Special Issue "AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication"

Submission Deadline: 31 May 2021 (closed)
Guest Editors
Dr. Muhammad Ahmad, National University of Computer & Emerging Sciences, Pakistan.
Prof. Dr. Ali Kashif Bashir, Manchester Metropolitan University, United Kingdom.
Prof. Dr. Diego Alberto Oliva Navarro, Universidad de Guadalajara, Mexico.


Smartphone and Smartwatch users exponentially increased by 3 billion and are expected to further grow by several hundred million in near future. Boosted by information and communication technologies, smartphones and Smartwatches are becoming a more and more powerful and thus trustworthy inseparable companion of our lives. Moreover, Smartphones and Smartwatches have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification and authentication processes in a controlled environment (laboratory process). Therefore, when a Smartphone or Smartwatch is stolen, a thief can have access to the owner's personal information and services against the stored passwords that have forced the community to study the security implications of these devices. As a result of this potential scenario, this Special Collection aims to collect new automatic legitimate user identification systems and possible innovative/technical reviews for future research directions.

Smartphone and Smartwatch based Physical Activity Recognition
Legitimate User Identification / Authentication
Information and Communication Technologies
Multi-level and Multi-sensor data fusion
IoT and security

Published Papers
  • Towards Securing Machine Learning Models Against Membership Inference Attacks
  • Abstract From fraud detection to speech recognition, including price prediction, Machine Learning (ML) applications are manifold and can significantly improve different areas. Nevertheless, machine learning models are vulnerable and are exposed to different security and privacy attacks. Hence, these issues should be addressed while using ML models to preserve the security and privacy of the data used. There is a need to secure ML models, especially in the training phase to preserve the privacy of the training datasets and to minimise the information leakage. In this paper, we present an overview of ML threats and vulnerabilities, and we highlight current progress… More
  •   Views:722       Downloads:239        Download PDF

  • A Machine Learning Approach for Early COVID-19 Symptoms Identification
  • Abstract Symptom identification and early detection are the first steps towards a health condition diagnosis. The COVID-19 virus causes pneumonia-like symptoms such as fever, cough, and shortness of breath. Many COVID-19 contraction tests necessitate extensive clinical protocols in medical settings. Clinical studies help with the accurate analysis of COVID-19, where the virus has already spread to the lungs in most patients. The majority of existing supervised machine learning-based disease detection techniques are based on clinical data like x-rays and computerized tomography. This is heavily reliant on a larger clinical study and does not emphasize early symptom detection. The aim of this… More
  •   Views:293       Downloads:239        Download PDF

  • A Compromise Programming to Task Assignment Problem in Software Development Project
  • Abstract The scheduling process that aims to assign tasks to members is a difficult job in project management. It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process. This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically. The generated schedule directs the project to be completed with the shortest critical path, at the minimum cost, while maintaining its quality. There are several real-world business constraints related to human resources, the similarity of the tasks added to the optimization model, and the literature’s traditional rules. To support the decision-maker… More
  •   Views:500       Downloads:267        Download PDF

  • Evolution-Based Performance Prediction of Star Cricketers
  • Abstract Cricket databases contain rich and useful information to examine and forecasting patterns and trends. This paper predicts Star Cricketers (SCs) from batting and bowling domains by employing supervised machine learning models. With this aim, each player’s performance evolution is retrieved by using effective features that incorporate the standard performance measures of each player and their peers. Prediction is performed by applying Bayesian-rule, function and decision-tree-based models. Experimental evaluations are performed to validate the applicability of the proposed approach. In particular, the impact of the individual features on the prediction of SCs are analyzed. Moreover, the category and model-wise feature evaluations… More
  •   Views:686       Downloads:668        Download PDF

  • Adversarial Attacks on Featureless Deep Learning Malicious URLs Detection
  • Abstract Detecting malicious Uniform Resource Locators (URLs) is crucially important to prevent attackers from committing cybercrimes. Recent researches have investigated the role of machine learning (ML) models to detect malicious URLs. By using ML algorithms, first, the features of URLs are extracted, and then different ML models are trained. The limitation of this approach is that it requires manual feature engineering and it does not consider the sequential patterns in the URL. Therefore, deep learning (DL) models are used to solve these issues since they are able to perform featureless detection. Furthermore, DL models give better accuracy and generalization to newly… More
  •   Views:1136       Downloads:676        Download PDF