Diagram Examples from Real Visitor Prompts

Create an accurate, simple, hand-drawable flowchart for my Python Bubble Sort practical program. Follow this exact code logic: def bubble_sort(arr): n = len(arr) for i in range(n): for j in range(0, n - i - 1): if arr[j] > arr[j + 1]: arr[j], arr[j + 1] = arr[j + 1], arr[j] return arr numbers = list(map(int, input("Enter numbers: ").split())) print("Sorted list:", bubble_sort(numbers)) Flowchart sequence: 1. Start. 2. Input the list of numbers. 3. Set "n = len(arr)". 4. Initialize "i = 0". 5. Decision: "i < n?" - Yes: initialize "j = 0". - No: display the sorted list and Stop. 6. Decision: "j < n - i - 1?" - Yes: compare adjacent elements. - No: set "i = i + 1" and return to "i < n?". 7. Decision: "arr[j] > arr[j + 1]?" - Yes: swap "arr[j]" and "arr[j + 1]". - No: skip the swap. 8. Join both branches and set "j = j + 1". 9. Return to "j < n - i - 1?". 10. When the outer loop finishes, display the sorted list and Stop. Required symbols: Oval for Start/Stop; parallelogram for input/output; rectangle for assignments, swapping, and incrementing; diamond for every decision. Label every decision arrow Yes/No. Connect every loop back to the correct decision. Do not add a "swapped" flag, an early-stop condition, or any extra decision that is not present in the code. Keep the flowchart neat, logically correct, and suitable for drawing by hand in a practical notebook.

Family tree

un arbre décisionnel qui part d'une douleur au genou, puis qui dit les différentes douleurs que ça pourrait être, puis si c'est une douleur face antérieur ça dit ce que ça pourrait être, si c'est une douelur au repos ça réduit aussi etc... pour savoir en fonction des symptômes quel pathologie ça pourrait être

视频文件 解码帧 (序号, 时间戳, BGR) ————————————————————————————▶ 采样 采样帧 序号 % 3 = 0，从第 0 帧起 ————————————————————————————▶ 不落盘，检测后释放 文本检测 检测记录 boxes, scores, timestamp ————————————————————————————▶ detections.jsonl，每个采样帧一行 粗过滤 过滤记录 boxes, scores, statuses, discard_reasons, timestamp ————————————————————————————▶ filtered_detections.jsonl，行数与检测记录相同 候选分组 字幕候选 start_time, end_time, representative, members ————————————————————————————▶ subtitle_candidates.jsonl 复制 已选字幕 与字幕候选相同 ————————————————————————————▶ selected_subtitles.jsonl

→ A → B → D → G → ↘ E ↗ A → C → E y C → F → G

Inicio → A → B → D → G → Fin ↘ E ↗ A → C → E y C → F → G

Haz un flujograma de como prepar un sandwich INICIO -> ¿Hay pan e ingredientes? - SI -> Lavar vegetales (lechuga y tomate) - NO -> Comprar ingredientes necesarios -> Regresar a casa -> Lavar vegetales (lechuga y tomate) Lavar vegetales -> Cortar tomate en rodajas y secar la lechuga Cortar tomate -> Colocar la primera rebanada de pan en la tabla o plato Colocar primera rebanada -> ¿Quieres untar algo (mayonesa, mantequilla)? - SI -> Untar en ambas rebanadas -> Poner hoja de lechuga sobre la primera rebanada - NO -> Poner hoja de lechuga sobre la primera rebanada Poner hoja de lechuga -> Colocar rodajas de tomate sobre la lechuga Colocar rodajas de tomate -> Añadir láminas de jamón dobladas Añadir jamón -> Agregar las láminas de queso Agregar queso -> Colocar la segunda rebanada de pan (tapa superior) Colocar segunda rebanada -> ¿Prefieres el sándwich caliente? - SI -> Tostar en sartén o plancha -> Cortar a la mitad (en diagonal) - NO -> Cortar a la mitad (en diagonal) Cortar a la mitad -> FIN

Results of the Proposed System : Successfully developed MalVision, an automated malware family classification system. Supports both .bytes and .exe malware files for analysis. .exe files are automatically converted into byte representation before processing. Malware byte sequences are successfully transformed into 64 × 64 grayscale fingerprints. The trained model successfully identifies malware families from generated grayscale images. The system provides prediction confidence and entropy for every analyzed sample. Grayscale fingerprints and pixel-intensity distributions provide visual insights into malware characteristics.

