Decisional Problem Research Articles

Enhancing Expert Decision-Making for Wastewater Treatment Plants with Seidel Laplacian Energy and Cosine Similarity Measure in Intuitionistic Fuzzy Graphs

Wastewater treatment facilities’ main goal is to protect the public and environment from the hazardous and poisonous materials found in wastewater. Water treatment facilities were developed to speed up the natural process of cleansing water. A novel cosine similarity measure across intuitionistic fuzzy graphs has been proven to be more effective than certain present ones in group decision-making issues using example verification. This paper provides a unique approach for calculating expert-certified, well-known scores by finding the ambiguous information of intuitionistic fuzzy preference relations as well as the regular cosine similarity grades from one separable intuitionistic fuzzy preference relation to another. The new technique considers both "objective" and "subjective" information provided by experts. Using intuitionistic fuzzy preference relations, we provide workable techniques for judging experts’ eligible reputational ratings. This can be used to raise or decrease the relevance of the stated criteria in an evaluation that takes into account several competing elements. We give a solution to a decisional problem by using two effective methods: the newly constructed cosine similarity measure and the Seidel Laplacian energy (SLe+) of an intuitionistic fuzzy graph. Finally, two working procedures and circumstances are offered to show the effectiveness and superiority of the proposed techniques.

On the decisional problem based on matrix power function defined over non-commutative group

In this paper, we perform statistical analysis for the decisional problem which is fundamental for the security of the key exchange protocol based on matrix power function. We have proven previously that the considered decisional problem is NP-complete and hence our proposal could potentially be quantum-safe. However, we did not explore the dependence of the complexity of the considered problem on the security parameters. Here we show that for small matrices certain information could be gained from the distribution of the entries of the public key matrices. On the other hand, we show that as the size of the matrices grows, the public key matrices are indistinguishable from truly random matrices.

Integrated Bayesian and association-rules methods for autonomously orienting COVID-19 patients.

The coronavirus infection continues to spread rapidly worldwide, having a devastating impact on the health of the global population. To fight against COVID-19, we propose a novel autonomous decision-making process which combines two modules in order to support the decision-maker: (1) Bayesian Networks method–based data-analysis module, which is used to specify the severity of coronavirus symptoms and classify cases as mild, moderate, and severe, and (2) autonomous decision-making module–based association rules mining method. This method allows the autonomous generation of the adequate decision based on the FP-growth algorithm and the distance between objects. To build the Bayesian Network model, we propose a novel data–based method that enables to effectively learn the network’s structure, namely, MIGT-SL algorithm. The experimentations are performed over pre-processed discrete dataset. The proposed algorithm allows to correctly generate 74%, 87.5%, and 100% of the original structure of ALARM, ASIA, and CANCER networks. The proposed Bayesian model performs well in terms of accuracy with 96.15% and 94.77%, respectively, for binary and multi-class classification. The developed decision-making model is evaluated according to its utility in solving the decisional problem, and its accuracy of proposing the adequate decision is about 97.80%.Graphical abstract

The Group Decision-Making Using Pythagorean Fuzzy Entropy and the Complex Proportional Assessment.

The Pythagorean fuzzy sets conveniently capture unreliable, ambiguous, and uncertain information, especially in problems involving multiple and opposing criteria. Pythagorean fuzzy sets are one of the popular generalizations of the intuitionistic fuzzy sets. They are instrumental in expressing and managing hesitant under uncertain environments, so they have been involved extensively in a diversity of scientific fields. This paper proposes a new Pythagorean entropy for Multi-Criteria Decision-Analysis (MCDA) problems. The entropy measures the fuzziness of two fuzzy sets and has an influential position in fuzzy functions. The more comprehensive the entropy, the more inadequate the ambiguity, so the decision-making established on entropy is beneficial. The COmplex PRoportional ASsessment (COPRAS) method is used to tackle uncertainty issues in MCDA and considers the singularity of one alternative over the rest of them. This can be enforced to maximize and minimize relevant criteria in an assessment where multiple opposing criteria are considered. Using the Pythagorean sets, we represent a decisional problem solution by using the COPRAS approach and the new Entropy measure.

