An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The reciprocal preference relation (RPR) is a powerful tool to represent decision makers’ preferences in decision making problems. In recent years, various types of RPRs have been reported and investigated, some of them being the ‘classical’ RPRs, interval-valued RPRs and hesitant RPRs. Additive consistency is one of the most commonly used property to measure the consistency of RPRs, with many methods developed to manage additive consistency of RPRs. To provide a clear perspective on additive consistency issues of RPRs, this paper reviews the consistency measurements of the different types of RPRs. Then, consistency-driven decision making and information fusion methods are also reviewed and classified into four main types: consistency improving methods; consistency-based methods to manage incomplete RPRs; consistency control in consensus decision making methods; and consistency-driven linguistic decision making methods. Finally, with respect to insights gained from prior researches, further directions for the research are proposed

Network access selection in heterogeneous wireless networks

In heterogeneous wireless networks (HWNs), both single-homed and multi-homed terminals are supported to provide connectivity to users. A multiservice single-homed multi-mode terminal can support multiple types of services, such as voice call, file download and video streaming simultaneously on any one of the available radio access technologies (RATs) such as Wireless Local Area Network (WLAN), and Long Term Evolution (LTE). Consequently, a single-homed multi-mode terminal having multiple on-going calls may need to perform a vertical handover from one RAT to another. One of the major issues in HWNs is how to select the most suitable RAT for multiple handoff calls, and the selection of a suitable RAT for multiple-calls from a single-homed multi-mode terminal in HWNs is a group decision problem. This is because a single-homed multi-mode terminal can connect to only one RAT at a time, and therefore multiple handoff calls from the terminal have to be handed over to the same RAT. In making group decision for multiple-calls, the quality of service (QoS) requirements for individual calls needs to be considered. Thus, the RAT that most satisfies the QoS requirements of individual calls is selected as the most suitable RAT for the multiple-calls. Whereas most research efforts in HWNs have concentrated on developing vertical handoff decision schemes for a single call from a multi-mode terminal, not much has been reported in the literature on RAT-selection for multiple-calls from a single-homed multi-mode terminal in next generation wireless networks (NGWNs). In addition, not much has been done to investigate the sensitivity of RAT-selection criteria for multiple-calls in NGWNs. Therefore, this dissertation addresses these issues by focusing on following two main aspects: (1) comparative analysis of four candidate multi-criteria group decision-making (MCGDM) schemes that could be adapted for making RAT-selection decisions for multiple-calls, and (2) development of a new RAT-selection scheme named the consensus RAT-selection model. In comparative analysis of the candidate RAT-selection schemes, four MCGDM schemes namely: distance to the ideal alternative-group decision making (DIA-GDM), multiplicative exponent weighting-group decision making (MEW-GDM), simply additive weighting-group decision making (SAW-GDM), technique for order preference by similarity to Ideal solution-group decision making (TOPSIS-GDM) are considered. The performance of the multiple-calls RAT-selection schemes is evaluated using the MATLAB simulation tool. The results show that DIA-GDM and TOPSIS-GDM schemes are more suitable for multiple handoff calls than SAW-GDM and MEW-GDM schemes. This is because they are consistent and less-sensitive in making RAT-selection decision than the other two schemes, with regards to RAT-selection criteria (service price, data rate, security, battery power consumption and network delay) in HWNs. In addition, the newly developed RAT-selection scheme incorporates RAT-consensus level for improving RAT-selection decisions for multiple-calls. Numerical results conducted in MATLAB validate the effectiveness and performance of the newly proposed RAT-selection scheme for multiple-calls in HWNs

Dynamics under Uncertainty: Modeling Simulation and Complexity

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc

Multiple-Criteria Decision Making

Decision-making on real-world problems, including individual process decisions, requires an appropriate and reliable decision support system. Fuzzy set theory, rough set theory, and neutrosophic set theory, which are MCDM techniques, are useful for modeling complex decision-making problems with imprecise, ambiguous, or vague data.This Special Issue, “Multiple Criteria Decision Making”, aims to incorporate recent developments in the area of the multi-criteria decision-making field. Topics include, but are not limited to:- MCDM optimization in engineering;- Environmental sustainability in engineering processes;- Multi-criteria production and logistics process planning;- New trends in multi-criteria evaluation of sustainable processes;- Multi-criteria decision making in strategic management based on sustainable criteria

A Corpus Driven Computational Intelligence Framework for Deception Detection in Financial Text

Financial fraud rampages onwards seemingly uncontained. The annual cost of fraud in the UK is estimated to be as high as £193bn a year [1] . From a data science perspective and hitherto less explored this thesis demonstrates how the use of linguistic features to drive data mining algorithms can aid in unravelling fraud. To this end, the spotlight is turned on Financial Statement Fraud (FSF), known to be the costliest type of fraud [2]. A new corpus of 6.3 million words is composed of102 annual reports/10-K (narrative sections) from firms formally indicted for FSF juxtaposed with 306 non-fraud firms of similar size and industrial grouping. Differently from other similar studies, this thesis uniquely takes a wide angled view and extracts a range of features of different categories from the corpus. These linguistic correlates of deception are uncovered using a variety of techniques and tools. Corpus linguistics methodology is applied to extract keywords and to examine linguistic structure. N-grams are extracted to draw out collocations. Readability measurement in financial text is advanced through the extraction of new indices that probe the text at a deeper level. Cognitive and perceptual processes are also picked out. Tone, intention and liquidity are gauged using customised word lists. Linguistic ratios are derived from grammatical constructs and word categories. An attempt is also made to determine ‘what’ was said as opposed to ‘how’. Further a new module is developed to condense synonyms into concepts. Lastly frequency counts from keywords unearthed from a previous content analysis study on financial narrative are also used. These features are then used to drive machine learning based classification and clustering algorithms to determine if they aid in discriminating a fraud from a non-fraud firm. The results derived from the battery of models built typically exceed classification accuracy of 70%. The above process is amalgamated into a framework. The process outlined, driven by empirical data demonstrates in a practical way how linguistic analysis could aid in fraud detection and also constitutes a unique contribution made to deception detection studies

