In this paper, we had built the online model which are built incrementally by using online outlier detection algorithms under the streaming environment. We identified that there is highly necessity to have the streaming models to tackle the streaming data. The objective of this project is to study and analyze the importance of streaming models which is applicable in the real-world environment. In this work, we built various Outlier Detection (OD) algorithms viz., One class Support Vector Machine (OC-SVM), Isolation Forest Adaptive Sliding window approach (IForest ASD), Exact Storm, Angle based outlier detection (ABOD), Local outlier factor (LOF), KitNet, KNN ASD methods. The effectiveness and validity of the above-built models on various finance problems such as credit card fraud detection, churn prediction, ethereum fraud prediction. Further, we also analyzed the performance of the models on the health care prediction problems such as heart stroke prediction, diabetes prediction and heart stroke prediction problems. As per the results and dataset it shows that it performs well for the highly imbalanced datasets that means there is a majority of negative class and minority will be the positive class. Among all the models, the ensemble model strategy IForest ASD model performed better in most of the cases standing in the top 3 models in almost all of the cases.

The incredible amount of data that will be generated in the next few years demands help to manage, mostly in the form of machine learning algorithms. Our intelligent devices generate more data than ever before. Today's population of IoT devices numbers more than 10 billion worldwide, and by some estimates, there will be more than 25.4 billion devices by 2025, generating an unfathomable 73.1 ZB (zettabytes) of data. It is not humanly possible to track even a minuscule fraction of that incoming telemetry and analyze it to quickly extract needed business intelligence or spot issues and growing trends in real time. Consider a nationwide fleet of long-haul trucks that needs to meet demanding schedules and can't afford unexpected breakdowns.

Let's talk about this feature of Azure called stream analytics and how to detect an anomaly before it becomes a failure. Data stream is a set of data that is coming through and is very transient, it's not sitting in a traditional SQL database. If we had so, we can just run a batch job and run SQL query over that data and extract whatever insights we want under that data. But what if we have data that is just passing through an event hub? How do we run queries, get reports, raise alerts if something becomes unusual?

Combining intelligent streaming analytics with real-time digital twins for aggregate analysis offers several benefits in a variety of real-world applications. Digital twins are finding broader use and playing a more important role in innovation. Many digital twins rely on continuous intelligence (CI) and artificial intelligence (AI) to ingest streams of data from sensors. The real-time analysis of that data use then used to make sense of current conditions, the status of different elements in a system, and determine what actions should be taken (if any). Their rising importance was noted in a recent MIT Sloan Management Review article.

Predictive analysis can potentially be pretty complex and intimidating to set up. While the applications for it are practically endless, the learning curve can be a challenge. However, whether you're a data scientist, or simply a developer that wants to start working with machine learning, SAP HANA streaming analytics provides a simple interface to get you started. In streaming analytics (and of course, SAP HANA itself), you don't need to be an expert on data mining to have data work for you. On top of that, you can do practically everything through your browser – which is where we get to the HANA Web IDE.

In the United States, around 200,000 manned U.S. general aviation aircraft have been registered over the last 50 years. By contrast, 750,000 unmanned aircraft systems -- aka drones -- have now been registered, including more than 40,000 in the last two weeks of December 2016 alone. It exemplifies the dramatic influx of "things," which carries unprecedented opportunity for digital disruption. They're typically full of sensors, increasingly connected, produce enormous amounts of data and can be the source of newer, smarter business models that touch every industry. For example, in the past decade, wind turbines have quickly evolved from isolated standalone machines to connected, sensor-laden, intelligent devices.

Spark is recognized as a great analytics engine. One of its benefits is its inclusion of machine learning algorithms that can group, predict, classify and even recommend based on the analysis of a corpus of data. Once the model is created, it can be applied to new data. In some situations, there is a need to score the model while the data is streaming to decide what to do with the new data. In this latest Data Science Central Webinar event, you will learn how to process the data to arrive at a model and how the resulting model can be used to score streaming data using IBM Streams.

Prescriptive analytics is about using data and analytics to improve decisions and therefore the effectiveness of actions. Isn't that what all analytics should be about? A hearty "yes" to that because, if analytics does not lead to more informed decisions and more effective actions, then why do it at all? Many wrongly and incompletely define prescriptive analytics as the what comes after predictive analytics. Our research indicates that prescriptive analytics is not a specific type of analytics, but rather an umbrella term for many types of analytics that can improve decisions. Think of the term "prescriptive" as the goal of all these analytics -- to make more effective decisions -- rather than a specific analytical technique.

With the recent success of depth cameras such as Kinect, gesture and posture recognition has become easier. Using depth sensors, the 3D locations of body-joints can be reliably extracted to used with any machine learning framework, specific gestures or posture can be modelled and inferred. Real world applications in Virtual Reality can be used for Yoga, Ballet training, Golf, anything related to activity recognition and proper postures. I also see application of it in the Architectural, Engineering, Construction and Manufacturing Industry by sending depth sensor data to the cloud to identify correct configurations. This is a proof of concept to detect pose "Y", "M", "C", "A" and stream the result back to the browser. This video explains a littlebit how it's hooked up together.

With the recent success of depth cameras such as Kinect, gesture and posture recognition has become easier. Using depth sensors, the 3D locations of body-joints can be reliably extracted to used with any machine learning framework, specific gestures or posture can be modelled and inferred. Real world applications in Virtual Reality can be used for Yoga, Ballet training, Golf, anything related to activity recognition and proper postures. I also see application of it in the Architectural, Engineering, Construction and Manufacturing Industry by sending depth sensor data to the cloud. This is a proof of concept to detect pose "Y", "M", "C", "A" and stream the result back to the browser. This video explains a littlebit how it's hooked up together.