Intelligent Data Processing Research Articles

CONTACTLESS PAYMENTS FRAUD DETECTION METHODS AND IS SOCIETY PREPARED TO RESIST: A CASE STUDY

The ability to use contactless payment technologies, non-cash payments and credit card payments is becoming almost an essential requirement for consumers and merchants in today's economic conditions. Different market sectors are rapidly adapting to these technologies and looking for the most convenient, secure, and fastest possible solutions that combine intelligent data processing, security, and business management functions. Millions of debit and credit card holders care about secure payments, the businesses that receive these payments are secure in terms of security, and the operators that process such incoming and outgoing payments are interested in innovative solutions that set them apart from the competition. Amid the COVID-19 pandemic, when e-commerce was growing exponentially, the global market for fraud detection and prevention, currently stands at USD 20.9 billion, and is expected to grow, until 2025 will rise to USD 38.2 billion by the end of the year; holds the market at 12.8 % annually. The US remains the dominant region in this market segment, but European countries are also increasingly investing in fraud prevention and detection solutions, which are growing in demand in Europe due to an increase in cybercrime as well as advanced bots and cyber-attack.

Agile Services Provisioning for Learning-Based Applications in Fog Computing Networks

Fog computing has emerged as a promising solution for service provisioning. To support the increasing number of smart end devices and their requirement for intelligent data processing, numerous learning-based applications will be deployed at the edge network to provide services with an acceptable level of latency. However, such applications assume that the training and the actual data are independent and identically distributed, which is impractical in the dynamic and heterogeneous environment of the real world. This makes service matchmaking a challenge because even if the service functionality matches user requirements, the mismatched actual data will inevitably result in the degradation of service quality. This paper targets the mismatching issue of learning-based applications and the high requirement of intelligent data processing at the network edge and proposes a novel service provisioning model. This model introduces a novel service description model to resolve data mismatch, a clustering algorithm that pre-processes requests to deal with high concurrency requirements, and a heuristic joint service searching method to reduce traffic costs. This work was evaluated through a case study and simulations. Evaluation results show that the proposed model can achieve a good quality of services and reduce response time as well as traffic costs.

A Semantics-preserving Approach for Extracting RDF Knowledge from Object-oriented Databases

The Resource Description Framework and RDF Schema recommended by the World Wide Web Consortium provide a flexible model for semantically representing information about resources on the Web, which are playing an increasingly important role in intelligent processing of large-scale data. With the widespread acceptance and applications of RDF(S), construction of RDF(S) is of increasing importance. Automatic construction of RDF(S) with diverse data has attracted more attention. In this paper, we propose a novel approach for constructing an RDF(S) with object-oriented databases that are suitable for non-traditional applications. We propose the formal rules of mapping an object-oriented database model into a RDF(S) model based on the formal definitions of these two models. We develop a tool named OODB2RDF to verify our approach.

System-Information and Cognitive Technologies of Man-Made Infrastructure Cyber Security

The complication of technological processes due to the modernization of complex technological processes aggregates with a distributed spatial infrastructure requires the use of new control systems and computer and information network technologies. Accordingly, this poses the problem of revising the basic concepts of information-measuring systems building, developing software and hardware for the implementation of the target management process. This requires development of new approaches to presentation, processing, and display of data about the aggregates state of energy-active objects and the entire information structure. For management under threat conditions, it is necessary to take into account the features of the information infrastructure, data selection and processing methods, methods and algorithms for classifying the situation, which are formed from blocks of data obtained from each unit and the technological process as a whole. Information about the current state of system and infrastructure is necessary for the formation, adoption and implementation of management decisions which is the basis for coordination strategies synthesis. Appropriate target orientation, reasonable indicators of real process trajectories divergence from the target state determine the probability of object attack. Knowledge and decision-making procedures for the coordination of managerial actions is based on the strategic target orientation of the structure, their professionalism and the level of intellectual, cognitive and scientific training which is the basis for correct situation interpretation of countermeasures against threats formation. In the event of active threats complex on man-made systems in a certain region (resource, cognitive, system, information) and natural disasters or military operations, the threats lead to active destruction or failure of the production process. In order to functionally withstand related production structures, when loyal to the industrial relations concept, they need to integrate at the strategic management level on common goal basis to reduce risks. If necessary, to increase sustainability, they can be integrated at operational management level using interconnections at the production and resource levels. To do this, it is necessary to develop a strategic and goal-oriented management system behaviour line, which requires informational and intelligent data processing at the highest level using cognitive creative methods. For each level of the infrastructure hierarchy, oriented towards strategic goals in the global infrastructure dynamic environment, methods of assessing the situation to detect failures and the actions of attacks have been developed, based on which countermeasures are formed depending on the type of threats.

