Application Of Intelligent System Research Articles

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

IntroductionThe application of artificial intelligence (AI) systems in ophthalmology is rapidly expanding. Early detection and management of keratoconus is important for preventing disease progression and the need for corneal transplant. We review studies regarding the utility of AI in the diagnosis and management of keratoconus and other corneal ectasias.MethodsWe conducted a systematic search for relevant original, English-language research studies in the PubMed, Web of Science, Embase, and Cochrane databases from inception to October 31, 2023, using a combination of the following keywords: artificial intelligence, deep learning, machine learning, keratoconus, and corneal ectasia. Case reports, literature reviews, conference proceedings, and editorials were excluded. We extracted the following data from each eligible study: type of AI, input used for training, output, ground truth or reference, dataset size, availability of algorithm/model, availability of dataset, and major study findings.ResultsNinety-three original research studies were included in this review, with the date of publication ranging from 1994 to 2023. The majority of studies were regarding the use of AI in detecting keratoconus or subclinical keratoconus (n=61). Among studies regarding keratoconus diagnosis, the most common inputs were corneal topography, Scheimpflug-based corneal tomography, and anterior segment-optical coherence tomography. This review also summarized 16 original research studies regarding AI-based assessment of severity and clinical features, 7 studies regarding the prediction of disease progression, and 6 studies regarding the characterization of treatment response. There were only three studies regarding the use of AI in identifying susceptibility genes involved in the etiology and pathogenesis of keratoconus.DiscussionAlgorithms trained on Scheimpflug-based tomography seem promising tools for the early diagnosis of keratoconus that can be particularly applied in low-resource communities. Future studies could investigate the application of AI models trained on multimodal patient information for staging keratoconus severity and tracking disease progression.

Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation

In satellite health management, anomalies are mostly resolved after an event and are rarely predicted in advance. Thus, trend prediction is critical for avoiding satellite faults, which may affect the accuracy and quality of satellite data and even greatly impact safety. However, it is difficult to predict satellite operation using a simple model because satellite systems are complex and telemetry data are copious, coupled, and intermittent. Therefore, this study proposes a model that combines an attention mechanism and bidirectional long short-term memory (attention-BiLSTM) with telemetry correlation to predict satellite behaviour. First, a high-dimensional K-nearest neighbour mutual information method is used to select the related telemetry variables from multiple variables of satellite telemetry data. Next, we propose a new BiLSTM model with an attention mechanism for telemetry prediction. The dataset used in this study was generated and transmitted from the FY3E meteorological satellite power system. The proposed method was compared with other methods using the same dataset used in the experiment to verify its superiority. The results confirmed that the proposed method outperformed the other methods owing to its prediction precision and superior accuracy, indicating its potential for application in intelligent satellite health management systems.

The application of artificial neural networks to forecast financial time series

Abstract The amount of information that is produced on a daily basis in the financial markets is vast and complex; consequently, the development of systems that simplify decision-making is an essential endeavor. In this article, several intelligent systems are proposed and tested to predict the closing price of the IBEX 35 index using more than ten years of historical data and five distinct architectures for neural networks. A multi-layer perceptron was the first step, followed by a simple recurrent neural network, a gated recurrent unit network and two long-short-term memory (LSTM) networks. The results of the analyses performed on these models have demonstrated a powerful capacity for prediction. Additionally, the findings of this research point to the fact that the application of intelligent systems can simplify the decision-making process in financial markets, which is a substantial advantage. Furthermore, by comparing the predicted outcome errors between the models, the LSTM presents the lowest error with a higher computational time in the training phase. The LSTM was able to accurately forecast the closing price of the day as well as the price for the following one and two days in advance. In conclusion, the empirical results demonstrated that these models could accurately predict financial data for trading purposes and that the application of intelligent systems, such as the LSTM network, represents a promising advancement in financial technology.

