Autoencoderpekerjaan

...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 rule installation at RSUs & switches Rate limiting and blacklisting of attack sources VANET-Specific Security Awareness Handles high mobility and dynamic topology Maintains low latency for safety messages Technologies Used SDN Controller: Ryu / ONOS VANET Simulator: SUMO + Mininet-WiFi ML/DL Model: LSTM Autoencoder / SVM / Random Forest/ use other technique than LSTM can use multimodel for transfer learning Dataset: SDN Specifc dataset Protocols: OpenFlow, TCP/UDP Programm...

...learn old operating regimes *Features lose meaning due to control changes *Label scarcity blocks retraining Include one mini-example from a paper: *“Model trained on Month A fails on Month B” type result *You cite it and explain why it happened 4. Methods and drift-handling solutions (1.5–2 pages) Compare only 4 method families to keep scope doable: Pick 2 detector families: *Unsupervised (Autoencoder, Isolation Forest, PCA) *Time-series deep models (LSTM/GRU/Transformer) *Graph-based (GNN) if you want topology awareness Pick 2 drift-handling strategies: *Drift detectors (ADWIN, Page-Hinkley, DDM) *Sliding window + periodic retraining *Online learning (River-style) *Ensembles that replace stale models *Physics-informed constraints (power laws, limits) Delivera...

...ready for you to turn into a robust signal-generating engine. The dataset must first be converted to Renko bricks; from there, I want both supervised and unsupervised deep-learning approaches explored so we can isolate the technique that flags high-probability entry and exit points most reliably. The core need is model generation. You’ll select or design the architecture—LSTM, Transformer, autoencoder hybrids, or any framework that proves effective—then train, validate, and benchmark it. I can supply continuous data feeds for additional walk-forward testing once an initial model is in place. You’ll run the experiments on your own GPU or on AWS you provision; if a different cloud or on-prem solution will accelerate training, outline that and we’ll s...

...with nothing skipped . 3) MATLAB Illustrative Example(s) MATLAB 2025 has many AI tools — prefer built-in functionality only for visualization and diagnostics; the core algorithm must be derived and coded manually for educational clarity. Topics to Cover 1. Regression 2. Classification 3. kNN & Decision Tree 4. Clustering: K-means 5. Dimension Reduction 6. Artificial Neural Networks (ANN) 7. Autoencoder 8. Convolutional Neural Networks (CNN) 9. Explainable AI (XAI) 10. Recurrent Neural Networks (RNN) 11. Transfer Learning 12. Physics Informed Neural Network 13. Generative AI diffusion models, transformers Deliverables For each topic: - Microsoft PowerPoint presentations using standard slide layout (size is wide screen 16:9), readable fonts, and color contrast suit...

...Learning, and Software Defined Networking (SDN) to develop a complete project titled: “Intelligent Detection Mechanism for DDoS Attack Prevention in SDN-VANET.” The goal is to design and implement an intelligent, ML/DL-based detection and mitigation system that can detect Distributed Denial of Service (DDoS) attacks in SDN-enabled Vehicular Ad-hoc Networks (VANETs). 1. Detection Module: Use LSTM Autoencoder (unsupervised) or any efficient deep learning algorithm for DDoS anomaly detection. Optionally integrate Federated Learning (FedAvg) across RSUs/OBUs to simulate distributed learning without sharing raw data. Use datasets like CIC-DDoS2019, CIC-IDS2017, or CAIDA DDoS dataset for training and evaluation. Implement feature extraction (e.g., via CICFlowMeter or ...

...to learn normal behaviour on its own, then flag anything unusual in real time. Preferred approach My goal is to train an autoencoder and an Isolation Forest on historical data so the model understands the “normal envelope” of the environment. Once trained, the lightweight inference layer should run locally on the ESP32 if memory allows; otherwise a small client on the board may stream data to a cloud endpoint where the model executes. In either case the detection latency must stay low enough for prompt alerts. Scope of work • Prepare or augment the dataset, handle scaling and any time-windowing that benefits the model. • Build, train, and evaluate both the autoencoder and the Isolation Forest, selecting whichever meets accuracy and footprint require...