Learning a crowd-powered perceptual distance metric for facial blendshapes

It is known that purely geometric distance metrics cannot reflect the human perception of facial expressions. A novel perceptually based distance metric designed for 3D facial blendshape models is proposed in this paper. To develop this metric, comparative evaluations of facial expressions were collected from a crowdsourcing experiment. Then, the weights of a distance metric, based on descriptive features of the models, were optimized to match the results with crowdsourced data, through a metric learning process. The method incorporates perceptual properties such as curvature and visual saliency. A formal analysis of the results proves the high correlation between the metric output and human perception. The effectiveness and success of the proposed metric were also compared to other distance alternatives. The proposed metric will enable intelligent processing of 3D facial blendshapes data in several ways. It will be possible to generate perceptually valid clustering and visualization of 3D facial blendshapes. It will help reduce storage and computational requirements by removing redundant expressions that are perceptually identical from the overall dataset. It can also be used to assist novice animators while creating plausible and expressive facial animations.

Research on Insulator Defect Detection System Based on Edge Intelligence

The paper aims at the problems of the high occupancy rate of communication resources and heavy server load caused by the image data taken during the inspection of the substation and sent to the remote server for processing. An edge intelligent inspection data processing architecture is constructed, and an insulator defect detection model based on transfer learning and YOLO v5 is proposed. The model can meet the training of a small sample network model in the case of scarcity of power equipment defect data. The proposed algorithm is compared with typical target detection algorithms, and the experimental results show that the proposed algorithm has an excellent performance in model recognition accuracy and recall rate. The trained insulator defect detection model is further deployed on the developed edge intelligent terminal for testing, and its image processing speed can meet the image processing requirements for intelligent inspection of substations. The system has good application value.

ARTIFICIAL INTELLIGENCE IN PUBLIC HEALTH DENTISTRY

Artificial intelligence (AI) is now playing a greater role in the healthcare sector. It can be helpful in many situations when new technologies can benefit people. Artificial intelligence (AI) is the term used to describe the capacity of machines to perform human-like tasks. Intelligent data processing software is now necessary due to the substantial increase in documented information and patient data. From data processing and information retrieval to the use of neural networks for diagnosis and the incorporation of augmented reality and virtual reality into dental education, artificial intelligence has a wide range of applications in medicine and dentistry. Artificial intelligence (AI) is being studied in dentistry for a variety of purposes, including the identification of normal and anomalous structures, disease diagnosis, and treatment outcome prediction. This review looks at some current and future applications of AI in Public Health Dentistry.

A Critical Review for Trustworthy and Explainable Structural Health Monitoring and Risk Prognosis of Bridges with Human-In-The-Loop

Trustworthy and explainable structural health monitoring (SHM) of bridges is crucial for ensuring the safe maintenance and operation of deficient structures. Unfortunately, existing SHM methods pose various challenges that interweave cognitive, technical, and decision-making processes. Recent development of emerging sensing devices and technologies enables intelligent acquisition and processing of massive spatiotemporal data. However, such processes always involve human-in-the-loop (HITL), which introduces redundancies and errors that lead to unreliable SHM and service safety diagnosis of bridges. Comprehending human-cyber (HC) reliability issues during SHM processes is necessary for ensuring the reliable SHM of bridges. This study aims at synthesizing studies related to HC reliability for supporting the trustworthy and explainable SHM of bridges. The authors use a bridge inspection case to lead a synthesis of studies that examined techniques relevant to the identified HC reliability issues. This synthesis revealed challenges that impede the industry from monitoring, predicting, and controlling HC reliability in bridges. In conclusion, a research road map was provided for addressing the identified challenges.

Analysis on quantifiable and controllable assembly technology for aeronautical thin-walled structures

