My Experience with Machine Learning : A Future Prospective

As someone who is delving on machine learning projects, I want to share my research experience and future prospective of this exciting field. In this article, I will discuss two projects that I have worked on, which highlight the potential of machine learning for improving healthcare and assisting individuals with disabilities. By sharing my experience, I hope to inspire others to explore the field of machine learning and contribute to its growth and development in the years to come.

Machine Learning — An Introduction

Machine learning is a type of artificial intelligence that allows computer systems to learn and improve from experience without being explicitly programmed. It involves the use of algorithms that can analyze large amounts of data, identify patterns, and make predictions or decisions based on that data. A common example used to explain machine learning is that of a learning child. Just as a child can learn from experience, make predictions, and adjust their behavior based on feedback, a machine learning algorithm can do the same. Machine learning has a wide range of applications in various fields, including healthcare, education, finance, and more.

Two of my Projects on Machine Learning

The two projects that I will discuss in this article are focused on early detection of Parkinson’s disease using machine learning algorithms and assisting visually challenged individuals using machine vision. Both of these projects have the potential to improve the lives of individuals with disabilities and highlight the broad range of applications for machine learning.

Project 1 : Early Detection of Parkinson’s Disease

Parkinson’s disease is a neurodegenerative disorder that affects millions of people worldwide. It is a progressive disease that can lead to tremors, stiffness, and difficulty with movement. Early detection of Parkinson’s disease is critical for effective treatment and management of the disease. As such, the potential impact of a machine learning algorithm for early detection of Parkinson’s disease is significant.

But, Why I choose Parkinson’s Disease ?

Parkinson’s disease is a neurodegenerative disorder that affects millions of people worldwide. It is a progressive disease that can lead to tremors, stiffness, and difficulty with movement. Early detection of Parkinson’s disease is critical for effective treatment and management of the disease. Personally, I forget things very easily and carry a personal diary with me always, I am acutely aware of the importance of memory and the impact it can have on daily life.

Since forgetfulness is also a potential sign of neurodegenerative diseases, including Parkinson’s disease, I was interested in exploring the potential of machine learning to aid in the early detection of the disease. Additionally, witnessing the suffering of Parkinson’s disease victims, who often experience tremors and other debilitating symptoms, motivated me to contribute to efforts to improve the detection and management of the disease using machine learning

How did I do it ?
I began my project by researching the various methods used for the early detection of Parkinson’s disease. I found that speech signals contain a wealth of information that can be used to identify patterns that could indicate the presence of the disease.

In fact, our forefathers could often identify these disability just by speaking to us (Insight I got from a traditional medical practitioner). I also discovered that the Mel-frequency cepstral coefficients (MFCCs) feature extraction technique could be used to analyze speech signals and identify patterns associated with Parkinson’s disease.

Using this knowledge, I developed my own machine learning algorithm based on Big Bang Algorithm for identifying speech signals that had the potential of Parkinson’s disease threat. This involved training the algorithm on a dataset of speech samples from individuals with Parkinson’s disease and healthy individuals, and using a support vector machine (SVM) classifier to predict the presence or absence of Parkinson’s disease based on the features extracted from the speech samples. By developing my own algorithm, I was able to customize it to the specific needs of the project and achieve a high degree of accuracy (73% of trails was successful)in detecting Parkinson’s disease.

Now, with more data sets, I feel : Machine Learning techniques would train themselves would predict disease as this in future.

This algorithm when incorporated in a Smart Watch would assist in early prediction of Parkinson’s Disease.

Project 2: Assistacles.

“Assistacles” is a simple spectacles with a microphone arrangement that uses machine learning to assist visually challenged individuals. The camera in the spectacles are designed to recognize and identify objects and people in the vicinity of the user, and communicate this information to the user through audio feedback. The goal of this project is to provide visually challenged individuals with greater independence and autonomy in their daily lives.

How does it work ?

The machine learning algorithm used in “Assistacles” is designed to recognize and identify objects and people in real-time using a camera and microphone array. The algorithm utilizes a customized convolutional neural network (CNN) to analyze the visual input and identify objects and pre-trained faces in the user’s surroundings. The audio input is analyzed using a recurrent neural network (RNN) to detect and identify sounds such as voices, footsteps, and other environmental sounds. The identified objects and sounds are then communicated to the user through audio feedback.

Potential impact of the project

The “Assistacles” project has the potential to make a significant impact on the lives of visually challenged individuals. By providing real-time information about objects and people in the user’s surroundings, the spectacles could increase their independence and mobility. The machine learning algorithms used in the project have achieved high accuracy rates in object and sound recognition, making the system reliable and effective. Future iterations of the project could incorporate additional features, such as emotion detection, to further enhance the user’s experience.

What I see for Future:

In the future, I believe that machine learning will continue to evolve and open up new possibilities for us. As technology advances and new algorithms are developed, we will be able to create even more accurate and precise models for prediction and diagnosis. This can lead to better and earlier detection of diseases, more personalized recommendations and services, and increased efficiency and productivity across a wide range of industries.

Additionally, the integration of machine learning with other fields like robotics and automation has the potential to revolutionize the way we work and interact with technology. However, as with any new technology, it will be important for us to consider the potential ethical and societal implications of machine learning as it continues to evolve and shape our world.

Overall, I believe that the possibilities for machine learning to evolve and shape our future are endless, and I am excited to be a part of this ongoing journey of discovery and innovation.

What I see in future with Machine Learning is “Possibilities to Evolve”

Let’s Learn… Learn… and Learn…. with Machines…..

As our Poet Avvaiyar says :

“கற்றது கை மண் அளவு. கல்லாதது உலகளவு.

கலை மகளும் தினமும் படித்துக் கொண்டு இருக்கிறாள்.”

Translation :

“What is learned is a handful of sand, what is unknown is the size of the world. The art is learning (evolving )every day.”