Intelligent Analysis System Research Articles

Advanced Personal Budget Analytics: Combining Optical Character Recognition and Natural Language Processing for Automated Budget Categorization and Insight Extraction

A budget tracking system is a tool designed to help users monitor their financial resources, categorize expenses and control their financial resources. An Intelligent Personal Budget Analysis System is designed to redefine personal finance management. IntelliFinance is a user- friendly app designed for seamless and efficient daily expense management removing the necessity for conventional paper documentation. This project inculcates the concepts of Computer Vision, Machine Learning and Data Analytics, aims to surpass traditional budget tracking by incorporating innovative features such as receipt scanning and analysis. The project is implemented using Android Studio and Firebase, incorporating advanced features such as Optical Character Recognition (OCR) to capture receipt images, extract text, and process it for further analysis. Firebase ML Kit is utilized for scanning and text recognition, while Natural Language Processing (NLP) is applied for categorizing the extracted text. The system provides a personal budget analysis that enhances the accuracy of financial tracking by offering users a comprehensive overview of their spending habits, complemented by detailed reports and visualizations. Ultimately, this project serves as a financial companion, empowering users to take control of their finances, make informed decisions, and achieve their financial goals.

Flexible electrochemical sensor with 2D-on-2D layered structure for real-time on-site intelligent detection of carbendazim residues on fresh tea leaves

Excessive use of carbendazim (CBZ) for controlling fungal disease may result in pesticide residues in growth environments. There is an urgent requirement for real-time detection of CBZ content to enable rapid assessment of pesticide residues. Herein, a novel electrochemical sensor was created, which utilizes a laser-induced graphene (LIG) electrode modified with two-dimensional (2D) tungsten disulfide (WS2) nanosheets and a poly(L-lysine) (PLL) film to construct a 2D-on-2D layered structure. The PLL/WS2/LIG electrode exhibits strong charge transfer, excellent electrocatalytic performance, and high CBZ adsorption capacity, thus leading to enhanced sensitivity. As a result, the sensor exhibits a low detection limit of 1.2 ng/mL (6.2 nM), high sensitivity up to 135.1 μA(μg/mL)−1cm−2, and a wide response range of 0.01 to 1.5 μg/mL. Moreover, an intelligent analysis system, consisting of a portable device that connects to an online cloud platform equipped with a machine learning model and visualization software, has been developed for the real-time detection of CBZ residues on tea leaves in tea plantations. This study demonstrates that the PLL/WS2/LIG sensor and the intelligent analysis system have great potential to monitor pesticide residues to guarantee food safety.

The architecture of the system of intelligent analysis of competencies for updating university educational programs

The problem of updating educational programs and educational content is closely related to the need to adapt them to the changing conditions of regional labor markets. The solution to the problem is achieved through the collection and analysis of data on the competence requirements of employers for the qualifications of specialists (the data itself can be extracted from job advertisements on open resources on the Internet). The article presents a method of using search robots and text data processing modules to obtain formalized descriptions of competence requirements and compare them by degree of similarity with competence descriptions from educational programs. For a comparative analysis of competency requirements, a clustering method was developed using fuzzy logic and a neural network. The architecture of the collection and intelligent analysis system includes search crawlers, a module for parsing text data with cleaning from information noise, a data consolidation module, a keyword dictionary generation module, a module for synthesizing competency vectors, a clustering module, a distributed registry of formalized descriptions of competencies, etc. The results of the comparative analysis are crucial for assessing the compliance of competencies of specialists trained in regional universities with the requirements of employers in the regions. The assessment allows to develop recommendations for updating educational programs and educational content of an educational institution, as well as to carry out adaptive adjustment of information and educational environment components of higher educational institutions.

