Search

The security of wireless sensor networks is a hot topic in current research. Game theory can provide the optimal selection strategy for attackers and defenders in the attack-defense confrontation. Aiming at the problem of poor generality of previous game models, we propose a generalized Bayesian game model to analyze the intrusion detection of nodes in wireless sensor networks. Because it is difficult to solve the Nash equilibrium of the Bayesian game by the traditional method, a parallel particle swarm optimization is proposed to solve the Nash equilibrium of the Bayesian game and analyze the optimal action of the defender. The simulation results show the superiority of the parallel particle swarm optimization compared with other heuristic algorithms. This algorithm is proved to be effective in finding optimal defense strategy. The influence of the detection rate and false alarm rate of nodes on the profit of defender is analyzed by simulation experiments. Simulation experiments show that the profit of defender decreases as false alarm rate increases and decreases as detection rate decreases. Using heuristic algorithm to solve Nash equilibrium of Bayesian game provides a new method for the research of attack-defense confrontation. Predicting the actions of attacker and defender through the game model can provide ideas for the defender to take active defense. [ABSTRACT FROM AUTHOR]

The recognition of individual activity has proven its importance in many application areas. Even after the pandemic crisis worldwide, the remote monitoring of human actions and their activities has increased a lot. In such situations, the smart sensors are utilized to capture the details remotely, share, and process intelligently using the computer-assisted machine. This paper introduces the new variant of the machine learning classifier, named Grid Search Based Nonlinear Multi-task Least Squares Twin Support Vector Machine (GMLT-SVM), to recognize human actions using sensory data. The proposed approach uses the grid search for hyperparameter tuning with a Nonlinear kernel for Multi-task classification in a twin support vector machine. This not only increases the efficacy of the model but also reduces the computation time for finding the optimal values of the hyper-parameters. The proposed approach when experimented with the sensory data of Human Action Recognition (HAR) gives an accuracy of 96.8%. The comparative analysis of the proposed method with existing other ML classifiers gives an increase in the accuracy of 94.2±2.6%. The experimental results suggest that the gained results are statistically significant enough to recognize human actions with sensory data. [ABSTRACT FROM AUTHOR]

Copyright of Nankai Business Review is the property of Nankai Business Review Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent days, people must deal with stress brought on by the demands of modern living, which constantly presents new obstacles. Stress, a state of mental tension triggered by challenging circumstances, has become a global risk factor impacting individual well-being. Understanding variations in stress resilience is crucial for tailoring treatment strategies. Previous studies have explored stress prediction using measures like electroencephalography (EEG), blood pressure (BP), heart rate (HR), and interventions such as Kriya Yoga and mindfulness meditation. The experimentation is done on the data collected from people who practice heartfulness meditation regularly. The research employs machine learning (ML) algorithms alongside physiological parameters such as EEG, BP, HR, and psychological parameters, perceived stress scale (PSS), to precisely classify, measure, and predict stress levels. The investigations are done using K-nearest neighbor (KNN), random forest (RF), and kernel-support vector machine (k-SVM). An accuracy of 98.27% accuracy was achieved with the RF algorithm in classifying stressed and non-stressed individuals. [ABSTRACT FROM AUTHOR]

In this study, an intelligent computing framework is presented to explore the influence of unpredictable environmental factors on the spread of infections. A stochastic non-linear SIS epidemic model (SISEM) is examined that incorporates a non-linear incidence rate, and impact of random fluctuations on the disease transmission. The knacks of artificial neural networks Levenberg–Marquardt (ANNLMA) technique are exploited to illustrate the behavioral complexities of stochastic non-linear SISEM, which explores the dynamics of disease transmission in a stochastic environment and provides insights into the interactions between susceptible and infectious populations. The Milstein approach of order 1 generates a dataset for the proposed ANNLMA technique by varying integrated parameters, such as the rates of recovery, death, disease transmission, noise standard deviation and the entire population, for various SISEM scenarios. The referenced dataset is randomly divided into test, validation and train sets for the weight learning of the proposed ANNLMA. The viability of the ANNLMA is measured using intensive simulation-based investigations through analysis of mean squared errors, histograms, regression and autocorrelation studies. The investigations highlighted that the ANNLMA results closely match the reference results, illustrating convergence within the 10−2 to 10−7 range, indicating the validity of the proposed methodology. [ABSTRACT FROM AUTHOR]

