Pembelajaran Mendalam Jobs

Project Title: Iraqi License Plate Detection and Recognition using Python (YOLO + OCR) Description: I am looking for a developer to build a complete Python project for automatic Iraqi vehicle license plate detection and recognition. Project Requirements: 1. Detect Iraqi license plates from vehicle images using a deep learning model (preferably YOLOv8). 2. Crop the detected license plate from the image. 3. Apply OCR to recognize the license plate number and characters. 4. The system should support Arabic letters, English letters, and Arabic-Indic numbers used in Iraqi license plates. 5. Use data augmentation techniques (rotation, blur, illumination changes) to improve model performance. 6. Provide training and testing scripts. 7. Provide the full Python source code. Tools preferr...

I need a YOLOv8 model trained to detect and classify approximately 12 types of fishing vessels from provided images. The dataset will be in YOLO/COCO format. Requirements: - Target accuracy: ~90%+ - Input image resolution: 1280x1280 - Performance metrics: Precision, Recall, F1 Score - Inference optimization: Balanced between speed and accuracy Deliverables: - Trained weights (.pt) - Training script - Inference code Ideal Skills & Experience: - Expertise in YOLOv8 and PyTorch - Experience with object detection and model training - Familiarity with performance metrics and optimization techniques

I want to see a working proof-of-concept that can watch a live webcam feed in an indoor setting and reliably decide whether someone is merely holding a phone or actively using it. The prototype must process the video stream in real time, recognise the presence of a smartphone, then look for behavioural cues—hand placement, posture and, ideally, gaze direction—to confirm active usage. Whenever the model judges that the phone is being used, it should trigger an audible or visible alarm on the host machine instantly; no other logging or alert channels are required for this first iteration. I am happy for you to choose your preferred computer-vision stack (e.g. OpenCV, MediaPipe, PyTorch, TensorFlow, ONNX) as long as the end result runs on a typical workstation without specia...

**Project Title:** Machine Learning Model to Analyze Price Behaviour Around Moving Average (Forex – XAUUSD) **Project Description:** We are building a quantitative research project focused on understanding and modeling **price behaviour around a Moving Average** using machine learning techniques. The objective is to develop a **statistical/ML model that can estimate the probability and confidence of price movement relative to a moving average**. This project is specifically focused on **XAUUSD (Gold) in the Forex market**, using **1-minute historical data**. The key requirement is that the **model must analyze price dynamics strictly around the Moving Average**, without relying on additional technical indicators such as RSI, MACD, Bollinger Bands, etc. We want to understand and ...

I’m finalising an academic paper and need a short-list of fresh, publishable ideas in Artificial Intelligence, Machine Learning or Deep Learning. The goal is to walk away with several concrete topics—each practical enough for a semester-length implementation in Python yet still offering a genuine research gap I can highlight as my own contribution. Here’s what I expect to receive: • 3–5 clearly worded research topics, phrased as potential paper titles. • For every topic, a crisp explanation of what makes it novel in light of recent literature. Cite the gap or twist that turns it from a replication study into new work. • A brief outline of the methodology: suggested datasets, baseline models (e.g., CNNs, Transformers, LSTMs, or classic scikit-lea...

Need tgood tutors for data science and computer science for large number number of tasks only indvial tutors no agencies and middle man

We are looking for a web designer to create a corporate website (14 pages) for a company specializing in construction material installation and public facility construction. Requirements: Responsive design (PC / Tablet / Mobile) Clean and modern design Attention to detail -We Provide: Text and images Preference: Female designers preferred. Please send: Portfolio Estimated timeline Your price. reference site:

We are looking for an experienced Senior AI/ML Engineer to join our team and help build advanced AI-driven solutions. The ideal candidate will have a strong background in machine learning, deep learning, and data science, with expertise in deploying models at scale. If you're passionate about tackling complex challenges and working with cutting-edge AI technologies, we want to hear from you! We are looking for an individual, not agency. Scope of work - Collaborate with the team to integrate AI/ML technologies into current projects. - Develop and optimize machine learning algorithms. - Perform ongoing analysis and validation of AI models. Key Responsibilities: ** Fluent English Skill is MUST!!! So if you don't have ENGLISH SKILLs don't apply for this position. Develop a...

