Computational Intelligence Systems Research Articles

Metaheuristic method for searching quasi-optimal route based on the ant algorithm and annealing simulation

Today, for intelligent computer systems of general and special purpose, the task of finding the optimal route is actual. Currently, there is a problem of lack of efficiency of methods for finding the quasi-optimal route. The object of the research is the process of solving optimization problems of finding a route. The subject of the research is a method for finding a quasi-optimal route based on metaheuristics. The current work increases the efficiency of searching for a quasi-optimal route using a metaheuristic method based on the ant algorithm. To achieve this goal, the work was created a method based on the ant algorithm and simulated annealing for the traveling salesman problem, was formulated the problem of the shortest path in the world of tiles, was developed a method based on the ant algorithm and simulated annealing for the problem of the shortest path in the world of tiles. Advantages of the proposed methods include the following. First, for calculating the probability of an ant moving from the current vertex to other vertices at the initial iterations, the random pheromone level plays the main role, which makes it possible to implement a random search, and at the final iterations, the normalized previous pheromone levelplays the main role, which makes it possible to implement directed search. This is ensured by the use of simulated annealing and increases the accuracy of finding a quasi-optimal route. Second, for calculating the change in the pheromone level at the initial iterations, the pheromone increment plays the main role, which ensures the breadth of the search, and at the final iterations, the previous pheromone level plays the main role, which ensures the convergence of the method. This is ensured by the use of simulated annealing and increases the accuracy of finding a quasi-optimal route. Third, the modification of the ant algorithm by calculating the length of the edges based on the Chebyshev distance, placing all ants in the initial vertex, checking for a dead-end, checking that the target vertex has been reached, and using Moore's neighborhood allows solving problems of the shortest path in the world of tiles. The performed numerical study made it possible to evaluate both methods (for the first method, the root-mean-square error was 0.04, and for the second method it was 0.03). The proposed methods make it possible to expand the area of application of metaheuristics based on the ant algorithm, which is confirmed by its adaptation for the specified optimization problems and contributes to an increase in the efficiency of intelligent computer systems for general and special purposes. The prospects for future research are the study of the proposed methods for a wide class of artificial intelligence problems.

A Comprehensive Survey on Nanophotonic Neural Networks: Architectures, Training Methods, Optimization, and Activations Functions.

In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective, and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materialization of neuromorphic circuits of neural networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization was realized along with the study of special issues such as optical activation functions and cost functions. The main contribution of this research work is that it is the first time in the literature that the most well-known architectures, training methods, optimization and activations functions of the nanophotonic networks are presented in a single paper. This study also includes an extensive detailed meta-review analysis of the advantages and disadvantages of nanophotonic networks.

A Literature Survey of Recent Advances in Chatbots

Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and algorithms from two Artificial Intelligence domains: Natural Language Processing and Machine Learning. However, there are many challenges and limitations in their application. In this survey we review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation.

Lessons Learned from Developing an Industry 4.0 Mobile Process Management System Supported by Artificial Intelligence

Research, development and innovation (RDI) projects are undertaken in order to improve existing, or develop new, more efficient products and services. Moreover, the goal of innovation is to produce new knowledge through research, and disseminating it through education and training. In this line of thinking, this paper reports and discusses the lessons learned from the undertaken project, regarding three areas: machine learning (artificial intelligence), computational intelligence, and database management systems (DBMS). As nowadays, a numerous of the RDI projects are oriented towards the development of dataintensive solutions, the authors are confident that these lessons will be valuable not only for data engineers, but also for those researchers and practitioners who are dealing with the issues related to building and validating machine learning models, applications of moving averages to high-frequency data streams, and the implementation and deployment of DBMS.

Cubic Soft Graphs with Application in Decision Making

A graph structure is a useful tool in solving the combinatorial problems in different areas of computer science and computational intelligence systems. In this paper, we introduce the concept of cubic soft graph, complete cubic soft graph, (internal, external) cubic soft graphs and investigate some of their properties. Then we deal with fundamental operations, union, intersection, Cartesian product, composition of cubic soft graphs and illustrate these notions by several examples. We prove that cubic soft graphs under these operations are also a cubic soft graph. Finally, we describe an application of cubic soft graphs in decision making.

