | — | |
Risk Response 21 | — | — | 5740 | — | 9000 | — | — | 7510 | 4500 | 4500 | |
Risk Response 22 | — | — | 6210 | — | 4670 | 4810 | 6070 | — | — | — | |
Risk Response 23 | — | 4670 | — | 7900 | 9800 | — | 7240 | — | — | — | |
Risk Response 24 | — | 4120 | 3980 | 5600 | — | — | — | 3650 | — | — | |
Risk Response 25 | — | — | 7820 | — | 4680 | 7800 | — | — | — | 10,110 | |
Risk Response 26 | — | — | 6450 | 8210 | — | 8090 | 8000 | — | — | — | |
Risk Response 27 | 9200 | — | 6480 | 4040 | — | — | 5200 | — | 4860 | — | |
Risk Response 28 | — | 6390 | — | — | 4920 | — | — | 7500 | — | — | |
Risk Response 29 | — | 5890 | — | 6400 | — | — | 7560 | — | — | — | |
Risk Response 30 | — | 4620 | 3980 | — | 6540 | — | 4890 | 4040 | — | 10,200 | |
Risk Response 31 | 7200 | — | 7560 | — | 7560 | — | 3960 | — | — | 7200 |
Risks | |||||||
---|---|---|---|---|---|---|---|
Risk 11 | Risk 12 | Risk 13 | Risk 14 | Risk 15 | Risk 16 | ||
Risk responses strategies | Risk Response 1 | 9300 | — | — | — | 7200 | 4620 |
Risk Response 2 | — | — | 4550 | 3500 | — | — | |
Risk Response 3 | — | — | 3900 | — | — | — | |
Risk Response 4 | 7220 | — | 7890 | — | 3560 | 4780 | |
Risk Response 5 | — | 9520 | — | — | — | 8630 | |
Risk Response 6 | — | — | — | — | — | — | |
Risk Response 7 | — | 9700 | — | 7560 | — | — | |
Risk Response 8 | 8200 | 9200 | — | — | 4560 | — | |
Risk Response 9 | — | — | 9400 | 5670 | — | 7200 | |
Risk Response 10 | 7500 | 10,400 | 11,630 | 4750 | 9600 | 7450 | |
Risk Response 11 | 5040 | — | — | — | 7500 | 8040 | |
Risk Response 12 | — | 11,230 | — | — | — | 7400 | |
Risk Response 13 | — | — | — | 7800 | — | — | |
Risk Response 14 | 8750 | — | — | 4620 | 10,400 | — | |
Risk Response 15 | — | — | 12,400 | 4590 | 6870 | 4700 | |
Risk Response 16 | 9500 | — | — | 10,400 | — | — | |
Risk Response 17 | — | 7800 | — | — | — | 9500 | |
Risk Response 18 | — | 8600 | — | 7500 | — | — | |
Risk Response 19 | 8620 | — | — | — | — | — | |
Risk Response 20 | — | 5600 | — | — | 9500 | 8720 | |
Risk Response 21 | — | — | — | 7120 | — | — | |
Risk Response 22 | — | 4810 | 9450 | — | — | — | |
Risk Response 23 | 8200 | — | — | — | 8620 | — | |
Risk Response 24 | — | — | 10,410 | — | — | 8300 | |
Risk Response 25 | — | — | — | 10,420 | — | — | |
Risk Response 26 | — | — | 7800 | 4860 | — | — | |
Risk Response 27 | — | — | 8600 | — | 8200 | — | |
Risk Response 28 | — | — | — | — | — | 8400 | |
Risk Response 29 | 4680 | — | 5600 | 4560 | 11,110 | 12,300 | |
Risk Response 30 | — | 7510 | — | 8600 | — | — | |
Risk Response 31 | — | — | 4810 | — | — | — |
Risks | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Risk 17 | Risk 18 | Risk 19 | Risk 19 | Risk 20 | Risk 21 | Risk 22 | Risk 23 | Risk 24 | ||
Risk responses strategies | Risk Response 1 | 3320 | 7260 | — | — | — | — | — | — | — |
Risk Response 2 | — | — | 4730 | 4120 | — | — | — | 8510 | — | |
Risk Response 3 | — | — | 4850 | 4670 | 4230 | — | 4450 | — | — | |
Risk Response 4 | — | 5630 | — | — | — | 7640 | — | — | — | |
Risk Response 5 | 5700 | — | — | — | — | — | 5410 | — | — | |
Risk Response 6 | — | — | — | — | 5100 | — | — | 6730 | — | |
Risk Response 7 | 5220 | 4040 | — | — | — | — | — | — | 8300 | |
Risk Response 8 | — | — | — | 4210 | — | — | — | 7100 | — | |