Financial fraud detection has been an urgent technical demand in cyberspace. It highly relies on clear extraction and deep representation toward complex relationships inside financial social networks. As consequence, this study combines both knowledge graph and deep learning to deal with such issue. Thus, an intelligent financial fraud detection model based on knowledge graph guidance and deep neural network is proposed in this paper. First, a new knowledge graph based on financially related systems is constructed, which includes multiple entities and relationships. Then, an adversarial learning-based neural network structure is formulated to extract financial attributes. Finally, the detection results can be output according to the extracted factors. Empirically, the proposal is implemented on a real-world dataset for performance evaluation. The experimental results show that it has higher accuracy and effectiveness compared to traditional fraud detection methods. The proposed detection model can not only identify known fraudulent behaviors, but also predict potential fraud patterns based on consumer habits, thereby improving the security and reliability of financial transactions. It can also update the knowledge graph in real-time, enabling it to cope with emerging fraud methods and variants. [ABSTRACT FROM AUTHOR]

Abstract The security of wireless sensor networks is a hot topic in current research. Game theory can provide the optimal selection strategy for attackers and defenders in the attack-defense confrontation. Aiming at the problem of poor generality of previous game models, we propose a generalized Bayesian game model to analyze the intrusion detection of nodes in wireless sensor networks. Because it is difficult to solve the Nash equilibrium of the Bayesian game by the traditional method, a parallel particle swarm optimization is proposed to solve the Nash equilibrium of the Bayesian game and analyze the optimal action of the defender. The simulation results show the superiority of the parallel particle swarm optimization compared with other heuristic algorithms. This algorithm is proved to be effective in finding optimal defense strategy. The influence of the detection rate and false alarm rate of nodes on the profit of defender is analyzed by simulation experiments. Simulation experiments show that the profit of defender decreases as false alarm rate increases and decreases as detection rate decreases. Using heuristic algorithm to solve Nash equilibrium of Bayesian game provides a new method for the research of attack-defense confrontation. Predicting the actions of attacker and defender through the game model can provide ideas for the defender to take active defense.

The surging popularity of generative adversarial networks (GANs) has ignited a wave of innovation in the realm of computer vision, a highly explored subfield of deep learning. GANs are revolutionizing the area of machine learning because they use a game-based training technique. This is in contrast to traditional approaches to machine learning, which center on feature learning and picture production. Several subfields of computer vision have seen tremendous progress thanks to the integration of numerous processing approaches, including image processing, dynamic processing, text, audio, and video processing, as well as generalized generative adversarial networks (GANs). Nevertheless, despite the fact that GANs have made great progress, they still offer promise that has not been fully realized and space for additional development. GANs have a wide range of applications within computer vision, including data augmentation, displacement recording, dynamic modeling, and image processing. This article digs into recent advances made by GAN researchers working in the realm of AI-based security and defense and discusses their accomplishments. In particular, we investigate how well image optimization, image processing, and image stabilization are incorporated into GAN-driven picture training. We want to achieve our goal of providing a complete overview of the present status of GAN research by carefully evaluating research articles that have been subjected to peer review. [ABSTRACT FROM AUTHOR]

The field of infrastructure security has garnered significant research attention. By integrating complex network theory with game theory, researchers have proposed many methods for studying the interactions between the attacker and the defender from a macroscopic viewpoint. We constructed a game model of infrastructure networks to analyze attacker-defender confrontations. To address the challenge of finding the Nash equilibrium, we developed a novel algorithm—node-incremental greedy algorithm (NIGA)—which uses less strategy space to solve the problem. The experiments performed further showed that NIGA has better optimization ability than other traditional algorithms. The optimal defense strategies under different conditions of initial strategy ratios and attacker-defender resources were analyzed in this study. Using intelligent computing to solve the Nash equilibrium is a new approach by which for researchers to analyze attacker-defender confrontations. [ABSTRACT FROM AUTHOR]

This paper aims to establish a panoramic foundation for investigating the impact of sunlight on a recently formulated water-based tri-nano hybrid Sutterby liquid (TNHF) under provided magnetic field, directed through photovoltaic solar panels by exploiting the knacks of machine intelligent computing paradigm. The comparative performance of hybrid and tri-nano fluidic system with water as a base fluid is exhaustively analyzed and discussed. This research is unique, as it has a distinguished figurative comparison analysis between two different types of nanofluidic materials, which helps to choose the best one for use in agrivoltaics systems, and replace the materials already in use to enhance the efficiency and performance coefficients. Furthermore, the description of the composition scheme makes this research more feasible and applicable. A numerical dataset of nonlinear mathematical model is generated by employing finite difference scheme in the recently introduced Python bvp-solver algorithm, then it is embedded into artificial intelligence (AI)-based Levenberg Marquardt neural network algorithm (LMNNA). A significant outcome of the research indicates that the integration TNHF results in a notably faster enhancement of heat transfer rate and temperature framework as compared to traditional hybrid fluid. It is observed that introducing three distinct nanomaterials of specific thermophysical characteristics enhances the thermal exchanging profile and faces an obvious flow rate dissipation in solar plate channels. The standard numerical and AI-generated results are documented to portray the stability, accuracy and efficacy of scheme in terms of iterative learning curves on MSE, error analysis, histograms and regression statistics. Additional perquisites of the methodology include cost effectiveness, time-saving ability, robustness, stability and its extendibility. [ABSTRACT FROM AUTHOR]

