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The present communication examines the unsteady-two-dimensional (2-D) squeezing flow of magnetohydrodynamic (MHD) Jeffrey fluid between two parallel plates (HT2DUSMHDJF). In its own plane, the bottom channel plate is extended while the upper plate squeezes towards the lower plate. The complete structure is takan is a rotating frame. Cattaneo-Christov heat flux model (CCHFM) is forced to explore the features of heat transfer. Distinct the conventional position, Instead of the Fourier heat conduction law, the heat flux is implemented by the Cattaneo-Christov theory. The resultant systems are computed through Artificial Neural Network (ANN). The behaviors of a number of relevant parameters are analyzed through graphs and numerical data. The velocity profile increases for Deborah number β and squeezing parameter sq and decreases for rotation, magnetic and relaxation time parameter ω , M, and λ 1 respectively. Also the Skin friction coefficient decreases for λ 1 and Sq and increases for high value of Deborah numbers β , ω and M. [ABSTRACT FROM AUTHOR]

The nanomaterials are frequently employed in a variety of heat transfer applications arising in energy generation, engine cooling, extrusion procedures, heat exchanger, thermos-chemical systems, manufacturing structures, powered plants etc. Experts and researchers in these fields often encounter such materials, leading to discernible impacts on velocity, temperature and concentration profiles. The objective of this study is to represent the mathematical structures for Sutterby nanomaterial fluidic model involving porous medium BFM-SNM using nonlinear autoregressive exogeneous networks backpropagated with Levenberg-Marquardt technique (NARX-LMT). The mathematical design of the model is originally presented by PDEs, which are converted into ODEs system through suitable modifications with alternative transformation incorporating numbers, i.e., Deborah, Prandtl and Reynold, as well as parameters, i.e., magnetic thermophoresis, radiation and temperature. The synthetic data is produced numerically by simulating Adams numerical method for BFM-SNM and the supervised computing paradigm of NARX-LMT is applied to the obtained datasets, and the results of NARX-LMT consistently align with numerical observations for each variant of the presented model, exhibiting negligible errors. The NARX-LMT performance on exhaustive experimentation is effectively illustrated through iterative convergence curves on mean squared error, control metrics of the optimization, distribution of error on histograms and regression outputs for Sutterby nanomaterial fluidic model. [Display omitted] [ABSTRACT FROM AUTHOR]

• The proposed expert system has a hybridized inference engine. • The proposed expert system has a mechanism for drug allergy management. • The proposed expert system's prediction mechanism grows in intelligence through continues mining of patient's medical history. When we fall sick, we seek professional medical assistance if situations warrant. Medical diagnostic processes are usually based on signs, symptoms and laboratory investigations. Some patients respond to treatment while others react (or are allergic) to treatment. Substances or drugs that cause allergies are called Allergens. Allergies, when not managed, can be chaotic to human health. Considering these scenarios and a lot of others, an expert system (ES) for differential symptoms-based diagnosis and drug allergy management comes in handy. This paper implements 'Dr. Flynxz – A First Aid Mamdani-Sugeno-type Fuzzy Expert System for Differential Symptoms-Based Diagnosis'. It also introduced three novel algorithms that were used to implement Dr. Flynxz's Ailment Prediction (Forward Chaining Approach and Backward Chaining Approach) and Allergy Management mechanisms. A comparison of the proposed FMI Ailments Prediction Algorithm with ada 2.37.2 (a rule based approach) and fuzzy logic both confirm Dr. Flynxz's prediction. Experimental results indicate performance improvement in terms of prediction accuracy. The implemented system can be useful in the area of medical diagnosis and allergy management while the proposed algorithms will be useful in the development of Mamdani-Sugeno-type fuzzy inference systems. [ABSTRACT FROM AUTHOR]