Risk Response 9 | — | 9000 | 4690 | — | 10,200 | — | 9000 | 3320 | 4800 | |
Risk Response 10 | 4250 | 4670 | 7450 | — | — | 7310 | 4870 | — | — | |
Risk Response 11 | — | 9800 | — | — | 8510 | 4700 | 7200 | — | — | |
Risk Response 12 | — | — | 9600 | — | — | 4840 | — | — | 4500 | |
Risk Response 13 | — | 4680 | — | 7890 | — | 7120 | 4680 | 5700 | — | |
Risk Response 14 | 5600 | — | — | — | — | 6400 | — | — | — | |
Risk Response 15 | — | — | — | 4680 | 6730 | — | — | 5220 | — | |
Risk Response 16 | — | 4920 | — | 7420 | — | — | 4920 | — | — | |
Risk Response 17 | — | — | 4760 | 6900 | 7100 | 6070 | — | — | 6780 | |
Risk Response 18 | 6230 | — | — | — | — | 7240 | — | 4250 | — | |
Risk Response 19 | 5520 | — | — | — | 12,400 | — | — | — | 4700 | |
Risk Response 20 | — | 4890 | — | — | — | — | 4870 | — | 6970 | |
Risk Response 21 | — | — | — | 9640 | — | 8010 | — | — | — | |
Risk Response 22 | — | — | 6710 | — | 10,700 | 5400 | — | — | — | |
Risk Response 23 | 6200 | — | — | — | — | 7800 | 4790 | 9870 | — | |
Risk Response 24 | 3750 | — | — | — | — | — | 8920 | 6540 | 5470 | |
Risk Response 25 | — | 4790 | — | — | — | 3450 | — | — | — | |
Risk Response 26 | — | 9760 | 4500 | — | 11,450 | — | — | 4800 | — | |
Risk Response 27 | — | 7450 | — | — | — | — | 7890 | — | — | |
Risk Response 28 | — | 8960 | — | 4750 | — | 9700 | — | — | — | |
Risk Response 29 | 4200 | 4300 | — | — | — | 4780 | 7450 | — | 4600 | |
Risk Response 30 | — | — | 6710 | — | 10,010 | — | — | 4500 | — | |
Risk Response 31 | — | — | 7890 | 7200 | 9800 | — | — | 8040 | — |
Risks | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Risk 24 | Risk 25 | Risk 26 | Risk 27 | Risk 28 | Risk 29 | Risk 30 | Risk 31 | Risk 32 | Risk 33 | ||
Risk responses strategies | Risk Response 1 | — | — | 9500 | — | — | — | — | 7800 | 7800 | — |
Risk Response 2 | — | — | — | — | 9000 | — | 4200 | — | 3320 | — | |
Risk Response 3 | — | 9320 | — | — | 4670 | — | — | — | — | — | |
Risk Response 4 | — | — | 8620 | — | 9700 | 4230 | — | — | — | — | |
Risk Response 5 | — | — | — | 9520 | — | — | — | 10,400 | — | — | |
Risk Response 6 | — | — | — | — | — | — | — | — | 5700 | — | |
Risk Response 7 | 8300 | — | — | 9700 | — | 5100 | — | 7890 | — | — | |
Risk Response 8 | — | — | 8200 | 9200 | — | — | — | 5470 | 5220 | 8090 | |
Risk Response 9 | 4800 | 4580 | — | — | 4920 | — | — | — | — | — | |
Risk Response 10 | — | 7400 | — | — | — | 7500 | — | — | — | 4750 | |
Risk Response 11 | — | 6520 | — | 6970 | 6870 | — | — | — | 4250 | 9840 | |
Risk Response 12 | 4500 | — | — | 4580 | — | 8510 | 8090 | 12,300 | — | — | |
Risk Response 13 | — | — | — | — | 6980 | — | — | — | — | — | |
Risk Response 14 | — | — | 7310 | — | — | — | 4750 | — | — | — | |
Risk Response 15 | — | — | 4500 | — | 4120 | — | 9640 | — | 7560 | — | |
Risk Response 16 | — | — | — | 7450 | 3640 | 4800 | — | 7540 | 4520 | — | |
Risk Response 17 | 6780 | — | 7120 | 6420 | — | 9200 | — | 6420 | — | — | |
Risk Response 18 | — | — | 6400 | — | — | 3480 | — | 4120 | — | 7890 | |
Risk Response 19 | 4700 | 8090 | — | — | — | 5800 | — | — | — | 3320 | |
Risk Response 20 | 6970 | — | — | 4780 | — | 6700 | — | — | 6340 | — | |
Risk Response 21 | — | 4750 | 6070 | — | 6980 | — | — | — | 4120 | — | |
