Emerging Technologies in Computing

4th EAI/IAER International Conference, iCETiC 2021, Virtual Event, August 18–19, 2021, Proceedings

Häftad, Engelska, 2021

Del i serien Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Beskrivning

This book constitutes the refereed conference proceedings of the 4th International Conference on Emerging Technologies in Computing, iCEtiC 2021, held in August 2021.

Produktinformation

Utgivningsdatum:2021-11-05
Mått:155 x 235 x 12 mm
Vikt:324 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Antal sidor:195
Förlag:Springer Nature Switzerland AG
ISBN:9783030900151

Utforska kategorier

Programvaruutveckling inom Data och IT
Artificiell intelligens inom Data och IT
Hårdvara inom Data och IT

Innehållsförteckning

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.