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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

Computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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Solving Sparse & High-Dimensional-Output Regression via Compression 38

Solving Sparse & High-Dimensional-Output Regression via Comp...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Li, Renyuan Chen, Zhehui Wang, Guanyi Department of Industrial Systems Engineering & Management National University of Singapore Singapore Google United States

Multi-Output Regression (MOR) has been widely used in scientific data analysis for decision-making. Unlike traditional regression models, MOR aims to simultaneously predict multiple real-valued outputs given an input. However, the increasing dimensionality of the outputs poses significant challenges regarding interpretability and computational scalability for modern MOR applications. As a first step to address these challenges, this paper proposes a Sparse & High-dimensional-Output REgression (SHORE) model by incorporating additional sparsity requirements to resolve the output interpretability, and then designs a computationally efficient two-stage optimization framework capable of solving SHORE with provable accuracy via compression on outputs. Theoretically, we show that the proposed framework is computationally scalable while maintaining the same order of training loss and prediction loss before-and-after compression under arbitrary or relatively weak sample set conditions. Empirically, numerical results further validate the theoretical findings, showcasing the efficiency and accuracy of the proposed framework. © 2024 Neural information processing systems foundation. All rights reserved.

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Genetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems

Genetic Algorithms with Neural Cost Predictor for Solving Hi...

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作者： Sobhanan, Abhay Park, Junyoung Park, Jinkyoo Kwon, Changhyun Department of Industrial and Management Systems Engineering University of South Florida TampaFL33620 United States Department of Industrial and Systems Engineering KAIST Daejeon34141 Korea Republic of OMELET Daejeon34141 Korea Republic of

When vehicle routing decisions are intertwined with higher-level decisions, the resulting optimization problems pose significant challenges for computation. Examples are the multi-depot vehicle routing problem (MDVRP), where customers are assigned to depots before delivery, and the capacitated location routing problem (CLRP), where the locations of depots should be determined first. A simple and straightforward approach for such hierarchical problems would be to separate the higher-level decisions from the complicated vehicle routing decisions. For each higher-level decision candidate, we may evaluate the underlying vehicle routing problems to assess the candidate. As this approach requires solving vehicle routing problems multiple times, it has been regarded as impractical in most cases. We propose a novel deep learning-based approach called the genetic algorithm with neural cost predictor to tackle the challenge and simplify algorithm developments. For each higher-level decision candidate, we predict the objective function values of the underlying vehicle routing problems using a pretrained graph neural network without actually solving the routing problems. In particular, our proposed neural network learns the objective values of the HGS-CVRP open-source package that solves capacitated vehicle routing problems. Our numerical experiments show that this simplified approach is effective and efficient in generating high-quality solutions for both MDVRP and CLRP and that it has the potential to expedite algorithm developments for complicated hierarchical problems. We provide computational results evaluated in the standard benchmark instances used in the literature. © 2024 INFORMS.

关键词： Genetic algorithms

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Technical Note—Conic Mixed-Binary Sets: Convex Hull Characterizations and Applications

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Operations Research 2025年第1期73卷 251-269页

作者： Kılınç-Karzan, Fatma Küçükyavuz, Simge Lee, Dabeen Shafieezadeh-Abadeh, Soroosh Tepper School of Business Carnegie Mellon University PittsburghPA15213 United States Department of Industrial Engineering and Management Sciences Northwestern University EvanstonIL60208 United States Department of Industrial and Systems Engineering KAIST Daejeon34141 Korea Republic of

We consider a general conic mixed-binary set where each homogeneous conic constraint j involves an affine function of independent continuous variables and an epigraph variable associated with a nonnegative function, fj, of common binary variables. Sets of this form naturally arise as substructures in a number of applications, including mean-risk optimization, chance-constrained problems, portfolio optimization, lot sizing and scheduling, fractional programming, variants of the best subset selection problem, a class of sparse semidefinite programs, and distributionally robust chance-constrained programs. We give a convex hull description of this set that relies on simultaneous characterization of the epigraphs of fj’s, which is easy to do when all functions fj’s are submodular. Our result unifies and generalizes an existing result in two important directions. First, it considers multiple general convex cone constraints instead of a single second-order cone type constraint. Second, it takes arbitrary nonnegative functions instead of a specific submodular function obtained from the square root of an affine function. We close by demonstrating the applicability of our results in the context of a number of problem classes. © 2023 INFORMS.