In the artificial neural networks domain, the Levenberg-Marquardt technique is novel with convergent stability and generates a numerical solution of the wire coating system for Sisko fluid flow (WCS-SFF) through regression plots, histogram representations, state transition measures, and means squared errors. In this paper, the analysis of fluid flow problem based on WCS-SFF is studied with a new application of intelligent computing system via supervised learning mechanism using the efficacy of neural networks trained by Levenberg-Marquardt algorithm (NN-TLMA). The original mathematical formulation in terms of PDEs for WCS-SFF is converted into dimensionless nonlinear ODEs. The data collection for the projected NN-TLMA is produced for parameters associated with the system model WCS-SFF influencing the velocity using the explicit Runge-Kutta technique. The training, validation, and testing processes of NN-TLMA are utilized to evaluate the obtained results of WCS-SFF for various cases, and a comparison of the obtained results is performed with reference data set to check the accuracy and effectiveness of the proposed algorithm NN-TLMA for the analysis of non-Newtonian fluid problem-related WCS-SFF. The proposed NN-TLMA for solving the WCS-SFF is effectively confirmed through state transition dynamics, mean square error, regression analyses, and error histogram studies. The powerful consistency of suggested outcomes with reference solutions indicates the validity of the framework, and the accuracy of 10 - 8 to 10 - 6 is also achieved. [ABSTRACT FROM AUTHOR]

The purpose of this research study is to evaluate the solutions of Titanium and Aluminum alloy nanofluidic model with gyrotactic microorganisms (TAA-NFMGM) by applying stupendous knacks of Bayesian distributed adaptive neural structure (BDANS). The use of nanofluids containing gyrotactic microorganisms in combination with titanium and aluminum alloys have potential applications in diverse fields, including engineering, bioremediation, biomedical devices, biotechnology, and research depending on different factors like design of the system and use of certain microorganism. A dataset for BDANS is generated for the eight different events with Adam numerical technique for TAA-NFMGM by varying Prandtl number (Pr), mixed convection parameter (λ), microorganisms concentration parameter (Ω), bioconvection peclet number (Pe), and bioconvection Lewis number (Lb). The reference data set generated with Adam numerical technique is utilized for numerical calculation of different parameters on TAA-NFMGM by employing the artificial intelligence based BDANS. The accuracy and justification of the performance of BDANS is efficaciously substantiated through negligible level of MSE around 10–10 to 10–13, calculation of regression metrics and distribution of instances of error on histograms with values 10–04 to 10–09 near to reference line. [ABSTRACT FROM AUTHOR]

With the popularization of the intelligent manufacturing, much attention has been paid in such intelligent computing methods as deep learning ones for machinery fault diagnosis. Thanks to the development of deep learning models, the interference of the human experience can be greatly reduced, and the fault diagnosis accuracy can also be increased under certain conditions. To improve the generalization ability of the intelligent fault diagnostics, the deep transfer learning consisting of both transfer learning and deep learning components was accordingly developed. This paper reviews the research progress of the deep transfer learning for the machinery fault diagnosis in recently years. It is summarizing, classifying and explaining many publications on this topic with discussing various deep transfer architectures and related theories. On this basis, this review expounds main achievements, challenges and future research of the deep transfer learning. This provides clear directions for the selection, design or implementation of the deep transfer learning architecture in the field of the machinery fault diagnostics. [ABSTRACT FROM AUTHOR]