Intelligent Health Care Data Analysis System Using Swarm based Optimization Algorithm

Large amounts of clinical data present both opportunities and challenges in modern healthcare. Effective data analysis improves patient care, early disease detection, and personalised treatment. This paper proposes an Intelligent Health Care Data Analysis System employing PSO and neural networks to improve disease detection. Missing values, normalisation, and feature selection ensure high-quality analysis inputs. Disease classification and prediction from clinical data are improved by PSO optimising neural network weights and biases. This method works on Hepatitis, Wisconsin Breast Cancer, and Cleveland Heart Disease datasets with and without missing values. PSO-optimized neural networks outperform regular neural networks in accuracy and robustness. Scalable and adaptable for real-time diagnostic applications, this intelligent system may improve healthcare delivery by enabling more precise and timely interventions. Using neural networks and advanced swarm-based optimisation, medical data analytics has improved patient outcomes and healthcare system efficiency. Particle Swarm Optimization-based Neural Network (IPSONN) diagnoses clinical illnesses better. IPSONN was created using K-means clustering and IPSO-trained Neural Networks. K-means clusterings optimal cluster number determines the hidden layers neurones. Cluster approximation error determines best clusters. Mean square error calculates hidden and output layer weights in QPSO. The classifier was tested with four clinical datasets from the University of California, Irvine (UCI) machine learning repository: Pima Indian Diabetes, Hepatitis, Wisconsin Breast Cancer, and Cleveland Heart Disease.

Intelligent Video Monitoring and Analysis System for Power Grid Construction Site Safety Using Wireless Power Transfer

Power grid construction significantly enhances power grid management and risk control. Inconsistent operations at construction sites can jeopardize grid stability and crew safety. Traditional power lines are less favored due to mobility limitations, while batteries add burden and impracticality. To address this, a wireless power transfer and video monitoring system is developed using RF technology and Yolo V3 model. This enables continuous monitoring and employee safety analysis. The system's detection performance is optimized using HPSO, surpassing existing methods in accuracy and speed. It ensures real-time monitoring and improves the detection of potential risk sources, crucial for construction site safety.

Study on Integrated Systems for Enhanced Forest Fire Prevention: An Embedded Intelligent Video Analysis and Collaborative IoT–Fog–Cloud Framework Approach

Introduction:: Forest fires have been a major hazard to forest management, needing sophisticated monitoring and management techniques. By creating an embedded intelligent video analysis system, this research proposed a complete strategy for addressing this difficulty. Method:: The system's hardware architecture was explained, and the operating system software was detailed, using a software and hardware design based on the ZynqSoC. At the same time, an emphasis on forest fire prevention applications was maintained. Furthermore, the study investigated a unique technique for forest fire detection using Arduino as a field data collector and a fuzzy logic algorithm to improve accuracy. Results:: The proposed IoT-Fog-Cloud collaboration infrastructure offered a patented contribution to real-time wildfire monitoring, prediction, and forecasting. The framework achieved excellent accuracy in determining wildfire proneness levels and real-time alert production by utilizing fuzzy K-nearest-neighbor classification and Holt-Winter's forecasting model. Conclusion:: The findings demonstrated the integrated system's ability to reduce the impact of wildfires, serving as a significant reference for future forest fire prevention scenarios.

Sentiment Analysis Based on Transformer

This article aims to explore the sentiment analysis of Transformer comments on Weibo to understand the attitudes and emotional tendencies of social media users regarding the movie The boy and the heron. This paper uses natural language processing technology and sentiment analysis model Transformer to crawl the Weibo comment data set, analyzes the anti-crawling mechanism of Weibo, adds HEADERS and REFERER directly in the code to bypass the inspection, and preprocesses it. Sentiment analysis was conducted on a large number of Weibo comments related to the boy and the heron. The results of the study showed that the vast majority of the comments expressed positive emotional attitudes and held positive and appreciative attitudes towards the boy and the heron. This study reveals that social media users have positive attitudes and emotional identification with the boy and the heron, and the audience really likes the film, which is of great help for the filmmakers to further understand the advantages and disadvantages of the film. Performing Weibo sentiment analysis based on advanced natural language processing technologies such as transformers can not only improve the accuracy and efficiency of sentiment analysis, but also help promote the progress and application of artificial intelligence technology. This is important for the development of more intelligent social media analysis tools and systems.

