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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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A Survey on Diagnosis of Hypoglycemia and Hyperglycemia Using Backpropagation Algorithm in Deep Learning 6th

A Survey on Diagnosis of Hypoglycemia and Hyperglycemia Usin...

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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Rajeshram, V. Karthika, M. Meena, C. Srimugi, V. Kaushik Karthikeyan, K. Department of Computer Science and Engineering M. Kumarasamy College of Engineering Tamil Nadu Karur639113 India

ISBN: (纸本)9789811935701

Diabetes is a metabolic issue. It has a great impact on uncountable individuals from one side of the planet to the other. Its annual occurrence rates are startling. Diabetes-related disorders in several important organs of the body can be lethal if left untreated. Diabetes must be detected early in order to receive adequate treatment and prevent the condition from escalating to severe consequences. A smart analytics model using deep learning should predict the risk factor about the patient and rigorous of diabetics using an unknown dataset. Deep neural network approach helps to find the optimal results using predictive analytics. Already available predictive models are used to predict whether the disease is normal or not based on the data processed. At first in this paper, the survey tells us that the ML algorithm is utilized for identifying the disease. Secondly, the deep neural network uses the backpropagation neural network as the base unit for breaking down the information by relegating loads to each part of the neural network. the main focus of the survey is to analyze and discuss about the machine learning and deep learning algorithms for the prediction of diabetics and investigate the solutions for getting best results in diabetic prediction with forecasting. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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Features of Constructing Information models of Power Electronics Devices as Part of a SCADA System

Features of Constructing Information Models of Power Electro...

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international Youth conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Ivan I. Zhuravlev Denis P. Kmelyuk Andrey E. Bannov Industrial Electronic Department National Research University “MPEI” Moscow Russian Federation

the paper proposes a concept of introduction and realization of the extended information model (EIM) of electric power facilities as part of SCADA systems. Implementation of EIM provides data verification from sensors of power electronics devices as part of power grids connected to a single SCADA-system, which leads to a reduction in the impact of sensor measurement errors in power facilities, as well as allows you to diagnose sensor failure. the essence of the method is the use of mathematical models of electric power objects together with real sensor readings. the peculiarities of software implementation for reconciliation of mathematical model parameters with real sensor readings are considered. An example of EIM realization as a part of SCADA system and reactive power compensator based on AC voltage regulator is given.

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SDCM: An Efficient Camellia Oleifera Fruits Detection Algorithm in Dense Occlusion Scenes 6

SDCM: An Efficient Camellia Oleifera Fruits Detection Algori...

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6th international conference on Computing and Big Data, ICCBD 2023

作者： Zhang, Youyang Chen, Qiuxia Cui, Wenhua Yang, Jinfeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence The Guang-dong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9798350317008

To realize intelligent management in the Camellia oleifera fruit industry and foster the efficient growth of its supply chain, the accurate identification and detection of Camellia oleifera fruits are essential. However, in natural environments, these fruits exhibit challenges such as clustering, mutual occlusion, and obstruction by leaves. this paper proposes a Camellia oleifera fruits detection algorithm based on an improved YOLOv7 deep convolutional neural network, which is referred to as the SDCM network model, and add the function of counting the number of fruits. We introduce an enhancement to the backbone layer of the YOLOv7 model by incorporating a module called SDCM (Shortcut and Dilation Convolution based Module). this module enriches multiscale edge information, enabling the network to extract features more effectively, thus improving detection accuracy. the experimental results show that the detection accuracy of the SDCM network model is significantly improved compared with the original YOLOv7 model, and the detection performance of the model in this paper is superior compared with other classical detection models. By enhancing the detection accuracy of the model and adding the function of fruit counting, the model in this paper can provide more reliable and accurate detection performance and the function of predicting fruit yield for the development of the Camellia oleifera fruit industry, improve the production efficiency, and reduce the artificial resources. © 2023 IEEE.

关键词： Supply chains

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Development and analysis of whispering gallery mode model for refractive index sensing 6

Development and analysis of whispering gallery mode model fo...

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6th Photonics Meeting 2023, PM 2023

作者： Batumalay, M. Zain, H.A. Rahim, H.R.A. Harun, S.W. Department of Electrical Engineering University of Malaya Kuala Lumpur50603 Malaysia Faculty of Data Science and Information Technology INTI International University Nilai Negeri Sembilan71800 Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia

A refractive index sensor based on a whispering gallery mode resonator is numerically investigated in this paper. Whispering gallery mode resonators confine light and can be used in sensing applications, and this work presents the model built using COMSOL software to analyze their sensing performance. this work has investigated the WGM resonators' response to refractive index changes numerically using 2D models in the refractive index ranges 1.000 and 1.333. the resonator showed a sensitivity of 167 nm/RIU in the refractive index range of 1.3331.4 and a sensitivity of 95 nm/RIU in the refractive index range of 1.000-1.010. the sensing resonator has potential applications for biological and chemical analytes. © Published under licence by IOP Publishing Ltd.