Reducing Road Mishaps: A CNN Model for Driver Fatigue Detection

Over the last few decades, there have been substantial developments in a variety of domains, including computer science, artificial intelligence, and machine learning, which has accelerated the evolution and application of intelligent systems in specific areas such as transportation. One way these systems are used in transportation is through fatigue detection, and to enable this technology, Convolutional Neural Networks serve as the foundation on which this system is built. CNNs are frequently employed in computer vision applications because they can automatically extract pertinent features from input data without the need for manual feature engineering. This CNN-based fatigue detection system was built in-order to address the repercussions of fatigued driving by monitoring the driver’s facial features in real-time so as to predict the fatigue levels and send out an alert to restore the driver’s alertness level so as to reduce road mishap. The system relies on visual input from a camera, which sends this input to a fatigue detection program installed on a Raspberry Pi microprocessor and sends out via alerts via a display and audio alerts via a piezoelectric buzzer. The model shows excellent performance, consistently achieving high accuracy, peaking at 97.34% on the 64th epoch, and consistently maintaining high validation accuracy, reaching 96.02% in the 67th epoch, indicating its ability to function effectively.

THE IMPACT OF ARTIFICIAL INTELLIGENCE APPLICATIONS ON THE TRANSPARENCY OF FINANCIAL REPORTS

This study aims to investigate the role of artificial intelligence applications in improving the transparency of financial reports. The main results of the study show that the application of artificial intelligence systems contributes to improving the reliability and transparency of financial reports, which enhances investor confidence and improves corporate governance. Therefore, companies should keep pace with developments and use technology, especially artificial intelligence technologies and systems, to improve and enhance the transparency of financial reports. Artificial intelligence can help detect financial fraud patterns in financial data, which leads to protecting investors from financial losses and potential risks. It also leads to improving investment and risk management decisions. Artificial intelligence can also help in creating interactive financial reports that users can customize according to their needs, which can lead to increased transparency and improved understanding of financial information

Research and Application of Large Model-Based Intelligent Customer Service System

With the rapid development of artificial intelligence technology, intelligent customer service systems have been widely used. This paper addresses the limitations of traditional intelligent customer service systems, such as limited language understanding ability, narrow knowledge coverage, and insufficient personalized service. It proposes an intelligent customer service system design scheme based on the RAG model. The scheme leverages the powerful language understanding and generation capabilities of large models, combined with dialogue management and knowledge base retrieval enhancement techniques, to build an efficient and intelligent customer service system. This paper introduces the overall architecture of the system, the design and implementation of each module, and comprehensively evaluates the system through experiments. The experimental results show that the system can provide accurate and fluent customer service, significantly improving customer satisfaction. The research in this paper provides new ideas and references for the development of intelligent customer service systems.

Application and comparative analysis of Intelligent Monitoring Technology for Grouted Pile Construction based on abaqus

In order to realize the intelligent monitoring of the high-precision positioning of the hole position and the real-time control of the verticality of the pile, an intelligent monitoring system was developed based on the combination positioning technology of BDS and UWB and the biaxial tilt sensor, and the numerical simulation and comparative analysis of the verticality of the pile were carried out by abaqus. The deviation of pile foundation in different directions and the deviation of pile body are controlled by the monitoring system, and abnormal warning is made when the deviation exceeds the permissible range.Through the application of intelligent monitoring system in the pile foundation engineering area of Changshui Airport, it is found that the plane offset and perpendicularity of all piles meet the standard requirements and the construction error is controlled at a small value. The results show that the application of intelligent inspection system can not only ensure the construction quality of pile foundation, but also meet and improve the level of digitization and information technology of smart construction site.

Development and Application of Big Data-Driven Intelligent Evaluation System for Physical and Aesthetic Education

Big data driven intelligence evaluation system for physical and aesthetic education are used to leverage vast amounts of data to provide more objective, nuanced, and personalized assessments of student performance and progress. This paper proposes a hybrid approach for development of big data driven intelligence evaluation system for physical and aesthetic education. The proposed hybrid approach is the combined performance of both the Dual attention graph convolution network (DAGCN) and Weibull time to event Recurrent Neural Network (WRNN).Commonly it is named as WRNN-DAGCN technique. The major objective of the proposed approach is to provide for development of big data driven intelligence evaluation system for physical and aesthetic education. WRNN is design to enhancing the efficiency of physical training. Enhance the physical training from the WRNN by using the DAGCN. By then, the MATLAB/Simulink working platform has the suggested model implemented, and the present processes are used to calculate the execution. The proposed method shows better results in all existing methods. The accuracy level of proposed BDEPA-WRNN-DAGCN approach is 98% that is higher than the other existing methods. From the result, it is conclude that the proposed approach based error rate is less compared to existing techniques.

