Intelligent Analysis Research Articles

Exploring the chemical compositions of Fufang Yinhua Jiedu granules based on ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry combined with multistage intelligent data annotation strategy

Fufang Yinhua Jiedu granules (FYJG) is a Traditional Chinese Medicine (TCM) compound formulae preparation comprising ten herbal drugs, which has been widely used for the treatment of influenza with wind-heat type and upper respiratory tract infections. However, the phytochemical constituents of FYJG have rarely been reported, and its constituent composition still needs to be elucidated. The complexity of the natural ingredients of TCMs and the diversity of preparations are the major obstacles to fully characterizing their constituents. In this study, an innovative and intelligent analysis strategy was built to comprehensively characterize the constituents of FYJG and assign source attribution to all components. Firstly, a simple and highly efficient ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-QTOF MSE) method was established to analyze the FYJG and ten single herbs. High-accuracy MS/MS data were acquired under two collision energies using high-definition MSE in the negative and positive modes. Secondly, a multistage intelligent data annotation strategy was developed and used to rapidly screen out and identify the compounds of FYJG, which was integrated with various online software and data processing platforms. The in-house chemical library of 2949 compounds was created and operated in the UNIFI software to enable automatic peak annotation of the MSE data. Then, the acquired MS data were processed by MS-DIAL, and a feature-based molecular networking (FBMN) was constructed on the Global Natural Product Social Molecular Networking (GNPS) to infer potential compositions of FYJG by rapidly classifying and visualizing. It was simultaneously using the MZmine software to recognize the source attribution of ingredients. On this basis, the unique chemical categories and characteristics of herbaceous plant species are utilized further to verify the accuracy of the source attribution of multi-components. This comprehensive analysis successfully identified or tentatively characterized 279 compounds in FYJG, including flavonoids, phenolic acids, coumarins, saponins, alkaloids, lignans, and phenylethanoids. Notably, twelve indole alkaloids and four organic acids from Isatidis Folium were characterized in this formula for the first time. This study demonstrates a potential superiority to identify compounds in complex TCM formulas using high-definition MSE and computer software-assisted structural analysis tools, which can obtain high-quality MS/MS spectra, effectively distinguish isomers, and improve the coverage of trace components. This study elucidates the various components and sources of FYJG and provides a theoretical basis for its further clinical development and application.

Audio analysis speeding detection techniques based on metaheuristic-optimized machine learning models

Law enforcement plays an important role in road safety across the world. Speed enforcement merits special attention as the correlation between exceeding speed limits and the risk of fatalities and serious injuries is well-known. Methods based on radar and light detection and ranging techniques have established themselves as effective methods for measuring vehicle speeds in real time on roads. However, the price of instruments used for these measurements limits their widespread applicability. This work explored the potential of coupling artificial intelligence and audio analysis for vehicle speed assessment. The potential of deep neural networks and extreme gradient boosting is explored on a recently proposed real-world vehicle speed dataset. Frequency analysis techniques are also applied to help reduce the computational demands of experimentation. Additionally, a modified optimization algorithm is introduced to help select optimal control parameters and improve the performance of the suggested method. Five experiments were executed to demonstrate the potential for detecting the exact vehicle speed using regression techniques as well as utilizing classifications to detect vehicles breaking the speed limit. The proposed methods demonstrated promising potential when applied to this pressing challenge.

Initial Fire Detection Method for Intelligent CCTV Using Deep Learning Technology

Pre-prediction and early detection are important for the prevention of fires. Hence, the integration of intelligent CCTV image analysis technology, deep learning, and artificial intelligence is actively being studied. Intelligent CCTV can analyze visible light images of fires, but the accuracies of the detection targets are low. To solve this problem, a dual sensing experimental device was constructed by combining a visible light sensor and a thermal image sensor, and an algorithm was implemented to predict fire events. An algorithm was constructed to detect three classes (Fire, Smoke, and Person) through RGB sensors and thermal imaging sensors. Additionally, a CNN deep learning method and the YOLOv5 method were used to build a classification neural network. The dual sensing device was able to act as a trigger to predict and detect a fire based on the size of the flame and changes in temperature.

