Learning Technology Research Articles

Machine-learning-based identification of patients with IgA nephropathy using a computerized medical billing database.

The billing database of the universal healthcare system in Japan potentially includes large-cohort data of patients with immunoglobulin A nephropathy, diagnosis codes aimed at billing should not be directly used for clinical research because of the risk of misdiagnosis. To solve this problem, we aimed to develop a novel method for identifying patients with immunoglobulin A nephropathy from billing data using machine learning. The medical records and bills of 3,743 patients who consulted nephrologists at a single center were extracted. Patients were labeled to have been diagnosed with immunoglobulin A nephropathy through a review of medical records. A manual analysis of the diagnostic accuracy and machine learning was performed. For machine learning, the datasets were preprocessed in three patterns and assigned to the XGBoost program using five-fold cross-validation. Of all the participants, 437 were labeled as having been diagnosed with immunoglobulin A nephropathy. Bill codes for immunoglobulin A nephropathy were provided to approximately half of them. The manually created criteria consisting of the recommended examinations and treatments in the Japanese guidelines for immunoglobulin A nephropathy showed both specificity and sensitivity < 0.8. In contrast, with the receiver operating characteristic curve analysis, the machine learning process yielded area under the curve values over 0.9 with preprocessing from the clinical viewpoint. Applying machine learning technology to a dataset preprocessed from a clinical viewpoint achieved a high performance in detecting patients with immunoglobulin A nephropathy. This methodology contributes to the construction of a disease-specific cohort using big bill data.

ANALYSIS OF CUSTOMER CHURN PREDICTION USING LOGISTIC REGRESSION, -NEAREST NEIGHBORS, DECISION TREE AND RANDOM FOREST ALGORITHMS

Customer churn predictions (CCPs) and their comprehensive analysis have become prevalent in the global telecom industry over the last five years, driven by advancements in machine learning (ML) technologies. In addition, AI (artificial intelligence) and ML-based predictive methods are currently employed for CCP applications to enhance customer retention. This predictive CCP methodology streamlines customer management processes and ensures sustainable profit growth. The machine learning models focus on identifying features derived from data that is rich in various types of information. This study analyzes CCP for a specific telecom company’s customer dataset using ML methods such as logistic regression (L.R.), -nearest neighbor (-NN), decision tree (D.T.), and random forest (R.F.). The UCI Iranian telecom churn dataset was utilized, and the influence of potential factors leading to customer churn was also considered. Results show that the tuned RF method yielded the best outcomes, with churn tendency analysis achieving a higher AUC score at 0.9042 with the accuracy of 0.9562. The most important feature of the dataset affecting the customer churn was identified as complains whereas the least important feature happened to be tariff plan.

AI and Student Engagement: A Comparative Analysis

This study will investigate in detail about the impact of AI on student engagement by using a comparative analysis of different AI-powered educational technologies. The study focuses on various tools include Knewton, GenAI, Smart Sparrow, DreamBox Learning, Carnegie and other technologies. Secondly, the research examines in detail about the influence of these technologies on student motivation, personalized learning, participation, and overall educational outcomes. For conducting required results, the study employs a quantitative methodology by using surveys for obtaining data on satisfaction, student engagement, and adaptive feedback effectiveness in traditional and AI driven learning environments. Moreover, the results shows that the AI technologies are significantly enhancing engagement level of students particularly with those platforms that are providing personalized learning, real-time feedback, and interactive content. This study also identifies various challenges related to ethical concerns, user interface, accessibility, and concerns like data privacy. The findings are suggesting that AI has the potential to transform education. However, addressing these challenges is also important for ensuring equitable learning experience for the students and teachers. Received: 11 September 2024 / Accepted: 10 November 2024 / Published: 3 December 2024

Exploring Blended Learning Models Enhanced by Mobile Interactive Technology in Higher Education

With the continuous advancement of information technology, mobile interactive technology has been increasingly applied in higher education, providing robust support for blended learning models. These models integrate the advantages of traditional classroom teaching and online learning, enhancing the flexibility and interactivity of the educational process. In recent years, research on mobile interactive technology and blended learning has expanded. However, most studies have primarily focused on the application of technological tools and platform construction, with limited exploration of specific interaction models and learning experiences. Furthermore, research on student learning objective identification and group stratification have been constrained, failing to effectively meet the needs of personalized learning. In response, this study explores the integration of interaction models and learning experiences within a mobile environment for university students in blended learning settings. Additionally, methods for identifying learning objectives and stratifying student groups based on mobile technology were investigated, aiming to provide theoretical and practical guidance for the implementation of blended learning in higher education.

