Application Of Intelligent System Research Articles

PH-responsive collagen nanocomposite films reinforced by curcumin-loaded Laponite nanoplatelets for dynamic visualization of shrimp freshness

Sustainable and intelligent packaging systems, utilizing biomass-derived matrix and bioactive indicators represent promising approaches for real-time detection and monitoring of freshness of food, as well as prolonging the shelf-life and improving the quality and safety of food products. Herein, we reported pH-responsive collagen nanocomposite films reinforced by curcumin-loaded Laponite nanoplatelets which could act as smart indicators for monitoring the freshness of shrimps. Caffeic acid, as a bio-based modifier, was covalently grafted onto the surfaces of the nanoplatelets loaded with curcumin. The collagen nanocomposite films were fabricated by the incorporation of functionalized nanoplatelets through all-aqueous casting-evaporation approach. The triple-helical structure of collagen molecules was stabilized by the functionalized nanoplatelets mainly through hydrogen bonding and electrostatic interactions with uniform distribution into the film matrix. Moreover, the resultant nanocomposite films not only demonstrated favorable hydrophobicity and water vapor barrier properties, but also exhibited effective antioxidant activity and anticipated color responsiveness to pH changes and NH3 presence. The consistent changes in color of the nanocomposite film with the shrimps following days of storage supported its feasibility and potential for monitoring the freshness of shrimps. Therefore, we envision that the developed collagen-based nanocomposite films showcase great potential in the application of sustainable and intelligent packaging system for dynamic visualization of seafood freshness.

Design and Application of Intelligent Subject System Based on TPACK Framework

With the continuous advancement of educational informatization deepening of teachers' professional development, educational technology ability has become a necessary quality and skill for college teachers. This article introduces the basic concept of the TPACK framework and the design concept of intelligent disciplinary systems and elaborates in detail on how to apply the TPACK framework to the design and development of intelligent disciplinary systems. Through case analysis, this article demonstrates the advantages of intelligent subject systems in improving teaching quality and promoting active learning among students. Finally, this article discusses the future development direction and application prospects of intelligent disciplinary systems. The intelligent subject system based on the TPACK framework provides a new teaching solution for the education field, which helps to promote the process of educational informatization and personalized teaching. Cultivate students' rigorous scientific literacy and good practice habits, and master standardized experimental analysis methods.

Research and Application of Intelligent Gel Chemical Sand Protection System in Sand Producing Gas Wells

Research and Application of Intelligent Gel Chemical Sand Protection System in Sand Producing Gas Wells

Human Skin-Mimicking Ionogel-Based Electronic Skin for Intelligent Robotic Sorting.

Creating bionic intelligent robotic systems that emulate human-like skin perception presents a considerable scientific challenge. This study introduces a multifunctional bionic electronic skin (e-skin) made from polyacrylic acid ionogel (PAIG), designed to detect human motion signals and transmit them to robotic systems for recognition and classification. The PAIG is synthesized using a suspension of liquid metal and graphene oxide nanosheets as initiators and cross-linkers. The resulting PAIGs demonstrate excellent mechanical properties, resistance to freezing and drying, and self-healing capabilities. Functionally, the PAIG effectively captures human motion signals through electromechanical sensing. Furthermore, a bionic intelligent sorting robot system is developed by integrating the PAIG-based e-skin with a robotic manipulator. This system leverages its ability to detect frictional electrical signals, enabling precise identification and sorting of materials. The innovations presented in this study hold significant potential for applications in artificial intelligence, rehabilitation training, and intelligent classification systems.

Development and application of intelligent judicial trial assistance system based on generative artificial intelligence and machine learning technology

The intelligent judicial trial assistance system is an intelligent auxiliary judicial trial tool based on generative artificial intelligence and machine learning technology. It can achieve auxiliary functions such as case upload, trial result prediction, and legal document generation through automated, digital, and informational means. It utilizes technologies such as Recurrent Neural Networks (RNN) and Long Short-Term Memory networks (LSTM) to train generative models for generating trial recommendations and assisting in decision-making. Through in-depth research on the working principles and optimization paths of the intelligent judicial trial assistance system, the best applicable path for the system in judicial trial practice is explored. By establishing optimized models, risks and biases are minimized to the greatest extent.

Advances in Brain-Inspired Deep Neural Networks for Adversarial Defense

Deep convolutional neural networks (DCNNs) have achieved impressive performance in image recognition, object detection, etc. Nevertheless, they are susceptible to adversarial attacks and interferential noise. Adversarial attacks can mislead DCNN models by manipulating input data with small perturbations, causing security risks to intelligent system applications. Comparatively, these small perturbations have very limited perceptual impact on humans. Therefore, the research on brain-inspired adversarial robust models has gained increasing attention. Beginning from the adversarial attack concepts and schemes, we present a review of the conventional adversarial attack and defense methods and compare the advantages and differences between brain-inspired robust neural networks and the conventional adversarial defense methods. We further review the existing adversarial robust DCNN models, including methods inspired by the early visual systems and supervised by neural signals. Representative examples have validated the efficacy of brain-inspired methods for designing adversarial robust models, which may benefit the further research and development of brain-inspired robust deep convolutional neural networks and the intelligent system applications.

