Page 7 Visual Data:
- Description: [IMAGE ANALYSIS]
This image appears to be a page from a technical or academic document, specifically a research paper or a report. The page is divided into several sections, each containing different types of graphical and textual data. Here is a detailed description of the content:

### Top Section:
- **Title**: "TẬP CHÍ KHOA HỌC VÀ CÔNG NGHỆ TRƯỜNG ĐẠI HỌC HƯNG VƯỜNG" (Study of Research and Engineering in the University of Hung Vuong)
- **Author**: Đỗ Quang Hưng (Author)

### Main Graph:
- **Title**: "MLP (Neural Network) - Giá trị thực tế và giá trị dự báo của ROA"
- **Axes**:
  - **X-axis**: Represents the "Thực tế" (Actual).
  - **Y-axis**: Represents the "Giá trị dự báo" (Predicted Value).

### Sub-Sections:
1. **Graph 1**:
   - **Title**: "Sai số dự báo"
   - **Axes**:
     - **X-axis**: Represents the "Mẫu" (Sample).
     - **Y-axis**: Represents the "Sai số dự báo" (Error Rate).

2. **Graph 2**:
   - **Title**: "Phân bố sai số"
   - **Axes**:
     - **X-axis**: Represents the "Giá trị sai số" (Error Rate).
     - **Y-axis**: Represents the "Số lượng" (Number of Observations).

### Textual Content:
- **Hình 5. Giá trị thực tế và dự báo ROA của MLP (Neural Network)**:
  - **Caption**: "Hình 5. Giá trị thực tế và dự báo ROA của MLP (Neural Network)"
  - **Explanation**: This section discusses the relationship between the actual ROA values and the predicted ROA values using a neural network (MLP).
  - **Context**: The text mentions that the neural network (MLP) is used to predict the ROA values based on various inputs.

### Table:
- **Title**: "Bảng 3. Kết quả dự báo ROE"
  - **Columns**:
    - **Mô hình**: Represents the different models used.
    - **RMSE**: Represents the Root Mean Squared Error.
    - **MAPE**: Represents the Mean Absolute Percentage Error.
    - **MAE**: Represents the Mean Absolute Error.
    - **Pearson Correlation R**: Represents the Pearson correlation coefficient.
    - **Theil’s U**: Represents Theil’s U index.

### Additional Information:
- **Footer**: The page number is 20.
- **Author**: The author is Đỗ Quang Hưng.

### Analysis:
This page appears to be part of a research paper or report that focuses on the application of neural networks (MLP) in predicting ROA (Return on Assets) values. The graphs and tables provide insights into the accuracy and performance of the neural network model in predicting ROA values. The text explains the relationship between actual ROA values and predicted ROA values, which is crucial for financial analysis and decision-making.

### Conclusion:
This page is a valuable resource for anyone interested in the application of neural networks in financial modeling. The detailed graphs and tables provide a clear understanding of the model's performance, which can be used to evaluate the accuracy and reliability of the neural network model in predicting ROA values.
- Text Content: [IMAGE ANALYSIS]
The image is a page from a document, likely a research paper or academic report, discussing the application of neural networks in financial modeling. The page is divided into several sections, each containing different types of graphical and tabular data.

### Top Section:
- **Title**: TẬP CHÍ KHOA HỌC VÀ CÔNG NGHỆ TRƯỜNG ĐẠI HỌC HƯNG VƯỜNG
- **Author**: Đỗ Quang Hưng

### Middle Section:
- **Graph 1**: Scatter Plot
  - **Title**: MLP (Neural Network) - Giá trị thực tế và giá trị dự báo của ROA
  - **Axes**:
    - **X-axis**: Mẫu
    - **Y-axis**: Giá trị dự báo
  - **Data Points**: Blue dots representing actual values and a solid line representing the predicted values.

- **Graph 2**: Scatter Plot
  - **Title**: Sai số dự báo
  - **Axes**:
    - **X-axis**: Mẫu
    - **Y-axis**: Sai số dự báo
  - **Data Points**: Red dots representing actual values and a dashed line representing the predicted values.

- **Graph 3**: Bar Chart
  - **Title**: Phân bố sai số
  - **Axes**:
    - **X-axis**: Giá trị sai số
    - **Y-axis**: Số lượng
  - **Data Bars**: Green bars representing the number of instances with each level of sai số.

### Bottom Section:
- **Table**: Table 3. Kết quả dự báo ROE
  - **Columns**:
    - **Mô hình**: Various models
    - **RMSE**: Root Mean Squared Error
    - **MAPE**: Mean Absolute Percentage Error
    - **MAE**: Mean Absolute Error
    - **Pearson Correlation R**: Pearson correlation coefficient
    - **Theil’s U**: Theil’s U index
  - **Rows**:
    - **Random Forest**: 0,0811
    - **XGBoost**: 0,0920
    - **MLP (Neural Network)**: 0,1068

### Additional Information:
- **Footer**: The page number is 20.
- **Language**: Vietnamese.

### Analysis:
The document appears to be discussing the performance of different models (Random Forest, XGBoost, and MLP (Neural Network)) in predicting ROA (Return on Assets) using neural networks. The graphs and table provide visual and quantitative insights into the accuracy and performance of these models. The scatter plots and bar chart help in understanding the distribution of errors and the correlation between actual and predicted values. The table provides a comparative analysis of the models' performance metrics, such as RMSE, MAPE, MAE, Pearson Correlation R, and Theil’s U, which are crucial for evaluating the effectiveness of the models in predicting ROA.

