Computational Intelligence Systems Research Articles

Guest Editorial Special Issue on Computational Intelligence for Perception and Decision-Making of Autonomous Systems

The papers in this special section focus on computational intelligence for perception and decision making of autonomous systems. Due to powerful capabilities in environmental perception, real-time computing, and intelligent decision-making, autonomous systems have demonstrated their great potential to efficiently accomplish a variety of complex tasks that humans cannot. Hence, autonomous systems are able to facilitate the development of almost every walk of life and have attracted increasing attention from both academia and industry. However, given high dimensional, heterogeneous, unstructured, and unpredictable data sampled from different modalities of sensors, autonomous systems with conventional algorithms may fail to acquire the accurate information related to the environment, and make the appropriate decision to complete assigned tasks. Notice that recent advanced computational intelligence algorithms including deep neural networks and evolutionary algorithms have the unique ability to efficiently extract useful information from the multi-source heterogeneous data, and thus have been successfully applied in the fields of computer vision, natural language processing, and so on. Therefore, it is promising to have a thorough and tight integration between computational intelligence and autonomous systems by upgrading advanced and innovative computational intelligence algorithms to ensure high-level environmental perception and decision-making of autonomous systems.

Conversational AI Assistant

Abstract: A conversational assistant is an intelligent conversational computing system designed to mimic human speech to provide automated online guidance and support. The growing benefits of conversational support have led to widespread adoption across many industries to provide virtual support to their customers. Conversation assistance uses methods and algorithms from his two fields of artificial intelligence: natural language processing and machine learning. However, the application has many challenges and limitations. This research reviews recent advances in conversation support using artificial intelligence and natural language processing. We highlight the main challenges and limitations of the current work and provide recommendations for future research investigations.

Guest editorial: intelligent ubiquitous computing and advanced learning systems for biomedical engineering

This special issue editorial introduces the latest development in emerging technologies of biomedical engineering, including big medical data, artificial intelligence, cloud/fog computing, federated learning, ubiquitous computing and communication, internet of things, wireless technologies, and security and privacy. In this special issue, nine manuscripts are published related to advanced learning and computing systems for biomedical engineering.

Research on intelligent information system of user intelligent behavior data based on computer big data

This paper collects and analyzes users' online behaviors through computer big data technology, uses computer big data intelligent analysis system to analyze users' online shopping behavior, and uses information-based data analysis system to detect consumers' online shopping needs under the e-commerce platform. The main technique used in this paper is the computer browser log mining method. In the user's click stream data, the function keys of Tmall and Taobao webpages are used as data information for classification and collection. This paper uses the Bisecting K-means clustering algorithm to mine the state of interest. Finally, the feature maps of interests and behaviors are summarized. By processing four typical types of e-commerce user demand status, including background management type, continuous search type, product browsing type and information search data, and clustering based on page type, an effective method for dynamically changing demand judgment is obtained. The state of online shoppers is analyzed through data processing, which also proves the effectiveness of the computer intelligent information system.

Benefits and Challenges of Artificial Intelligence in HealthCare

Artificial intelligence is the simulation of human intelligence processes by machine , it plays a crusial role in our day to day life. Artificial intelligence has possibly changed wellbeing to an extremely incredible degree. The execution of Artificial Intelligence has expanded in numerous areas including the Medical sector . Artificially intelligent computer systems are used extensively in medical science. The common application of AI in medical sector include diagnosing patients, end-to-end drug discovery and development, improving communication between physician and patient , transcribing medical documents ,such as prescriptions, and remotely treating patients. Man-made intelligence is the boundless branch worried about building savvy machine equipped for performing undertakings that require human knowledge. AI in medical sector improves so many benefits to the peoples and also the organizations . This study investigates the advantages and difficulties associated with AI involving in medical services . It also consider the future grow of Ai in medical sector and what are the remedies for the challenges.

Multi-level object detection by multi-sensor perception of traffic scenes

Object detection is a hot research topic in the communities of computer vision and intelligent transportation system. In this paper, we propose a novel framework for multi-level object detection by mutli-sensor perception from road scenes. Firstly, two types of 2D object detection methods are proposed based on multi-level feature processing. Among them, an improved 2D object detection method is designed based on CenterNet, which introduces a centripetal offset module and a deformable convolution module to improve the corner matching accuracy and solve the problem of missed detection. Moreover, an improved 2D object detection method based on RetinaNet is designed by the optimization of the sub-network of ResNet. The coordinate attention mechanism and bidirectional feature fusion mechanism are incorporated. Finally, we propose a 3D object detection method based on an improved PointNet network. The point cloud data are projected into a frustum according to the 2D detection result, then the frustum is segmented according to the linearly increased steps. The experimental results on the KITTI dataset and TSD-max dataset well demonstrate the effectiveness of the proposed framework.