Enhancing chromosomal analysis efficiency through deep learning-based artificial intelligence graphic analysis

The objective of this study is to evaluate the efficacy and diagnostic utility of an advanced chromosomal analysis approach. A total of 2663 amniotic fluid samples were chosen for chromosomal karyotype profiling between January 2022 and June 2023. Two sets of tests were carried out: experiment 1 involved randomly selecting 1168 examples to test the accuracy of machine learning-based chromosomal karyotypes. The aim was to determine the method’s general applicability when cases were naturally dispersed. Experiment 2 concentrated on randomly selecting the most common examples of chromosomal number anomalies and cases with structural defects that did not affect the visual assessment of chromosome categories. The goal was to investigate the diagnostic efficacy of the artificial intelligence (AI) analysis system in detecting these flaws. The results of experiment 1 demonstrated the resilience of the intelligent analysis system in cases with significant differences in chromosomal karyotypes, resulting from manual shooting and film-making. Experiment 2 results showed that the intelligent analysis system surpassed the standard chromosomal image analysis program in terms of automated analysis accuracy, for both normal and defect cases. Furthermore, the intelligent analysis system demonstrated detection and analysis speeds that were 3–15 times faster. The average speed of regular case analysis increased by a factor of 4–6, cases with quantitative defects increased by a factor of 3–5, and cases with structural defects increased by a factor of 5–7. Implementing a chromosome intelligence analysis system in clinical practice could improve the efficiency of chromosome identification and analysis, allow for more widespread chromosomal examination, and reduce the likelihood of congenital defects.

StructDiffusion: End-to-end intelligent shear wall structure layout generation and analysis using diffusion model

Shear wall structures are widely used in high-rise buildings. However, the design process of shear wall structures suffers from inefficiencies. This paper introduces StructDiffusion, an end-to-end intelligent system for shear wall layout generation and analysis. The proposed approach tackles the layout design task by employing a novel diffusion model architecture to generate conditional images. Key components of the proposed method include pretrained diffusion models, ControlNet for conditional control, and LoRA for efficient adaptation. Notably, the method allows for architectural plan images and basic textual design conditions as inputs, enabling manipulation of the generated layouts by adjusting attributes such as building height and seismic intensity. To evaluate the quality of the model's design swiftly, this paper presents a comprehensive evaluation framework that incorporates perceptual and structural validity metrics. Through experimental analyses, this paper demonstrates the effectiveness of the proposed model in generating layouts, surpassing the capabilities of GAN-based methods. Furthermore, our study investigates model-specific parameter configuration, text-guided capabilities, code compliance, and computational efficiency. StructDiffusion significantly enhances the automation and intelligence of structural engineering workflows. The framework effectively addresses challenges associated with instability, data scarcity, and limitations in assessment. This pioneering application of diffusion models opens up promising avenues for advancing data-driven structural design.

Intelligent Analysis System for Teaching and Learning Cognitive Engagement Based on Computer Vision in an Immersive Virtual Reality Environment

The 20th National Congress of the Communist Party of China and the 14th Five Year Plan for Education Informatization focus on digital technology and intelligent learning and implement innovation-driven education environment reform. An immersive virtual reality (IVR) environment has both immersive and interactive characteristics, which are an important way of virtual learning and are also one of the important ways in which to promote the development of smart education. Based on the above background, this article proposes an intelligent analysis system for Teaching and Learning Cognitive engagement in an IVR environment based on computer vision. By automatically analyzing the cognitive investment of students in the IVR environment, it is possible to better understand their learning status, provide personalized guidance to improve learning quality, and thereby promote the development of smart education. This system uses Vue (developed by Evan You, located in Wuxi, China) and ECharts (Developed by Baidu, located in Beijing, China) for visual display, and the algorithm uses the Pytorch framework (Developed by Facebook, located in Silicon Valley, CA, USA), YOLOv5 (Developed by Ultralytics, located in Washington, DC, USA), and the CRNN model (Convolutional Recurrent Neural Network) to monitor and analyze the visual attention and behavioral actions of students. Through this system, a more accurate analysis of learners’ cognitive states and personalized teaching support can be provided for the education field, providing certain technical support for the development of smart education.