Modern technology and analysis of emotions play a crucial role in enabling intelligent healthcare systems to provide diagnostics and self-assistance services based on observation. However, precise data predictions and computational models are critical for these systems to perform their jobs effectively. Traditionally, healthcare monitoring has been the primary emphasis. However, there were a couple of negatives, including the pattern feature generating the method's scalability and reliability, which was tested with different data sources. This paper delves into the Discriminant Input Processing Scheme (DIPS), a crucial instrument for resolving challenges. Data-segmentation-based complex processing techniques allow DIPS to merge many emotion analysis streams. The DIPS recommendation engine uses segmented data characteristics to sift through inputs from the emotion stream for patterns. The recommendation is more accurate and flexible since DIPS uses transfer learning to identify similar data across different streams. With transfer learning, this study can be sure that the previous recommendations and data properties will be available in future data streams, making the most of them. Data utilization ratio, approximation, accuracy, and false rate are some of the metrics used to assess the effectiveness of the advised approach. Self-assisted intelligent healthcare systems that use emotion-based analysis and state-of-the-art technology are crucial when managing healthcare. This study improves healthcare management's accuracy and efficiency using computational models like DIPS to guarantee accurate data forecasts and recommendations. [ABSTRACT FROM AUTHOR]

This study investigates the application of artificial intelligence (AI) in fluid dynamics, mainly using a neural network trained by the Levenberg – Marquardt method (NN-BLMM), to model magnetohydrodynamic (MHD) stagnation point Ree – Eyring flow. We focus on this flow over a convectively heated stretched surface by integrating the Cattaneo – Christov heat model. The initial complex nonlinear partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs) using suitable similarity variables. A dataset was generated using the Lobatto IIIA numerical solver to analyze the effects of various fluid flow and thermal parameters. The NN-BLMM model was then rigorously evaluated through training, testing, and validation phases and compared with reference data. This ensures the model’s precision and effectiveness. We observe that an increase in the Powell – Eyring fluid parameter notably reduces the fluid’s shear resistance, implying a decrease in viscosity. Concurrently, the heat transfer rate within the fluid medium increases with an increase in the internal heat generation parameter. These findings highlight the robustness of NN-BLMM in simulating complex fluid dynamics, emphasizing AI’s potential to provide a deeper understanding of non-Newtonian fluid behaviors. This research has important implications for industrial applications in which precise control over fluid properties and heat transfer is crucial. [ABSTRACT FROM AUTHOR]

The current investigation formulated an efficient computing technique by incorporating the algorithm of Artificial Neural Network Levenberg Marquardt Method (ANNLMM) to approximate Soret and Dufour effects on Magnetohydrodynamic squeezing flow of Jeffrey fluid (MHDSFJF) in MATLAB environment. Due to its efficiency and flexibility, the applied neural network model can accurately predict the output results. In the ANNLMM, 80% of the data is used for training and 10% is set for both the testing and validation processes. The flow under consideration is mathematically formulated with the help of Navier Stokes Equations. The partial differential equations (PDEs) are converted into ordinary differential equations (ODEs) by similarity variables. The effects of the physical parameters are discussed briefly. When the Hartmann number and Jeffrey fluid parameter increase, the concentration, temperature, and velocity profiles decreases. The temperature profile and heat transfer rate are improved by the effects of joule dissipation, Dufour numbers, and heat generation/absorption. On the other hand, as thermal radiation rises, the temperature profile decreases and the heat transfer rate increases. With an augment in Soret number, the mass transfer rate increases and the concentration profile slows down. Additionally, increasing chemical reactions, the mass transfer rate increases, but in convective chemical reactions, the contradiction is observed.

