Department of Electrical Engineering
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Item Routing algorithms in internet of things complex network with the role of machine learning(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2023) Kamalov, Firuz; Sayari, Zahra; Gheisari, Mehdi; Lee, Cheng-Chi; Moussa, SherifIn recent years, the growth of internet-based technologies increased at a rapid pace. The development of technologies such as the Internet of Things (IoT) influenced the enormous increase in the use of data and internet services. IoT devices use different algorithms for facilitating connectivity between devices and control them. However, ensuring smooth connectivity using protocols across a shared medium of network resources is challenging. IoT ecosystems utilize several routing algorithms to deliver the best path/ route for network traffic to control cyber physical systems. These routing algorithms allow IoT networks to use network routes, thereby increasing network traffic mobility effectively. So, a comprehensive survey is needed, which paves the path for researchers. Specifically, this survey investigates and compares the routing solutions in the IoT environment from different perspectives than current surveys such as safety, flow control of data and other essential parameters in IoT physical and nonphysical systems. © 2023, Institute for Problems in Mechanical Engineering, Russian Academy of Sciences. All rights reserved.Item Enough of the chit-chat: A comparative analysis of four AI chatbots for calculus and statistics(Kaplan Singapore, 2023-06-29) Calonge, David Santandreu; Smail, Linda; Kamalov, FiruzThis article presents a comparative analysis of four AI chatbots with potential utilization in the fields of mathematics education and statistics, namely ChatGPT, GPT-4, Bard, and LLaMA. Our objective is to evaluate and compare the features, functionalities, and potential applications of these platforms within the domains of calculus and statistics. By examining their strengths and limitations, this study aims to provide insights into the selection and implementation of AI chatbots in calculus and statistics to enhance student learning. The results of the comparative analysis reveal that, while not perfect, GPT-4 outperforms ChatGPT, Bard, and LLaMA as a learning tool in calculus and statistics. Findings also reveal that chatbots may have a positive transformational impact on higher education. © 2023 David Santandreu Calonge, Linda Smail and Firuz Kamalov..Item Forward feature selection: empirical analysis(American Scientific Publishing Group (ASPG), 2024) Kamalov, Firuz; Elnaffarr, Said; Cherukuri, Aswani; Jonnalagadda, AnnapurnaItem Semi-analytic solutions and sensitivity analysis for an unsteady squeezing MHD Casson nanoliquid flow between two parallel disks(2023) Umavathi, J.C.; Thameem Basha, H.; Noor, N.F.M.; Kamalov, F.; Leung, H.H.; Sivaraj, R.Item Computation of inclined magnetic field, thermophoresis and Brownian motion effects on mixed convective electroconductive nanofluid flow in a rectangular porous enclosure with adiabatic walls and hot slits(World Scientific, 2023) Sumithra, A.; Sivaraj, R.; Prasad, V. Ramachandra; Bég, O. Anwar; Leung, Ho-Hon; Kamalov, Firuz; Kuharat S.; Kumar, B. RushiItem Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review(Multidisciplinary Digital Publishing Institute (MDPI), 2023-11) Pazderin, Andrey; Kamalov, Firuz; Gubin, Pavel Y.; Safaraliev, Murodbek; Samoylenko, Vladislav; Mukhlynin, Nikita; Odinaev, Ismoil; Zicmane, IngaNontechnical losses of electrical energy (NTLEE) have been a persistent issue in both the Russian and global electric power industries since the end of the 20th century. Every year, these losses result in tens of billions of dollars in damages. Promptly identifying unscrupulous consumers can prevent the onset of NTLEE sources, substantially reduce the amount of NTLEE and economic damages to network grids, and generally improve the economic climate. The contemporary advancements in machine learning and artificial intelligence facilitate the identification of NTLEE sources through anomaly detection in energy consumption data. This article aims to analyze the current efficacy of computational methods in locating, detecting, and identifying nontechnical losses and their origins, highlighting the application of neural network technologies. Our research indicates that nearly half of the recent studies on identifying NTLEE sources (41%) employ neural networks. The most utilized tools are convolutional networks and autoencoders, the latter being recognized for their high-speed performance. This paper discusses the main metrics and criteria for assessing the effectiveness of NTLEE identification utilized in training and testing phases. Additionally, it explores the sources of initial data, their composition, and their impact on the outcomes of various algorithms. © 2023 by the authors.Item Digital twin technology: Security implications and issues(Nova Science Publishers, Inc., 2023) Nikhil, C.; Rahul, K.; Cherukuri, Aswani Kumar; Kamalov, Firuz; Srinivasan, KathiravanItem