Harnessing the Power of Artificial Intelligent System in the advancements of Pharmaceuticals

The advancement of artificial intelligent systems is allowing the pharmaceutical industries to expand rapidly. In recent years, the pharmaceutical industries has witnessed the significant advancements in the development of artificial intelligent system. Pharmaceutical industries are adopting artificial intelligent machines across a variety of regions, particularly in drug distribution, clinical trials and personalized healthcare. Overall, the application of artificial intelligent system in these areas can revolutionize the pharmaceutical industries by streamlining processes, increasing efficiency and reducing costs. In order to provide improved care to the patients, pharmacists have to keep updated on the most recent advances within the field of artificial intelligent system, which are most likely to become even more common throughout the pharmaceutical industries during the next several decades. Intelligent systems are useful in estimating the safety and effectiveness of medications, discovering innovative medications, improving clinical studies and enhancing the formulation of drugs. By analyzing the vast amount of data, these machines can quickly identify the risks and benefits of medications, ensuring the patient safety. Artificial intelligent systems can enhance the efficiency and accuracy of clinical studies by providing real-time monitoring of patient responses to medications, enabling personalized treatments, and reducing the risk of adverse reactions. Lastly, their ability to identify variations and outcomes that may not meet standards can help to improve drug formulations and ensure consistent quality in personalized healthcare. Moreover, they can aid in the discovery of new drugs by efficiently screening and analyzing compounds, potentially accelerating the development of life-saving treatments. Further, it may decrease the overall price for development and research, eliminate significant errors, and enhance the ability to identify variations and outcomes that may not comply with standards. Artificial intelligent system has started playing an essential part in pharmaceutical industries and is going to keep increasing its importance over the upcoming years. Keywords: - Artificial intelligent system, Pharmaceutical industries.

Design and Application of Intelligent Control System for Molten Iron Transportation Based on 5G Technology

Molten transport is an important link in the iron and steel enterprise production, involves many complex factors, artificial management is difficult. Therefore, puts forward a kind of molten iron transport wisdom control system based on 5G technology, which mainly contains the intelligent identification tracking system, equipment status collection information acquisition system, locomotive vehicle terminal system, etc. Combined with the analysis of the actual application situation, the system could integrate all the processes and elements of molten iron production and transportation, realize the integration of operation and management, and also promote the improvement of the turnover efficiency of molten iron tank, reduce the demand for personnel, and reduce the labor cost.

THE ROLE OF ARTIFICIAL INTELLIGENCE IN MAKING FOREIGN POLICY DECISIONS IN THE UKRAINIAN-RUSSIAN WAR

The article considers the application of the artificial intelligence systems as a new tool in making foreign policy decision. The content of the concept of artificial intelligence is studied, the scope of the use of artificial intelligence systems in everyday life and in international relations is studied. It can be noted that AI, as a progressive and rapidly developing technology, has a large set of tools that help people make decisions and increase the efficiency of their work, for example, save time and spent resources to achieve a particular result. With the development of technology, more and more machines with artificial intelligence are used in various areas of life. For example, the field of medicine, mechanical engineering, data analysis, public administration and politics - all these fields are actively developing with using of artificial intelligence technologies. Special attention is paid to how artificial intelligence affects in making decision, event forecasting, and automation of data analysis. The use of artificial intelligence systems in international diplomacy was analyzed. Attention is focused on the weak and strong sides and what risks this technology can carry for foreign policy decisions. The given statistical data show how artificial intelligence is treated in Ukraine. Based on the research, it can be concluded that for the widespread use of artificial intelligence, it is necessary to develop convenient and transparent rules and algorithms, norms for the using of technology and its interaction with people. Summarizing the discussed topic, we will come to the conclusion that artificial intelligence will soon become a powerful tool in international relations, diplomacy, and in other areas of our life, which will bring benefit and a real threat. But in order for the benefit to be greater, it is necessary that the people who will use artificial intelligence be trained and knowledgeable in the technology.

Правоотношения по коммерческому учету электрической энергии при применении интеллектуальных систем учета

The legal model of the application of intelligent electric energy metering systems provided for by the current regulatory regulation imposes the obligation of commercial energy accounting on entities that are not parties to the energy supply contract, which determines the task of creating an effective legal mechanism for its execution. Evaluation of the effectiveness of the legal protection of the parties to the energy supply contract within the framework of the legal relationship on commercial accounting of electric energy with the use of intelligent electric energy accounting systems; identification of problems arising in the implementation of such protection and justification of proposals to overcome them. The necessity of regulatory fixing of the penalty ensuring the fulfillment of the obligation tric energy with the use of intelligent accounting systems by guaranteeing suppliers and grid organizations in favor of the parties to the energy supply contract is substantiated.

