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MCI-GAN: a novel GAN with identity blocks inspired by menstrual cycle behavior for missing pixel imputation

Neural Computing and Applications 2025年 1-35页

作者： Marie, Hanaa Salem Elbaz, Mostafa Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Department of Computer Science Faculty of Computers and Informatics Kafrelsheikh University Kafrelsheikh Egypt

This paper presents MCI-GAN, a novel menstrual cycle imputation (MCI) and generative adversarial network (GAN) framework designed to address the challenge of missing pixel imputation in medical images. Inspired by the intelligent behavior of the endometrial lining during the menstrual cycle, our method introduces four key innovations. First, we propose a novel metaheuristic algorithm that assigns weights to surround pixels based on menstrual cycle behavior, ensuring that the imputed pixels maintain structural integrity and coherence with their neighbors, thus preserving overall image quality. Second, to enhance the learning capability of the GAN, identity blocks are integrated into the network architecture, improving the network’s ability to capture complex spatial relationships and leading to more accurate and consistent imputation of missing pixels. Third, we introduce an adaptive loss function that dynamically adjusts the penalty for pixel discrepancies based on local image context, allowing the model to focus on areas where accurate imputation is most critical and thereby enhancing overall image fidelity. Fourth, the framework incorporates a multi-scale feature extraction mechanism, enabling the GAN to process and combine information at various levels of detail, ensuring that both fine-grained textures and larger structural patterns are accurately captured during the imputation process. The efficacy of MCI-GAN is demonstrated across three diverse medical imaging datasets: mammograms, magnetic resonance imaging (MRI) scans, and skin lesion images. Our results show that the proposed method significantly outperforms existing approaches in terms of accuracy and structural coherence, offering a robust solution for missing pixel imputation in medical imaging. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Numerical and spatiotemporal features fusion for video summarization

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Multimedia Tools and Applications 2025年 1-16页

作者： Aboelenien, Mohamed Salem, Mohammed A.-M. Faculty of Media Engineering and Technology German University In Cairo Cairo11865 Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo11566 Egypt

In this paper, we propose a numerical spatiotemporal approach for video summarization. The current solutions leverage deep learning techniques to tackle this task. However, existing methods do not employ the video shots’ length data in their networks. We first introduce a new ground truth labelling for video summarization. This ground truth tackles multiple users’ annotations and is inclusive of video shot length information. We then propose a novel network architecture Numerical SpatioTemporal Fusion Net, NSTFNet. The proposed architecture leverages the temporal modelling ability of the self-attention mechanism to model video’s vision data to be fused with structured numerical features of video shots. We evaluate our results on TVSum and SumMe datasets. Experimental results show that the proposed model outperforms state-of-the-art performance on the SumMe dataset with 51.29% and 54.42% f-scores on the canonical and augmented configurations, demonstrating the effectiveness of our proposed model compared to state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Video analysis

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JLOS: a Cooperative UAV-Based Optical Wireless Communication With Multi-Agent Reinforcement Learning

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1345-1356页

作者： Liu, Jiangang Luo, Hanjiang Tao, Hang Liu, Jiahong Zhou, Jiehan Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland

In maritime Internet of Things (IoT) systems, leveraging a swarm of Unmanned Aerial Vehicles (UAVs) and optical communication can achieve a variety of potential maritime missions. However, due to the high directionality of the optical beam and interference from the marine environment, the optical link via UAVs as relays is prone to interruption. To address this challenge, we propose a Joint Link Optimization Scheme (JLOS) that includes Wind Disturbance Resistance (WDR) and Adaptive Beamwidth Adjustment (ABA). In WDR, we first model the problem as a Partially Observed Markov Decision Process (POMDP), and then design a collaborative Multi-Agent Reinforcement Learning (MARL) approach to control a swarm of UAVs in windy conditions, to maintain mechanical stability and prevent link interruption. Furthermore, in ABA, to reduce uncertainties from control activities and environmental factors like sunlight and fog, we design an adaptive algorithm using distributed MARL. It adjusts beamwidth based on historical UAV locations and link Bit Error Ratio (BER) to improve communication reliability. Numerical simulations confirm its effectiveness in enhancing robust data transmission. © 2004-2012 IEEE.

