Journal of Software Engineering and Applications 

About: Journal of Software Engineering and Applications is an academic journal published by Scientific Research Publishing. The journal publishes majorly in the area(s): Software development & Software. It has an ISSN identifier of 1945-3116. Over the lifetime, 958 publications have been published receiving 8114 citations. The journal is also known as: JSEA.

Blockchain Platform for Industrial Internet of Things

Abstract: Internet of Things (IoT) are being adopted for industrial and manufacturing applications such as manufacturing automation, remote machine diagnostics, prognostic health management of industrial machines and supply chain management Cloud-Based Manufacturing is a recent on-demand model of manufacturing that is leveraging IoT technologies While Cloud-Based Manufacturing enables on-demand access to manufacturing resources, a trusted intermediary is required for transactions between the users who wish to avail manufacturing services We present a decentralized, peer-to-peer platform called BPIIoT for Industrial Internet of Things based on the Block chain technology With the use of Blockchain technology, the BPIIoT platform enables peers in a decentralized, trustless, peer-to-peer network to interact with each other without the need for a trusted intermediary

The 3+1 SysML View-Model in Model Integrated Mechatronics

Abstract: Software is becoming the driving force in today’s mechatronic systems. It does not only realize a significant part of their functionality but it is also used to realize their most competitive advantages. However, the traditional development process is wholly inappropriate for the development of these systems that impose a tighter coupling of software with electronics and mechanics. In this paper, a synergistic integration of the constituent parts of mechatronic systems, i.e. mechanical, electronic and software is proposed though the 3+1 SysML view-model. SysML is used to specify the cen-tral view-model of the mechatronic system while the other three views are for the different disciplines involved. The widely used in software engineering V-model is extended to address the requirements set by the 3+1 SysML view-model and the Model Integrated Mechatronics (MIM) paradigm. A SysML profile is described to facilitate the application of the proposed view-model in the development of mechatronic systems.

Challenges for Software Engineering in Automation

Abstract: This paper gives an introduction to the essential challenges of software engineering and requirements that software has to fulfill in the domain of automation. Besides, the functional characteristics, specific constraints and circumstances are considered for deriving requirements concerning usability, the technical process, the automation functions, used platform and the well-established models, which are described in detail. On the other hand, challenges result from the circumstances at different points in the single phases of the life cycle of the automated system. The requirements for life-cycle-management, tools and the changeability during runtime are described in detail.

Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Abstract: Many companies like credit card, insurance, bank, retail industry require direct marketing. Data mining can help those institutes to set marketing goal. Data mining techniques have good prospects in their target audiences and improve the likelihood of response. In this work we have investigated two data mining techniques: the Naive Bayes and the C4.5 decision tree algorithms. The goal of this work is to predict whether a client will subscribe a term deposit. We also made comparative study of performance of those two algorithms. Publicly available UCI data is used to train and test the performance of the algorithms. Besides, we extract actionable knowledge from decision tree that focuses to take interesting and important decision in business area.

Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis

Abstract: In the absence of medical diagnosis evidences, it is difficult for the experts to opine about the grade of disease with affirmation. Generally many tests are done that involve clustering or classification of large scale data. However many tests could complicate the main diagnosis process and lead to the difficulty in obtaining the end results, particularly in the case where many tests are performed. This kind of difficulty could be resolved with the aid of machine learning techniques. In this research, we present a comparative study of different classification techniques using three data mining tools named WEKA, TANAGRA and MATLAB. The aim of this paper is to analyze the performance of different classification techniques for a set of large data. A fundamental review on the selected techniques is presented for introduction purpose. The diabetes data with a total instance of 768 and 9 attributes (8 for input and 1 for output) will be used to test and justify the differences between the classification methods. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.