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Health Care: Cardiac Monitoring

AI Applications - Healthcare

Health Care: Cardiac Monitoring

AI can significantly improve cardiac monitoring in the areas of productivity and process efficiency, real-time notifications, and future event prediction. Analyzing Holter ECG signals manually to detect ECG abnormalities is an intense task. But applying machine learning (ML) and digital signal processing technologies such as noise reduction, beat identification, and event classification can drastically improve efficiency. AI applications can analyze millions of heartbeats from Holter device ECG signals, make conclusions on heart health, and create detailed reports and labels for physicians to inspect.
Technology:
Machine Learning, Digital Signal Processing, ML Models of Support Vector Machines, Gradient-Boosting Trees, Random Forest, 1D Convolutional Networks, LSTM & GRU Deep Neural Networks, Python, TensorFlow
Results:
99% accuracy in predicting ECG abnormal beats and events. 100%+ improvement in process efficiency and productivity.

Health Care: Provider Recommendation

In the growing medical tourism industry and provider network, finding suitable physicians or providers for a patient is increasingly challenging. Manual processes, human bias, and lack of insight about providers are roadblocks when it comes to finding a good match. AI & ML technology solutions can simplify provider recommendations by leveraging data such as disease profiles, gender, social profiles, financial profiles, insurance coverage, preferences, provider details, specialization of relevance, and social ratings. AI applications can reduce friction and increase customer satisfaction and quality service opportunities for providers.
Technology:
Scikit-learn, Ensemble Models such as Gradient-Boosting Trees, Python, Flask, Content-Based Recommendation, and Collaborative Filtering
Results:
Having an AI strategy can reduce errors and friction in the process and increase customer satisfaction and quality service opportunities for providers.

Health Care: Provider Recommendation

AI Applications - Healthcare

Finance: Fraud Detection

AI Applications - Finance

Finance: Fraud Detection

Application:

The financial sector has been using a rule-based fraud detection system but suffers from high false positive rates. Deep learning has revolutionized how we identify money laundering. ML model development has provided excellent results in finding fraud and suspicious behavior. Supervised anomaly detection allows historical data to be labeled as “normal” and “abnormal” so that models can be developed to apply those labels to new data. Anomaly detection can also be applied to unlabeled data in unsupervised machine learning, using historical data to analyze the probability distribution of values. These values then determine if a new value is unlikely and therefore, an anomaly. Single variables or combinations of variables can be used to detect anomalies.

Technology:
Cluster-Based Local Outlier Factor, Histogram-Based Outlier Detection, Isolation Forest, K-nearest Neighbors, Gradient Boost Machine (GBM), Generative Adversarial Networks (GANs)
Results:
Improve efficiency by 50% and reduce false positives from 80% to 20%.

Finance: Credit Risk Assessment

Application:
Micro-financing is a viable option for millions of under-banked and unbanked customers without enough credit history. But lack of alternate mechanisms to evaluate customers’ credit worthiness is a challenge. An AI-based psychometric model to assess the creditworthiness of the customers can reduce lending risk. The model makes a personality assessment of the applicant by analyzing their behaviors in a gamified KYC. A credit model is established based on using machine learning, cognitive analytics, and predictive analytics on customer response-based psychometric data. An AI engine then calculates alternate credit scores based on customer traits.
Technology:
Python, Flask, Mongo-Db, AWS, Decision Trees, Support Vector Machines, Logistic Regression, Plotly, and Bokeh
Results:
New technology innovation to increase credit access to millions of people as well as provide an alternative option to banks to mitigate risk.

Finance: Credit Risk Assessment

AI Applications - Finance

Social Media Sentiment Analysis

AI Applications - Social Media

Social Media Sentiment Analysis

Application:

Companies are struggling to keep up with customer feedback on social media platforms, which can negatively affect their brand. AI-based sentiment analysis can track public sentiment on social media, tackle negative feedback, and take action to improve customer experience (CX). The AI-based platform gathers social media feeds to analyze the data, segments the customers and sentiments, and performs deep sentiment analysis. The platform also integrates with ticketing systems to trigger resolution. This type of AI-based sentiment analysis can be deployed in various customer service industries.

Technology:

Text Blob, LSTM, Bidirectional LSTM, Transformers – BERT, Gensim – Word2Vec & Glove, BOW, Tf-idf, Python, TensorFlow, Pytorch, Flask, NER, NLTK, Spacy

Results:

Improves brand reputation, triggers swift action to customer feedback, delivers tailored promotions, and offers to positively influence customer behavior, understand customer concerns, and keep customers satisfied.

Logistics : Route Optimization

The world economy is going through a supply chain crisis. AI applications can significantly improve logistics by optimizing transportation capacity, accurately predicting delivery schedules, and reducing overall cost. Machine learning models can be developed by applying constraints such as least time, maximum capacity, and least transportation costs. Model output is key to driving automation and providing an optimized route and schedule information via channels like mobile apps.

Technology:

ANN (Artificial Neural Networks), MLR (Multilinear Regression), PR (Polynomial Regression), RFR (Random Forest Regressor), SVR (Support Vector Regressor)

Results:

AI-based transportation and route optimization can reduce costs by 25%, increase delivery rate by 31%, and significantly improve first-attempt delivery dates.

Logistics : Route Optimization

AI Applications - Logistics

Computer Vision to Detect Defects on Field Assets

AI Applications - Computer Vision

Computer Vision to Detect Defects on Field Assets

It is difficult to make a proper assessment of age or defects in installed assets ranging from solar farms to roofs of buildings. AI can help analyze these assets using computer vision algorithms. Satellite and aerial imagery of these assets can be extracted using location coordinates. The area of the assets can be identified using object detection and masking for further analysis. We can also determine that various properties of the assets can be identified by a computer vision time series analysis.

Technology:

CNN, ResNet-50, Variational AutoEncoders, XGB, Random Forest Trees, Masked RCNN, Pattern Detection Models, OpenCV, PIL, Skimage, Python, TensorFlow, Pytorch, Flask, AWS

Results:

A cost-effective, reliable, and scalable mechanism to study various field assets.

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