关键词： Adaptive algorithms

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Stylometry-driven framework for Urdu intrinsic plagiarism detection: a comprehensive analysis using machine learning, deep learning, and large language models

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Neural Computing and Applications 2025年第9期37卷 6479-6513页

作者： Manzoor, Muhammad Faraz Farooq, Muhammad Shoaib Abid, Adnan Department of Computer Science University of Management and Technology Lahore Pakistan Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

Detecting plagiarism in documents is a well-established task in natural language processing (NLP). Broadly, plagiarism detection is categorized into two types (1) intrinsic: to check the whole document or all the passages have been written by a single author;(2) extrinsic: where a suspicious document is compared with a given set of source documents to figure out sentences or phrases which appear in both documents. In the pursuit of advancing intrinsic plagiarism detection, this study addresses the critical challenge of intrinsic plagiarism detection in Urdu texts, a language with limited resources for comprehensive language models. Acknowledging the absence of sophisticated large language models (LLMs) tailored for Urdu language, this study explores the application of various machine learning, deep learning, and language models in a novel framework. A set of 43 stylometry features at six granularity levels was meticulously curated, capturing linguistic patterns indicative of plagiarism. The selected models include traditional machine learning approaches such as logistic regression, decision trees, SVM, KNN, Naive Bayes, gradient boosting and voting classifier, deep learning approaches: GRU, BiLSTM, CNN, LSTM, MLP, and large language models: BERT and GPT-2. This research systematically categorizes these features and evaluates their effectiveness, addressing the inherent challenges posed by the limited availability of Urdu-specific language models. Two distinct experiments were conducted to evaluate the impact of the proposed features on classification accuracy. In experiment one, the entire dataset was utilized for classification into intrinsic plagiarized and non-plagiarized documents. Experiment two categorized the dataset into three types based on topics: moral lessons, national celebrities, and national events. Both experiments are thoroughly evaluated through, a fivefold cross-validation analysis. The results show that the random forest classifier achieved an ex

关键词： Deep learning

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Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks

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IEEE Access 2025年 13卷 81312-81325页

作者： Roozbehi, Zahra Narayanan, Ajit Mohaghegh, Mahsa Saeedinia, Samaneh-Alsadat Auckland University of Technology School of Electrical and Computer Engineering Auckland New Zealand Auckland University of Technology Faculty of Design and Creative Technologies Auckland New Zealand Iran University of Science and Technology Department of Electrical and Computer Engineering Tehran Iran

Sound event detection and classification present significant challenges, particularly in noisy environments with multiple overlapping sources. This paper introduces an innovative architecture for multiple sound event detection and classification utilizing recurrent spiking neural networks (SNNs). Our method uniquely leverages temporal data to detect and classify multiple sound sources simultaneously, integrating the physical concept of signal power matching with neuronal output power and employing a binaural strategy to enhance detection accuracy in real-world scenarios. The architecture processes spatiotemporal data to dynamically update synaptic weights, enabling precise identification of sound event categories and their occurrences. Our simulations reveal substantial performance improvements, achieving the highest precision of 73% in classification tasks, including multilayer perceptrons (MLP), convolutional recurrent neural networks (CRNN), and recurrent neural networks (RNN). Statistical analysis indicates that these improvements are significant (p-value © 2013 IEEE.

关键词： Recurrent neural networks

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Behavioral Gait Biometrics in VR: Is the Use of Synthetic Samples Able to Increase Person Identification Metrics?

Behavioral Gait Biometrics in VR: Is the Use of Synthetic Sa...

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作者： Sawicki, Aleksander Faculty of Computer Science Bialystok University of Technology Bialystok Poland

In this paper, we present an approach to build a biometric system capable of identifying subjects based on gait. The experiments were carried out with a proprietary gait corpus collected from 100 subjects. In the data acquisition process, we used a commercially available perception neuron body suit equipped with motion sensors and dedicated to entertainment in the VR domain. Classification was performed using two variants of the CNN architecture and evaluated using cross-day validation. A novelty in the presented approach was the exploration of research areas related to the usage of synthetically generated samples. Experiments were conducted for two types of preprocessing—a low-pass filtering of the signals using a 3rd- or 1st-order Butterworth filter. For the first variant, the synthetic samples generated by the long short-term memory-mixture density network (LSTM-MDN) model allowed us to increase the F1-score from 0.928 to 0.966. Meanwhile, in the second case from 0.970 to 0.978 F1-score. © 2025 John Wiley & Sons Ltd.