Abstract The integration of edge intelligence (EI) in animation design, particularly when dealing with large models, represents a significant advancement in the field of computer graphics and animation. This survey aims to provide a comprehensive overview of the current state and future prospects of EI-assisted animation design, focusing on the challenges and opportunities presented by large model implementations. Edge intelligence, characterized by its decentralized processing and real-time data analysis capabilities, offers a transformative approach to handling the computational and data-intensive demands of modern animation. This paper explores various aspects of EI in animation and then delves into the specifics of large models in animation, examining their evolution, current trends, and the inherent challenges in their implementation. Finally, the paper addresses the challenges and solutions in integrating EI with large models in animation, proposing future research directions. This survey serves as a valuable resource for researchers, animators, and technologists, offering insights into the potential of EI in revolutionizing animation design and opening new avenues for creative and efficient animation production.

Anomaly Detection (AD) in Pedestrian Walkways (PWs) is critical to urban security and safety systems. It is widely used to detect abnormal or unusual behaviours, situations, or events in areas dedicated to pedestrian traffic, namely crosswalks, sidewalks, or pedestrian bridges. The main objective is to improve efficiency, safety, and security in the urban environment by identifying deviations and monitoring pedestrian activities from established norms. This kind of AD typically includes surveillance cameras, sensors, and advanced software algorithms. Using advanced machine learning (ML) and computer vision (CV) approaches, this technique continuously monitors the pedestrian area to detect potential threats and irregularities. Deep Learning Assisted AD in Pedestrian Walkways presents a novel and very efficient method to enhance security and safety in urban environments. Therefore, this study designs an Intelligent Multi-Group Marine Predator Algorithm with Deep Learning Assisted Anomaly Detection (MMPADL-AD) in Pedestrian Walkways. The MMPADL-AD system aims to ensure security in PWs via the AD process. The MMPADL-AD technique incorporates a NASNet feature extractor that proficiently extracts high-level features from surveillance data, allowing a deep understanding of pedestrian behaviours. Besides, the MMPADL-AD technique applies convolutional long short-term memory (ConvLSTM), inheriting the benefits of convolutional neural networks) and LSTM for the AD process. Finally, the MMPA has been used for the hyperparameter tuning mechanism, which optimizes the model’s performance, assuring accuracy and adaptability. Benchmark data accompanied an extensive set of experiments to ensure the higher effectiveness of the MMPADL-AD approach. The experimental values highlighted the supremacy of the MMPADL-AD approach over other DL methods.

It is difficult to describe the complex relationship between spare parts consumption and influencing factors accurately in the prediction process. The resons are listed as the randomness, diversity, time-varying and insufficient information of spare parts demand. The theories has been developed rapidly when dealing with uncertainty based on the Intelligent computing and tools. It was summarized about relevant literature on handling the uncertainty of spare parts demand prediction, according to the four categories of uncertainty, namely, randomness, fuzziness, incompleteness and compound uncertainty. It was summarized about the limitations and development direction of relevant research. The research results can be used to serve as references for equipment spare parts management. [ABSTRACT FROM AUTHOR]

Processed big data has social significance for the development of society and industry. Intelligent processing of big data is a condition for creating a collective mind of a social group, company, state and the planet as a whole. At the same time, the economy of big data (Data Economy) takes first place in the evaluation of processing mechanisms, since two parameters are very important: speed of data processing and energy consumption. Therefore, mechanisms focused on parallel processing of large data within the data storage center will always be in demand on the IT market. Objective. The goal of the investigation is to increase the economy of big data (Data Economy) thanks to the analysis of data as truth table addresses for the identification of patterns of production functionalities based on the similarity-difference metric. Method. Intelligent computing architectures are proposed for managing cyber-social processes based on monitoring and analysis of big data. It is proposed to process big data as truth table addresses to solve the problems of identification, clustering, and classification of patterns of social and production processes. A family of automata is offered for the analysis of big data, such as addresses. The truth table is considered as a reasonable form of explicit data structures that have a useful constant – a standard address routing order. The goal of processing big data is to make it structured using a truth table for further identification before making actuator decisions. The truth table is considered as a mechanism for parallel structuring and packing of large data in its column to determine their similarity-difference and to equate data at the same addresses. Representation of data as addresses is associated with unitary encoding of patterns by binary vectors on the found universe of primitive data. The mechanism is focused on processorless data processing based on read-write transactions using in-memory computing technology with significant time and energy savings. The metric of truth table big data processing is parallelism, technological simplicity, and linear computational complexity. The price for such advantages is the exponential memory costs of storing explicit structured data. Results. Parallel algorithms of in-memory computing are proposed for economic mechanisms of transformation of large unstructured data, such as addresses, into useful structured data. An in-memory computing architecture with global feedback and an algorithm for matrix parallel processing of large data such as addresses are proposed. It includes a framework for matrix analysis of big data to determine the similarity between vectors that are input to the matrix sequencer. Vector data analysis is transformed into matrix computing for big data processing. The speed of the parallel algorithm for the analysis of big data on the MDV matrix of deductive vectors is linearly dependent on the number of bits of the input vectors or the power of the universe of primitives. A method of identifying patterns using key words has been developed. It is characterized by the use of unitary coded data components for the synthesis of the truth table of the business process. This allows you to use read-write transactions for parallel processing of large data such as addresses. Conclusions. The scientific novelty consists in the development of the following innovative solutions: 1) a new vector-matrix technology for parallel processing of large data, such as addresses, is proposed, characterized by the use of read-write transactions on matrix memory without the use of processor logic; 2) an in-memory computing architecture with global feedback and an algorithm for matrix parallel processing of large data such as addresses are proposed; 3) a method of identifying patterns using keywords is proposed, which is characterized by the use of unitary coded data components for the synthesis of the truth table of the business process, which makes it possible to use the read-write transaction for parallel processing of large data such as addresses. The practical significance of the study is that any task of artificial intelligence (similarity-difference, classification-clustering and recognition, pattern identification) can be solved technologically simply and efficiently with the help of a truth table (or its derivatives) and unitarily coded big data . Research prospects are related to the implementation of this digital modeling technology devices on the EDA market. [ABSTRACT FROM AUTHOR]

