In broad, three machine learning classification algorithms are used to discover correlations, hidden patterns, and other useful information from different data sets known as big data. Today, Twitter, Facebook, Instagram, and many other social media networks are used to collect the unstructured data. The conversion of unstructured data into structured data or meaningful information is a very tedious task. The different machine learning classification algorithms are used to convert unstructured data into structured data. In this paper, the authors first collect the unstructured research data from a frequently used social media network (i.e., Twitter) by using a Twitter application program interface (API) stream. Secondly, they implement different machine classification algorithms (supervised, unsupervised, and reinforcement) like decision trees (DT), neural networks (NN), support vector machines (SVM), naive Bayes (NB), linear regression (LR), and k-nearest neighbor (K-NN) from the collected research data set. The comparison of different machine learning classification algorithms is concluded.

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Object oriented design metrics are most essential part of software development environment and being more popular day by day. This study focus on a set of object oriented metrics that can be used to measure the quality of an object oriented design. The object oriented design metrics focus on the measurements of class and design characteristics. These measurements permit designers to access the software in the early stage of the process and changes accordingly to reduce complexity and improve the continuing capability of the design. This paper summarizes the existing metrics those guide the designers to support their design. We categorized and discussed metrics in such a way that novice designers can apply metrics in their design as needed.

The World Health Organization (WHO) report shows that Heart disease is the major cause of death all over the world i.e. nearly 21.2 million people die every year directly or indirectly from Cardiovascular (Heart) diseases estimated 32% of all deaths worldwide. Whereas, Diabetes is at the ninth position for the death all over the world i.e. nearly 3.7 million people die every year from diabetes estimated 6.61% of all deaths worldwide. The Heart and Pancreas organ play a most important role in human being. The blood flows in all parts of the body through heart. The function of pancreas is to regulate maintain the insulin levels that is responsible for diabetes. The detection of heart disease and diabetes takes too much time and very costly process. In our research, we develop a Heart Disease and Diabetes Identification System based on Iris Healthcare Kiosk. We proposed a desktop system application that detects these diseases through the Iris. The process starts by taking the left Eye photograph of the patient's through Eyeronec (company name) camera and perform intermediates operations of target cropping, pre-processing, auto-cropping (through integral projection and removing sclera), heart regions of interest (ROI) measuring, pancreatic measuring, extracting the feature and finally classify in the result. The classification result shows that 83% tests are successful, 11% tests are scant whereas 6% tests became fail. The operation is performed on 32 different training digital data sets and final result is labelled as normal or abnormal. The result shows that accuracy of our proposed system in heart disease and diabetes are 86.36% and 90.91% respectively.