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SSRN 2025年

作者： Pannu, Rabia Zubair, Muhammad Owais, Muhammad Hassan, Shoaib Umair, Muhammad Usman, Syed Muhammad Al-Bashrawi, Mousa Ahmed Hussain, Irfan Faculty of Information Technology and Computer Science University of Central Punjab Lahore Pakistan Interdisciplinary Research Center for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Mechanical Nuclear Engineering Khalifa University Abu Dhabi United Arab Emirates School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Computer Science Bahria School of Engineering and Applied Sciences Bahria University Islamabad Pakistan Department of Information Systems and Operations Management King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

Glaucoma is a progressive eye condition caused by high intraocular fluid pressure, damaging the optic nerve, leading to gradual, irreversible vision loss, often without noticeable symptoms. Subtle signs such as mild redness in the eyes, slightly blurred vision, and occasional headaches or eye pain may go unnoticed, earning it the nickname "silent thief of sight." Its prevalence is increasing with an aging population, driven by advances in healthcare care and increased life expectancy. Most computer-aided diagnosis (CAD) systems rely on the cup-to-disc ratio (CDR) for the diagnosis of glaucoma. This study introduces a novel approach by integrating CDR with the neuro-retinal rim ratio (NRR), which quantifies rim thickness within the optic disc (OD). NRR improves diagnostic accuracy by recording additional head changes of the optic nerve, such as rim thinning and tissue loss, which were overlooked using CDR alone. We proposed a modified ResUNet architecture for OD and optic cup (OC) segmentation, combining residual learning and U-Net to capture spatial context for semantic segmentation. Its performance was evaluated on the DRISHTI-GS and RIM-ONE datasets. For OC segmentation, the model achieved Dice Coefficient (DC) scores of 0.942 and 0.872 and Intersection over Union (IoU) values of 0.891 and 0.773 for DRISHTI-GS and RIM-ONE, respectively. For OD segmentation, the model achieved DC of 0.972 and 0.950 and IoU values of 0.945 and 0.940 for DRISHTI-GS and RIM-ONE, respectively. CDR and NRR were calculated from segmentation masks and used to train an SVM with a radial basis function, classifying the eyes as healthy or glaucomatous. The model achieved accuracies of 0.969 on DRISHTI-GS and 0.977 on RIM-ONE. © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Shedding Light on Glowapp: A Usability Test Evaluation for Enhanced User Experience

Shedding Light on Glowapp: A Usability Test Evaluation for E...

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International Conference on Information Management and Technology (ICIMTech)

作者： Sergio Immanuela Puspasari Saputro Yulius Denny Prabowo Voice Esther Ticoalu Karunia Agung Mahardini Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Cosmetics is one of the emerging industries in Indonesia. Cosmetology is the practice of using cosmetics and cosmetic products to alter the appearance of the face. When applying makeup, it is essential to tailor it to the occasion and consider the facial structure. Applying appropriate cosmetics may enhance self-assurance. According to the questionnaire findings, women apply makeup more than twice. However, not all of them possess the talent to apply makeup on themselves. They struggle to locate a makeup artist with a style and availability that meets their requirements. Glowapp was developed with booking functionalities. Customers will find it more convenient to schedule an appointment. Blackbox testing was utilized to assess the functionality of the system. The results indicate that the features performed effectively and satisfied user requirements. The usability test is conducted by integrating the eight golden guidelines and ISO-9241-11. The goal is to reduce the time respondents spend filling questionnaires while maintaining usability principles to ensure more accurate answers. Glowapp has a performance score of 93%, with efficiency scoring the highest and effectiveness scoring the lowest.

关键词： Industries Schedules Closed box User experience Filling Information management Usability Faces Testing Guidelines

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Comparison of Cellular Network Controllers for Quadrotors Experiencing Time Delay

Comparison of Cellular Network Controllers for Quadrotors Ex...

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Mediterranean Conference on Control and Automation (MED)

作者： Mohammad T. Ramezanlou Howard Schwartz Ioannis Lambadaris Michel Barbeau Department of Systems and Computer Engineering Carleton University Ottawa Canada School of Computer Science Carleton University Ottawa Canada

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This paper presents a control framework including delay estimator, state estimator, and controller to compensate for random time delays in cellular networks. The effect of network delay on the control of a quadrotor is investigated. A comparative study is done between a linear-based PD and nonlinear Backstepping controller. A time delay estimator based on the Markov stochastic model is developed. The combination of the time delay estimator and the state estimator is used to compute the control signal. Results show that the performance of both controllers in low-variation delays is approximately equivalent. According to the results, the linear-based PD controller is a good choice since it satisfies the problem conditions with a more straightforward design process.

关键词： Cellular networks Backstepping Delay effects Process control Markov processes Position measurement Time measurement

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Survey of Texture Based Image Processing and Analysis with Differential Fractional Calculus Methods

Survey of Texture Based Image Processing and Analysis with D...

