Emerging Technologies in Computing (eBook, PDF)

4th EAI/IAER International Conference, iCETiC 2021, Virtual Event, August 18-19, 2021, Proceedings
Redaktion: Miraz, Mahdi H.; Soomro, Safeeullah; Ware, Andrew; Ali, Maaruf; Southall, Garfield

• Format: PDF

Produktbeschreibung

This book constitutes the refereed conference proceedings of the 4th International Conference on Emerging Technologies in Computing, iCEtiC 2021, held in August 2021. Due to VOVID-19 pandemic the conference was helt virtually. The 15 revised full papers were reviewed and selected from 44 submissions and are organized in topical sections covering Information and Network Security; Cloud, IoT and Distributed Computing; AI, Expert Systems and Big Data Analytics

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 195
• Erscheinungstermin: 3. November 2021
• Englisch
• ISBN-13: 9783030900168
• Artikelnr.: 62880557

Inhaltsangabe

Information and Network Security 1 Hardware Assisted Protocol for Attacks Prevention in ad hoc Networks.- Android Malicious Software using Boosting Algorithms Deepon Nath.- Privacy and Security Factors of Government Websites versus Private Websites in Bangladesh and USA: A Comparative Study.- Protecting Web Applications from Web Scraping.- Cloud, IoT and Distributed Computing.- 5 Integrated CMOS Active Low-pass Filter for IoT RFID Transceiver.- LoRa IoT WSN for E-Agriculture.- Integrating the Meteorological Data into a Smart City Service using Cloud of Things (CoT) .- Data Handling and Transparency Enhancement in Central Sterile Service Department.- AI, Expert Systems and Big Data Analytics .- Data Mining Approach Improving Decision-Making Competency along the Business Digital Transformation Journey: A Literature Review.- An Event-level Clustering Framework for Process Mining Using Common Sequential Rules.- Fuzzy Logic enabled Stress Detection using Physiological Signals.- The Auxiliary Parametric Sensitivity Method as a Means of Improving Project Management Analysis and Synthesis of Executive Elements.- Predicting Diabetes Using Diabetes Datasets and Machine Learning Algorithms: Comparison and Analysis.