Frog calling activity detection using lightweight CNN with multi-view spectrogram: A case study on Kroombit tinker frog

Frogs play an important role in ecological systems, while frog species across the globe are threatened and declining. Therefore, it is valuable to estimate the frog population based on an intelligent computer system. Due to the success of deep learning (DL) in various pattern recognition tasks, previous studies have used DL-based methods for frog call analysis. However, the performance of DL-based systems is highly affected by their input (feature representation). In this study, we develop a frog calling activity detection system for continuous field recordings using a light convolutional neural network (CNN) with multi-view spectrograms. To be specific, a sliding window is first applied to continuous recordings for obtaining audio segments with a fixed duration. Then, the background noise is filtered out. Next, a multi-view spectrogram is used for characterizing those segments, which has more distinctive information than a single-view spectrogram. Finally, a lightweight CNN model is used for the detection of frog calling activity with a twin loss, where different train and test sets are used to validate the model’s robustness. Our experimental results indicate that the highest macro F1-score was 99.6 ± 0.2 and 96.4 ± 2.0 using 2016 and 2017 as the train data respectively, where CNN-GAP is used as the model with multi-view spectrogram as the input.

Self‐Assembled Nanostructures of Quantum Dot/Conjugated Polymer Hybrids for Photonic Synaptic Transistors with Ultralow Energy Consumption and Zero‐Gate Bias

AbstractHerein, it is reported the influence of solution processing and treatments, such as adding marginal solvent, ultrasonication, and UV treatment, on the resulting perovskite (CsPbBr3) quantum dot (QD)/poly(3‐hexylthiophene) (P3HT) composite nanofibril films (CNFs) to improve the charge dissociation and photonic synaptic performance. A photonic synaptic transistor with CNFs can perform fundamental functions, including short‐term plasticity, long‐term plasticity, spike‐number‐dependent, and spike‐time‐dependent plasticity, to mimic sensing, computing, and memory functions. Notably, a synaptic device with CNFs presents an ultralow energy consumption of 0.18 fJ and zero‐gate operation. The superior performance of synaptic devices with CNFs can be attributed to two factors: (i) homogeneous axial distribution of the QDs and (ii) the formation of P3HT nanofibrils and co‐aggregates. Therefore, enhanced interfacial charge transfer between QDs and P3HT, ensuring decent carrier transport capability, is achieved. Collectively, the composite artificial synapse successfully provides an effective guide that offers a new perspective for the fabrication of one‐dimensional self‐assembled nanostructure‐based artificial synapses emulating human‐like memory, neuromorphic computing, and artificial intelligent systems.

Computer Intelligent Comprehensive Rapid Risk Assessment System of Barrier Dam by Fuzzy Analytic Hierarchy Process and Big Data

Barrier dam overall stability and dam break influence degree are the two risks. In order to comprehensively and quickly evaluate the risk of barrier dams, the dam height, the capacity of the barrier lake and the material composition of the dam body are selected as the stability evaluation indexes; the dam failure degree and the risk population and the potential economic loss are taken as the evaluation indexes. Based on the fuzzy hierarchy theory, this comprehensive and rapid risk assessment system of barrier dam is obtained, which is clear, intuitive and rapid, combining qualitative indexes and quantitative indexes.

DBP-GAPred: An intelligent method for prediction of DNA-binding proteins types by enhanced evolutionary profile features with ensemble learning.

DNA-binding proteins (DBPs) perform an influential role in diverse biological activities like DNA replication, slicing, repair, and transcription. Some DBPs are indispensable for understanding many types of human cancers (i.e. lung, breast, and liver cancer) and chronic diseases (i.e. AIDS/HIV, asthma), while other kinds are involved in antibiotics, steroids, and anti-inflammatory drugs designing. These crucial processes are closely related to DBPs types. DBPs are categorized into single-stranded DNA-binding proteins (ssDBPs) and double-stranded DNA-binding proteins (dsDBPs). Few computational predictors have been reported for discriminating ssDBPs and dsDBPs. However, due to the limitations of the existing methods, an intelligent computational system is still highly desirable. In this work, features from protein sequences are discovered by extending the notion of dipeptide composition (DPC), evolutionary difference formula (EDF), and K-separated bigram (KSB) into the position-specific scoring matrix (PSSM). The highly intrinsic information was encoded by a compression approach named discrete cosine transform (DCT) and the model was trained with support vector machine (SVM). The prediction performance was further boosted by the genetic algorithm (GA) ensemble strategy. The novel predictor (DBP-GAPred) acquired 1.89%, 0.28%, and 6.63% higher accuracies on jackknife, 10-fold, and independent dataset tests, respectively than the best predictor. These outcomes confirm the superiority of our method over the existing predictors.

