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nPGSAO: A Hybrid Particle Swarm Optimization and Genetic Algorithm with Niching technology for Edge Server Placement

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IEEE Internet of Things Journal 2025年第12期12卷 19370-19383页

作者： Wang, Bo Zhang, Zhifeng Song, Ying Chen, Ming Liu, Dongqing Software Engineering College Zhengzhou University of Light Industry Zhengzhou China Computer School Beijing Information Science and Technology University Beijing China School of Mathematics and Computer Science Tongling University Tongling China National Engineering Laboratory for Internet Medical Systems and Applications the First Affiliated Hospital of Zhengzhou University Zhengzhou China

In this paper, we focus on the edge server placement problem (ESPP). ESPP is to decide positions (edge stations) where purchased edge servers (ESs) are placed when a service provider builds or upgrades its edge computing solution. The decision of ESPP largely determines the overall service quality and resource efficiency. We aim at providing ESPP solutions for optimizing the service quality in terms of the system response time, given a set of purchased ESs. To achieve this aim, we first deduce the optimal request dispatch for each edge station based on queue theory and Binomial theorem. Then, based on the deduced result, we formulate ESPP into a discrete nonlinear constrained optimization model. Because ESPP is NP-hard and generally multi-modality, we designed a hybrid metaheuristic algorithm to combine advantages of particle swarm optimization (PSO) and genetic algorithm (GA) as well as the niching technology, which is named nPGSAO. The basic idea of nPGSAO is grouping similar individuals into a niche and finding the niche best. Then, nPGSAO evolves each individual by integrating the swarm intelligence of PSO into the evolutionary process of GA based on its personal and niching best. To evaluate the performance of nPGSAO, we conducted extensive simulated experiments. The results show that nPGSAO achieves 5.78%–40.4% better response time with varied ESPP scale, compared to 11 up-to-date algorithms, and has a linear time complexity with the ESPP scale. These confirm the superior performance and scalability of nPGSAO. © 2014 IEEE.

关键词： Genetic algorithms

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Deep Video Demoireing via Compact Invertible Dyadic Decomposition

Deep Video Demoireing via Compact Invertible Dyadic Decompos...

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IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Quan, Yuhui Huang, Haoran He, Shengfeng Xu, Ruotao South China University of Technology School of Computer Science and Engineering Guangzhou 510006 China Pazhou Lab Guangzhou 510335 China Singapore Management University School of Computing and Information Systems 188065 Singapore

ISBN: (纸本)9798350307184

Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, which thus are easily confused with moire patterns and can be significantly erased in the removal process. By interpreting video demoireing as a multi-frame decomposition problem, we propose a compact invertible dyadic network called CIDNet that progressively decouples latent frames and the moire patterns from an input video sequence. Using a dyadic cross-scale coupling structure with coupling layers tailored for multi-scale processing, CIDNet aims at disentangling the features of image patterns from that of moire patterns at different scales, while retaining all latent image features to facilitate reconstruction. In addition, a compressed form for the networks output is introduced to reduce computational complexity and alleviate overfitting. The experiments show that CIDNet outperforms existing methods and enjoys the advantages in model size and computational efficiency.

关键词： Deep Video Computational Efficiency Image Features Model Size Video Sequences Image Pattern Latent Features Compressed Format Complex Texture Multiscale Process Latent Image Deep Neural Network Attention Mechanism Video Frames Peak Signal-to-noise Ratio Multiscale Analysis Feature Alignment Adjacent Frames Supplement For Details Discrete Cosine Transform Frame Alignment U-shaped Structure Alignment Blocks Multi-scale Structure Reversible Loss Color Shift information Fidelity Latent Code Spatial Pyramid Pooling Spatial Pyramid

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Triple Diseases Prediction System using Deep Learning Techniques

Triple Diseases Prediction System using Deep Learning Techni...

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Emerging Trends in information technology and Engineering (ic-ETITE), Conference on

作者： R. Vijayan V. Mareeswari A. B. Jaswanth Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu Department of Software and Systems School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu MTech (Software Engineering) School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu

With the existing deep learning models in predicting multiple diseases primarily focus on analyzing individual diseases in isolation, lacking a unified system for multi-disease prediction. This project presents an approach to predict multiple diseases using Flask API, with a specific focus on brain tumors, COVID-19 and pneumonia. The proposed work represents a significant contribution to the field of disease prediction, harnessing the power of deep learning algorithms and modern web application development. The primary focus is on disease prediction, with a particular emphasis on ensuring accuracy and accessibility for end-users. The initial phase of this research involves data collection, where relevant datasets of various diseases are gathered. These datasets serve as the foundation for training and validating the deep learning models. Two prominent deep learning algorithms, Sequential CNN and VGG16, are employed for this purpose. These algorithms are chosen for their ability to handle complex data and recognize patterns within medical images and other health-related data. The core of the research involves training the deep learning models using the collected datasets. This step is crucial in enabling the models to learn and generalize from the provided data, ultimately enhancing their predictive capabilities. The models are modified to elevate their performance and accuracy. Following the training phase, the models are rigorously tested to evaluate their predictive accuracy. This assessment is vital in gauging the real-world applicability of the models in medical diagnosis. To make these powerful disease prediction models accessible to a wider audience, a front-end web application is developed.

