Intelligent Analysis Research Articles

РОЗРОБКА КІБЕРФІЗИЧНОЇ СИСТЕМИ ДЛЯ АВТОМАТИЗАЦІЇ ТА КЕРУВАННЯ ІНТЕРНЕТОМ РЕЧЕЙ З ВИКОРИСТАННЯМ ПЛАТФОРМИ HOME ASSISTANT

The paper presents the development of a cyber-physical system based on the HomeAssistant platform for efficient automation and control of Internet of Things (IoT) devices. The architectural features, technical implementation and prospects for the development of the system are considered, with an emphasis on the ability to integrate a wide range of sensors and IoT devices into a single network to create adaptive and intelligent solutions. The main focus is on the implementation of automation scenarios for indoor climate control, which optimise living conditions depending on the internal needs of users and external weather conditions, significantly increasing energy efficiency and overall comfort. Data storage and analysis strategies are described in detail, including the use of NAS servers for backup, the use of MariaDB for storage and InfluxDB and Grafana for analytics and visualisation, ensuring a high level of reliability and availability of information. Testing was conducted to assess the delay of notifications transmitted via Telegram and the internal network of the cyber-physical system. The obtained results confirm the high efficiency of the implemented cyber-physical system in ensuring instant delivery of notifications, which is a key aspect for rapid response to critical situations in a dynamic environment. The Home Assistant platform has been found to have extensive capabilities to support artificial intelligence services. It integrates with a variety of services such as Google Assistant, TensorFlow, DeepStack, Amazon Alexa, and allows you to easily expand its functionality with additional components and plug-ins. Using intelligent algorithms and data analysis, the system can independently make decisions on the optimal use of resources, monitor devices in accordance with changing conditions and user needs, and respond to dangerous or unpredictable situations.

МЕТОД ДВОКАСКАДНОЇ ІМПЛАНТАЦІЇ ПРИХОВАНОЇ ІНФОРМАЦІЇ НА ОСНОВІ СТЕГАНОКОМПРЕСІЙНИХ ПЕРЕТВОРЕНЬ

In the article, the need for further development of the state is directly related to: solving issues of increasing defense capability and information security; development of information and intelligent systems. The necessity of ensuring the required level of completeness of information, compliance with the requirements of its relevance, achievement and maintenance of the appropriate level of integrity, accessibility and confidentiality is substantiated. The article emphasizes that for this purpose, complex systems of coding and information protection are being built. It is shown that recently, in order to further increase the level of security of information resources, methods of hidden embedding of information have been used. In this field of scientific and applied research, the direction of timely delivery of integral video information in a secure mode is outlined. For protection, the following can be used: meta-messages, which are formed on the basis of intelligent analysis of video frames; separate video segments of aerial photographs containing the most important information for decision-making. This article examines a class of methods of steganographic transformations, which are associated with embedding messages in digital containers, which are formed by a stream of video segments (VS). However, existing steganographic systems are based mainly on the use of the amount of psychovisual (PSV) redundancy available for reduction. Therefore, an increase in steganographic capacity leads to a loss of integrity and efficiency of delivery of video-container information. Steganocompression coding in the stegano-polyadic basis was created on the basis of taking into account the amount of redundancy permissible for reduction. In this case, in the process of compression of the components of the VS, the direct embedding of hidden information is organized. Concealment of information is carried out by technological stages, depending on the compression transformation of the transitional syntax of the quilted sequence. The key stage is the technology of two-stage implementation of the elements of the concealed message added to the stegano sequences to the syntax of the steganocompression representation of the BC components.

Comprehensive translational profiling and STE AI uncover rapid control of protein biosynthesis during cell stress.

Translational control is important in all life, but it remains a challenge to accurately quantify. When ribosomes translate messenger (m)RNA into proteins, they attach to the mRNA in series, forming poly(ribo)somes, and can co-localize. Here, we computationally model new types of co-localized ribosomal complexes on mRNA and identify them using enhanced translation complex profile sequencing (eTCP-seq) based on rapid in vivo crosslinking. We detect long disome footprints outside regions of non-random elongation stalls and show these are linked to translation initiation and protein biosynthesis rates. We subject footprints of disomes and other translation complexes to artificial intelligence (AI) analysis and construct a new, accurate and self-normalized measure of translation, termed stochastic translation efficiency (STE). We then apply STE to investigate rapid changes to mRNA translation in yeast undergoing glucose depletion. Importantly, we show that, well beyond tagging elongation stalls, footprints of co-localized ribosomes provide rich insight into translational mechanisms, polysome dynamics and topology. STE AI ranks cellular mRNAs by absolute translation rates under given conditions, can assist in identifying its control elements and will facilitate the development of next-generation synthetic biology designs and mRNA-based therapeutics.