Assembly affects the product's performance and reliability directly. The current assembly method based on geometric deviation quantity controlling, cannot guarantee the physical performance for complex aeronautical thin-walled structures effectively, such as assembly geometry accuracy and internal interactive stress. And assembly performance controlling is taken as the bottleneck problem that restricting the new aviation requirement of sub-millimeter assembly. In this paper, by proposing the accurate prediction and process-oriented adjustment&controlling strategies on assembly quality, construction on working mode with “quantifiable and controllable” characteristic was proposed, whose aim was to reduce the phenomenon of out-of-tolerance and deformation rebound error, and the ultimate goal is to reduce the uncertainty of assembly performance parameters. At the technical level, the academic development context and existing problems for assembling thin-walled structures were reviewed and analyzed, such as assembly process parameters optimization, assembly error transfer and accumulation, comprehensive adjustment on assembly quality, and virtual assembly simulation validation. Then the key future research trends for aeronautical structure assembly were also put forward, i.e. the force/deformation coordination among multi-type finite units for non-ideal assemblies, the dynamic construction of stiffness matrix for intermediate assemblies considering geometric nonlinearity, the adaptive balance on assembly performance driven by physical modeling and measured data, and the inverse optimization on assembly quality and parameters with intelligent data processing. This paper would lay a solid foundation for achieving the accurate assembly mode with the characteristics of “intelligent/scientific, and active/collaborative controlling on geometric shape and physical performance”, and higher assembly quality and efficiency could also be gained.

GA-DCTSP: An Intelligent Active Data Processing Scheme for UAV-Enabled Edge Computing

With the prosperous development of the Internet of Things (IoT), large numbers of IoT devices have been deployed in many scenarios to perceive the data of surroundings. To provide real-time service, devices prefer to transfer the data to servers for faster processing. However, in some special areas with limited communication infrastructure, devices cannot be directly connected to the Internet, so the requirements of data collection and processing are agnostic, which brings a huge challenge for real-time and dynamic data processing. We make the first attempt to tackle this challenge by proposing an Intelligent Active Data Processing Scheme (IADP) in unmanned aerial vehicle (UAV)-enabled edge computing. The main innovations of IADP are as follows: 1) when IoT devices generate or sense data, they can intelligently and proactively spread data or data information to other areas to inform UAV to collect; 2) our scheme combines active data processing with data deadline priority and artificial intelligence (AI)-based UAV trajectory optimization to achieve maximum data processing rate and minimum UAV flight cost. Specifically, genetic algorithm-deadline constraint traveling Salesman problem (GA-DCTSP) algorithm is proposed based on improved GA to achieve it; and 3) an intelligent data processing decision mechanism of optimally determining transmitting to UAV or edge server is proposed to further optimize network performance. Finally, the experimental results show that our proposed IADP increases the data processing rate by 48.77%–232.54% and reduces the UAV flight distance by 63.45%–88.36% compared with the existing schemes.

Design of wind power equipment monitoring system based on Huawei AI chip

With the gradual development of wind power, developing a wind power equipment monitoring system for wind farms is of great significance for ensuring the normal operation of wind farms and subsequent data analysis and processing. This paper develops a wind farm equipment monitoring system based on Huawei AI chips. The frame structure, R&D process, system construction process, and corresponding electronic circuits of the power vision edge computing system design based on Huawei AI chips are introduced in detail. The system can realize the intelligent processing of the data of the main electrical equipment of the wind farm and has functions such as the perception and acquisition of wind power resource data, the monitoring of the operating status of wind power equipment, and the advanced functions and intelligent processing of data.

Organization of online learning using the intelligent metasystem of open semantic technology for intelligent systems

Distance learning today allows you to create a system of mass continuous self-learning, universal exchange of information, regardless of the presence of time and space zones. For more effective online learning, it is necessary to introduce artificial intelligence technologies and methods for implementing learning systems. In this paper, the objects of research were: 1) the capabilities of the OSTIS open semantic technology, the capabilities of the IMS.OSTIS metasystem for organizing online learning; 2) machine learning method – classification. Results of the study: 1) an online course was organized using the IMS.OSTIS metasystem of the OSTIS open semantic technology; 2) for the analysis and visualization of training data, a machine learning method is implemented – classification. The results of the implementation of the online course were obtained using the semantic technology for designing an intelligent learning system: the IMS.OSTIS metasystem using the graphical semantic code SCg. The OSTIS kernel requires a machine with the Ubuntu operating system installed, which is a GNU/Linux distribution based on Debian GNU/Linux, an operating system based on the Linux kernel. The paper also shows an example of using the machine learning method – classification. This method allows you to classify data. Intelligent processing and visualization of data were carried out based on the results of testing students in order to classify them into letter categories A, B, C, D according to a set of features: scores and points of the average score. The high-level Python library Pandas was used, this is a library for data analysis. To visualize the results of data processing, the Matplotlib library in Python was used