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Data Assetization via Resources-decoupled Federated Learning

arXiv

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arXiv 2025年

作者： Zhao, Jianzhe Zhu, Feida He, Lingyan Tang, Zixin Gao, Mingce Yang, Shiyu Guo, Guibing Software college Northeastern University Liaoning Shenyang China School of Computing & Information Systems Singapore Management University Singapore Singapore

With the development of the digital economy, data is increasingly recognized as an essential resource for both work and life. However, due to privacy concerns, data owners tend to maximize the value of data through the circulation of information rather than direct data transfer. Federated learning (FL) provides an effective approach to collaborative training models while preserving privacy. However, as model parameters and training data grow, there are not only real differences in data resources between different data owners, but also mismatches between data and computing resources. These challenges lead to inadequate collaboration among data owners, compute centers, and model owners, reducing the global utility of the three parties and the effectiveness of data assetization. In this work, we first propose a framework for resource-decoupled FL involving three parties. Then, we design a Tripartite Stackelberg Model and theoretically analyze the Stackelberg-Nash equilibrium (SNE) for participants to optimize global utility. Next, we propose the Quality-aware Dynamic Resources-decoupled FL algorithm (QD-RDFL), in which we derive and solve the optimal strategies of all parties to achieve SNE using backward induction. We also design a dynamic optimization mechanism to improve the optimal strategy profile by evaluating the contribution of data quality from data owners to the global model during real training. Finally, our extensive experiments demonstrate that our method effectively encourages the linkage of the three parties involved, maximizing the global utility and value of data assets. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Optimization of Precision and Speed in ADCP 14

Optimization of Precision and Speed in ADCP

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14th International Conference on Advanced Computer information Technologies, ACIT 2024

作者： Mychuda, Lesya Mychuda, Zynowij Korobeinikova, Tetiana Zhuravel, Ihor Romanyuk, Olexandr Kotlyk, Sergii Lviv Polytechnic National University Department of Information Technology Security Lviv Ukraine Lviv Polytechnic National University Department of Computerized Automatic Systems Lviv Ukraine Vinnytsia National Technical University Department of Software Engineering Vinnytsia Ukraine National University of Technology Department of Information Technology and Cybersecurity Odesa Ukraine

ISBN: (纸本)9798350350036

This article introduces a novel principle for the design of interpolating logarithmic Analog-to-Digital Functional Converters (ADFCs) that enhances conversion speed and accuracy. The proposed principle leverages the phenomena of charge redistribution and accumulation during coarse and precise conversion stages, respectively. By simplifying the conversion algorithm and eliminating the need to reproduce previous compensating voltage levels during the coarse conversion stage, the new ADFC design outperforms existing analogs in both speed and precision. The methodological foundations discussed include the selection of conversion steps and the determination of logarithm bases that directly influence the conversion error. The article presents a simplified functional diagram of the ADFC, detailing the operational amplifier's resistance configuration and the role of capacitive cells in the conversion process. The results demonstrate that the ADFC's performance can be significantly improved without compromising accuracy, offering advancements in automation, control systems, and information-measurement technology. © 2024 IEEE.

关键词： Operational amplifiers

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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Computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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Normalized Least Dependent Difference: A Method in Solving Incorrectly Identified Monotonic Linearity Relationship in Linear Regression

Normalized Least Dependent Difference: A Method in Solving I...

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2025 International Workshop on Advanced Imaging technology, IWAIT 2025

作者： Yeap, Herrick Han Lin Lim, Le Ying Lim, Michelle Chee Ern Chow, Brandon Chen Hong Ong, Hui Yan Kimberly Eu, Kok Seng Yap, Kian Meng Research Centre for Human-Machine Collaboration Department of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Petaling Jaya47500 Malaysia School of Engineering Faculty of Innovation & Technology Taylor's University Subang Jaya47500 Malaysia

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

In machine learning, the reliability of an analytical method is crucial in validating the assumption of linearity for any linear regression *** existing analytical method of proving the assumption linearity, such as Pearson's Correlation Coefficient (PCC), Spearman's Rank Correlation Coefficient (SRCC) and Kendall's Tau Correlation Coefficient (KTCC), has its limitation as it does not work in monotonic relationship *** this paper, we propose a Normalized Least Dependent Difference (NLDD) method to improve the limitation of existing linearity method in identifying monotonic relationship *** calculating the difference between each data point and its predicted value on the regression line, we can determine how much the predicted value deviates from the observed value.A consistent difference between each data point and its predicted value, represented by a relative standard deviation in the y-axis that is near to its mean, suggests that the model accurately reflects the relationship between the dependent variable and the independent *** findings show that our NLDD is effective in identifying linearity in linear relationship graph and non-linearity in monotonic relationship graph. © 2024 SPIE.