关键词： Refractive index

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predictive Modeling for Early Identification of Disease Severity in Acute Respiratory Infections: A Case Study with COVID-19 5

Predictive Modeling for Early Identification of Disease Seve...

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5th international conference on Advancements in Computing, ICAC 2023

作者： Kalapuge, Vindhya Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Department of Computer Science & Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

the COVID-19 pandemic, declared a global emergency by WHO in March 2020, has had a profound impact on global health. With millions affected, the pandemic's magnitude became evident. Even advanced healthcare systems grappled with resource shortages, from protective gear to ventilators. Late diagnosis and the inability to adapt to new SARS-CoV-2 variants led to misdiagnoses and high casualties. Overburdened healthcare facilities and the absence of a centralized patient data system hindered swift decision-making. the lack of real-time monitoring meant many didn't receive timely, personalized care, underscoring the need for an integrated healthcare response during global emergencies. To tackle these challenges and improve hospital resource management during pandemics, this study presents a web approach harnessing big data analytics to develop a machine learning predictive model. this model has been developed to enable hospitals to assess patients' severity in real-time, taking into account a wide range of factors such as symptoms, medical history, and demographics. To acquire data regularly, a streaming data pipeline was established using Apache Kafka and Apache Streaming. In predicting the severity levels of patients, both Multinomial Logistic Regression and Multilayer Perceptron (MLP) models were employed. One noteworthy finding was that using datasets with balanced classes yielded significantly higher accuracy for the MLP model compared to unbalanced datasets. Specifically, MLP achieved the highest accuracy of 60.4% when trained on balanced classes. this model's real-time severity insights have the potential to revolutionize decision-making and resource allocation in healthcare. It represents a significant step towards a more effective response to future pandemics and the strengthening of the global healthcare system. © 2023 IEEE.

关键词： COVID-19

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SURFACE ROUGHNESS PREDICTION FOR ADDITIVELY MANUFACTURED TI-6AL-4V COMPONENTS BASED ON SUPERVISED LEARNING models 17

SURFACE ROUGHNESS PREDICTION FOR ADDITIVELY MANUFACTURED TI-...

引用

ASME 17th international Manufacturing Science and engineering conference (MSEC) held jointly with the 50th North American Research conference (NAMRC)

作者： Maitra, Varad Shi, Jing Univ Cincinnati Dept Mech & Mat Engn Cincinnati OH 45221 USA

ISBN: (纸本)9780791885802

To avoid redundant trial and error experiments in hope of achieving acceptable surface roughness, reliable predictive models must be engineered to anticipate surface characteristics based on process parameter inputs. In the present study, two rigorously tested supervised machine-learning based models are proposed to predict the arithmetic average (Ra) of profile deviation for the surface of Ti-6Al-4V alloy manufactured via selective laser melting (SLM). Firstly, a Gaussian Process Regression (GPR) model with Rational Quadratic kernel function is constructed after ten-fold cross validation of the training data. Secondly, using the same training data and the same ten-fold cross validation, a feed-forward narrow neural network (NN) is employed. Primary input parameters of SLM process, namely laser power, scanning speed, hatch spacing, layer thickness and volumetric energy density are mined from literature investigating as-built surface characteristics of SLMed Ti-6Al-4V alloy. To further test the developed machine-learning models, ten 8x8x8 mm Ti-6Al-4V cubes are manufactured and a comparative between two non-parametric (GPR and NN) models is performed by predicting the surface roughness (Ra) of the ten samples. It is discovered that the NN model underperforms with a root mean squared error (RMSE) of 2.76 mu m, as opposed to its counterpart GPR model, exhibiting RMSE of 0.82 mu m. Additionally, analyses of the surface characteristics of the fabricated samples, the surface profiles, and impact of such a predictive model are provided.

关键词： Surface Roughness Ti-6Al-4V alloy Selective Laser Melting Gaussian Process Regression Neural Network Predictions

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Voltage Stability Enhancement of Power System with FACTS Devices Based on Model predictive Control

Voltage Stability Enhancement of Power System with FACTS Dev...

