Suppose we're speaking in a hall before a hige audience but are unable to know whether or not the audience is interested so that we can mix up with lightning talks, motivational stories, quotes, etc. At that time, our talk starts becoming boring and we start getting the tag of 'unwanted speaker'. This will never happen if we have the data, specifically data of dynamic and running time users' rating of the speakers. There just needs to be a few Realsense Cameras placed optimally in the hall and a person/bot with AR and or VR headset tracks the user's rating collectively (heatmap). Realsense cameras and the headset are connected. This promises to make every talk in the world interesting after gathering enough data.
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Iam currently working on a life saving IoT device which helps diabetic type-1 patients to automatically regulate their insulin levels. And also gives notification on high or low blood sugar..
Now I just started new company call warung, warung is a small shop that sells cold bottled drinks, candy, cigarettes, snacks and other daily necessities, while the larger ones are small restaurant establishments. But instead of using POS to track the transaction we will build speech recognition and computer vision If you have any comment or suggestion for my project feel free to comment Thanks
How we can differentiate garbage types whether "bottle", "pet bottle", "burnable" and so on? we sometimes hard to differentiate it. My experience when visited Japan, there are several waste bin types like i mentioned before. which one is correct when i have "banana peel" ? . In my country also has several types of waste bin types like "organic" and "not organic" , which one is correct when i have "bottle"? we don't know. So this project is created to differentiate garbage type based on images.
how it works? the user just upload image of garbage to system and the system will response the kind of garbages correctly! the garbage image will be forwarded to inference machine that i prepared before. the model in inference machine is Convolutional Neural Network. this system accuracy in predicting garbage type reach 90%.
Step-by-step of implementation:
Download dataset from Image-net Stanford (imagenet.stanford.edu). This dataset contains 1000 class of images. we select only image that appropriate for our system like: Banana, Bottle, Pet Bottle, and so on. we additionally augment other image from other source to enrich dataset
Train the model on dataset based on Convolutional Neural Network (see my previous project: https://devmesh.intel.com/projects/landuse-classification-convolutional-neural-network)
After obtaining a model, we try to inference a new image to model. let say image captured by camera will be forwarded to this machine.
create Front-end like Android or web apps that perform capturing images and forwarded to model. this sometimes use web service to connect front-end with model. in this case i use Flask micro-framework in Python environment as web apps and web service.
I am working on a AI with basically a high EQ. It is a kind of chatbot which can help people to get out of a emotional stress and it will make life if a emotionally weak people really easy in my opinion.
I was told about the 'Artificial Intelligence' by one of my friends in University. I was just taken away! Currently pursuing a BTech Degree in Computer Science, I am keen and eager to explore into the world of Artificial Intelligence, especially the state-of-the-art Deep Learning techniques.
How great is this World!
I had few experiences in image processing using OpenCV. A lover of Python, I had done all my projects using it. Also, an expertise in C, C++ and Java. Now, working on a clustering project to retrieve relevant news events from Social Media. Need some collaborations!
With over 10,000 people dying every year in Nigeria due to lack of access to blood at the time of need, this project is designed to help hospitals predict the amount of blood that will be needed at a particular point in time for treatment even before the need arises so that adequate preparations are made to make blood (with different blood types) available and accessible when it is needed.
In this project there will be need for data to build a predictive model with a machine learning algorithm. However, I have had a a lot of issues trying to access the data in my home country because of some health policies in place. Any ideas on how to get an open and relevant data from another country to work with
Solving decision making problems is the essence of any intelligent autonomous agent. The objective of this problem is finding the most beneficial course of action, in relation to some revenue measure. In the aspect, robots which are set in the real world are often required to account for its uncertainty when making decisions, in order to provide reliable and robust results. There are multiple possible sources for this uncertainty, e.g. a dynamic environment in which unpredictable events might occur; noisy or limited observations, such as a limited camera range and an inaccurate GPS signal; and inaccurate delivery of actions. Relevant problems include: simultaneous localization and mapping (SLAM), path planning, sensor deployment, robotic arm manipulation, and in recent years even more profound problems such as natural language processing (NLP).
Yet, making a decision (in a naive way) requires calculation of the revenue for every candidate action, in a possibly very large group of actions. Also, uncertainty measures, such as entropy, are expensive to calculate. Overall the total computational cost of the problem can turn exceptionally expensive, thus making it challenging for online systems, or when having a limited processing power (e.g. in drones). Recently several approaches have been introduced in order to improve this high computational cost, considering only specific problems. Moreover, while some of these approaches demonstrate good performance, the induced error from using them is not modeled nor bounded, and therefore the results can not be guaranteed.
In this work, we look for computationally efficient algorithms to solve the decision making and planning problems. Until now we have introduced a new method for analysis of states in this context, which is then used to generate sparse and efficient approximations. Thanks to the pre-analysis, we can also bound the approximation error and guarantee the quality of solution. In several demonstrations, using our algorithms showed a significant improvement in run time.
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