You will get gender identification from sensor signals
Project details
Gender identification (GI) is to determine the sex of the individual based on the characteristics that distinguish between male and female. In this study, three different feature extraction methods are proposed for gender identification by using signals obtained from accelerometers, magnetometers and gyroscope sensors installed in 5 different body parts of the individuals. Feature extraction from signals is one of the most critical stages of GI. Because the success of GI depends on the features, different transformation methods have been applied to the signals obtained from sensors such as One Dimensional Local Binary Patterns (1D-LBPs), One Dimensional Robust Local Binary Patterns (1D-RLBPs) and Weighted One-Dimensional Robust Local Binary Patterns (W-1D- RLBPs). By using these features, different machine learning methods (SVM, RF, ANN, Knn) were elaborated for classification.
Machine Learning Tools
Keras, MATLAB, NumPy, Python, WekaWhat's included
| Service Tiers |
Starter
$600
|
Standard
$700
|
Advanced
$800
|
|---|---|---|---|
| Delivery Time | 25 days | 25 days | 25 days |
Number of Revisions | 2 | 2 | 2 |
Model Validation/Testing | |||
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$100About Yilmaz
Data Scientist & Machine Learning Engineer | Deep Learning, Statistica
Batman, Turkey - 8:22 am local time
1- Develop Machine Learning models? — YES
2-Design Deep Learning architectures? — YES
3-Perform Time-Series Modeling & Forecasting (ECG, EEG, EMG, Vibration, Sensor Streams)? — YES
4-Apply Signal Processing & Feature Extraction (Time/Frequency/Time-Frequency)? — YES
5-Build Statistical Learning Pipelines (Classification/Regression)? — YES
6-Perform Dimensionality Reduction (PCA, UMAP, t-SNE)? — YES
7-Conduct Exploratory Data Analysis (EDA)? — YES
8-Execute Data Cleaning & Preprocessing (Missing, Noise, Outliers)? — YES
9-Train/Validate/Tune ML models with proper CV strategies? — YES
10-Evaluate Model Performance (ROC, PR, F1, RMSE, MAE, R²)? — YES
11-Build Custom Loss Functions & Training Loops? — YES
12-Engineer Features (Statistical/Domain/Spectral)? — YES
13-Visualize Data & Results (Plots, Heatmaps, Trends, Signals)? — YES
14-Process Multi-Channel Signals (ECG, EEG, EMG, IMU, Accelerometer)? — YES
15-Apply Statistical Testing & Inference (ANOVA, Correlation, Hypothesis Tests)? — YES
16-Optimize GPU Training (Batching, Mixed Precision, Memory)? — YES
17-Implement End-to-End ML Pipelines (Data → Model → Metrics → Reports)? — YES
18-Build Multi-Class & Multi-Label Models? — YES
19-Conduct Research-Grade Experimentation & Benchmarking? — YES
20-Generate Technical Reports, Figures, Tables & Summaries? — YES
21-Provide Technical Consulting & Model Design Guidance? — YES
22-Mentor on ML/DL/Time-Series topics (Academic or Industrial)? — YES
23-Build Computer Vision Models (Image)? — YES
24-Apply Image Preprocessing & Feature Extraction (Filters, Edges, Keypoints)? — YES
25-Perform Object Detection/Tracking (YOLO, Faster-RCNN, SORT, DeepSORT)? — YES
26-Implement Image Classification & Segmentation (CNN/UNet/ViT)? — YES
27-Conduct Text Mining & NLP (Tokenization, Lemmatization, N-grams)? — YES
28-Build Text Classification & Topic Modeling (TF-IDF, LDA, BERT)? — YES
29-Perform Semantic Similarity & Embedding Analysis (Word2Vec, SBERT)? — YES
30-Develop NLP Pipelines for Sentiment/Entity/Keyword Extraction? — YES
31-Integrate LLMs for Knowledge Retrieval & Reasoning (RAG/Vector DB)? — YES
32-Fine-Tune or Customize LLMs (Instruction-Tuning/Domain Adaption)? — YES
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After purchasing the project, send requirements so Yilmaz can start the project.
Delivery time starts when Yilmaz receives requirements from you.
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