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

Submission Deadline: 30 August 2021 (closed)

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.


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 This special issue focuses on original and unpublished articles in the area of IoT supported smart infrastructures, smart healthcare solutions, remote drug behavioural analysis and data science-based drug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.



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


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

Published Papers

  • Open Access


    Invariant of Enhanced AES Algorithm Implementations Against Power Analysis Attacks

    Nadia Mustaqim Ansari, Rashid Hussain, Sheeraz Arif, Syed Sajjad Hussain
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1861-1875, 2022, DOI:10.32604/cmc.2022.023516
    (This article belongs to the Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
    Abstract The security of Internet of Things (IoT) is a challenging task for researchers due to plethora of IoT networks. Side Channel Attacks (SCA) are one of the major concerns. The prime objective of SCA is to acquire the information by observing the power consumption, electromagnetic (EM) field, timing analysis, and acoustics of the device. Later, the attackers perform statistical functions to recover the key. Advanced Encryption Standard (AES) algorithm has proved to be a good security solution for constrained IoT devices. This paper implements a simulation model which is used to modify the AES algorithm… More >

  • Open Access


    Enhancing Parkinson's Disease Prediction Using Machine Learning and Feature Selection Methods

    Faisal Saeed, Mohammad Al-Sarem, Muhannad Al-Mohaimeed, Abdelhamid Emara, Wadii Boulila, Mohammed Alasli, Fahad Ghabban
    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5639-5658, 2022, DOI:10.32604/cmc.2022.023124
    (This article belongs to the Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
    Abstract Several millions of people suffer from Parkinson's disease globally. Parkinson's affects about 1% of people over 60 and its symptoms increase with age. The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners, but which could be analyzed using recorded speech signals. With the huge advancements of technology, the medical data has increased dramatically, and therefore, there is a need to apply data mining and machine learning methods to extract new knowledge from this data. Several classification methods were used to analyze medical data sets and diagnostic… More >

  • Open Access


    Cardiovascular Disease Prediction Among the Malaysian Cohort Participants Using Electrocardiogram

    Mohd Zubir Suboh, Nazrul Anuar Nayan, Noraidatulakma Abdullah, Nurul Ain Mhd Yusof, Mariatul Akma Hamid, Azwa Shawani Kamalul Arinfin, Syakila Mohd Abd Daud, Mohd Arman Kamaruddin, Rosmina Jaafar, Rahman Jamal
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1111-1132, 2022, DOI:10.32604/cmc.2022.022123
    (This article belongs to the Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
    Abstract A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The Malaysian Cohort project. An automated peak detection algorithm to detect nine fiducial points of electrocardiogram (ECG) was developed. Forty-eight features were extracted in both time and frequency domains, including statistical features obtained from heart rate variability and Poincare plot analysis. These include five new features derived from spectrum counts of five different frequency ranges. Feature selection was then made based on p-value and correlation matrix. Selected features were used as… More >

  • Open Access


    Multilingual Sentiment Mining System to Prognosticate Governance

    Muhammad Shahid Bhatti, Saman Azhar, Abid Sohail, Mohammad Hijji, Hamna Ayemen, Areesha Ramzan
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 389-406, 2022, DOI:10.32604/cmc.2022.021384
    (This article belongs to the Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
    Abstract In the age of the internet, social media are connecting us all at the tip of our fingers. People are linkedthrough different social media. The social network, Twitter, allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights. This paper serves the purpose of text processing of a multilingual dataset including Urdu, English, and Roman Urdu. Explore machine learning solutions for sentiment analysis and train models, collect the data on government from Twitter, apply sentiment analysis, and provide a python More >

  • Open Access


    MELex: The Construction of Malay-English Sentiment Lexicon

    Nurul Husna Mahadzir, Mohd Faizal Omar, Mohd Nasrun Mohd Nawi, Anas A. Salameh, Kasmaruddin Che Hussin, Abid Sohail
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1789-1805, 2022, DOI:10.32604/cmc.2022.021131
    (This article belongs to the Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
    Abstract Currently, the sentiment analysis research in the Malaysian context lacks in terms of the availability of the sentiment lexicon. Thus, this issue is addressed in this paper in order to enhance the accuracy of sentiment analysis. In this study, a new lexicon for sentiment analysis is constructed. A detailed review of existing approaches has been conducted, and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated. Constructing MELex involves three activities: seed words selection, polarity assignment, and synonym expansions. Our approach differs from previous works in that MELex can analyze text More >

  • Open Access


    Benchmarking Performance of Document Level Classification and Topic Modeling

    Muhammad Shahid Bhatti, Azmat Ullah, Rohaya Latip, Abid Sohail, Anum Riaz, Rohail Hassan
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 125-141, 2022, DOI:10.32604/cmc.2022.020083
    (This article belongs to the Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
    Abstract Text classification of low resource language is always a trivial and challenging problem. This paper discusses the process of Urdu news classification and Urdu documents similarity. Urdu is one of the most famous spoken languages in Asia. The implementation of computational methodologies for text classification has increased over time. However, Urdu language has not much experimented with research, it does not have readily available datasets, which turn out to be the primary reason behind limited research and applying the latest methodologies to the Urdu. To overcome these obstacles, a medium-sized dataset having six categories is… More >

  • Open Access


    A Novel AlphaSRGAN for Underwater Image Super Resolution

    Aswathy K. Cherian, E. Poovammal
    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1537-1552, 2021, DOI:10.32604/cmc.2021.018213
    (This article belongs to the Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
    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… More >

  • Open Access


    Competency Driven Resource Evaluation Method for Business Process Intelligence

    Abid Sohail, Dhanapal Durai Dominic, Mohammad Hijji, Muhammad Arif Butt
    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1141-1157, 2021, DOI:10.32604/cmc.2021.018023
    (This article belongs to the Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
    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 More >

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