ItemA NOTE ON THE AUTOCOVARIANCE OF p-SERIES LINEAR PROCESS(Canadian University of Dubai, 2020-12-01) Kamalov, FiruzIn this note, we provide tight boundaries for the autocovariance function of a stochastic linear process with p-series coefficients. © 2020, Canadian University of Dubai. All rights reserved. ItemDirections of Application of Phasor Measurement Units for Control and Monitoring of Modern Power Systems: A State-of-the-Art Review(Multidisciplinary Digital Publishing Institute (MDPI), 2023-09) Pazderin, Andrey; Zicmane, Inga; Senyuk, Mihail; Gubin, Pavel; Polyakov, Ilya; Mukhlynin, Nikita; Safaraliev, Murodbek; Kamalov, FiruzThe development of modern power systems is directly related to changes in the traditional principles of management, planning, and monitoring of electrical modes. The mass introduction of renewable energy sources and control devices based on power electronics components contributes to changing the nature of the flow of transient and quasi-established electrical modes. In this area, the problem arises of conducting a more accurate and rapid assessment of the parameters of the electrical regime using synchronized vector measurement devices. The paper presents an extensive meta-analysis of the modern applications of phasor measurement units (PMUs) for monitoring, emergency management and protection of power systems. As a result, promising research directions, the advantages and disadvantages of the existing approaches to emergency management, condition assessment, and relay protection based on PMUs are identified. © 2023 by the authors. ItemNested ensemble selection: An effective hybrid feature selection method(Elsevier Ltd, 2023-09) Kamalov, Firuz; Sulieman, Hana; Moussa, Sherif; Reyes, Jorge Avante; Safaraliev, MurodbekIt has been shown that while feature selection algorithms are able to distinguish between relevant and irrelevant features, they fail to differentiate between relevant and redundant and correlated features. To address this issue, we propose a highly effective approach, called Nested Ensemble Selection (NES), that is based on a combination of filter and wrapper methods. The proposed feature selection algorithm differs from the existing filter-wrapper hybrid methods in its simplicity and efficiency as well as precision. The new algorithm is able to separate the relevant variables from the irrelevant as well as the redundant and correlated features. Furthermore, we provide a robust heuristic for identifying the optimal number of selected features which remains one of the greatest challenges in feature selection. Numerical experiments on synthetic and real-life data demonstrate the effectiveness of the proposed method. The NES algorithm achieves perfect precision on the synthetic data and near optimal accuracy on the real-life data. The proposed method is compared against several popular algorithms including mRMR, Boruta, genetic, recursive feature elimination, Lasso, and Elastic Net. The results show that NES significantly outperforms the benchmarks algorithms especially on multi-class datasets. © 2023 The Author(s) ItemSuspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention(Multidisciplinary Digital Publishing Institute (MDPI), 2023-07) Nazir, Amril; Mitra, Rohan; Sulieman, Hana; Kamalov, FiruzThe rise in crime rates in many parts of the world, coupled with advancements in computer vision, has increased the need for automated crime detection services. To address this issue, we propose a new approach for detecting suspicious behavior as a means of preventing shoplifting. Existing methods are based on the use of convolutional neural networks that rely on extracting spatial features from pixel values. In contrast, our proposed method employs object detection based on YOLOv5 with Deep Sort to track people through a video, using the resulting bounding box coordinates as temporal features. The extracted temporal features are then modeled as a time-series classification problem. The proposed method was tested on the popular UCF Crime dataset, and benchmarked against the current state-of-the-art robust temporal feature magnitude (RTFM) method, which relies on the Inflated 3D ConvNet (I3D) preprocessing method. Our results demonstrate an impressive 8.45-fold increase in detection inference speed compared to the state-of-the-art RTFM, along with an F1 score of 92%,outperforming RTFM by 3%. Furthermore, our method achieved these results without requiring expensive data augmentation or image feature extraction. © 2023 by the authors. ItemEmotion Recognition from Speech Using Convolutional Neural Networks(Springer Science and Business Media Deutschland GmbH, 2023) Mahfood, Bayan; Elnagar, Ashraf; Kamalov, Firuz ItemDigital Solution: Breaking the Barriers to Address Stigma of Mental Health(Institute of Electrical and Electronics Engineers Inc., 2023) Karthi, Madhulika; Alsager, Mayar; Metha, Rahul; Fatima, Nash Namulondo; Al-Gindy, Ahmed ItemLeveraging computer algebra systems in calculus: A case study with SymPy(IEEE Computer Society, 2023) Kamalov, Firuz; Santandreu, David; Leung, Ho Hon; Johnson, Jason; El Khatib, Ziad ItemNew Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution(Multidisciplinary Digital Publishing Institute (MDPI), 2023-08) Kamalov, Firuz; Santandreu Calonge, David; Gurrib, IkhlaasThe recent high performance of ChatGPT on several standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education. As deep learning is poised to shift the teaching paradigm, it is essential to have a clear understanding of its effects on the current education system to ensure sustainable development and deployment of AI-driven technologies at schools and universities. This research aims to investigate the potential impact of AI on education through review and analysis of the existing literature across three major axes: applications, advantages, and challenges. Our review focuses on the use of artificial intelligence in collaborative teacher–student learning, intelligent tutoring systems, automated assessment, and personalized learning. We also report on the potential