关键词： Long short-term memory

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An Improved Algorithm for Accelerating Modular Exponentiation with Periodic Bit Pattern Exponents

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KSII Transactions on Internet and Information Systems 2025年第4期19卷 1267-1285页

作者： Somsuk, Kritsanapong Panit, Nirundon Phromloungsri, Ravee Department of Computer and Communication Engineering Faculty of Technology and Engineering Udon Thani Rajabhat University UDRU Udon Thani Thailand

This paper presents a novel approach to improve the efficiency of modular exponentiation computation. In fact, modular exponentiation is essential in public key cryptography, as it can be applied to many algorithms, including RSA, which has been selected for the implementation in this study. In addition, Periodic Left-to-Right Binary Exponentiation (PLR-BE) is introduced, utilizing exponents characterized by periodic bit patterns to reduce the modular multiplication operations. In fact, the proposed method improves the conventional binary exponentiation algorithm by leveraging the repetitive nature of periodic bit patterns, enabling the reutilization of results and thus reducing computational overhead. The implementation of PLR-BE is demonstrated in conjunction with the Chinese Remainder Theorem (CRT) in RSA. This algorithm is called Modified RSA with Periodic Patterns of Sub-private Keys (MRSA-PP). The experimental results show that PLR-BE outperforms the compared techniques, including Left-to-Right Binary Exponentiation (LR-BE) and Right-to-Left Binary Exponentiation (RL-BE), particularly for exponents with high Hamming weights. The efficiency improvements are especially pronounced in the decryption phase of the MRSA-PP. Although, the proposed method does not reduce key sizes, it substantially decreases the time to complete the computation. Furthermore, the proposed method is adaptable and can be applied to other algorithms requiring modular exponentiation for the computation to decrease computation resources. Copyright © 2025 KSII.

关键词： Public key cryptography

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Systems on Time Scales: Theory and Applications in Modeling 2nd

Systems on Time Scales: Theory and Applications in Modeli...

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2nd International Conference on Mathematical Analysis and Applications in Modeling, ICMAAM 2023

作者： Bartosiewicz, Zbigniew Faculty of Computer Science Bialystok University of Technology Bialystok Poland

ISBN: (纸本)9789819791934

Systems on time scales incorporate continuous- and discrete-time dynamical systems. They can serve as models of real systems from various areas. Positive systems on time scales are studied here in more detail. Such systems appear in biology, medicine and chemistry. The language of time scales allows for natural description of time discretization in continuous-time models. During such discretization some properties of the system may be lost, while other may be preserved. Invariance under discretization of positivity and uniform exponential stability is investigated, both for linear and nonlinear systems. One of the results says that if discretization preserves positivity of the system, then it also preserves uniform exponential stability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Continuous time systems

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Knowledge Discovery of the Internet of Things (IoT) Using Large Language Model

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 993-998页

作者： Alsulami, Bassma Saleh Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Internet of Things (IoT) technology quickly transformed traditional management and engagement techniques in several sectors. This work explores the trends and applications of the Internet of Things in industries, including agriculture, education, transportation, water management, air quality monitoring, underground mining, smart retail, smart home systems, and weather forecasting. The methodology involves a comprehensive review of the literature, followed by data extraction and analysis using BERT to identify key insights and patterns in IoT applications. The findings show that IoT significantly impacts the improvement of real-time monitoring, increasing efficiency, and encouraging innovative solutions in various sectors. Despite its transformative potential, cybersecurity threats, data privacy concerns, and the need for strong policy frameworks persist. The study emphasizes the necessity of multidisciplinary approaches to address these difficulties and optimize IoT implementation. Future research should focus on establishing secure IoT systems, maintaining data integrity, and encouraging collaboration between disciplines to realise the benefits of IoT technology. © (2025), (Science and Information Organization). All rights reserved.

关键词： Data integrity

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SELL:A Method for Low-Light Image Enhancement by Predicting Semantic Priors

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IEEE Signal Processing Letters 2025年 32卷 1785-1789页

作者： Xiao, Quanquan Jin, Haiyan Su, Haonan Yan, Ruixia Faculty of Computer Science and Engineering Xi'an University of Technology Xi'an China Guizhou Minzu University Guiyang China

In recent years, low-light image enhancement techniques have made significant progress in generating reasonable visual details. However, current methods have not yet fully utilized the full semantic prior of visual elements in low-light environments. Therefore, images generated by these low-light image enhancement methods often suffer from degraded visual quality and may even be distorted. To address this problem, we propose a method to guide low-light image enhancement by predicting semantic priors. Specifically, we train a semantic prior predictor under standard lighting conditions, which is made to learn and predict semantic prior features for low-light images by knowledge distillation on high-quality standard images. Subsequently, we utilize a semantic-aware module that enables the model to adaptively integrate these learned semantic priors, thus ensuring semantic consistency of the enhanced images. Experiments show that the method outperforms several current state-of-the-art methods in terms of visual performance on the LOL-v2 and SICE benchmark datasets. © 1994-2012 IEEE.

关键词： Economic and social effects

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