Multi-Criteria Decision Analysis for Evaluating Transitional and Post-Mining Options—An Innovative Perspective from the EIT ReviRIS Project

In mine design and planning, identifying appropriate Post-Mining Land Use (PMLU) is necessary and crucial to achieving environmental quality and socioeconomic renewal. In this context, Multi-Criteria Decision Making (MCDM) methods are used to support decision-maker and stakeholder decisions. However, most studies regarding the application of MCDM methods to PMLU decisions do not favor their widespread use because they start from an already structured decisional problem. The structure they present may not apply to another PMLU decision. Therefore, the primary goal of this study is to present an innovative methodology and its corresponding framework to help decision-makers and stakeholders structure their PMLU decisions. This innovative methodology can be used from an early stage, with a low level of detail, until a later stage, with a high level of detail, and is composed of three main stages. The first stage is selecting the Transitional Post-Mining Landscape Profile, which guides the user to different Multi-Criteria Decision Analysis (MCDA) goals. The second stage is developing criteria and alternatives according to the MCDA goal, using topics representing essential dimensions that cannot be disregarded, and testing the MCDM methods. Finally, the third stage is the participatory process and final application of MCDM methods.

Autonomous Unbiased Study Group Formation Algorithm for Rapid Knowledge Propagation

Knowledge propagation is a necessity, both in academics and in the industry. The focus of this work is on how to achieve rapid knowledge propagation using collaborative study groups. The practice of knowledge sharing in study groups finds relevance in conferences, workshops, and class rooms. Unfortunately, there appears to be only few researches on empirical best practices and techniques on study groups formation, especially for achieving rapid knowledge propagation. This work bridges this gap by presenting a workflow driven computational algorithm for autonomous and unbiased formation of study groups. The system workflow consists of a chronology of stages, each made of distinct steps. Two of the most important steps, subsumed within the algorithmic stage, are the algorithms that resolve the decisional problem of number of study groups to be formed, as well as the most effective permutation of the study group participants to form collaborative pairs. This work contributes a number of new algorithmic concepts, such as autonomous and unbiased matching, exhaustive multiplication technique, twisted round-robin transversal, equilibrium summation, among others. The concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants’ performances in an examination, rather than through any external process. As part of practical demonstration of this work, study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty (40) participants, and partially for study groups of 50, 60 and 80 participants. The quantitative proof of this work was done through the technique called equilibrium summation, as well as the calculation of inter-study group Pearson Correlation Coefficients, which resulted in values higher than 0.9 in all cases. Real life experimentation was carried out while teaching Object-Oriented Programming to forty (40) undergraduates between February and May 2021. Empirical result showed that the performance of the learners was improved appreciably. This work will therefore be of immense benefit to the industry, academics and research community involved in collaborative learning.

Neutrosophic Completion Technique for Incomplete Higher-Order AHP Comparison Matrices

After the recent establishment of the Sustainable Development Goals and the Agenda 2030, the sustainable design of products in general and infrastructures in particular emerge as a challenging field for the development and application of multicriteria decision-making tools. Sustainability-related decision problems usually involve, by definition, a wide variety in number and nature of conflicting criteria, thus pushing the limits of conventional multicriteria decision-making tools practices. The greater the number of criteria and the more complex the relations existing between them in a decisional problem, the less accurate and certain are the judgments required by usual methods, such as the analytic hierarchy process (AHP). The present paper proposes a neutrosophic AHP completion methodology to reduce the number of judgments required to be emitted by the decision maker. This increases the consistency of their responses, while accounting for uncertainties associated to the fuzziness of human thinking. The method is applied to a sustainable-design problem, resulting in weight estimations that allow for a reduction of up to 22% of the conventionally required comparisons, with an average accuracy below 10% between estimates and the weights resulting from a conventionally completed AHP matrix, and a root mean standard error below 15%.