Intelligent and robust computational prediction model for DNA N4-methylcytosine sites via natural language processing

DNA N4-methylcytosine (4 ​mC) is an essential epigenetic modification and performs crucial roles in restriction-modification systems. The 4 ​mC involves many essential cellular processes, namely: correcting DNA replication and controlling DNA replication errors in the prokaryotic organism. In order to understand their biological functional mechanisms, the prediction of 4 ​mC modification is indispensable. Although computationally, it was targeted but the desired outcomes were not obtained. Thus, the development of an intelligent computational prediction system that truly expresses 4 ​mC modification sites is imperative. An efficient and high throughput discriminative intelligent computational system called “iDNA-4mC-DL” is introduced using the natural language processing method “word2vec” along with a convolution neural network. The obtained outcomes authenticated that the proposed iDNA-4mC-DL system performs outstandingly on six publicly available benchmark and independent datasets compared to current tools. It is, thus, highly estimated that the proposed model might be a more supportive and applied tool for rudimentary research and academia.

ONLINE PROBABILISTIC FUZZY CLUSTERING METHOD BASED ON EVOLUTIONARY OPTIMIZATION OF CAT SWARM

Context. The problems of big data clustering today is a very relevant area of artificial intelligence. This task is often found in many applications related to data mining, deep learning, etc. To solve these problems, traditional approaches and methods require that the entire data sample be submitted in batch form.
 Objective. The aim of the work is to propose a method of fuzzy probabilistic data clustering using evolutionary optimization of cat swarm, that would be devoid of the drawbacks of traditional data clustering approaches.
 Method. The procedure of fuzzy probabilistic data clustering using evolutionary algorithms, for faster determination of sample extrema, cluster centroids and adaptive functions, allowing not to spend machine resources for storing intermediate calculations and do not require additional time to solve the problem of data clustering, regardless of the dimension and the method of presentation for processing.
 Results. The proposed data clustering algorithm based on evolutionary optimization is simple in numerical implementation, is devoid of the drawbacks inherent in traditional fuzzy clustering methods and can work with a large size of input information processed online in real time.
 Conclusions. The results of the experiment allow to recommend the developed method for solving the problems of automatic clustering and classification of big data, as quickly as possible to find the extrema of the sample, regardless of the method of submitting the data for processing. The proposed method of online probabilistic fuzzy data clustering based on evolutionary optimization of cat swarm is intended for use in hybrid computational intelligence systems, neuro-fuzzy systems, in training artificial neural networks, in clustering and classification problems.

TSK-Streams: learning TSK fuzzy systems for regression on data streams

The problem of adaptive learning from evolving and possibly non-stationary data streams has attracted a lot of interest in machine learning in the recent past, and also stimulated research in related fields, such as computational intelligence and fuzzy systems. In particular, several rule-based methods for the incremental induction of regression models have been proposed. In this paper, we develop a method that combines the strengths of two existing approaches rooted in different learning paradigms. More concretely, our method adopts basic principles of the state-of-the-art learning algorithm AMRules and enriches them by the representational advantages of fuzzy rules. In a comprehensive experimental study, TSK-Streams is shown to be highly competitive in terms of performance.

C-CADZ: computational intelligence system for coronary artery disease detection using Z-Alizadeh Sani dataset

C-CADZ: computational intelligence system for coronary artery disease detection using Z-Alizadeh Sani dataset

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Experience in developing and using intelligent graphical computer systems

The article discusses the conceptual, methodological and practical aspects of building information and analytical systems, including forecasting and evaluation systems based on artificial intelligence and the use of knowledge bases, as well as the implementation of these principles in the research version of the developed intellectual and graphical system based on parametric models of deposits and ore fields of the Rudno-Altai mineragenic zone, used to assess the prospects of ore fields of the Zmeinogorsky ore district.

Backpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid

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.