Abstract— Brain tumor classification using Magnetic Resonance Imaging (MRI) is essential for early detection and effective treatment planning. This paper proposes an explainable hybrid Deep Learning and Machine Learning framework for automated brain tumor classification. Initially, MRI images are preprocessed to improve image quality and consistency. A Convolutional Neural Network (CNN) is used to extract significant features from MRI images, while a Support Vector Machine (SVM) classifier utilizes these features to classify brain tumors. To improve model transparency, Gradient-weighted Class Activation Mapping (Grad-CAM) is incorporated to visualize the important regions influencing classification decisions. The proposed framework is evaluated using performance metrics such as accuracy, precision, recall, and F1-score. By combining automated feature extraction, machine learning-based classification, and explainable AI, this approach aims to improve classification reliability and provide interpretable predictions for supporting medical image analysis. Keywords: Brain Tumor Classification, MRI, Deep Learning, Machine Learning, CNN, SVM, Explainable Artificial Intelligence, Grad-CAM. i need workflow diagram

faz me um ecomapa deste genero mas com as informaçoes que te vou dar ( SÃO UM CASAL- mulher - E.D., 73 anos; homem R.P., 74 anos, ( ligações dele- Cáritas Diocesana de coimbra (LIGAÇÃO FORTE), USF Manuel Cunha), ligações conjuntas- Familia, vizinhos. (RESPEITA TODOS OS SIMBOLOS TENDO EM CONTA A INFORMAÇÃO

. Logigramme synthétique de la procédure • Étape 1 : Préparation et vérification du dossier d’importation • Étape 2 : Dépôt du dossier au bureau des Douanes de l’aéroport • Étape 3 : Contrôle documentaire et enregistrement officiel du dépôt • Étape 4 : Retrait du dossier visé et vérification de la conformité des pièces • Étape 5 : Présentation du dossier auprès de la compagnie aérienne • Étape 6 : Établissement de la facture et règlement des frais aéroportuaires et de fret • Étape 7 : Retrait du reçu de paiement et du bon de livraison (BL) • Étape 8 : Réalisation des photocopies d’usage pour l’archivage et le contrôle • Étape 9 : Présentation des documents au magasin sous douane • Étape 10 : Contrôle documentaire final et validation de la mise à disposition • Étape 11 : Pointage physique des colis, vérification du poids et des marques • Étape 12 : Signature de la décharge de sortie par le transitaire • Étape 13 : Chargement des marchandises et évacuation du magasin • Étape 14 : Contrôle final au poste de sortie du parc fret (1er et 2e postes) • Étape 15 : Sortie effective de la marchandise et acheminement vers le client

Create a professional 16:9 enterprise architecture diagram titled "IGW – REST to IMS Transaction Flow". Show these components from left to right: REST Client → IGW (Java REST API on Open Liberty) → IMS Connect → OTMA → IMS TM / JMP Region → i4j Framework → IMS Java Application. Use arrows to show the synchronous request moving from the REST client to the IMS application and the response returning along the same path in reverse, ending with an HTTP JSON response. Important: i4j runs inside the IMS Java application environment, not as a separate network service. IMS Connect communicates with IMS TM through OTMA. IGW communicates with IMS Connect over TCP/IP. Use a clean white background, rounded cards, simple icons, purple and blue accents, teal highlights, thin directional arrows, and readable labels. Keep the diagram technically accurate and uncluttered.

