Logical Aggregation Research Articles

IBA-VNS: A Logic-Based Machine Learning Algorithm and Its Application in Surgery

The interpretability and explainability of machine learning (ML) approaches play a key role in the trustworthiness of ML models in various applications. The objective of this paper is to incorporate a logic-based reasoning in the ML model that is not only accurate but also interpretable and easily applied. More precisely, we propose a hybrid IBA-VNS approach based on interpolative Boolean algebra (IBA) and variable neighborhood search (VNS). IBA is chosen over traditional multi-valued and/or fuzzy logic techniques due to its consistency in preserving all Boolean axioms. The VNS heuristic is used for model training, i.e., determining the optimal logical aggregation function within the IBA framework for solving observed prediction problems. Obtained logic aggregation functions are easy to understand and may provide additional insight to the decision-maker. The proposed approach does not require any domain knowledge and is applicable in various domains. IBA-VNS is evaluated on several standard datasets. Further, IBA-VNS is applied to the real-world problem of predicting hospital length of stay (LOS), showing exceptional results in terms of interpretability and accuracy. In fact, the dataset is collected from the LabSerb program regarding colorectal surgeries in the period 2015–2023. The proposed approach extracted knowledge regarding the problem, i.e., the causal relations between the patient’s health condition and LOS, along with achieving an MAE of 1.144 days.

Solution strategy for sustainable additive manufacturing design problem using Pythagorean fuzzy MCGDM methodology

To solve difficulties involving various groups’ decision-making problems, this work has been proposed to develop a logical aggregation approach to aggregate decision-makers’ crisp data into Pythagorean fuzzy numbers. By combining the established strategy with the Pythagorean fuzzy TOPSIS method, a hybrid Pythagorean fuzzy multiple criteria group decision-making methodology is presented. Based on fuzzy rules inference and the Takagi–Sugeno technique, a novel function is created to represent the degrees of uncertainty in decision-makers’ data. As an example, the material selection process in practical additive manufacturing designs is provided to show how the proposed methodology may be applied to actual applications. Sensitivity analysis is used to evaluate the effectiveness of the suggested methodology. The outcomes demonstrate that the plan was successful in producing a PFN that accurately reflects the decision-maker’s knowledge.

Agile Logical Semantics for Natural Languages

This paper presents an agile method of logical semantics based on high-order Predicate Logic. An operator of predicate abstraction is introduced that provides a simple mechanism for logical aggregation of predicates and for logical typing. Monadic high-order logic is the natural environment in which predicate abstraction expresses the semantics of typical linguistic structures. Many examples of logical representations of natural language sentences are provided. Future extensions and possible applications in the interaction with chatbots are briefly discussed as well.

Towards reliable IoT communication and robust security: investigating trusted schemes in the internet of medical things using blockchain

The Internet of Things (IoT) is evolving in various sectors such as industries, healthcare, smart homes, and societies. Billions and trillions of IoT devices are used in e-health systems, known as the Internet of Medical Things (IoMT), to improve communication processes in the network. Scientists and researchers have proposed various methods and schemes to ensure automatic monitoring, communication, diagnosis, and even operating on patients at a distance. Several researchers have proposed security schemes and approaches to identify the legitimacy of intelligent systems involved in maintaining records in the network. However, existing schemes have their own performance issues, including delay, storage efficiency, costs, and others. This paper proposes trusted schemes that combine mean and subjective logic aggregation methods to compute the trust of each communicating device in the network. Additionally, the network maintains a blockchain of legitimate devices to oversee the trusted devices in the network. The proposed mechanism is further verified and analyzed using various security metrics, such as reliability, trust, delay, beliefs, and disbeliefs, in comparison to existing schemes.

High-Speed Object Recognition Based on a Neuromorphic System

Neuromorphic systems are bio-inspired and have the potential to break through the bottleneck of existing intelligent systems. This paper proposes a neuromorphic high-speed object recognition method based on DVS and SpiNNaker and implements a system in which an OR logic aggregation algorithm is used to acquire sufficient effective information and the asynchronous sparse computing mechanism of SNNs is exploited to reduce the computation. The experiment’s results show that the object detection rate of the designed system is more than 99% at the rotating speed of 900~2300 rpm; its response time is within 2.5 ms; and it requires 96.3% less computation than traditional recognition systems using the same scaled ANN.