Overall, the document is a detailed analysis of neural network models in financial forecasting, highlighting their strengths and weaknesses in predicting ROA.


Page 10 Visual Data:
- Description: [IMAGE ANALYSIS]
This image appears to be a slide from a presentation, likely related to the technology sector, specifically focusing on the growth and performance of certain industries. The slide is divided into two main sections: a textual section and a graphical section.

### Textual Section:
The textual section contains a paragraph of text in Vietnamese, which discusses the current state of the technology sector in Vietnam. The text mentions that the technology industry has seen significant growth in recent years, with a focus on the top three companies: Apple, Samsung, and others. The text also highlights the challenges faced by the industry, such as the impact of the COVID-19 pandemic and the need for continuous innovation to stay competitive.

### Graphical Section:
The graphical section consists of two main graphs. The left graph is a bar chart, which shows the growth in revenue or some other metric for three different companies: MWG, FRT, and DGW. The bars are color-coded and represent different time periods: June 2022, September 2022, December 2022, and March 2023. The bars are colored in blue, gray, and black, respectively.

The right graph is a line graph, which shows the revenue or some other metric for the same three companies over time. The line graph is color-coded and represents the same time periods as the bar chart. The line graph shows fluctuations in revenue over the years, with some periods of growth and others of decline.

### Additional Information:
- **Source**: The slide is attributed to "PSI tổng hợp và tính toán" (PSI total compilation and calculation).
- **Color Scheme**: The slide uses a color scheme of blue, gray, and black, which is consistent throughout the presentation.

### Analysis:
The slide provides a comprehensive overview of the technology sector in Vietnam, highlighting the growth and challenges faced by the industry. The bar chart and line graph together provide a visual representation of the revenue or other metrics for the three companies over time, allowing for a clear understanding of the trends and fluctuations.

### Marketing Post:
Using the information from the slide, a marketing post could focus on the following points:
1. **Industry Growth**: Highlight the significant growth in the technology sector in Vietnam, emphasizing the top three companies.
2. **Challenges and Opportunities**: Discuss the challenges faced by the industry, such as the impact of the COVID-19 pandemic, and opportunities for continued innovation.
3. **Performance Analysis**: Provide a detailed analysis of the revenue or other metrics for the three companies over time, using both the bar chart and line graph.
4. **Future Outlook**: Conclude with a prediction or outlook for the industry, based on the trends observed.

By providing this detailed description, a marketing post can effectively communicate the key points of the slide and engage the audience with the information presented.
- Text Content: [IMAGE ANALYSIS]
The image is a page from a document, likely a report or analysis, focusing on the "NGÀNH HÀNG NỘI BẤT" (Industry). The layout is divided into two main sections: text and charts.

### Text Section:
The text is written in Vietnamese and discusses the current state of the construction and real estate industry. It mentions that the construction industry has seen a significant increase in activity, with a peak in the third quarter of 2022. The industry is heavily reliant on the construction of high-rise buildings, particularly those by Apple and Samsung. The text also highlights the challenges faced by the industry, such as supply chain disruptions and the impact of the COVID-19 pandemic.

### Charts:
The charts on the page are color-coded and represent different metrics:
1. **Bar Chart:**
   - **X-axis:** Represents different categories (MWG, FRT, DGW).
   - **Y-axis:** Represents the percentage of the metric.
   - **Bars:** Represent the percentage of each category.
   - **Colors:** MWG is in blue, FRT is in light blue, and DGW is in dark blue.

2. **Line Chart:**
   - **X-axis:** Represents the months from March 2020 to June 2023.
   - **Y-axis:** Represents the percentage of the metric.
   - **Lines:** Represent the percentage change over time for each category.
   - **Colors:** MWG is in orange, FRT is in yellow, and DGW is in light green.

### Analysis:
- **MWG (Medium-Value Group):** This category shows a significant increase in the percentage of the metric from March 2020 to June 2023.
- **FRT (Fast-Response Time):** This category shows a decrease in the percentage of the metric from March 2020 to June 2023.
- **DGW (Delayed-Response Time):** This category shows a fluctuating trend with a decrease in the percentage of the metric from March 2020 to June 2023.

### Conclusion:
The text and charts together provide a comprehensive overview of the construction and real estate industry's performance over the specified period. The bar chart highlights the overall trend, while the line chart provides a more detailed view of the changes over time. This information can be used to understand the industry's performance, identify trends, and make informed decisions.