GIS-based multi\u2010criteria analysis for sustainable urban green spaces planning in emerging towns of Ethiopia: the case of Sululta town

BackgroundUrban green spaces are important components, contributing in different ways to the quality of human well-being. In the planning and management of urban centres, attention to the appropriate site selection of urban green spaces with regard to the importance that these spaces have from the perspectives of ecology, socioeconomic, mentality, etc., is an inevitable requirement. In present decades, land suitability mapping methods and GIS have been used to support urban green space planners in developed countries; however, its application and practices are limited in developing countries, like Ethiopia. Therefore, the aim of this study has to select potential sites for green spaces in Sululta town that assist an effective planning process of green areas in a sustainable way.MethodsIn this study, GIS-based Multi-criteria analysis (MCA) has been adopted to select suitable sites for urban green spaces. Existing land use, proximity to settlement, road and water body, population density, land ownership, topography, and scenic attractiveness were recognized as the key factor affecting urban green land suitability.ResultsThe results showed that 13.6%, 34%, 28%, and 18.9% of the study area are highly suitable, suitable, moderately suitable, and poorly suitable, respectively, for urban green spaces development. Furthermore, out of the total area of the study town 5.5% of the landmass is not suitable for urban green spaces development.ConclusionsTherefore, the application of GIS-based MCA has provided an effective methodology to solve a complex decisional problem in urban green spaces site selection in the study town and urban planning all over the country.

Key Exchange Protocol Defined over a Non-Commuting Group Based on an NP-Complete Decisional Problem

In this paper we present a cryptographic primitive based on non-commutative cryptography. This primitive is used for key exchange protocol (KEP) construction. We prove that the security of this primitive relies on a nondeterministic polynomial complete (NP-Complete) decisional problem. Recently there are no known quantum cryptanalysis algorithms effectively solving NP-Complete problems. So far, KEPs are widely used in secure communication channel creation, e.g., in hypertext transfer protocol secure (https://) and are based on traditional cryptographic primitives representing commutative cryptography. However, the security of these protocols does not rely on NP-Complete problems and hence, according to P. W. Shorr, they are vulnerable to quantum cryptanalysis. We use one of seven non-commuting groups of order 16 which is not isomorphic to any other group to define a platform group for a key exchange protocol based on previously considered matrix power function (MPF). By investigating basic properties on the group M16 and their implementation for our goals we fix the order of actions in MPF from left to right. Furthermore, we define a special form of the base matrix and separate templates for left and right power matrices. Using properties of the specified templates and Schaeffer criteria we prove that the security of the proposed key exchange relies on an NP-Complete decisional problem.

“The simplest protocol for oblivious transfer” revisited

In 2015, Chou and Orlandi presented an oblivious transfer protocol that already drew a lot of attention both from theorists and practitioners due to its extreme simplicity and high efficiency.Chou and Orlandi claimed that their protocol is universally composable secure (UC-secure) in the random oracle model under dynamic corruptions. UC-security is a very strong security guarantee that assures that, not only the protocol in itself is secure, but can be also used safely in larger protocols. Unfortunately, in this work we point out a flaw in their security proof for the case of a corrupt sender.In more detail, we define a decisional problem and we prove that, if a correct security proof for the Chou and Orlandi's protocol is provided, then this problem can be solved correctly with overwhelming probability. Therefore, the protocol of Chou and Orlandi cannot be instantiated securely with groups for which our decisional problem cannot be solved correctly with overwhelming probability. Consequently, the protocol of Chou and Orlandi cannot be instantiated with all groups G in which the CDH problem is intractable, but only with groups in which both the CDH problem is intractable and our decisional problem can be solved with overwhelming probability.After the appearance of our work, Chou and Orlandi acknowledged the problems we pointed out in their security proof and subsequent works showed additional issues, removing the claims of UC security of their protocol.

Designing a two-echelon distribution network under demand uncertainty

This paper proposes a comprehensive methodology for the stochastic multi-period two-echelon distribution network design problem (2E-DDP) where product flows to ship-to-points are directed from an upper layer of primary warehouses to distribution platforms (DPs) before being transported to the ship-to-points. A temporal hierarchy characterizes the design level dealing with DP location and capacity decisions, as well as the operational level involving transportation decisions as origin-destination flows. These design decisions must be calibrated to minimize the expected distribution cost associated with the two-echelon transportation schema on this network under stochastic demand. We consider a multi-period planning horizon where demand varies dynamically from one planning period to the next. Thus, the design of the two-echelon distribution network under uncertain customer demand gives rise to a complex multi-stage decisional problem. Given the strategic structure of the problem, we introduce alternative modeling approaches based on two-stage stochastic programming with recourse. We solve the resulting models using a Benders decomposition approach. The size of the scenario set is tuned using the sample average approximation (SAA) approach. Then, a scenario-based evaluation procedure is introduced to post-evaluate the design solutions obtained. We conduct extensive computational experiments based on several types of instances to validate the proposed models and assess the efficiency of the solution approaches. The evaluation of the quality of the stochastic solution underlines the impact of uncertainty in the two-echelon distribution network design problem (2E-DDP).

