Mapping muscle activity data to the gestures that they result in.

Photo by Yohanen on Dribbble

Electromyography is a technique for recording and evaluating electrical activity produced in the skeletal muscles.

The readings from the muscle activity sensor are then fed to a model that can be trained on gestures, separately for each user (customization for a user) or to obtain a pre-trained model ready for direct implementation in various applications like prosthetic arms, game control, etc.

Implementation of the idea on cAInvas — here!

The dataset

On Kaggle by Kirill Yashuk

Four different motion gestures were recorded using the MYO armband.

Each reading has 8 consecutive…


Detect cracks in concrete surfaces.

Photo by Tevis Godfrey on Dribbble

Concrete surface cracks are major defects in civil structures. Identifying them is an important part of the building inspection process where the rigidity and tensile strength of the building are evaluated.

Automating this process involves using a mobile bot with a camera input that scans the surfaces of the building for cracks and logs the locations for the same.

In order to make predictions on a larger surface, the camera is to be moved over the surface, covering it in small sections. The trained model is then applied to the image of this smaller section.


Identify sounds that indicate possible danger in the surrounding.

Photo by Ekrem EDALI on Dribbble

What would your response be if you heard a gunshot or glass break while inside your home? The natural inclination would be to call for help. In most cases, dial the police helpline, a neighbour, a relative, or a friend.

A system to recognize that sounds that indicate possible danger would benefit the neighbourhood. The model underlying the system should be able to recognize the distinct sounds among other background noises. …


Predict the quantity of fuel consumed during drives.

Photo by Tim Constantinov on Dribbble

The mileage of a vehicle is defined as the average distance traveled on a specified amount of fuel. But distance is not the only factor that affects fuel consumption.

Here, we take into account multiple factors like speed, temperatures inside and outside, AC, and other weather conditions like rain or sun besides distance to predict the consumption of different types of fuels during drives.

Predicting the fuel consumption given distance and other factors vice versa (predicting distance given fuel) can prove useful in planning trips as well as performing real-time predictions during…


Classifying Indian currency notes using their images and deep learning.

Photo by Alexander Barton for NJI Media on Dribbble

Currency notes have identifiers that allow the visually impaired to identify them easily. This is a learned skill.

On the other hand, classifying them using images is an easier solution to help the visually impaired identify the currency they are dealing with.

Here, we use pictures of different versions of the currency notes taken from different angles, with different backgrounds and covering different proportions.

Implementation of the idea on cAInvas — here!

The dataset

On Kaggle by Gaurav Rajesh Sahani

The dataset contains 195 images of 7 categories of Indian Currency Notes — Tennote, Fiftynote, Twentynote, 2Thousandnote, 2Hundrednote, Hundrednote, 1Hundrednote.

There are…


Identify the type of star using its characteristics and neural networks.

Photo by Alex Kunchevsky for OUTLΛNE on Dribbble

Classification of stars based on their characteristics is called stellar classification.

Here we classify them into 6 classes — Brown Dwarf, Red Dwarf, White Dwarf, Main Sequence, Supergiant, Hypergiant.

Implementation of the idea on cAInvas — here!

Dataset

On Kaggle by Deepraj Baidya | Github

The dataset took 3 weeks to collect for 240 stars which are mostly collected from the web. The missing data were manually calculated using equations of astrophysics.

The dataset is a CSV file with characteristics of a star like luminosity, temperature, colour, radius, etc that help classify them into one of the 6 classes — Brown…


Are they being sarcastic?

Photo by Su for RaDesign on Dribbble

Sarcasm is the use of words that convey a meaning opposite to the one you actually intend to pass on. It has the ability to flip the sentiment of the sentence. This makes sarcasm detection an important part of sentiment analysis.

Most of the datasets available for this purpose rely on tweets written by the public. This can result in noisy data with improper labeling. The context of tweets is dependent on the thread (in case of replies) and thus, understanding the context of the conversation becomes crucial to labeling the text.

To overcome this, here we use a dataset…


Are they being sarcastic?

Photo by Su for RaDesign on Dribbble

Sarcasm is the use of words that convey a meaning opposite to the one you actually intend to pass on. It has the ability to flip the sentiment of the sentence. This makes sarcasm detection an important part of sentiment analysis.

Most of the datasets available for this purpose rely on tweets written by the public. This can result in noisy data with improper labeling. The context of tweets is dependent on the thread (in case of replies) and thus, understanding the context of the conversation becomes crucial to labeling the text.

To overcome this, here we use a dataset…


Predict a customer’s behaviour in online shopping websites for KPI and marketing analysis.

Photo by Karol Cichoń on Dribbble

How do we know if a customer is going to shop or walk away? Understanding the customers is crucial to any seller/store/online platform. This understanding can be important in convincing a customer who is just browsing to buy a product.

In offline stores, the inferences derived influence the placement of objects in the store. When the same experience is translated to an online store, the sequence of web pages browsed to reach a product becomes important.

Here, we analyze the behaviour of customers as they browse through the pages to predict if they will make a purchase or not.

Implementation…


Predicting the age of abalone (sea snails) from their physical measurements.

Photo by Nico Medina on Dribbble

Abalone is a common name for sea snails. Determining their age is a detailed process. Their shell is cut through the cone, stained and the rings are counted using a microscope.

This is a time-consuming process that can be simplified by using neural networks to predict their age using the physical measurement of the abalone

Here, we use measurements such as length, height, weight, and other features to predict their age.

Implementation of the idea on cAInvas — here!

The dataset

Data comes from an original (non-machine-learning) study: Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and Wes…

AI Technology & Systems

Simplifying AI development for EDGE devices

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