Artificial Intelligence (AI), in at least one of its various forms, has had an impact on all major industries in the world today. AI has been growing rapidly in the past few years, as there have been several advancements in data collection, analysis and processing.
The key contributors to these advancements are robust Internet of Things (IoT) connectivity and high-speed processors to fortify it.
At Embitel, we are constantly reimagining the boundaries of artificial intelligence and machine learning to help global businesses efficiently utilize their assets.
Various Streams of Artificial Intelligence
Artificial intelligence, as is commonly prevalent today, is also referred to as Narrow AI (ANI). This stream of AI pertains to technology outperforming humans in a narrow spectrum of cognitive abilities. Some examples of narrow AI are:
- Facial recognition tools
- Spam filters that segregate emails
- Chatbots for customer service on ecommerce webpages
- Self-driving cars
- Google search engine technology
- Product recommendation engines on ecommerce websites
Today, a large portion of business investments in artificial intelligence are for narrow AI.
Another stream of AI, referred to as Artificial General Intelligence (AGI), enables a machine to apply skills pertaining to multiple streams of cognitive abilities. This is a replica of human intelligence, as it includes independent learning and problem solving.
Machine Learning (ML) and Deep Learning are Subsets of Artificial Intelligence
Machine learning involves the usage of complex algorithms that automatically learn and refine the learning from a vast amount of data and data patterns. The performance of these systems plateau after an extended period of training.
Deep learning, on the other hand, is a subset of machine learning. Large neural networks (complex algorithms with brain-like functions) are constructed and trained with a huge amount of data continuously. The performance of these networks improves as the training increases. This results in the development of machines that can predict outcomes through deductive reasoning and logic.
Our AI and ML Services for IoT Applications
We assist customers in identifying AI opportunities for improved efficiency of operations. Our decade long expertise in AI and ML software development can be leveraged to build intelligent systems that effectively automate tedious or repetitive tasks.
Data collected by IoT sensors can be in the form of text, videos or images. Data mining activities include the consolidation of raw data, cleansing, analysis and segmentation. Only the data in a suitable format is used for further processing.
Our team of ML experts support customers in developing self-learning algorithms that accurately process large sets of data and deliver insights. We perform feature engineering, model training and model validation activities to ensure maximum accuracy of predictions.
We assist you in developing AI-powered mobile, web and desktop applications that can monitor assets over time and predict possible failures before they even occur. Such predictive analytics systems are widely used in industrial and enterprise operations. We also help you develop ML-powered wearable devices that can track human movements or vital signs and derive valuable insights.
Our data science team provides support for enhancing or optimizing existing ML-based solutions. This includes the addition of new features, analysis of fresh data resources, improvement in AI predictions, and enhancement of accuracy to cover new business requirements.
Handbook: IoT Solutions Powered by Artificial Intelligence and Machine Learning
Partner with us for unlocking the potential of Machine Learning algorithms for your business challenges. Our solutions drive business automation through improved and accurate decision-making.