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international conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Vadim I. Zakutsky Magomed G. Gadzhiev R&D Department Unigreen Engineering LLC Moscow Russia Department of Electric Power Systems Moscow Power Engineering Institute Moscow Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

this paper describes an improved voltage stability control strategy based on Model predictive Control (MPC) of power system with FACTS (Flexible Alternating Current Transmission System) devices installed. In the proposed approach, FACTS devices are coordinated in such a way as to ensure an acceptable voltage profile inc ase o f power system oscillations. the predictive models of FACTS devices are obtained and described. the modified IEEE 9 -bus system was used for calculations to show effectiveness of the proposed approach.

关键词： Reactive power Power system stability predictive models Stability analysis Real-time systems Phasor measurement units Power grids Time factors Oscillators predictive control

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Intelligent System for Predicting the Integrity of Operating Pipelines

Intelligent System for Predicting the Integrity of Operating...

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international conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Kristina Evseenkova Galina Kuznetsova Department of Computer-Aided Design and Control Systems Saint-Petersburg State Institute of Technology Saint Petersburg Russian Federation

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

An intelligent system for monitoring the integrity of operating pipelines is presented. the software package is designed to assess the corrosion rate and calculate the residual life of pipelines, which takes into account the parameters of climatic and transported environments, the characteristics of pipelines and their insulating coatings. the complex allows us to consider different types of corrosion: atmospheric, electrochemical and mechanical. A functional structure is proposed that describes the operating principle of the software package, calculation results and examples of intelligent system interfaces are presented.

关键词： software packages Atmospheric modeling Corrosion Pipelines predictive models Control systems Mathematical models Energy efficiency Intelligent systems Monitoring

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SCAF-DG: A Multi-site Medical Image Denoising with A Domain-Generalized Spatial-Channel Attention Fusion 6

SCAF-DG: A Multi-site Medical Image Denoising with A Domain-...

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6th IEEE international conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Fiasam, Linda Delali Rao, Yunbo Sey, Collins Klugah-Brown, Benjamin Tettey, Obed Nartey Aggrey, Esther Stacy E.B. Agyemang, Isaac Osei Adjei-Mensah, Isaac Yang, Yang University of Electronic Science Technology of China School of Information and Software Engineering Chengdu China University of Electronic Science Technology of China School of Life Science and Technology Chengdu China University of Electronic Science Technology of China School of Information and Communication Engineering Chengdu China Sichuan Institute of Computer Sciences Chengdu China

ISBN: (纸本)9798350325485

Images from multiple medical sites usually contain varying noise levels that can affect the generalization performance of the denoising models. Domain generalization (DG), which seeks to learn a model that can generalize to an unseen test domain, has not been extensively explored in medical image denoising for multi-site images. We present an approach for multi-site medical image denoising, which addresses the issue of varying noise levels across different imaging sites. Our proposed method, called SCAF-DG (Spatial-Channel Attention Fusion for Domain Generalization), employs two attention units to extract both domain-specific channel features and mutually-invariant spatial features from the noisy images. these features are then combined to form a rich feature space. Additionally, we utilized skip connections to preserve structural information during the denoising process. We evaluate SCAF-DG on publicly available brain magnetic resonance image (MRI) datasets from three different imaging sites, and demonstrate an average increase of 1.01 in peak signal-To-noise ratio (PSNR) and 0.016 in structural similarity index measure (SSIM) for denoising images. Our results demonstrate that SCAF-DG has the potential to generalize well to new, unseen test domains, and outperforms existing state-of-the-Art denoising models on multi-site medical images. © 2023 IEEE.

关键词： Image denoising

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Towards Interpreting the Behavior of Large Language models on software engineering Tasks

Towards Interpreting the Behavior of Large Language Models o...

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international conference on software engineering - Companion, ICSE, Companion

作者： Atish Kumar Dipongkor Kevin Moran University of Central Florida Orlando Florida USA

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

Large Language models (LLMs) have ushered in a significant break-through within the field of Natural Language Processing. Building upon this achievement, analogous language models have been developed specifically for code-related tasks, commonly referred to as Large Language models for Code (LLMsC). Notable examples of LLMsC include CodeBERT, UnixCoder, CoPilot, among others. these models have demonstrated exceptional performance across various software engineering (SE) tasks, encompassing code summarization, test case generation, natural language to code conversion, bug triaging, malware detection, program repair, and more. Despite the promising results achieved by LLMsC in SE tasks, there remains fundamental questions regarding their decision-making processes. Understanding these model decision mechanisms is crucial for further enhancing the performance of LLMsC. In pursuit of this objective, my PhD dissertation aims to pioneer novel methodologies for interpreting and comprehending the behavior of LLMsC.

关键词： Codes Decision making Computer bugs Medical services predictive models Natural language processing Trajectory

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