Intelligent Analysis of E-commerce Reviews based on Complex-Valued Spatio-temporal Graph Convolutional Neural Network Optimized Tyrannosaurus Optimization Algorithm

Intelligent analysis of E-commerce reviews based on multimodal social media big data using applies the benefit of innovative artificial intelligence method, especially deep learning algorithms, to study and realize web-based product consider and social network data connected to e-commerce. In this manuscript, Intelligent Analysis of E-commerce reviews based on Complex-Valued Spatio-temporal Graph Convolutional Neural Network Optimized Tyrannosaurus Optimization Algorithm (IAER-CVSGCNN-TOA)is proposed for Intelligent Analysis of E-commerce Reviews. The input data are collected from E-commerce big Dataset. Then data are pre-processing utilizing federated neural collaborative filtering (FNCF) to remove the noise and clean the data. The pre-processed data is provided to the CVSGCNN is used for intelligent analysis of e-commerce reviews. In general, CVSGCNN does no express adapting optimization approaches to determine optimal parameters to ensure accurate prediction. Hence, proposed to utilize the Tyrannosaurus Optimization Algorithm enhancement CVSGCNN for intelligent analysis of e-commerce reviews. The proposed IAER-CVSGCNN-TOA method is implemented on python. Then performance of proposed technique is analyzed with other existing techniques. The proposed technique attains 30.26%, 28.70% and 46.25% higher accuracy, 20.53%, 32.60%, and 53.43% higher precision, 25.23%, 23.15%, and 22.33%higher recall, 25.75%, 44.30% and 53.70%higher specificity comparing with the existing methods such as a Retracted: Online Troll Reviewer Detection Utilizing Deep Learning Methods (IAER-CNN), Developing an Intelligent System and DL Algorithms for Sentiment Analysis of E-Commerce Product Reviews(IAER-LSTM), Intelligent Perception System of Big Data Decision in Cross-Border e-Commerce Depend on Data Fusion(IAER-DFN) respectively.

Method for evaluating efficiency of refrigeration units based on compression cycle with economizer

The transition to the so-called “digital economy” requires the development of automation tools, and, therefore, algorithms based on which it will be possible to create intelligent control systems.
 At the same time, do not forget that the control and monitoring tools themselves are not an end in themselves. Automation must solve certain problems, relying on a data collection system (monitoring system). One of these tasks is energy saving. Since the share of electricity costs for refrigeration equipment is a significant part of the country’s energy balance, energy conservation issues are paid a lot of attention at the state level (Federal Law No. 261-FZ “On Energy Saving and Energy Efficiency and on Amending Certain Legislative Acts of the Russian Federation”).
 Existing systems for monitoring the operating parameters of a refrigeration plant are often used only to detect emergencies and alarm them, however, an intelligent analysis system can be built on the basis of recorded data to optimize the operation of the refrigeration plant in real time.
 This article describes the methodology for assessing the energy efficiency of refrigeration plants operating on a cycle with an economizer and allowing to evaluate both at the design stage and during the operation of the refrigeration plant, and allows to obtain a distribution of losses by elements and processes of the refrigeration plant.

Preliminary Study on the Identification of Aerobic Vaginitis by Artificial Intelligence Analysis System