Project Title: MERN Stack Web Application Development Project Description: I am looking for a developer who can build a web application using the MERN Stack (MongoDB, , React.js, Node.js). This project is for academic purposes. Requirements: • Frontend: React.js with responsive UI • Backend: Node.js and • Database: MongoDB • Features: User authentication (login/signup), CRUD operations, basic dashboard • Clean and well-structured code Additional Requirements: • Source code must be provided • Basic documentation of the project • Proper comments in the code Budget: Open for discussion Deadline: 5–7 days If you have experience with MERN Stack development, please place your bid and share your previous work if available.

**Project Title:** Source Detection using Human Behaviour Dynamics using Machine Learning / Deep Learning / AI **Project Description:** I am looking for an experienced Machine Learning / Deep Learning developer to build a model also novelty is must **Scope of Work:** * Dataset preprocessing and exploration * Model development using ML/DL techniques * Training and testing the models * Performance evaluation (Accuracy, Precision, Recall, Confusion Matrix) * Comparison of different models * Clear documentation of the workflow **Preferred Tech Stack:** * Python * Machine Learning / Deep Learning models * TensorFlow / PyTorch / OpenCV * Data visualization libraries **Deliverables:** * Complete source code * Trained model * Results and performance comparison * Brief documentation of th...

Project Overview Designed and implemented an intelligent DDoS detection and mitigation framework for Software-Defined Networking (SDN) based Vehicular Ad-hoc Networks (VANETs) to ensure secure, reliable, and low-latency vehicle-to-infrastructure (V2I) communication. The system leverages SDN’s centralized control to dynamically monitor network traffic and mitigate DDoS attacks in real time, ensuring uninterrupted safety-critical vehicular services. Key Innovations: SDN-Based Centralized Security Control OpenFlow-enabled SDN controller Global view of VANET traffic behavior Intelligent DDoS Detection Flow-level traffic analysis Anomaly detection using ML / Deep Learning (LSTM Autoencoder / Hybrid model) Real-Time Mitigation Strategy Malicious vehicle flow isolation Dynamic ru...

Explainable AI Models for Any topic with novelty Project Description: Build a complete end-to-end Machine Learning project using dataset (CSV) to identify the source of infection using Explainable AI models. The project should include data loading, EDA (missing values, distributions, correlations, visualizations), data preprocessing (handling missing data, encoding categorical features, scaling numeric data, removing outliers), and feature selection. Train and compare multiple ML models with cross-validation and hyperparameter tuning, and include explainability using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). Deploy the system with a Streamlit web UI where users can upload datasets, select the target column, run preprocessing, train...

I’m working through an year long online course on Artificial Intelligence and Machine Learning and I’m running short on time for several graded assignments and mini-projects. Rather than general tutoring or technical platform troubleshooting, I specifically need someone who can step in, finish the required tasks, and submit clean, well-commented solutions on my behalf. This will be an ongoing project and will span an year. Typical exercises range from data preprocessing and feature engineering to training and evaluating models with libraries such as Python, NumPy, pandas, scikit-learn, TensorFlow or PyTorch. Code must run flawlessly in the course’s Jupyter-based environment and meet the rubric laid out in each brief (accuracy thresholds, narrative explanations, and any...

Tôi cần một chuyên gia hỗ trợ xây dựng model phân tách neutron/gamma bằng Deep Learning sử dụng Python. Phạm vi công việc • Phân tích yêu cầu và đề xuất kiến trúc mạng phù hợp (CNN, RNN, hoặc hybrid nếu cần). • Xử lý và chuẩn hóa dữ liệu đầu vào; nếu thiếu dữ liệu, hỗ trợ tạo hoặc mở rộng bộ dữ liệu mô phỏng. • Huấn luyện, đánh giá và tinh chỉnh mô hình để đạt độ chính xác tối ưu. • Bàn giao mã nguồn Python kèm hướng dẫn chạy, file trọng số đã huấn luyện và báo cáo tóm tắt kết quả. Kỳ vọng • Ưu tiên giải pháp gọn nhẹ, dễ t&aac...

