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
This paper presents an innovative technique for solving the Traveling Salesman Problem (TSP) using Ant Colony Optimization (ACO) and Tabu Search (TS). This paper focuses on the variation of Euclidean Traveling Salesman Problem (TSP) and Generalized Traveling Salesman Problem (GTSP), extending the Ant Colony Optimization method from TSP to the region. The goal of the algorithm is to find the shortest route from source to destination and the total number of cities is 25. The technique of graphs is used to find the shortest path to the problem in the literature. The main goal is to reduce the travel costs and time. First of all, most researchers try to solve this problem by using different algorithms to get efficient results. Our proposed algorithm has optimized the implementation of our algorithm faster and more efficient to solve the well-known TSP problem. Our algorithm using ASO and TS has made significant improvements in accuracy compared to the sophisticated TS algorithm to solve the same TSP. An efficient MATLAB implementation system prepares our code for deployment in an environment with limitations in memory and speed. The results confirm the efficiency of the proposed algorithm. The performance obtained is better than the genetic algorithm and the Tabu Search algorithm.
The need to develop great quality software with timely delivery and tested components gave birth to reuse. Component reusability entails the use (re-use) of existing artifacts to improve the quality and functionalities of software. Many researches have considered and justified common reusability factors, such as customizability, portability, interface complexities, understandability/documentability etc., but with limited work on stability as a factor. The need to establish stability (in the context of volatility) as a factor for determining component reusability, is an attempt to lend our voice to the domain of component reusability. This study introduces and justifies stability, in the context of volatility of software component, as a factor that determines the reusability of software components. As part of the study methodology, sixty- nine (69) software components were collected from third party, and data extracted from their features were used to compute the metric values of stability. The conducted experiment proved the stability status of the various component types considered.
The daunting and challenging tasks of specifying the optimal network architecture and its parameters are a major area of research in the field of Machine Learning (ML) to date. Although these tasks determine the success of building and training an effective and accurate model are yet to be considered on a deep network having three hidden layers with varying optimized parameters to the best of our knowledge. This is due to expert's opinion that it is practically difficult to determine a good Multilayer Perceptron (MLP) topology with more than two or three hidden layers, without considering the number of samples and complexity of the classification to be learned. In this study, a novel approach is proposed that combining an evolutionary genetic algorithm and an optimization algorithm, and a supervised Deep Neural Network (Deep-NN) using alternative activation functions with the view of modeling the prediction for the admission of prospective university students. The genetic algorithm is used to select the optimal network parameters for the Deep-NN. Thus, this study presents a novel methodology that is effective, automatic and less human-dependent in finding optimal solution to diverse binary classification benchmarks. The model is trained, validated and tested using various performance metrics to measure the generalization ability and its performance.
Coordination and collaboration play an important role during requirements collection and implementation for any software system. In Global Software Development (GSD), its significance is increased more as stakeholders are far away across the globe. GSD has critical challenges such as language differences, geographical distance, culture differences, time zone differences exist and proper requirements collection and implementation become more difficult, which can affect the quality of software and increase cost and time estimation of software projects. The need of collaboration and coordination become more increase in GSD. This study aims to identify the possible practices from literature for implementing practices that bring collaboration and coordination among vendors and clients of GSD. To achieve goals, Systematic Literature Review (SLR) is conducted to identify the best practices for collaboration and coordination. Through SLR, 13 practices are identified. Among these, use of collaborative software and tools, effective communication, informal communication, infrastructure and organization improvement and awareness of other cultures are most identified practices for coordination and collaboration in GSD.
Requirements collection and management for software is very difficult phase of software engineering, especially when clients and vendors are far away from each other. In Global Software Development (GSD), there exist challenges such as language differences, culture differences and differences in time zones that are difficult to communicate properly. The purpose of this research was to validate the critical challenges during requirements implementation in the context of GSD through questionnaire survey that were previously identified using Systematic Literature Review (SLR). The results of survey show that factors such as “Lack of effective and proper way of communication”, “Lack of coordination and collaboration”, “Requirement management”, “Culture issue, time zone differences, language problem” are strongly agreed with frequency above 50%. As a result, these challenges are identified as critical in both literature and industry.