Risk Response 22 | — | 9640 | 7240 | — | — | — | — | 10,230 | — | — | |
Risk Response 23 | — | — | — | — | — | — | 4890 | — | 7890 | 5700 | |
Risk Response 24 | 5470 | — | — | — | — | — | — | 11,100 | 6420 | — | |
Risk Response 25 | — | — | 8000 | — | 6980 | — | 9560 | — | — | 5220 | |
Risk Response 26 | — | — | — | 7890 | — | 4780 | 6340 | — | — | — | |
Risk Response 27 | — | — | — | — | — | — | 7800 | — | — | — | |
Risk Response 28 | — | — | 7450 | — | — | — | 9870 | — | — | 10,500 | |
Risk Response 29 | 4600 | 8000 | 6320 | — | — | 8970 | — | — | 6740 | — | |
Risk Response 30 | — | — | — | 7120 | 9780 | — | 6700 | — | — | — | |
Risk Response 31 | — | — | — | — | 5700 | — | — | 7400 | — | — |
Projects Human resources (HR) Machinery Material Time period Work packages Risk events (RE) Candidate RRSsSets
Max accessible HR i in time t (person-hours).
Demand of HR i in j (person-hours).
Max available machine-hour k in time t.
Demand of machine-hour k in j.
Max accessible material o in time t.
Demand of material o in j.
Maximum available project budget for j in period t.
Hourly cost of HR i in period t.
Hourly cost of machine k in time
Unit cost of material o in time
Work packages w.
Risk response (RR) r.
Candidate RRS a.
Cost of implementing risk response strategy a.
Total Net Profit (NP) worth of j in time t.
RoR for j in time t.
MARR during period t.
Period of project j in time t.
if project j is chosen for investment in time t, 1; otherwise, 0.
1 if RRS a is applied for RE r for project j; 0, otherwise.
Authors are listed below with their open access chapters linked via author name:
",metaTitle:"IntechOpen authors on the Global Highly Cited Researchers 2018 list",metaDescription:null,metaKeywords:null,canonicalURL:null,contentRaw:'[{"type":"htmlEditorComponent","content":"New for 2018 (alphabetically by surname).
\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nJocelyn Chanussot (chapter to be published soon...)
\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nYuekun Lai
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\\n\\nAbdul Latif Ahmad 2016-18
\\n\\nKhalil Amine 2017, 2018
\\n\\nEwan Birney 2015-18
\\n\\nFrede Blaabjerg 2015-18
\\n\\nGang Chen 2016-18
\\n\\nJunhong Chen 2017, 2018
\\n\\nZhigang Chen 2016, 2018
\\n\\nMyung-Haing Cho 2016, 2018
\\n\\nMark Connors 2015-18
\\n\\nCyrus Cooper 2017, 2018
\\n\\nLiming Dai 2015-18
\\n\\nWeihua Deng 2017, 2018
\\n\\nVincenzo Fogliano 2017, 2018
\\n\\nRon de Graaf 2014-18
\\n\\nHarald Haas 2017, 2018
\\n\\nFrancisco Herrera 2017, 2018
\\n\\nJaakko Kangasjärvi 2015-18
\\n\\nHamid Reza Karimi 2016-18
\\n\\nJunji Kido 2014-18
\\n\\nJose Luiszamorano 2015-18
\\n\\nYiqi Luo 2016-18
\\n\\nJoachim Maier 2014-18
\\n\\nAndrea Natale 2017, 2018
\\n\\nAlberto Mantovani 2014-18
\\n\\nMarjan Mernik 2017, 2018
\\n\\nSandra Orchard 2014, 2016-18
\\n\\nMohamed Oukka 2016-18
\\n\\nBiswajeet Pradhan 2016-18
\\n\\nDirk Raes 2017, 2018
\\n\\nUlrike Ravens-Sieberer 2016-18
\\n\\nYexiang Tong 2017, 2018
\\n\\nJim Van Os 2015-18
\\n\\nLong Wang 2017, 2018
\\n\\nFei Wei 2016-18
\\n\\nIoannis Xenarios 2017, 2018
\\n\\nQi Xie 2016-18
\\n\\nXin-She Yang 2017, 2018
\\n\\nYulong Yin 2015, 2017, 2018
\\n"}]'},components:[{type:"htmlEditorComponent",content:'New for 2018 (alphabetically by surname).
