MINSKY AI HEALTH ASSISTANT

AI Labs, Inc. has developed the Minsky Health Assistant, an innovative AI-powered healthcare solution built on their proprietary Minsky platform. This intelligent voice assistant transforms how individuals access preliminary healthcare guidance by allowing users to describe symptoms in natural language and receiving personalized diagnostic suggestions, medication recommendations, and precautionary advice through an intuitive voice interface.

SAR REPORTING USING GEN AI

A Suspicious Activity Report (SAR) is a document that financial institutions use to report potential suspicious financial transactions or activities to relevant authorities. SARs are a crucial tool in the detection and prevention of financial crimes, including money laundering, fraud, and other illicit activities.

CO2 EMISSIONS FROM AGRICULTURE

The agriculture sector is a significant contributor to carbon dioxide (CO2) emissions, primarily due to practices like deforestation, fertilizer use, and transportation. Reducing agricultural emissions is crucial for mitigating climate change, yet accurately quantifying these emissions is a complex challenge.

HYDROCEPHALUS

Hydrocephalus (HYC) is the segregation of fluid in the cavities (ventricles) deep within the brain. The excess fluid increases the size of the ventricles and puts pressure on the brain. Cerebrospinal fluid normally flows through the ventricles and bathes the brain and spinal column. Hydrocephalus is a frequent complication which is affecting almost all age groups following subarachnoid haemorrhage. Few studies investigated the association between laboratory parameters and shunt-dependent hydrocephalus. Non-contrast materialenhanced head computed tomographic (CT) examination is an important method for the diagnosis of HYC because it can observe the enlargement of the ventricles, and sometimes determine the cause of HYC. However, due to the lack of uniform standards, different range of patients’ ages and the various levels doctors’ expertise, it is rather difficult to reach a diagnosis. Therefore, after a detailed analysis and research, Ai labs developed and implemented a Proof of Concept (POC) using its proprietary engine Minsky™ to highlight the need for a shunt for an individual and mortality caused by Hydrocephalus.

HEART DISEASE PREDICTION

Globally, Heart Disease has become a major cause of morbidity. The deaths are rising significantly annually due to this disease. Of all heart diseases, coronary heart disease (heart attack) is the most common and fatal. The silver lining is that these heart attacks can be highly preventable by maintaining healthy lifestyle (such as reducing alcohol and tobacco use, eating healthily and exercising) coupled with early treatment that greatly improves its prognosis. It is, however, difficult to identify high risk patients because of the multi-factorial nature of several contributory risk factors such as diabetes, high blood pressure, high cholesterol, etc. This is where machine learning and data analytics can be used to analyse the co-relation between factors/parameters and predict the risk of heart disease. The objective of the project is to predict the risk of heart attacks using MinskyTM Machine Learning Models that can help clinically in analysing the risk factors of the disease and interpretation of the important factors affecting the particular patience.

HEALTHCARE INSURANCE

This case study is based on data from a large global insurance company which is engaged in providing health insurance. With offices in multiple countries, the claims department handles various activities such as processing and approving claims etc. Each of these processes comprise of several activities that are time consuming but critical for employee satisfaction. This solution leverages our Minsky™ AI engine to categorize each claim as High Risk or Low Risk based on the customers historical data. Minsky™ then creates accurate Modes that can be used to process live data for high risk/low risk to be handled by the appropriate claims adjuster based on the rating for further processing. This real time monitoring/actions helped us automate the claims processing across various geo locations offices while also reducing fraudulent claims. Solution was integrated with an existing system.

CUSTOMER CHURN

Customer churn has created huge concerns in the highly competitive service sectors and especially in the telecom sector. The aim of this project was to build a customer churn model using AI (Artificial Intelligence) to predict whether certain customers would leave in the future. This helped the client to retain customers for the long run which improved revenues and business continuity.

SEMICONDUCTOR WAFER-DEFECT ANALYSIS

Typically, fabrication of good quality semiconductor wafers without defects is challenging for any semiconductor manufacturer. It consists of sequential process steps performing physical and chemical operations on wafers. Usually, wafers are aggregated into so-called lots of size 25 or 50, which always pass through some operations in the production chain. Therefore, the entire manufacturing process can involve lengthy repetitive steps, and takes place in a sterile clean fabrication room designed to prevent even the tiniest speck of dust from falling on the pristine wafers. The fragile wafers may get scratched or get particulates from the clean room that could cause the micro circuits to malfunction when tested after the manufacturing process. Often, these flaws are microscopic and completely invisible to the naked eye which leads to poor production quality. If these flaws are not addressed at contamination phase, then there might be decrease in test yield that results in the wafer manufacturing costs. After a detailed analysis and research, Ai Labs developed and implemented a Proof of Concept (POC) using its proprietary engine Minsky to highlight the defects in wafer fabrication process. The objective of this case study is to automate inspections process and identify the defects by categorizing them as (good, Bad) in the process using Deep learning classification model depending on the response value set (Threshold value) by the semiconductor company.