negative aspects, ethical issues, and possible future routes for AI implementation in education. Ultimately, we find that the only way forward is to embrace the new technology, while implementing guardrails to prevent its abuse. © 2023 by the authors. ItemIntelligent Indoor Positioning Systems: The Case of Imbalanced Data(Springer Science and Business Media Deutschland GmbH, 2023) Kamalov, Firuz; Moussa, Sherif; Reyes, Jorge Avante ItemRegularized Information Loss for Improved Model Selection(Springer Science and Business Media Deutschland GmbH, 2023) Kamalov, Firuz; Moussa, Sherif; Reyes, Jorge Avante ItemKeep it simple: random oversampling for imbalanced data(Institute of Electrical and Electronics Engineers Inc., 2023) Kamalov, Firuz; Leung, Ho-Hon; Cherukuri, Aswani Kumar ItemCritical Controlling for the Network Security and Privacy Based on Blockchain Technology: A Fuzzy DEMATEL Approach(Multidisciplinary Digital Publishing Institute (MDPI), 2023-07) Kamalov, Firuz; Gheisari, Mehdi; Liu, Yang; Feylizadeh, Mohammad Reza; Moussa, SherifThe Internet of Things (IoT) has been considered in various fields in the last decade. With the increasing number of IoT devices in the community, secure, accessible, and reliable infrastructure for processing and storing computed data has become necessary. Since traditional security protocols are unsuitable for IoT devices, IoT implementation is fraught with privacy and security challenges. Thus, blockchain technology has become an effective solution to the problems of IoT security. Blockchain is an empirical data distribution and storage model involving point-to-point transmission, consensus mechanism, asymmetric encryption, smart contract, and other computer technologies. Security and privacy are becoming increasingly important in using the IoT. Therefore, this study provides a comprehensive framework for classifying security criteria based on blockchain technology. Another goal of the present study is to identify causal relationship factors for the security issue using the Fuzzy Decision-Making Trial-and-Evaluation Laboratory (FDEMATEL) approach. In order to deal with uncertainty in human judgment, fuzzy logic is considered an effective tool. The present study’s results show the proposed approach’s efficiency. Authentication (CR6), intrusion detection (CR4), and availability (CR5) were also introduced as the most effective and essential criteria, respectively. © 2023 by the authors. ItemImpact of the First-Year Seminar Course on Student GPA and Retention Rate across Colleges in Qatar University(Society for Research and Knowledge Management, 2023-05) Elobaid, Manal; Elobaid, Rafida M.; Romdhani, Lamia; Yehya, ArijThe first year is known to be challenging for students at university. If students fail to transition to college successfully, it can result in a low GPA and they may eventually drop out of university. Fortunately, higher education systems opt to add various types of support for students during this important transition. One possible support is to implement a first-year seminar course, a hybrid-course which teaches academic and non-academic skills that help students to be successful. Optimally, teaching students' skills for college success might help them manage their academic needs and increase retention rates. Following this proposal for best practice, Qatar University added a compulsory first-year seminar course in six colleges across different programs. The course included cognitive, non-cognitive and performance skills. In this retrospective study, we assessed the impact of this course on the retention rate at the university and students' academic performance over time. We reviewed a large sample of over 3000 students who started their college journey at Qatar University over four consecutives semesters. Students were classified into two groups to allow for comparison between those who took the first-year seminar course and those who did not take it, in terms of their retention rates and GPA. Our findings show that students who successfully completed the course had a higher retention rate, especially in the first semester. Furthermore, the GPA, for those who took the course and continued to attend the university, was higher across semesters. In conclusion, within our sample, the first-year seminar course was successful in supporting student success as evidenced by higher GPAs, and an increased retention rate. © Authors. ItemProactive AI Enhanced Consensus Algorithm with Fraud Detection in Blockchain(Springer, 2023) Das, Vinamra; Cherukuri, Aswani Kumar; Hu, Qin; Kamalov, Firuz; Jonnalagadda, Annapurna ItemDevelopment of Synthetic Data Benchmarks for Evaluating Feature Selection Algorithms(Institute of Electrical and Electronics Engineers Inc., 2022) Mitra, Rohan; Varam, Dara; Ali, Eyad; Sulieman, Hana; Kamalov, Firuz ItemMachine learning applications for COVID-19: a state-of-the-art review(Elsevier, 2022-01-01) Kamalov, Firuz; Cherukuri, Aswani Kumar; Sulieman, Hana; Thabtah, Fadi ItemMachine learning and blockchain integration for security applications(River Publishers, 2022-11-11) Bhandari, Aradhita; Cherukuri, Aswani Kumar; Kamalov, Firuz ItemIntegrating digital technology in enterprise and entrepreneurship education(Springer International Publishing, 2022-03-17) Al-Gindy, Ahmed; Yasin, Naveed; Aerabe, Mariam; Al-Chikh Omar, Aya ItemDynamic Bayesian network-based operational risk assessment for industrial water pipeline leakage(Elsevier Ltd, 2023-09) Abdelhafidh, Maroua; Fourati, Mohamed; Chaari, Lamia ItemA note on time series differencing(Canadian University of Dubai, 2021-05-11) Kamalov, FiruzDifferencing is one of the key tools time series analysis. It is com-monly used to obtain stationary time series. In this note, we show that the nth difference of a weakly stationary time series is weakly stationary. Similarly we prove that the nth difference of a strictly stationary time series is strictly stationary. We also consider the effect of differencing on the time series auto-covariance. © 2021, Canadian University of Dubai. All rights reserved.