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7th International Conference on Information Technology Research, ICITR 2022

作者： Haputhanthri, Manula Himasha, Charana Balasooriya, Hansi Herath, Malithi Rajapaksha, Samantha Harshanath, S.M.B. Faculty of Computing Computer Systems Engineering Malabe Sri Lanka

ISBN: (数字)9798350332032

ISBN: (纸本)9798350332032

The majority of industrial environments and homewares need help when exploring unknown locations owing to a lack of understanding about the building structure and the various impediments that may be faced while transporting products from one spot to another. This is because there is a lack of knowledge about the building structure and the potential obstacles that may be encountered. This paper provides 'computer Vision-Based Navigation Robot' as a strategy for indoor navigation with optimal accessibility, usability, and security, decreasing issues that the user may encounter when traveling through indoor and outdoor areas with real-time monitoring of the most up to date IoT technology. The article is titled 'Indoor Navigation with Optimal Accessibility, Usability, and Security.' This article proposes 'computer Vision-Based Navigation Robot' as a solution for interior navigation that provides optimum accessibility, usability, and security. This is done in order to tackle the issue that was presented before. Since the readers of this post include people who work in industry as well as physically challenged people who live alone, CVBN Robot takes object-based inputs from its surroundings. This is because the audience for this essay includes both groups of people. This study also covers a variety of methods for localization, sensors for the detection of obstacles, and a protocol for an Internet of Things connection between the server and the robot. This connection enables real-time position and status updates for the robot as it navigates a known but unknown interior environment. In addition, this study covers a variety of methods for localization, sensors for the detection of obstacles, and a protocol for an Internet of Things connection between the server and the robot. © 2022 IEEE.

关键词： computer vision

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A linear partitioning diversity metric for evaluation of permutation-based metaheuristic algorithms

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Evolutionary Intelligence 2025年第4期18卷 1-67页

作者： Shahbazi, Majid Safi-Esfahani, Faramarz Shahbazi, Sara Mirjalili, Seyedali Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran Big Data Research Center Najafabad Branch Islamic Azad University Najafabad Iran School of Information Systems and Modelling Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia University Research and Innovation Center Obuda University Budapest Hungary Faculty of Electrical Engineering and Computer Science VSB - Technical University of Ostrava Ostrava Czech Republic

Metaheuristic algorithms, implicitly or explicitly, typically aim to achieve a delicate balance between exploration and exploitation when improving a population of solutions for a given optimization problem. Diversity metrics are crucial indicators for both exploration and exploitation, which serves as a valuable tool for assessing the performance of metaheuristic algorithms. Although several metrics for diversity have been proposed, the majority of them are only suitable for problems with continuous space. However, many optimization problems are defined/modeled based on discrete variables, and permutation representation is widely used for modeling real-world problems. Many existing metrics are not applicable to permutation-based problems. Applying other existing metrics to such problems frequently encounters two primary drawbacks: computational cost and effectiveness to represent the actual diversity of the population. To address these drawbacks, a new exclusive diversity metric is proposed in this work for optimization problems modeled by permutations. This method calculates diversity by linearizing and partitioning the problem space based on the population distribution in the space and counting the number of individuals in the partitions. Compared to the eleven available metrics (including both permutation-based and usable non-permutation-based metrics) in the literature, the proposed method demonstrates superior performance, exhibiting acceptable accuracy, stability, sensitivity, and robustness, particularly in the presence of outliers and requiring minimal computational cost. The accuracy of the results of the method is, on average, 35% better than baseline metrics. The computational cost of the method is also nearly 50% better than the fastest available method. These characteristics make the metric a valuable mean for analyzing existing algorithms’ behavior or designing new ones. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of

关键词： Linear programming

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Prediction of room temperature in Trombe solar wall systems using machine learning algorithms

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Energy Storage and Saving 2024年第4期3卷 243-249页

