Transform your Business with AI, ML & NLP
Dikite provides your business with the tools to succeed in the digital age.
Get StartedEverything your need to Scale with AI, ML NLP, LLMs, Data Science and Deep Learning
We provide a wide range of services to help you get the most out of your data. We can help you with everything from data collection to data analysis and visualization. Our team has years of experience in the field and we are always looking for new ways to improve our services.
Artificial Intelligence
Dikite helps you setup your Artificial Intelligence projects with ease, we have a team of experts that can help you with your AI projects.
Machine Learning
We help you setup your Machine Learning projects with ease, we have a team of experts that can help you with your ML projects.
Natural Language Processing
Dikite helps you setup your Natural Language Processing projects with ease, we have a team of experts that can help you with your NLP projects.
Large Language Models
Dikite helps you setup your Large Language Models projects with ease, we have a team of experts that can help you with your LLM projects.
Data Science
Dikite helps you setup your Artificial Intelligence projects with ease, we have a team of experts that can help you with your AI projects.
Use Cases
Machine learning (ML) with IoT (Internet of Things) devices to detect alcohol usage in truck drivers.
We use Sensor Data from IoT devices such as breathalyzers, accelerometers, and GPS trackers, these collect continuous data from the truck and its driver. These sensors record information like alcohol levels, driver behavior, and location. The with those data, we Preprocess the collected sensor and remove noise, outliers, and irrelevant information. Data preprocessing techniques we use like filtering, normalization, and feature extraction.
Model Training
We then train the ML model using the prepared dataset. We use different ML algorithms, such as decision trees, random forests, or deep learning models, to develop a predictive model and check the model accuracy. Over time, the ML model will be updated by our team with new data to improve its accuracy and adapt to changing conditions.
Classification Tasks
For classification tasks, we use Gradient Boosting Algorithms like XGBoost or LightGBM as they work well with sensor data.
Alerting System
Once the ML model detects signs of alcohol impairment based on the sensor data, it triggers alerts. These alerts are then sent to the driver, fleet manager and centralized monitoring system for further actions by our client.
Using AI for Predictive maintenance of multiple rice processing machines at a factory.
Using AI for predictive maintenance of multiple rice processing machines helps to prevent unexpected breakdowns, reduce downtime, and optimize maintenance schedules.
Working with Data
We start by collecting data from the rice processing machines. This data can include information such as temperature, humidity, vibration, power consumption, and other relevant sensor readings. We use IoT sensors and data loggers to continuously monitor and record machine performance data.
Once the data is gathered, we then clean and preprocess the collected data. We manage handling missing values, outliers, and noise in the data. And then Normalize the data to ensure that all features have the same scale which is especially important.
We then extract relevant features from the data that can be indicative of machine health and performance. For example, rolling averages, statistical measures, or time-based features. With client support we also define what constitutes a failure or an anomaly in the machines. This involves setting thresholds or using historical maintenance records.
AI Model Selection
Once again, we experimented using different AI models like
Prognostic Model:
Recurrent Neural Networks (RNNs), Since RNNs are well-suited for time-series data, making them an excellent choice if you have historical sensor data from the rice processing machines. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) variants of RNNs are used for sequence prediction tasks in predictive maintenance.
Classification Model:
- Random Forest classifiers are versatile and perform well on structured data. They can be used to predict whether a machine is likely to fail within a specified time frame.
- Support Vector Machines (SVMs), SVMs is used for binary classification tasks to predict machine failures or anomalies based on feature vectors extracted from sensor data.
- Gradient Boosting Algorithm CatBoost is powerful for classification tasks and can manage complex relationships in the data.
Regression Model
Time Series Forecasting technique ARIMA (Autoregressive Integrated Moving Average) and Prophet are being applied for predicting future sensor readings and estimating the time until maintenance is needed.
Anomaly Detection Model
Autoencoders, Variational Autoencoders (VAEs) or traditional autoencoders is used for anomaly detection by reconstructing sensor data and identifying deviations from the normal reconstruction.
Continuous Improvement
We periodically retrain the AI model with new data to adapt to changing conditions and improve accuracy and refine the model by incorporating feedback from maintenance actions and historical data.
Documentation and Reporting
We keep records of maintenance actions, model performance, and any insights gained from predictive maintenance and generate reports and visualizations to communicate the benefits and ROI of the predictive maintenance system to stakeholders.
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Get in touch
We are here to answer any questions you may have about our services.
- Telephone
- 044 - 28152511
- contact@dikite.com