Explainable-by-Design Deep Learning

Machine learning, and more specifically, deep learning, have attracted the attention of media and the broader public in the last decade due to its potential to revolutionize industries, public services, and society. Deep learning achieved or even surpassed human experts’ performance in terms of accuracy for different challenging problems such as image recognition, speech, and language translation. However, deep learning models are often characterized as a “black box” as these models are composed of many millions of parameters, which are extremely difficult to interpret by specialists. Complex “black box” models can easily fool users unable to inspect the algorithm’s decision, which can lead to dangerous or catastrophic events. Therefore, auditable explainable AI approaches are crucial for developing safe systems, complying with regulations, and accepting this new technology within society. This thesis tries to answer the following research question: Is it possible to provide an approach that has a performance compared to a Deep Learning and the same time has a transparent structure (non-black box)? To this end, it introduces a novel framework of explainable- by-design Deep Learning architectures that offers transparency and high accuracy, helping humans understand why a particular machine decision has been reached and whether or not it is trustworthy. Moreover, the proposed prototype-based framework has a flexible structure that allows the unsupervised detection of new classes and situations. The approaches proposed in thesis have been applied to multiple use cases, including image classification, fairness, deep recursive learning interpretation, and novelty detection

Algebraic Structures of Neutrosophic Triplets, Neutrosophic Duplets, or Neutrosophic Multisets

Neutrosophy (1995) is a new branch of philosophy that studies triads of the form (, , ), where is an entity {i.e. element, concept, idea, theory, logical proposition, etc.}, is the opposite of , while is the neutral (or indeterminate) between them, i.e., neither nor .Based on neutrosophy, the neutrosophic triplets were founded, which have a similar form (x, neut(x), anti(x)), that satisfy several axioms, for each element x in a given set.This collective book presents original research papers by many neutrosophic researchers from around the world, that report on the state-of-the-art and recent advancements of neutrosophic triplets, neutrosophic duplets, neutrosophic multisets and their algebraic structures – that have been defined recently in 2016 but have gained interest from world researchers. Connections between classical algebraic structures and neutrosophic triplet / duplet / multiset structures are also studied. And numerous neutrosophic applications in various fields, such as: multi-criteria decision making, image segmentation, medical diagnosis, fault diagnosis, clustering data, neutrosophic probability, human resource management, strategic planning, forecasting model, multi-granulation, supplier selection problems, typhoon disaster evaluation, skin lesson detection, mining algorithm for big data analysis, etc

Regime analysis of the rheology of spherical and non-spherical particles

In the early stages of granular rheology, the majority of analytical studies were based on granular assembly consisting of spherical particles. This was due to geometric simplicity and feasibility when calculating dynamic variables. Furthermore system limitation emerged as a problem when investigating more complex and realistic considerations. However, in the contemporary research field, with the steadily increasing ability to perform more complex computations and with available resources, attention has focused on non-spherical particles because of their deeper relevance to practical applications. In this work, a 3D shear cell model is developed based on the Discrete Element Method using the commercial software platform “PFC” to study non-spherical particles’ flow characteristics. A comparison is made with those of spherical assemblies. Firstly, the simulation model of annular shear cell consisting of spherical particles is tested with PFC and this agreed well with previous results, thus justifying the use of this tool to analyse the nonspherical level. Then the effect of platen roughness is investigated on spherical particle assembly from the microdynamic perspective, in order to establish a correlation between platen roughness and granular flow dynamics. This is undertaken in terms of particle size that is used to construct the platens. It is found that linearity and non-linearity of gradient profile across several important parameters are distinguishing features affected by variations in platen texture. The externally applied load is the most important aspect that bridges studies where gravity is considered and yet often overlooked. This point is established through in-depth investigation of granular flow in presence and absence of gravity where comparison of an number of flow characteristics is presented. Following this, the effects of particle shape are microdynamically investigated with reference to aspect ratio of non-spherical (ellipsoidal) particles and compared with spherical particles. The following key properties - particle linear velocity, angular velocity, contact normal force, contact shear force, total contact force, total contact moment and porosity - are 4 analysed to explain the effect of variation of the above-mentioned geometric properties on each of these parameters. Then, macrodynamic analysis is performed in a comparative study between spherical particles and ellipsoidal particles of varying aspect ratios with focus on the variables that are important in general constitutive model such as velocity, density and stress tensors. Physics underlying the observation is discussed to highlight effect of particle aspect ratio. Finally and most importantly, regime transition of ellipsoidal particle assembly is contrasted with the findings for spherical particles. In this study, the techniques that are generally used to identify regime transition for granular rheology of spherical particles are tested on flow of non-spherical (ellipsoidal) particles of varying shapes (aspect ratios). This includes correlation between elastically scaled force, kinetically scaled force, coordination number, apparent coefficient of friction and porosity. Some observations are found to be similar and useful for non-spherical particles while others found not suitable for nonspherical particles