The Application of Intelligent Information Systems Driven by 6G Big Data in Product Sales Traceability

The Application of Intelligent Information Systems Driven by 6G Big Data in Product Sales Traceability

Application of intelligent decision system based on artificial neural network in free forging of the engine main shaft

Application of intelligent decision system based on artificial neural network in free forging of the engine main shaft

Using Artificial Intelligence in the Forensic Science for the Analysis of Microparticles: A Systematic Review

Technological advances in different industries have a tremendous impact on various aspects of human activities, including criminal activities. More complex and well-organized crimes often leave no room for the traditional analytical techniques, and require analysis of the tiniest pieces of the evidence – microobjects. Detection and study of these pieces of information obviously are time-consuming and demands more sophisticated equipment. This systematic review estimated the current status of the application of artificial intelligence systems in analysis of a specific type of the forensic evidence such as microparticles. Analysis of 27 articles extracted according to the PRISMA guidelines confirms the rationale behind using the AI for forensic investigation mainly to achieved automation of the most laborious aspects of the evidence investigation: image processing, matching a piece of the evidence to the created database and identification of the evidence. The AI technologies assist in identification of the victim or suspect personality thorough AI-assisted analysis of the DNA and RNA from the blood, saliva, urine; time of death and place of death via AI-assisted investigation of soil and fabric traces, and specific microbiome; tracking abusive substances; identification the cause of fires. The AI mainly serves as assistant to the convenient forensic methods, such as microscopy and spectroscopy, to process a big amount of data generated by the traditional techniques or to enhance the outcomes of the traditional techniques, such as image processing. The types of the AL the most widely used in the forensic science are machine learning algorithm.

Construction of neural network model for exercise load monitoring based on yoga training data and rehabilitation therapy

The Internet of Things is based on the traditional Internet and its purpose is to achieve information exchange between users and devices, as well as between devices. The rapid development of sensor technology, communication network technology, and computer technology has enriched the coverage of the Internet of Things, including a wide range of intelligent applications such as healthcare, smart cities, and smart homes. The development of high-performance computing and machine learning technologies has promoted the wide application of intelligent auxiliary systems in sports medicine. With the rapid development of yoga in the field of sports, athletes can play the various functions of yoga, improve their physical strength and quality, and improve their strength, flexibility, etc., cultivate positive, optimistic, and healthy emotions, and these are conducive to rehabilitation treatment after sports injuries. Therefore, it is feasible and feasible to introduce yoga training into the monitoring of the exercise load of athletes. In this paper, neural network technology was used to break the traditional training method based on experience. Based on yoga training data, through experimental exercise research, it could explore a new effective way to monitor exercise load and rehabilitation treatment, and build an exercise load monitoring model of the Ant Colony Optimization (ACO) neural network. By sorting out the data, statistics and analysis of the data, this article confirmed the effect of yoga training on reducing fatigue after exercise. The experimental results showed that the prediction value obtained by the ACO neural network model was 9.106, and the error was only −0.003 compared to the actual detection value of 9.109. This result showed that the ACO neural network model can perfectly fit the functional relationship between yoga training level and exercise load and has high prediction accuracy. This also marked that the development of high-performance computing systems has entered a new journey in the field of sports and health.

Application of intelligent nursing system based on big data in maintenance hemodialysis patients

Maintenance hemodialysis (MHD) is one of the most important renal replacement therapies for patients with end-stage renal disease. However, long-term and frequent treatment not only damages the physiological functions of patients but also leads to serious economic burdens and mental stress. This can easily cause a series of psychological disorders in patients, resulting in severe rejection and fear of MHD. To reduce patient resistance and improve the quality of life of MHD, this article built an intelligent nursing system based on big data and then used the constructed intelligent nursing system to research MHD. Through experiments, it has been found that compared to self-efficacy intervention, intelligent nursing systems can control the concurrent rate of MHD patients below 14 %, and self-efficacy intervention methods can control the concurrent rate of MHD patients above 13 %. Moreover, using intelligent nursing systems can improve the ability of MHD patients to self-care. At the same time, before the use of intelligent nursing systems, the nursing satisfaction of MHD patients also varied greatly, with the overall satisfaction rate after use being 70 % higher than before. Using intelligent nursing systems can improve the satisfaction of MHD patients with nursing outcomes.

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

The 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. We 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. Ninety-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. Algorithms 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.

An Intelligent Non-Invasive Blood Pressure Monitoring System Based on a Novel Polyvinylidene Fluoride Piezoelectric Thin Film.

Hypertension is a common cause of cardiovascular diseases, closely associated with the high mortality and disability rates of cardiovascular diseases such as stroke and coronary heart disease. Therefore, developing a comfortable and sustainable device for monitoring human pulse signals holds practical significance for the prevention and treatment of hypertension and cardiovascular diseases. PVDF flexible pressure sensors possess the characteristics of high sensitivity, good flexibility, and strong biocompatibility, thereby demonstrating extensive application potential in areas such as health monitoring, wearable devices, and electronic skins. This paper focuses on the development of a modified piezoelectric polymer and its application in an intelligent blood pressure monitoring system, demonstrating its outstanding performance and feasibility through a series of experiments. This research provides innovative material choices for the development of intelligent medical devices and offers beneficial guidance for the design and application of future intelligent health monitoring systems.

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.