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All Outputs (23)

A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis (2022)
Journal Article
Rehman, M. U., Shafique, A., Ghadi, Y. Y., Boulila, W., Jan, S. U., Gadekallu, T. R., Driss, M., & Ahmad, J. (2022). A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis. IEEE Transactions on Network Science and Engineering, 9(6), 4322-4337. https://doi.org/10.1109/tnse.2022.3199235

Early cancer identification is regarded as a challenging problem in cancer prevention for the healthcare community. In addition, ensuring privacy-preserving healthcare data becomes more difficult with the growing demand for sharing these data. This s... Read More about A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis.

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review (2022)
Journal Article
Gulzar Ahmad, S., Iqbal, T., Javaid, A., Ullah Munir, E., Kirn, N., Jan, S. U., & Ramzan, N. (2022). Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review. Sensors, 22(12), Article 4362. https://doi.org/10.3390/s22124362

Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential... Read More about Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review.

IoT-Enabled Vehicle Speed Monitoring System (2022)
Journal Article
Khan, S. U., Alam, N., Jan, S. U., & Koo, I. S. (2022). IoT-Enabled Vehicle Speed Monitoring System. Electronics, 11(4), Article 614. https://doi.org/10.3390/electronics11040614

Millions of people lose their lives each year worldwide due to traffic law violations, specifically, over speeding. The existing systems fail to report most of such violations due to their respective flaws. For instance, speed guns work in isolation... Read More about IoT-Enabled Vehicle Speed Monitoring System.

Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing (2022)
Journal Article
Saeed, U., Yaseen Shah, S., Aziz Shah, S., Liu, H., Alhumaidi Alotaibi, A., Althobaiti, T., Ramzan, N., Ullah Jan, S., Ahmad, J., & H. Abbasi, Q. (2022). Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing. Sensors, 22(3), Article 809. https://doi.org/10.3390/s22030809

Wireless sensing is the utmost cutting-edge way of monitoring different health-related activities and, concurrently, preserving most of the privacy of individuals. To meet future needs, multi-subject activity monitoring is in demand, whether it is fo... Read More about Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing.

Transfer learning-based method for detection of COVID-19 using X-Ray Images (2021)
Presentation / Conference Contribution
Rehman, A., Tariq, Z., Jan, S. U., Aziz, S., Khan, M. U., & Chaudry, H. N. (2021, October). Transfer learning-based method for detection of COVID-19 using X-Ray Images. Presented at 2021 International Conference on Robotics and Automation in Industry (ICRAI), Rawalpindi, Pakistan

In this paper, we have performed transfer learning using different pre-trained convolutional neural networks for binary classification of X-ray images into COVID-19 disease and normal. The dataset is gathered from two open sources. Our dataset is con... Read More about Transfer learning-based method for detection of COVID-19 using X-Ray Images.

A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT (2021)
Journal Article
Almas Khan, M., Khan, M. A., Ullah Jan, S., Ahmad, J., Jamal, S. S., Shah, A. A., Pitropakis, N., Buchanan, W. J., Alonistioti, N., Panagiotakis, S., & Markakis, E. K. (2021). A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT. Sensors, 21(21), Article 7016. https://doi.org/10.3390/s21217016

A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or ev... Read More about A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT.

A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration (2021)
Journal Article
Liaqat, S., Dashtipour, K., Zahid, A., Arshad, K., Ullah Jan, S., Assaleh, K., & Ramzan, N. (2021). A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration. Frontiers in Communications and Networks, 2, Article 679502. https://doi.org/10.3389/frcmn.2021.679502

Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, with a prevalence of 1–2% in the community, increasing the risk of stroke and myocardial infarction. Early detection of AF, typically causing an irregular and abnormally... Read More about A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration.

A Lightweight Chaos-Based Medical Image Encryption Scheme Using Random Shuffling and XOR Operations (2021)
Journal Article
Masood, F., Driss, M., Boulila, W., Ahmad, J., ur Rehman, S., Jan, S. U., Qayyum, A., & Buchanan, W. J. (2022). A Lightweight Chaos-Based Medical Image Encryption Scheme Using Random Shuffling and XOR Operations. Wireless Personal Communications, 127, 1405-1432. https://doi.org/10.1007/s11277-021-08584-z

Medical images possess significant importance in diagnostics when it comes to healthcare systems. These images contain confidential and sensitive information such as patients’ X-rays, ultrasounds, computed tomography scans, brain images, and magnetic... Read More about A Lightweight Chaos-Based Medical Image Encryption Scheme Using Random Shuffling and XOR Operations.

CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning (2021)
Journal Article
Saeed, U., Lee, Y., Jan, S. U., & Koo, I. (2021). CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning. Sensors, 21(2), Article 617. https://doi.org/10.3390/s21020617

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These... Read More about CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning.

Fault diagnosis based on extremely randomized trees in wireless sensor networks (2020)
Journal Article
Saeed, U., Jan, S. U., Lee, Y., & Koo, I. (2021). Fault diagnosis based on extremely randomized trees in wireless sensor networks. Reliability Engineering and System Safety, 205, Article 107284. https://doi.org/10.1016/j.ress.2020.107284

Wireless Sensor Network (WSN) being highly diversified cyber–physical system makes it vulnerable to numerous failures, which can cause devastation towards safety, economy, and systems’ reliability. Precise detection and diagnosis of failures or fault... Read More about Fault diagnosis based on extremely randomized trees in wireless sensor networks.

