BIG Data Analytics & Machine Learning
Strategic Consulting
OUR SERVICES
Big Data Sciences and Machine Learning
During the past ~20 years, our research has been focused on development of novel and complex algorithms. We have applied computational intelligence techniques in a wide variety of fields such as intelligent data and decision analysis and support systems (such as personalized date matching, user profiling and social behavior prediction, university admission, and credit card ranking), personalized-targeted advertising and the customer satisfaction and marketing strategy, fault diagnosis and anomaly detection such as automated sensory inspection systems with application to medical diagnosis and prognosis and predictive and personalized medicine, fraud detection and detection and analysis of the anomaly and rare events, hybrid expert systems and crowd sourcing, improvement of capabilities of internet and search engines and personalization, signal processing, building dynamic ontology from data and expert knowledge, advanced control systems, oil and gas exploration, earth sciences, and geophysical analysis such as reservoir modeling and characterization to decide where to drill the next economically viable oil well.
Analytics for Politics
While we know the formal party affiliation (Republican or Democrat) of every congressperson, it is not clear and not everyone would agree on the precise degree in which a congressperson being Republication/Democrat or being Leftist/Right-Wing? Using data from the U.S. House of Representatives and the vote history of the Congressperson on selected key issues, our Artificial Intelligence technique which is based on both quantitative and qualitative/ perceptual information has been able to predict both the Congressperson affiliation and the degree in which he/she is affiliated to the specific party with 96-98 percent of the accuracy. Our system can also be used to predict 1) what would be the most likelihood of his/her vote/stand/position on a specific issue? And 2) to what degree one congressperson would agree or disagree with another congressperson on a specific issue? Of course, this type of problem has many commercial applications (i.e. predicting the type of customer based on their purchase history).
Analytics for BioMedical
After 15 years of research in the field of AI and Bio-Medical applications, we have finished our extensive report on the use of AI and Machine learning for Bio-Medical applications. This includes classification of varieties of Cancers using Gene Expression using microarray data some with over 37,000 attributes (Gene’s expressions). We have focused on many key types of Cancers such as Breast Cancer, Colon Tumor, Leukemia, Lung Cancer, Ovarian Cancer, Prostate Cancer, and other Bio-Medical applications such as Central Nervous Systems, Genomic Sequences, and Diffuse Large B-Cell Lymphoma. We have used the public dataset, and we have showed that our techniques outperform of the existing state-or-the-art techniques, with accuracy of %95-%98 (test runs) and %100 for optimized models.
Analytics for Predicting Social Behavior
Strategic Marketing and Customer Retention: With the number of competing services available, businesses need to maintain their consumers, reward their loyalty, and proactively understand their customer behavior or reactions to a given marketing campaigns prior to lunch of the new promotion, lucrative offers or marketing strategy. Successful efforts to proactively convert marginally dissatisfied customers to satisfied ones by even a few percentage points will benefit most companies. Preventing “decay” in the other way is equally important and beneficial. Using thousands of customer records and surveys, our Artificial Intelligence technique for customer satisfaction and retention has been able to predict the satisfied and dissatisfied customers with 90-95 percent of the accuracy. Our decision trees will also walk you through the casual and effect, provide insight, reveals interesting facts and is opening windows into better understanding the customer behavior, proactively.
Predictive Analytics
Predictive analytics aims at extracting relevant information and insight from data to predict trends, behavior patterns (social, political and economic behavior), and forecasting with a wide range of applications such as identifying credit card fraud (Fraud is a big problem for many businesses and can be of various types: inaccurate credit applications, fraudulent transactions, identity thefts and false insurance claims), identify when, and why customer are leaving, customer retention strategy including direct marketing and/or marketing campaigns, evaluate the risk or opportunity of a given customer or transaction, or simulating human behavior or reactions to given stimuli or scenarios, predict customers' buying habits in order to promote relevant products and customer services, and collection analytic to identifying the most effective strategies to minimize wasted on customers who are impossible to recover, to name a few.
