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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Haque, Md Ehsanul Jahidul Islam, S.M. Maliha, Jeba Hossan Sumon, Md Shakhauat Sharmin, Rumana Rokoni, Sakib Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Mathematics Bangladesh Agricultural University Mymensingh Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Electrical and Computer Engineering North South University Dhaka Bangladesh Department of Food and Nutrition University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (纸本)9798331531935

Chronic Kidney Disease (CKD) is a major global health issue which is affecting million people around the world and with increasing rate of mortality. Mitigation of progression of CKD and better patient outcomes requires early detection. Nevertheless, limitations lie in traditional diagnostic methods, especially in resource constrained settings. This study proposes an advanced machine learning approach to enhance CKD detection by evaluating four models: Random Forest (RF), Multi-Layer Perceptron (MLP), Logistic Regression (LR), and a fine-tuned CatBoost algorithm. Specifically, among these, the fine-tuned CatBoost model demonstrated the best overall performance having an accuracy of 98.75%, an AUC of 0.9993 and a Kappa score of 97.35% of the studies. The proposed CatBoost model has used a nature inspired algorithm such as Simulated Annealing to select the most important features, Cuckoo Search to adjust outliers and grid search to fine tune its settings in such a way to achieve improved prediction accuracy. Features significance is explained by SHAP-a well-known XAI technique-for gaining transparency in the decision-making process of proposed model and bring up trust in diagnostic systems. Using SHAP, the significant clinical features were identified as specific gravity, serum creatinine, albumin, hemoglobin, and diabetes mellitus. The potential of advanced machine learning techniques in CKD detection is shown in this research, particularly for low income and middle-income healthcare settings where prompt and correct diagnoses are vital. This study seeks to provide a highly accurate, interpretable, and efficient diagnostic tool to add to efforts for early intervention and improved healthcare outcomes for all CKD patients. © 2025 IEEE.

关键词： Patient treatment

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Thermal-Aware Communication

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IEEE Transactions on Information Theory 2025年第6期71卷 4145-4155页

作者： Chee, Yeow Meng Etzion, Tuvi Immink, Kees A. Schouhamer Nguyen, Tuan Thanh Vu, Van Khu Weber, Jos H. Yaakobi, Eitan Singapore University of Technology and Design Singapore Technion — Israel Institute of Technology Department of Computer Science Haifa3200003 Israel National University of Singapore Department of Industrial Systems Engineering and Management Singapore Turing Machines Inc Rotterdam3016 DK Netherlands Singapore University of Technology and Design Science Mathematics and Technology Cluster 487372 Singapore Delft University of Technology Department of Applied Mathematics Delft2628 CD Netherlands

Temperature control is of utmost importance in transmission systems. In this paper, a binary channel model is considered in which the transmission of a one causes a temperature increase while communicating a zero causes a temperature drop. By putting constraints on the input sequences, it is guaranteed that the channel temperature will not exceed a certain pre-determined maximum. In the asymptotic regime, the capacity of such a channel is studied. For the non-asymptotic regime, fixed-length codes are presented, with the property that codewords can be freely cascaded without violating the temperature constraint. Optimization of the code size is investigated and codewords are enumerated using generating functions. © 1963-2012 IEEE.

关键词： Channel capacity

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Real-Time EEG-Based BCI for Self-Paced Motor Imagery and Motor Execution Using Functional Neural Networks

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IEEE Access 2025年 13卷 91232-91247页

作者： Heim, Mavin Heinrichs, Florian Hueppe, Michael Nunez, Fran Szameitat, Alexander Reuter, Muriel Goetz, Stefan M. Weber, Corinna SNAP DISCOVERY AG BCI Research Bochum44801 Germany Rheinland-Pfälzische Technische Universität Kaiserslautern Department of Electrical and Computer Engineering Kaiserslautern67663 Germany FH Aachen - Applied Sciences Department of Medical Engineering and Technomathematics Aachen52066 Germany

This paper introduces a novel application of functional neural networks (FNNs) in the domain of electroencephalography-based (EEG-based) brain-computer interfaces (BCIs), targeting self-paced motor execution (ME) and motor imagery (MI). FNNs represent a neural network architecture tailored to smooth processes, and as such have been applied to EEG data classification recently. The paper proposes a comprehensive pipeline encompassing data acquisition, synchronization, pre-processing, training of FNNs, and real-time inference to enable the seamless integration of FNNs into real-world BCI applications. For the first time, FNNs are integrated into an end-to-end pipeline and serve for live inference outside a strict laboratory setting. In pursuit of a more accessible electroencephalography (EEG) artificial intelligence (AI) training scenario, the paper introduces a self-paced environment for auto-labeling EEG data. A custom-designed Pong game serves as the training task and enables subjects to engage in MI/ME tasks while receiving immediate visual feedback. To automate the labeling process of the recorded EEG data, the movements of both arms are captured with inertial measurement units (IMUs) and analyzed through gesture recognition. This novel training framework contributes to more natural and engaging data collection and reduces pre-processing for model training. To provide a comprehensive evaluation, the paper compares the performance of FNN and EEGNet in the self-paced MI/ME tasks. The comparative analysis addresses factors such as classification accuracy, real-time processing speed, and power consumption. Furthermore, the study explores various auto-labeling methods within the self-paced environment, analyzing their impact on the classification performances of both architectures. By evaluating these labeling methods, this work addresses the challenge of accurate and efficient EEG data labeling, crucial for training robust models for prediction of time critical events