关键词： Constrained optimization

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Minimizing D.C. Metrorail Disturbances: A Multi-Objective Decision Making Analysis Framework for Strategic Resilience

Minimizing D.C. Metrorail Disturbances: A Multi-Objective De...

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2024 systems and Information engineering Design Symposium, SIEDS 2024

作者： Foley, Claire Walker, Amaya Yang, Ziqian Mohammed, Salah Dano, Eric Jachimowicz, Adam George Washington University Department of Engineering Management and Systems Engineering United States Washington Metropolitan Area Transit Authority United States

ISBN: (纸本)9798350385144

The Washington Metropolitan Area Transit Authority (WMATA) faces frequent disruptions in its metro system, impacting operational efficiency and rider satisfaction. WMATA's disruptions are categorized into 12 incident levels from A1 (most severe) to D3 (least severe). This research introduces an analytical framework to determine which incident level's disturbances should be mitigated to minimize both downtime and costs due to disturbances. The number of occurrences and total downtime for each incident level were found through data processing on the 18 months of logged disturbances. Multiple WMATA Subject Matter Experts (SMEs) conducted an Analytic Hierarchy Process (AHP) assessment to determine the relative disturbance costs. These costs were subsequently converted into actual costs for all incident levels, leveraging known D2 incident costs. An exploratory interview was performed with WMATA SMEs to obtain weights for the importance of disturbance downtime relative to the cost. The resulting weights were applied to separate normalized downtime and cost scores to yield a ranked list of incident levels that need to be prioritized to best reduce disturbance downtime and cost. These findings directly allow WMATA to prioritize disruption mitigation and ensure operational continuity, benefiting WMATA and its stakeholders. © 2024 IEEE.

关键词： Data handling

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Design of a CNN1D for Control and Monitoring of Blood Pressure 1

Design of a CNN1D for Control and Monitoring of Blood Pressu...

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1st IEEE Colombian Caribbean Conference, C3 2023

作者： Barrios, Vanessa Susana Rodriguez Torrenegra, Fernando Miguel Mendez Molinares, Daladier Jabba Universidad Simón Bolívar Systems Engineering Program Barranquilla Colombia Universidad Del Norte Department of Systems Engineering Barranquilla Colombia

ISBN: (纸本)9798350341799

The ability to continuously monitor the parameters associated with the state of health of an individual results in a large volume of data, which presents both challenges and opportunities for data analysis. There are several studies of convolutional neural networks (CNN) used successfully in the implementation of tools for the monitoring and analysis of a large amount of medical data, accomplishing faster and more accurate data processing for the forecasting of health-related variables. This paper will address the design of a predictive model of ECG and PPG signals to estimate blood pressure (BP) with an one dimensional convolutional neural network (CNN1D) that optimizes performance to reduce the amount of processing on devices. © 2023 IEEE.

关键词： Electrocardiograms

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Personal rapid transit capacity evaluation for inner-city:A detailed micro-simulation study

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International Journal of Transportation Science and Technology 2023年第1期12卷 119-135页

作者： Ran Etgar Erel Avineri Yuval Cohen Ruppin Academic Center Faculty of EngineeringEmek Hefer4025000Israel Afeka College of Engineering Head of Program-Energy and Power Systems EngineeringMivtza Kadesh 38Tel-Aviv 6910717Israel Afeka College of Engineering School of Industrial Engineering and ManagementMivtza Kadesh 38Tel-Aviv 6910717Israel

This paper describes a detailed, discrete-event, micro-simulation study of an inner-city personal rapid transit (PRT) system, for the purpose of evaluating the capacity of such a system and the effect of various parameters on this capacity. While former studies considered various parameters, most considered only a partial set of the overall set of parameters considered by this paper. In many cases these parameters were treated differently from the model proposed here, which thus provides valuable insights for future research. This evaluation is crucial for justifying construction of such a system, and for judging its feasibility and may provide an initial step towards creating a PRT manual, similar to the Transit Capacity and Quality of Service Manual (TCQSM). The complex operation and structure of the problem suggests micro-simulation as a viable tool for this study. This necessitates tailoring a solution using general simulation software rather than a transportation-oriented tool. The simulation evaluated the capacity under three major scenarios related to passenger arrival characteristics. The results show that the capacity is not sensitive to pod velocity, nor to acceleration/deceleration rates, but is sensitive to the number of pods and suggests an optimal pod quantity.