To develop an artificial intelligence vaginal secretion analysis system based on deep learning and to evaluate the accuracy of automated microscopy in the clinical diagnosis of aerobic vaginitis (AV). In this study, the vaginal secretion samples of 3769 patients receiving treatment at the Department of Obstetrics and Gynecology, West China Second Hospital, Sichuan University between January 2020 and December 2021 were selected. Using the results of manual microscopy as the control, we developed the linear kernel SVM algorithm, an artificial intelligence (AI) automated analysis software, with Python Scikit-learn script. The AI automated analysis software could identify leucocytes with toxic appearance and parabasal epitheliocytes (PBC). The bacterial grading parameters were reset using standard strains of lactobacillus and AV common isolates. The receiver operating characteristic (ROC) curve analysis was used to determine the cut-off value of AV evaluation results for different scoring items were obtained by using the results of manual microscopy as the control. Then, the parameters of automatic AV identification were determined and the automatic AV analysis scoring method was initially established. A total of 3769 vaginal secretion samples were collected. The AI automated analysis system incorporated five parameters and each parameter incorporated three severity scoring levels. We selected 1.5 μm as the cut-off value for the diameter between Lactobacillus and common AV bacterial isolates. The automated identification parameter of Lactobacillus was the ratio of bacteria ≥1.5 μm to those <1.5 μm. The cut-off scores were 2.5 and 0.5, In the parameter of white blood cells (WBC), the cut-off value of the absolute number of WBC was 103 μL－1 and the cut-off value of WBC-to-epithelial cell ratio was 10. The automated identification parameter of toxic WBC was the ratio of toxic WBC toWBC and the cut-off values were 1% and 15%. The parameter of background flora was bacteria<1.5 μm and the cut-off values were 5×103 μL－1 and 3×104 μL－1. The parameter of the parabasal epitheliocytes was the ratio of PBC to epithelial cells and the cut-off values were 1% and 10%. The agreement rate between the results of automated microscopy and those of manual microscopy was 92.5%. Out of 200 samples, automated microscopy and manual microscopy produced consistent scores for 185 samples, while the results for 15 samples were inconsistent. We developed an AI recognition software for AV and established an automated vaginal secretion microscopy scoring system for AV. There was good overall concordance between automated microscopy and manual microscopy. The AI identification software for AV can complete clinical lab examination with rather high objectivity, sensitivity, and efficiency, markedly reducing the workload of manual microscopy.

Song Emotion Intelligence Analysis for Psychological Stress Relief

In today's digital and networked era, multimedia content such as images, audio, and video have become an important part of the data transmitted on the Internet's information superhighway. How to manage the stress in the college student population is directly related to the future life and development of college students. In this article, based on domestic and international data mining technology, the authors designed an intelligent analysis system for college students' mental health, pre-processed the data, then analysed these data in detail by using the outlier analysis algorithm in clustering algorithm, and finally mined the intrinsic connection between the psychological problems and attributes by using the Apriori association rule algorithm, so as to provide the decision makers with a reliable basis. This study puts forward reasonable solutions and suggestions for the problems existing in the psychological level of contemporary college students.

Research on the application of big data mining technology based on neural network

In the development of social construction, all walks of life have the application of data mining technology, with the continuous development of science and technology, data mining technology can not only use the hidden law to solve the problem of time, but also can grasp more valuable data in the increasingly competitive social environment. Originally, after understanding the application research status of big data mining technology, based on the application of big data mining technology with neural network as the core, empirical analysis was carried out with Guanhealth big data intelligent analysis system as an example, in order to verify the unique value of data mining technology.

The communication of artificial intelligence and deep learning in computer tomography image recognition of epidemic pulmonary infectious diseases.

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.

Prevalence of and risk factors for myopia among urban and rural children in Northeast China: protocol for a school-based cross-sectional study

IntroductionThe worldwide prevalence of myopia is high and continues to increase. In this study, a school screening programme for myopia will be implemented using the whole-process information method. The purpose...