Прогнозирование и минимаксное оценивание состояний производственной системы при наличии рисков

The solution of the problem of forecasting the state of complex socio-economic systems is possible only on the basis of appropriate dynamic economic and mathematical models that describe their main parameters, the presence of control actions and risks. In this paper, it is proposed to use a deterministic minimax approach for modeling and solving the problem of estimating the predicted states of a production system in the presence of risks. To make managerial decisions at a manufacturing enterprise aimed at improving the efficiency of its functioning, it is necessary to have high-quality information support, the basis of which is the solution of the corresponding problem of predicting the states of its basic parameters. In this article, to describe the functioning of a production system, it is proposed to use a discrete-time controlled dynamical system in the presence of risks. It is assumed that the values of the control action (admissible control scenarios) are realized from a finite set of admissible elements of the corresponding finite-dimensional vector space, and the realizations of the values of the phase vector of the model and the risk vector are limited by the given compact polyhedrons in the corresponding finite-dimensional vector spaces. Application of the developed discrete-time controlled dynamical model that describes the output products of an enterprise in the presence of risks, and the developed methodology for the formation and minimax estimation of the predictive set of its phase states in a given period of time, allow us to develop appropriate numerical algorithms that can be used in the development and creation of computer intelligent information systems that provide support for making effective management decisions at manufacturing enterprises. The main results of this work is the development of a new economic-mathematical model that describes the dynamics of the output products of an enterprise in the presence of risks and the creation on its basis of a methodology for constructing and minimax estimation of the predictive set of its phase states in the form of implementing a finite number of one-step operations that allow their algorithmization. The results obtained in this work can serve as a basis for developing methods for optimizing the management of enterprise production processes and creating computer intelligent information systems to support managerial decision-making.

Stochastic Matrix Modelling and Scheduling Algorithm of Distributed Intelligent Computing System

Parallel and distributed processing has always been a hot field of scientific and technological research, development, and application. It is an important solution in the fields of scientific computing and data service processing, such as weather prediction, wind tunnel Reynolds numerical calculation, and financial services. Intelligent cloud computing has higher requirements for high-capacity and efficient computing. The ability of existing computing system has been difficult to meet its needs. It is necessary to establish an intelligent computing system with the self-organizing ability and realize efficient task scheduling. Since the coordination of computing and storage resource scheduling becomes the key to scheduling, this study designs scheduling tasks based on a large-scale multi-task distributed system, establishes the model of distributed intelligent computing system and the multi-objective optimization model of the task scheduling problem, and designs the IPSO algorithm combined with improved particle swarm optimization algorithm according to the model. First, the particle swarm optimization algorithm is used to generate the initial scheduling scheme, then the ant colony algorithm is initialized, and the final scheduling results are generated. Simulation results show that the performance of the algorithm has obvious performance advantages compared with the improved particle swarm optimization algorithm and the improved ant colony algorithm. In addition, this study presents the task migration conditions and optimization methods under the dual objectives of makespan and availability. This optimization operation increases the system availability without increasing the scheduling length. In the distributed system with heterogeneous availability, the algorithm is effective in the dual objective performance optimization of task completion time and system availability.

Bioinspired Scene Classification by Deep Active Learning With Remote Sensing Applications

Accurately classifying sceneries with different spatial configurations is an indispensable technique in computer vision and intelligent systems, for example, scene parsing, robot motion planning, and autonomous driving. Remarkable performance has been achieved by the deep recognition models in the past decade. As far as we know, however, these deep architectures are incapable of explicitly encoding the human visual perception, that is, the sequence of gaze movements and the subsequent cognitive processes. In this article, a biologically inspired deep model is proposed for scene classification, where the human gaze behaviors are robustly discovered and represented by a unified deep active learning (UDAL) framework. More specifically, to characterize objects' components with varied sizes, an objectness measure is employed to decompose each scenery into a set of semantically aware object patches. To represent each region at a low level, a local-global feature fusion scheme is developed which optimally integrates multimodal features by automatically calculating each feature's weight. To mimic the human visual perception of various sceneries, we develop the UDAL that hierarchically represents the human gaze behavior by recognizing semantically important regions within the scenery. Importantly, UDAL combines the semantically salient region detection and the deep gaze shifting path (GSP) representation learning into a principled framework, where only the partial semantic tags are required. Meanwhile, by incorporating the sparsity penalty, the contaminated/redundant low-level regional features can be intelligently avoided. Finally, the learned deep GSP features from the entire scene images are integrated to form an image kernel machine, which is subsequently fed into a kernel SVM to classify different sceneries. Experimental evaluations on six well-known scenery sets (including remote sensing images) have shown the competitiveness of our approach.