Research on mood monitoring and intervention for anxiety disorder patients based on deep learning wearable devices

Background Anxiety disorders are common mental health issues that have a significant effect on people's quality of life. Conventional techniques for tracking emotional states frequently lack the accuracy and sensitivity needed for successful intervention. Objectives This project aims to create a sophisticated monitoring system that uses deep learning methods to evaluate physiological data from wearables, emphasizing heart rate variability (HRV), to forecast patients’ emotional states who suffer from anxiety disorders. Methods Wearable equipment monitors physiological characteristics, which we used to obtain patient HRV data. We processed the data using a Bidirectional Long-Short-Term Memory (Bi-LSTM) network to evaluate time-dependent variables and enhance the precision of emotional state predictions. The physiological signals were used to teach the model to recognize different emotional states, such as neutral, happy, and sad. Results Outperforming conventional machine learning models, the Bi-LSTM model showed a high accuracy rate of up to 97% in predicting emotional states. The findings suggest that ongoing HRV monitoring can accurately track shifts in emotional states and enable prompt responses. Conclusion This work emphasizes the possibility of real-time emotional state monitoring in patients with anxiety disorders with wearable technology and deep learning. The results point to the potential benefits of this strategy for improving emotional regulation and improving anxiety sufferers’ quality of life, opening new avenues for investigation and advancement in the field of mental health therapies.

Application of deep learning models with spectral data augmentation and Denoising for predicting total phosphorus concentration in water pollution

BackgroundWith the increasing severity of global water pollution, accurate prediction models of water pollution content are critical for effective environmental management. However, traditional methods often exhibit low prediction accuracy for pollutant concentrations when data samples are limited and do not adequately address data noise. This study focuses on predicting total phosphorus (TP) concentrations in the Yangtze River Basin by integrating data augmentation and denoising methods with spectral technology and deep learning, using water samples collected from Wuhan to Anhui, China. MethodThe study utilized an improved Conditional Generative Adversarial Networks (CGAN) for data augmentation, increasing dataset diversity and training effectiveness. Adaptive threshold wavelet denoising is applied to reduce noise and improve data quality. A Convolutional Neural Network (CNN) with a coordinate attention (CA) mechanism is used to extract key spectral features linked to TP concentration prediction. Significant FindingsThis study introduces an innovative approach that combines advanced CGAN-based data augmentation, adaptive threshold wavelet denoising, and a CNN model incorporating a CA mechanism, achieving high accuracy in TP concentration prediction. The proposed model outperforms traditional methods, achieving R² = 0.9805, RMSE = 0.0019, and MAE = 0.0009. This novel method significantly enhances prediction performance, providing an effective solution particularly in scenarios with limited data samples.

BD-StableNet: a deep stable learning model with an automatic lesion area detection function for predicting malignancy in BI-RADS category 3–4A lesions

The latest developments combining deep learning technology and medical image data have attracted wide attention and provide efficient noninvasive methods for the early diagnosis of breast cancer. The success of this task often depends on a large amount of data annotated by medical experts, which is time-consuming and may not always be feasible in the biomedical field. The lack of interpretability has greatly hindered the application of deep learning in the medical field. Currently, deep stable learning, including causal inference, make deep learning models more predictive and interpretable. In this study, to distinguish malignant tumors in Breast Imaging-Reporting and Data System (BI-RADS) category 3-4A breast lesions, we propose BD-StableNet, a deep stable learning model for the automatic detection of lesion areas. In this retrospective study, we collected 3103 breast ultrasound images (1418 benign and 1685 malignant lesions) from 493 patients (361 benign and 132 malignant lesion patients) for model training and testing. Compared with other mainstream deep learning models, BD-StableNet has better prediction performance (accuracy = 0.952, area under the curve = 0.982, precision = 0.970, recall = 0.941,F1-score = 0.955 and specificity = 0.965). The lesion area prediction and class activation map results both verify that our proposed model is highly interpretable. The results indicate that BD-StableNet significantly enhances diagnostic accuracy and interpretability, offering a promising noninvasive approach for the diagnosis of BI-RADS category 3-4A breast lesions. Clinically, the use of BD-StableNet could reduce unnecessary biopsies, improve diagnostic efficiency, and ultimately enhance patient outcomes by providing more precise and reliable assessments of breast lesions.