Strategic Intelligence: A Semantic Leadership Perspective

This paper explores the notion of strategic intelligence, namely with respect to leadership and business-oriented areas. Strategic intelligence is a researched concept that primarily concerns the collection and analysis of intelligence for policy and military planning, mainly at national and international levels, whereas this study of management and organizational decision-making application is an extension of that. This article’s subject is the necessity for strategic intelligence, a crucial competence for leaders, managers, and companies striving to remain competitive, to be utilized and best leveraged in today’s volatile and dynamic business contexts. This study follows a research design that includes a thorough literature review and bibliometric research, executed via VoSViewer, illustrating the concept of strategic intelligence from a leadership point of view as a driver of competitive advantage, enhanced by knowledge dynamics. Furthermore, this article points out that the limitations are acknowledged because the literature on the matter is limited, as strategic intelligence is relatively new to the business sector; moreover, more research is required to fully understand and use the potential of strategic intelligence in business growth. Overcoming these limitations and continuing to study the strategic intelligence concept could, on the one hand, use multiple platforms, such as scholarly articles or encyclopedias, and, on the other hand, support businesses in gaining a competitive edge and making informed decisions that can fuel their success in an ever-evolving market.

Research on Intelligent Identification and Analysis Algorithm of Tunnel Engineering Geological Information

When tunneling, detecting abrupt changes in geological circumstances can be difficult. In recent years, the proliferation of tunneling characteristics has been strongly related to the surrounding geology. These parameters offer significant potential for using data-driven artificial intelligence (AI) approaches to infer patterns from information without reference to known consequences. This research introduces the Simulated Fire Hawk Optimizer-based Deep Action Selection Network (SFHO-DASN) model, which uses a Simulated Fire Hawk Optimizer to anticipate the geological conditions needed for tunneling. The optimized hybrid neural network technique can anticipate geological conditions effectively, as demonstrated by a case study of the constructed model. It is especially significant for rock types, water intrusion, karst caverns, and surface subsidence, for which the predicted accuracy is greater than 95%. These findings imply that the geological circumstances behind the tunnel face could be reliably and correctly predicted by a DASN that has received the proper training. This procedure's most significant advantage is its ability to adjust every scored parameter's weighting in response to variations in geological circumstances. The accuracy performance of the proposed neural network outperforms the conventional neural network, as indicated by the area under the curve (AUC) and performance analysis. A proposed model for geology prediction can attain predictive accuracy with a small number of tunneling parameters, according to an analysis of the feature relevance of each tunneling parameter. The suggested approach ought to be more practical for proposals about tunnel support architecture in the East Asian geological region and for future tunnel building.

Analysis of Artificial Intelligence (AI) Utilization for Improving Motor Skills Learning Outcomes among Elementary School Teacher Education (PGSD) Students

This study explores the use of artificial intelligence (AI) to improve motor skills learning outcomes for elementary school teacher education (PGSD) students. The research investigates how AI can provide timely feedback and personalized instruction in teaching motor skills. Data analysis methods include surveys, observations, and evaluations of learning outcomes. The research findings reveal the following: 1) Female students' motor skills are classified as moderate, with an average score of 200. The classification criteria for female students' motor skills are excellent (5.67%), good (24.33%), moderate (30.33%), poor (32.67%), and very poor (3%). 2) Male students' motor skills are also categorized as moderate, with an average score of 200. The percentage breakdown for male students' motor skills is as follows: excellent (7.32%), good (25.61%), moderate (37.80%), poor (29.39%), and very poor (4.88%). The results indicate that integrating AI into motor skills learning significantly enhances the academic performance of PGSD students. These findings highlight the importance of incorporating AI technology in teacher education, particularly to enhance the motor skills development of PGSD students. The research supports the concept that AI can effectively support motor skills learning at this educational level.

Course genres classification of music e-learning platform based on deep learning big data intelligent processing algorithm

At present, the growth trend of digital music information is showing an exponential form. Various songs can be classified according to their unique genres and styles, forming various “genres”. So, how to effectively manage the massive amount of music data has become a problem that needs to be solved to promote the balanced development of different types and genres of music. On this basis, this article adopts deep learning technology to study the classification methods of music genres oriented towards big data. Firstly, this article studies LSTM based on long and short term memory networks and compares its advantages and disadvantages with convolutional neural network CNN. Secondly, this article calculates the drift patterns of big data intelligent processing algorithms and conducts relevant performance tests on big data intelligent processing algorithms to verify the effectiveness of the proposed algorithms. Finally, this article addresses the problems in current music genre classification research by introducing Mel filters into the extraction of sound features to further improve the efficiency and speed of the model. And deep learning technology is adopted to construct a music genre classification model based on big data intelligent analysis, further promoting the effective improvement of music genre classification effectiveness.