Proposed Paper Title: “AI-Driven Fraud Detection in Financial Transactions Using Hybrid Machin...financial stability, data integrity, and cyber risk mitigation. Key Methodology Components: Data Collection & Preprocessing – Simulate or use anonymized bank transaction data (Kaggle, IEEE DataPort, or proprietary datasets). Feature Engineering – Identify behavioral and transactional anomalies. Modeling Techniques – Combine: Random Forest or XGBoost (for structured fraud classification) Autoencoder or LSTM (for time-series anomaly detection) Generative AI (for synthetic fraud pattern generation) System Architecture – Implement a real-time fraud detection pipeline on cloud (AWS or Azure). Evaluation Metrics – Precision, Recall, F1-score, a...

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...learn normal behaviour on its own, then flag anything unusual in real time. Preferred approach My goal is to train an autoencoder and an Isolation Forest on historical data so the model understands the “normal envelope” of the environment. Once trained, the lightweight inference layer should run locally on the ESP32 if memory allows; otherwise a small client on the board may stream data to a cloud endpoint where the model executes. In either case the detection latency must stay low enough for prompt alerts. Scope of work • Prepare or augment the dataset, handle scaling and any time-windowing that benefits the model. • Build, train, and evaluate both the autoencoder and the Isolation Forest, selecting whichever meets accuracy and footprint requ...

The task is to build a concise yet fully functional variational autoencoder that learns meaningful latent representations from X-ray images and accurately reconstructs them. I will provide an anonymised chest X-ray subset, or you may suggest a publicly available alternative that is easy to obtain and free of personal identifiers. Python 3 with PyTorch or TensorFlow/Keras is preferred. Keep the code modular, well-commented and GPU-ready so it can double as teaching material for my master’s project. The training routine should accept configurable hyper-parameters from a single file, log losses per epoch and save the best model checkpoints automatically. Deliverables • Clean source code with or • Jupyter notebook demonstrating data loading, training, reconstru...

I need an AutoEncoder specifically designed for compressing Computational Fluid Dynamics (CFD) images and making predictions based on them. Key Requirements: - Primary application: Image compression and prediction - Type of data: CFD images - Output format: To be determined (preferably JPEG, PNG, or TIFF) Ideal Skills and Experience: - Proficiency in deep learning and neural networks - Experience with AutoEncoders, especially for image processing - Knowledge of CFD data and its intricacies - Familiarity with image output formats and conversion Please share your relevant experience and proposed approach.

...Interpretation**: 1–3 bullets explaining results. MATLAB 2025 has many AI tools — prefer built-in functionality **only for visualization and diagnostics**; the **core algorithm must be derived and coded manually** for educational clarity. ## Topics to Cover 1. Regression 2. Classification 3. kNN & Decision Tree 4. Clustering: K-means 5. Dimension Reduction 6. Artificial Neural Networks (ANN) 7. Autoencoder 8. Convolutional Neural Networks (CNN) 9. Explainable AI (XAI) 10. Recurrent Neural Networks (RNN) 11. Transfer Learning --- ## Deliverables For **each topic**: - A **step-by-step mathematical explanation** (clear, no skipped steps). - A **numerical method/algorithm section** (flow + pseudocode). - A **MATLAB example** (synthetic dataset, training, visual...

...scalability (batch processing, streaming optional). Document logic and recommend improvements to enrich pattern recognition over time. Ideal Skillset: Strong Python skills (especially in data wrangling and NLP). Experience with MongoDB (PyMongo, aggregation pipelines). Familiarity with Hugging Face Transformers (T5, FLAN-T5, Mistral, etc.). Experience building or fine-tuning LSTM, Transformer, or Autoencoder models. Regex generation/mining tools (e.g., refex, AutoRegex, regexgen). Clustering algorithms and similarity measures (Levenshtein, n-gram, FAISS). Familiarity with web scraping or URL structure analysis is a plus. Comfortable working with large datasets (millions of rows). Tools & Frameworks You Might Use: Python (Pandas, Numpy, Scikit-learn, PyTorch/TensorFl...

