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High quality dehazed image and video based on enhanced multi-scale guided filtering dehazing technique

Cluster Computing 2025年第5期28卷 1-24页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. El-Rabaie, El-Sayed M. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Haze is one of the most common challenges faced by outdoor imaging systems. Hazy images are characterized by low contrast and a white overlay, which make it difficult to produce high-quality results. To address this issue, researchers have developed various haze removal methods aimed at achieving superior image enhancement, efficient processing, and cost-effectiveness. This paper introduces an enhanced Multi-scale Guided Filtering (MGF) technique for dehazing. The proposed technique incorporates several key components: a fast dehazing method, homomorphic processing, and Contrast Limited Adaptive Histogram Equalization (CLAHE) as preprocessing steps, followed by a fuzzy logic approach to enhance contrast. The core of the technique is the MGF dehazing method, which is supported by fast dehazing and fuzzy logic proessing as enhancement strategies to reduce noise and improve the dynamic range of images. The MGF dehazing process gets the transmission map and atmospheric light at the coarsest level, and then guided filtering is employed iteratively to produce a smoothed transmission map, resulting in dehazed images free from artifacts. The haze model is applied to obtain the final dehazed image. This technique effectively combines two traditional approaches—fast dehazing and MGF dehazing—into a unified framework. The performance of the proposed technique is evaluated using visible and Near Infrared (NIR) video frames, as well as real hazy images. To demonstrate its efficacy, its results are compared with those of the standalone MGF dehazing method. Additionally, a comparative analysis is conducted with other dehazing techniques using both visible and NIR frames. Evaluation metrics include both referenced and non-referenced quantitative measures such as Peak Signal-to-Noise Ratio (PSNR), correlation (to measure similarity between hazy and dehazed images), entropy (quantifying the amount of information), Feature Similarity Index (FSIM), Chronic Feature Similarity index (FSI

关键词： Image enhancement

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Conditions to guarantee the existence of solutions for a nonlinear and implicit integro-differential equation with variable coefficients

Conditions to guarantee the existence of solutions for a non...

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作者： Li, Chenkuan Saadati, Reza Allahviranloo, Tofigh Department of Mathematics and Computer Science Brandon University BrandonMB Canada School of Mathematics Iran University of Science and Technology Tehran Iran Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey

Using Babenko's approach, multivariate Mittag–Leffler (MM–L) function, and Krasnoselskii's fixed point theorem, we first investigate the existence of solutions to a Liouville–Caputo nonlinear integro-differential equation with variable coefficients and initial conditions in a Banach space. Then the existence of a positive solution for a variant equation is studied. Finally, we provide examples to illustrate the applications of the main results obtained. © 2022 John Wiley & Sons, Ltd.

关键词： Integrodifferential equations

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A decentralized and privacy-preserving framework for electronic health records using blockchain

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Alexandria engineering Journal 2025年 126卷 196-203页

作者： Alahmari, Saad Alshardan, Amal Al-Wesabi, Fahd N. Sorour, Shaymaa Alghushairy, Omar Alsini, Raed Khadidos, Alaa O. Duhayyim, Mesfer Al Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj16273 Saudi Arabia

As healthcare services have become increasingly digitized, Electronic Health Records (EHRs) have become widely adopted, providing seamless data exchange among providers. Conventional EHRs, however, are extremely vulnerable to cyber threats because patients' sensitive data is centralized and transmitted electronically. The paper proposes a decentralized, privacy-preserving framework for managing EHRs on blockchains in order to address these security and privacy concerns. Using cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, the proposed system enhances security and ensures data integrity. Additionally, the model facilitates scalable, efficient, and secure access to patient records through the integration of cloud-based storage and blockchain. Using smart contracts, we also ensure compliance with healthcare regulations by regulating access control and authentication. As a result of performance evaluations, the proposed approach is demonstrated to be feasible, and the advantages it offers in terms of security, privacy, and efficiency are highlighted. © 2025 The Authors

关键词： Electronic health record

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IoT Based Monitoring System for Health During Menstruation 4

IoT Based Monitoring System for Health During Menstruation

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Khadse, Chinmaya Hajare, Shital Sheikh, Nishad Dahule, Roshni Kate, Priya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawani Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. Data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD. © 2025 IEEE.

关键词： Electronic health record

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Classifying white blood cells using combining different convolutional neural networks

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Multimedia Tools and Applications 2025年 1-24页

作者： Toptaş, Murat Toptaş, Buket Hanbay, Davut Software Eng. Dept. Engineering and Natural Science Faculty Bandırma Onyedi Eylül University Balıkesir Turkey Computer Eng. Dept. Engineering Faculty Inonu University Malatya44280 Turkey

White blood cells are warrior cells that protect the human body against external factors. Each of these warrior cells performs a distinct task, making every piece of information about them highly valuable in the medical field. In this article, a classification framework for the four known types of white blood cells is proposed. It is hoped that the classification of these types will contribute to the prediction of diseases such as AIDS, malaria, leukemia, and many others. In the proposed method, images of white blood cells from the Blood Cell Classification and Detection dataset were used as input to Convolutional Neural Networks. The feature vectors extracted using these Convolutional Neural Network architectures were combined into a single vector. A Minimum Redundancy Maximum Relevance algorithm was then employed to identify the most effective features within the feature vector. Experiments were conducted using these selected features, and the analysis of each experiment was reported in detail. The Support Vector Machines classifier achieved an accuracy of 98.63% in classifying white blood cell types by combining features from multiple deep learning architectures. The experimental results demonstrated that the features obtained from different layers of the Convolutional Neural Networks had varying impacts on the classification performance. © The Author(s) 2025.

