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Artificial neural network (ANN) has been well applied in pattern recognition, classification and machine learning thanks to its high performance. Most ANNs are designed by a static structure whose weights are trained during a learning process by supervised or unsupervised methods. These training methods require a set of initial weights values, which are normally randomly generated, with different initial sets of weight values leading to different convergent ANNs for the same training set. Dealing with these drawbacks, a trend of dynamic ANN was invoked in the past year. However, they are either too complex or far from practical applications such as in the pathology predictor in binary multi-input multi-output (MIMO) problems, when the role of a symptom is considered as an agent, a pathology predictor's outcome is formed by action of active agents while other agents' activities seem to be ignored or have mirror effects. In this paper, we propose a new dynamic structural ANN for MIMO problems based on the dependency graph, which gives clear cause and result relationships between inputs and outputs. The new ANN has the dynamic structure of hidden layer as a directed graph showing the relation between input, hidden and output nodes. The properties of the new dynamic structural ANN are experienced with a pathology problem and its learning methods' performances are compared on a real well known dataset. The result shows that both approaches for structural learning process improve the quality of ANNs during learning iteration. [ABSTRACT FROM AUTHOR]

This special issue of the Journal of Intelligent & Fuzzy Systems contains selected articles of complex evolutionary artificial intelligence in cognitive digital twinning. [ABSTRACT FROM AUTHOR]

Soft set is the power tool to deal with uncertainty in a parametric manner. In applications of soft set, one of the most important steps is to define mappings on soft sets. In this study, we model theory of game under theory of soft set which is an effective tool for handling uncertainties events and problems that may exist in a game. To this end, we first define some expected impact functions of players in soft games. Then, we propose three new decision making algorithms to solve the 2.2 × p, 2. n × p and m. 2 × p soft matrix games, which cannot be settled by the relevant soft methods such as saddle points, lover and upper values, dominated strategies and Nash equilibrium. The proposed soft game algorithms are illustrated by examples. [ABSTRACT FROM AUTHOR]

Picture fuzzy graph (PFG) is an extended version of intuitionistic fuzzy graph (IFG) to model the uncertain real world problems, in which IFG may fail to model those problems properly. PFG is more precise, flexible and compatible than IFG to deal the real-life scenarios which consists of information these types: yes, abstain, no and refusal. The main focus of our study is to present the concept of isomorphic PFG, regular PFG (RPFG) and picture fuzzy multigraph. In this paper, we present the notation of RPFG. Many different types of RPFGs such as regular strong PFG, regular complete PFG, complete bipartite PFG and regular complement PFG are introduced. We also describe the concepts of dn and tdn-degree of a vertex in a RPFG. Based on those two types of degrees, we classify the regularity of PFG into 3 type's namely, dn- RPFG, tdn-RPFG and n- highly irregular PFG. Several theorems of those RPFG are presented here. We define the busy vertex and free vertex in a RPFG. We present the notations of μ-complement, homomorphism, isomorphism, weak isomorphism and co weak isomorphism of RPFG. Some significant theorems on isomorphism and μ- complement of RPFG are derived here. We also introduce the notation of picture fuzzy multigraph. We present a mathematical model of communication network and transportation network by using picture fuzzy multigraph and real time data are collected so that the transportation network/communication network can work efficiently. [ABSTRACT FROM AUTHOR]

Transfer learning (TL) is further investigated in computer intelligence and artificial intelligence. Many TL methodologies have been suggested and applied to figure out the problem of practical applications, such as in natural language processing, classification models for COVID-19 disease, Alzheimer's disease detection, etc. FTL (fuzzy transfer learning) is an extension of TL that uses a fuzzy system to pertain to the vagueness and uncertainty parameters in TL, allowing the discovery of predicates and their evaluation of unclear data. Because of the system's increasing complexity, FTL is often utilized to further infer proper results without constructing the knowledge base and environment from scratch. Further, the uncertainty and vagueness in the daily data can arise and modify the process. It has been of great interest to design an FTL model that can handle the periodicity data with fast processing time and reasonable accuracy. This paper proposes a novel model to capture data related to periodical phenomena and enhance the quality of the existing inference process. The model performs knowledge transfer in the absence of reference or predictive information. An experimental stage on the UCI and real-life dataset compares our proposed model against the related methods regarding the number of rules, computing time, and accuracy. The experimental results validated the advantages and suitability of the proposed FTL model. [ABSTRACT FROM AUTHOR]