I need an expert to develop a privacy-preserving and anomaly detection for Intrusion Detection System (IDS) for IoT networks. The system should leverage: - Semi-supervised learning - Unsupervised feature extraction - Federated learning Key functionalities include: - Anomaly detection using an autoencoder - K-Means clustering - Distributed training across edge devices Ideal skills and experience: - Strong background in machine learning and cybersecurity - Experience with federated learning and anomaly detection - Familiarity with IoT networks and privacy-preserving technologies Please provide relevant work experience.

... --- 3. Market Access FIX API Live Market Data Stream: port 5211 Trade Execution Stream: port 5212 Assets Forex Majors & Crosses Indices: US30, NAS100, SPX500, DAX40 Commodities: Gold, Silver, Oil Crypto: BTC, ETH, EOS --- 4. Core Functions (Grouped) 4.1 AI Strategy Engine (Python) Multiple AI models working in parallel: Trend Following (Transformer, LSTM) Mean Reversion (Autoencoder + XGBoost) Scalping (Reinforcement Learning with PPO/DQN) Breakout Detection (CNN + LSTM) Ensemble Learning selects best strategy based on market phase 4.2 Adaptive Trade Execution (C++) Smart Order Selection: Market, Limit, Iceberg, Dark Pool routing Order modification & cancellation Real-time latency & slippage measurement Auto-adaptive Grid + Martingale sys...

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I need a Machine Learning expert to refine and extend an existing dimensionality reduction analysis on a financial dataset. The current implementation includes PCA & Variational Autoencoder (VAE), but requires further modifications: ✅ Implement a third Autoencoder (e.g., Convolutional Autoencoder) ✅ Apply PCA & Autoencoders on both training and test sets and analyze generalization ✅ Compare reconstruction errors between training & test data ✅ Analyze key components/features for all four methods (PCA, VAE, Autoencoders) ✅ Fix minor visualization issues (integer axis labels, English-only text, formatting) Requirements: ? Strong expertise in Python (TensorFlow, PyTorch, NumPy, Pandas, Scikit-learn) ? Experience with Autoencoders & Dimensionality Reductio...

I'm seeking an expert in reinforcement learning and machine learning with a focus on digital communication systems. The project involves utilizing RL-based autoencoders to enhance an end-to-end wireless communication system, specifically dealing with noisy feedback scenarios. Key Tasks: - Design an RL-based autoencoder system aimed at optimizing channel use and enhancing overall communication efficiency. - Apply machine learning techniques to improve aspects of digital communication such as error correction, signal processing, and channel estimation. - Ensure the system is resilient to noise and can effectively handle digital communication challenges. Ideal Skills: - Proficiency in reinforcement learning and machine learning. - Extensive knowledge of digital communication sys...

I'm looking for a professional to create a detailed and high-quality visualization of my deep learning model, which is a CNN + Transformer Autoencoder. The diagram should clearly illustrate the encoder, decoder, latent space, feature maps, and their connections. Key requirements for the project: - The final architecture diagram should be SVG or vector graphics. - The diagram must use contrasting colors and be structured in a clear and easy to understand manner. - The diagram needs to include layer names, feature map dimensions, and additional comments. Specifics of the model: - The encoder consists of Conv1D, Dense, and Transformer (Multi-Head Attention). - The latent space is used for feature extraction. - The decoder includes Conv1DTranspose and Dense. - The input to the mo...

...Age Weight Lifestyle & Dietary Preferences Health Markers: NAD+ Levels Cortisol Levels Sleep Patterns Nutrient-Frequency Mapping: Data Sources: USDA FoodData Central Spectral Absorption Databases Molecular Vibration Data Mapping Mechanism: Fourier Transform of Nutrient Vibrational Frequencies Subharmonic and Harmonic Frequency Computation 2.2 AI Processing Layer Data Preprocessing: Variational Autoencoder (VAE) encodes user vitality states. RNN-based models predict optimal RFM sequences based on past biofeedback. Biofield Resonance Analysis: Quantum magnetometry assesses biofield entropy fluctuations. Predictive AI models real-time energy alignment using AI-driven resonance algorithms. RFM Generation Model: Generative AI synthesizes soundscapes based on 432 Hz tuning, binaura...