The growing attractiveness of Artificial Neural Networks (ANNs) derives from their exceptional effectiveness in handling difficult and exceptionally nonlinear mathematical ideas. In complicated disciplines such as fluid mechanics, biological computation, and the field of biotechnology ANNs provide a diverse computing framework that is extremely valuable. This article's major aim is to harness the capabilities of the Levenberg-Marquardt technique with backpropagation intelligent neural networks (LM- BPINNs) to study there is still a lack of clarity regarding the mechanics underlying the increased heat transfer caused by dispersed nanoparticles. The using proposed LM-BPINNs to improve the heat transmission use activation energy and Hall current phenomena in nanofluid (HTAHCNF). The data set is obtained by using Lobatto-III. A method and then ANNs is applied. The LM- BPINNs technique is applied by utilizing reference datasets, with 80% of the dataset devoted to training, 10% to testing, and 10% to verification. The precision/accuracy and converging of developed LM- BPINNs are validated based on the obtained reliability via efficient fitness achieved on mean squared error (MSE), comprehensive regression analysis, and appropriate error histogram visualizations. A diminished MSE indicates that the model's predictions are more reliable. The outcome is consistent with getting a minimal absolute error close to zero, exhibiting the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract In this paper, strength of recently introduced nature inspired heuristic computing technique named fireworks algorithms (FWA) and its variants are exploited in the field of system identification based on active noise control (ANC) systems. Residual error based fitness function is constructed for ANC system and optimization of parameter is carried out with the help of fireworks, enhance fireworks and adaptive fireworks algorithms. The design scheme is applied to number of scenarios of ANC system for different primary and secondary paths. The designed methodology is evaluated for three different noise interferences based on sinusoidal and random signals. The scheme validated by means of accuracy and convergence operators through the results of statistics based on ample number of independent trails. The comparative study of results is also made for three variants of FWA to establish their worth in terms of precision, stability and complexity measures. [ABSTRACT FROM AUTHOR]

In current study, the effects of magnetic field and Joule heating in flow of (Cu+CuO) hybrid nanomaterial model with blood as a base fluid is analyzed effectively by exploiting novel design of intelligent Bayesian regularization neural networks (IBRNN) for several important physical aspects. Mathematical models of the systems are derived by considering the small Eckert and Prandtl numbers with thermo-physical characteristics of Cu+CuO/blood hybrid nanoliquids while the heat emission/absorption is added in energy equation to maintain the temperature for blood flow. The effects of various parameters of interest like wall deformation rate, Joule heating parameter, magnetic parameter, volume fraction and permeability parameter on velocity and temperature distribution have been studied using viable numerical approach of Adams method as well as presented intelligent Bayesian networks. The reliability of the Bayesian networks is endorsed by convergence plots of MSEs, effective outputs of adaptive controlling parameters of optimization algorithm, error based histograms, and regression statistics for exhaustive simulations studies in case of sundry scenarios. [ABSTRACT FROM AUTHOR]

The development of 5G brings convenience to people, but it also brings threats. It is particularly important to analyze attack–defense confrontation from a macro perspective. Network security of graph is an important research problem in current network environment. A Stackelberg game model based on 5G network graph is established to analyze the attack–defense confrontation in this paper. In order to solve the Nash equilibrium, a compact particle swarm optimization based on t-Location-Scale distribution (tCPSO) is proposed. The tCPSO uses less memory space and has better optimization performance. The experiments prove that tCPSO has better optimization performance than other heuristic algorithms on CEC2013. The clustering experiments further show that tCPSO has better optimization ability than other heuristic algorithms. The optimal defense strategies under different attack–defense resources are analyzed in this paper. It is concluded that increasing the successful defense rate is more effective for the defender to obtain greater profit than reducing the successful attack rate. Using intelligent computing to solve Nash equilibrium is a new idea for researchers to analyze attack–defense confrontation. • The tCPSO proposed in this paper has strong optimization ability. • Intelligent computing can efficiently solve Nash equilibrium. • More defense resources and fewer attack resources are better for the defender. • Increasing the successful defense rate is more beneficial to the defender. [ABSTRACT FROM AUTHOR]

Abstract Bacterial foraging optimization (BFO), based on the social foraging behaviors of bacteria, is a new intelligent optimizer. It has been widely accepted as an optimization algorithm of current interest for a variety of fields. However, compared with other optimizers, the BFO possesses a poor convergence performance over complex optimization problems. To improve the optimization capability of the BFO, in this paper a bare bones bacterial foraging optimization (BBBFO) algorithm is developed. First, a chemotactic strategy based on Gaussian distribution is incorporated into this method through making use of both the historical information of individual and the share information of group. Then the swarm diversity is introduced in the reproduction strategy to promote the exploration ability of the algorithm. The performance of BBBFO is verified on various benchmark functions, the comparative results reveal that the proposed approach is more superior to its counterparts. [ABSTRACT FROM AUTHOR]

