This paper deals with the makespan minimization for Job Scheduling . Research on optimization techniques of the Job Scheduling Problem (JSP) is one of the most significant and promising areas of an optimization. Instead of the traditional optimization method, this paper presents an investigation into the use of an Ant Colony optimization (ACO) to optimize the JSP. The numerical experiments of ACO were implemented in a small JSP. In the natural environment, the ants have a tremendous ability to team up to find an optimal path to food resources. An ant algorithm stimulates the behavior of ants. The main objective of this paper is to minimize the makespan time of a given set of jobs and achieved optimal results are encroached.

No abstract

No abstract

Information-Centric Networking (ICN) empowered by information-centric paradigm takes popular paradigm place of hostcentric networking of communication networks, which in turn helps prioritizing the labeled content delivery, with no information on the origin of the contents. Security of client and content, originating place, and identity privacy are inherent in ICN paradigm design in contrast to present host centric concept where they are introduced as a second-thought. But, with its genesis, the ICN paradigm exhibits different unresolved challenges in privacy and security. In this work, current literature in ICN privacy and security are explored and open challenges are presented. Especially, three extensive subjects: security threats, risks involved with privacy, access control management techniques are explored. Primary objective of ICN is to modify the present location-based IP network architecture to location-free and content-oriented network framework. ICN can satisfy the demands for caching to the neighbouring edge devices with no more storage deployed. In this work, an several architecture for effective caching at the edge devices for data-centric IoT applications and a rapid content access that depends on novel deep learning techniques and caching processes in ICN. The novel learning-oriented effective caching technique yields the solution to the problem involving the available hash and on-path caching techniques, and the newly introduced content popularity scheme improves the availability content at the devices in the vicinity for minimizing the content transfer time and packet loss ratio.

Knowledge Discovery in Databases (KDD) process is also known as data mining. It is a most powerful tool for medical diagnosis. Due to hormonal changes, diabetes may  occur during pregnancy is referred as Gestational diabetes mellitus (GDM). Pregnant Women with GDM are at highest risk of future diabetes, especially type-2 diabetes. This paper focuses on designing an automated system for diagnosing gestational diabetes using hybrid classifiers as well as predicting the highest risk factors of getting Type 2 diabetes after delivery. One of the common   predictive data mining tasks is classification. It classifies the data and builds a model based on the test data values and attributes to produce the new classified data. For detecting GDM and also its risk factors, two classifier models namely modified SVM and modified J48 classifier models are proposed. The data set were collected from various hospitals and clinical labs and preprocessed with discretize filter using weka tool. Missing values are replaced by the suitable values. The final preprocessed data applied in the proposed classifier Model.  The output of the proposed model is compared with all the other existing methodologies. Since the proposed model modified J48 classifier model produces more accuracy and low error rate against other existing classifier models.