Deep Learning is a specialized form of machine learning which utilizes supervised, unsupervised and semi-supervised learning to learn data representations. It is similar to the structure and function of the human nervous system, where a complex network of interconnected computing units works in a coordinated way to process complex information. An artificial neural network is a computer system made up of several simple and highly interconnected processing elements which process information by their dynamic state response to external inputs. Feature of Neural Network - Cluster and classify the raw input. Naive Bayes - Is a simple but surprisingly powerful algorithm for predictive modeling.

IBM today announced it will apply a range of artificial intelligence (AI) technologies to automate the management of IT operations and modernize applications, also known as AIOps. Announced at IBM's Think Digital conference, Watson AIOps leverages algorithms to continuously optimize IT operations while Accelerator for Application Modernization with AI is a suite of tools that leverage continuous learning and interpretable AI models to help organizations stay current as modern of technology and platforms evolve. Made available as part of a Cloud Modernization Service engagement, the Application Modernization with AI tools span a range of tasks from applying AI to more accurately identifying software components that should be deployed as containers or converted into microservices to applying symbolic reasoning to dynamically define modernization steps that are surfaced via a natural language processing (NLP) engine. Watson AIOps, meanwhile, applies machine and deep learning algorithms to time series data, semi-structured logs, structured data and unstructured data spanning both IT incidents and human conversations to track the timeline of an issue. It then uses that data to correlate root causes, create an explainable diagnosis and generate a recommendation for the best course of action. Built on top of an instance of the OpenShift platform that IBM gained via its acquisition of Red Hat, the Watson AIOps also leverages semantic search techniques that can relate the current incident to past incidents to provide additional context.

In recent years, the growth of Internet of Things (IoT) as an emerging technology has been unbelievable. The number of networkenabled devices in IoT domains is increasing dramatically, leading to the massive production of electronic data. These data contain valuable information which can be used in various areas, such as science, industry, business and even social life. To extract and analyze this information and make IoT systems smart, the only choice is entering artificial intelligence (AI) world and leveraging the power of machine learning and deep learning techniques. This paper evaluates the performance of 11 popular machine and deep learning algorithms for classification task using six IoT-related datasets. These algorithms are compared according to several performance evaluation metrics including precision, recall, f1-score, accuracy, execution time, ROC-AUC score and confusion matrix. A specific experiment is also conducted to assess the convergence speed of developed models. The comprehensive experiments indicated that, considering all performance metrics, Random Forests performed better than other machine learning models, while among deep learning models, ANN and CNN achieved more interesting results.

Building so-called Smarter Cities has long been touted as a major goal for municipalities around the globe. But building systems capable of responding to events in real time is a major challenge for any government operating on a limited IT budget. The hope is that processes will become integrated enough to create a massive pool of data that will then be employed to drive any number of artificial intelligence (AI) applications. The problem is that while city governments typically have access to massive amounts of data, most of it resides in isolated systems run by departments that are often at odds with one another. In fact, Daniel Newman, principal analyst with Futurum, says smart cities are mostly a figment of vendor marketing imagination. "The trouble with smart cities is they don't exist yet," says Newman.

An ERP application has always been an attempt to wrap code around a business process that enables both automation and standardization. Organizations that embrace packaged ERP applications would then write a lot of code to fill in the workflow gaps between business processes. But as ERP applications move into the cloud, providers of ERP applications are starting to significantly increase the number of business processes that can be automated by applying both machine and deep learning algorithms. Case in point is the latest release of SAP S/4 HANA Cloud, which among other new capabilities can now automatically extract payment information from PDF documents. Previously, that task inside most organizations was either performed manually or via a separate application that needed to be developed or acquired.