An urban pluvial flood is a devastating, costly natural disaster requiring effective rescue path planning to mitigate the loss of lives and property. The inherent uncertainty and complexity of the risks associated with urban flooding limit the ability to plan optimal rescue paths that prioritize both timeliness and safety. This study addresses the challenge by proposing an innovative assessment methodology to output risk values and probability representing safety and timeliness in each passable area while simulating real-world flood scenarios. Furthermore, the paper develops a pioneering path-planning algorithm based on deep reinforcement learning, incorporating improved stochastic reward exploitation and heterogeneous reward exploration mechanisms to function in a simulation rescue path-planning scenario with uncertainty and complexity. According to the findings, the proposed algorithm outperforms current state-of-the-art algorithms in converging to the optimal path, fully sampling, and running efficiency. The study contributes to theoretical progress on urban pluvial flood rescue, deep reinforcement learning, risk assessment, and decision intelligence while offering practical implications for smart cities, emergency management, and optimizing real-world problems by employing artificial intelligence. • Urban pluvial flood rescue path planning using deep reinforcement learning. • Probability-based risk assessment methodology for urban pluvial flood rescue. • Environmental simulation based on real-world rescue scenarios. • Improving the exploitation and exploration to stochastic and heterogeneous reward. • Expanding deep reinforcement learning to solve real-world problems. [ABSTRACT FROM AUTHOR]

A novel adaptive algorithm of particle swarm optimization is proposed in this paper, where the learning strategy is designed by simulating features of human social learning intelligence. By the principles of this strategy, the swarm is first adaptively grouped into a number of subswarms based on the fitness levels of particles, as well as no need of fixing the population sizes as done in the existing algorithms. Then, the particles in each subswarm constitute three different types classified by their evolutionary levels, which adopt different learning modes defined by distinct adaptive inertia weighting rules and dynamical learning mechanisms. Consequently, all the particles play their respective roles in promoting the global optimization performance of our algorithm, just like all the individuals in society. Numerical tests of our algorithm are conducted by solution of the standard benchmark test problems from the CEC 2014 and CEC 2017 test suites. The test results show the advantages of the proposed learning strategy in this paper, and demonstrate that the overall performance of our algorithm outperforms both of the other compared PSO variants and the other state-of-the-art evolutionary algorithms, in terms of global search ability, solution accuracy and convergence speed. Statistical tests also validate the promising performance of our algorithm. [ABSTRACT FROM AUTHOR]

In addition to dominance-based and decomposition-based algorithms, performance indicator-based algorithms have been widely used and investigated in the field of evolutionary multi-objective optimisation. This study proposes a multi-objective artificial bee colony optimisation method called ε -MOABC based on performance indicators to solve multi-objective and many-objective problems. The proposed algorithm develops an external archive on the basis of both Pareto dominance and preference indicators to save the non-dominated solutions produced in each generation. The population of the presented algorithm includes employed bees, onlooker bees, and scout bees. Employed bees adjust their trajectories according to the information provided by other employed bees. Motivated by employed bees, onlooker bees select food sources to update their positions according to a power law probability, with which the food sources with high quality have a high probability to be selected for exploration. The quality of food sources is calculated on the basis of the quality indicator I ε + . Scout bees dispose of food sources with poor quality. The proposed algorithm proves to be competitive in dealing with multi-objective and many-objective optimisation problems in comparison with other state-of-the-art algorithms for CEC09, LZ09, and DTLZ test instances. [ABSTRACT FROM AUTHOR]

