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Analysis of microarray data is a highly challenging problem due to the inherent complexity in the nature of the data associated with higher dimensionality, smaller sample size, imbalanced number of classes, noisy data-structure, and higher variance of feature values. This has led to lesser classification accuracy and over-fitting problem. In this work, the authors aimed to develop a deep feedforward method to classify the given microarray cancer data into a set of classes for subsequent diagnosis purposes. They have used a 7-layer deep neural network architecture having various parameters for each dataset. The small sample size and dimensionality problems are addressed by considering a well-known dimensionality reduction technique namely principal component analysis. The feature values are scaled using the Min–Max approach and the proposed approach is validated on eight standard microarray cancer datasets. To measure the loss, a binary cross-entropy is used and adaptive moment estimation is considered for optimisation. The performance of the proposed approach is evaluated using classification accuracy, precision, recall, f-measure, log-loss, receiver operating characteristic curve, and confusion matrix. A comparative analysis with state-of-the-art methods is carried out and the performance of the proposed approach exhibit better performance than many of the existing methods.

To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping's preference on the similarity measures significantly reduces the alignment's quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of the authors' approach.

Existing multifactorial particle swarm optimisation (MFPSO) algorithms only explore a relatively narrow area between the inter-task particles. Meanwhile, these algorithms use a fixed inter-task learning probability throughout the evolution process. However, the parameter is problem dependent and can be various at different stages of the evolution. In this work, the authors devise an inter-task learning-based information transferring mechanism to replace the corresponding part in MFPSO. This inter-task learning mechanism transfers the searching step by using a differential term and updates the personal best position by employing an inter-task crossover. By this mean, the particles can explore a broad search space when utilising the additional searching experiences of other tasks. In addition, to enhance the performance on problems with different complementarity, they design a self-adaption strategy to adjust the inter-task learning probability according to the performance feedback. They compared the proposed algorithm with the state-of-the-art algorithms on various benchmark problems. Experimental results demonstrate that the proposed algorithm can transfer inter-task knowledge efficiently and perform well on the problems with different complementarity.

In social network analysis, community detection is one of the significant tasks to study the structure and characteristics of the networks. In recent years, several intelligent and meta-heuristic algorithms have been presented for community detection in complex social networks, among them label propagation algorithm (LPA) is one of the fastest algorithms for discovering community structures. However, due to the randomness of the LPA, its performance is not suitable for the general purpose of network analysis. In this study, the authors propose an improved version of the label propagation (called AntLP) algorithm using similarity indices and ant colony optimisation (ACO). The AntLP consists of two steps: in the first step, the algorithm assigns weights for edges of the input network using several similarity indices, and in the second step, the AntLP using ACO tries to propagate labels and optimise modularity measure by grouping similar vertices in each community based on the local similarities among the vertices of the network. In order to study the performance of the AntLP, several experiments are conducted on some well-known social network datasets. Experimental simulations demonstrated that the AntLP is better than some community detection algorithms for social networks in terms of modularity, normalised mutual information and running time.

This study presented a multi-objective linear fractional inventory (LFI) problem with generalised intuitionistic fuzzy numbers. In modelling, the authors have assumed the ambiances where generalised trapezoidal intuitionistic fuzzy numbers (GTIFNs) used to handle the uncertain information in the data. Then, the given multi-objective generalised intuitionistic fuzzy LFI model was transformed into its equivalent deterministic linear fractional programming problem by employing the possibility and necessity measures. Finally, the applicability of the model is demonstrated with a numerical example and the sensitivity analysis under several parameters is investigated to explore the study.

The smart grid technology permits the revolution of the electrical system from a conventional power grid to an intelligent power network which has led the improvements in electrical system in terms of energy efficiency and sustainable energy integration. This study presents the energy management/coordination scheme for domestic demand using the key strategy of smart grid energy efficiency modelling. The structure consists of combining renewable energy resources, photovoltaic (PV) and wind power generation connected to the utility grid with energy storage system (ESS) in an optimal control manner to coordinate the power flow of a residential home. Based on the demand response schemes in the framework of real-time electricity pricing, this work designs a closed-loop optimal control strategy that is created by the dynamic model of the ESS to compute the system performance index, which is formulated by the cost of the energy flows. A dynamic distributed energy storage strategy (DDESS) is implemented to optimally coordinate the energy system, which reduces the total energy consumption from the main grid of more than 100% of the load demand. The designed model introduces a payback scheme while robustly optimising the energy flows and minimising the utility grid's energy consumption cost.