The Cluster Method of Heterogeneous Distributed Units in a Low Voltage Distribution Network

With the large amounts of small capacity and heterogeneous distributed electricity units connected to the distribution power network, there exist increasingly complex management challenges. In this paper, a new management scheme that can classify and divide the distributed units according to their adjustable characteristics is proposed, which consequently forms an effective collection of fragmented adjustable ability and promotes the utilization of micropower resources. Inspired by the social division of labor in the biological community, the approach is based on a logical aggregation with the division of labor. A feature extraction method was acquired on the basis of the daily output curve, which reduces the data dimension and, subsequently, clusters the output feature points by the K-means algorithm. The simulation is performed by taking the measured output curve of low voltage distributed units on the low voltage side. The experimental results analyze the characteristics of seven classes of distributed units, allocate two main features, and reorganize them into a cluster; so, the “5-dimensional feature array” is reduced to “2-dimensional feature points”. The results demonstrate that the proposed cluster method can enable the power grid to identify and classify the distributed units automatically.

Blockchain enabled trusted task offloading scheme for fog computing: A deep reinforcement learning approach

AbstractWith the recent advancements in the Internet of Things, cloud computing has emerged as an important industrial technology that assists in various data analysis operations. However, the remote locality of cloud servers and scalability issues of cloud computing make it unsuitable for real‐time computing‐intensive applications. Fog computing strives to support cloud computing in meeting scalability demands by providing location‐sensitive services closer to end devices. With decentralized heterogeneous resource capabilities, fog architecture can handle several computation‐intensive and delay‐sensitive user requests. Although deploying service providers in an untrustworthy environment makes it challenging to assess the trustworthy acquired services. Conspicuously, in this article, we present a trusted task offloading and resource allocation using blockchain technology. To start with, we analyze direct and indirect trust with a subjective logical aggregation approach using a distributed trust assessment approach. Additionally, we examined the various quality of service parameters and constructed a smart contract that utilizes the state‐of‐the‐art deep reinforcement learning algorithm, namely Deep Deterministic Policy Gradient, to maximize fog revenue while serving as many user requests as possible. The entire process from task generation to results calculation is assisted by blockchain and offloading task transactions are stored in the secure, immutable, and tamper‐resistant ledger. To assess the effectiveness of our proposed scheme, we compared the simulation results with other baseline schemes over different performance metrics in terms of reward, service latency, energy consumption, task drop ratio, and transaction success rate. The results suggest that enabling trust computation improves transaction success by 21%.

Measuring pro-poor growth: a comparative study and a fuzzy logic-based method

PurposeThis paper has two purposes. The first is to provide a critical evaluation of current methods of measuring monetary versus non-monetary pro-poor growth. The second is to propose an alternative method based on the fuzzy logic aggregation approach, which allows including both monetary and non-monetary indicators simultaneously for measuring the “global pro-poor growth”.Design/methodology/approachThe methodology that we propose is based on the fuzzy logic approach to aggregate both monetary and non-monetary indicators simultaneously and thus to calculate the “Global Welfare Index”. This index will be considered as the main global wellbeing indicator based on which a “Global Growth Incidence Curve” is constructed to analyze the pro-poor growth. 10; Also, an application of the main previous procedures for measuring monetary vs non-monetary pro-poor growth is presented to compare their results and to discuss their advantages and limitations.FindingsEmpirical validation using Tunisian data reveals that on one hand, results of the pro-poor growth analysis are very sensitive to the used measurement method and may lead to different conclusions. On the other hand, our alternative procedure may provide a more appropriate analysis of pro-poor growth given that it takes into consideration the multidimensional aspect of poverty while remaining faithful to the fundamental principle of pro-poor growth measurement.Originality/valueThe proposed method for constructing the “Global Growth Incidence Curve” is original given that it presents a new procedure to take into account both monetary and non-monetary indicators simultaneously, which allows having a more global view of the phenomenon. Also, the comparative study of the different proposed methods in the literature of measuring pro-poor growth is useful to identify their limitations and advantages.

Evaluating the Quality of Social Web Applications Using the LSP Method

Quality is an essential determinant of the success of every type of software and social Web applications are not an exception. It is therefore of great importance that the examination of the degree to which social Web applications meet predefined requirements related to particular facets of quality is performed effectively and frequently. With an objective to facilitate evaluation procedure and enable comparison of social Web applications at all levels of the quality model, we initiated a research into development of a methodology that will aggregate quality requirements into a single score. The work presented in this paper draws on the employment of the logic scoring of preference (LSP) method and outlines only some parts of the aforementioned methodology. After identifying quality attributes that constitute the requirement tree, elementary criteria for both objective and subjective performance variables were introduced. As a follow up, field experts were included in the study in order to determine weights of performance variables within particular performance subsystem. Finally, the appropriate logic aggregation operators were selected based on the relevance of performance variables

Fuzzy logic aggregation of crisp data partitions as learning analytics in triage decisions