关键词： PRT Simulation Urban transport

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Fusing clinical and image data for detecting the severity level of hospitalized symptomatic COVID-19 patients using hierarchical model

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Research on Biomedical engineering 2023年第1期39卷 209-232页

作者： Ershadi, Mohammad Mahdi Rise, Zeinab Rahimi Department of Industrial Engineering and Management Systems Amirkabir University of Technology No. 350 Hafez Ave Valiasr Square Tehran*** Iran

Purpose: Based on medical reports, it is hard to find levels of different hospitalized symptomatic COVID-19 patients according to their features in a short time. Besides, there are common and special features for COVID-19 patients at different levels based on physicians’ knowledge that make diagnosis difficult. For this purpose, a hierarchical model is proposed in this paper based on experts’ knowledge, fuzzy C-mean (FCM) clustering, and adaptive neuro-fuzzy inference system (ANFIS) classifier. Methods: Experts considered a special set of features for different groups of COVID-19 patients to find their treatment plans. Accordingly, the structure of the proposed hierarchical model is designed based on experts’ knowledge. In the proposed model, we applied clustering methods to patients’ data to determine some clusters. Then, we learn classifiers for each cluster in a hierarchical model. Regarding different common and special features of patients, FCM is considered for the clustering method. Besides, ANFIS had better performances than other classification methods. Therefore, FCM and ANFIS were considered to design the proposed hierarchical model. FCM finds the membership degree of each patient’s data based on common and special features of different clusters to reinforce the ANFIS classifier. Next, ANFIS identifies the need of hospitalized symptomatic COVID-19 patients to ICU and to find whether or not they are in the end-stage (mortality target class). Two real datasets about COVID-19 patients are analyzed in this paper using the proposed model. One of these datasets had only clinical features and another dataset had both clinical and image features. Therefore, some appropriate features are extracted using some image processing and deep learning methods. Results: According to the results and statistical test, the proposed model has the best performance among other utilized classifiers. Its accuracies based on clinical features of the first and second datasets are 92%

关键词： COVID-19

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Arabic text sentiment analysis and emotion classification using transformers 11

Arabic text sentiment analysis and emotion classification us...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Mossad, Nada Mohamed, Youssef Fares, A. Zaky, Ahmed B. Alexandria21934 Egypt Benha University The Computer Systems Engineering Program Faculty of Engineering at Shoubra The Department of Electrical Engineering Egypt

ISBN: (纸本)9798350369328

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately. © 2023 IEEE.

关键词： Sentiment analysis

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Evaluating the Economic Impact of Users’Personality on Selection of Demand Response programs

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CSEE Journal of Power and Energy systems 2023年第3期9卷 1036-1050页

作者： Hossein Khorramdel Mohsen Gitizadeh Pierluigi Siano Sara Bakhtiari Department of Electronics and Electrical Engineering Shiraz University of TechnologyShirazIran Department of Management&Innovation Systems University of SalernoVia Giovanni PaoloⅡ132Fisciano(SA)-84084Italy Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburg 2006South Africa Power Systems Studies Center of Shiraz University of Technology ShirazIran

Demand response creates an opportunity for consumers to play an important role in the development of smart *** the advent of renewable energies and their uncertainties,demand response provides a possible solution to resolve these *** addition to demand response schemes in the presence of renewable energy,the personality types of consumers can influence the choice of tariffs and change their electricity *** this paper,first,household residents with different types of personalities are considered as energy ***,the uncertainty of renewable energy sources is considered for the distributed generations scheduling by using a stochastic method called the Here-and-Now approach and considering three tariffs,time of use,real-time pricing,and direct load control in the residential sector to reduce total ***,the tariff choice is compared based on people preferences via various personality types,the Myers-Briggs Type Indicator test,and simulations ***,a probabilistic unit commitment methodology is used for distributed generations scheduling to minimize the total *** financial losses caused by non-optimal tariffs selection are determined through the comparison of *** results show that time of use and direct load control tariffs are optimal ones in summer and winter seasons,respectively.

关键词： Demand response renewable energy smart grid unit commitment

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