Due to the depletion of traditional fossil energy, to improve energy efficiency and build a cost-effective integrated energy system has become an inevitable choice. Aiming at the problems that the traditional centralised scheduling method is difficult to reflect the multi-dimensional interests of different agents in the multi-energy microgrid system, and the application of artificial intelligence technology in integrated energy scheduling still needs further exploration, this manuscript proposed a hierarchical control optimisation learning method with consideration of multi-agent game. Firstly, the multi-energy microgrid was taken as the research object, the microgrid system architecture was analysed, and the multi-agent partition in the system was pursued based on different economic interests. Secondly, for the technical aspects involved in the integrated energy regulation and management, the management layers of the multi-energy microgrid were divided, and the functions of different management layers were analysed. Based on this, the regulation functions were realised by considering the Nash Q-learning and the artificial intelligence method of Petri-net. Finally, the learning and decision-making ability of the method through practical cases were analysed, and the effectiveness and applicability of the proposed method were explained. This study explores the application of artificial intelligence technology in energy Internet energy management.

Here, a data mining–driven scheme based on discrete wavelet transform (DWT) is proposed for high impedance fault (HIF) detection in active distribution networks. Correlation between the phase current signal and the related details of the current wavelet transform is presented as a new index for HIF detection. The proposed HIF detection method is implemented in two subsequent stages. In the first stage, the most important features for HIF detection are extracted using support vector machine (SVM) and decision tree (DT). The parameters of SVM are optimised using the genetic algorithm (GA) over the input scenarios. In second stage, SVM is utilised to classify the input data. The efficiency of the utilised SVM-based classifier is compared with a probabilistic neural network (PNN). A comprehensive list of scenarios including load switching, inrush current, solid short-circuit faults, HIF faults in the presence of harmonic loads is generated. The performance of the proposed algorithm is investigated for two active distribution networks including IEEE 13-Bus and IEEE 34-Bus systems.

Generation maintenance scheduling (GMS) is one of the most important scheduling problems in the restructured power systems. The maintenance time interval of generation units is the crucial factor of GMS for an operation lifespan of generation units, particularly within the smart grid which needs high reliability. Accordingly, this study proposes a multi-objective-GMS (MO-GMS) optimisation model for maintenance scheduling of generation units based on the global criterion approach, adopting a suitable compromise function. The proposed MO-GMS model determines the maintenance intervals, aims to maximise both the generation company's (GenCo's) financial returns from selling electricity and the system reserve at every time interval from the independent system operator (ISO) standpoint. This method searches the optimal maintenance weeks for each generation unit, considering the objectives of both GenCo and ISO, simultaneously. The proposed MO-GMS model is formulated as a mixed-integer non-linear programming problem and examined on the IEEE 24-bus and IEEE 118-bus test systems. The success of the proposed multi-objective model is validated by comparing the obtained results with intelligent optimisation algorithms.

This study proposes a mathematical model of an intelligent multi-objective home energy management (HEM) scheme with the integration of small-scale renewable energy sources. The main aim of the proposed model is to handle the residential load demand in a smart way to minimise both the consumer's energy bill and the system peak demand simultaneously. To generate the best compromise solution of the proposed multi-objective problem, a cooperative game theory approach is used in this study on the basis of super-criterion and a Pareto optimal solution concept. In the cooperative game process, each HEM objective is assigned as a player and every player tries to maximise their own payoff. Bargaining model in the form of super criterion is considered in this game approach. Finally, all players can get win–win nature with collective negotiations. Generally, HEM method deals with various controllable devices having distinct operating characteristics. Because of this, the proposed HEM problem is modelled as a mixed-integer problem. Consequently, a mixed-integer non-linear programming is applied in this game process to maximise the super-criterion. To show the effectiveness of the proposed model, different case studies and various scenarios are carried out.