Showing papers in "Engineering Applications of Artificial Intelligence in 2020"

TL;DR: Simulation results demonstrate that TSA generates better optimal solutions in comparison to other competitive algorithms and is capable of solving real case studies having unknown search spaces.

TL;DR: The comparison results on the benchmark functions suggest that MRFO is far superior to its competitors, and the real-world engineering applications show the merits of this algorithm in tackling challenging problems in terms of computational cost and solution precision.

TL;DR: The results indicate that the proposed BWO algorithm has numerous advantages in different aspects such as early convergence and achieving optimized fitness value compared to other algorithms, and has the capability of providing competitive and promising results.

TL;DR: The journey of Differential Evolution is shown through its basic aspects like population generation, mutation schemes, crossover schemes, variation in parameters and hybridized variants along with various successful applications of DE.

TL;DR: The statistical simulation results revealed that the LFD algorithm provides better results with superior performance in most tests compared to several well-known metaheuristic algorithms such as simulated annealing (SA), differential evolution (DE), particle swarm optimization (PSO), elephant herding optimization (EHO), the genetic algorithm (GA), moth-flame optimization algorithm (MFO), whale optimization algorithm

TL;DR: A novel bio-inspired optimization algorithm namely the Barnacles Mating Optimizer (BMO) algorithm to solve optimization problems that mimics the mating behaviour of barnacles in nature for solving optimization problems.

TL;DR: A thorough evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and Health Management (PHM) applications can be found in this paper.

TL;DR: An overview of the most relevant work in the area of type-2 fuzzy logic, including its theoretical and practical implications, as well as envisioning possible future works and trends in this area of research.

TL;DR: It can be concluded that an innovative hybrid prediction model in view of the Volterra adaptive filter and an improved whale optimization algorithm to predict the short-term natural gas consumption may provide a reference for natural gas companies to achieve intelligent scheduling.

TL;DR: A novel framework is elaborated which combines AHP and TOPSIS with a spherical fuzzy set, which is effective in handling uncertainty in decision making and leads to robust and competitive results compared with state-of-the-art multi-criteria decision-making (MCDM) approaches.

TL;DR: The proposed prediction method for short-term wind speed based on local mean decomposition and combined kernel function least squares support vector machine (LSSVM) has higher prediction accuracy and is able to reflect the laws of wind speed correctly.

TL;DR: This paper analyzes both the independent and interdependent relationship that exist between the input arguments based on the arithmetic mean and the Maclaurin symmetric mean (MSM) respectively and develops some new Hamacher operations for q-ROFS.

TL;DR: A predictive model based on a Bayesian Filter, a tool from the Machine Learning field, to estimate and predict the gradual degradation of such machinery, permitting the operators to make informed decisions regarding maintenance operations is described.

TL;DR: A systematic literature review was conducted to explore smart wearables by reviewing previous studies from 2010 to 2019, and the results show that the Technology Acceptance Model (TAM) is the most commonly adopted theory in the smart wearable studies.

TL;DR: With the monitoring data of a gear life cycle test, the comparative experiments show that the proposed gear remaining life prediction method has higher prediction accuracy than the conventional prediction methods.

TL;DR: In a case study considering a local subsystem of a 220kV power system, the diagnosis results of five test cases prove that the proposed FD-WCFRSNPS is viable and effective.

TL;DR: A healthcare device selection problem is analyzed from the perspectives of clinicians, biomedical engineers, and healthcare investors and a comparison of the results derived from different multi-criteria solution processes is presented.

TL;DR: The CMVHHO showed the best results than other algorithms in terms of the performance measures as well as in engineering problems and it outperformed the state-of-the-art algorithms in all problems.

TL;DR: A dynamic neighborhood-based learning strategy is introduced to replace the global learning strategy, which enhances the diversity of the population and has competitive performance compared with 5 state-of-the-art multimodal multi-objective algorithms.

TL;DR: A conceptual framework is outlined whereby DB is viewed in terms of different dimensions established within the Driver–Vehicle–Environment (DVE) system, and an interpretive framework incorporating multiple dimensions influencing the driver’s conduct is identified.

TL;DR: The importance ratings and global weights of customer requirements (CR) and improvement directions of design requirements (DR) are successfully represented by using spherical fuzzy sets.

TL;DR: Attention is brought to the promising applicability of artificial neural networks applied to the control of microgrid distributed generation sources, as well as in scheduling, power sharing, supervisory control and optimization.

TL;DR: The proposed technique is a population based meta-heuristic method which is inspired from the strategies used by players in a well known tag-team game played in India, i.e. Kho-Kho, and its performance and superiority with respect to other existing methods are evaluated.

TL;DR: The results show that the Lt-SFS-MABAC method is sensitive to weights, decision makers can use the Lt/SFS/TODIM method to make a realistic evaluation based on the actual environment.

TL;DR: A clear summary of the latest progress in the context of intrusion detection methods is prepared, a technical background on boosting is presented, and the ability of the three well-known boosting algorithms as IDSs is demonstrated by using five IDS public benchmark datasets.

TL;DR: Comparisons demonstrate that the proposed RLWOA shows better or at least competitive performance against the standard WOA, WOA variants and other state-of-the-art meta-heuristic algorithms for solving high-dimensional numerical optimization, practical engineering design optimization, and photovoltaic model parameter estimation problems.

TL;DR: This paper addresses for the first time this problem using deep learning approaches to predict exactly those pixels that belong to each class, i.e., diatom and non diatom, in microscopic algae segmentation.

TL;DR: The uniform ultimate boundedness stability of the closed-loop control system is proved via a Lyapunov-based stability synthesis and it is demonstrated that the posture tracking errors converge to a vicinity of the origin with a guaranteed prescribed performance during the tracking mission without velocity measurements.

TL;DR: In this article, a scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks is proposed for solving increasingly complicated multiobjective problems.

TL;DR: The results show that the proposed target threat assessment method can effectively deal with dynamic uncertain situation information, turn the traditional ranking results of two-way decisions to the objective classification results of three- way decisions and can flexibly reflect the acquisition of situation information by setting the risk avoidance coefficient.