Periapical Inflammation (PI) is one of the most popular diseases in adults due to complication of endodontitis or dental trauma with corresponding consequences to quality-of-life like tiredness and signs of infection. Specifically, patients having severe PI are often tiredness and high fever accompanied by signs of infection such as dry lips, dirty tongue, lymph node reaction in the area under the jaw. In X-Ray images, PI could be recognized by vague boundaries with signs of periapical ligament extensions. It is necessary to design a computerized diagnosis system based on the Deep Learning models for supporting clinicians in diagnosis of PI from X-Ray images. In this paper, we propose a new medical system called VNU for diagnosis of PI from X-Rays images. The VNU system uses Deep Learning to classify whether X-Ray images being PI or not. The Residual Neural Network (ResNet) with 34 layers is utilized with proper data augmentation and learning algorithms. The system is designed based on 7-layer enterprise architecture (User, Business, Application, Data, Technology, Infrastructure, and Security). It is used by both the clinicians and IT operators. The system has been validated on real data from Hanoi Medical University, Vietnam consisting of 900 images with PI and 500 normal images. Two scenarios of validation namely hyperparameter selection and performance comparison with other CNN-based Deep Learning models have been performed. It has been found from the experiments that the proposed system has better performance than the others in terms of sensitivity and specificity with the corresponding values of 96.70% and 93.87%. The system is deployed on the web interface that offers flexibility for clinicians in diagnosis and training. [ABSTRACT FROM AUTHOR]

Data acquisition problem on large distributed wireless sensor networks (WSNs) is considered as a challenge in the growth of Internet of Things (IoT). Recently, the combination of compressive sensing (CS) and routing techniques has attracted much attention of researchers. An open question of this combination is how to integrate these techniques effectively for specific tasks. On the other hand, CS data reconstruction process is considered as one of the CS challenges because it requires to recover N data from only M measurement where M<

At present, there are still many deficiencies in Chinese-Japanese machine translation methods, the processing of corpus information is not deep enough, and the translation process lacks rich language knowledge support. In particular, the recognition accuracy of Japanese characters is not high. Based on machine learning technology, this study combines image feature retrieval technology to construct a Japanese character recognition model and uses Japanese character features as the algorithm recognition object. Moreover, this study expands image features by generating a brightness enhancement function using a bilateral grid. In order to exclude the influence of the edge and contour of the image scene on the analysis of the image source, the brightness value of the HDR image is used instead of the pixel value of the image as the image data. In addition, this research designs experiments to study the translation effects of this research model. The research results show that the model proposed in this paper has certain effects and can provide theoretical references for subsequent related research. [ABSTRACT FROM AUTHOR]

The reasons for consumers' resale behavior are complex and sometimes diverse, and the investigation of consumer resale behavior is not a simple matter. Therefore, only through a lot of investigation and inquiry can we reach relevant conclusions. Based on machine learning and BP neural network, this paper constructs a consumer online resale behavior measurement model. The contraction-expansion factor can balance the global search and local search capabilities in different iteration periods, and the differential evolution operator is introduced to solve the problem of lack of population diversity. After building the model, this study collects data through questionnaires, and combines neural network training models to take data training and data prediction. In addition, this study compares and analyzes real data with predicted data, and visually displays the comparison results through statistical graphs. The results show that the method proposed in this paper has certain effects and can provide theoretical references for subsequent related research. [ABSTRACT FROM AUTHOR]

In order to improve the performance of entrepreneurship and innovation education in colleges and universities, this study attempts to build an evaluation system and model of innovation and entrepreneurship in colleges and universities to provide a complete and practical tool for government education authorities and universities to evaluate the implementation of innovation and entrepreneurship education. In this research, decision tree and fuzzy mathematics are used as the basis of the model algorithm, and the algorithm is improved based on the analysis of traditional algorithms. Moreover, based on the improved decision tree algorithm, an evaluation index system for university innovation and entrepreneurship education is constructed. After determining the evaluation indicators of innovation and entrepreneurship education in colleges and universities, this study uses several universities as examples to analyze and define the definitions of various indicators. In addition, this study statistically analyzes the results of entrepreneurship and innovation education in colleges and universities through simulation. The research shows that the model proposed in this paper has a certain practical effect, and based on the simulation results, this study makes several suggestions. [ABSTRACT FROM AUTHOR]

With the enhancement of people's environmental awareness, improving environmental performance has become an important way for manufacturing enterprises to achieve sustainable development. At present, one of the key challenges facing enterprises in terms of environmental sustainability is to extend it to other supply chain members. In this paper, the author analyzes the integration performance statistics of green suppliers based on fuzzy mathematics and BP neural network. Supply chain integration represents the company's ability to formulate strategic alliances, integrate resources, establish seamless processes and share information. The empirical results show that it is feasible to evaluate the performance of green logistics enterprise integration. Through reasonable calculation method and model, the effective and reasonable evaluation results can be obtained. Enterprise managers can reasonably evaluate the key links of enterprise management, allocate enterprise resources correctly, completely and reasonably, minimize costs and maximize profits. Therefore, for the green logistics enterprises, should pay attention to the enterprise's own performance evaluation, timely adjust their own development direction, plan and goal. [ABSTRACT FROM AUTHOR]