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2021 International Conference on System, Computation, Automation and Networking, ICSCAN 2021

作者： Ganga, M. Janakiraman, N. Sivaraman, Arun Kumar Balasundaram, A. Vincent, Rajiv Rajesh, M. K.L.N.College of Engineering Department of ECE Madurai India School of Computer Science and Engineering Center for Cyber Physical Systems Chennai India

ISBN: (纸本)9781665439862

In image processing systems, image denoising, and image enhancement is an essential problem but it is difficult to analyze. These are aim to achieve the enhancing the quality of image which helps to achieve the human observer or the computer vision system in the preprocessing stage. The efficient technique of developing a fractional-based convolution mask based on the image denoising and image enhancement approaches which are having the ability to identify the edges in a detailed manner very substantially. The credible way of the above approaches is to differentiate between the important image features which are kept or evenly enhanced. In this paper, a review of various image processing application texture image enhancement processes using mathematical formulas like fractional derivative calculus with various conventional works. Here review the various existing methods in enhancement of image processing methods. © 2021 IEEE.

关键词： Image enhancement

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A hybrid healthy diet recommender system based on machine learning techniques

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computers in Biology and Medicine 2025年 184卷 109389-109389页

作者： Sweidan, Sara Askar, S.S. Abouhawwash, Mohamed Badr, Elsayed Department of Artificial Intelligence Faculty of Computer and Artificial Intelligence Benha University Benha Egypt Faculty of Computer Science and Engineering New Mansoura University New Mansoura Egypt Department of Statistics and Operations Research College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia College of Engineering Michigan State University East LansingMI48824 United States Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt Department of Scientific Computing Faculty of Computer and Artificial Intelligence Benha University Benha Egypt Department of Information Systems College of Information Technology Misr University for Science & Technology Giza Egypt

Obesity is a chronic disease correlated with numerous risk factors that not only negatively affect all body functions but also increase the chances of developing chronic diseases and the associated morbidity and mortality rates. This study proposes a novel system that bridges the gap between healthcare providers and patients by offering both parties some tools for navigating the intricacies of dietary planning. In this system, machine learning techniques are used to determine the required calories before starting an obesity treatment. A hybrid precision model with minimal parameters is also developed to estimate the appropriate number of calories for losing weight and to formulate a healthy diet plan. A real dataset of 15 anthropometric measurements is analyzed using SVR, LR, and DTR regression models, and all the data are preprocessed before analysis to enhance model performance. Results show that the required calories can be estimated with a high correlation (R = 0.985) from independent measurements. The proposed model also calculates the healthy daily percentages of fats, proteins, and carbohydrates based on a knowledge base of medical rules and functions, thus facilitating the sequential treatment of obese patients. In sum, this study applies different models to design a practical, cost-effective approach for accurately determining the required calories and formulating valuable diet plans for obesity treatment and management. © 2024 Elsevier Ltd

关键词： Calorie Machine learning Regression Obesity treatment Expert system

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Prediction Aided by Surrogate Training

arXiv

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arXiv 2024年

作者： Xia, Eric Wainwright, Martin J. Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Electrical Engineering & Computer Sciences Massachusetts Institute of Technology CambridgeMA United States Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States

We study a class of prediction problems in which relatively few observations have associated responses, but all observations include both standard covariates as well as additional "helper" covariates. While the end goal is to make high-quality predictions using only the standard covariates, helper covariates can be exploited during training to improve prediction. Helper covariates arise in many applications, including forecasting in time series;incorporation of biased or mis-calibrated predictions from foundation models;and sharing information in transfer learning. We propose "prediction aided by surrogate training" (PAST), a class of methods that exploit labeled data to construct a response estimator based on both the standard and helper covariates;and then use the full dataset with pseudo-responses to train a predictor based only on standard covariates. We establish guarantees on the prediction error of this procedure, with the response estimator allowed to be constructed in an arbitrary way, and the final predictor fit by empirical risk minimization over an arbitrary function class. These upper bounds involve the risk associated with the oracle data set (all responses available), plus an overhead that measures the accuracy of the pseudo-responses. This theory characterizes both regimes in which PAST accuracy is comparable to the oracle accuracy, as well as more challenging regimes where it behaves poorly. We demonstrate its empirical performance across a range of applications, including forecasting of societal ills over time with future covariates as helpers;prediction of cardiovascular risk after heart attacks with prescription data as helpers;and diagnosing pneumonia from chest X-rays using machine-generated predictions as helpers. © 2024, CC BY.

关键词： Prediction models

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CLEARING SECTIONS OF LATTICE LIABILITY NETWORKS

arXiv

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arXiv 2025年

作者： Ghrist, Robert Gould, Julian Lopez, Miguel Riess, Hans Department of Mathematics and Electrical & Systems Engineering University of Pennsylvania Philadelphia PA Department of Mathematics University of Pennsylvania Philadelphia PA Applied Mathematics & Computational Science University of Pennsylvania Philadelphia PA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