Create a professional system architecture diagram for a Student Interview Experience Portal. Arrange three rectangular layers vertically: 1. Presentation Layer: Web Browser, 12 HTML Pages, HTML5, CSS3, JavaScript. 2. Application Layer: Python Flask Server, 7 Route Modules, JWT Authentication Check, Analytics Service. 3. Data Layer: MySQL 8 Database, Automatic SQLite Fallback when MySQL is unavailable. Connect the Presentation Layer and Application Layer with a double-headed arrow labelled "HTTP Requests / JSON Responses". Connect the Application Layer and Data Layer with a double-headed arrow labelled "SQL Queries / Results". Use light blue, light green and light orange backgrounds respectively. Use a white canvas, clear labels, aligned boxes and a clean academic report style. Do not add any technologies or components not specified. Title: Student Interview Experience Portal — System Architecture.

Abstract— Brain tumor classification using Magnetic Resonance Imaging (MRI) is essential for early detection and effective treatment planning. This paper proposes an explainable hybrid Deep Learning and Machine Learning framework for automated brain tumor classification. Initially, MRI images are preprocessed to improve image quality and consistency. A Convolutional Neural Network (CNN) is used to extract significant features from MRI images, while a Support Vector Machine (SVM) classifier utilizes these features to classify brain tumors. To improve model transparency, Gradient-weighted Class Activation Mapping (Grad-CAM) is incorporated to visualize the important regions influencing classification decisions. The proposed framework is evaluated using performance metrics such as accuracy, precision, recall, and F1-score. By combining automated feature extraction, machine learning-based classification, and explainable AI, this approach aims to improve classification reliability and provide interpretable predictions for supporting medical image analysis. Keywords: Brain Tumor Classification, MRI, Deep Learning, Machine Learning, CNN, SVM, Explainable Artificial Intelligence, Grad-CAM. NEEED WORK FLOW AND ARCTICTURE DIAGRAM

Abstract— Brain tumor classification using Magnetic Resonance Imaging (MRI) is essential for early detection and effective treatment planning. This paper proposes an explainable hybrid Deep Learning and Machine Learning framework for automated brain tumor classification. Initially, MRI images are preprocessed to improve image quality and consistency. A Convolutional Neural Network (CNN) is used to extract significant features from MRI images, while a Support Vector Machine (SVM) classifier utilizes these features to classify brain tumors. To improve model transparency, Gradient-weighted Class Activation Mapping (Grad-CAM) is incorporated to visualize the important regions influencing classification decisions. The proposed framework is evaluated using performance metrics such as accuracy, precision, recall, and F1-score. By combining automated feature extraction, machine learning-based classification, and explainable AI, this approach aims to improve classification reliability and provide interpretable predictions for supporting medical image analysis. Keywords: Brain Tumor Classification, MRI, Deep Learning, Machine Learning, CNN, SVM, Explainable Artificial Intelligence, Grad-CAM.

An event-driven process chain for responsible/safer gambling

a flowchart for responsible gambling

An event-driven process chain for purchase approval from requisition received through manager approval, procurement, and order placed

B. Data Flow Diagram (DFD – Level 1) The primary data flow in the system is: Customer Dataset → Data Preprocessing → Feature Engineering → Model Training and Evaluation → Saved Model → Streamlit Application → Churn Prediction and Explanation The explainability component uses model predictions and customer feature values to generate SHAP-based explanations, which are presented through the application interface.

The workflow consists of the following stages: Load the IBM Telco Customer Churn dataset. Clean the data and perform feature engineering. Split the dataset into training and testing sets. Train and compare five machine learning classifiers. Evaluate model performance using appropriate classification metrics. Select the final model and generate SHAP explanations. Load the saved model into the Streamlit application. Display churn predictions, risk categories, and explanatory visualizations.

Total Savings AED 5,150 ╱ │ ╲ ╱ │ ╲ 🌿 Diesel 🌿 Loading 🌿 Man-hour AED 1500 AED 1800 AED 1200 │ ╱──────┴──────╲ ╱ ╲ 🌿 Air Bag 🌿 Overtime AED 700 AED 950 aro smart vabe cororate style baniye daw

Accounting scope or basic formula

A flowchart for off-cycle claim checking

financial administration Budget Budgetary