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15th International Conference on Parallel Processing and applied mathematics, PPAM 2024

作者： Gacek, Paulina Bocheńczyk, Dominika Krajewski, Maciej Kruczek, Bartosz Lazarz, Krzysztof Magnuszewski, Pawel Zięciak, Antoni Kamiński, Filip Wąs, Jaroslaw Department of Applied Computer Science Faculty of Electrical Engineering Automatics IT and Biomedical Engineering AGH University of Krakow Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031857027

In recent years, a variety of new frameworks streamlining the process of agent-based modeling has emerged. These frameworks serve different purposes and each offers a unique set of features. In this practical comparative study, we evaluate the performance of various ABM frameworks through a series of benchmark simulations. By comparing the distinct functionalities offered by these tools, we aim to assist in the selection of an appropriate ABM toolkit for developing system models. This review presents a concise overview of seven popular agent-based modeling tools aiming to inspire further exploration and investigation into this subject. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital elevation model

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A novel partitioned random forest method based Facial Emotion Recognition

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IEEE Access 2025年 13卷 75089-75103页

作者： Heidari, Hanif Murugappan, M. Shaikh-Mohammed, Javeed Chowdhury, Muhammad E. H. Damghan University Department of Applied Mathematics Damghan Iran Research Lab Department of Electronics and Communication Engineering Block 4 Doha13133 Kuwait Vels Institute of Sciences Technology and Advanced Studies Department of Electronics and Communication Engineering Faculty of Engineering Tamilnadu Chennai India Prince Sattam bin Abdulaziz University College of Applied Medical Sciences Department of Biomedical Technology Al-Kharj11942 Saudi Arabia Qatar University Department of Electrical Engineering Doha2713 Qatar

Facial emotion recognition (FER) has been applied to various sectors, including e-learning, marketing, humanoid robot design, HMI/HCI applications, and medicine. The rapid development of intelligent technologies has led researchers to strive to improve facial emotion recognition techniques. A range of machine learning (ML) methods can be used to recognize facial expressions based on data from small to large datasets. Random Forest (RF) is simpler and more efficient than other ML algorithms. Some modified versions of RF have been developed to improve classification accuracy in the literature. Most improved RF versions modify attribute selection processes or combine them with other machine learning algorithms, increasing their complexity. Identifying an appropriate training dataset and determining its size remain open questions. The partitioned random forests (PRFs) approach is proposed as a modified strategy for improving FER. The proposed method divides multiple regions (different data lengths) into the feature space, allowing the algorithm to learn more complex decision boundaries. Using three statistical measures Lyapunov exponents (LE), Correlation Dimension (CD), and approximate entropy (AE), we evaluated the performance of machine learning algorithms over different data lengths. A crucial role for classification accuracy is played by the Lyapunov exponent or LE and the size of the dataset. A PRF is more effective on smaller datasets and with higher LE values. The proposed method for partitioning the datasets has been successfully tested on the FER dataset to classify six basic emotions (sadness, anger, fear, surprise, disgust, and happiness). Based on our numerical results, PRF performed better than traditional RF and other ML methods for FER, providing 98.37% mean absolute accuracy. Thus, a robust and useful method for improving classification rates is proposed for both small and large datasets. © 2013 IEEE.

关键词： Adversarial machine learning

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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Personalized Learning Path Problem Variations: Computational Complexity and AI Approaches

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 574-588页

作者： Mochocki, Sean A. Reith, Mark G. Borghetti, Brett J. Peterson, Gilbert L. Jasper, John D. Merkle, Laurence D. Air Force Institute of Technology Department of Electrical and Computer Engineering Wright-Patterson AFBOH45433 United States Air Force Institute of Technology Department of Mathematics and Statistics Wright-Patterson AFBOH45433 United States

E-learning courses often suffer from high dropout rates and low student satisfaction. One way to address this issue is to use personalized learning paths (PLPs), which are sequences of learning materials that meet the individual needs of students. However, creating PLPs is difficult and often involves combining knowledge graphs (KGs), student profiles, and learning materials. Researchers typically assume that the problem of creating PLPs belong to the nondeterministic polynomial (NP)-hard class of computational problems. However, previous research in this field has neither defined the different variations of the PLP problem nor formally established their computational complexity. Without clear definitions of the PLP variations, researchers risk making invalid comparisons and conclusions when they use different metaheuristics for different PLP problems. To unify this conversation, this article formally proves the NP-completeness of two common PLP variations and their generalizations and uses them to categorize recent research in the PLP field. It then presents an instance of the PLP problem using real-world data and shows how this instance can be cast into two different NP-complete variations. This article then presents three artificial intelligence (AI) strategies, solving one of the PLP variations with back-tracking and branch and bound heuristics and also converting the PLP variation instance to XCSP{}^{3}, an intermediate constraint satisfaction language to be resolved with a general constraint optimization solver. This article solves the other PLP variation instance using a greedy search heuristic. The article finishes by comparing the results of the two different PLP variations. © 2024 The Authors.