作者： Seyed Hossein Hashemi Zahra Besharati Seyed Abdolrasoul Hashemi Seyed Ali Hashemi Aziz Babapoor Petroleum Systems Engineering Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSK S4S 0A2Canada Department of Chemical Engineering University of Mohaghegh ArdabiliArdabil 13131-56199Iran Department of Chemistry School of ScienceAlzahra UniversityTehran ***Iran Computer Engineering Gachsaran Oil and Gas Exploitation CompanyGachsaran ***Iran Architectural Engineering Shiraz Islamic Azad UniversityShiraz ***Iran

A Trombe wall-heating system is used to absorb solar energy to heat *** parameters affect the system performance for optimal *** study evaluated the performance of four machine learning algorithms—linear regression,k-nearest neighbors,random forest,and decision tree—for predicting the room temperature in a Trombe wall *** accuracy of the algorithms was assessed using R^(2)and root mean squared error(RMSE)*** results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance,with R^(2)and RMSE values of 1 and *** contrast,linear regression and decision tree showed weaker *** findings highlight the potential of advanced machine learning algorithms for accurate room temperature prediction in Trombe wall systems,enabling informed design decisions to enhance energy efficiency.

关键词： Trombe wall Solar energy Thermal storage wall Machine learning algorithms

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Evaluating the Impact of Pulse Oximetry Bias in Machine Learning Under Counterfactual Thinking 3rd

Evaluating the Impact of Pulse Oximetry Bias in Machine L...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Martins, Inês Matos, João Gonçalves, Tiago Celi, Leo A. Wong, An-Kwok Ian Cardoso, Jaime S. Faculty of Engineering University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science Porto Portugal Duke University Durham United States Massachusetts Institute of Technology Cambridge United States

ISBN: (纸本)9783031820069

Algorithmic bias in healthcare mirrors existing data biases. However, the factors driving unfairness are not always known. Medical devices capture significant amounts of data but are prone to errors;for instance, pulse oximeters overestimate the arterial oxygen saturation of darker-skinned individuals, leading to worse outcomes. The impact of this bias in machine learning (ML) models remains unclear. This study addresses the technical challenges of quantifying the impact of medical device bias in downstream ML. Our experiments compare a "perfect world", without pulse oximetry bias, using SaO2 (blood-gas), to the "actual world", with biased measurements, using SpO2 (pulse oximetry). Under this counterfactual design, two models are trained with identical data, features, and settings, except for the method of measuring oxygen saturation: models using SaO2 are a "control" and models using SpO2 a "treatment". The blood-gas oximetry linked dataset was a suitable test-bed, containing 163,396 nearly-simultaneous SpO2 - SaO2 paired measurements, aligned with a wide array of clinical features and outcomes. We studied three classification tasks: in-hospital mortality, respiratory SOFA score in the next 24 h, and SOFA score increase by two points. Models using SaO2 instead of SpO2 generally showed better performance. Patients with overestimation of O2 by pulse oximetry of ≥ 3% had significant decreases in mortality prediction recall, from 0.63 to 0.59, P © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Oximeters

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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Polarization Insensitive Broadband Zero Indexed Nano-Meta Absorber for Optical Region Applications

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computers, Materials & Continua 2022年第4期71卷 993-1009页

作者： Ismail Hossain Md Samsuzzaman Ahasanul Hoque Mohd Hafiz Baharuddin Norsuzlin Binti Mohd Sahar Mohammad Tariqul Islam Space Science Center(ANGKASA) Universiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityDumki8602PatuakhaliBangladesh Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia

Broadband response metamaterial absorber(MMA)remains a challenge among researchers.A nanostructured new zero-indexed metamaterial(ZIM)absorber is presented in this study,constructed with a hexagonal shape resonator for optical region *** design consists of a resonator and dielectric layers made with tungsten and quartz(Fused).The proposed absorbent exhibits average absorption of more than 0.8972(89.72%)within the visible wavelength of 450–600 nm and nearly perfect absorption of 0.99(99%)at 461.61 *** on computational analysis,the proposed absorber can be characterized as *** developments of ZIM absorbers have demonstrated plasmonic resonance characteristics and a perfect impedance *** incidence obliquity in typically the range of 0◦–90◦both in TE and TM mode with maximum absorbance is more than 0.8972(∼89.72%),and up to 45◦angular stability is suitable for solar cell applications,like exploiting solar *** proposed structure prototype is designed and simulated by studying microwave technology numerical computer simulation(CST)*** finite integration technique(FIT)based simulator CST and finite element method(FEM)based simulator HFSS also helps validate the numerical data of the proposed ZIM *** proposed MMA design is appropriate for substantial absorption,wide-angle stability,absolute invisible layers,magnetic resonance imaging(MRI),color images,and thermal imaging applications.

关键词： Hexagonal resonator loaded star shaped polarization insensitive compact broadband metamaterial absorber optical region applications

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Online Mechanism Design for Differentially Private Data Acquisition

Online Mechanism Design for Differentially Private Data Acqu...

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IEEE Conference on Decision and Control

作者： Ameya Anjarlekar Rasoul Etesami R. Srikant Department of Electrical and Computer Engineering UIUC Faculty of Electrical and Computer Engineering and Industrial and Systems Engineering UIUC Faculty of Electrical and Computer Engineering UIUC

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We address a problem involving a buyer seeking to train a logistic regression model by acquiring data from privacy-sensitive sellers. Along with compensating the sellers for their data, the buyer provides differential privacy guarantees to them where the payments depend on the privacy guarantees. In addition, each seller has a different privacy sensitivity associated with their data, which is the cost per unit of loss of privacy. The buyer transacts sequentially with the sellers, wherein the seller will disclose their privacy sensitivity, and the buyer immediately provides a payment and guarantees differential privacy. After receiving the payment, the seller provides their data to the buyer. The buyer’s goal is to optimize a weighted combination of test loss and payments, i.e., achieve a tradeoff between getting a good ML model and limiting its payments. Additionally, the buyer must design the payments and differential privacy guarantees in an online fashion. Further, the online problem is historydependent, which adds to the challenge. Consequently, we design a payment mechanism that ensures incentive compatibility and individual rationality and is asymptotically optimal. Additionally, we also provide experimental results to validate our findings.

关键词： Differential privacy Privacy Logistic regression Sensitivity Limiting Costs Data acquisition Mechanism design Games Data models

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IOT Based Smart Parking System Using Ensemble Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

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computers, Materials & Continua 2023年第1期74卷 1819-1835页

作者： Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil Radzi Ambar Ahmed Abdu Alattab Anwar Ali Yahya Yousef Asiri Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn MalaysiaParit RajaBatu Pahat86400JohorMalaysia Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran61441Saudi Arabia Department of Computer Science Faculty of Computer Science and Info.SystemsThamar UniversityDhamar87246Yemen Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or ***,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different ***,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and *** was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable *** experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,*** model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification ***,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

关键词： Cardiac short-axis MRI images automatic left ventricle localization deep learning models faster R-CNN

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Smart Home User Interface: Overview

Smart Home User Interface: Overview

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2023 International Conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2023

作者： Faizrakhmanov, Renat Platunov, Alexey Bahrami, Mohammad Reza Innopolis University Cyberphysical Systems Laboratory Kazan Russia Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia

ISBN: (纸本)9781665475952

This paper overviews the Smart Home User Interfaces (UI) in the following aspects: general definition of Human-Machine Interface (HMI), types of common Smart Home UI, market analysis of HMI, voice assistants and chatbots. This study was conducted to determine existed Smart Home UI and evaluate them in the future works. This comparing and evaluation needed to choose appropriate and convenient Smart Home UI for certain application. The overview leaded to the following findings: Smart Home market is growing;certain global trends influenced on this market and on HMI in general;certain groups of people are more interested in Smart Home technologies;each type of Smart Home User Interface has its leaders. This study can help researchers learn about the main types of Smart Home User Interfaces and trends in this market. © 2023 IEEE.

关键词： User interfaces

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