Analysis and Synthesis of Technology for Textual Information Classification

The task of developing effective text information classification systems requires the thoughtful analysis and synthesis of variable components of technology. These components strongly affect the practical efficiency and the requirements to the data. For this purpose, a typical technology was discussed, comparing the regular “learning from features” approach versus the more advanced “deep learning” approach, that studies from data. In order to implement the technology, the first approach was tested, which included the means (methods, algorithms) for analysis of the features of the source text, by applying the dimensionality transformation, and building model solutions that allow the correct classification of data by a set of features. As a result, all the steps of the technology are described, which allowed to determine the way of presenting data in terms of hidden features in data, their presentation in a standard visual form and evaluate the solution, as well as its practical efficiency, based on this set of features. In a depth study, the informational core of the document was studied, using the regression and T-stochastic grouping of features for dimensionality reduction.The separate results contain estimation of practical efficiency of the algorithms in terms of time and relative performance for each step of the proposed technology. This estimation gives a possibility to obtain the best algorithm of intelligent data processing that is useful for a given dataset and application. In order to estimate the best suited algorithm for separation in reduced dimension an experiment was carried out which allowed the selection of the best range of data classification algorithms, in particular boosting methods. As a result of the analysis of the technology, the necessary steps of this technology were discussed and the classification on real text data was conducted, which allowed to identify the most important stages of the technology for text classification.

Programming for High-Performance Computing on Edge Accelerators

The field of edge computing has grown considerably over the past few years, with applications in artificial intelligence and big data processing, particularly due to its powerful accelerators offering a large amount of hardware parallelism. As the computing power of the latest edge systems increases, applications of edge computing are being expanded to areas that have traditionally required substantially high-performant computing resources such as scientific computing. In this paper, we review the latest literature and present the current status of research for implementing high-performance computing (HPC) on edge devices equipped with parallel accelerators, focusing on software environments including programming models and benchmark methods. We also examine the applicability of existing approaches and discuss possible improvements necessary towards realizing HPC on modern edge systems.

Low-Cost Data Acquisition System for Automotive Electronic Control Units.

The vehicle testing-validation phase is a crucial and demanding task in the automotive development process for vehicle manufacturers. It ensures the correct operation, safety, and efficiency of the vehicle. To meet this demand, some commercial solutions are available on the market, but they are usually expensive, have few connectivity options, and are PC-dependent. This paper presents an IoT-based intelligent low-cost system for vehicle data acquisition during on-road tests as an alternative solution. The system integrates low-cost acquisition hardware with an IoT server, collecting and transmitting data in near real-time, while artificial intelligence (AI) algorithms process the information and report errors and/or failures to the manufacturing engineers. The proposed solution was compared with other commercial systems in terms of features, performance, and cost. The results indicate that the proposed system delivers similar performance in terms of the data acquisition rate, but at a lower cost (up to 13 times cheaper) and with more advanced features, such as near real-time intelligent data processing and reduced time to find and correct errors or failures in the vehicle.

Real-Time Diagnosis of Plasma Molecular Temperature Based on OES and Elastic-Net Regression Analysis

The diagnosis of atmospheric pressure low-temperature plasma parameters is one of the research hot spots in plasma science. As important parameters, rotational temperature and vibrational temperature need to be measured in many application fields, such as plasma medicine, spraying, and combustion technology. Nowadays, as nonimmersion measurement technology, the plasma temperature diagnostic method based on optical emission spectroscopy (OES) shows greater advantages because it causes no pollution and interference to the plasma. However, the current measurement of molecular rotational and vibrational temperatures by OES is based on manual comparison and fitting, which has the disadvantages of cumbersome operation, heavy workload, and low efficiency. Therefore, an efficient method based on linear regression to process emission spectrum data has been proposed in relevant literature, but there is still room for improvement in accuracy and applicable range of temperature prediction. In this article, a theoretical spectral dataset with uniform and extensive temperature distribution is established, and some improvements and optimizations are made to the training variables and elastic-net regression model to obtain better fitting results. The fitting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$</tex-math> </inline-formula> of the experimental test dataset reaches 0.99427 and 0.96435 for these two temperatures, and the prediction time of a single spectrum data is less than 5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $</tex-math> </inline-formula> s, almost real time, which contributes new insights into the intelligent and efficient processing of plasma emission spectrum data.