关键词： Gesture recognition

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Enhancing the Swanepoel method: precise envelope detection of thin-film transmission spectra

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Optics Express 2025年第6期33卷 13376-13400页

作者： Ballester, Manuel Marquez, Almudena P. Lopez-Tapia, Santiago Fernandez, Susana Bass, John Minkov, Dorian Wuersch, Christoph Willomitzer, Florian Marquez, Emilio Katsaggelos, Aggelos K. Computer Science Department Northwestern University EvanstonIL60208 United States Department of Mathematics Faculty of Science University of Cadiz Puerto Real11510 Spain Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States CIEMAT Avenida Complutense 40 Madrid28040 Spain Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States Sofia1000 Bulgaria Institute for Computational Engineering ICE OST Eastern Switzerland University of Applied Sciences Switzerland Department of Condensed-matter Physics Faculty of Science University of Cadiz Puerto Real11510 Spain

The Swanepoel method is a widely used optical technique for characterizing thin films through normal-incidence transmission measurements. A critical step in this approach involves extracting the upper and lower envelopes of the measured oscillatory spectrum. By analyzing the transmission spectrum and its corresponding envelopes, the Swanepoel procedure enables precise determination of the film refractive index and extinction coefficient. However, even minor inaccuracies in envelope construction can propagate significant errors into the final characterization results. To address this challenge, we present what we believe to be a novel physics-informed optimization algorithm for envelope detection. Our mathematical model reformulates the envelope detection problem as a global optimization task that enforces the physical properties of the Swanepoel envelopes. Extensive validation on fifty randomly generated transmission spectra demonstrates unprecedented accuracy: the method achieves root-mean-square errors (RMSE) below 0.10% for the upper envelope and 0.06% for the lower envelope (more than doubling the accuracy of current state-of-the-art approaches). Furthermore, the proposed model has been assessed with two experimental transmission spectra, demonstrating its robustness and accuracy with real noisy data. We have developed an open-source Python software package (see text below). This software includes not only our innovative envelope construction algorithm but also additional envelope drawing algorithms for comparison and an efficient implementation of the Swanepoel method, enabling complete optical characterization of thin films. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Mean square error

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Optimizing Software Defect Classification with Locally Linear Embedding Techniques 71

Optimizing Software Defect Classification with Locally Linea...

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71st Annual Reliability and Maintainability Symposium, RAMS 2025

作者： Salboukh, Fatemeh Saki, Hesam Fiondella, Lance Department of Applied Science and Engineering University of Massachusetts - Dartmouth 285 Old Westport Road North DartmouthMA02747 United States Department of Computer Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Massachusetts - Dartmouth 285 Old Westport Road North DartmouthMA02747 United States

ISBN: (纸本)9798350367744

Software defect classification is crucial for enhancing the quality and reliability of software. This research explores the integration of Locally Linear Embedding (LLE) into the preprocessing stages of classification models to improve accuracy. LLE is particularly effective as it transforms complex, nonlinear data into a simpler, linear format. This transformation is instrumental in reducing the complexity of the data, facilitating a more straightforward analysis and potentially improving the predictive accuracy of classifiers. For this study, we used the KC2 dataset from the PROMISE repository and applied several feature selection techniques, including Correlation-Based Feature Selection, Gain Ratio, Sequential Forward Selection, and K-nearest neighbors. After selecting the most informative features, LLE was employed to linearize this data, aiming to enhance the classifiers’ performance by presenting them with data that is easier to interpret and analyze. The impact of LLE was evaluated by comparing the outcomes of five different classifiers, such as Random Forest, Gradient Boosting, Support Vector Machines, Non-Linear Support Vector Machines, and Naive Bayes, on both the original and the LLE-processed datasets. Our results demonstrated an average improvement of 4.5% in accuracy and 4% in F1-Score across the classifiers with the LLE-processed data, confirming the effectiveness of transforming nonlinear data into a linear space for enhancing software defect classification. © 2025 IEEE.