...compression, high quality, and low maintenance costs: 1. Data Preparation: • Frame Extraction: Decompose the video into individual frames. • Block Segmentation: Divide each frame into non-overlapping blocks (e.g., 8x8 pixels) to create a set of image patches. 2. Feature Extraction with Deep Learning: • Autoencoder Training: • Architecture Design: Develop an autoencoder neural network tailored for image data, comprising an encoder and a decoder. • Training: Train the autoencoder on the image patches to learn a compact representation (latent space) of the input data. • Latent Vector Generation: Utilize the encoder to transform each image patch into its corresponding latent vector, capturing essential features in a reduced-dimens...

...formatting standards and includes placeholders for each section 3. Implementation and Validation of Cryptographic Mechanisms Objective: Recreate or develop code to implement the hybrid cryptographic mechanism (ALO-DHT) described in the original article. Steps: Reproduce the Ant Lion Optimization algorithm and its integration with the Diffie-Hellman and Twofish cryptographic techniques. Integrate Autoencoder neural networks for anomaly detection as described in the article. Expected Output: Well-documented code with explanations for each component, ideally in Python. 4. Conducting Tests and Gathering Results Objective: Run simulations or tests to gather results comparable to those in the original article, specifically metrics like accuracy, precision, recall, time consumption, and...

I am in need of an image compression model implemented in Python, or by Matlab for medical datasets. The primary aim is to achieve high compression levels on MRI and CT scans while maintaining diagnostic quality. Key Requirements: - High Compression Rate: We are prioritizing a small file size to allow for efficient storage and transfer of image data. - Image Quality: The final compressed images should retain a high level of visual quality, critical for accurate medical diagnosis. - Dataset Expertise: This project is focused on MRI scans and CT scans. Any experience with handling similar medical imaging datasets is a plus. Ideal Skills and Experience: - Strong programming skills in Python or Matlab. - Profound understanding of image processing. - Experience with autoencoders, particular...

...code base and dataset I provide, construct an Abstract Syntax Tree (AST) based on them, and then create an Autoencoder using TensorFlow to compress the resulting AST for a classification problem. Key requirements for this project include: - Proficiency in Python: You will be working with a Python code base and dataset. Familiarity with Python is essential for this project. - Understanding of AST: You should be able to construct an AST tree based on the provided dataset and code base. - TensorFlow Experience: I would like the Autoencoder to be developed using TensorFlow, so prior experience with this framework is a must. - Output format: The desired output format for the Autoencoder is JSON, so you should be comfortable working with this format. This project is i...

...knowledge in the use of autoencoders for feature extraction and GRU models. Key project elements include: * Professionals will need to implement an autoencoder for feature extraction with the core goal of reducing data dimensionality. Prior experience in working with high-dimensional data and in deploying autoencoders is necessary. * Subsequently, the system built should be capable of classifying Denial of Service (DoS), User to Root (U2R), and Probe attacks within the KDD dataset. Good working knowledge of GRU models and the mentioned attacks is required. * The project includes steps to ensure the accuracy and reliability of both the autoencoder and GRU model. This includes: 1. Hyperparameter Tuning: Applicant should be dexterous in practicing various optimization te...

The main task is to create the variational autoencoder.

For this project, I'm seeking a skilled machine learning engineer with proficiency using TensorFlow jupyter notebook to create a variational autoencoder model. Your task will be to detect anomalies related to mobility patterns within an Excel-format dataset. Key Tasks Include: - Analyzing a large dataset with more then am million rows and 32 columns. - Building a variational autoencoder in TensorFlow specifically designed to identify anomalies in mobility patterns. -Visualize the result between two time Ideal skills and experience: - Proficient in TensorFlow and machine learning algorithms. - Experience with variational autoencoder . - Demonstrable expertise in anomaly detection algorithms, particularly in mobility patterns data.