Divide and conquer: Ill-light image enhancement via hybrid deep network

Intelligent system applications in computer vision suffer detection and identification problems in ill lighting conditions (i.e., non-uniform illumination), where under-exposed and over-exposed regions coexist in the captured images. Processing on these images results in over and under enhancement with colour and contrast distortions. The traditional methods design some handcrafted constraints and rely on image pairs and priors, whereas existing deep learning-based methods rely on large scale and even paired training data. But these method’s capacity is limited to specific scenes (i.e., lighting conditions). In this paper, we present a deep-hybrid ill-light image enhancement method and propose a contrast enhancement strategy based on the decomposition of the input images into reflection J and illumination T. A Divide to Glitter network (D2G-Net) is designed to learn from the few-shots of training samples and do not require paired and large quantity training data. D2G-Net is comprised of a multilayer Division-Net for image division and a Glitter-Net to amplify the illumination map. We propose to regularize learning using a correlation consistency of decomposition extracted from the input data itself. Extensive experiments are organized under ill-lighting conditions, where a new test dataset is also proposed with robust lighting variation to evaluate the performance of the proposed method. Experimental results prove that our method has superior performance for preserving structural and texture details compared to state-of-the-art approaches, which suggests that our method is more practical in interactive computer vision and intelligent expert system applications.

Research on the Application of Computer Intelligent Proofreading System in College English Teaching

With the rapid development of computer technology, people have higher requirements for English Teaching (hereinafter referred to as ET) tools. However, in ET, teachers need to correct students’ homework, which will take a lot of time. Therefore, we can develop intelligent proofreading system (hereinafter referred to as IPS), which can improve the translation and proofreading of English syntax and phrases. Through corpus and other databases, we can calibrate English phrases and syntax with high accuracy, which will improve phrase translation and poor sentence coherence. By creating semantic ontology model and phrase translation combination translation algorithm, we can create semantic ontology model, which will better realize online collaborative translation and intelligent school peer-to-peer work, which will play an important role in ET. Through the IPS, we can display the situation in a graphical way, which will better carry out teaching activities. Firstly, this paper analyzes the positioning of IPS for College ET. Then, this paper analyzes the working principle of the IPS. Finally, some suggestions are put forward.

Extended high dimensional indexing approach for reachability queries on very large graphs

Given a directed acyclic graph G=(V,A) and two vertices u,v∈V, the reachability problem is to answer if there is a path from u to v in the graph. In the context of very large graphs, with millions of vertices and a series of queries to be answered, it is not practical to search the graph for each query. On the other hand, the storage of the full transitive closure of the graph is also impractical due to its O(|V|2) size. Scalable approaches aim to create indices used to prune the search during its execution. Negative indices may be able to determine (in constant time) that a query has a negative answer while positive indices may determine (again in constant time) that a query has a positive answer. In this paper we propose a novel scalable approach called LYNX that uses a large number of topological sorts of G as a negative cut index without degrading the query time. A similar strategy is applied regarding a positive cut index. In addition, LYNX proposes a user-defined index size that enables the user to control the ratio between negative and positive cuts depending on the expected query pattern. We show by computational experiments that LYNX consistently outperforms the state-of-the-art approach in terms of query-time using the same index-size for graphs with high reachability ratio. In intelligent computer systems that rely on frequent tests of connectivity in graphs, LYNX can reduce the time delay experience by end users through a reduced query time. This comes at the expense of an increased setup time whenever the underlying graph is updated.

Neuromorphic extreme learning machines with bimodal memristive synapses

The biology-inspired intelligent computing system for the neuromorphic hardware implementation is useful in high-speed parallel information processing. However, the traditional Von Neumann computer architecture and the unsatisfactory signal transmission approach have jointly limited the overall performance of the specific hardware implementation. In this paper, a compact extreme learning machine (ELM) architecture synthesized with the spintronic memristor-based synaptic circuit, the biasing circuit, and the activation function circuit is presented. Notably, due to the threshold characteristic of the memristive device, the synaptic circuit has a bimodal behavior. Namely, it is capable to provide the constant and adjustable network weights between the adjacent layers in the ELM. Furthermore, two major limitations (process variations and sneak path issue) are taken into account for the detailed robustness analysis of the whole network. Finally, the entire scheme is verified with case studies in single image super-resolution (SR) reconstruction.