The establishment of a performance evaluation model using education informatization to evaluate teacher morality construction in colleges and universities

Abstract To improve the effectiveness of the construction of teacher morality in colleges and universities, this article employs the performance evaluation model of education informatization to construct an evaluation system for the construction of teacher morality in colleges and universities. The evaluation of the construction effect of teacher morality is carried out through intelligent data analysis, and the significant error is used as the parameter of the teacher’s morality to make estimations. Moreover, a binary variable is introduced to judge whether the data have a significant error. Mixed integer linear and nonlinear programming methods are employed to detect significant errors. In addition, the idea of synchronizing teachers’ moral data is added. The effectiveness of the proposed method is verified by a simulation study using MATLAB 2017. The results show that it is necessary to establish and improve the talent flow mechanism that conforms to the requirements to promote the rational flow of teachers.

Predicting Airbnb pricing: a comparative analysis of artificial intelligence and traditional approaches

Airbnb's distinctive model accommodates a broad spectrum of hosts, ranging from non-professionals to traditional establishments, resulting in a nuanced pricing system that poses challenges for prediction. This paper delves into Airbnb pricing and, to address this exercise, introduces computational approaches that combine traditional linear methods and advanced artificial intelligence techniques. Utilizing real data from the Netherlands, the study specifically focuses on all active Airbnb listings until September 2019. Our contribution stands out by incorporating the financial history of diverse rental offerings, a novel aspect compared to existing literature. Unlike other studies, our investigation spans various cities in the Netherlands, providing a comprehensive view beyond individual regions. Our results highlight the robust predictive capabilities of Artificial Intelligence techniques, that lead to lower susceptibility to overfitting and to superior overall performances. However, traditional methods also prove to be valuable, aiding in determining the significance of predictors in the predicting exercise. Collectively, these approaches offer valuable insights to enhance pricing strategies and overall performances within the Airbnb market.

Sexual and Gender Minority Status and Suicide Mortality: An Explainable Artificial Intelligence Analysis.

Objectives: Suicide risk is elevated in lesbian, gay, bisexual, and transgender (LGBT) individuals. Limited data on LGBT status in healthcare systems hinder our understanding of this risk. This study used natural language processing to extract LGBT status and a deep neural network (DNN) to examine suicidal death risk factors among US Veterans. Methods: Data on 8.8 million veterans with visits between 2010 and 2017 was used. A case-control study was performed, and suicide death risk was analyzed by a DNN. Feature impacts and interactions on the outcome were evaluated. Results: The crude suicide mortality rate was higher in LGBT patients. However, after adjusting for over 200 risk and protective factors, known LGBT status was associated with reduced risk compared to LGBT-Unknown status. Among LGBT patients, black, female, married, and older Veterans have a higher risk, while Veterans of various religions have a lower risk. Conclusion: Our results suggest that disclosed LGBT status is not directly associated with an increase suicide death risk, however, other factors (e.g., depression and anxiety caused by stigma) are associated with suicide death risks.

An intelligent model to decode students’ behavioral states in physical education using back propagation neural network and Hidden Markov Model

This paper highlights the need for intelligent analysis of students’ behavioral states in physical education tasks. The hand-ring inertial data is used to identify students’ motion sequence states. First, statistical feature extraction is performed based on the acceleration and angular velocity data collected from the bracelet. After completing the filtering and noise reduction of the data, we perform feature extraction by Back Propagation Neural Network (BPNN) and use the sliding window method for analysis. Finally, the classification capability of the model sequence is enhanced by the Hidden Markov Model (HMM). The experimental results indicate that the classification accuracy of student action sequences in physical education exceeds 96% after optimization by the HMM method. This provides intelligent means and new ideas for future student state recognition in physical education and teaching reform.

