Optimization Techniques for Decision-making and Information Security

In this investigation, we deal with a finite capacity Markovian feedback queue with threshold-based recovery policy, server breakdown, and customers’ impatience. For this system, the Successive Over-Relaxation (SOR) method is used to obtain the steady-state probabilities. Besides, the queueing system’s characteristics are also derived. Then, cost optimization of the system is carried out using the Quasi-Newton method. Further, numerical studies are performed to support the theoretically obtained results.

We deal with a parametric set-valued optimization problem (in brief, PSOP), where set-valued functions (in brief, SVFs) are used for the constraint and objective functions. We use the idea of higher-order p-cone convexity of SVFs (introduced by Das and Nahak [1] as a generalization of cone convex SVFs. We provide the Karush-Kuhn-Tucker (in brief, KKT) criteria of sufficiency for the presence of the minimizers of the PSOPs under higher-order p- cone convexity assumption. Further, we constitute the duality models of Mond-Weir kind and demonstrate the strong, weak, and converse duality theorems under higher-order contingent epi-derivative and higher-order p-cone convexity assumption to a couple of set-valued optimization problems (in brief, SOPs). We provide some examples to justify our results. As a special case, our results reduce to the existing ones of scalar-valued parametric optimization problems

The primary objective of the study is to investigate the trigeneration system for the simultaneous production of power, heating, and cooling, driven by solar power towers employing molten salt as the heat transfer fluid. A comparative analysis is provided between refrigerants (LiNO3-H2O and LiBr-H2O), so as to evaluate the best thermodynamic performance for the vapor absorption refrigeration system among them. A novel concept of uncertainty analysis is introduced which is a prime instance in this research area so as to provide much better results with precision removing all human and machine errors stipulated to be 5.34 % which is to be found in the desired range. Combined energy and exergy analyses are performed to investigate the variation in efficienices while altering various performance parameters for the trigeneration system. The highest exergy destruction was found to be 33.6% by the central receiver, 24.9% by heliostat, and 7.8% by heat recovery steam generators. The highest energy and exergy efficiencies (62.6% and 20.6%) were obtained by LiBr-H2O, whereas (60.9% and 19.6%) were obtained from LiNO3-H2O refrigerant. 

In the current situation, ranking a general fuzzy number is a difficult task, and various ranking methods have been developed, but no perfect ranking method exists. To solve fully fuzzy linear programming problems, many ranking functions have been developed and implemented in the literature. However, all of these methods have some limitations. In this chapter, we propose a new method for comparing two triangular fuzzy numbers in a generalised form. The Ezzati method [1] has been expanded upon using the suggested approach to handle fully fuzzy linear programming issues (FFLPP). The implementation of the developed algorithm has been illustrated through numerical illustrations. The proposed algorithm has been applied to a transportation problem in light of extensive testing, and it has been discovered that it is effective and generally offers a better solution.

This research article establishes anti-synchronization between the three-dimensional non-identical nonlinear Chen-Lee, Lorenz-Stenflo and Liu-Chen chaotic systems via active nonlinear control techniques. Phase portraits of master and slave systems in the form of antisynchronization are investigated. The stability results are discussed by the stability theory of Lyapunov function. Anti-synchronization of chaotic Chen-Lee system and chaotic Lorenzstenflo systems as well as anti-synchronization of chaotic Chen-Lee and Liu-Chen systems have been established using active control methodologies. The active control method is more efficient to obtain the anti-synchronization between different chaotic systems. Numerical results are also discussed by the proposed method.

This chapter considers a fixed charged solid transportation problem where all parameters of the problem have uncertainty due to ambiguity in the data set. These uncertain parameters (destination budget, accessibility, necessity, conveyance capacity, and transportation cost) are considered bounded. The problem’s solution is obtained by applying a transformation of the interval-based model into a conventional nonlinear programming problem. The transformed model is solved using any nonlinear programming technique. The solution obtained by the deterministic model is an acceptable solution to the main model. An examples with hypothetical data are explained to validate the developed model and methodology.

Recently, many soft computing methods have been implemented to extract information from big data. A standardized format for evaluating the expression levels of thousands of genes is made available by DNA microarray technology. Cancers of several anatomical regions can be identified with the help of patterns developed by gene expressions in microarray technology. Since the microarray data is too huge to process due to the curse of dimensionality problem.

Methodology: Therefore, in this chapter, a setup based on a hybrid machine learning framework using soft computing techniques for feature selection is designed and executed to eliminate unnecessary genes and identify important genes for the identification of cancer. In the first stage, the genes or the features are taken out with the aid of the higher-order Independent Component Analysis (ICA) technique. Then, a wrapper algorithm that is based on Spider Monkey Optimization (SMO) with Genetic Algorithm (GA) is used to find the set of genes that improve the classification accuracy of Naïve Bayes (NB) classifiers and Support Vector Machine (SVM). For comparison purposes, three other optimization techniques considered in this chapter are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Genetic Algorithm (GA). After the selection of relevant expressed genes, the most popular classifiers namely Naïve Bayes (NB) and Support Vector Machine (SVM)) are trained with selected genes, and in the end, the accuracy of classification is determined using test data.

 Result: The experimental results with five benchmark microarray datasets of cancer prove that Genetic Spider Monkey (GSM) is a more efficient approach to improve the classification performance with ICA for both classifiers.

Technology has enhanced the jeopardy of misuse of individual confidential data and identity theft attacks, where securing the content of communication and confidentiality has become one of the chief requirements. Here Cryptography operates as the cornerstone of computer and communications security. Certificateless cryptography, together with digital signatures, is the best solution for achieving fundamental security, i.e., confidentiality, data integrity, non-repudiation, and authentication. Correspondingly, certificateless cryptography (CLS) abolishes certificate management problems and the key escrow problems of public-key cryptography (PKC) and identity-based cryptography (IBC). CLS is the optimum combination of PKC and ID-based PKC, hence making it more efficient. Many signature schemes rely on certificateless schemes (CLS) and are formed using bilinear pairing and the MTP hash functions. An MTP (map-to-point) hash function and elliptic curve bilinear pairing are lavish procedure schemes, and they enhance computation expenses in the schemes and are timeconsuming. In contrast, pairing-free CLS schemes rely on elliptic curve cryptography (ECC) and have comparatively less computational cost, providing the desirable time requirement and making the scheme more efficient. In this chapter, we have done a survey of various pairingfree CLS schemes and compared them with each other. We have analyzed the performance evaluation of various schemes’ security and efficiency. Some are even observed to be precarious under various attacks. 

This work focuses on tie-breaking procedures that employ the fuzzy technique for order performance by similarity to the ideal solution. A tie situation occurs when two or more competitors receive the same score in a competition. Fuzzy technique for order performance by similarity to ideal solution provides efficient and effective decision making. The fuzzy technique for order performance by similarity to ideal solution assesses accurate and systematic decision-making based on multiple criteria among a finite set of possible alternatives. The proposed tie-breaking approach establishes an ordering relationship and a ranking among a set of alternatives under certain objective and subjective decision criteria. A numerical example is considered to demonstrate the computing process.