Special Issue "Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems"

Submission Deadline: 30 August 2021 (closed)
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Guest Editors
Dr. Dhanapal Durai Dominic Selvam, Universiti Teknologi Petronas, Malaysia.
Dr. KottiLingam Kottursamy, SRMIST, India.
Dr. Abid Sohail, COMSATS university, Pakistan.
Dr. Korhan Cengiz, Trakya University, Turkey.

Summary

The Data Science and Internet of Things (IoT) fields are blooming bright in this era. IoT is one of the major contributors to data in today’s technological world. The data that is produced from the IoT devices are massive which paves the way for the many data-intensive system and applications. The Natural Language Processing (NLP) is used for the machine to machine correspondence and the NLP based frameworks, have developed in the new past which assisted with expanding the proficiency of administrations offered absent a lot of need of human connection. This huge technological revolution unveils many opportunities to solve socio-economical problems. The large-scale application ranges from biometric systems to remote patient monitoring. Smart environments include smart mobility, vehicular systems, smart grids, waste management; environmental monitoring, water management; surveillance/intelligence; smart services, and crowdsensing. The global IoT in the healthcare market size is projected to reach USD 534.3 billion by 2025 expanding at a CAGR 19.9% over the forecast period, according to a new report by a global research forum. Hence, smart healthcare becomes very important. IoT-based healthcare services are expected to reduce costs, increase the quality of life, and enrich the user’s experience. This special issue focuses on original and unpublished articles in the area of IoT supported smart infrastructures, smart healthcare solutions, remote drug behavioural analysis and data science-based drug recommendation systems, Inventive smart vehicular systems, motion control and medical imaging, healthcare embedded smart homes and other broad domains of healthcare decision support systems.

 

Subtopics:

The subtopics to be covered within this issue are listed below:

1. Healthcare information systems

2. Ambient assisted living

3. The internet of m-health things (m-IoT)

4. Information sciences and Adverse drug reaction

5. AI in Community healthcare

6. Children health information systems

7. Wearable device access

8. AI for Remote Medical Imaging

9. Smart systems for electrocardiogram monitoring

10. Smart systems for oxygen saturation monitoring

11. Smart systems for body temperature monitoring

12. Smartphone healthcare solutions

13. Crowd-sensing, human-centric sensing

14. Deployment and field testing

15. e-Health, Assisted Living and e-Wellness

16. IoMT for contact tracing and monitoring in pandemic environments

17. Correlation of Health and IoT based air quality index monitoring

18. Data-Sciences and Smart Cities

19. Security, privacy, the integrity of multimodal data in IoMT

20. New data science algorithms geospatial systems

21. Business Process Improvement

22. Natural Language Processing


Keywords
• Data Science
• IoT
• Health Information Systems
• AI
• Smart Systems
• Business Process Improvement

Published Papers
  • A Novel AlphaSRGAN for Underwater Image Super Resolution
  • Abstract Obtaining clear images of underwater scenes with descriptive details is an arduous task. Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition errors. Consequently, a need for a system that produces clear images for underwater image study has been necessitated. To overcome problems in resolution and to make better use of the Super-Resolution (SR) method, this paper introduces a novel method that has been derived from the Alpha Generative Adversarial Network (AlphaGAN) model, named Alpha Super Resolution Generative Adversarial Network (AlphaSRGAN). The model put forth in this paper helps in enhancing the… More
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  • Competency Driven Resource Evaluation Method for Business Process Intelligence
  • Abstract Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge. One of the established ways of improving process performance is to assign the most appropriate resources to each task of the process. However, evaluations of business process improvement approaches have established that a method that can guide decision-makers to identify the most appropriate resources for a task of process improvement in a structured way, is missing. It is because the relationship between resources and tasks is less understood and advancement in business process intelligence is also ignored. To address this problem an integrated resource classification… More
  •   Views:724       Downloads:486        Download PDF