In this paper, we introduce two algorithms for reasoning with words on fuzzy dynamic system. The systems that use linguistic variables which are variables whose values may be expressed in terms of a specific natural or artificial language, for example 𝕃= very less true; less true; true; more true; very true; very very true .... In language of hedge algebra ( ℍ𝔸 ), 𝕃 set which is generated from ℍ𝔸 is the POSET (partial order set). The algorithms are Static reasoning and Dynamic reasoning. The former traverses the branch of the fuzzy graph whereas the later transform according to the equation of state and create a space of states of the system. Algorithms performed on linguistic variables and applied labeling techniques. And finally, the application of the algorithm on the mobile network model is also investigated.
This paper represents verification algorithms and register transfer level (RTL) specification as algebraic aspects proposed to validate the results of RTL synthesis. Major properties of this approach, the conception of an algebraic semantics-based model (ASM), to be interpreted as a Chu space, is viewed as an algebraic semantics foundation for the RTL formalization and the conception of algebraic semantics-based specification automata ( ASA_SPEC ) are given for formal correctness of the results of RTL synthesis. Approaching formal verification is focused on functional equivalence examining to define if the algebraic RTL automata ( ASA_RTL ) are equivalent to ASA_SPEC . To put it another way, the comparison is determined as an assessing that examines the synthesis algorithm is produced an effective RTL specification.
The application of Artificial Intelligence (AI) and ontologies to the COVID-19 pandemic has been an active area of research and development. AI techniques such as machine learning, computer vision, and natural language processing have been used to analyze vast amounts of data generated by the pandemic, such as medical records, scientific literature, and social media posts. Ontologies, on the other hand, provide a structured representation of knowledge, which can be used to standardize data and facilitate data integration, enabling more efficient and effective data analysis.
English is the most common language globally, and it is increasingly important. English has been compiled in most online documents, information, and contents. However, with a considerable vocabulary, learning English is difficult for many people to remember. Therefore, many modern technologies have been proposed to support English learning, such as English learning technology through word-matching games to help children become excited and easily approach English from an early age. In addition, translation tools can help users look up vocabularies, antonyms, synonyms, and examples. This study presents a method to support learning English via object detection in videos, images, or even live-stream videos in real-time using deep learning architectures such as You Look Only Once (YOLO) - one of the finest families of object detection models with state-of-the-art performances. The method to obtain an mAP is 55.6 with 17GFlops. The results are vocabulary, meaning, and making sentences with that. Our method has good accuracy in data of 2786 images belonging to 59 classes.
Online learning has gained significant popularity, but maintaining learner focus remains a challenge, especially in financial enterprise training systems. The need for training has increased with banking and finance digitalization trends, yet high learning curves and prolonged sessions often lead to distractions. This research introduces an online learning tool that monitors and quantifies learner attention in real-time. Using the MobileNet Convolutional Neural Network, we detect seven core emotions, which, combined with attention scores, form a Concentration Index (CI). Learners are then categorized as “Highly-engaged,” “Normally Engaged,” or “Disengaged.” With 70
This research employs the design of experimental (DoE) to examine how various parameters impact the economic feasibility and overall satisfaction of enterprises operating within eco-industrial parks (EIPs). A full factorial design is constructed, using economic feasibility and overall satisfaction as response variables, and experimental data is generated by simulating diverse scenarios. Each iteration of the experiment utilizes a single-leader multi-follower (SLMF) game optimization model, focusing on designing water exchange networks within EIPs. The investigation encompasses several parameters in a case study involving ten follower enterprises aiming to minimize their annual operational costs. Concurrently, the EIP authority assumes the leader role with the objective of reducing the collective freshwater consumption of the EIP. Furthermore, this study employs binary logistic and multi-linear regressions to establish causal relationships. These relationships link input parameters with economic feasibility and overall satisfaction of operating businesses within EIPs. Ultimately, the reliability of the DoE methodology is showcased, offering valuable insights into enterprise parameters, EIP design, economic feasibility, and overall satisfaction.
In recent years, technological advances in various fields of human activity have enabled the development of smart city applications that can help improve life in modern cities. In order to validate that the functional requirements of the applications are met and, above all, to ensure security and resilience against vulnerabilities and constantly evolving cyber threats, it is imperative that these applications are adequately tested before being put into operation. The aim of this paper is to present an analysis of the use of fuzz testing to test the stability, correctness and security of applications and information systems that are applicable in the smart city domain. The paper presents an analysis of the possibilities of using different types of fuzz testing and maps the different fuzz testing tools that are applicable in implementation projects in different areas of smart cities. Furthermore, a testing method for the use of fuzz testing is proposed and presented. This method is then validated using a set of proposed tests and outputs for a selected project.
This paper studies on linguistic topological sapces which are generate from Hedge algebra. We also indicate homotopy classes of homotopic functions on this spaces as well as its equivalence relations.