Analytics for Airline
Have you ever been stuck in an airport due to flight delay or cancellation? What are the reasons for such flight delayed and cancellations? Is it the older planes cause more delays, or is it the number of people is flying? Is it the weather that causes the delay or the flying between different locations? Is there any critical link in the system or is it delayed due to cascading failures? If you have been wondering about these causes and effects, and if you ever wanted to know if you could have predicted such delayed and avoid them, it’s your chance to find out. Using 100s of thousands to 10s of Millions of flight records, our Artificial Intelligence technique for Airline on-time performance and Delay Prediction has been able to predict the delays and on time with 94-97 percent of the accuracy. Our Decision Trees will also walk you through the casual and effect.
Analytics for Banking
Data Analytic for Banking- Campaigns and Risk Analytic: Often time, more than one phone call to the same client is required for a product to be subscribed or purchased. Our Artificial Intelligence techniques can help to predict the outcome of the campaigns and if direct marketing campaigns work. Using data from direct phone marketing campaigns of banking, our Artificial Intelligence technique has been able to predict the outcome with 97-99 percent of the accuracy. Our system can also predict, whether a loan approved is good or bad credit risk with 96-98 Percent of the accuracy
Analytics for Energy
Electricity Price Forecast: In free Market, what are the prices of the Electricity when prices are not fixed and are affected by demand and supply of the Market? They are set every five minutes and soon will be even every minute. In this study, we look at 10s-100s of thousands of the records and we will find the changes of the price relative to a moving average of the last 24 hours. Our Artificial Intelligence technique has been able to predict the Up and Down of the price with 96-98 percent of the accuracy.
The Emergence of Machine Learning, Recommender Systems, Sentiment Analysis and Classification, Social Media, and Wisdom of Crowd
Recommender systems have become extremely common in recent years, and are applied in a variety of applications. Recommender systems are systems that predict the user’s rating and/or preference for movies, products, restaurants, life insurance, financial services, online dating, and Twitter.
Social media such as Facebook, MySpace, and Twitter have exploded as online services where people create and share content and network at a fast rate. In addition, due to their ease of use, speed and reach, social media is setting trends in topics that range from politics to technology and the entertainment. Social media is also form of collective wisdom (crowd sourcing) and combined with Machine Learning techniques is powerful tool at predicting real-world outcomes that can be used to make quantitative and quantitative predictions that outperform those of artificial markets.
Our Artificial Intelligence system based on Machine Learning, recommender system, wisdom of crowd and sentiment analysis of Tweets can outperform market-based predictors and showed the effetiveness of the forecasting power of the social media. In addition, our Artificial intelligence system is used to predict rating of product reviews.
The Convergence of Machine Learning-Deep Learning and Big Data
Deep Learning is a step towards realizing strong Artificial Intelligence. While, Deep Learning is rebranding of Neural Network which goes back to 1980s and even 1960s, many organizations including Google, Facebook, Microsoft, Baidu, among others showed interest in its use for specific applications. Facebook hired Yann LeCun to head its artificial intelligence and later hired Vladimir Vapnik the co-inventor of the support vector machine method. Google hired Geoffrey Hinton to focus on improving machine learning and deal with the growing amount of its data. Microsoft established the Deep Learning Technology Center while Baidu hired Andrew Ng to head its Silicon Valley-based research lab focusing on deep learning.
We use state-of-the art Neuro Computing and Deep Learning architectures such as deep neural networks, deep Boltzmann and Restricted Boltzmann machine, convolutional deep neural networks, deep belief networks and recurrent neural networks for advanced analytics, fraud detection, anomaly and novelty detection, medical diagnosis and prognosis, automated sensory information systems. classification, and clustering. Our technology go beyond the traditional method and replay on Artificial Intelligence/Machine Learning-deep learning technologies and advanced analytics utterly embedded and complemented with advanced platform that can extract actionable insights at speed and scale. Our technology can monitor, identify, classify, and predict, abnormal or possible suspicious user activity and behavior, including possible frauds. The system can also predict anomaly and novelty as it happens and as it develops in real time and provide real time alert/notification and insights.