关键词： Contrastive Learning

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Fuzzy-Deep Learning-Based Artificial Intelligence for Edge Computing and Real-Time Decision-Making in Uncertain IoT Environments 1

Fuzzy-Deep Learning-Based Artificial Intelligence for Edge C...

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1st International Conference on Advances in computer Science, electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Joshi, B.P. Singh, Akhilesh Kumar, Nagendra Rautela, Siddharth Graphic Era Hill University Department of Mathematics Department of Computer Science & Engineering India VMSB UTU Department of Electrical Engineering Campus Pithoragarh India GL Bajaj Institute of Technology & Management Department of Electrical & Electronics Engineering Greater Noida201306 India Central Board of Secondary Education Department of Mathematics Uttarakhand Bhimtal India

ISBN: (纸本)9798331518578

Owing to the Internet of Vehicles (IoV) quick growth, both academics and the sector have paid close emphasis to vehicular edge computation (VEC). Nevertheless, because of the unbalanced congestion and the strict delay requirements, task offloading in various junction situations continues to struggle from inefficient resource allocated and poor operation implementation standards. This study proposes a task-offloading technique using a fuzzy decision-making method to deal with ambiguity and uncertainties to solve these problems. Roadside Utilities (RSUs) placed alongside remote roadways typically have limited energy resources, thus they must offer energy-effective planning assistance with the distribution of duties to VEC. However, planning decisions for regional task execution incur computational costs, and assigning duties to edge automobiles incurs transmission costs, making energy usage management difficult. Task data transmission to edge automobiles results in increased RSU power usage even while task offloading lowers response delay. To meet task schedule and supply restrictions, this study proposes an energy-effective automobile planning issue for offloading functions to mobile edge units. This study develops a planning technique depending on on-policy deep reinforcement learning (DRL) and a fuzzy-based DRL to address the extremely complex problem brought on by a rise in the number of automobiles under RSU service. When contrasted to the Q-learning method, this FRL not only speeds up the learning procedure but also enhances long-term payoff. © 2025 IEEE.

关键词： Energy utilization

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New Results on Interval Type-3 Fuzzy Control for Nonlinear Time-Delay Systems Using Convex Relaxation Technique

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3630-3641页

作者： Harikaran, B. Harshavarthini, S. Lee, Sangmoon Sakthivel, R. Sathiyaraj, T. School of Advanced Sciences Department of Mathematics Chennai600127 India Kyungpook National University School of Electronic and Electrical Engineering Daegu41566 Korea Republic of Bharathiar University Department of Applied Mathematics Coimbatore641046 India Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia

In this article, the interval type-3 fuzzy-based state feedback control is proposed for the stabilization problem of interval type-3 fuzzy systems (IT3FSs) subject to time-varying delay. Specifically, to improve the model accuracy and control capabilities, we proposed a nonparallel distributed compensation-based controller approach, be precise, the system and controller adopts the distinct membership functions. Moreover, the stability analysis of IT3FSs in the state space form is studied newly in this article. Besides the primary membership function that exists for interval type-2 fuzzy systems (IT2FSs), the IT3FSs possess the secondary membership function to handle the overall uncertainty which enacts the superiority of the proposed work. Notably, the adequate stability criteria are derived in the form of linear matrix inequality (LMI) using the Lyapunov stability theorem. Additionally, the convex relaxation technique is used to strengthen the design flexibility and achieve the faster convergence rate, which converts an optimization problem with a vector variable to a convex program with a matrix variable, via a lifting technique. A noteworthy aspect is that the application of the convex relaxation technique in the context of IT3FSs is proposed for the first time in literature. Finally, the comparative results between IT2FSs and IT3FSs are shown via two illustrative examples, including the inverted-pendulum model, to underscore the efficacy and practical implementations of the developed control methodology. © 2013 IEEE.

关键词： Membership functions

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Fundamental Limits of Covert Communication Over Classical-Quantum Channels

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IEEE Transactions on Information Theory 2025年第4期71卷 2741-2762页

作者： Bullock, Michael S. Sheikholeslami, Azadeh Tahmasbi, Mehrdad Macdonald, Robert C. Guha, Saikat Bash, Boulat A. The University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States Suffolk University Department of Mathematics and Computer Science BostonMA02108 United States University of Illinois at Urbana-Champaign Department of Computer Science UrbanaIL61801 United States Sandia National Laboratories LivermoreCA94550 United States The University of Arizona Wyant College of Optical Sciences TucsonAZ85721 United States University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States

We investigate covert communication over general memoryless classical-quantum channels with fixed finite-size input alphabets. We show that the square root law (SRL) governs covert communication in this setting when product a of n input states is used: LSRL (Formula Presented) covert bits (but no more) can be reliably transmitted in n uses of classical-quantum channel, where LSRL > 0 is a channel-dependent constant that we call covert capacity. We also show that ensuring covertness requires JSRL (Formula Presented) bits secret key shared by the communicating parties prior to transmission, where JSRL ≥ 0 is a channel-dependent constant. We assume a quantum-powerful adversary that can perform an arbitrary joint (entangling) measurement on all n channel uses. We determine the single-letter expressions for LSRL and JSRL, and establish conditions when JSRL = 0 (i.e., no pre-shared secret key is needed). Finally, we evaluate scenarios where covert communication is not governed by the SRL. © 1963-2012 IEEE.