Synthetic Data for Feature Selection(Springer Science and Business Media Deutschland GmbH, 2023) Kamalov, Firuz; Sulieman, Hana; Cherukuri, Aswani KumarFeature selection is an important and active field of research in machine learning and data science. Our goal in this paper is to propose a collection of synthetic datasets that can be used as a common reference point for feature selection algorithms. Synthetic datasets allow for precise evaluation of selected features and control of the data parameters for comprehensive assessment. The proposed datasets are based on applications from electronics in order to mimic real life scenarios. To illustrate the utility of the proposed data we employ one of the datasets to test several popular feature selection algorithms. The datasets are made publicly available on GitHub and can be used by researchers to evaluate feature selection algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item EMI investigation of a Bridgeless-PFC LED Driver with Direct Power Transfer Circuit(Institute of Electrical and Electronics Engineers Inc., 2023-08) Hamza, Djilali; Pahlevani, MajidItem A Secure Peer-to-Peer Image Sharing Using Rubik's Cube Algorithm and Key Distribution Centre(Sciendo, 2023-09-01) Cherukuri, Aswani Kumar; Sannuthi, Shria; Elagandula, Neha; Gadamsetty, Rishita; Singh, Neha; Jain, Arnav; Sumaiya Thaseen I.; Priya V.; Jonnalagadda, Annapurna; Kamalov, FiruzIn this work, we build upon an implementation of a peer-to-peer image encryption algorithm: "Rubik's cube algorithm". The algorithm utilizes pixel-level scrambling and XOR-based diffusion, facilitated through the symmetric key. Empirical analysis has proven this algorithm to have the advantage of large key space, high-level security, high obscurity level, and high speed, aiding in secure image transmission over insecure channels. However, the base approach has drawbacks of key generation being handled client-side (at nodes) and the process is time-consuming due to dynamically generating keys. Our work solves these issues by introducing a Key Distribution Center (KDC) to distribute symmetric keys for transmission, increasing confidentiality, and reducing key-generation overhead on nodes. Three approaches utilizing the KDC are presented, communicating the dimensions with KDC to generate keys, standardizing any image to fixed dimensions to standardize key-generation, and lastly, using a single session key which is cyclically iterated over, emulating different dimensions. © 2023 Aswani Kumar Cherukuri et al., published by Sciendo.Item A 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.Item Directions 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.Item Nested 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)Item Suspicious 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.Item Emotion Recognition from Speech Using Convolutional Neural Networks(Springer Science and Business Media Deutschland GmbH, 2023) Mahfood, Bayan; Elnagar, Ashraf; Kamalov, FiruzItem Digital 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, AhmedItem Leveraging 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, ZiadItem New 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.Item Intelligent Indoor Positioning Systems: The Case of Imbalanced Data(Springer Science and Business Media Deutschland GmbH, 2023) Kamalov, Firuz; Moussa, Sherif; Reyes, Jorge AvanteThe ubiquity of Wi-Fi over the last decade has led to increased popularity of intelligent indoor positioning systems (IPS). In particular, machine learning has been recently utilized to develop intelligent IPS. Most of the existing research focus on developing intelligent IPS using balanced data. In this paper, we investigate a hitherto unexamined issue of imbalanced data in the context of machine learning-based IPS. We consider several traditional machine learning algorithms to determine the optimal method for training IPS on imbalanced data. We also analyze the effect of imbalance ratio on the performance of the IPS. The results show that the k-nearest neighbors algorithm provides the best approach to developing intelligent IPS for imbalanced data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Regularized Information Loss for Improved Model Selection(Springer Science and Business Media Deutschland GmbH, 2023) Kamalov, Firuz; Moussa, Sherif; Reyes, Jorge AvanteInformation criteria are used in many applications including statistical model selection and intelligent systems. The traditional information criteria such as the Akaike information criterion (AIC) do not always provide an adequate penalty on the number of model covariates. To address this issue, we propose a novel method for evaluating statistical models based on information criterion. The proposed method, called regularized information criterion (RIL), modifies the penalty term in AIC to reduce model overfitting. The results of numerical experiments show that RIL provides a better reflection of model predictive error than AIC. Thus, RIL can be a useful tool in model selection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.