Deep Neural Network Based Disease Prediction Analysis

With the rapid development of social and economic construction as well as medical informatization, the medical industry has gradually entered the era of big data and artificial intelligence, using the medical big data accumulated in the hospital information system for pooling, modeling, analysis, and the use of neural networks can predict and determine the type of patient's disease and the source of the cause, which can not only reduce the complexity of the disease diagnostic test but also achieve the effect of predictive diagnosis, and provide theoretical basis and practical experience for the establishment, improvement and practical application of the intelligent medical system. It provides theoretical basis and practical experience for the establishment, improvement and practical application of intelligent medical system.

Application of Bayesian Networks and Reinforcement Learning in Intelligent Control Systems in Uncertain Environments

<p>Reinforcement learning is a machine learning paradigm that focuses on how an agent can perform actions in an environment to achieve a certain goal. The agent learns through interaction with the environment, observing the state and making decisions to maximize its reward. Reinforcement learning has wide applications in intelligent control systems. However, one limitation of reinforcement learning is the uncertainty in handling the environment model. Usually, reinforcement learning is performed without a clear model, which requires estimating environmental uncertainty and state transitions. Bayesian Networks are effective in modeling uncertainty, which can aid in establishing a probabilistic model of environmental dynamics. This allows for the integration of uncertainty information into the environmental model, leading to a more accurate understanding of the dynamic characteristics of the environment. In this study, we propose a reinforcement learning algorithm based on Bayesian Networks. We utilize optimal generalized residual differentiation, parallel integration causal directional reasoning, and other modeling techniques to address reinforcement learning tasks. The main idea is to utilize the prior distribution to estimate the uncertainty of unknown parameters. Then, the obtained observation information is used to calculate the posterior distribution in order to acquire knowledge. Experiments demonstrate that this approach is feasible in intelligent control systems operating in uncertain environments.</p> <p> </p>

Application of Conversational Intelligent Reporting System Based on Artificial Intelligence and Large Language Models

As large language models gain traction in the financial sector, they are revolutionizing the workflows of financial professionals. From data analysis and market forecasting to risk assessment and customer management, these models demonstrate significant potential and value. By automating data processing tasks, they enhance productivity and empower professionals to derive deeper insights and make more precise decisions. This article explores the application of conversational intelligent reporting systems, leveraging artificial intelligence and large language models, within the financial industry. It examines how these systems streamline reporting processes, facilitate efficient communication, and contribute to informed decision-making, ultimately reshaping the landscape of financial market operations.

Use of artificial intelligence and human chipping in forensic medicine: a review

This article outlines some aspects of the use of artificial intelligence and human chipping in forensic medicine.
 An overview of the use of artificial intelligence and human chipping in forensic medical examination is provided, as well as a definition of the ethical and legal issues of integrating artificial intelligence and human chipping into forensic medical examination.
 This article discusses the work of scientists of Kazakhstan and foreign countries aimed at studying the use of artificial intelligence and human chipping in forensic medical examination. Notably, the greatest application of artificial intelligence systems at this stage of development of science and technology has been found in forensic psychiatry. We noted the lack of research on the use of human microchipping for forensic purposes. In general, studies addressing the synergy between artificial intelligence and human chipping for use in forensic medicine and the legal and ethical aspects of their use in this area are limited.
 Considering the generalization of scientific works and analysis of domestic and foreign experience on the issue, we present the ethical and legal aspects of the integration of artificial intelligence and human chipping in forensic medicine. Owing to the lack of sufficient sources in modern scientific literature devoted to the integrated use of artificial intelligence and human chipping in forensic examination as a whole, a SWOT analysis was conducted, which showed that these technologies have some advantages and opportunities regarding efficiency and accuracy.
 Based on a study of the literature and practice of using artificial intelligence and human chipping in forensic medicine, legal aspects related to the synergy of artificial intelligence and human chipping have been identified, which can be divided into several key areas: confidentiality and data security, informed consent, ownership, and control.
 Furthermore, based on the literature and the practice of using artificial intelligence and human chipping in forensic medicine, legal aspects related to the synergy of artificial intelligence and human chipping were identified, which can be divided into several key areas: respect for autonomy, confidentiality and ethics of data processing, lack of harm, and informed consent.

Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment.

Care for patients with heart failure (HF) causes a substantial load on health care systems where a prominent challenge is the elevated rate of readmissions within 30 days following initial discharge. Clinical professionals face high levels of uncertainty and subjectivity in the decision-making process on the optimal timing of discharge. Unwanted hospital stays generate costs and cause stress to patients and potentially have an impact on care outcomes. Recent studies have aimed to mitigate the uncertainty by developing and testing risk assessment tools and predictive models to identify patients at risk of readmission, often using novel methods such as machine learning (ML). This study aims to investigate how a developed clinical decision support (CDS) tool alters the decision-making processes of health care professionals in the specific context of discharging patients with HF, and if so, in which ways. Additionally, the aim is to capture the experiences of health care practitioners as they engage with the system's outputs to analyze usability aspects and obtain insights related to future implementation. A quasi-experimental design with randomized crossover assessment will be conducted with health care professionals on HF patients' scenarios in a region located in the South of Sweden. In total, 12 physicians and nurses will be randomized into control and test groups. The groups shall be provided with 20 scenarios of purposefully sampled patients. The clinicians will be asked to take decisions on the next action regarding a patient. The test group will be provided with the 10 scenarios containing patient data from electronic health records and an outcome from an ML-based CDS model on the risk level for readmission of the same patients. The control group will have 10 other scenarios without the CDS model output and containing only the patients' data from electronic medical records. The groups will switch roles for the next 10 scenarios. This study will collect data through interviews and observations. The key outcome measures are decision consistency, decision quality, work efficiency, perceived benefits of using the CDS model, reliability, validity, and confidence in the CDS model outcome, integrability in the routine workflow, ease of use, and intention to use. This study will be carried out in collaboration with Cambio Healthcare Systems. The project is part of the Center for Applied Intelligent Systems Research Health research profile, funded by the Knowledge Foundation (2021-2028). Ethical approval for this study was granted by the Swedish ethical review authority (2022-07287-02). The recruitment process of the clinicians and the patient scenario selection will start in September 2023 and last till March 2024. This study protocol will contribute to the development of future formative evaluation studies to test ML models with clinical professionals. PRR1-10.2196/52744.

Speech recognition based on mobile sensor networks application in English education intelligent assisted learning system

With the popularity of English learning and the development of mobile technology, the development of intelligent assisted learning system has become an effective learning method. The purpose of this study is to improve the speech recognition performance of English education intelligent learning assistance system by using mobile sensor networks, and improve the learning effect of learners. This paper uses a speech recognition algorithm based on mobile sensor network to acquire learners' speech signals with multiple sensor nodes. By preprocessing and feature extraction of acquired signals, machine learning algorithm is applied to recognize them, thus improving the recognition accuracy. The experimental results show that compared with traditional speech recognition methods, the speech recognition system based on mobile sensor network has better performance in the case of background noise and speaker change. When learners use the system to learn English, they can get more accurate and reliable speech recognition results, improve the learning effect and interactive experience, and provide a more effective learning tool for English learners.

Formation and analysis of networks of events in the field of parliamentary control based on the application of artificial intelligence systems

The article introduces a methodology for forming and analyzing the network of events in news reports related to parliamentary control. This methodology relies on the application of generative artificial intelligence, and the article provides examples of its practical implementation. The revolution in artificial intelligence enables the solution of tasks not only related to identification but also to the formation of causal networks of events, where the causes and consequences are clearly presented. The utilization of large linguistic models has yielded convenient methods for extracting events from texts, filtering them, and clustering. Artificial intelligence is employed for identifying cause-and-effect relationships, significantly simplifying the processing of natural language. Visualization and cluster analysis of the formed networks can be performed using traditional tools for network analysis.