Research on Computer Intelligent Proofreading System for English Translation Based on Deep Learning

English, as a global language, is widely used in daily life, which leads to higher requirements for people’s language proficiency. Grammar, as an essential part of English, plays a significant role in language acquisition. Although the needs for English learning have become greater, the resources of English teaching are quite limited. Under this circumstance, it is imperative to apply information technology to alleviate the burden of lacking sufficient educational resources. The intelligent grammar correction system is in special needs. In this study therefore, the deep learning-based CNN and RNN bidirectional propagation model were utilized to construct a feedback filtering function, screen the suggested text, and select the high-quality suggested text for the retraining of the error correction model. The proofreading optimization algorithm and feedback filtering algorithm were used in practice to implement each functional module. The core function service module was designed with a cluster deployment scheme with high system performance and response speed to ensure system stability and availability. Experimental results demonstrate that the correct rate gradually increased with increasing the number of tests and finally stabilized at about 86%. The overall average correcting rate obtained from the present model was verified to be superior to that from the GRU model and the MGB model.

Computational intelligence-enabled prediction and communication mechanism for IoT-based autonomous systems

Autonomous systems and the Internet of Things (IoT) have become more sophisticated research areas and entered successfully into various daily living activities such as smart homes & buildings, autonomous cars, drones, robots, etc. A crucial and essential aspect of these systems is the precision and accuracy of the decision-making process, i.e., the decision support system. Likewise, developing a completely autonomous system is an open research problem. This paper proposes a computational intelligence-based prediction and communication mechanism for the independent system where IoT is used as a data collection tool. Initially, energy gauge (EG) devices collect helpful information about neighboring devices in the IoT networks. Then, information about the potential relaying devices is broadcasted by the concerned EG device, which uses every member device to adjust routing path(s) in the autonomous system. Furthermore, every EG device has an embedded computational intelligent decision support system that is used to precisely predict the criticality of a neighboring device (preferably relay) in the autonomous systems. Therefore, every device must ensure data transmission via the most reliable path(s), i.e., avoiding critical devices if possible. A device is assumed critical if either its residual energy or received signal strength indicator value is less than the defined threshold values for the autonomous systems. Additionally, the proposed mechanism has ensured a uniform traffic distribution of the transmitted packets in the autonomous system. The operational applicability of the proposed computational intelligence-enabled prediction mechanism in the autonomous system is verified by comparing it with the existing approaches. Simulation results show that the proposed scheme has enhanced the accuracy of the concerned autonomous systems more than other schemes.

Recommendation System for Privacy-Preserving Education Technologies.

Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the data over which they were trained, data that have privacy and other sensitivities associated with the education abilities of learners, which can be vulnerable. This work proposes a recommendation system for privacy-preserving education technologies that uses machine learning and differential privacy to overcome this issue. Specifically, each student is automatically classified on their skills in a category using a directed acyclic graph method. In the next step, the model uses differential privacy which is the technology that enables a facility for the purpose of obtaining useful information from databases containing individuals' personal information without divulging sensitive identification about each individual. In addition, an intelligent recommendation mechanism based on collaborative filtering offers personalized real-time data for the users' privacy.