The field of infrastructure security has garnered significant research attention. By integrating complex network theory with game theory, researchers have proposed many methods for studying the interactions between the attacker and the defender from a macroscopic viewpoint. We constructed a game model of infrastructure networks to analyze attacker-defender confrontations. To address the challenge of finding the Nash equilibrium, we developed a novel algorithm—node-incremental greedy algorithm (NIGA)—which uses less strategy space to solve the problem. The experiments performed further showed that NIGA has better optimization ability than other traditional algorithms. The optimal defense strategies under different conditions of initial strategy ratios and attacker-defender resources were analyzed in this study. Using intelligent computing to solve the Nash equilibrium is a new approach by which for researchers to analyze attacker-defender confrontations.

Modern technology and analysis of emotions play a crucial role in enabling intelligent healthcare systems to provide diagnostics and self-assistance services based on observation. However, precise data predictions and computational models are critical for these systems to perform their jobs effectively. Traditionally, healthcare monitoring has been the primary emphasis. However, there were a couple of negatives, including the pattern feature generating the method’s scalability and reliability, which was tested with different data sources. This paper delves into the Discriminant Input Processing Scheme (DIPS), a crucial instrument for resolving challenges. Data-segmentation-based complex processing techniques allow DIPS to merge many emotion analysis streams. The DIPS recommendation engine uses segmented data characteristics to sift through inputs from the emotion stream for patterns. The recommendation is more accurate and flexible since DIPS uses transfer learning to identify similar data across different streams. With transfer learning, this study can be sure that the previous recommendations and data properties will be available in future data streams, making the most of them. Data utilization ratio, approximation, accuracy, and false rate are some of the metrics used to assess the effectiveness of the advised approach. Self-assisted intelligent healthcare systems that use emotion-based analysis and state-of-the-art technology are crucial when managing healthcare. This study improves healthcare management’s accuracy and efficiency using computational models like DIPS to guarantee accurate data forecasts and recommendations.

In this article, some complex parameters of the product and design processes, how to match and optimise the sub-parts of related industrial products and how to improve the quality of the corresponding products and the competitiveness of the product in the international market are discussed in this article. We also build an algorithm based on the particle swarm and XGBoost algorithms, combined with the intelligent computing of the Internet of Things (IoT). We transform some uncertain factors in the process of the industrial product design process through the fuzzy matrix, select the optimal design through the optimised intelligent computing of the IoT scheme and compare the influence of the scheme before and after optimisation on production efficiency. The results show that the method proposed in this article can reduce the time-consumption of optimal solution selection by 42.85%–52.94%. In addition, selecting the optimal solution for each field in a targeted manner can increase the overall production efficiency of the product by about 5%, reaching between 93.6% and 96.5%, which may save raw materials and create more economic value.