\n\n\n\n\n\n\n\n\n\nJocelyn Chanussot (chapter to be published soon...)
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nYuekun Lai
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPrevious years (alphabetically by surname)
\n\nAbdul Latif Ahmad 2016-18
\n\nKhalil Amine 2017, 2018
\n\nEwan Birney 2015-18
\n\nFrede Blaabjerg 2015-18
\n\nGang Chen 2016-18
\n\nJunhong Chen 2017, 2018
\n\nZhigang Chen 2016, 2018
\n\nMyung-Haing Cho 2016, 2018
\n\nMark Connors 2015-18
\n\nCyrus Cooper 2017, 2018
\n\nLiming Dai 2015-18
\n\nWeihua Deng 2017, 2018
\n\nVincenzo Fogliano 2017, 2018
\n\nRon de Graaf 2014-18
\n\nHarald Haas 2017, 2018
\n\nFrancisco Herrera 2017, 2018
\n\nJaakko Kangasjärvi 2015-18
\n\nHamid Reza Karimi 2016-18
\n\nJunji Kido 2014-18
\n\nJose Luiszamorano 2015-18
\n\nYiqi Luo 2016-18
\n\nJoachim Maier 2014-18
\n\nAndrea Natale 2017, 2018
\n\nAlberto Mantovani 2014-18
\n\nMarjan Mernik 2017, 2018
\n\nSandra Orchard 2014, 2016-18
\n\nMohamed Oukka 2016-18
\n\nBiswajeet Pradhan 2016-18
\n\nDirk Raes 2017, 2018
\n\nUlrike Ravens-Sieberer 2016-18
\n\nYexiang Tong 2017, 2018
\n\nJim Van Os 2015-18
\n\nLong Wang 2017, 2018
\n\nFei Wei 2016-18
\n\nIoannis Xenarios 2017, 2018
\n\nQi Xie 2016-18
\n\nXin-She Yang 2017, 2018
\n\nYulong Yin 2015, 2017, 2018
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. 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However, it is difficult to find them manually because of disturbing factors such as baseline wander and high-frequency noise. In this chapter, we propose a method based on variational autoencoder to distinguish these distortions automatically and efficiently. We test our method on three ECG datasets from Physionet by adding some tiny artificial distortions. 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Furthermore, the research will suggest two algorithms to calculate the 67 measurements with precision; assuming that the algorithms output will be used as guideline to human body modelers in simulation, gaming, plastic surgery, as well as the world of biometrics or wherever human body measurements and calculations is needed like prosthetic limbs, spatial design, and machine learning of human biometrics. Furthermore, building proportional models creates visual harmony in measurements and visual parity model. Hence, the chapter facilitates and explains for the human modeler the process of human modeling from within an algorithm. This research is an expanded work based on two published conference papers listed in the references section.",book:{id:"6563",slug:"machine-learning-and-biometrics",title:"Machine Learning and Biometrics",fullTitle:"Machine Learning and Biometrics"},signatures:"Evon Abu-Taieh and Hamed S. 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One of the primary data analysis tasks for gene expression studies involves data-mining techniques such as clustering and classification. Clustering, which is an unsupervised learning technique, has been widely used as a computational tool to facilitate our understanding of gene functions and regulations involved in a biological process. Cluster analysis aims to group the large number of genes present in a sample of gene expression profile data, such that similar or related genes are in same clusters, and different or unrelated genes are in distinct ones. Classification on the other hand can be used for grouping samples based on their expression profile. There are many clustering and classification algorithms that can be applied in gene expression experiments, the most widely used are hierarchical clustering, k-means clustering and model-based clustering that depend on a model to sort out the number of clusters. Depending on the data structure, a fitting clustering method must be used. In this chapter, we present a state of art of clustering algorithms and statistical approaches for grouping similar gene expression profiles that can be applied to RNA-seq data analysis and software tools dedicated to these methods. In addition, we discuss challenges in cluster analysis, and compare the performance of height commonly used clustering methods on four different public datasets from recount2.",book:{id:"8734",slug:"applications-of-pattern-recognition",title:"Applications of Pattern Recognition",fullTitle:"Applications of Pattern Recognition"},signatures:"Ismail Jamail and Ahmed Moussa",authors:[{id:"322618",title:"Ph.D.",name:"Ismail",middleName:null,surname:"Jamail",slug:"ismail-jamail",fullName:"Ismail