Matthew McMullen
Bio
11+ Years Experience in machine learning and AI for collecting and providing the training data sets required for ML and AI development with quality testing and accuracy. Equipped with additional qualification in machine learning.
Stories (30/0)
Annotation Strategies for Computer Vision Training Data
It is well known that data science teams dedicate significant time and resources to developing and managing training data for AI and machine learning models — and the principal requisite for it is high-quality computer vision datasets. It is common for problems to stem from poor in-house tooling, labeling rework, difficulty locating data, and difficulties in collaborating & iterating on distributed teams’ data.
By Matthew McMullen about a year ago in Proof
AI in Healthcare: Trends and Applications
Growing populations around the world are experiencing (and contributing to) a shortage of healthcare workers, and the gap is expected to widen in the future. “The world will be short of 12.9 million healthcare workers by 2035; today, that figure stands at 7.2 million,” as per a recent report by the World Health Organization (WHO). Globally, billions of people will suffer serious health consequences if the findings in today’s WHO report are not addressed. Experts believe that integrating technology into healthcare and digitalizing the system can address future challenges.
By Matthew McMullen 2 years ago in Journal
Challenges to Successful AI Implementation in Healthcare
– By Benjamin Bell (Scottish Scientific Surgeon) Artificial intelligence (AI) and machine learning (ML) have received widespread interest in recent years due to their potential to set new paradigms in healthcare delivery. It is being said that machine learning will transform many aspects of healthcare delivery, and radiology & pathology are among the specialties set to be among the first to take advantage of this technology.
By Matthew McMullen 2 years ago in Journal
Incorporating Sentiment Analysis into E-commerce
Studying users’ behavior and understanding their sentiments have become substantial to businesses with the increasing platforms operating in the digital space and users’ traffic rapidly growing on these online platforms. The Internet replaced sources such as friends, relatives, and consumer reports which used to be the primary sources of opinions on products and services. As a method of determining users’ opinions and subjectivity, sentiment analysis is an emerging new source.
By Matthew McMullen 2 years ago in Journal
Applications and Role of AI in Smart Cities for Sustainable Development
As per the international organization reports, globally more than fifty percent population is living in urban areas, and by 2050, more than two-third population will live in cities providing big investment opportunities of tech development companies.
By Matthew McMullen 2 years ago in Journal
Filtering Out the Bad: 5 Reasons to Use Content Moderation
Publishing user-generated content is one approach to increase brand awareness and trust. Even the most well-known companies rely on user-generated content to get top results in search engines. However, releasing this material has a risk: you must guarantee that people depict your business in a positive way. This is where the concept of content moderation comes into play.
By Matthew McMullen 2 years ago in Journal
Semantic role labeling
In natural language processing for machine learning models, semantic role labeling is associated with the predicate, where the action of the sentence is depicted. SRL or semantic role labeling does the crucial task of determining how different instances are related to the primary predicate. Semantic Role Labelling is also referred to as thematic role labeling and goes systematically for interpreting the syntactic expression of a sentence, ideally, with the parsing tree method.
By Matthew McMullen 2 years ago in Journal
Text Annotation, Use Cases, and Its Usability to Machine Learning
Although the world has shifted rapidly towards digitization, some documents and papers still contain some of the most complex information. As public information becomes more abundant, the challenge of making raw, unstructured data machine-readable arises. As a matter of fact, video and images are easier to comprehend than text. As an example, we can take "what's up." It is likely the human brain will interpret this phrase as a question, concern, or inquiry about someone. However, a machine may perceive the meaning of the text as it intends to, i.e., what is literally up, such as the fan, the ceiling, the ceiling.
By Matthew McMullen 2 years ago in Journal
Healthcare: AI-led Medical Coding and Billing Favoring Sectoral Growth
The Healthcare sector is among the largest and most critical service sectors, globally. Recent events like the Covid-19 pandemic have furthered the challenge to handle medical emergencies with contemplative capacity and infrastructure. Within the healthcare domain, healthcare equipment supply and usage have come under sharp focus during the pandemic. The sector continues to grow at a fast pace and will record a 20.1% CAGR of surge; plus, it is estimated to surpass $662 billion by 2026.
By Matthew McMullen 2 years ago in Journal
How Sentiment Analysis is used for Effective Stock Market Predictions?
The stock market is one of the most sensitive fields, where sentiments of the people can change the trend of the entire market. Actually, there are many factors, affect the movement of the stock market and, the sentiments of the traders are also one of them drive the market.
By Matthew McMullen 2 years ago in Journal
Image Annotation: A Quick Insight into How We at Cogito Annotate Images
A major step in the development of computer vision systems, AI-based machine learning models, and prediction applications is building well-optimized training data, i.e., the training data that consists of high-quality image annotation and labeling. The AI training data, as a matter of fact, is a principal prerequisite for enabling computer vision systems to recognize, obtain, characterize, and interpret results. Autonomous vehicles, medical imaging, and security & surveillance are some of the AI applications that use computer vision. Image annotation eventually tends to be the most critical part of the AI implementation plan for almost every industry that aims to implement automation in their business or industrial process.
By Matthew McMullen 2 years ago in Journal
Computer Vision Technologies in Robotics: State Of the Air
Introduction: In the face of the rise of artificial intelligence and computer vision has become a critical part of applying the technology to everyday problems. Computer vision robotics is one of the most used AI techniques to improve human performance.
By Matthew McMullen 2 years ago in Journal