Comparative Analysis of Artificial Intelligence (AI) Languages in Predicting Sequential Organ Failure Assessment (SOFA) Scores.

The Sequential Organ Failure Assessment (SOFA) score plays a crucial role in intensive care units (ICUs) by providing a reliable measure of a patient's organ function or extent of failure. However, the precise assessment is time-consuming, and daily assessment in clinical practice in the ICU can be challenging. Realistic scenarios in an ICU setting were created, and the data mining precision of ChatGPT 4.0 Plus, Bard, and Perplexity AI were assessed using Spearman's as well as the intraclass correlation coefficients regarding the accuracy in determining the SOFA score. The strongest correlation was observed between the actual SOFA score and the score calculated by ChatGPT 4.0 Plus (r correlation coefficient 0.92) (p<0.001). In contrast, the correlation between the actual SOFA and that calculated by Bard was moderate(r=0.59, p=0.070), while the correlation with Perplexity AI was substantial, at 0.89, with a p<0.001. The interclass correlation coefficient analysis of SOFA with those of ChatGPT 4.0 Plus, Bard, and Perplexity AI was ICC=0.94. Artificial intelligence (AI) tools, particularly ChatGPT 4.0 Plus, show significant promise in assisting with automated SOFA score calculations via AI data mining in ICU settings. They offer a pathway to reduce the manual workload and increase the efficiency of continuous patient monitoring and assessment. However, further development and validation are necessary to ensure accuracy and reliability in a critical care environment.

Automating Threat Intelligence Analysis with Retrieval Augmented Generation (RAG) for Enhanced Cybersecurity Posture

Automating Threat Intelligence Analysis with Retrieval Augmented Generation (RAG) for Enhanced Cybersecurity Posture

Analysis of Henan Tourism Demand Difference Based on Data Mining and Intelligent Analysis

Relevant tourists fields in Henan have conducted in-depth investigations on Henan tourism, in order to provide reference for subsequent tourism research; Based on the increase in the number of Henan tourists, studying outbound tourism activities from the perspective of tourists can explore its development law and formulate effective marketing strategies. Based on this, this paper proposes a data mining method to analyze the differences between different consumer groups in tourism needs and marketing strategies. The joint example analysis shows that our data mining analysis is very reasonable and effective. Our data experiments show that based on the increase in the number of Henan tourists going abroad, the tourism strategy designed in this paper can promote tourists' interest and increase the number of trips.

Validation of the Quality Analysis of Medical Artificial Intelligence (QAMAI) tool: a new tool to assess the quality of health information provided by AI platforms.

The widespread diffusion of Artificial Intelligence (AI) platforms is revolutionizing how health-related information is disseminated, thereby highlighting the need for tools to evaluate the quality of such information. This study aimed to propose and validate the Quality Assessment of Medical Artificial Intelligence (QAMAI), a tool specifically designed to assess the quality of health information provided by AI platforms. The QAMAI tool has been developed by a panel of experts following guidelines for the development of new questionnaires. A total of 30 responses from ChatGPT4, addressing patient queries, theoretical questions, and clinical head and neck surgery scenarios were assessed by 27 reviewers from 25 academic centers worldwide. Construct validity, internal consistency, inter-rater and test-retest reliability were assessed to validate the tool. The validation was conducted on the basis of 792 assessments for the 30 responses given by ChatGPT4. The results of the exploratory factor analysis revealed a unidimensional structure of the QAMAI with a single factor comprising all the items that explained 51.1% of the variance with factor loadings ranging from 0.449 to 0.856. Overall internal consistency was high (Cronbach's alpha = 0.837). The Interclass Correlation Coefficient was 0.983 (95% CI 0.973-0.991; F (29,542) = 68.3; p < 0.001), indicating excellent reliability. Test-retest reliability analysis revealed a moderate-to-strong correlation with a Pearson's coefficient of 0.876 (95% CI 0.859-0.891; p < 0.001). The QAMAI tool demonstrated significant reliability and validity in assessing the quality of health information provided by AI platforms. Such a tool might become particularly important/useful for physicians as patients increasingly seek medical information on AI platforms.