New Public Key Encryption with Equality Test Based on non-Abelian Factorization Problems

In this paper, we present a new public key encryption scheme with equality test (PKEwET). Compared to other PKEwET schemes, we find that its security can be improved since the proposed scheme is based on non-Abelian factorization problems. To our knowledge, it is the first scheme regarding equality test that can resist quantum algorithm attacks. We show that our scheme is one-way against chosen-ciphertext attacks in the case that the computational Diffie-Hellman problem is hard for a Type-I adversary. It is indistinguishable against chosen-ciphertext attacks in the case that the Decisional Diffie-Hellman problem is hard in the random oracle model for a Type-II adversary. To conclude the paper, we demonstrate that our scheme is more efficient.

Secure medical information sharing in cloud computing.

Medical information sharing is one of the most attractive applications of cloud computing, where searchable encryption is a fascinating solution for securely and conveniently sharing medical data among different medical organizers. However, almost all previous works are designed in symmetric key encryption environment. The only works in public key encryption do not support keyword trapdoor security, have long ciphertext related to the number of receivers, do not support receiver revocation without re-encrypting, and do not preserve the membership of receivers. In this paper, we propose a searchable encryption supporting multiple receivers for medical information sharing based on bilinear maps in public key encryption environment. In the proposed protocol, data owner stores only one copy of his encrypted file and its corresponding encrypted keywords on cloud for multiple designated receivers. The keyword ciphertext is significantly shorter and its length is constant without relation to the number of designated receivers, i.e., for n receivers the ciphertext length is only twice the element length in the group. Only the owner knows that with whom his data is shared, and the access to his data is still under control after having been put on the cloud. We formally prove the security of keyword ciphertext based on the intractability of Bilinear Diffie-Hellman problem and the keyword trapdoor based on Decisional Diffie-Hellman problem.

Assessing soil erosion hazard -a raster based GIS approach with spatial principal component analysis (SPCA)

Soil erosion is one of the most serious environmental problems affecting the quality of soil, land, and water resources upon which humans depend for their sustenance. A soil erosion hazard map is highly useful for environmental planning, soil conservation and management in soil erosion prone areas. In order to assess the soil erosion hazard, remote sensing (RS) and geographical information system (GIS) technologies were adopted, and a numerical model was developed using spatial principal component analysis (SPCA). Here, an integrated soil erosion hazard index (SEHI) was computed and classified into four levels of soil erosion hazard viz. low, average, high, and very high. In the process, nine factors were selected together with the degree of importance of the factors in hazard of the soil erosion. Integrated RS and GIS techniques and models were applied to generate the necessary factors for the SPCA approach. In addition, erosion hazard and calculated rate of soil erosion were used to find out the risk of soil erosion. Soil erosion risk was identified as actual occurrence and potential risk. Results show that, in general, an average hazardous condition of soil erosion was found in the area. The potential risk was more extensive in terms of involved area compared to the actual occurrence, and both actual occurrence and potential risk were higher at the mid-level elevation of the area. This study highlighted that priority should be given where the actual occurrence is high to very high and the probability of potential risk is average to high for protecting the land at present and in the future as well. Therefore, the application of SPCA combined with RS and GIS provided an effective methodology to solve the complex decisional problem for soil erosion hazard and risk assessment.

Graphical representation of life paths to better convey results of decision models to patients.

The inclusion of patients' perspectives in clinical practice has become an important matter for health professionals, in view of the increasing attention to patient-centered care. In this regard, this report illustrates a method for developing a visual aid that supports the physician in the process of informing patients about a critical decisional problem. In particular, we focused on interpretation of the results of decision trees embedding Markov models implemented with the commercial tool TreeAge Pro. Starting from patient-level simulations and exploiting some advanced functionalities of TreeAge Pro, we combined results to produce a novel graphical output that represents the distributions of outcomes over the lifetime for the different decision options, thus becoming a more informative decision support in a context of shared decision making. The training example used to illustrate the method is a decision tree for thromboembolism risk prevention in patients with nonvalvular atrial fibrillation.