Jamail"},{id:"322619",title:"Dr.",name:"Ahmed",middleName:null,surname:"Moussa",slug:"ahmed-moussa",fullName:"Ahmed Moussa"}]},{id:"62526",title:"Introductory Chapter: Machine Learning and Biometrics",slug:"introductory-chapter-machine-learning-and-biometrics",totalDownloads:1131,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"6563",slug:"machine-learning-and-biometrics",title:"Machine Learning and Biometrics",fullTitle:"Machine Learning and Biometrics"},signatures:"Jucheng Yang, Yarui Chen, Chuanlei Zhang, Dong Sun Park and\nSook Yoon",authors:[{id:"220565",title:"Dr.",name:"Jucheng",middleName:null,surname:"Yang",slug:"jucheng-yang",fullName:"Jucheng Yang"}]},{id:"73909",title:"Incomplete Data Analysis",slug:"incomplete-data-analysis",totalDownloads:306,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"This chapter discusses missing-value problems from the perspective of machine learning. Missing values frequently occur during data acquisition. When a dataset contains missing values, nonvectorial data are generated. This subsequently causes a serious problem in pattern recognition models because nonvectorial data need further data wrangling before models are built. In view of such, this chapter reviews the methodologies of related works and examines their empirical effectiveness. At present, a great deal of effort has been devoted in this field, and those works can be roughly divided into two types — Multiple imputation and single imputation, where the latter can be further classified into subcategories. They include deletion, fixed-value replacement, K-Nearest Neighbors, regression, tree-based algorithms, and latent component-based approaches. In this chapter, those approaches are introduced and commented. 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The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}},{id:"441116",title:"Dr.",name:"Jovanka M.",middleName:null,surname:"Voyich",slug:"jovanka-m.-voyich",fullName:"Jovanka M. Voyich",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Montana State University",country:{name:"United States of America"}}},{id:"330412",title:"Dr.",name:"Muhammad",middleName:null,surname:"Farhab",slug:"muhammad-farhab",fullName:"Muhammad Farhab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"349495",title:"Dr.",name:"Muhammad",middleName:null,surname:"Ijaz",slug:"muhammad-ijaz",fullName:"Muhammad Ijaz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}}]}},subseries:{item:{id:"26",type:"subseries",title:"Machine Learning and Data Mining",keywords:"Intelligent Systems, Machine Learning, Data Science, Data Mining, Artificial Intelligence",scope:"The scope of machine learning and data mining is immense and is growing every day. It has become a massive part of our daily lives, making predictions based on experience, making this a fascinating area that solves problems that otherwise would not be possible or easy to solve. This topic aims to encompass algorithms that learn from experience (supervised and unsupervised), improve their performance over time and enable machines to make data-driven decisions. It is not limited to any particular applications, but contributions are encouraged from all disciplines.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11422,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"43680",title:"Prof.",name:"Ciza",middleName:null,surname:"Thomas",slug:"ciza-thomas",fullName:"Ciza Thomas",profilePictureURL:"https://mts.intechopen.com/storage/users/43680/images/system/43680.jpeg",institutionString:null,institution:{name:"Government of Kerala",institutionURL:null,country:{name:"India"}}},{id:"16614",title:"Prof.",name:"Juan Ignacio",middleName:null,surname:"Guerrero Alonso",slug:"juan-ignacio-guerrero-alonso",fullName:"Juan Ignacio Guerrero Alonso",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6HB8QAM/Profile_Picture_1627901127555",institutionString:null,institution:{name:"University of Seville",institutionURL:null,country:{name:"Spain"}}},{id:"3095",title:"Prof.",name:"Kenji",middleName:null,surname:"Suzuki",slug:"kenji-suzuki",fullName:"Kenji Suzuki",profilePictureURL:"https://mts.intechopen.com/storage/users/3095/images/1592_n.jpg",institutionString:null,institution:{name:"University of Chicago",institutionURL:null,country:{name:"United States of America"}}},{id:"214067",title:"Dr.",name:"W. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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