Exploration on the Application of ChatGPT in Corporate Financial Management

The report of the 20th National Congress of the Communist Party of China proposed to accelerate the development of the digital economy and promote the deep integration of the digital economy and the real economy. With the continuous development of generative artificial intelligence technologies such as ChatGPT, the financial industry has been profoundly affected and impacted. This article mainly combines the specific characteristics of each link of financial management, taking a listed company as an example, to explore the feasibility of the practical application of ChatGPT in specific financial management links such as financial forecasting, financial decision-making, financial control and financial analysis. After research, it is concluded that, ChatGPT can generate financial forecast reports and decision-making suggestions based on the provided data, identify and evaluate financial risks, and assist accounting personnel in conducting intelligent financial analysis. Its specific application in financial management will undoubtedly contribute to the digital transformation of enterprises.

Network Anomaly Traffic Analysis

This paper rigorously analyzes two principal methodologies in network traffic anomaly detection: feature detection and anomaly detection. Each methodology exhibits distinct strengths and confronts specific challenges. The study elucidates how the integration of deep learning with artificial immune systems could potentially transform feature detection. Moreover, it illustrates the enhancement of anomaly detection through the synthesis of machine learning techniques with traditional methods. Looking ahead, the paper delineates research trajectories that concentrate on merging deep learning, artificial intelligence, and behavioral analysis. This integration aims to augment the precision, efficiency, and adaptability of network anomaly traffic monitoring systems. Proposed future strategies include advanced methods in data preprocessing, model development, pattern recognition, and adaptive adjustments. These strategies are directed towards fortifying network defenses in response to the dynamically changing spectrum of cyber threats.

Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students

With the development of Internet of Things (IoT) technology, the use of running APP to analyse college students' physical fitness indicators has gradually become a commonly used sports analysis method. Aiming at the problems of insufficient precision of the running APP usage analysis method, easy to fall into the local optimum, and insufficiently comprehensive evaluation effect, this paper proposes a running APP usage analysis method based on deep learning network for some college students' physical fitness indicators. Firstly, feature vectors are taken from the running APP user behaviour data to analyse the values of college students' physical fitness indicators and construct a mapping model of running APP usage analysis for the effects of college students' physical fitness indicators; then, the BiGRU neural network is improved by using the driver-training heuristic optimisation algorithm to construct a running APP usage analysis model for some of the college students' physical fitness indicators; finally, a mapping model is constructed for the effect of running APP usage analysis for some of the college students' physical fitness indicators by using college student-oriented running APP Finally, the effectiveness and robustness of the DTBO algorithm are compared with the user behaviour dataset of the running APP for college students. Finally, the effectiveness and robustness of the DTBO algorithm are compared using the user behaviour data set of the running app platform for college students.

Research on Chinese Financial Industry Application Scenarios based on ChatGPT

The emergence of the artificial intelligence ChatGPT has brought a huge information impact to the traditional industry, promoting the deep integration of ChatGPT and financial institutions, stimulating its innovation vitality, and comprehensively empowering financial services. This technology is the key to promote the digital intelligence transformation of the financial industry. Based on the systematic study of the integration scenario of ChatGPT and innovative application in the financial industry and related limitations, this paper constructively puts forward suggestions and expectations for the future development of the financial industry under the background of artificial intelligence, to make some innovative exploration for the application of ChatGPT in the financial industry. According to the research, ChatGPT-like artificial intelligence's powerful natural language processing, data sorting, and analysis capabilities have great application value for financial institutions covering customer service, operation, marketing, risk control, investment and budget, etc., but there are also problems such as high cost, low credibility and security, and technical limitations. Finally, this paper puts forward effective empowerment suggestions based on three aspects: government policy, institutions and technology development.

Reactive UAV-based automatic tunnel surface defect inspection with a field test

This work addresses the problem of automatic tunnel surface defect inspection using unmanned aerial vehicles (UAVs). The research aims at proposing a robust and efficient monitoring method for image data acquisition and processing in complex and dark tunnel environments. A method, called Proximity Move-Pause-Photo for Surface Defect Inspection (PMPP-SDI), is proposed by combining reactive flying control strategies with a grid scanning pattern to capture high-quality image data from multiple views and angles. The image data is then used to generate a 3D point cloud model of the tunnel surface for structural condition assessment. The method is tested in a field experiment in a railway tunnel in Ireland, and the results show that it can achieve stable navigation, high-resolution reconstruction, and accurate defect detection. The paper discusses the advantages and limitations of the method, and suggests improving the control/navigation intelligence, data quality, and defect analysis as the future research directions.