This paper provides an analytical learning system based on Fuzzy Logic AGgregation (FLAG) of crisp data partitions to improve the Triage process in a hospitality emergency department. The method compares patient rankings made by nurses with those made by an Expert to detect points for improvement. Specifically, a normalized concordance index per nurse and the average of them allow for the evaluation of the ability of the nurses of well aggregating the cases in Triage process. The proposed FLAG system is tested through an empirical case study by simulating the patients arriving at two Emergency Departments Triage. The main contribution is the definition of the global performance index combining both the nurse’s partitioning concordance with respect to the Expert’s one and the accuracy in class assignment. The empirical distribution function of the global concordance index is derived through permutation method. In this way, Kolmogorov-Smirnov testing provides the comparison of the performances of the two healthcare units. The pay-off table concordance-accuracy allows to address improvement actions. Another tool of the system is the correspondence analysis to visualize the accuracy of decisions on the class priority as well as the sharing behaviours that influence the nurse’s judgements. All this becomes part of the FLAG learning analytics system which is able to outline critical points by red flags. to improve further assignments and the overall management organization. FLAG system can be adapted in all other situations of risk where cognitive heuristics face an accuracy-effort trade-off such that their simplified decision process leads to reduced accuracy.

Logic gate aggregation of poly(N-isopropylacrylamide) nanogels with catechol substituents that respond to body heat

Smart hydrogel materials are a popular topic of investigation because they have the potential to be used as smart drug carriers and soft actuators. Hydrogel materials showing responsiveness to logic gate-type stimuli are of special interest because their responsiveness can be regulated more accurately than that of common stimuli-responsive materials. In this study, poly(N-isopropylacrylamide) nanogels containing catechol substituents in their polymer network structure were prepared via precipitation polymerization. The prepared nanogels showed AND-type logic gate thermal aggregation behavior. The possible use of this logic gate-type smart nanogel aggregating system was investigated in a smart valve system. Poly(N-isopropylacrylamide) nanogels having catechol substituents were prepared. It was found that the aqueous dispersion of the nanogel showed thermal aggregation responding to body temperature only in the case that Fe3+ was mixed and then pH increased. It can be said that this thermal aggregation system of the nanogel is one kind of AND-type logic gate aggregation system. We also showed the possibility that this AND-type nanogel aggregation can be used as a smart valve system regulating water flow.

Logic-based aggregation methods for ranking student applicants

In this paper, we present logic-based aggregation models used for ranking student applicants and we compare them with a number of existing aggregation methods, each more complex than the previous one. The proposed models aim to include depen- dencies in the data using Logical aggregation (LA). LA is a aggregation method based on interpolative Boolean algebra (IBA), a consistent multi-valued realization of Boolean algebra. This technique is used for a Boolean consistent aggregation of attributes that are logically dependent. The comparison is performed in the case of student applicants for master programs at the University of Belgrade. We have shown that LA has some advantages over other presented aggregation methods. The software realization of all applied aggregation methods is also provided. This paper may be of interest not only for student ranking, but also for similar problems of ranking people e.g. employees, team members, etc.

Idempotent Weighted Aggregation Based on Binary Aggregation Trees

We propose weighted aggregation algorithms for creating general idempotent weighted aggregators of n variables derived from related symmetric idempotent aggregators of two variables. This computational method, together with interpolative aggregation, can be used for the development of general idempotent logic aggregators that satisfy a variety of conditions necessary for building decision models in the area of weighted compensative logic.

Advanced manufacturing systems: supply–demand matching of manufacturing resource based on complex networks and Internet of Things

ABSTRACTAfter investigation on the existing advanced manufacturing systems (AMSs), it is found that supply–demand matching of manufacturing resource is one of the common issues to be addressed in all AMSs, and methods for addressing this issue have evolved from P2P (peer-to-peer)-based, to information centre-based, and to platform (or system)-based matching, and are moving towards socialisation and service-based solutions. In order to adapt to this trend, a new method for manufacturing resource supply–demand matching based on complex networks and Internet of Things (IoT) is proposed, and a four-layered architecture for implementing this method is designed. In this method, IoT technology is employed to realise the intelligent perception and accessing of various manufacturing resources and capabilities (MR&C), which enables logical aggregation of various distributed MR&C in the form of services. Then complex networks model and theory are used to realise the efficient manufacturing service management, optimal-allocation, and supply–demand matching. In this article, the specific key technologies for implementing the method are presented, including key technologies for manufacturing service generation and aggregation, manufacturing demand/task management, supply–demand matching of MR&C in the form of services, and value/utility adding based on manufacturing service network (MSN), manufacturing task network (MTN) and manufacturing enterprises collaborative network (ECN).

Using ILP to Identify Pathway Activation Patterns in Systems Biology.