HYBRID MULTI-AGENT METHOD FOR OPTIMIZATION OF FUZZY COMPUTER SYSTEMS

Recently, intelligent computer systems based on fuzzy logic and soft computing are used quite effectively to solve a wide range of complex applied problems in various fields of human activity. One of the promising areas of modern research in the field of artificial intelligence is the creation and testing of bioinspired multi-agent and evolutionary methods for the synthesis and optimization of fuzzy automatic control systems (ACS) and decision support systems (DSS). This paper is devoted to the development and research of a multi-agent method of parametric optimization of fuzzy systems (FS) based on hybrid improved grey wolf algorithms. The proposed method allows optimizing the parameters of fuzzy computer systems more efficiently, compared to the basic and improved grey wolf methods. This method uses group hunting and dimensional learning-based hunting strategies, as well as local search strategies based on algorithms of gradient descent and extended Kalman filter, which significantly reduces computational costs and increases the rate of convergence to optimal solutions when optimizing parameters of complex fuzzy systems. To study the effectiveness of the developed hybrid multi-agent method in this work, the synthesis and parametric optimization of the fuzzy flight control system for the unmanned aerial vehicle (UAV) is carried out. In particular, the optimization of the vectors of normalizing coefficients, adjustable parameters of linguistic terms (LT), and weight coefficients of the rule's consequences of the rule base (RB) for the altitude controller of the fuzzy ACS for the UAV is performed. The obtained results of the comparative analysis confirm the significant advantages of the developed hybrid multi-agent method of parametric optimization, as well as the feasibility of its application for the synthesis of different types of ACSs and DSSs based on fuzzy logic.

Solid‐State Electrolyte Gate Transistor with Ion Doping for Biosignal Classification of Neuromorphic Computing

AbstractAs the core component of an intelligent neuromorphic computer system, reliable synaptic devices process vast amounts of data with high computing speed and low energy consumption. In this work, the ion‐doped eco‐friendly solution‐processed indium oxide (InOx)/aluminum oxide (AlOx) electrolyte gate transistors (EGTs) with typical and reliable synaptic behavior are proposed. The lithium ions doped into the AlOx solid‐state layer to facilitate the generation of electrical double layers and doped into InOx to improve the stability of long‐term potentiation/depression cyclic update and enhance the synaptic plasticity. Finally, an artificial neural network simulator is well designed to electrocardiogram signal recognition based on the Gmax/Gmin ratio and nonlinearity of weight update curve. According to the results, the device possesses tremendous potential for biosignal prediction and neural intervention. Moreover, for the first time, the recognition accuracy of the abnormality of the cardiovascular can reach over 94.8% obtained from the confusion matrix. Consequently, this research article presents a stable and robust neuromorphic device for biosignal recognition based on solid‐state EGTs via the synaptic long‐term plasticity.

AoI research on PMU cloud side Cooperative System of active distribution network

Phasor measurement units (PMUs) are being widely installed in transmission networks. Many PMU based power system applications have been developed or are under development in academia and industry. For these applications, data status update is usually a key factor affecting its performance, especially for distribution network equipment information acquisition and control computing delay performance cannot accurately ensure the timely processing of services. Therefore, an index of information freshness is proposed—age of information(AoI). According to the model of intelligent computing system of distribution network equipment, the average peak information age of the system is calculated. Finally, the DQN method is adopted to minimize the peak information age. Simulation results show that the proposed service unloading optimization scheme based on DQN can effectively reduce the peak AoI of the system.

FAST FUZZY CREDIBILISTIC CLUSTERING BASED ON DENSITY PEAKS DISTRIBUTION OF DATA BROAKYSIS

Context. The problem of clustering (classification without a teacher) is often occures when processing data arrays of various natures, which is quite an interesting and integral part of artificial intelligence. To solve this problem, there are many known methods and algorithms based on the principles of the distribution density of observations in the analyzed data. However, these methods are rather complicated in software implementation and are not without drawbacks, namely: the problem of determining significant clusters in datasets of different densities, multiepoch self-learning, getting stuck in local extrema of goal functions, etc. It should be noted that the methods based on the analysis of the peaks of the data distribution density are clear in nature, therefore, to expand the capabilities of these methods, it is advisable to introduce their fuzzy modification. Objective. The aim of the work is to introduce fast fuzzy data clustering using density peaks distribution of the datasets, that can find the prototypes (centroids) of clusters that overlapping regardless of the amount of incoming data. Method. The problem of fuzzy clustering data arrays based on a hybrid method that based on the simultaneous use of a credibilistic approach to fuzzy clustering and an algorithm for finding the types of distribution density of the initial data is proposed. A feature of the proposed method is computational simplicity and high speed, due to the fact that the entire array is processed only once, that is, eliminates the need for multi-era self-learning, implemented in traditional fuzzy clustering algorithms. Results. A feature of the proposed method of fast fuzzy credibilistic clustering using of density peaks distribution is characterized by computational simplicity and high speed due to the fact that the entire array is processed only once, that is, the need for multiepoch self-learning is eliminated, which is implemented in traditional fuzzy clustering algorithms. The results of the computational experiment confirm the effectiveness of the proposed approach in clustering problems under conditions in the case when the clusters are ovelap. Conclusions. The experimental results allow us to recommend the developed method for solving the problems of automatic clustering and data classification, as quickly as possible to find the centroids of clusters. The proposed method of fast fuzzy credibilistic clustering using of density peaks distribution of dataset is intended for use in computational intelligence systems, neuro-fuzzy systems, in training artificial neural networks and in clustering problems.

