ICICT 2020, London — Volume 1
In February 2020, researchers, academics, and industry practitioners gathered in London for the Fifth International Congress on Information and Communication Technology. This volume collects the first group of papers from that congress, published as Volume 1183 in Springer’s Advances in Intelligent Systems and Computing series.
The congress brief was broad by design — e-business fields such as e-agriculture, e-education and e-mining were the stated focus, but the accepted submissions range much further. The result is a wide-angle view of where ICT research stood at the turn of the decade, from deep-learning methods to the practical politics of data protection in small companies.
What the Papers Cover 💻
The contents move across a genuinely varied set of applied problems. Among the areas represented in this volume:
- Deep learning and multimodal biometric identification systems
- Machine learning for advanced driver-assistance systems
- Blockchain in logistics and supply chain management, plus blockchain-based frameworks for decentralised IoT security
- Digital transformation in Swiss hospitals and in enterprise more generally
- Network performance and wireless coverage, including low-power wide area networks and Wi-Fi radio cartography
- Text and sentiment analysis, with a study on Vietnamese aspect-based sentiment classification
- Technology in teaching: flipped classrooms for C programming, augmented reality storytelling with preschool children, and soft skills in agile teams
- Policy and organisation questions, including GDPR impacts on small and medium-sized enterprises and the under-representation of women in ICT
Research Connected to Practice
Conference proceedings of this kind are valuable for a specific reason: they capture work in progress while it is still close to the problem it was designed to solve. Several papers here are explicitly case-driven — a remote monitoring system for a smart laboratory, a radio cartography methodology, a practical study within a national telecom operator. Others take a systems or methodological angle, such as measuring the complexity of legislation or estimating exceedance probability in air-pollution time series.
Read together, the volume shows how quickly ideas move between disciplines in this field. The same underlying techniques appear in healthcare, logistics, education, and public administration, adapted to each setting.
Who This Volume Serves 📚
This is a research-level collection. It suits postgraduate students and doctoral researchers looking for current work and citation material, academics tracking developments in intelligent systems and communication technology, and industry engineers who want to see how comparable problems have been approached in other sectors and countries. Libraries maintaining holdings in computing and engineering will find it a straightforward addition alongside the rest of the AISC series.
Publication Details
Edited by Xin-She Yang, R Simon Sherratt, Nilanjan Dey, and Amit Joshi, and published by Springer Nature Singapore in 2021. The congress received submissions from experts across more than 45 countries; 120 papers were accepted, with 105 presented across 14 technical sessions, and ten Best Paper Awards announced by Springer at the closing ceremony.
Available from Digital Delights as a digital edition for immediate download.
User Reviews
Only logged in customers who have purchased this product may leave a review.
Introduction to Algorithms for Data Mining and Machine Learning
Sold by Xin-She Yang
Original price was: $5.00.$2.50Current price is: $2.50.

There are no reviews yet.