关键词： Quantum channel

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Fabrication and Characterization of Aluminum-Doped ZnO Nanorods for Biosensor Applications

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IEEE Sensors Journal 2025年第10期25卷 16648-16657页

作者： Chou, Hsin-Yu Chen, Yu-Wei Yu, Chang-Tze Ricky Chen, Jo-Mei Maureen Chiang, Wei-Hsiang Singh, Anoop Kumar Kung, Po-Kai Chiang, Jung-Lung Wuu, Dong-Sing National Chung Hsing University Department of Materials Science and Engineering Taichung40227 Taiwan National Chi Nan University Department of Applied Chemistry Nantou54561 Taiwan National Chi Nan University Department of Applied Materials and Optoelectronic Engineering Nantou54561 Taiwan National Chin-Yi University of Technology Graduate Institute Prospective Technology of Electrical Engineering and Computer Science Taichung411030 Taiwan

In this study, we prepared various aluminum-doped zinc oxide (AZO) surface structures via chemical bath deposition (CBD) for use in biomedical sensors. Indium tin oxide (ITO) conductive films were deposited on glass substrates via electron beam evaporation, and AZO nanorods were subsequently grown on the ITO layer using CBD at a bath temperature of 90 °C. The AZO/ITO sensor electrode was further optimized through furnace annealing, improving its structural and electrical properties. This nanostructure enhances the ability of biomedical sensor films to adsorb H+ and OH- ions by increasing the reactive surface area between the film and the solution under test, thereby improving pH sensitivity. In addition, we used an extended gate field effect transistor (EGFET) module to measure glucose concentrations ranging from 0 to 10 mg/ml, achieving sensitivity values of 55.4 mV/pH and 8.07 mA/mg·ml-1. These findings have significant implications for the future development of blood glucose monitoring and microenvironmental sensing systems. © 2001-2012 IEEE.

关键词： Nanorods

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Enhancing Urban Air Quality with Smart Vehicle Exhaust Management

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Operations Research Forum 2025年第2期6卷 1-24页

作者： Tyagi, Vaishali Dubey, Roshan Singh, Suraj Ram, Mangey Department of Applied Sciences & Humanities ABES Institute of Technology Uttar Pradesh Ghaziabad India Department of Computer Science & Engineering ABES Institute of Technology Uttar Pradesh Ghaziabad India Department of Mathematics Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

Air pollution is the contamination of the indoor or outdoor environment by any chemical, physical, or biological agent that modifies the natural characteristics of the atmosphere. Burning gasoline and diesel fuel creates harmful byproducts like nitrogen dioxide, carbon monoxide, hydrocarbons, benzene, and formaldehyde. This study proposes a sensor-based model to control vehicular pollution by monitoring emission levels and preventing vehicles from operating if their pollution exceeds government standards. The proposed model consists of a smoke sensor, particulate sensors, carbon monoxide sensors, along with Arduino based microcontroller, data logger, LCD display, and GPRS modem. Using the block diagram, a mathematical model has been developed and some differential equations by using Markov process have been generated. This Markov-based model gives the information about the failure causes and working of the proposed system. Some reliability matrices like availability, mean time between failure (MTBF), and sensitivity analysis have been evaluated with the help of Laplace transform and illustrated with the help of graphs. Also, the failure cases of the system and its cost analysis have been done along with all the numerical calculations required for the successful operation of the model. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Air pollution Cost analysis Failure cases Markov process Reliability Sensor system

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One-Dimensional Kalman Filter with Measurement Noise Replaced by Quantization 59

One-Dimensional Kalman Filter with Measurement Noise Replace...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： De Alcantara, Pedro Aquino Bezerra Costa, Flavio Fuhrmann, Daniel R. Michigan Technological University Department of Electrical and Computer Engineering Houghton United States Michigan Technological University Department of Applied Computing Houghton United States

ISBN: (纸本)9798331513269

Several communication systems applications rely on state estimators for dynamic monitoring and control. Kalman Filters have been widely used for state estimation of stochastic processes. Low communication bandwidth adds more complexity to state estimator models, as state quantization should be considered. This paper studies a one-dimensional Kalman Filter with a 1-bit quantization process replacing noisy measurements. 2-bin and 3-bin 1-bit quantizers are considered. State distributions are calculated using discrete convolutions on a fine grid, and with Gaussian approximations. For the 3-bin 1-bit quantizer, two variations of the Gaussian approximation are developed. MATLAB simulations for the five resulting methods indicate that discrete convolutions are not worth the high computational cost, and that the 3-bin 1-bit quantizer eliminates the granular noise of the conventional 2-bin 1-bit quantizer. © 2025 IEEE.

关键词： Kalman filters

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