Two Blind Individuals in a Forest: Implementing Project-Based Learning in Nutrition and Food Technology in Secondary Schools in Kampala, Uganda

Purpose: In this study, we explored how project-based learning is used in Nutrition and Food Technology in Uganda. Methodology: Using a mixed methods research approach, 12 focus group discussions were conducted with student project group leaders and considered nine for analysis (n = 50) 12 key informant interviews were done with NFT teachers and considered six for analysis because saturation point was reached. Also, quantitative data was collected from 197 learners who were not group leaders using questionnaires. Findings: The study revealed that the majority of projects in the NFT syllabus were only on food processing with none on topics that link nutrition to health. It further revealed that PBL was used as an additional practice for material taught initially by other means (Mean = 2.72; SD = 0.58), with limited writing of proposals (Mean = 1.47; SD = 0.85) with a narrow focus on 21st-century skills. Also, PBL strongly focused on the originality of the products (Mean = 2.53; SD = 0.82) with overall, an overemphasis on projects for assessment instead of learning. Unique Contribution to Theory, Practice and Policy: The study was limited to Kampala, so further research across the country is recommended. In addition, further studies on assessment of projects for learning are recommended.

Enhancing Information Technology Governance in Universities: A Smart Chatbot System based on Information Technology Infrastructure Library

The rapid evolution of information and communication technologies has created a pressing need for higher education institutions to modernize their Information Technology (IT) governance practices. This article proposes an innovative solution to enhance the governance and efficiency of IT services while optimizing and personalizing user experience. The proposed solution consists of a chatbot using Artificial Intelligence (AI) and Natural Language Processing (NLP) combined with the Information Technology Infrastructure Library (ITIL) standard to automate the management of IT services in the digital work environment (ENT). Intended for students, teachers, and administrators, this chatbot provides reactive support by responding to requests, reducing waiting times, and improving satisfaction. It also helps decrease operational costs and the workload of support teams by autonomously handling recurring requests. Beyond efficiency improvements, the chatbot contributes significantly to IT governance by providing structured service management, improving decision-making through real-time data, and supporting compliance with governance frameworks. An online survey conducted among 120 students revealed slow processing of requests and unavailability of services, justifying the need for this chatbot. The chatbot was designed with advanced NLP and Machine Learning (ML) technologies. Preliminary tests demonstrate the chatbot’s response reliability, with an accuracy rate of 96% and a response time decrease to an average of 4.17 seconds. The use of chatbots has considerable potential for universities to improve the efficiency of digital services offered to students.

Highway Foreign Body Intrusion Detection System Based on Deep Learning

This paper introduces the expressway intrusion detection system based on deep learning to improve traffic safety. The system adopts deep learning, image recognition, and foreign body detection technology to monitor the road condition in real-time through lidar and binocular camera groups to detect and distance the foreign body on the road. The system visualizes the detection results on the onboard screen to assist the driver to avoid and improve the safety of highway driving. In addition, the system also includes emergency braking, blind spot monitoring, lane departure warning, and other functions. The system has wide application prospects and development potential and is expected to be widely used in the future, providing a strong guarantee for the safe operation of expressways in China.

Accurate prediction of colorectal cancer diagnosis using machine learning based on immunohistochemistry pathological images

Colorectal cancer (CRC) ranks as the third most prevalent tumor and the second leading cause of mortality. Early and accurate diagnosis holds significant importance in enhancing patient treatment and prognosis. Machine learning technology and bioinformatics have provided novel approaches for cancer diagnosis. This study aims to develop a CRC diagnostic model based on immunohistochemical staining image features using machine learning methods. Initially, CRC disease-specific genes were identified through bioinformatics analysis, SVM-RFE and Random Forest algorithm utilizing RNA-seq data from both GEO and TCGA databases. Subsequently, verification of these genes was performed using proteomics data from CPTAC and HPA database, resulting in identification of target proteins (AKR1B10, CA2, DHRS9, and ZG16) for further investigation. SVM and CNN were then employed to analyze and integrate the characteristics of immunohistochemical images to construct a reliable CRC diagnostic model. During the training and validation process of this model, cross-validation along with external validation methods were implemented to ensure accuracy and reliability. The results demonstrate that the established diagnostic model exhibits excellent performance in distinguishing between CRC and normal controls (accuracy rate: 0.999), thereby presenting potential prospects for clinical application. These findings are expected to provide innovative perspectives as well as methodologies for personalized diagnosis of CRC while offering more precise references for promising treatment.