In order to ensure the safe production of mine threatened by water hazard, speed up the intelligent process of mine water hazard prediction and early warning technology, and improve the effect of mine water hazard prediction and early warning, based on the research status of water hazard mechanism and monitoring and early warning at home and abroad, four types of key technical issues for constructing water hazard monitoring and intelligent early warning systems are analyzed. The complexity of early warning requirements and data access standards, the classification and spatio-temporal matching of multi-source heterogeneous big data information, the intelligent processing and analysis of water hazard big data information, and the timeliness of early warning and intelligent decision information release are discussed in detail. From the perspective of early warning system resource integration and data drive, water hazard warning resources are divided into information collection resources and computing resources, water hazard warning big data information is divided into static source information and dynamic monitoring information, and data processing is divided into basic geological model data processing, numerical processing and Computational simulation and information fusion data processing divide coal mine disaster early warning into primary monitoring parameter early warning, intermediate index grading early warning, and advanced intelligent model early warning. The key technical architecture of an intelligent warning system for coal mine water hazards is proposed and analyzed. A software service architecture that meets the technical requirements is proposed, including infrastructure layer, data resource layer, application support layer, business application layer, and user presentation layer. Based on the water hazard warning construction process, a Gated Recurrent Unit algorithm warning model for water hazard monitoring data is proposed, and the network structure of the warning model is given. The forward calculation, backward propagation calculation, and weight gradient calculation methods of the warning model are studied. The classification of different types of perception data access, storage, encoding, models, construction and testing of intelligent deep learning models, and technical paths for warning information release are analyzed. It provides a reference for the intelligent construction of coal mine water hazard early warning.

This study investigates the possessions of a dual stratified common on the diverse convection barrier layer discharge of an Eyring-Powell fluid (CBLFEPL) induced by an prone extensive barrel. The temperature and concentration at the exterior of the barrel are assumed to be larger than the moving fluid. To solve the resulting flow equations, an brilliant numerical established aggregating solver is employed using the Levenberg-Marquardt neural network scheme (LMNNS). The governing equations of partial differential are converted into interconnected nonlinear ordinary differential equations using appropriate transformations. First, a dataset is generated for two distinct cases, one with a zero curvature parameter (plate) and the other with a non-zero curvature parameter (cylinder). The behaviors of skin-friction coefficient, Sherwood number and Nusselt number are presented over chart obtained using the BVP4C technique. Subsequently, an intelligent computing algorithm nftool, is utilized for training, validation, and testing steps to approximate solutions for various cases. The designed solver, LMNNS, is applied to solve the CBLFEPL problem through regression, mean squared error (MSE), histogram studies, and gradient analysis. The study of double-layered combined convection flow of Eyring-Powell fluid around an elevated stretched cylinder provides insights into heat and mass transfer characteristics, crucial for applications in engineering and fluid dynamics. This study aims to contribute insights that can be applied to engineering and fluid dynamics, aiding in the optimization of relevant processes and applications. The research methodology involves employing numerical technique, three-stage Lobatto IIIa formula, to solve the governing equations of the double-layered combined convection flow of Eyring-Powell fluid around an elevated stretched cylinder, while systematically varying key parameters to analyze their impact on heat and mass transfer phenomena. The speed of the fluid experiences a notable rise with higher values of curvature parameter K, fluid parameter M, mixed convection parameter λm, and the ratio of buoyancy forces N. Conversely, the velocity profile exhibits a contrasting behavior concerning the thermal stratification parameter ϵ1, solutal stratification parameter ϵ2, and an inclination angle α.

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.

The cost control challenge in construction and installation projects has always been a critical concern for construction entities. The complexity of task collaboration among various equipment and nodes during the installation process leads to extended construction duration, resulting in increased construction costs. To address this issue, this paper proposes a heuristic two-stage optimal deployment approach called MERD. The MERD approach incorporates intelligent computing principles from computer science into the resource scheduling of the construction process, modelling the installation scheduling problem into a combinatorial optimisation problem. Designing the probability method based on Bayes theorem, the MERD approach carries out an installation provisioning mechanism to optimise personnel and device allocation in the selected area. As a result, the MERD approach minimises construction hours and reduces labour costs in the construction process. Experimental results demonstrate the effectiveness and efficiency of the MERD approach in reducing work time and cost in engineering projects. [ABSTRACT FROM AUTHOR]

The echo state network is a unique form of recurrent neural network. Due to its feedback mechanism, it exhibits superior nonlinear behavior compared to traditional neural networks and is highly regarded for its simplicity and efficiency in computation. In recent years, as network development has progressed, the security threats faced by networks have increased. To detect and counter these threats, the analysis of network traffic has become a crucial research focus. The echo state network has demonstrated exceptional performance in sequence prediction. In this article, we delve into the impact of echo state networks on time series. We have enhanced the model by increasing the number of layers and adopting a different data input approach. We apply it to predict chaotic systems that appear ostensibly regular but are inherently irregular. Additionally, we utilize it for the classification of sound sequence data. Upon evaluating the model using root mean squared error and micro-F1, we have observed that our model exhibits commendable accuracy and stability. [ABSTRACT FROM AUTHOR]