In recent years, the concept and implementation of smart cities has become an important topic at the level of countries, regions and cities. However, it is currently not clearly defined what all areas are part of a smart city, or what functional domains a smart city addresses. The aim of this paper is to analyse the current state of smart city concepts in the world and then to identify the functional domains that define the areas belonging to a smart city. This analysis represents an input to propose a further model of functional domains for Central Europe. The relevance of the model is verified by comparing the identified functional domains with existing regional and national smart city strategies in the Czech Republic.
The term Smart City represents a strategic concept for a city or region that involves the use of modern technologies to influence the quality of life in the city. At the technological level, a wide range of IoT devices are used, which are interconnected through modern low-latency networks to enable the creation of intelligent applications with added value for their users. However, this relatively simple and noble idea represents a wide range of technologies and approaches, making the idea of ensuring Cyber Security in Smart Cities difficult. When implementing any technology in an organization, the processes, assets, and people that bring the technology to life, are crucial. The aim of this paper is to analyze the key capabilities, frameworks and standards that would facilitate and support the possibility of developing Smart Cities. The first part of the article introduces the issue of Cyber Security and Smart Cities. Subsequently, the key approaches for ensuring security in creating Smart Cities are analyzed. The final part presents the BPMN-SC data model based on business process model notation and key security standards while incorporating the specifics of Smart Cities.
This paper focuses on addressing the challenge of maintaining information coherence and robustness within a multi-agent system (MAS) that aggregates information from distributed sources, some of which may be defective intentionally or unintentionally. We propose a self-organizational approach in this context, emphasizing a systemic perspective that considers structural coupling across two levels: direct information gathering and communication. Specifically, we integrate a trust mechanism with local behavioral rules and selective environmental pressures to facilitate the emergence of two co-evolving organizations: one at the social level and the other at the spatial level. The social organization mirrors the trust relationships developed among the agents, while the spatial organization represents the deployment of agents in the environment to encourage exploration. The local behavioral rules encompass three categories: deployment rules, communication rules, and retro-action rules governing communication and deployment. We conduct simulations to experiment with the combination of these behavioral rules, observing the emergence of organizational structures and roles within the system.
Stock market prediction holds significant importance in the world of finance, captivating the attention of both investors and financial researchers. The integration of artificial intelligence and advancements in computational power has led to substantial improvements in predicting stock prices, surpassing the effectiveness of traditional programmed prediction methods. In this paper, we explore three distinct and innovative methods for stock price prediction: Long Short-Term Memory (LSTM), LSTM combined with Simple Moving Average (LSTM-SMA), and LSTM combined with Exponential Moving Average (LSTM-EMA). Our analysis is conducted using a comprehensive historical dataset of Apple’s stock prices, and the performance of each model is rigorously evaluated using critical metrics, including Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2 score. Additionally, the training time for each model is taken into account. The results show that all three models LSTM, LSTM-SMA, and LSTM-EMA give good prediction results for Apple’s stock price, in which the LSTM model gives the best prediction results for the 21-day cluster. However, in terms of computational time, the LSTM-SMA model and LSTM-EMA model are more efficient than the LSTM model. These findings highlight the potential of integrating advanced techniques to achieve more accurate and efficient stock price predictions.
The aim of the paper is to design and verify a solution for collecting and analysing logs of a distributed application, which is operated as Software as a Service (SaaS) in the cloud environment in Kubernetes technology. Applications running in cloud environment are not monolithic in most cases, but consist of a large number of co-operating microservices. Providing logging for such distributed applications presents a complex issue, where to provide a comprehensive view of the application state, it is necessary to provide logging across all microservices representing the application. This paper first introduces modern approaches for application development using the technical means of virtualization, containerization and orchestration with an emphasis on Kubernetes technology. Next, approaches and analysis of application logging options are presented with the emphasis on the use of ELK and PLG stack technologies. Based on the analysis, a technical solution for logging applications in Kubernetes environment, operated in the form of SaaS, is proposed and verified.
Palmprint-based biometrics has received a lot of attention for personal identification. The paper proposes a novel learning discriminant feature technique for palmprint recognition, called the Learning Discriminant Line Direction Descriptor (LDLDD), that learns separately all three kind of directional pattern code. The dominant direction number (DDN) map is calculated first in this method. Then, this technique computes direction pattern maps with three multi-direction encoding methods based on the DDN map, where pixels with the same DDN values will use the same encoding strategy and belong to the same feature map. Finally, (2D)2LDA is used to train new feature subspaces that project these maps from a high-dimensional space to a discriminant space with lower dimensions. Experiments on Hong Kong Polytechnic University’s (PolyU and IITD) public databases show that the proposed method outperforms existing techniques in terms of accuracy.
