Emerging Technologies in Computing

4th EAI/IAER International Conference, iCETiC 2021, Virtual Event, August 18¿19, 2021, Proceedings
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ISBN-13:
9783030900151
Veröffentl:
2021
Einband:
Paperback
Erscheinungsdatum:
05.11.2021
Seiten:
208
Autor:
Mahdi H. Miraz
Gewicht:
324 g
Format:
235x155x12 mm
Serie:
395, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Sprache:
Englisch
Beschreibung:

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
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.

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