Steps to Develop Machine Learning IoT Applications
IoT Sensors for Data Collection
•Hardware and software development of IoT sensors with FOTA updates
•Data storage and handling in the event of gateway connectivity issues
• Power management and optimisation of network design / data transfer
Data Mining, Filtering and Feature Extraction
• Analysing the problem definition and researching on the data to be collected
• Examining the data collected by sensors to discover patterns and trends
• Data preprocessing to convert raw data into efficient data for training and validating the ML model
Evaluation and Identification of the ML Model
• Identifying a suitable Machine Learning Model (neural networks, decision trees, regression models, classification models, etc.) based on the problem-statement and parameters
• Analysing hardware requirements for the application that hosts the ML algorithm
Training the ML Model
• Segregation of filtered data into ‘training data’ and ‘validation data’
• Training data is given to the algorithm with definite output labels, while validation data is given without these labels. The difference between predicted and actual labels is redirected into the training loop to improve the accuracy of predictions
Outcome Prediction, Powered by ML
• Training the ML model through exposure to a large amount of real-time and historical data so that it makes predictions with a high degree of accuracy
• Implementing ML model performance tuning and interference to optimise it and validate it in the real world
Develop IoT Machine Learning Application
• Design and development of IoT cloud-based applications, other web and desktop applications, mobile apps, HMI/UI for applications
• Specialisation in predictive and recommendation systems, object detection algorithm, driver monitoring system, wearable devices
IoT Machine Learning Use Cases
AI based software solutions for mobility/autonomous cars:
Smart location tracking
Navigation
Control of cabin conditions
Automotive entertainment
Connectivity with mobile devices
Control of drive modes
Parking assistance
Intelligent mobile app
Driver behaviour monitoring
Assessment of road conditions
Other avenues:
Sports applications/wearables powered by machine learning
AI-enabled health monitoring devices/wearable
Predictive maintenance for battery monitoring and solar tracking systems in Industry 4.0
Our Expertise in IoT and ML Based Projects
1
Our certified technology professionals have deep domain knowledge in IoT, artificial intelligence and machine learning to take up consultation and development projects.
2
We are agile, flexible and transparent. Our in-house reusable software stacks expedite project development and reduce time-to-market, considerably.
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We have over 16 years of experience in IoT application and leading-edge technology solution development.
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As an organisation, we have large teams to scale according to the project requirements. At the same time, we take pride in our ability to cater to our customer’s unique requirements with utmost care and consideration.
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Data safety and quality of deliverables are attributed top priority at Embitel.
Our Customer Success Stories
Predictive Maintenance Solution for Industrial Battery Monitoring System
Embitel Solution
We designed and developed a predictive maintenance solution for battery monitoring. Our industrial grade network of sensors collects data and stores it on a local storage system or an external server.
The collected voltage and temperature data is sent to the local monitoring unit for decision making. The system monitors the rate of battery discharge and notifies the administrator about weakening batteries.
Business Impact:
The solution enables our customer to address load balance challenges during charging and discharging cycles. The customer also has the ability to ensure zero system downtime. This has resulted in reduced cost of ownership.
Mobile App and HMI Development to Monitor Patient Health
Embitel Solution:
We developed an Android-based mobile application that monitors vital body parameters and keeps track of user activities. Nutrition tracking, mood monitoring and maintenance of medication logs were added features. Users can update vital parameters even when they are offline.
Third-party services like FitBit, Jawbone and Google-Fit API were integrated and a graphical data dashboard was developed for reporting. Based on the data collected, the app guides the user to adhere to specific food habits or make lifestyle changes.
Business Impact:
The cloud-based mobile app enables users to manage their health through a single interactive platform. Our team integrated the features and functionalities expected by the customer, while also delivering an intuitive HMI.
IoT Platform Development for Predictive Maintenance of Solar Tracking System
Embitel Solution:
Our IoT team developed the hardware and software for the embedded control systems that were retrofitted on the field-deployed solar panels. The control system changes the orientation of the solar panels according to the movement of the Sun.
We also designed and developed the IoT platform that connected the network of solar trackers to the cloud.
Business Impact:
This intelligent IoT solution mitigated the customer’s challenges related to solar panel monitoring and efficient power generation. The administrators were able to increase field coverage and reduce the cost of field operations.
Our solution also improved the plant’s power output by 20%.
Video
Here’s a demo of Project Genie, a driver monitoring app, developed by our engineers at Embitel’s IoT Innovation Lab. The app is powered by machine learning algorithms that monitor road conditions and the patterns of your driving.
Our Chief Innovation Officer, Mr. Sitaram Naik, shows us how the app works on the road!
The Key to Unlock Success Through AI and ML
The essential factors that guarantee success of an artificial intelligence implementation are as follows:
- Collaborate with partner companies driving IoT innovation. The alliance should augment the goals of both companies.
- Aim for a harmonious collaboration between human and machinery assets in your organization.
- Ideate the incorporation of IoT across segments and in your overall business operations for improved productivity.