关键词： Software quality

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Hesitant Bi-fuzzy Dombi Operator Based Group Decision-making Approach for Electric Vehicle Selection

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Process Integration and Optimization for Sustainability 2025年 1-35页

作者： Golui, S. Das, Bidyut Mahapatra, G.S. Mahapatra, B.S. Department of Engineering Sciences and Humanities Siliguri Institute of Technology Siliguri734009 India Department of Applied Mathematics Maulana Abul Kalam Azad University of Technology Haringhata741249 India Department of Master of Computer Applications Siliguri Institute of Technology Siliguri734009 India Department of Mathematics National Institute of Technology Puducherry Karaikal609609 India School of Applied Science and Humanities Haldia Institute of Technology Haldia721657 India

Electric vehicles (EVs) are one of the sustainable modes of transportation that can reduce the air pollution caused by fossil fuel-powered automobiles. The literature overlooks non-technical criteria, including environmental considerations and social responsibility in the EV selection process, which may navigate the adoption of EVs. This study addresses the EV selection process in the hesitant bi-fuzzy context, considering technical and non-technical EV features. A multi-criteria group decision-making approach is developed to evaluate EVs utilizing a modified hesitant bi-fuzzy COmbinative Distance-based Assessment technique. This study proposes a novel hesitant bi-fuzzy Dombi weighted average operator to integrate the decision maker’s opinions with their weightage. The proposed approach incorporates the importance of the criteria evaluated by the method based on the removal effects of criteria and hesitant information in Euclidean and Taxicab metrics to produce the ranking sequence of EVs. The Python code for the suggested group decision-making model is provided to rationalize the computational process. This study highlights the relative importance of charging time, price, and range compared to the remaining features in selecting EVs. The study’s findings indicate that the technical features of an EV are still a primary consideration for the customers. These findings will assist policymakers and EV industry stakeholders in making decisions to focus on EV modification. A comparative analysis with the existing decision-making models encompasses the applicability of the proposed technique. In contrast, the sensitivity analysis demonstrates the suggested approach’s robustness through the alteration in decision makers’ s preferences, internal parameters, and criteria importance to assess their impact on the final rankings of EVs. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric vehicles

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Gamma Sampling for Intrusion Detection with Imbalanced Data 10th

Gamma Sampling for Intrusion Detection with Imbalanced Dat...

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10th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2024

作者： Kamalov, Firuz Mitra, Rohan Sulieman, Hana Department of Electrical Engineering Canadian University Dubai Dubai United Arab Emirates Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates Department of Mathematics and Statistics American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9789819637737

Recent advances in artificial intelligence have prompted the use of machine learning methods in network security. In this paper, we address the issue of imbalanced data that is often present in network security datasets used in machine learning. We propose an oversampling method based on the Gamma distribution to balance the data prior to training. The results on several imbalanced datasets show the potential of the proposed method as a viable tool to build intrusion detection systems based on artificial intelligence. The accompanying code for the study is available on Github. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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MEMS-based twisted bilayer flat optics for multidimensional optical modulation and hyperimaging 14

MEMS-based twisted bilayer flat optics for multidimensional ...

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High Contrast Metastructures XIV 2025

作者： Tang, Haoning Lou, Beicheng Du, Fan Gao, Guangqi Zhang, Mingjie Ni, Xueqi Yacoby, Amir Cao, Yuan Fan, Shanhui Mazur, Eric School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Applied Physics Ginzton Laboratory Stanford University StanfordCA94305 United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States

Our MEMS-based twistoptics device enables precise control of interlayer gaps and twist angles in photonic crystals, offering high-accuracy, multidimensional light manipulation. This finding holds potential for applica... 详细信息

ISBN: (纸本)9781510685048

关键词： Nanophotonics

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Convex Optimization Boundary-Control Policies for Stability and Invariance of Traffic Flow Dynamics

arXiv

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arXiv 2025年

作者： Chiri, Maria Teresa Guglielmi, Roberto Notomista, Gennaro Department of Mathematics and Statistics Queen’s University KingstonON Canada Department of Applied Mathematics University of Waterloo ON Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

In this letter we propose an optimization-based boundary controller for traffic flow dynamics capable of achieving both stability and invariance conditions. The approach is based on the definition of Boundary Control Barrier Functionals (BCBFal), from which sets of invariance-preserving boundary controllers are derived. In combination with sets of stabilizing controllers, we reformulate the problem as a convex optimization program solved at each point in time to synthesize the boundary control inputs. Sufficient conditions for the existence of optimal controllers are derived in the case of traffic flow models with stability and invariance conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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THE INCLUSION-EXCLUSION PRINCIPLE AND RECURRENCES FOR PARTITION NUMBERS

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UPB Scientific Bulletin, Series A: applied mathematics and Physics 2025年第1期87卷 97-106页

作者： Ballantine, Cristina Merca, Mircea Department of Mathematics and Computer Science College of The Holy Cross WorcesterMA01610 United States Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center National University of Science and Technology Politehnica Bucharest BucharestRO-060042 Romania Academy of Romanian Scientists BucharestRO-050044 Romania

The inclusion-exclusion principle together with Legendre type theorems for number of distinct restricted partitions weighted by the parity of their length are used to give several recurrence relations for restricted partition numbers as well as for overpartition numbers. In the case of overpartitions, POD partitions and 3-color partitions, we give combinatorial proofs for the Legendre type theorems and arrive to combinatorial proofs for the recurrence relations. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Combinatorial mathematics

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