The Construction of Big Data Computational Intelligence System for E-Government in Cloud Computing Environment and Its Development Impact.

The traditional E-government big data system fills and classifies algorithms with low accuracy and poor work efficiency. With the development and wide application of big data, the internet of things, and other technologies, the integration of information resources has become the key to information construction. In the process of information resource integration, there are still outstanding problems such as incomplete government information resource system, different standards of government information resource management system construction, and serious threats to network and information security. In order to solve this problem, a new E-government big data system filling and classification algorithm is studied in the cloud computing environment; E-government big data filling is carried out on the basis of complete compatibility theory; and the E-government big data computing intelligence system in the cloud computing environment is constructed and its development impact, so as to parallelize the data, classify the data through decision trees, and realize incremental update decision forest parallelization processing. To verify the effectiveness of the method, comparative experiments are set, and the results demonstrate that experiment one is randomly built into the classification model, and according to the decision forest algorithm, the optimal number of decision trees is 24.

New concepts of domination in fuzzy graph structures with application

Fuzzy graphs have a prominent place in the mathematical modelling of the problems due to the simplicity of representing the relationships between topics. Gradually, with the development of science and in encountering with complex problems and the existence of multiple relationships between variables, the need to consider fuzzy graphs with multiple relationships was felt. With the introduction of the graph structures, there was better flexibility than the graph in dealing with problems. By combining a graph structure with a fuzzy graph, a fuzzy graph structure was introduced that increased the decision-making power of complex problems based on uncertainties. The previous definitions restrictions in fuzzy graphs have made us present new definitions in the fuzzy graph structure. The domination of fuzzy graphs has many applications in other sciences including computer science, intelligent systems, psychology, and medical sciences. Hence, in this paper, first we study the dominating set in a fuzzy graph structure from the perspective of the domination number of its fuzzy relationships. Likewise, we determine the domination in terms of neighborhood, degree, and capacity of vertices with some examples. Finally, applications of domination are introduced in fuzzy graph structure.

Traveling Transporter Problem: Arranging a New Circular Route in a Public Transportation System Based on Heterogeneous Non-Monotonic Urban Data

Hybrid computational intelligent systems that synergize learning-based inference models and route planning strategies have thrived in recent years. In this article, we focus on the non-monotonicity originated from heterogeneous urban data, as well as heuristics based on neural networks, and thereafter formulate the traveling transporter problem (TTP). TTP is a multi-criteria optimization problem and may be applied to the circular route deployment in public transportation. In particular, TTP aims to find an optimized route that maximizes passenger flow according to a neural-network-based inference model and minimizes the length of the route given several constraints, including must-visit stations and the requirement for additional ones. As a variation of the traveling salesman problem (TSP), we propose a framework that first recommends new stations’ location while considering the herding effect between stations, and thereafter combines state-of-the-art TSP solvers and a metaheuristic named Trembling Hand , which is inspired by self-efficacy for solving TTP. Precisely, the proposed Trembling Hand enhances the spatial exploration considering the structural patterns, previous actions, and aging factors. Evaluation conducted on two real-world mass transit systems, Tainan and Chicago, shows that the proposed framework can outperform other state-of-the-art methods by securing the Pareto-optimal toward the objectives of TTP among comparative methods under various constrained settings.

Dynamic Monitoring of Football Training Based on Optimization of Computer Intelligent Algorithm

Nowadays, with the development of computer science and technology, computer intelligent algorithms are more and more widely used in various industries. Every calculation formula in the computer intelligent algorithm has systematic logic and singleness, in order to expound the dynamic algorithm of football training optimized by the computer intelligent algorithm in detail. In this paper, the monitoring system using the computer intelligent algorithm can dynamically observe people or objects and systematically analyze them. This paper mainly studies the research of a football training dynamic monitoring system based on the computer intelligent algorithm and the design and optimization of the computer intelligent dynamic monitoring system in football training. Finally, the overall composition of the computer intelligent dynamic monitoring system and the application of the optimized computer intelligent dynamic monitoring system to the analysis of sample data are studied.