The Future of Distance Education: Exploring the Potential of Adaptive Learning Systems

Adaptive learning technologies are used in many areas of education, including online distance learning. This study investigates the applications of Adaptive Learning technology in distance education environments. Following the steps of systematic literature review and using bibliometric analysis, the study examines a total of 1071 publications. Accordingly, time trend analysis has increased steadily within recent years, and Educational Research, Computer Science, and Engineering are the leading subject areas in research on Adaptive Learning in distance education. China and USA are the countries that make the most of the contribution, followed by Taiwan, Spain, and India. Kinshuk is the author who contributed the most, followed by Caballé and Santi. The most collaborative authors are Capuano and Nicola, Ritrovato and Pierluigi, Cabelle and Santi, and Gaeta and Matteo. Pierri and Anna hold central positions in the co-authoring network. The top three journals with the most publications are Computers &amp;amp; Education, International Journal of Distance Education, and Academic Medicine, all with significant citation counts. The most frequently used keywords are “e-learning”, followed by “adaptive learning”, and “online learning”. Integrating artificial intelligence and machine learning techniques presents significant potential for enhancing adaptive learning technologies. Utilizing frameworks such as learning analytics and the Technology Acceptance Model can help identify effective strategies to increase system acceptability.

The analysis of rural tourism image optimization under the internet of things and deep learning

This study aims to utilize deep learning technology to optimize rural tourism image, enhance visitor experience, and promote sustainable development. By deploying sensors for real-time monitoring of the environment and visitor flow in rural scenic areas, combined with a Dense Convolutional Neural Network (DenseNet), automatic identification and analysis of rural landscapes are achieved. Using rural tourism along the Yellow River as a case study, this study constructs a tourism image evaluation and optimization model based on big data. The results indicate that the model performs excellently in terms of accuracy and robustness, significantly improving the presentation of rural tourism images. The study shows that realism and service facilities have the greatest impact on rural tourism image, underscoring the value of technological means in optimizing the rural tourism image.

Practical Application and Management of Information Communication Technology (ICT) to Enhance the Performance of Ugandan Secondary Schools in West Nile

This study focuses on the contribution that Information and Communication Technology (ICT) can make to improving teacher and student performance in secondary schools in West Nile Region, Uganda. Examines the existing ICT implementation, assesses integration and potential benefits issues, and analyses the relationship between ICT usage and learning outcomes. The research is focused on showing that ICT can go global and how it connects Uganda’s secondary schools in West Nile to global education networks. ICT enhances global learning through active participation, innovation, and flexibility through access to international resources and embracing cross-boundary collaboration and virtual interchanges. This has improved student achievement but also endows students and teachers with the competencies to prosper in a globally connected environment. Ugandan schools have poorly developed ICT facilities, but schools embrace ICT education and facilities for operations. The study centers on specific ICT issues in West Nile schools and explores the possibility of using ICT to raise aggregate performance and efficiency in communication, collaboration, and organizational management. Quantitative and qualitative data were collected from 400 respondents, including teachers and students from 10 secondary schools. Data collection tools included questionnaires and interviews. Quantitative analysis was performed using SPSS, while NVivo was used for qualitative analysis. Ethical considerations were strictly adhered to protect participants' rights. While observing ICT integration in teaching across the ten selected secondary schools in West Nile and surveying 100 teachers, researchers found that 55.6% of them sometimes, 33.3% consistently, and 11% seldom integrate ICT in their teaching. This limited integration is due to a lack of ICT equipment, for example, computers, projectors, and internet connections; inadequate teacher education, where the majority of the teachers are found to be either lacking skills or self-confidence to incorporate ICT in teaching; and limited resource availability where even schools that have procured ICT tools are most often found to be having very few that are inadequacy for the needs of both teacher and students to make effective use of. These results raise concerns regarding the existing disparities in developments and funds for ICT training in West Nile’s secondary schools, with recommendations being made to enhance specific plans to reduce the digital divide. These include making ICT tools a focal area, increasing internet connection, and providing training activities that would increase and develop the competencies of the teaching staff. They seek to devise a technological learning atmosphere to enhance education, teacher, and student learning outcomes

Novel study on the prediction of BI-RADS 4A positive lesions in mammography using deep learning technology and clinical factors.