A distributed sensor-fault detection and diagnosis framework using machine learning (2020)
Journal Article
Jan, S. U., Lee, Y. D., & Koo, I. S. (2021). A distributed sensor-fault detection and diagnosis framework using machine learning. Information Sciences, 547, 777-796. https://doi.org/10.1016/j.ins.2020.08.068

The objective of this work is to design a sensor-fault detection and diagnosis system for the Internet of Things and Cyber-Physical Systems. The challenge is, however, achieving this objective within the limited computation, memory, and energy resour... Read More about A distributed sensor-fault detection and diagnosis framework using machine learning.

Machine Learning-based Real-Time Sensor Drift Fault Detection using Raspberry Pi (2020)
Presentation / Conference Contribution
Saeed, U., Ullah Jan, S., Lee, Y., & Koo, I. (2020, January). Machine Learning-based Real-Time Sensor Drift Fault Detection using Raspberry Pi. Presented at 2020 International Conference on Electronics, Information, and Communication (ICEIC), Barcelona, Spain

From smart industries to smart cities, sensors in the modern world plays an important role by covering a large number of applications. However, sensors get faulty sometimes leading to serious outcomes in terms of safety, economic cost and reliability... Read More about Machine Learning-based Real-Time Sensor Drift Fault Detection using Raspberry Pi.

Machine Learning for Detecting Drift Fault of Sensors in Cyber-Physical Systems (2020)
Presentation / Conference Contribution
Jan, S. U., Saeed, U., & Koo, I. (2020, January). Machine Learning for Detecting Drift Fault of Sensors in Cyber-Physical Systems. Presented at 2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan

Cyber-Physical System (CPS) emerges as a potential direction to improve the applications relating to object-to-object, human-to-human and human-to-object communications in both the real world and virtual world. The examples of CPSs include Smart Gird... Read More about Machine Learning for Detecting Drift Fault of Sensors in Cyber-Physical Systems.

On Lightweight Method for Intrusions Detection in the Internet of Things (2019)
Presentation / Conference Contribution
Shakhov, V., Jan, S. U., Ahmed, S., & Koo, I. (2019, June). On Lightweight Method for Intrusions Detection in the Internet of Things. Presented at 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Sochi, Russia

Integration of the internet into the entities of the different domains of human society is emerging as a new paradigm called the Internet of Things. At the same time, the ubiquitous and wide-range systems make them prone to attacks. Security experts... Read More about On Lightweight Method for Intrusions Detection in the Internet of Things.

Toward a Lightweight Intrusion Detection System for the Internet of Things (2019)
Journal Article
Jan, S. U., Ahmed, S., Shakhov, V., & Koo, I. (2019). Toward a Lightweight Intrusion Detection System for the Internet of Things. IEEE Access, 7, 42450-42471. https://doi.org/10.1109/access.2019.2907965

Integration of the Internet into the entities of the different domains of human society (such as smart homes, health care, smart grids, manufacturing processes, product supply chains, and environmental monitoring) is emerging as a new paradigm called... Read More about Toward a Lightweight Intrusion Detection System for the Internet of Things.

Performance Analysis of Support Vector Machine-Based Classifier for Spectrum Sensing in Cognitive Radio Networks (2018)
Presentation / Conference Contribution
Jan, S. U., Vu, V. H., & Koo, I. S. (2018, October). Performance Analysis of Support Vector Machine-Based Classifier for Spectrum Sensing in Cognitive Radio Networks. Presented at 2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Zhengzhou, China

In this work, the performance of support vector machine (SVM)-based classifier, applied for spectrum sensing in cognitive radio (CR) networks, is analyzed. A single observation given input to classifier is composed of three statistical features extra... Read More about Performance Analysis of Support Vector Machine-Based Classifier for Spectrum Sensing in Cognitive Radio Networks.

A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification (2018)
Journal Article
Jan, S. U., & Koo, I. (2018). A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification. Journal of Sensors, 2018, Article 7467418. https://doi.org/10.1155/2018/7467418

The efficiency of a binary support vector machine- (SVM-) based classifier depends on the combination and the number of input features extracted from raw signals. Sometimes, a combination of individual good features does not perform well in discrimin... Read More about A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification.

Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio (2018)
Journal Article
Jan, S. U., Vu, V., & Koo, I. (2018). Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio. Applied Sciences, 8(3), Article 421. https://doi.org/10.3390/app8030421

A framework of spectrum sensing with a multi-class hypothesis is proposed to maximize the achievable throughput in cognitive radio networks. The energy range of a sensing signal under the hypothesis that the primary user is absent (in a conventional... Read More about Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio.

Sensor faults detection and classification using SVM with diverse features (2017)
Presentation / Conference Contribution
Jan, S. U., & Koo, I. S. (2017). Sensor faults detection and classification using SVM with diverse features. In 2017 International Conference on Information and Communication Technology Convergence (ICTC). https://doi.org/10.1109/ictc.2017.8191044

Sensors in industrial systems fault frequently leading to serious consequences regarding cost and safety. The authors propose support vector machine-based classifier with diverse time- and frequency-domain feature models to detect and classify these... Read More about Sensor faults detection and classification using SVM with diverse features.

Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features (2017)
Journal Article
Jan, S. U., Lee, Y., Shin, J., & Koo, I. (2017). Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features. IEEE Access, 5, 8682-8690. https://doi.org/10.1109/access.2017.2705644

This paper deals with the problem of fault detection and diagnosis in sensors considering erratic, drift, hard-over, spike, and stuck faults. The data set containing samples of the above mentioned fault signals was acquired as follows: normal data si... Read More about Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features.