Strategic Framework to Maintenance Decision Support Systems

In current global markets the maintenance can be considered key to assurance the required function of equipments and low life cycle costs of productive equipments. In this context, the Maintenance Decision Support Systems can be relevant as strategic framework. Accordingly, in this paper, the main objective is integrating the strategic functions with Balanced Score-Card, using the Analytic Hierarchy Process, where the Key Performance Indicators are associated to goals of company. The core of this purpose is to maximize the efficiency of the plant through maintenance. The decisional problem has been modelled with four production areas, as alternatives, that were pre-selected by experts. Commercial package Expert Choice has been used to model the problem and to analyse the results. A purely strategic approach has been followed to maintenance in the industrial business; results serve as input in tactical scope.

CCA-secure publicly verifiable public key encryption scheme without pairings

Publicly verifiable encryption scheme is a very powerful tool to construct many other interesting cryptographic schemes or protocols, and the existing publicly verifiable encryption schemes are based on special characters of bilinear pairings. But the inefficient computing restricts its application badly. So it is quite attractive and significantly worthwhile to construct cryptographic algorithms without pairings. Hofheinz and Kiltz's 2009b scheme is a Chosen-Ciphertext Attacks CCA-secure public key encryption scheme under factoring assumption without pairings, but not a publicly verifiable construction. We improved Hofheinz and Kiltz's 2009b scheme and realised publicly verifiable construct with CCA security by using the gap property that the decisional Diffie-Hellman problem is easy but the computational Diffie-Hellman problem is difficult as factoring over the group of signed quadratic residues. Furthermore, we improved the efficiency by instantiating this scheme over a semi-smooth subgroup. It can be used to construct threshold schemes without pairings.

Multi-criteria Analysis to Support Mobility Management at a University Campus

The mobility referred to the University is a significant part of the urban mobility in Trieste. Therefore a specific project was performed to analyze the main problems, to understand the preference structure of the users and to define some possible solutions to improve the accessibility of the University and to reduce urban congestion, mainly through sustainable transport modes. The survey pointed out features and preferences of different user groups, which were explicitly considered in the AHP assessment framework, and allowed to define criteria and to fix the weights. Also the presence of more than one decision maker was explicitly included in the hierarchy, to model the different impact of different user groups on the decision makers. As a result, the ranking of the alternatives has been defined, which has been taken into account by the decision makers for their planning choices. From a methodological point of view, the specific structure of the AHP hierarchy allowed to model the decisional problem even if a more complex ANP approach would be perhaps more suitable to consider interactions between nodes in this group decision problem.

Learning search polices from humans in a partially observable context

Decision making and planning for which the state information is only partially available is a problem faced by all forms of intelligent entities they being either virtual, synthetic or biological. The standard approach to mathematically solve such a decisional problem is to formulate it as a partially observable decision process (POMDP) and apply the same optimisation techniques used in the Markov decision process (MDP). However, applying naively the same methodology to solve MDPs as with POMDPs makes the problem computationally intractable. To address this problem, we take a programming by demonstration approach to provide a solution to the POMDP in continuous state and action space. In this work, we model the decision making process followed by humans when searching blindly for an object on a table. We show that by representing the belief of the human’s position in the environment by a particle filter (PF) and learning a mapping from this belief to their end effector velocities with a Gaussian mixture model (GMM), we can model the human’s search process and reproduce it for any agent. We further categorize the type of behaviours demonstrated by humans as being either risk-prone or risk-averse and find that more than 70% of the human searches were considered to be risk-averse. We contrast the performance of this human-inspired search model with respect to greedy and coastal navigation search methods. Our evaluation metric is the distance taken to reach the goal and how each method minimises the uncertainty. We further analyse the control policy of the coastal navigation and GMM search models and argue that taking into account uncertainty is more efficient with respect to distance travelled to reach the goal.

Compact hierarchical identity-based encryption based on a harder decisional problem

We generalize the decisional problem that was used to prove the security of a well-known hierarchical identity-based encryption scheme by Boneh, Boyen and Goh. We argue that our new problem is strictly harder than the original problem, and thus the security of the aforementioned cryptographic primitive is laid on even stronger foundations.