Analysis of Artificial Intelligence Based MPPT in PV Grid Connected System

The There has been a rise in the demand for electrical power during the past 10 years. Installing a new power generator (PG) is a costly and time-consuming procedure. For this reason, solar power plants are considered a viable alternative for meeting the current energy demand. But crucial maintenance and output power balance are the key issues facing solar power facilities. In order to reduce output power balance and maintenance problems in solar plants, an appropriate approach is required. This study offers a novel method for tracking the maximum power for hybrid photovoltaic (PV) and wind energy systems (WES): single maximum power point tracking, or MPPT. In the proposed MPPT technique, a radial basis function network (RBFN) control mechanism is based on an artificial neural network (ANN).

Integrated Two-Level New Energy Monitoring and Big Data Centers: A Comprehensive Approach for Urban Sustainability

This study examines the transition to green energy in Wenzhou City and suggests a novel plan for integrating an energy monitoring and data center. The aim is to improve the city's sustainability through the building of a large-scale wind-solar integrated energy monitoring system by a group in Wenzhou City. The solution uses the Internet of Things and intelligent data analysis technology to realize real-time data collection, processing and operation and maintenance decision support for distributed power stations. The system adopts storage-computer separation and data encryption technology to ensure data security and system stability and improves data processing efficiency through containerization and microservice architecture. In addition, the study also explores the application potential of this technical scheme in regional energy planning, which provides a new idea for Wenzhou to achieve green energy transformation and low-carbon development.

Analysis of Intellectual Intelligence (IQ) and Emotional Intelligence (EQ) on Employee Performance with Spiritual Intelligence (SQ) as a mediating variable

This research starts from the problem of whether Intellectual Intelligence (IQ) and Emotional Intelligence (EQ) affect employee performance with Spiritual Intelligence (SQ) as a mediating variable. This research aims to determine and analyze the influence of Intellectual Intelligence (IQ) and Emotional Intelligence (EQ) on employee performance with Spiritual Intelligence (SQ) as a mediating variable. This type of research is quantitative research with correlational problem construction. This quantitative research takes the form of numbers and is analyzed using statistics. The population in this study were employees of the distribution department of PT Saribumi Sriguna Putra, Palembang City, PT Sriguna Patra Niaga, and PT Graha Patra Saribumi in 2023, totaling 105 people. The sample in this study was taken using a saturated sampling technique, namely a sample determination technique when all members are used as samples. Thus, the sample in this study was taken equal to the population, namely 105 people. The data analysis technique in this research uses the SPSS application. factors such as intellectual intelligence, emotional intelligence and spiritual intelligence affect the creation of employee performance, and spiritual intelligence is able to mediate between intellectual intelligence factors, emotional intelligence and employee performance. the findings in this study are that this research is expected to be a view for company owners to pay more attention to factors to improve maximum employee performance.

Prospective deep learning-based quantitative assessment of coronary plaque by computed tomography angiography compared with intravascular ultrasound: the REVEALPLAQUE study.

Coronary computed tomography angiography provides non-invasive assessment of coronary stenosis severity and flow impairment. Automated artificial intelligence (AI) analysis may assist in precise quantification and characterization of coronary atherosclerosis, enabling patient-specific risk determination and management strategies. This multicentre international study compared an automated deep learning-based method for segmenting coronary atherosclerosis in coronary computed tomography angiography (CCTA) against the reference standard of intravascular ultrasound (IVUS). The study included clinically stable patients with known coronary artery disease from 15 centres in the USA and Japan. An AI-enabled plaque analysis was utilized to quantify and characterize total plaque (TPV), vessel, lumen, calcified plaque (CP), non-calcified plaque (NCP), and low-attenuation plaque (LAP) volumes derived from CCTA and compared with IVUS measurements in a blinded, core laboratory-adjudicated fashion. In 237 patients, 432 lesions were assessed; mean lesion length was 24.5 mm, and mean IVUS-TPV was 186.0 mm3. AI-enabled plaque analysis on CCTA showed strong correlation and high accuracy when compared with IVUS; correlation coefficient, slope, and Y intercept for TPV were 0.91, 0.99, and 1.87, respectively; for CP volume 0.91, 1.05, and 5.32, respectively; and for NCP volume 0.87, 0.98, and 15.24, respectively. Bland-Altman analysis demonstrated strong agreement with little bias for these measurements. AI-enabled CCTA quantification and characterization of atherosclerosis demonstrated strong agreement with IVUS reference standard measurements. This tool may prove effective for accurate evaluation of coronary atherosclerotic burden and cardiovascular risk assessment.