Development of intelligent system of global bibliographic search

The paper presents scientifically grounded principles of formation and algorithms of a global bibliographic retrieval system based on modern intelligent technologies (intelligent linguistic analysis of texts and database management) and Internet search methods. Within the framework of theoretical bibliography, for the first time taking into account the specifics of modern intelligent software, a new algorithm of an integrated automated system for global bibliographic retrieval as well as search for specialized publications to publish research papers was developed. The developed methods include the formation of a normalized database of keywords based on the Universal Decimal Classification (UDC) categories and the automatic assignment of keywords from this normalized database to research papers by an intelligent system for linguistic analysis of texts. The proposed use of keywords as data labels in the form of UDC categories, covering all branches of knowledge, makes it possible to precisely label and describe research papers and scientific publications (journals, proceedings of scientific conferences) to the fullest extent possible. The search for research papers by the proposed bibliographic retrieval system as well as the search for specialized publications to publish those research papers is carried out automatically by normalized keywords assigned to research papers and scientific journals based on intelligent linguistic analysis of the content of those research papers. The study makes a social contribution and shows how it is important to create and utilize best practices, processes and strategies of bibliographic retrieval, which are essential in the development of science in modern society. Besides, in social terms, the search for bibliographic information is one of the methods to solve the problem of finding a relation between personal and public knowledge. The implementation of the study results in accordance with the presented algorithms consists in designing a special global public Internet search resource.

Quantitative identification of the co-exposure effects of e-waste pollutants on human oxidative stress by explainable machine learning

Global electronic waste (e-waste) generation continues to grow. The various pollutants released during precarious e-waste disposal activities can contribute to human oxidative stress. This study encompassed 129 individuals residing near e-waste dismantling sites in China, with elevated urinary concentrations of e-waste-related pollutants including heavy metals, polycyclic aromatic hydrocarbons (PAHs), organophosphorus flame retardants (OPFRs), bisphenols (BPs), and phthalate esters (PAEs). Utilizing an explainable machine learning framework, the study quantified the co-exposure effects of these pollutants, finding that approximately 23% and 18% of the variance in oxidative DNA damage and lipid peroxidation, respectively, was attributable to these substances. Heavy metals emerged as the most critical factor in inducing oxidative stress, followed by PAHs and PAEs for oxidative DNA damage, and BPs, OPFRs, and PAEs for lipid peroxidation. The interactions between different pollutant classes were found to be weak, attributable to their disparate biological pathways. In contrast, the interactions among congeneric pollutants were strong, stemming from their shared pathways and resultant synergistic or additive effects on oxidative stress. An intelligent analysis system for e-waste pollutants was also developed, which enables more efficient processing of large-scale and dynamic datasets in evolving environments. This study offered an enticing peek into the intricacies of co-exposure effect of e-waste pollutants.

CIRCA: Cloud-based intelligent robotic system for chemical analysis in streamlining and automating open-ended measurement tasks

The realm of chemical analysis has witnessed remarkable advancements through the automation of measurement techniques. This automation has played a pivotal role in reducing the labor-intensive and time-consuming aspects associated with chemical analysis, while simultaneously minimizing dependence on specialized knowledge. However, in practical analytical work, automated chemical measurement instrumentation faces persistent challenges due to uncertainties regarding the number and timing of sample measurement tasks reaching the laboratory for analysis. These challenges result in low instrument sharing rates and a deficiency in effective communication mechanisms between sample providers and administrators, impeding the further enhancement of automated chemical measurement efficiency. Here we proposed a Cloud-based Intelligent Robotic System for Chemical Analysis (CIRCA) and demonstrated its application by the analysis of soluble iron in water samples. CIRCA comprises a cloud platform and an analytical robot, which efficiently manages and executes measurement tasks using a Process Simulation Algorithm and a Task Coordination Algorithm. The Process Simulation Algorithm plans the mechanical actions of analytical robot modules, while simulating processing times for each task. Simultaneously, the Task Coordination Algorithm optimizes the timing of each measurement task. By adopting CIRCA, sample providers can remotely submit measurement tasks without negotiation, allowing for the seamless management of open-ended analytical tasks, measurement execution, and analytical result feedback, thereby enhancing the overall efficiency of the analytical workflow. CIRCA represents a pioneering cloud-based intelligent robotic system that revolutionizes chemical analysis by addressing persistent challenges in managing open-ended measurement tasks. Its innovative design and distinctive algorithms, coupled with cloud-based communication logic and architecture, as well as the robotic physical framework, empower seamless task coordination, remote synergism, automated execution, and result feedback, ultimately enhancing analytical efficiency.