Remote sensing, the Internet, the Internet of Things (IoT), artificial intelligence, and other technologies have become the core elements of modern agriculture and smart farming. Agricultural production and management modes guided by data and services have become a cutting-edge carrier of agricultural information monitoring, which promotes the transformation of the intelligent computing of remote sensing big data and agricultural intensive management from theory to practical applications. In this paper, the main research objective is to construct a new high-frequency agricultural production monitoring and intensive sharing service and management mode, based on the three dimensions of space, time, and attributes, that includes crop recognition, growth monitoring, yield estimation, crop disease or pest monitoring, variable-rate prescription, agricultural machinery operation, and other automatic agricultural intelligent computing applications. The platforms supported by this mode include a data management and agricultural information production subsystem, an agricultural monitoring and macro-management subsystem (province and county scales), and two mobile terminal applications (APPs). Taking Shandong as the study area of the application case, the technical framework of the system and its mobile terminals were systematically elaborated at the province and county levels, which represented macro-management and precise control of agricultural production, respectively. The automatic intelligent computing mode of satellite–air–ground spatiotemporal collaboration that we proposed fully couples data obtained from satellites, unmanned aerial vehicles (UAVs), and IoT technologies, which can provide the accurate and timely monitoring of agricultural conditions and real-time guidance for agricultural machinery scheduling throughout the entire process of agricultural cultivation, planting, management, and harvest; the area accuracy of all obtained agricultural information products is above 90%. This paper demonstrates the necessity of customizable product and service research in agricultural intelligent computing, and the proposed practical mode can provide support for governments to participate in agricultural macro-management and decision making, which is of great significance for smart farming development and food security. [ABSTRACT FROM AUTHOR]

Among recent state-of-the-art realistic image super-resolution (SR) intelligent algorithms, generative adversarial networks (GANs) have achieved impressive visual performance. However, there has been the problem of unsatisfactory perception of super-scored pictures with unpleasant artifacts. To address this issue and further improve visual quality, we proposed a perception-design-oriented PSRGAN with double perception turbos for real-world SR. The first-perception turbo in the generator network has a three-level perception structure with different convolution kernel sizes, which can extract multi-scale features from four  1 4  size sub-images sliced by original LR image. The slice operation expands adversarial samples to four and could alleviate artifacts during GAN training. The extracted features will be eventually concatenated in later 3 × 2 upsampling processes through pixel shuffle to restore SR image with diversified delicate textures. The second-perception turbo in discriminators has cascaded perception turbo blocks (PTBs), which could further perceive multi-scale features at various spatial relationships and promote the generator to restore subtle textures driven by GAN. Compared with recent SR methods (BSRGAN, real-ESRGAN, PDM_SR, SwinIR, LDL, etc.), we conducted an extensive test with a  × 4  upscaling factor on various datasets (OST300, 2020track1, RealSR-Canon, RealSR-Nikon, etc.). We conducted a series of experiments that show that our proposed PSRGAN based on generative adversarial networks outperforms current state-of-the-art intelligent algorithms on several evaluation metrics, including NIQE, NRQM and PI. In terms of visualization, PSRGAN generates finer and more natural textures while suppressing unpleasant artifacts and achieves significant improvements in perceptual quality. [ABSTRACT FROM AUTHOR]

The rapid development of key technologies for the intelligent research and development (R&D) of new materials has significantly promoted the R&D efficiency and industrialization of materials and attracted global attention. China’s development in this field lags behind the advanced international level in terms of key technologies and infrastructures, which has restricted the original innovation and industrial development of the material sector. This study summarizes the key technologies involving the intelligent R&D of new materials, explores the developing status of these key technologies in China and abroad, and analyzes the challenges faced by China. Moreover, the intelligent R&D technology system is summarized including intelligent computing and design technologies and software, autonomous/intelligent experiment technologies and equipment, artificial-intelligence-driven basic algorithms and technologies, digital twins, intelligent R&D platforms and collaborative innovation networks. Furthermore,countermeasures are proposed from the aspects of innovative ecology construction, industrial environment improvement, standards system establishment, talent training, and international cooperation.

The release of ChatGPT, sparked a wave of generative AI, representing the arrival of the singularity moment of general artificial intelligence and highly likely restructuring the information industry ecosystem.The domestic industry has strengthened the research in the field of intelligent computing represented by ChatGPT, and operators have become the main force in the construction of computing network infrastructure, ushering in new opportunities for the development of intelligent computing.A detailed analysis of the capabilities, development status, and application prospects of generative AI were provided, and the technical elements behind generative AI, the demands for computing network resources, the impact on the communication industry, the opportunities and challenges faced by operators in the development wave of generative AI were thought about.Finally, the positioning and response strategies of operators were discussed.