The classification of Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions in mammography is complicated by subjective interpretations and unclear criteria, which can lead to potential misclassifications and unnecessary biopsies. Thus, more accurate assessment methods need to be developed. This study aimed to improve the classification prediction of BI-RADS 4A positive lesions in mammography by combining deep learning (DL) technology with relevant clinical factors. A retrospective analysis of 590 patients diagnosed with BI-RADS 4A at Shenzhen People's Hospital and Shenzhen Luohu People's Hospital was conducted, and a multi-faceted approach was employed to construct a robust predictive model. The patients were divided into training, validation, and external validation sets. The classification results from a DL system applied to mammography were recorded, and data on relevant clinical factors were collected. Univariate and multivariate logistic regression analyses were performed to identify the independent predictive factors. A predictive model and nomogram integrating these factors were developed. Assessment metrics, such as the areas under the curve (AUCs), calibration curves, and a decision curve analysis (DCA), were employed to evaluate the diagnostic performance, calibration, and clinical net benefit of the model. External validation was conducted to assess the generalization ability of the model. Four independent predictive factors (i.e., age, nipple discharge, ultrasound BI-RADS assessment, and DL system classification results) were identified and included in the predictive model. The model showed commendable diagnostic performance with AUC values of 0.85, 0.82, and 0.84 for the training, validation, and external validation sets, respectively. There were no statistically significant differences in the AUCs of the predictive model between the training set, and the internal and external validation sets (P=0.543 and 0.842, respectively). The calibration curves showed excellent calibration in the training, validation, and external validation sets, indicating a minimal deviation between the predicted and actual positive risk probabilities (P=0.906, 0.890, and 0.769, respectively). The DCA results illustrated the clinical net benefit of the model for risk thresholds greater than 0.15 and less than 0.70 in both the internal validation and external validation sets. Our predictive model, which incorporated age, nipple discharge, ultrasound BI-RADS assessment, and DL system classification results, emerged as a powerful tool for accurately predicting BI-RADS 4A positive lesions. Its application holds significant promise in helping radiologists enhance diagnostic precision and reduce unnecessary biopsies in BI-RADS 4A positive lesion cases.

Aqua-stream: an IoT based smart water management system for sustainable living

&lt;p class="03IJISAEAbstract"&gt;Aqua-stream, an innovative internet of things (IoT) enabled water management system, utilizes the power of long short-term memory (LSTM) networks, a sophisticated time-series forecasting machine learning technique with Kafka. Aqua-stream seamlessly integrates LSTM within the Kafka streaming architecture for efficient real-time data processing, ensuring quick responses to emerging water management needs. LSTM is employed for real-time anomaly detection, dynamically analyzing streaming data to prevent leaks through automated shut-off valves. The system’s comprehensive dashboard utilizes LSTM insights for live water quality analysis; adaptive scheduling based on individual preferences and personalized recommendations, enhancing cost-effective water management. This streamlined approach extends to the smart gardening system, where LSTM guides automation for optimal plant care incorporating sensors to monitor soil moisture, temperature, and sunlight levels. This system automatically adjusts watering and lighting to ensure optimal conditions for plant growth. Users can control and monitor their garden remotely via a smartphone, facilitating plant care while saving water and energy. Aqua-stream redefines home water management, offering a holistic solution that combines intelligent water conservation with smart gardening for a sustainable and connected living experience. Aqua-stream represents a seamless integration of LSTM-based machine learning and IoT technologies, offering an intelligent, yet simplified, solution for sustainable and connected living.&lt;/p&gt;

Emerging trends and hotspots in lung cancer-prediction models research.

In recent years, lung cancer-prediction models have become popular. However, few bibliometric analyses have been performed in this field. This study aimed to reveal the scientific output and trends in lung cancer-prediction models from a global perspective. In this study, publications were retrieved and extracted from the Web of Science Core Collection (WoSCC) database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze hotspots and theme trends. A marked increase in the number of publications related to lung cancer-prediction models was observed. A total of 2711 institutions from in 64 countries/regions published 2139 documents in 566 academic journals. China and the United States were the leading country in the field of lung cancer-prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that lncRNA, tumor microenvironment, immune, cancer statistics, The Cancer Genome Atlas, nomogram, and machine learning were the current focus of research in lung cancer-prediction models. Over the last two decades, research on risk-prediction models for lung cancer has attracted increasing attention. Prognosis, machine learning, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of lung cancer-prediction models and reduce the global burden of lung cancer.