We show a logical aggregation method that, combined with propositionalization methods, can construct novel structured biological features from gene expression data. We do this to gain understanding of pathway mechanisms, for instance, those associated with a particular disease. We illustrate this method on the task of distinguishing between two types of lung cancer; Squamous Cell Carcinoma (SCC) and Adenocarcinoma (AC). We identify pathway activation patterns in pathways previously implicated in the development of cancers. Our method identified a model with comparable predictive performance to the winning algorithm of a recent challenge, while providing biologically relevant explanations that may be useful to a biologist.

Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control

This paper presents a proposed new approach for complex control combining several simpler individual fuzzy controllers. This method is particularly useful when the case of study is a multivariable control system. The proposed method has a hierarchical architecture with 2 levels (individual fuzzy systems and a superior control to adjust the global result). The behavior of the proposed method is illustrated with a problem of flight control that requires several individual controllers. In addition a statistical comparison is performed using the t student test, where the proposed control strategy is compared against a simple fuzzy control approach. Finally, an optimization method is also applied to achieve an optimal design of the fuzzy systems.

Methods to stimulate sustainable consumption in the system of promotion products in industrial markets

In recent years, the necessity to meet ecological needs has been occupying an important place in the system of consumption values.The key mission of marketing in the area is the formation and continuous development of the processes of mutually beneficial exchange of ecological products in the market. The aim of the research is the development of the mechanism of a model for the stimulation of sustainable consumption based on the use of contemporary marketing tools and the end-consumer “pull” methods. The object of the research is the theoretical and practical basis of ecological products promotion in the market. The theoretical and methodological basis of the research is scientific literature on the formation of a market of sustainable consumption and the stimulation of sales of ecological products. To establish the actions to be used for the promotion of ecological products in the market, 398 residents of Lithuania were surveyed over the period of 2013 to 2014. To solve the research problem, the methods of analysis, generalisation, and comparison were used. The use of the end-consumer “pull” methods in the consumption of ecological products was based on the analysis of academic literature, as well as the methods of analysis and synthesis, induction and deduction, and logical aggregation of the survey data. For the development of the mechanism of the model for the promotion of the sales of ecological products, the method of modelling was employed. The findings of the research: the mechanism of a model for stimulating the sales of ecological products has been developed, based on the integrated use of traditional and contemporary methods and the end-consumer “pull” tools. The process of management of the implementation of the mechanism of ecological products promotion has been formed, which revealed specific actions necessary for its success.

Judgment Aggregation Theory Can Entail New Social Choice Results

Judgment (or logical) aggregation theory is logically more powerful than social choice theory and has been put to use to recover some classic results of this field. Whether it could also enrich it with genuinely new results is still controversial. To support a positive answer, we prove a social choice theorem by using the advanced nonbinary form of judgment aggregation theory developed by Dokow and Holzman (2010c). This application involves aggregating classifications (specifically assignments) instead of preferences, and this focus justifies shifting away from the binary framework of standard judgement aggregation theory to a more general one.

Metaheuristic Patient Estimation Based Patient-Specific Fuzzy Aggregated Artificial Pancreas Design

Patient-specific artificial pancreas design has been receiving increasing attention lately. In this article, using the chaotic bat algorithm (CBA), Hovorka–Wilinska (H–W) model parameters are estimated from nominal H–W virtual patient data. Using this identified H–W model for the virtual patient, multiple empirical second-order plus delay time (SOPDT) models representing glucose–insulin dynamics are derived for the range of blood glucose concentrations (BGCs) considered. Clustering of these models using the k-means algorithm yields three distinct clusters. Implicitly enumerated multiparametric model predictive controllers (mpMPCs) are designed using the cluster representatives. A fuzzy logic aggregation (FLA) of prediction and control improves the design parsimony. An insulin on board (IOB) safety trigger is designed using FLA of multiple full-order linearized CBA-estimated H–W models. The FLA-based mpMPC along with IOB and meal estimation are implemented on an embedded platform and by hardware-in-the-loo...

On the Expansion of Access Bandwidth of Manufacturing Cloud Core Network

This paper discusses the construction and a logical aggregation method of multi-access link manufacturing cloud core networks. The key technologies involved in the logical aggregation of multi-access link manufacturing cloud core networks are discussed in detail. Focusing on the key technologies including access load scheduling, access link flow monitoring, link aggregation control, and gateway integration, the principle and an corresponding effective approach have been developed to effectively expand the access bandwidth of a manufacturing cloud core network through multi-access link aggregation. A preliminary link aggregation implementation model has been developed according to the proposed aggregation structure and the proposed key technologies, which provides supports to the maximization of the performance of manufacturing cloud core network.