The error measurement of fiscal accounting data can effectively slow down the change of financial assets. Based on deep neural network theory, we constructed an error measurement model for fiscal and tax accounting data, and we analyzed the relevant theories of fiscal and tax performance evaluation. By establishing a batch evaluation index of finance and tax accounting, the model can monitor the changing trend of the error of urban finance and tax benchmark data scientifically and accurately, as well as solve the problem of high cost and delay in predicting the error of finance and tax benchmark data. In the simulation process, based on the panel data of credit unions, the entropy method and a deep neural network were used to measure the fiscal and tax performance of regional credit unions. In the example application, the model, combined with MATLAB programming, calculated the contribution rate of regional higher fiscal and tax accounting input to economic growth. The data show that the contribution rates of some fiscal and tax accounting input, commodity and service expenditure, other capital expenditure and capital construction expenditure to regional economic growth are 0.0060, 0.0924, 0.1696 and -0.0822, respectively. The results show that the proposed method can effectively map the relationships between variables.

The numerical methods such as the artificial neural networks with greater probability and nonlinear configurations are more suitable for estimation and modeling of the problem parameters. The numerical methods are easy to use in applications as these methods do not require costly and time-consuming tests like the experimental study. In this study, we use the Levenberg–Marquardt-based backpropagation Process (LMP) to create a computing paradigm that makes use of the strength of artificial neural networks (ANN), known as (ANN-LMP). Here we use the ANN-LMP to obtain the solution of the second-grade fluid in a rotating frame in a porous material with the impact of a transverse magnetic field. The 1000 data set points in the interval [ 0 , 1 ] are used for the network training to determine the effect of various physical parameters of the flow problem under consideration. The experiment is executed of six scenarios with different physical paramaters. ANN-LMP is used for evaluating the mean square errors (MSE), training (TR), validation (VL), testing (TT), performance (PF) and fitting (FT) of the data. The problem has been verified by error histograms (EH) and regression (RG) measurements, which show high consistency with observed solutions with accuracy ranging from E-5 to E-8. Characteristics of various concerned parameters on the velocity and temperature profiles are studied. [ABSTRACT FROM AUTHOR]

This paper showcases some of the main contributions from the research project Switch2Steel, in which a web application is developed for the calculation and optimization of the cost of steel industrial sheds. Unprotected steel structures, compared to other construction materials, possess relatively low fire resistance. To enhance their fire performance, adequate fire protection is required. Simplified fire design, without considering the real influence of the main parameters on the development and propagation of natural fires, often leads to excessive amounts of costly protective material. On the other hand, the use of advanced software tools may be impractical because of iterative time‐consuming computational processes such as structural optimization. Hence, this work presents a simple user‐friendly calculation tool that allows users to define the characteristics of the sheds to be built and based on a set of metrics, weights, and advanced algorithms, to minimize their cost by reducing the consumption of steel and fire protection material. To illustrate the use of the tool, a reference case of a steel industrial shed is selected and the results obtained by the tool are compared with those obtained by FEM tools. The tool gives more optimized results than the use of prescriptive rules, and the results are obtained considerably faster than by advanced expensive computational tools. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present a novel design of intelligent neuro-supervised networks (INSNs) in order to study the dynamics of a mathematical model for Parkinson's disease illness (PDI), governed with three differential classes to represent the rhythms of brain electrical activity measurements at different locations in the cerebral cortex. The proposed INSNs are constructed by exploiting the knacks of multilayer structure neural networks back-propagated with the Levenberg–Marquardt (LM) and Bayesian regularization (BR) optimization approaches. The reference data for the grids of input and the target samples of INSNs were formulated with a reliable numerical solver via the Adams method for sundry scenarios of PDI models by way of variation of sensor locations in order to measure the impact of the rhythms of brain electrical activity. The designed INSNs for both backpropagation procedures were implemented on created datasets segmented arbitrarily into training, testing, and validation samples by optimization of mean squared error based fitness function. Comparison of outcomes on the basis of exhaustive simulations of proposed INSNs via both LM and BR methodologies was conducted with reference solutions of PDI models by means of learning curves on MSE, adaptive control parameters of algorithms, absolute error, histogram error plots, and regression index. The outcomes endorse the efficacy of both INSNs solvers for different scenarios in PDI models, but the accuracy of the BR-based method is relatively superior, albeit at the cost of slightly more computations. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 λ1 respectively. Also the Skin friction coefficient decreases for λ1 and Sq and increases for high value of Deborah numbers β, ω and M.