Inam Ullah (Member, IEEE) received a B.Sc. degree in Electrical Engineering (Telecommunication) from the Department of Electrical Engineering, University of Science and Technology Bannu (USTB), KPK, Pakistan, in 2016 and a Master's and Ph.D. degree in Information and Communication Engineering from the College of Internet of Things (IoT) Engineering, Hohai University (HHU), Changzhou Campus, 213022, China, in 2018 and 2022, respectively. He has completed his postdoc with Brain Korea 2021 (BK21) at the Chungbuk Information Technology Education and Research Center, Chungbuk National University, Cheongju 28644, S Korea, in March 2023. He is currently an Assistant Professor at the Department of Computer Engineering, Gachon University, S Korea. His research interests include Robotics, Internet of Things (IoT), Wireless Sensor Networks (WSNs), Underwater Communication and Localization, Underwater Sensor Networks (USNs), Artificial Intelligence (AI), Big data, Deep learning, etc. He has authored more than 100 peer-reviewed articles on various research topics. He is a TPC member of ACM RACS 2023, Poland, August 6-10, 2023, and IEEE ICC'24 - SAC-10, Denver, CO, USA), 2024. He served as Guest Editors for various journals such as Computers in Human Behavior, Sensors, Electronics, Journal of Marine Science and Engineering, Frontiers in Sensors, Artificial Intelligence and Applications, etc. He is the reviewer of many prominent journals, including IEEE Transactions on Industrial Informatics KSII Transactions on Internet & Information Systems, IEEE Transactions on Vehicular Technology, IEEE Transactions on Intelligent Transportation Systems, Transactions on Sustainable Computing, IEEE ACCESS, Sustainable Energy Technologies and Assessments, Future Generation Computer Systems (FGCS), Computers and Electrical Engineering (Elsevier), Internet of Things (IoT) Journal, Digital Communications & Networks (Elsevier), Springer Nature, Wireless Communication & Mobile Computing (WCMC), Alexandria Engineering Journal Sensors, Electronics, Remote Sensing, Applied Sciences, Computational Intelligence and Neurosciences, etc.
His awards and honors include the Best Student Award from the University of Science and Technology Bannu (USTB), KPK, Pakistan, in 2015 and the Prime Minister Laptop Scheme Award from the University of Science and Technology Bannu (USTB), KPK, Pakistan, in April 2015. Top-10 students award of the College of Internet of Things (IoT) Engineering, Hohai University, China in June 2019, Top-100 students award of Hohai University (HHU), China in June 2019, Jiangsu Province Distinguish International Students award (30,000 RMB) in 2019-2020, Certificate of Recognition from Hohai University (HHU), China in 2021 & 2022 both, Top-100 students award of Hohai University (HHU), China in May 2022, Top-10 Outstanding Students Award, Hohai University (HHU), China in June 2022, and Distinguished Alumni Award from University of Science and Technology Bannu (USTB), KPK, Pakistan in Oct. 2022.
In the modern world, disseminating false information is a problem that must be addressed, and algorithms based on machine learning are used to spot and stop the spread of incorrect information. Due to the current unregulated development of false news fabrication and dissemination, democracy is continuously under threat. Fake news may mislead individuals while influencing them because of its persuasiveness and life sciences. Using data from the Web of Science, this study undertakes a bibliometric analysis of research on the application of machine learning for fake news identification. The research underscores the need for a streamlined approach to analyze data exclusively from the Web of Scie... More >
In recent years, vehicular ad hoc networks (VANETs) have faced growing security concerns, particularly from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks flood the network with malicious traffic, disrupting services and compromising resource availability. While various techniques have been proposed to address these threats, this study presents an optimized framework leveraging advanced deep-learning models for improved detection accuracy. The proposed Intrusion Detection System (IDS) employs Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Deep Belief Networks (DBN) alongside robust feature selection techniques, Random Projecti... More >
This paper introduces a novel method for controlling a class of nonlinear non-affine systems with fractional-order dynamics, using an adaptive fuzzy technique. By incorporating a novel fractional update law in the design procedure, the controller can effectively suppress chaotic behaviour and smoothly track desired trajectories. The proposed method offers key advantages such as robustness against uncertainties, fast error convergence to the neighbourhood of zero, and satisfactory disturbance rejection performance. To demonstrate the capabilities of the proposed fractional controller, simulation results were conducted using Python on a fractional order Arneodo chaotic system. The results high... More >
The increasing prevalence of fake news on social media has become a significant challenge in today’s digital landscape. This paper proposes a hybrid framework for fake news detection, combining Natural Language Processing (NLP) techniques and machine learning algorithms. Using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction, and classifiers such as Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machines (SVM), the model integrates Maximum Likelihood Estimation (MLE) with Logistic Regression to achieve 95% accuracy and 93% precision on a Kaggle dataset. The results highlight the potential of combining statistical and NLP approaches to improve fake... More >
With great excitement and a sense of profound responsibility, I welcome you to the inaugural IECE Transactions on Emerging Trends in Network Systems issue. This journal has been established as a leading platform for sharing cutting-edge research and innovative practices in network systems. We aim to bridge the gap between theory and practical application, enabling researchers, practitioners, and policymakers to collaborate and advance the rapidly evolving field of network technologies. More >
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