Development of surface molecular-imprinted electrochemical sensor for palmitic acid with machine learning assistance

Palmitic acid (PA) is a kind of saturated high fatty acid, which is involved in physiological safety and food quality. A surface molecularly imprinted polymer (MIP) electrochemical sensor was prepared on MXene surface using dopamine (DA) as functional monomer. The electrode was modified with gold nanoparticles (AuNPs), ferrocene-graphene oxide-multiwalled carbon nanotubes (Fc-GO-MWCNT) composite to enhance the electroactive area and conductivity. The sensor was characterized by scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDS), electrochemical impedance spectroscopy (EIS) and Differential pulse voltammetry (DPV), respectively. The parameters concerning this assay and various regeneration conditions have been carefully studied. The sensor can detect PA in the range of 1 nM-1 mM (R2 = 0.995), the limit of detection (LOD) is 0.48 nM (S/N = 3), and the limit of quantification (LOQ) is 1.61 nM. The artificial neural network (ANN) model in machine learning is further used to analyze the data collected by the sensor. The results show that the back propagation (BP) neural network in ANN is more suitable for the intelligent analysis of PA. The practicality of the sensor was confirmed by detecting PA in pork samples. This is the first MIP-based electrochemical sensor for PA, and it has great potential in practical applications.

Study of the TMMS-24 test for the analysis of Emotional Intelligence considering the influence of sex in university students

Study of the TMMS-24 test for the analysis of Emotional Intelligence considering the influence of sex in university students

ChatGPT and Artificial Intelligence a Bibliometric Analysis in Latin America

The aim of this study was to perform a bibliometric analysis of ChatGPT and artificial intelligence in Latin America. Scientific productivity was quantified by analyzing bibliometric data. The 84 Scopus papers used for the study were chosen based on their relevance and keywords in English (ChatGPT, artificial intelligence). In 2023, the highest substantial growth in scientific production was achieved (n=68; 8%), with Brazil standing out as the most important country (n=41 publications, 47.7%). Important sources: Archives of Medical Research and Gaceta Medica de Mexico had three publications, while the most cited author, Kitamura, F.C., had 91 citations and two papers. Most of these papers were scientific articles (44%), of which 24% belonged to the field of social sciences and 21% to computer science. It is concluded that there is an increase in the topics covered, authors, sources and resources in general related to ChatGPT and artificial intelligence in Latin America, providing a more complete view of the region's scientific production in terms of its visibility, impact and importance. Therefore, this bibliometric study serves as a basis for future studies, providing evidence-based support that critically examines the research.

RotatedStomataNet: a deep rotated object detection network for directional stomata phenotype analysis.

Innovatively, we consider stomatal detection as rotated object detection and provide an end-to-end, batch, rotated, real-time stomatal density and aperture size intelligent detection and identification system, RotatedeStomataNet. Stomata acts as a pathway for air and water vapor in the course of respiration, transpiration, and other gas metabolism, so the stomata phenotype is important for plant growth and development. Intelligent detection of high-throughput stoma is a key issue. Nevertheless, currently available methods usually suffer from detection errors or cumbersome operations when facing densely and unevenly arranged stomata. The proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time, and intelligent phenotype analysis of stomata and apertures. The system is constructed based on the Arabidopsis and maize stomatal data sets acquired destructively, and the maize stomatal data set acquired in a non-destructive way, enabling the one-stop automatic collection of phenotypic, such as the location, density, length, and width of stomata and apertures without step-by-step operations. The accuracy of this system to acquire stomata and apertures has been well demonstrated in monocotyledon and dicotyledon, such as Arabidopsis, soybean, wheat, and maize. The experimental results that the prediction results of the method are consistent with those of manual labeling. The test sets, the system code, and their usage are also given ( https://github.com/AITAhenu/RotatedStomataNet ).

Prevalence, mortality, cost, and disparities in transcatheter mitral valve repair and replacement in cancer patients: Artificial intelligence and propensity score national 5-year analysis of 7495 procedures

IntroductionWe conducted the first comprehensive evaluation of the therapeutic value and safety profile of transcatheter mitral edge-to-edge repair (TEER) and transcatheter mitral valve replacement (TMVR) in individuals concurrently afflicted with cancer. MethodsUtilizing the National Inpatient Sample (NIS) dataset, we analyzed all adult hospitalizations between 2016 and 2020 (n = 148,755,036). The inclusion criteria for this retrospectively analyzed prospective cohort study were all adult hospitalizations (age 18 years and older). Regression and machine learning analyses in addition to model optimization were conducted using ML-PSr (Machine Learning-augmented Propensity Score adjusted multivariable regression) and BAyesian Machine learning-augmented Propensity Score (BAM-PS) multivariable regression. ResultsOf all adult hospitalizations, there were 5790 (0.004%) TMVRs and 1705 (0.001%) TEERs. Of the total TMVRs, 160 (2.76%) were done in active cancer. Of the total TEERs, 30 (1.76%) were done in active cancer. After the comparable rates of TEER/TMVR in active cancer in 2016, the prevalence of TEER/TMVR was significantly less in active cancer from 2017 to 2020 (2.61% versus 7.28% p < 0.001). From 2017 to 2020, active cancer significantly decreased the odds of receiving TEER or TMVR (OR 0.28, 95%CI 0.13–0.68, p = 0.008). In patients with active cancer who underwent TMVR/TEER, there were no significant differences in socio-economic disparities, mortality or total hospitalization costs. ConclusionThe presence of malignancy does not contribute to increased mortality, length of stay or procedural costs in TMVR or TEER. Whereas the prevalence of TMVR has increased in patients with active cancer, the utilization of TEER in the context of active cancer is declining despite a growing patient population.

