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Medical diagnosis through classification is often critical as the medical datasets are multilabel in nature, that is, a patient may have more than one health condition: high blood pressure, obesity, and diabetes. The aim of this article is to improve the accuracy and performance of multilabel classification using multilabel feature selection and improved overlapping clustering method. The proposed system consists of Optimized Initial Cluster Centers and Enhanced Objective Function technique to reduce the number of iterations in the clustering process thereby improving the clustering performance and to improve the clustering accuracy which will result in improving the accuracy and performance of multilabel classification. Ratios of clustering distance to class distance and execution time are used as the evaluation metric for accuracy and total execution time is used as the evaluation metric for performance. Based on the different combination with the number of labels, attributes, instances, and number of clusters, different values of accuracy and performance are obtained. The results on all 10 datasets show that the proposed technique is superior to the current technique. Furthermore, on average, the proposed technique has improved the classification accuracy by 5%–7%. Furthermore, the performance of new technique is improved by decreasing the processing time by 0.5–1 s on average. The proposed system targets on improving the accuracy and performance of the multilabel classification for medical diagnosis, which consists of multilabel feature selection and enhanced overlapping clustering technique. This study provides an acceptable range of accuracy with improved processing time, which assists the doctors in medical diagnosis (high blood pressure, obesity, and diabetes) of patients.

Online Teaching Learning (OTL) systems are the future of the education system due to the rapid developmentin the field of Information Technology. Many existing OTL systems provide distance education services in thepresent context as well. In this paper, several types of existing OTL systems are explored in order to identifytheir key features, needs, working, defects and sectors for future development. For this, different aspects, types,processes, impacts, and teaching-learning strategies of various OTL systems were studied. In addition, the paperconcludes with some future insights and personal interest in the further development of OTLs on the basis ofprevious research performed.

Advancement in IoT and cloud technology has opened room for various application services in different areas. With such popularity, the volume of data increases immensely and it is infeasible for cloud technology to provide real-time services in some cases. Fog computing is an extension of cloud technology which provides real-time and time-sensitive services. Data processing is done at fog nodes that allow seamless connectivity and application services. In this paper, various fog computing architectures, applications, and security issues are discussed. It aims to provide a comprehensive review of various aspects of fog computing