Artificial neural networks combined with deep learning (DL) techniques are becoming a very powerful tool that gives the best performance for many difficult problems such as: the speech recognition, the image recognition, etc. the language processing. The training of the neural network models takes place in many different languages, different techniques, different sizes of organizations. However, previous studies only focused on the model training techniques, the datasets, currently there is no research that fully introduces running artificial neural network models, the network models are run in the cloud connecting directly from RStudio. In this article, we focus on creating models and applying deep learning models of the artificial neural networks based on the cloud computing, in order to create a separate research direction. The results of this study open up a new approach to cloud-based deep learning programming, providing an additional choice of the deep learning approaches for those wishing to enter the field.
Gross Domestic Product (GDP) is an indicator used to measure the total market value of all final goods and services produced within a national territory during a given period. This is an essential indicator for formulating macroeconomic policies. This study presents a classical machine learning algorithm to forecast GDP in countries from 2013 to 2018 (with Economic Freedom Index’s Predicting GPD dataset). We use the Feature importance technique and incorporate other methods such as PCA and KBest; simultaneously, we tune the hyperparameters for the model to have more optimal results. We compare the predictive accuracy of Random Forest (RF) with other classical models such as Support Vector Machines (SVM). We find that RF KBest outperforms RF and SVM. The forecast accuracy is measured by $$R^2$$ has reached 0.904 in predicting GDP in 186 countries. This study encourages increasing the use of machine learning models in macroeconomic forecasting. Besides, we present GDP growth rates (as a percentage) by region. We also analyze and find some critical factors that can significantly affect GDP, such as Freedom from Corruption, Property rights, and the unemployment rate.
Human always has the ability to view things and phenomena, but to understand about it that depends on the ability to perceive as well as combine with existing experiences and knowledge which is available in every human to understand it. This research studies the human vision organs, infographics capture characteristics of the human eye, human vision awareness progress, and human visual viewing - thinking mechanisms to design visual analytics system of multivariate data (mdVAS), model of viewing - thinking visually analyzing, and visual graphs for discovering knowledge from multivariate data (mD). This mdVAS will support to upgrade human vision awareness progress that includes stages such as viewing - perceiving - cognizing - analyzing - understanding - remembering (vpcaurHs). This progress, especially the analysis stage plays a key that opens the door for human to understand the profound significance of things. Humans who want to understand the profound insights of things must bring out questions, at that time the mdVAS will enhance knowledge for humans to answer questions that humans themselves brought out. This approach will help humans, especially leaders, who have a profound insight into mD. This research illustrates experiments on educational data to manage the student intakes to help leadership on policy and decision making.
Time series are formed from data points collected over time. The prominent data points of time series are often minima or maxima; hence they have special values. Moreover, they are virtually turning points that change trend of time series. These prominent data points play an important role in determining the characteristics of time series so they are called important data points or major extrema. There are many methods to detect major extrema in time series in static context; however, in streaming context there have almost been no methods to carry out this task so far. In the paper, we propose a method for detecting major extrema in streaming time series. The method is of low computational time in identifying major extrema as soon as a newly in-coming data point of streaming time series is collected. The experimental results demonstrate that the proposed method exactly detects major extrema on the fly. Furthermore, the method could identify correlation of streaming time series thanks to their major extrema. An interesting application of the proposed method is to enable the task of online forecasting to predict future data points of streaming time series based on similarity search using major extrema.
On 1936, Birkhoff and von Newmann proposed the introduction of a “quantum logic”, as the lattice of quantum mechanical proposition which is not distributive and also not a Boolean. Seven years later, Mackey tried to provide a set of axioms for the propositional system to predict of the outcome set of experiments. He indicated that the system is an orthocomplemented partially ordered set. Physical complex systems can be modeled by using linguistic variables which are variables whose values may be expressed in terms of a specific natural or artificial language, for example $$\mathbb {L}$$ = {very less young; less young; young; more young; very young; very very young ...}. In language of hedge algebra ( $$\mathbb{H}\mathbb{A}$$ ), $$\mathbb {L}$$ set which is generated from $$\mathbb{H}\mathbb{A}$$ is the POSET (partial order set). In this paper, we introduce a quantum logic $$\ell $$ to assert that, let $$\bot $$ be the orthocomplementation map $$\bot : \ell \rightarrow \ell $$ , all $$\clubsuit , \spadesuit \in \bot $$ must satisfy the following conditions:
Today, software testing is already an integral part of every software development cycle. However, even this does not guarantee that the final product will be free of software bugs. Testing needs to be implemented from the lowest possible stage of development so that the bugs can be detected as early as possible to reduce the cost of fixing them at later stages. However, this is hampered by the ever-increasing demands for software from customers, increasing testing requirements to the point where it is impossible to meet them all. This is why automated software testing is becoming increasingly popular. Automated test scripts can partially replace manual user testing and help significantly improve the quality of the software. Automated testing using the Selenium framework is the focus of this paper. The functionality and process of testing in Selenium are demonstrated with five testing scripts in a sample scenario.