关键词： Multiple linear regression

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Learning a Coordinated Network for Detail-Refinement Multiexposure Image Fusion

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IEEE Transactions on Circuits and systems for Video technology 2023年第2期33卷 713-727页

作者： Li, Jiawei Liu, Jinyuan Zhou, Shihua Zhang, Qiang Kasabov, Nikola K. Ministry of Education School of Software Engineering Dalian University Key Laboratory of Advanced Design and Intelligent Computing Dalian116622 China Dalian University of Technology School of Software Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Nowadays, deep learning has made rapid progress in the field of multi-exposure image fusion. However, it is still challenging to extract available features while retaining texture details and color. To address this difficult issue, in this paper, we propose a coordinated learning network for detail-refinement in an end-to-end manner. Firstly, we obtain shallow feature maps from extreme over/under-exposed source images by a collaborative extraction module. Secondly, smooth attention weight maps are generated under the guidance of a self-attention module, which can draw a global connection to correlate patches in different locations. With the cooperation of the two aforementioned used modules, our proposed network can obtain a coarse fused image. Moreover, by assisting with an edge revision module, edge details of fused results are refined and noise is suppressed effectively. We conduct subjective qualitative and objective quantitative comparisons between the proposed method and twelve state-of-the-art methods on two available public datasets, respectively. The results show that our fused images significantly outperform others in visual effects and evaluation metrics. In addition, we also perform ablation experiments to verify the function and effectiveness of each module in our proposed method. The source code can be achieved at https://***/lok-18/LCNDR. © 1991-2012 IEEE.

关键词： Extraction

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Unveiling Public Perception of ChatGPT Through Twitter Data and Machine Learning 4

Unveiling Public Perception of ChatGPT Through Twitter Data ...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Abishethvarman, Vadivel Banujan, Kuhaneswaran Wijeratne, Ashansa Nirubikaa, Ravikumar Kumara, Samantha Prasanth, Senthan Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Software Engineering Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Belihuloya Sri Lanka

ISBN: (纸本)9798350369786

The study comprehensively analysed public sentiment towards ChatGPT, a language processing AI tool, using approximately 135,000 tweets from an initial dataset of 236,275 entries. After cleaning and pre-processing the data using basic NLP techniques, Machine Learning (ML) models - Long Short-Term Memory (LSTM), Decision Tree (DT), and Support Vector Machine (SVM) - were applied to classify the tweets into positive, negative, and neutral categories. The LSTM model demonstrated superior performance across all evaluation metrics - accuracy, precision, recall, and F1 score - for all tweet categories. Word clouds generated for each category revealed that the most frequent words in positive tweets included 'student,' 'education,' 'research,' and 'teacher,' indicating a strong association of ChatGPT with educational and research contexts. Similarly, 'AI,' 'student,' 'research,' 'course,' and 'education' dominated neutral and negative tweets, highlighting prevalent discussion topics. However, the study had limitations, including dependency on a single dataset, potential loss of contextual information during pre-processing, and a focus on English tweets only. Despite these limitations, the study provides valuable insights into the public's perception of ChatGPT, underscoring its significant association with education and research and the effectiveness of the LSTM model in classifying tweets. The findings suggest that future research should incorporate diverse data sources and languages better to understand global sentiment towards ChatGPT and similar AI tools. © 2023 IEEE.

关键词： Tweets

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A NEW HOPE: contextual privacy policies for mobile applications and an approach toward automated generation 24

A NEW HOPE: contextual privacy policies for mobile applicati...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Shidong Pan Zhen Tao Thong Hoang Dawen Zhang Tianshi Li Zhenchang Xing Xiwei Xu Mark Staples Thierry Rakotoarivelo David Lo School of Computing Australian National University and Software and Computational Systems Research Program CSIRO's Data61 Software and Computational Systems Research Program CSIRO's Data61 Khoury College of Computer Sciences Northeastern University School of Computing and Information Systems Singapore Management University

ISBN: (纸本)9781939133441

Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application's graphical user interfaces (GUIs). In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SEEPRIVACY, specifically designed to automatically generate CPPs for mobile applications. This method uniquely integrates vision-based GUI understanding with privacy policy analysis, achieving 0.88 precision and 0.90 recall to detect contexts, as well as 0.98 precision and 0.96 recall in extracting corresponding policy segments. A human evaluation shows that 77% of the extracted privacy policy segments were perceived as well-aligned with the detected contexts. These findings suggest that SEEPRIVACY could serve as a significant tool for bolstering user interaction with, and understanding of, privacy policies. Furthermore, our solution has the potential to make privacy notices more accessible and inclusive, thus appealing to a broader demographic. A demonstration of our work can be accessed at https://***/SeePrivacy/

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