This work aims to improve the accuracy of financial crisis early warnings for small and medium-sized enterprises (SMEs) to help them uncover hidden dangers during the latent period of a crisis and to respond effectively. First, this work analyses the development characteristics of SMEs, the crises they face, and a financial crisis early warning system. Then, a statistical method is used to determine which indicators are significant to implement the model for an early warning financial crisis to determine the index system. To do this, relevant machine learning (ML) algorithms are introduced to realize a corporate financial crisis management and early warning system (CFCM-EWS) using the stacking fusion method. The CFCM-EWS can mine relationships among the data and analyze nonlinear and difficult-to-explain problems. A comparison of classical financial early warning models (backpropagation neural network (BPNN) and logistic regression models) with the XGBoost model highlights the advantages of the XGBoost model. The model's discrimination results show that its prediction accuracy for special treatment and nonspecial treatment corporates is 85.8 % and 81.9 %, respectively. The logistic regression, XGBoost, and BPNN models are fused using the stacking method. ML provides a more practical prediction method with higher efficiency and accuracy than does traditional econometric models. The final fusion model outperforms the voting and averaging methods in prediction performance. The CFCM-EWS based on intelligent computing discussed here is of great value for SMEs to accurately predict financial crises and adopt timely countermeasures. • Established a financial crisis early warning system for the crisis of SMEs. • For CFCM and early warning systems, this study used the Sacking fusion method. • The voting and averaging methods are used for prediction performance. • CFCM early warning System is significant for taking countermeasures promptly. [ABSTRACT FROM AUTHOR]

The design of integrated intelligent computing solver with Morlet wavelet neural networks (MW-NNs) is presented for solving the mathematical predator–prey model by exploiting the strength of MW-NNs modeling, optimization ability of global search with genetic algorithms (GAs) and rapid local search eminence of sequential quadratic programming (SQP), i.e., MW-NNs-GA-SQP. The proposed MW-NNs-GA-SQP scheme is used to analyze the predator–prey dynamics for six different variables coefficient values. The validation, correctness and reliability of the presented MW-NNs-GA-SQP technique is attained through the consistent matched outcomes with the reference Adams numerical results. Moreover, statistics investigations have been accomplished to verify the precision and accuracy of the outcomes with proposed MW-NNs-GA-SQP solver via the performances of Theil's inequality coefficient, Nash Sutcliffe efficiency and mean absolute error. • The design of integrated intelligent computing solver with Morlet wavelet neural network (MW-NN) is presented for solving the mathematical predator–prey model. • We use the strength of MN-NN modeling, optimization ability of global search with genetic algorithms (GAs) and rapid local search eminence of sequential quadratic programming (SQP), i.e., MW-NN-GA-SQP. • The proposed MW-NN-GA-SQP scheme is used to analyze the predator–prey dynamics for six different variables coefficient values. • The validation, correctness and reliability of the presented MWNN-GA-SQP technique is attained through the consistent matched outcomes with the reference Adams numerical results. • Statistics investigations have been accomplished to verify the precision and accuracy of the outcomes with proposed MW-NN-GA-SQP solver via the performances of Theil's inequality coefficient, Nash Sutcliffe efficiency and mean absolute error. [ABSTRACT FROM AUTHOR]

Background /purpose: Establishing an accurate postmortem interval (PMI) is exceptionally crucial in forensic investigation. Artificial intelligence (AI) and Machine learning (ML) models are widely employed in forensic practice. ML is a part of AI, both terms are highly associated and sometimes used interchangeably. This systematic review aims to evaluate the application and performance of AI technology for the prediction of PMI.Methods: Systematic literature search across different electronic databases using PubMed/Google Scholar/EMBASE/Scopus/CINAHL/Web of Science/Cochrane library was conducted from inception to 3 December 2021 for preclinical and clinical studies reported ML models for PMI estimation.Results: We identified 18 studies (12 preclinical and 06 clinical) that met the inclusion criteria in the qualitative analysis. Most of the studies employed supervised learning (N = 15), and others employed unsupervised learning (N = 3). Due to the heterogeneity of the samples, quantitative analysis was not performed.Conclusion: In this systematic review, we discussed the performance of AI-based automated systems in PMI estimation. ML models have demonstrated accuracy and precision and the ability to overcome human errors and bias. However, the research is limited, conducted in primarily small, selected human populations. In addition, we suggest further research in larger population-based studies is needed to fully understand the extent of integrated ML models. [ABSTRACT FROM AUTHOR]