Optimizing warehouse management system with blockchain and machine learning predictive data analytics

Blockchain technology is proving to be a disruptive technology in many areas of supply chain, manufacturing, medical, agriculture, and so on. Warehouses are an inevitable part of the supply chain. Issues like space optimization, route optimization, quick item pick-up, demand forecasting, and transaction management are of importance to address in warehouse management systems (WMS). Traditional database systems have limitations of interoperability among different entities involved in warehouses. This paper presents an innovative application of blockchain technology and machine learning (ML) to build a smart warehouse management system in Web3 (SWMW3). We developed a decentralized application (DApp) using Web3.0 principles, integrating ReactJS for the frontend, express for the backend, and blockchain through smart contracts. This integration enhances security and transparency by storing WMS operational data in the blockchain and automating payments and verifications through smart contracts. Additionally, we implemented a ML model for predicting the total time from order receipt to delivery, leveraging historical data to optimize workflow, reduce delays, and improve overall efficiency. This combination of blockchain for secure transactions and ML for predictive analytics generates a robust, efficient, and optimized management system for the warehouse.

Fusion algorithms on identifying vacant parking spots using vision-based approach

&lt;div class="page" title="Page 1"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;p&gt;In densely populated cities, parking space scarcity results in issues like traffic congestion and difficulty finding parking spots. Recent advancements in computer vision have introduced methods to address parking lot management challenges. The availability of public image datasets and rapid growth in deep learning technology has led to vision-based parking management studies, offering advantages over sensor-based systems in comprehensive area coverage, cost reduction, and additional functionalities. This study presents an innovative fusion algorithm that integrates object detection with occupancy state algorithms to accurately identify vacant parking spaces. The employment of the YOLOv7 framework for vehicle instance segmentation, combined with three occupancy algorithms Euclidean distance (ED), intersection over reference (IoR), and intersection over union (IoU) are compared to determine the occupancy state of observed areas. The proposed method is evaluated using the CNRPark-EXT dataset, and its performance is compared with state-of-the-art methods. As a result, the proposed approach demonstrates robustness under varying conditions. It outperforms existing methods in terms of system evaluation performance, achieving accuracies of 98.88%, 97.99%, and 90.04% for ED, IoR, and IoU, respectively. This fusion detection method enhances adaptability and addresses occlusions, emphasizing YOLOv7’s advantages and accurate shape approximation for slot annotation. This study contributes valuable insights for effective parking management systems and has potential usage in the real-world implementation of intelligent transportation systems.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;

Icon similarity model based on cognition and deep learning

Human-computer cooperation guided by natural interaction, intelligent interaction, and human–computer integration is gradually becoming a new trend in human–computer interfaces. An icon is an indispensable pictographic symbol in an interface that can convey pivotal semantics between humans and computers. Research on similar icons’ cognition in humans and the discrimination of computers can reduce misunderstandings and facilitate transparent cooperation. Therefore, this research focuses on images of icons, extracted contours, and four features, including the curvature, proportion, orientation, and line of the contour, step by step. By manipulating the feature value change to obtain 360 similar icons, a cognitive experiment was conducted with 25 participants to explore the boundary values of the feature dimensions that cause different levels of similarity. Its boundary values were applied to deep learning to train a discrimination algorithm model that included 1500 similar icons. This dataset was used to train a Siamese neural network using a 16-layer network branch of a visual geometry group. The training process used stochastic gradient descent. This method of combining human cognition and deep learning technology is meaningful for establishing a consensus on icon semantics, including content and emotions, by outputting similarity levels and values. Taking icon similarity discrimination as an example, this study explored the analysis and simulation methods of computer vision for human visual cognition. The accuracy evaluated is 90.82%. The precision was evaluated as 90% for high, 80.65% for medium, and 97.30% for low. Recall was evaluated as 100% for high, 89.29% for medium, and 83.72% for low. It has been verified that it can compensate for fuzzy cognition in humans and enable computers to cooperate efficiently.