IoT Assistive Technology for People with Disabilities
Soulease: A Mind-Refreshing Application for Mental Well-Being
AI-Powered Weather System with Disaster Prediction
AI Driven Animal Farming and Livestock Management System
Advances in AI for Automatic Sign Language Recognition: A Comparative Study of Machine Learning Approaches
Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
Requirements elicitation is considered the most important step in software engineering. There are several techniques to elicit requirements, however they are limited. Most approaches are general qualitative approaches. Thus, they do not suite specific software domain, such as cyber security. This article proposes a new technique to elicit requirements from cyber security strategies. The approach is able to formally define requirements' strengths, and link them with respective analyst's expertise. Consequently, management can easily select the appropriate requirements to be implemented. The use of the proposed approach on a selected cyber security domain showed its applicability on cyber security framework implementations.
Software Product Line (SPL) is an emerging approach to satisfy the ever-increasing customization demands by reusing commonalities and variability's. Variability - aware design patterns can leverage SPL configuration management and evolution of new products. Design pattern is a blueprint or model solution to a frequently occurring design problem. Variability aware design patterns can address variability and help in customizing software products. Modularization of artefacts and reusability of them can be realized by using design patterns. Design patterns in SPL is relatively used in new research area. However, composite design patterns that are variability-aware can lead to the realization of high quality SPL. In this context, the configuration management and product derivation are to be conceived and handled. There are no dedicated efforts found in the literature to leverage the usage of design patterns in SPL. The authors proposed a framework and provided provision for variability-aware design patterns. They use the concept of roles and map them to variability model. Then they map design pattern roles to artefacts thus realizing variability with industry best practices. This will help in improving the dynamic reconfiguration of SPL artefacts. Their empirical evaluation shows that the approach improved performance up to 20% with respect to configuration management of SPL and product derivation. The prototype demonstrates the proof of concept.
Multicore platforms allow developers to optimize applications by intelligent partitioning at different workloads on different processor cores. Currently, application programs are optimized to use multiple processor resources, resulting in faster application performance. The authors earlier research work focused on native thread for Java on windows thread, Pthread, and Intel TBB. The authors also developed Native Threads, Native Pthread, Java Native Intel TBB beneath windows 32-bit platform. This article aims to identify the future directions of native thread for Java on windows thread, Pthread, and Intel TBB through JNI beneath windows 64-bit platforms and other platform besides. Furthermore, it articulates additional opening to pursue approaching developments on parallel programming models through Java.
Software defect prediction is an important research topic in the software engineering field, especially to solve the inefficiency and ineffectiveness of the existing industrial approach of software testing and reviews. The software defect prediction performance decreases significantly because the data set contains noisy attributes and class imbalance. Feature selection is generally used in machine learning when the learning task involves high-dimensional and noisy attribute datasets. In this survey, a Genetic Algorithm and a bagging technique is a research topic for Software Defect Prediction. The survey of publications on this topic leads to the conclusion that the field of genetic algorithms applications is growing fast. The authors overall aim is to provide an efficient feature selection for further development of the research.
Data mining is a process of extracting facts from a given huge set of data. Of the available huge data set, multimedia is one which contains diverse data such as audio, video, image, text and motion, and such video data play a vital role in the field of video data mining. For extracting information from this huge content, we need special techniques. Because of numerous devices like cell phones, tablets, and other electronic devices available today, we can upload images or video data very easily. Today information comes in the form of electronic information instead of text information. Most of the information like news, entertainment, books, healthcare and weather forecasts are in the electronic form. Among this information the acquisition and storage of video data is an easy task, but retrieval of information from video data is challenging. This paper brings some of these issues and challenges involved in image extraction using data mining techniques.
Traditional approaches of architecting software are incapable of providing all the solutions for developing scalable architectures. Uncertainty or lack of knowledge about which steps or guidelines to use, in order to obtain a good modularization of the architecture, is one of the major problems that keeps us from realizing a truly scalable architecture.