A STUDY ON IMPROVING SUPPLY CHAIN USING BUSINESS INTELLIGENCE CONCERNING DHARA LOGISTICS

Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs. BI tools enable business users to access different types of data — historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Users can analyze this information to gain insights into how the business is performing. According to CIO magazine: “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.” Organizations can use the insights gained from business intelligence and data analysis to improve business decisions, identify problems or issues, spot market trends, and find new revenue or business opportunities. BI platforms traditionally rely on data warehouses for their baseline information. A data warehouse aggregates data from multiple data sources into one central system to support business analytics and reporting. Business intelligence software queries the warehouse and presents the results to the user in the form of reports, charts and maps. Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. “OLAP provides powerful technology for data discovery, facilitating business intelligence, complex analytic calculations and predictive analytics,” says IBM offering manager Doug Dailey in his data warehousing blog. “One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data to improve product quality, customer interactions and process improvements.” Some newer business intelligence solutions can extract and ingest raw data directly using technology such as Hadoop, but data warehouses are still the data source of choice in many cases.

B-51 | 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 7,495 procedures

B-51 | 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 7,495 procedures

Research on the Integration Innovation of AI and E-commerce and Its Impact on Future Business Models

This paper aims to explore the specific application of Artificial Intelligence (AI) technology in the e-commerce industry and the changes it brings, given the industry's rapid development, intensifying market competition, and evolving user needs. By reviewing the history of e-commerce development, we gain an in-depth understanding of the status of AI application in meeting market demands. The study focuses on the specific application of AI technology in e-commerce operations and its effectiveness, deeply analyzing key areas such as intelligent recommendation systems and data analysis. It reveals how AI can reshape the operational mode and business model of the e-commerce industry by enhancing user experience, optimizing operational efficiency, and deeply exploring data value. Furthermore, the paper explores the potential impact of this integration on the future development of the e-commerce industry and makes predictions on the prospects of AI technology application, providing valuable references for relevant enterprises and researchers. Ultimately, this research concludes that AI technology holds great promise in driving the sustainable and healthy development of the e-commerce industry by enabling more effective market competition response and precise user need satisfaction.

Evaluating visitors’ experience in museum: Comparing artificial intelligence and multi-partitioned analysis

Analysing visitors' behaviour in a museum or in a cultural site is a crucial element to manage spaces and artworks arrangement as well as improving the visit experience. This paper presents the preliminary results of the ARTEMISIA project, exploiting Artificial Intelligence (AI) techniques to study, design and develop a methodology to interpret visitors' behaviour within a museum context, namely the Museum of Rome in Palazzo Braschi (Rome, Italy). The aim is to combine literature on users' experience (UX) analysis with experimental data coming from the visitor anonymous tracking out of motion sensors (users' stand-still positions, viewpoint direction, movements), merging approaches of different research domains. Through the use of agglomerative hierarchical clustering algorithms, four categories of visitors were identified, then associated to user profiles emerged by UX evaluations. Such analysis may lead to new forms of visitors profiling and to the development of a new generation of customised applications in public and private contexts. Identifying and predicting users’ patterns with respect to museum halls arrangement may also be useful to suggest improvement in the museum spaces and exhibitions (new indications, updated storytelling or changes in thematic configuration).© 2023 Elsevier Ltd. All rights reserved.

Teaching Reform and Exploration of "Financial Metrology" Course in the New Financial Era

At the present stage, financial measurement based on big data needs to decompose financial problems into a series of variables and parameters, take data as the base, combined with quantitative model and algorithm, intelligently and real-time perception of financial problems through data, to achieve the goal of quantitative analysis, quantitative control and quantitative operation. Based on this, this paper puts forward the overall goal of "four characteristics, eight forces and four feelings" financial metrology course construction, which aims to coordinate the development of professional ability, comprehensive literacy ability and value ability. It aims to build a course reform and innovation system that takes value power as the starting point, promotes the construction of "intelligent analysis platform" of financial metrology, promotes scientific research through educational reform, and finally realizes the matching of supply and demand. Form a closed-loop innovation system in which innovation promotes curriculum reform, talent reform, and talent reform promotes innovation.