Abstract: Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. [ABSTRACT FROM AUTHOR]

Abstract: This research presents the application of intelligent computing models in Air Traffic Flow Management (ATFM). Firstly, multi-agent system in grid computing environment is applied to deal with the problem of ATFM synchronization. The developed system consists of software agents, which are implemented in a Computational Grid platform for congestions identification, conflicts resolution and agreements negotiation among the participating airports. A metric criterion, called Agent’s Balancing Standard (ABS), is used as a basic index to measure the effectiveness of reducing both the amount of communication among agents and the delay of flights. Secondly, a brief discussion about the Meta-Level Control model is introduced in ATFM issues to improve the efficiency of communication among the agents. The system is developed to analyze the traffic flow information received, identify its importance and process it in the most adequate order. [Copyright &y& Elsevier]

Abstract: Now that intelligent computing era is coming, it becomes necessary to retrieve and process images according to human Kansei or preference. Application and research on Kansei information of image, especially, are mainly focused on satisfying individual taste. However, existing researches are focused on content-based retrieval such as low-level information and visual features that make it difficult to retrieve and process images according to human high-level Kansei information. Therefore, we suggest solving this problem by defining relations between visual information and Kansei and establishing “Kansei Factor Space” using Kansei vocabulary. The Kansei factor space defines the systematical Kansei information, a Kansei modeling, by use of its vocabulary and represents Kansei of each image. In addition, through Kansei factor space, Kansei can be measured in terms of quality. Thus, our study could serve as a basis for Kansei information image processing. [Copyright &y& Elsevier]

Abstract: Recently the methods of intelligent computing (IC) have been applied for the interpretation of well log data. This is due to the necessity to process well logs in the situations when complete information about them cannot be obtained. In this case, hybrid methods based on statistical analysis, fuzzy logic and evolutionary algorithms could be very useful. This paper presents such hybrid analysis of logging data of the wells from the Cantarell Oil Complex, in the Zonda of Campeche, Mexico. Different methods, such as principal component analysis, factor analysis, fuzzy classification and evolutionary optimization are used for analysis of the structure of porosity space given by primary, cavernous and micro-fractures porosity classes. Comparison and analysis of the obtained results show that IC methods can substantially compensate for the absence of exact information without losing the precision of data analysis and at the same time decrease the costs of well logging. [Copyright &y& Elsevier]

• The use of wireless sensor networks to monitor indoor temperature. • A cyber-physical design approach is proposed. • Propose an optimal delay algorithm for anycast protocol. • Implement using TelosB sensors with TinyOS. Social, Mobile, Analytics and Cloud (SMAC) technologies aim to bridge the cyber, physical and social spaces. The use of wireless sensor networks to monitor indoor temperature is a typical application in smart cities. Rather than splitting the measured temperature and the design of a sensor network, a cyber-physical design approach is proposed by this paper for indoor temperature monitoring using wireless sensor network. The source sensors adopt sleep/wake scheduling, that is, source nodes wake up and sense the temperature periodically. The temperature data is sent to the cloud server via multi-hop relaying sensor nodes in an anycast way. Each sensor decides how to route packets based on its local information and dynamically adjust the sleep/wake duty cycle according to the sensed temperature: if the measured temperature is within normal range, the sensor wakes up infrequently to achieve higher energy efficiency; and vice versa. We first propose an optimal delay algorithm for anycast protocol. The simulation results show that our approach outperforms other heuristic schemes. Furthermore, we implement the proposed algorithm using TelosB sensors with TinyOS. Experiments demonstrate that the designed system can report a temperature anomaly within a small delay and achieve good long-term energy efficiency at the same time. [ABSTRACT FROM AUTHOR]