I need a skilled freelancer to tackle a specific issue I'm encountering with my autoencoder model in python on the Colab platform. **My Requirements:** - Diagnose and resolve training issues. - Experience with large image data handling. **Skills and Experience Needed:** - Proficiency in Machine Learning and Neural Networks. - Hands-on experience with autoencoders, particularly with image data. - Familiarity with the Colab environment. - Strong problem-solving and analytical skills. The ideal candidate should clearly understand the typical challenges faced while dealing with autoencoders and have a proven record of fixing similar issues. If you have worked on similar tasks and have a knack for ironing out computational wrinkles, I would love to work with you.

I need a skilled freelancer to tackle a specific issue I'm encountering with my autoencoder model in python on the Colab platform. **My Requirements:** - Diagnose and resolve training issues. - Experience with large image data handling. **Skills and Experience Needed:** - Proficiency in Machine Learning and Neural Networks. - Hands-on experience with autoencoders, particularly with image data. - Familiarity with the Colab environment. - Strong problem-solving and analytical skills. The ideal candidate should clearly understand the typical challenges faced while dealing with autoencoders and have a proven record of fixing similar issues. If you have worked on similar tasks and have a knack for ironing out computational wrinkles, I would love to work with you.

I need a skilled freelancer to tackle a specific issue I'm encountering with my autoencoder model in python on the Colab platform. **My Requirements:** - Diagnose and resolve training issues. - Experience with large image data handling. **Skills and Experience Needed:** - Proficiency in Machine Learning and Neural Networks. - Hands-on experience with autoencoders, particularly with image data. - Familiarity with the Colab environment. - Strong problem-solving and analytical skills. The ideal candidate should clearly understand the typical challenges faced while dealing with autoencoders and have a proven record of fixing similar issues. If you have worked on similar tasks and have a knack for ironing out computational wrinkles, I would love to work with you.

This example shows how to train a deep learning variational autoencoder (VAE) to generate images. Ideal Skills and Experience: - Proficiency in MATLAB and experience with training Variational Autoencoders. - Strong understanding of image processing and deep learning techniques. - Ability to work with different input and output image sizes. - Familiarity with generating images using VAEs. If you are confident in your MATLAB skills and have experience with VAEs, please submit your proposal for this project.

...skilled Python developer to modify an existing autoencoder code. The code is written in Python and requires changes in the input of the autoencoder. Skills and Experience: - Proficiency in Python programming language - Strong understanding of autoencoders and their implementation - Experience in modifying existing code and making necessary changes - Familiarity with data preprocessing and manipulation techniques in Python Tasks: - Modify the input of the autoencoder code to meet the project requirements - Ensure the code runs efficiently and effectively - Debug and fix any issues that may arise during the modification process - Implement any additional features or improvements as needed Deliverables: - Updated Python code with the modified autoencoder inpu...

...changing market conditions Hybrid algorithm: RL algorithm: Deep Q-networks (DQNs) DL algorithm: Autoencoder EA: Neuroevolution Explanation: The RL algorithm would be used to learn a trading strategy that can adapt to changing market conditions. The autoencoder would be used to learn a compressed representation of market data. The neuroevolution algorithm would be used to evolve the RL algorithm to adapt to changing market conditions. The RL algorithm would be trained on a dataset of historical market data. The RL algorithm would learn to make trading decisions that are based on the current market conditions. The autoencoder would be trained on the same dataset of historical market data. The autoencoder would learn a compressed representation of market dat...

I am looking for a skilled freelancer who can develop an autoencoder dimensionality reduction code with good accuracy using Python. I have a code for PCA for dimensionality reduction on the same dataset I need a autoencoder code with fine-tuning which gives good accuracy. Dataset: I will provide the specific dataset that should be used for testing the code. Dataset is divided into 6 files 6 files needs to be dimensionality reduction using autoencoder Don't worry I have sample for that Accuracy: The desired accuracy level for the model is 80-90%. Ideal Skills and Experience: - Strong proficiency in Python programming language - Experience with developing autoencoder algorithms for dimensionality reduction - Knowledge of machine learning and deep...