Design and Application of Intelligent Scheduling System Based on Business and Labor Forecasting

Design and Application of Intelligent Scheduling System Based on Business and Labor Forecasting

Multi-scenario PM2.5 distribution and dynamic exposure assessment of university community residents: Development and application of intelligent health risk management system integrated low-cost sensors

Exposure scenario and receptor behavior significantly affect PM2.5 exposure quantity of persons and resident groups, which in turn influenced indoor or outdoor air quality & health management. An Internet of Things (IoT) system, EnvironMax+, was developed to accurately and conveniently assess residential dynamic PM2.5 exposure state. A university community “QC”, as the application area, was divided into four exposure scenarios and five groups of residents. Low-cost mobile sensors and indoor/outdoor pollution migration (IOP) models jointly estimated multi-scenario real-time PM2.5 concentrations. Questionnaire was used to investigate residents' indoor activity characteristics. Mobile application (app) “Air health management (AHM)” could automatic collect residents' activity trajectory. At last, multi-scenario daily exposure concentrations of each residents-group were obtained. The results showed that residential exposure scenario was the most important one, where residents spend about 60 % of their daily time. Closing window was the most significant behavior affecting indoor contamination. The annual average PM2.5 concentration in the studied scenarios: residential scenario (RS) < public scenario (PS) < outdoor scenario (OS) < catering scenario (CS). Except for CS, the outdoor PM2.5 in other scenarios was higher than indoor by 5–10 μg/m3. The multi-scenario population weighted annual average exposure concentration was 37.1 μg/m3, which was 78 % of the annual average outdoor concentration. The exposure concentration of 5 groups: cooks > outdoor workers > indoor workers > students > the elderly, related to their daily activity time proportion in different exposure scenario.

Sustainable Development of Industrial Engineering with the Application of Intelligent Systems

This Special Issue focuses on Intelligent Systems (ISs) in the context of sustainable development for industrial engineering [...]

Application of integrating reinforcement learning and intelligent scheduling in logistics distribution

Logistics distribution is an indispensable part of the modern economy and is crucial for ensuring the efficient operation of the supply chain. With the continuous progress of technology, the application of intelligent scheduling systems in the field of logistics distribution is becoming increasingly widespread. Reinforcement learning, as one of the hot technologies in the field of artificial intelligence, is gradually receiving attention in its application in intelligent scheduling. Reinforcement learning can continuously learn and predict the same thing to enhance memory, while intelligent scheduling requires continuous prediction and optimization of logistics distribution paths. In response to the current problems of slow logistics distribution efficiency and low customer satisfaction, this article analyzed the application of intelligent scheduling in logistics distribution from the aspects of basic data maintenance, basic data review, intelligent scheduling, scheduling result review, distribution information management, and vehicle tracking. By using reinforcement learning, the traffic network weight in logistics distribution was studied to improve logistics distribution efficiency and customer satisfaction. This article analyzed the efficiency of logistics distribution, vehicle tracking accuracy, vehicle scheduling ability, and logistics distribution costs under different logistics distributions. The results showed that the logistics distribution under the integration of reinforcement learning and intelligent scheduling reduced 12.047 km compared to traditional distribution paths, and its distribution cost decreased by 129.718 yuan compared to traditional logistics distribution costs. The efficiency of logistics distribution that integrates reinforcement learning and intelligent scheduling has significantly improved, with optimized distribution costs and paths. It also has a positive effect on improving the utilization rate of logistics distribution vehicles.

A zero-shot fault attribute transfer learning method for compound fault diagnosis of power circuit breakers

Diagnosis of compound mechanical faults for power circuit breakers (CBs) is a challenging task. In traditional fault diagnosis methods, however, all fault types need to be collected in advance for the training of diagnosis model. Such processes have poor generalization capabilities for industrial scenarios with no or few data when faced with new faults. In this study, we propose a novel zero-shot learning method named DSR-AL to address this problem. An unsupervised neural network, namely, depthwise separable residual convolutional neural network, is designed to directly learn features from 3D time-frequency images of CB vibration signals. Then we build fault attribute learners (ALs), for transferring fault knowledge to the target faults. Finally, the ALs are used to predict the attribute vector of the target faults, thus realizing the recognition of previously unseen faults. The orthogonal experiments are designed and conducted on real industrial switchgear to validate the effectiveness of the proposed diagnosis framework. Results show that it is feasible to diagnose target faults without using their samples for training, which greatly saves the costs of collecting fault samples. This will help to accurately identify the various faults that may occur during CB’s life cycle, and facilitate the application of intelligent fault diagnosis system.