Preventing Cyber Attacks using Artificial Intelligence
Exploring Advantages and Challenges of Chatbot Integration in Human-Robot Interaction
Counterfeit Currency Detection using Machine Learning
Artificial Intelligence use in Lung Cancer Screening to Improve Patient Recovery
An Analysis of Various Crypto Coins and their Suitability for Real-Time Applications
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
The Wireless Sensor Network is widely utilized technology in different sectors for collection of process field data and to control same. In a wireless sensor network the data acquisition and presentation play an important role in the signal monitoring and control system. Considering such facts, The Smart Graphical User Interface (GUI) is designed to demonstrate collected data at the base station from distributed sensor nodes, in a user friendly format and preserve the data of collected by, the sensor network for industrial application. Moreover, the smart GUI is developed to indicate faulty nodes and localize the sensor nodes from base station. Furthermore, the GUI is designed to achieve the goals such as like energy efficiency and database security. The details regarding the design and implementation of the GUI are discussed in this paper.
Increases in workplace productivity and their benefits have been documented since pre-civilisation with similar observable effects to date. Whilst such productivity increases lead to far better product output they have also historically displaced surplus labour into other new innovative forms of labour. Industry 4.0 seeks to once again causes weeping increases to productivity with large disagreement as to whether the effects of “the fourth industrial revolution” will be comparable to that witnessed over the previous century. This research investigates whether or not industry 4.0 will create secondary factors that affect the working environment that designers operate within. The arguments surrounding future labour opportunity and how current labour may be affected by industry 4.0 will be examined and compared to primary research findings in order to make any conclusions about the state of manufacture, white collar work, industry geography, labour opportunity and the designer’s individual roles.
In recent years, the concept of asset life extension has become increasingly important in a safety critical environment. All agents acting in a complex system act in their capacity to create a positive outcome for the organization and tasks at hand using available data and history. Decision-making tools in such areas as telecommunications, healthcare, and utility industries play an integral part in the establishment and maintenance of such environments. A broad range of potential errors, both technical and human, contribute to resolutions which may be less-then-optimal, leading to loss of resources, including time, money, and in severe situations, human life. With greater access to available data, a higher the potential for positive outcomes in decision-making is made possible. The goal is to produce an examination of the available tools in machine learning and algorithm design which aid in decision-making. The strengths and weaknesses of network architectures and algorithmic models is discussed, followed by conclusions and proposed solutions for improved efficiency and access to appropriate information systems.
Internet of Things (IoT) consists of a wide variety of devices with limited power sources. Due to the adhered reason, energy consumption is considered as one of the major challenges in the IoT environment. In this research article, an attempt is made to optimize the existing Routing Protocol (RPL) towards a green technology. It focuses on finding the most significant parameter in the RPL using Taguchi Design of Experiments. It emphasizes the effects of five input factors Network Size, Mobility Speed, DIO_DOUBLING, DIO_MIN_INTERVAL and Redundancy Constant on only one output parameter Power Consumption. The findings show that DIO_MIN_INTERVAL is the leading factor that has a significant effect on the power consumption in RPL. After determining the most significant factor that affects the power consumption, measures can be taken to optimize the performance of RPL by applying some optimization techniques. COOJA simulator is used to carry out the simulations required for this research article.
Software engineering may look like an old term, but the process to be followed to develop a product follows the Software Development Life Cycle (SDLC) method always for a successful product in the market. Succeeding in the market is always a tough task. Though one satisfies all the functional requirements of a client as a product development company, they need to move our focus more on the non-functional requirements and the users' experience. Only then, the product will do wonders in the market. In this paper, the authors propose a successful software model with users' experience as a key factor and also users' view on the product, where all the terms come under non-functional requirement.