Modern financial networks involve complex obligations that transcend simple monetary debts: multiple currencies, prioritized claims, supply chain dependencies, and more. We present a mathematical framework that unifies and extends these scenarios by recasting the classical Eisenberg-Noe model of financial clearing in terms of lattice liability networks. Each node in the network carries a complete lattice of possible states, while edges encode nominal liabilities. Our framework generalizes the scalar-valued clearing vectors of the classical model to lattice-valued clearing sections, preserving the elegant fixed-point structure while dramatically expanding its descriptive power. Our main theorem establishes that such networks possess clearing sections that themselves form a complete lattice under the product order. This structure theorem enables tractable analysis of equilibria in diverse domains, including multi-currency financial systems, decentralized finance with automated market makers, supply chains with resource transformation, and permission networks with complex authorization structures. We further extend our framework to chain-complete lattices for term structure models and multivalued mappings for complex negotiation systems. Our results demonstrate how lattice theory provides a natural language for understanding complex network dynamics across multiple domains, creating a unified mathematical foundation for analyzing systemic risk, resource allocation, and network *** Codes 91G40, 06B23, 91D30, 68Q85 Copyright © 2025, The Authors. All rights reserved.

关键词： Lattice theory

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QAOA-Assisted Benders' Decomposition for Mixed-integer Linear Programming

QAOA-Assisted Benders' Decomposition for Mixed-integer Linea...

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IEEE International Conference on Communications (ICC)

作者： Zhongqi Zhao Lei Fan Yuanxiong Guo Yu Wang Zhu Han Lajos Hanzo Department of Electrical and Computer Engineering University of Houston Houston TX USA Department of Engineering Technology University of Houston Houston TX USA Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio TX USA Department of Computer and Information Sciences Temple University Philadelphia PA USA School of Electronics and Computer Science University of Southampton Southampton UK

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Benders' decomposition (BD) algorithm constitutes a powerful mathematical programming method of solving mixed-integer linear programming (MILP) problems with a specific block structure. Nevertheless, BD still needs to solve an NP-hard quasi-integer programming master problem (MAP), which motivates us to harness the popular variational quantum algorithm (VQA) to assist BD. More specifically, we choose the popular quantum approximate optimization algorithm (QAOA) of the VQA family. We transfer the BD's MAP into a digital quantum circuit associated with a physically tangible problem-specific ansatz; and then solve it with the aid of a state-of-the-art digital quantum computer. Next, we evaluate the computational results and discuss the feasibility of the proposed algorithm. The hybrid approach advocated, which utilizes both classical and digital quantum computers, is capable of tackling many practical MILP problems in communication and networking, as demonstrated by a pair of case studies.

关键词： Performance evaluation Quantum computing Quantum algorithm Programming Approximation algorithms Mixed integer linear programming Quantum circuit

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Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study

Enhancing Transfer Learning for Medical Image Classification...

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International Conference on computer and Information Technology (ICCIT)

作者： Md Zehan Alam Tonmoy Roy H.M. Nahid Kawsar Iffat Rimi Department of Mathematics University of Dhaka Dhaka Bangladesh Department of Data Analytics and Information Systems Utah State University Logan Utah USA Department of Applied Mathematics Noakhali Science and Technology University Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain tumor class and diabetic retinopathy stage detection. The effectiveness of TL—using pre-trained models on the ImageNet dataset—varies due to domain-specific challenges. We evaluate five pre-trained models—MobileNet, Xception, InceptionV3, ResNet50, and DenseNet201—on two datasets: Brain Tumor MRI and APTOS 2019. Our results show that TL models excel in brain tumor classification, achieving near-optimal metrics. However, performance in diabetic retinopathy detection is hindered by class imbalance. To mitigate this, we integrate the Synthetic Minority Over-sampling Technique (SMOTE) with TL and traditional machine learning(ML) methods, which improves accuracy by 1.97%, recall (sensitivity) by 5.43%, and specificity by 0.72%. These findings underscore the need for combining TL with re-sampling techniques and ML methods to address data imbalance and enhance classification performance, offering a pathway to more accurate and reliable medical image analysis and improved patient outcomes with minimal extra computation powers.

关键词： Diabetic retinopathy Accuracy Sensitivity Transfer learning Brain tumors Brain modeling Reliability Medical diagnostic imaging Image classification Residual neural networks

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Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques

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SN computer science 2021年第3期2卷 177页

作者： Akter, Laboni Ferdib-Al-Islam Islam, Md. Milon Al-Rakhami, Mabrook S. Haque, Md. Rezwanul Department of Biomedical Engineering Khulna University of Engineering & Technology Khulna 9203 Bangladesh Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna 9203 Bangladesh Research Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Cervical cancer growth is the fourth maximum of regular diseases in females. It is one of the sicknesses which is compromising ladies' wellbeing everywhere in the world and it is difficult to notice any sign in the beginning phase. But the screening process of cervical cancer sometimes is being hampered due to some social-behavioral factors. There is still a limited number of researches directed in cervical cancer identification dependent on the behavior and machine learning in the area of gynecology and computer science. In this research, we have proposed three machine learning models such as Decision Tree, Random Forest, and XGBoost to predict cervical cancer from behavior and its variables and we got significantly improved outcomes than the current methods with 93.33% accuracy. Moreover, we have shown the top features from the dataset according to the feature important scores to know their impacts on the development of the classification model. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Behavior risk Cervical cancer Correlation Feature importance Machine learning

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