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An Architecture Supporting Intelligent Mobile Healthcare Using Human-Computer Interaction HCI Principles

Computer Systems Science & Engineering 2022年第2期40卷 557-569页

作者： Mesfer Alrizq Shauban ali Solangi Abdullah Alghamdi muham.a. ali nizamani muham.a. ali Memon Mohammed Hamdi College of Computer Science and Information Systems Najran UniversityNajranSaudi Arabia Faculty of Engineering&Technology University of SindhJamshoro78060Pakistan

Recent advancements in the Internet of Things IoT and cloud computing have paved the way for mobile Healthcare(mHealthcare)services.A patient within the hospital is monitored by several ***,upon leaving the hospital,the patient can be remotely monitored whether directly using body wearable sensors or using a smartphone equipped with sensors to monitor different user-health *** raises potential challenges for intelligent monitoring of patient's *** this paper,an improved architecture for smart mHealthcare is proposed that is supported by HCI design *** HCI also provides the support for the User-Centric Design(UCD)for smart mHealthcare ***,the HCI along with IoT's(Internet of Things)5-layered architecture has the potential of improving User Experience(UX)in mHealthcare design and help saving *** intelligent mHealthcare system is supported by the IoT sensing and communication layers and health care providers are supported by the application layer for the medical,behavioral,and health-related *** care providers and users are further supported by an intelligent layer performing critical situation assessment and performing a multi-modal communication using an intelligent *** HCI design focuses on the ease-of-use,including user experience and safety,alarms,and error-resistant displays of the end-user,and improves user's experience and user satisfaction.

关键词： Human computer interaction mhealthcare user-centric design sensor network nternet-of-things

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Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

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Intelligent Automation & Soft Computing 2024年第3期39卷 545-566页

作者： Asad Raza Shahzad Memon muham.a. ali nizamani Mahmood Hussain Shah Dr.AHS Bukhari Postgraduate Centre of Information and Communication Technology Faculty of Engineering and TechnologyUniversity of SindhJamshoro76060Pakistan Newcastle Business School Northumbria UniversityNewcastleNE1UK

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven ***,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the *** signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging ***,there is the need for an IDS which can learn from data and detect new *** Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking *** this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta *** proposed DSNN model was trained and evaluated with the next-generation dataset,TON_*** pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE ***,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble *** experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML *** addition,the model proposed for IDS has been compared with similar *** proposed DSNN achieved better performance metrics than the other *** proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

关键词： Industrial internet of things smart industrial environment cyber-attacks convolutional neural network ensemble learning

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