Methodology for forming a digital farming management system

The paper presents methodological approaches for the creation of a digital farming management system (DFMS). A convergent approach, based on cognitive (conceptual) analysis methods, is employed in the research and applied to the formation of adaptive landscape farming systems. The fundamental principles of organizing DFMS include crop and environmental monitoring (in situ, remote sensing); the formation of farming system archetypes based on the analysis of long-term field experiments; spatial object modeling and land typology using GIS; planning and support for agrotechnologies to adapt to natural and economic conditions; modeling ecosystem services and biodiversity; assessing the impact on the sustainability and economics of crop production. The system is implemented using geoinformation models in a specific geographic coordinate. DFMS involves conducting a "inventory" of natural and production resources, as well as identifying limits of climatic, soil, and agrolandscape parameters at different levels of land use intensity. At each stage of organizing system blocks, methods of intelligent data analysis and machine learning are used, with the core of the system relying on the use of knowledge bases and logical rules of the subject area. A key element of the system is the scaling of the results of long-term field experiments and accumulated knowledge in different management areas based on the parameterization of the multi-level variability of farming systems and the formation of their archetypes. The practical implementation of the main provisions of DFMS allows approaching the solution of key issues related to reducing the level of uncertainty and associated risks in agriculture. This is achieved through scientifically justified organization of rational land use, increasing the resilience of crop production in different land use conditions, and providing information support to rural producers.

Concrete structure assembly technology based on 3D intelligent image analysis

AbstractAt present, there is no standard practice for temporary connection of component installation. How to make temporary connections to components more efficient and accurate is the key to the construction of cast‐in‐place connection parts in concrete prefabricated buildings. In order to improve the assembly technology of concrete structures, this paper combines three‐dimensional intelligent image analysis technology for simulation, analyzes and compares several common electronic image stabilization algorithms, discusses the video image stabilization technology based on gray projection method in detail, and gives experimental results. Moreover, this paper analyzes and compares common moving target tracking algorithms, such as feature‐based tracking, region matching‐based tracking, dynamic contour‐based tracking, and 3D model‐based tracking. In addition, this paper studies the target tracking algorithm based on the Camshift algorithm, and constructs the assembly model of the intelligent concrete structure with the support of the algorithm. Through experimental verification, it is known that the performance distribution of the concrete structure assembly model based on 3D intelligent image analysis in experimental evaluation is between [81, 89], the experimental study shows that the concrete structure assembly model based on 3D intelligent image analysis can effectively improve the assembly effect of concrete structure.

Automatic classification of dog barking using deep learning

Barking and other dog vocalizations have acoustic properties related to emotions, physiological reactions, attitudes, or some particular internal states. In the field of intelligent audio analysis, researchers use methods based on signal processing and machine learning to analyze the digitized acoustic signals’ properties and obtain relevant information. The present work describes a method to classify the identity, breed, age, sex, and context associated with each bark. This information can support the decisions of people who regularly interact with animals, such as dog trainers, veterinarians, rescuers, police, people with visual impairment. Our approach uses deep neural networks to generate trained models for each classification task. We worked with 19,643 barks recorded from 113 dogs of different breeds, ages and sexes. Our methodology consists of three stages. First, the pre-processing stage prepares the data and transforms it into the appropriate format for each classification model. Second, the characterization stage evaluates different representation models to identify the most suitable for each task. Third, the classification stage trains each classification model and selects the best hyperparameters. After tuning and training each model, we evaluated its performance. We analyzed the most relevant features extracted from the audio and the most appropriate deep neural network architecture for that feature type. Even if the application of our method is not ready for being used in ethological practice, our evaluation showed an outstanding performance of the proposed method, surpassing previous research results on this topic, providing the basis for further technological development.