Preface

The 2022 International Conference on Intelligent Equipment and Data Processing (IEDP 2022) was successfully held on October 21st-23rd, 2022 in Guangzhou, China (virtual conference). IEDP 2022 encouraged exchange of information on frontiers of research in different areas, connected the most advanced academic resources in China and the world, and turned research results into industrial solutions.We had the honor of having invited Prof. Guixiong Liu from South China University of Technology, China to serve as our Conference General Chair. 90 individuals participated in the conference. Divided into three parts, the conference agenda covered keynote speeches, oral presentations, and online Q&A discussion. Firstly, keynote speakers were each allocated 30-45 minutes to hold their speeches. Then in the oral presentations, the excellent papers we had selected were presented by their authors one by one.During the conference, we were honored to invite four sophisticated keynote speakers to address their speeches. Among them, Prof. Wenbing Zhao from Cleveland State University, America delivered a speech on Video-Based Baseball Pitch Type Recognition. During the speech, he reported a preliminary study on video-based pitch type recognition using deep learning. A semi-automated way of building datasets based on publicly available video and pitch information for MLB games was proposed to facilitate the study and the two-stream inflated 3D convolutional neural network (I3D) was used for pitch type recognition. Next, Prof. Ming Jiang from Sun Yat-sen University, China addressed a presentation on Massive MIMO Beam Vector Assignment Based on Deep Learning and Location/Handover Information. In this paper, he proposed to apply the Gaussian anomaly detection (GAD) to improve the system performance, particularly in the handover zone, by exploiting the information of sea lane and locations of the user equipment (UE) and used convolutional deep belief networks (CDBN) and GAD in the further analysis. Their insightful speeches had triggered heated discussion in the third part of the conference. Besides, every participant praised this conference for disseminating useful and insightful knowledge.The proceedings of IEDP 2022 are a compilation of the accepted papers and represent an interesting outcome of the conference. These papers feature but are not limited to the following topics: Intelligent Inspection Technology, Hybrid Intelligent Systems, Integrated Data Terminals, Data Science and Big Data Technology, etc. All the papers have been checked through a rigorous review and process to meet the requirements of publication.We would like to acknowledge all of those who supported IEDP 2022 and made it a great success. Particularly, we would like to thank the Journal of Physics: Conference Series, for the efforts of all its colleagues in publishing this paper volume. All the help and contribution of each individual and institution was instrumental in the success of IEDP 2022.The Committee of IEDP 2022List of Committee member is available in this pdf.

Predictive maintenance in pharmaceutical manufacturing lines using deep transformers

Inherent complexities in pharmaceutical manufacturing lines of modern industrial facilities make precise and timely detection of malfunction occurrences necessary. In fact, unpredicted malfunctions in a production line can often provoke a cascade of adverse effects that can occur everywhere in the production chain bringing the manufacturing line to a halt for undefined time periods. Such events can have unfortunate consequences that are not always confined to the damaged part itself but propagate throughout the production line. Nevertheless, modern production lines are equipped with a multitude of data sensors that enable the real-time and fine-grained monitoring of each constituent part of the production process providing a richness of information that can be exploited by intelligent data processing methods.In this work, we present ManuTrans, a deep learning-based model for monitoring real-time raw sensor data, deriving the condition of a pharmaceutical manufacturing line and predicting the next moment in time when a malfunction can occur. The model is further able to predict the severity of the next malfunction and can contribute adjunct information in corporate decision-making. The suggested approach exploits the capacity of deep transformer models for extracting both long- and short-term correlations as well as patterns in sequential data and, combined with a linear output layer, conducts both classification and regression. The proposed approach was tested on a real dataset comprising raw data from two manufacturing lines, and it achieved promising results.

AI DBMS in modern-day applications

The future of computing is anticipated to be shaped by the fusion of DBMS (Database Management Systems) and AI (Artificial Intelligence). This union is of utmost importance to advance DBMS technology and enable next-generation computing Although DBMS and AI systems are well-established technology, research, and development into the combination of AI and databases is still in its early phases. The availability of vast volumes of shared data for knowledge processing, effective data, knowledge management, and intelligent data processing is the driving force behind this convergence. The Intelligent Database Interface (IDI) architecture was developed to protect the significant investments made in current databases. Numerous well-liked techniques and developments for fusing AI with databases have been examined in-depth through extensive study and published articles.

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People with physical disabilities need some assistive devices to perform their daily tasks. In these devices, higher latency factors need to be addressed appropriately. Therefore, the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation. The proposed architecture is capable to communicate between different modules of the system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion. Think-Net Convolutional Neural Network (TN-CNN) architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification. Data collection and processing are the responsibility of the central integrated server for system load minimization. Testing of implemented architecture and deep learning model shows excellent results. The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism. The training and testing results are 99% and 93% for custom model implementation based on TN-CNN. The proposed real-time architecture is capable of intelligent data processing unit with fewer errors, and it will benefit assistive devices working on the local server and cloud server.