Looking for help to complete the Project: (Communication Systems) In the ref paper shared for local model training they have used a unsupervised deep learning model called AMCNN LSTM , but we are planning to use supervised deep Learning model called Adversarial autoencoder, and the aggregation algorithm for federated learning used here is Fedavg, but we need FedProx and the gradient compression scheme mentioned in the same paper also needs to be implemented along with it .The datasets that needs to be used are 1) NF-TON-IOT dataset 2) NF-BOT-IOT dataset Reference document and more details on the Project are attached.

Hello, My name is Lucas, I am French and Data Scientist. I am looking for someone to do a deep learning study/model for me/with me. An autoencoder that will forecast a time series previously transformed into an image. You will find all the details in the attached document. Looking forward to discuss with you. Lucas

I am looking for a freelancer who can help me parallelise the training using autoencoders. Currently, I have a code written in Python, that works with any type of autoencoder – variational autoencoder (VAE), denoising autoencoder, or sparse autoencoder. The current code has 1-2 layers. I prefer to work on the model with the same layers, but if some further layers are required, I am open to discuss my project requirements. Furthermore, scalable and well-tested code is mandatory. To make this project successful, I need someone who has wide knowledge and experience of using different autoencoders, can work within time deadlines, and can handle tasks related to parallelizing models.

The objective is to build a system based on AutoEncoder to extract features from speech dataset for biometric access control

The research needs only review and working on certain notes. The objectives of the research are: • To filter and remove noise while preserving important features for improving the quality of low-resolution images by using novel filtering techniques. • To extract the most important facial features from the low-resolution images using Deep Autoencoder (DAE) for improved classification accuracy. • To design a low-resolution facial recognition model using a deep learning classifier NO AGENT MESSAGING PLEASE - WON'T RESPOND

Need to perform different SVM methods on a given dataset and use an autoencoder in another dataset. Need to use jupyter notebook.

I'm going to use a varational autoencoder to upload videos and sounds and then combine them all in one simple interface

1) Examine this implementation : 2)Swap CNN layers with CLTSM layers. () 3) Made CLSTM Autoencoder 4)Give input video, output gives dehazed video. Example Dataset in attachment. PLS write XX your at the beginning of the bid. So I can understand this is not AUTO-BID!

1) Examine this implementation : 2)Swap CNN layers with CLTSM layers. 3) Made CLSTM Autoencoder 4)Give input video, output gives dehazed video. Example Dataset in attachment. PLS write XX your at the beginning of the bid. So I can understand this is not AUTO-BID!

I have deep learning model for clustering, it does the clustering on data embedding learned by an Autoencoder. The problem is the AE weights seemed to be not effected by the clustering model being trained. This means that; I change the loss objective of the clustering model and it is still the "exact" same performance results (accuracy, precision, recall, F1 score) as with the previous loss objective.

Develop a CNN autoencoder for Image Analysis

I need a machine learning expert to change the autoencoder code of one file to reinforcements code. I have 3 more tasks after this. file: in

I need a ml expert to implement the autoencoder based on reinforcements for fibre optical communication. source code:

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The aim of this research project is to study and analyze the factors affecting the criticality of COVID-19 patients, and accurately predict the mortalit...COVID-19 patients, and accurately predict the mortality rate of the patients ahead of time. In this paper, COVID-19 data from the National Center for Data of Health which consists of data from 2019 to 2022. Different visualization techniques were used to extract patterns from the demographic and the clinical data of patients to determine the factors affecting COVID-19 patients. Random Forest and Autoencoder neural networks were trained to predict the mortality rate of the patients. Predictions were evaluated using AUC, ROC and accuracy scores. Neural Network resulted in an accuracy of 71.10% and Random Forest gave an accuracy of a...

Hello, I'm looking for an expert in deep learning especially the autoencoders to HELP me analyze some data(ionique images). The objectif is to find the most relevent ions and its parameters m/z. Each one of the files here(just a part of the data) is 1 ionique image, if we can fusion them all to have one complete image and then we can proceed to the analyzis with an autoencoder. It's my internship, so i need someone that will be open to have some videos calls to well discuss about the work.