Intelligent education is an intelligent education platform that integrates correct education concept and Internet of things, big data, cloud computing and other technologies. This paper hopes to use the construction of the composition scoring model to further construct a computer scoring system for college English translation, which can give students a translation score and give feedback evaluation based on the quality of the translation. In this paper, according to the knowledge of the existing automatic scoring system at home and abroad, the feature selection method (TF-IDF, IG, CHI) is discussed and analyzed. Moreover, this paper studies the impact of our composition automatic scoring from the perspective of linguistics. In addition, this paper uses the multiple regression method to evaluate the final score. The features considered in this paper mainly include simple linguistic features and complex linguistic features. Finally, performance analysis of the algorithm model is performed by setting up a control experiment. The research results show that the proposed algorithm model has certain effects. The future trend is to form adult auxiliary machines through various human-computer interaction technologies, which will reshape future learning and education and form a new teaching form. [ABSTRACT FROM AUTHOR]

From the point of view of urban landscape design objectives, techniques and evaluation, the continuous development of digital information and digital technology has a positive impact on urban landscape design. The core of landscape planning model is to explore various possibilities and determinants of landscape spatial system by changing experimental conditions or parameters.In this paper, the author analyzes the intelligent landscape design and land planning based on neural network and wireless sensor network. The digital generation and construction is through the use of relevant digital technology groups for landscape design and construction activities. Parametric design makes great changes in modern urban landscape planning and design, and has a significant impact on the concept of landscape design, the auxiliary means of design and the construction of landscape entities. It is an indispensable and important link in the process of digital landscape design. Reasonable planning and design of urban landscape can make better use of urban land resources, alleviate the waste of land resources, and optimize the use of resources. [ABSTRACT FROM AUTHOR]

The comprehensive indicators of the physical fitness of young athletes and the specific modes of transportation, working and leisure activities as explanatory variables are not in line with the normal distribution. Moreover, there is a high correlation between explanatory variables, and fitting traditional regression models does not meet the assumptions, and multiple collinearity problems will occur, and good results will not be obtained. The random forest regression model has excellent performance in overcoming these difficulties. Therefore, the random forest regression model is constructed to evaluate the impact of various factors on the physical fitness of young people. This paper studies the impact of various factors on the health level of young people's body and combines the source data and research goals to establish a comprehensive evaluation index system and an influential factor indicator system. In addition, this paper uses AHP to conduct comprehensive evaluation, and obtains the comprehensive physical quality of young people, and gives corresponding suggestions according to the actual situation. [ABSTRACT FROM AUTHOR]

The difficulty of sports gesture recognition is the effective cooperation of hardware and software. Moreover, there are few studies on machine learning in the capture of the details of sports athletes' gesture recognition. Therefore, based on the learning technology, this study uses the sensor with gesture recognition algorithm to analyze the detailed motion capture of sports athletes. At the same time, this study selects inertial sensor technology as the gesture recognition hardware through comparative analysis. In addition, by analyzing the actual needs of athletes' gesture recognition, the Kalman filter algorithm is used to solve the athlete's posture, construct a virtual human body model, and perform sub-regional processing, so as to facilitate the effective identification of different limbs. Finally, in order to verify the validity of the algorithm model, the basketball exercise is taken as an example for experimental analysis. The research results show that the basketball gesture recognition method used in this paper is quite satisfactory. [ABSTRACT FROM AUTHOR]

Traditional mathematical models have problems in the analysis of financial stocks that are not intuitive enough. In order to improve the intuitiveness of the stock forecasting model, based on the image recognition technology, this study normalizes the image and performs feature recognition with grayscale images. At the same time, this paper ignores the small fluctuations and combines the characteristics of stock images to remove the drying process and proposes an algorithm model based on feature recognition. In addition, in order to improve the image accuracy, the model combines the edge extraction technology to extract features, which reflects the actual rise and fall of the stock. Finally, this paper designs experiments to conduct research and analysis. The research results show that the proposed method has certain effects and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