I need a complete, reproducible deep-learning pipeline that takes raw leaf images, learns to recognise plant diseases, and then serves the prediction through a Streamlit interface. Because I do not yet have the images, the first task is to identify and download a suitable, well-labelled dataset from Kaggle. Feel free to compare a few candidates, but the final choice should give good class balance and enough samples per disease category. Once the data is in place, walk through exploratory data analysis, preprocessing, and augmentation inside a Jupyter notebook. From there, build and tune a convolutional neural network (TensorFlow / Keras or PyTorch are both fine) and report the usual metrics plus a confusion matrix so I can judge class-wise performance. When the model is satisfactory, save...

SCRM dashboard: Track victims, chat logs, stages (hunt → raise → kill), message history. • AI chat automation: LLM-powered bot (ChatGPT/Claude style) with pre-set grooming scripts for rapport, flirting, daily check-ins, objection handling, profit teasing, small deposit pushes. Auto 70-80% of early chats; manual takeover for big targets. • Fake crypto trading platform: Web + mobile app (React/Flutter). User side: login, fake wallet, live-looking charts, trade history, auto-show rising profits (2-10% daily). Admin side: control all user balances, see real deposits (crypto wallets), manually adjust fake gains, block withdrawals. • Bulk outreach basics: Tools/scripts for wrong-number SMS blasts, dating app auto-messages, profile generator (AI photos + bios). • ...

I have continuous video footage from live inspection of steel flat bars strips our production line and need a complete deep-learning pipeline that flags surface, structural, and functional defects in real time. The raw videos are already labeled by timestamp; frame-level annotation may still be required for optimal accuracy. Scope • Design and train a deep neural network—CNN, transformer, or hybrid model—that detects all three defect categories directly from video streams. • Implement preprocessing (frame extraction, augmentation, ROI isolation) and post-processing (tracking, alert generation) in Python using libraries such as PyTorch/TensorFlow and OpenCV. • Optimise for inference on an on-premise GPU; latency under 200 ms per frame is the target. &bull...

Build an end-to-end AI application that can reliably solve JEE-style math problems, explain solutions step-by-step, and improve over time. The goal of this assignment is not just model usage, but to evaluate whether you can: design a RAG pipeline build a multi-agent system handle image, text, and audio inputs introduce human-in-the-loop (HITL) implement memory & self-learning package everything into a working application and deploy it You do not need to be a DevOps or full-stack expert, but you must be able to build, run, and deploy a simple app.

project is to develop an image processing–based infrared small target detection system that identifies small targets in infrared images using a combination of local contrast analysis and nonlocal spatial information. The project will use a publicly available dataset The project will involve implementing and integrating two classical image processing methods: A. Dual-Window Local Contrast Method (DW-LCM) B. Multiscale Window Infrared Patch-Image Model (MW-IPI)

I’m building a ROS 2 pipeline that must reliably spot nuts and bolts in a live camera feed and publish their positions to the rest of my stack in real time. Your job is to create the complete vision-detection module—from model training or fine-tuning through to a clean ROS 2 node that subscribes to an image topic and spits out the detected objects with bounding boxes (or masks) and a confidence score. OpenCV, TensorFlow/PyTorch and any of the common ROS 2 image-transport plugins are all fine as long as the final node runs on Humble and stays GPU-agnostic (CUDA acceleration is a bonus, not a requirement). I already have a test rig with a standard USB camera; if you need specific calibration images I can capture them for you. Please deliver: • Source code for the detection...

More details: Which deep learning framework do you prefer for this project? TensorFlow Do you have a preferred dataset for brain MRI image segmentation? Please use a publicly available dataset Which style of output visualization do you prefer? 2D slices with segmentation overlay

The project centers on building a production-ready TensorFlow 2.x model that classifies tabular data delivered to us through an internal API. I have the API specifications and sample payloads ready; you will turn those streams into a clean training pipeline, engineer the right features, and iterate until the classifier meets our performance targets in real-world tests. Scope of work • Data pipeline – pull the API data, handle preprocessing, and produce TensorFlow-friendly datasets for train/val/test splits. • Model development – design, train, and tune a deep learning architecture suitable for tabular inputs (e.g., wide & deep, Transformer, or other proven structures). • Optimization – experiment with hyperparameters, regularization, and callback...