关键词： Convolutional neural networks

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RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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Performance Analysis and Power Allocation for Massive MIMO ISAC Systems

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IEEE Transactions on Signal Processing 2025年 73卷 1691-1707页

作者： Nguyen, Nhan Thanh Nguyen, Van-Dinh Nguyen, Hieu V. Ngo, Hien Quoc Swindlehurst, A. Lee Juntti, Markku University of Oulu Centre for Wireless Communications Oulu90014 Finland VinUniversity College of Engineering and Computer Science Hanoi100000 Viet Nam University of Science and Technology The University of Danang Faculty of Electronics and Telecommunication Engineering Da Nang50000 Viet Nam Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Kyung Hee University Department of Electronic Engineering Yongin-si17104 Korea Republic of University of California Center for Pervasive Communications and Computing IrvineCA92697 United States

Integrated sensing and communications (ISAC) is envisioned as a key feature in future wireless communications networks. Its integration with massive multiple-input-multiple-output (MIMO) techniques promises to leverage substantial spatial beamforming gains for both functionalities. In this work, we consider a massive MIMO-ISAC system employing a uniform planar array with zero-forcing and maximum-ratio downlink transmission schemes combined with monostatic radar-type sensing. Our focus lies on deriving closed form expressions for the achievable communications rate and the Cramér–Rao lower bound (CRLB), which serve as performance metrics for communications and sensing operations, respectively. The expressions enable us to investigate important operational characteristics of massive MIMO-ISAC, including the mutual effects of communications and sensing as well as the advantages stemming from using a very large antenna array for each functionality. Furthermore, we devise a power allocation strategy based on successive convex approximation to maximize the communications rate while guaranteeing the CRLB constraints and transmit power budget. Extensive numerical results are presented to validate our theoretical analyses and demonstrate the efficiency of the proposed power allocation approach. © 1991-2012 IEEE.

关键词： Budget control

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Federated transfer learning for distributed drought stage prediction

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Discover Artificial Intelligence 2025年第1期5卷 1-13页

作者： Raza, Muhammad Owais Umar, Aqsa Rasheed, Jawad Asuroglu, Tunc Alsubai, Shtwai Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Computer Science Sindh Madressatul Islam University Karachi74000 Pakistan Department of Software Engineering Istanbul Nisantasi University Istanbul34398 Turkey Applied Science Research Center Applied Science Private University Amman Jordan Faculty of Medicine and Health Technology Tampere University Tampere33720 Finland VTT Technical Research Centre of Finland Tampere33720 Finland Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Due to the uncertain nature of drought, it is one of the most menacing natural disasters. Drought modeling (Prediction, Detection, Forecasting, and Stage Prediction) is very essential for efficient policy making. But one of the key problems with drought modeling is the limited availability of centralized datasets. To address this problem, we are a novel proposing federated learning based transfer learning models for the prediction of drought stages. In this study, satellite image dataset was collected from the Tharparkar district (prone to drought) of Pakistan. We trained the dataset using traditional and federated learning approaches, comparing centralized ML models, pre-trained models, and their respective federated learning models (FL-ResNet, FL-DenseNet, FL-MobileNet). The development of these models is the novel aspect of the study specifically for the use case of drought stage prediction. Based on the final evaluation, FL-MobileNet achieved 82% precision while baseline MobileNet scored 68%. The results show the effectiveness of novelty (federated learning), that our proposed framework improves the performance of the drought stage classification task. © The Author(s) 2025.

关键词： Federated learning

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Data Aggregation and Scheduling To Optimize Information Freshness In Multi-Hop IoT Networks

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年

作者： Wu, Xueling Qu, Long Khabbaz, Maurice J. Ningbo University Faculty of Electrical Engineering and Computer Science Zhejiang Ningbo315211 China American University of Beirut Faculty of Arts and Science Department of Computer Science Beirut Lebanon

The rapid proliferation of Internet of Things (IoT) devices with sensing, monitoring, and control capabilities has fueled the emergence of diverse real-time IoT applications. These applications often require the efficient transmission of collected sensitive data to centralized or distributed processing units, such as cloud servers, edge nodes, or fog computing platforms—to enable timely responses. Ensuring the freshness of information while maintaining sustainable data transmission under energy constraints remains a critical challenge. To address this issue, we quantify information freshness using Age of Aggregated Information (AoAI) and investigate its minimization within energy-harvesting multi-hop IoT networks. Specifically, we develop data transmission schedules that jointly consider energy harvesting and packet aggregation. This problem is formulated as a Mixed-Integer Linear Programming (MILP) model, integrating data scheduling, packet aggregation, link interference, and energy resource management across multiple nodes. To tackle the computational complexity of this NP-hard problem, we propose an Energy-Aware Column Generation Algorithm (CGA). The original problem is decomposed into a Restricted Master Problem (RMP) and a Pricing Problem (PP). The RMP models data aggregation at Base Station (BS), while the PP incorporates an Energy-Aware Scheduling Algorithm (EASA) to generate feasible scheduling solutions. Simulation results demonstrate that the proposed CGA consistently outperforms EASA, the Genetic Algorithm (GA), and the Juventas Algorithm, achieving an average deviation of only 1.84% from the global optimal solution. © 2020 IEEE.

关键词： NP-hard

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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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