Artificial Intelligence is getting increasingly sophisticated at doing what humans do, but more efficiently, more quickly and at a lower cost. The potential for artificial intelligence in healthcare is vast, and are increasingly a part of our healthcare eco-system. When many of us hear the term “artificial intelligence”, we imagine robots doing our jobs, rendering people obsolete, and since artificial intelligence-driven computers are programmed to make decisions with little human intervention, some wonder if machines will soon make the difficult decisions we now entrust to our doctors. Artificial intelligence in healthcare mainly refers to doctors and hospitals accessing vast data sets of potentially life-saving information, this includes treatment methods and their outcomes, survival rates, and speed of care gathered across millions of patients, geographical locations, and innumerable and sometimes interconnected health conditions. New computing power can detect and analyze large and small trends from the data and even make predictions through machine learning that’s designed to identify potential health outcomes.Continue reading “Artificial Intelligence in Healthcare”
Aim of the project was to develop an animal image classifier in dense forest environments to achieve the desired accuracy, and aid ecologists and researchers in neural network/artificial intelligence & zoological domains to further study and/or improve habitat, environmental, and extinction patterns.
Efficient and reliable monitoring of wild animals in their natural habitats is essential to inform conservation and management decisions regarding wildlife species, migration patterns, habitat protection, and is possible, rehabilitation and grouping species of the same animals together. Processing a large volume of images and videos captured from camera traps manually is extremely expensive, time-consuming, and also monotonous. This presents a major obstacle to scientists and ecologists to monitor wildlife in an open environment. In particular, we intend to use animal images dataset and train a convolutional neural network, capable of classifying the image to a particular animal class accurately. This, in turn, can, therefore, speed up research findings, construct more efficient monitoring systems, and subsequent management decisions, having the potential to make significant impacts on the world of ecology and image analysis.Continue reading “Fauna Image Classification using Convolutional Neural Network”
Image classification refers to a process in computer vision that can classify an image according to its visual content.
Today, with the increasing volatility, necessity, and applications of artificial intelligence, fields like machine learning, and its subsets, deep learning and neural networks have gained immense momentum. The training needs software and tools like classifiers, which feed a huge amount of data, analyze them, and extract useful features. The intent of the classification process is to categorize all pixels in a digital image into one of several classes. Normally, multi-spectral data are used to perform the classification, and indeed, the spectral pattern present within the data for each pixel is used as the numerical basis for categorization. The objective of image classification is to identify and portray, as a unique gray level (or color), the features occurring in an image in terms of the object these features actually represent on the ground. Image classification is perhaps the most important part of digital image analysis. Classification between objects is a complex task and therefore image classification has been an important task within the field of computer vision. Image classification refers to the labeling of images into one of a number of predefined classes. There are potentially n number of classes in which a given image can be classified. Manually checking and classifying images could be a tedious task especially when they are massive in number and therefore it will be very useful if we could automate this entire process using computer vision. The advancements in the field of autonomous driving also serve as a great example of the use of image classification in the real-world. The applications include automated image organization, stock photography and video websites, visual search for improved product discoverability, large visual databases, image and face recognition on social networks, and many more; which is why, we need classifiers to achieve maximum possible accuracy.Continue reading “Image Classification Techniques”
I’m pursuing Bachelor of Technology (B.Tech.) in Computer Engineering from NMIMS University’s Mukesh Patel School of Technology Management & Engineering, Mumbai. I’ve completed my Diploma in Computer Engineering from Thakur Polytechnic, Mumbai. I’ve done various certifications that includes Java SE 8 Programmer (1Z0-808) by Oracle, Database Fundamentals (98-364) by Microsoft, Machine Learning by Stanford University on Coursera to name a few.
I’m keen on learning new things. Being a Computer Engineering student, I had basic knowledge of Android. So, I applied for the Android Developer track, which is the advance level course. I was very happy when I received the email from Udacity on 7th February 2018 at 8:33 am which stated, I was selected for the Android Developer track. I was among the few fortunate who got selected for the Phase 1 of Udacity Google India Challenge Scholarship.Continue reading “Udacity Google India Challenge Scholarship: Android Developer”