Information in many real life problems collect from multi agents, i.e., "multipolar information" exists. This multipolar information cannot be properly modeled by m- polar fuzzy graph or intutionistic fuzzy graph. An m-polar neutrosophic model is very much efficient for such real word problems which can construct more precise, flexible, and comparable system as compared to the classical, fuzzy and neutrosophic graph models. In this paper, we present the definition of m-polar neutrosophic graph model. Some new operations, such as union, join, composition and ring sum of two m-polar neutrosophic graph are defined here. We define six new products on m-polar neutrosophic graphs namely strong product, semi strong product, complete product, direct product, cartesian product and lexicographic product. Some idea of complement, isomorphism, weak and co weak isomorphism on m-polar neutrosophic graph are introduced here. We also present several associated properties and theorems of m-polar neutrosophic graph. We introduce a model of m-polar neutrosophic graph, which is applied in evaluating the teacher's performance of a college. The performances of the teachers are computed based on the response score (feedback) of the students of the college. We also present a numerical example to illustrate our proposed model. [ABSTRACT FROM AUTHOR]

Nowadays, the crime rate increases dramatically in every country. Therefore, it is an urgent need for governments and social associations to produce persistent solutions and disincentive penalties to prevent crime issues. Specifically, social media plays an important role in crime rate detection; thus, reducing crime rates significantly. It would be a good medium for the desired task. In this paper, we analyze Twitter data collected from Twitter accounts for seven different locations (Ghaziabad, Chennai, Bangaluru, Chandigarh, Jammu, Gujarat, and Hyderabad) from January 2014 to November 2018 in a case study of India, which is opted to illustrate the efficiency of the proposed work. Sentiment analysis has been used to analyze users' behavior and psychology through the tweets of people to track crime actions. Twitter part-of-speech tagger, which is a Markov Model of first-order entropy, has been used for part-of-speech in online conversational text. Brown clustering is used for a long set of unlabeled tweets. Comparisons are verified with real crime rates from an authorized source of information according to different locations. We also measure the latest crime trends for the highest (Ghaziabad, Uttar Pradesh) and lowest crime cities (Jammu) in India. It has been found that the latest crime trends have been recorded for the time duration of 7 days (23, January 2019 to 30, January 2019). The analyses demonstrate that the obtained results match with the real crime rate data. We believe that these types of studies will help to detect the real-time crime rate for different locations and detect the crime pattern easily. [ABSTRACT FROM AUTHOR]

In Botnet Detection, Domain generation algorithms are the most effective method to intercept and analyze captured package. In this article, we propose a new method to classify harmful domain names using Neutrosophic Sets. Data of domain name, after being selected featured and fuzzed into Neutrosophic Sets will be used to classify benign domain names, malicious domain names and indeterminacy domain names, minimizing false detection of benign domain names. The proposed model is going to be tested and evaluated with other malicious domain detection models in the aspects of accuracy points, Accuracy, Revocation, and F1, all of which show that our proposed model has good results. [ABSTRACT FROM AUTHOR]

A genetic algorithm (GA) belongs to the class of evolutionary algorithms and it is one of the most studied heuristic algorithms to solve graph coloring problems. In this paper, we propose a new GA algorithm for the total graph coloring problem. To the best of our knowledge, no algorithm based on a GA exists in the literature for total graph coloring. In the proposed approach, a novel encoding scheme is introduced, where all the edges and vertices of the graph are represented in a chromosome without any repetition. For the initialization of the population, a greedy algorithm is used to determine the total number of colors required for a total coloring of the graph. The number of colors is used as the fitness value of a chromosome which depends on the sequence of vertices and edges representing the chromosome. We introduce a convergence criteria for GA based on the total coloring conjecture. A two-point crossover and mutation operations, suitable for total coloring, are suggested. The proposed algorithm is applied on some well-known and standard graphs. In our computational tests, graphs are used with a maximum number of 690 vertices and 6650 edges of the graph, respectively. The proposed algorithm determines an optimal solution for 21 graphs among the 27 graphs. The solution of remaining the 6 graphs is near optimal and differs by at most one unit from the optimal value. The results show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Image steganography plays a vital role in hiding significant secret information within any input image. Numerous steganography techniques have been carried out to hide the secret information in images. In the current scenario, frequency domain techniques are widely preferred which are mostly transform based approaches. Conventionally, the secret data is hidden randomly in the coefficients of the transformed image. However, such random data hiding techniques lead to inferior performance of the overall system. Thus, using an optimization algorithm is obligatory to find out the optimal coefficients. Genetic Algorithm (GA) is normally used for selecting the optimal transform coefficients to enhance the system performance. However, conventional GA based approaches are highly random in nature which again leads to inaccurate results. In this work, a modified GA approach was proposed to determine the optimal coefficients in order to improve the embedding capacity and stego image quality. The achieved average peak signal to noise ratio (PSNR) was 50.29 dB with embedding capacity of 139361 bits. These experimental results validate the practical feasibility of the proposed methodology for image steganography. [ABSTRACT FROM AUTHOR]