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

共 3308 条记录，以下是611-620 订阅

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Prediction of short-term heat load of office buildings based on GNN-LSTM modeling

Prediction of short-term heat load of office buildings based...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Xuan Jia Hua Song Xinyuan Nan Xin Cai School of Electrical Engineering Xinjiang University Urumqi Xinjiang China Xinjiang Architectural Design Institute Co. Ltd Urumqi Xinjiang China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

To address the accuracy and real-time requirements of heat load forecasting in current office buildings, a hybrid forecasting framework (hybrid GNN LSTM model (HGLM)) based on hybrid graph neural network and long and short-term memory network is proposed, and the interrelationships between heat load feature nodes are fully considered in the graph data design to improve the accuracy of the forecasting target. In order to evaluate the performance of the model, the dataset was first constructed by simulating the data with weather factors using DeST software, and compared with several conventional forecasting models such as GNN, LSTM, MLP and RNN. the experimental results show that the accuracy of the coefficient of determination (R2) of the proposed HGLM model is improved by 20.7%, 1.8%, 10.6%, and 35.9% for short-term prediction (168 hours) the results show that the model performs well in the prediction of thermal loads in office buildings.

关键词： Heating systems Analytical models Accuracy Buildings predictive models Graph neural networks Data models Forecasting Long short term memory Load modeling

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Damping of Structures of Earthquake-Resistant Suspended Buildings 6th

Damping of Structures of Earthquake-Resistant Suspended Buil...

引用

6th international conference on Construction, Architecture and Technosphere Safety, ICCATS 2022

作者： Belash, T. Svitlik, I. JSC Research Center of Construction 6 2Nd Institutskaya Moscow109428 Russia Emperor Alexander I Petersburg State Transport University 9 Moskovsky Pr. Saint-Petersburg190031 Russia

ISBN: (纸本)9783031211195

During the construction of multi-storey and high-rise buildings, the principle of suspension of floors has been frequently applied to create the main system of load-bearing structures. Such a solution makes it possible to reduce the loads in load-bearing structures caused by dynamic influences. the effectiveness of the use of suspended structures in earthquake-resistant construction was confirmed by studies of the behavior of such objects in earthquake conditions. the most widespread structural solution of suspended type buildings is a single-core system with a cantilever head. there are many approaches to the realization of floor suspension in buildings with rigid core, each of which has certain advantages and disadvantages. this article discusses some possible options for joining elements of suspended and load-bearing structures of buildings, as well as analyzes the effectiveness of their use. the analysis of the effectiveness of the proposed solutions was carried out by evaluating the displacements of elements and stresses in the load-bearing elements of the calculated models under seismic impacts with different frequency spectra. the parameters of the calculated models were determined in the LIRA 10.12 software package by the finite element method in the temporary realm. the results of computational research of these structural solutions are presented in the article. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Floors

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CureIQ: FutureCare Analytics

CureIQ: FutureCare Analytics

引用

Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Nirmala M Prajwal D A K Sai Dinesh Bibiana Jennifer J G Vidya Shankar B Bharath Computer Science & Engineering New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

the growing complexity of healthcare data necessitates advanced techniques to extract meaningful insights and facilitate informed decision-making. this study presents “CureIQ,” a sophisticated platform that employs a blend of machine learning models, including Gaussian Naive Bayes, Random Forest, Logistic Regression, and Decision Tree algorithms, to enhance disease prediction and personalized medication recommendations. CureIQ also integrates a robust medication recommendation module, where Gaussian Naive Bayes and Random Forest models deliver flawless accuracy. Furthermore, the platform offers streamlined functionalities for appointment scheduling and patient-provider communication, ensuring user-centric design and operational efficiency. Building on existing methodologies, this study pushes the boundaries of diagnostic precision and therapeutic guidance, paving the way for innovations in intelligent healthcare solutions.

关键词： Technological innovation Processor scheduling Medical services predictive models Prediction algorithms Bayes methods Random forests Medical diagnostic imaging Regression tree analysis Mobile computing

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Platform of Formal Modeling and Analysis for Airborne software Requirements 6

Platform of Formal Modeling and Analysis for Airborne Softwa...

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6th international conference on Electronic Information Technology and Computer engineering, EITCE 2022

作者： Lyu, Jiarun Hu, Jun Wang, Lisong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781450397148

Airborne software systems play very important roles in modern civil aircraft systems, and there are several safety standards, including DO-178B/C, etc., that are compulsory to be satisfied before airborne software can be certificated by the authority of government. According to the DO-178B/C, the consistency and integrity of airborne software requirements must be analyzed and verified in the early stage of software development. In this paper, we introduce a formal modeling and analysis tool platform (ART: Avionics Requirement Tools) for airborne software natural language requirements, and a case study of the requirements of the software subsystem of the Indication-Recording System (IRS) is provided. Firstly, we give the semantics of a formal Variable Relationship Model (VRM), the platform architecture, and toolchain of ART. then a methodology of formal analysis of requirement consistency and integrity based on a multi-paradigm is given. After that, some details of the case study of IRS are shown including: how to make a preproccessing of original requirements and the automatic analysis process of the requirement model, such as the preprocessing and standardization of original requirement items, automatic generation of VRM models and multi-paradigm based formal analysis, etc. Lastly, some experiences of this case study are shown. © 2022 Association for Computing Machinery.

关键词： Semantics

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software Fault Prediction using Wrapper based Ant Colony Optimization Algorithm for Feature Selection

Software Fault Prediction using Wrapper based Ant Colony Opt...

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international conference on Information Systems and Computer Networks (ISCON)

作者： Sagnik Mondal Adarsh Kumar Sahu Hrishikesh Kumar Radha Mohan Pattanayak Mahendra Kumar Gourisaria Himansu Das School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India School of Comp. Sc. & Engg VIT-AP University Amravati AP India

Feature selection is both important and difficult part in classification technology. It is used to reduce the dimensionality of dataset and remove unwanted features. In this paper, we are going to discuss about a supervised feature selection method based on ant colony optimization for software fault prediction. software fault prediction helps to identify software fault at an early stage. We have used KNN, Naive Bayes and Decision Tree classifiers. We designed a fitness function and a two-step pheromone update rule is applied for effective elimination of duplicate features. this algorithm is inspired from real ants that search for the shortest path to the food source depending upon the concentration of the pheromone. We have used 12 different datasets and compared them using fitness plots. Each and every graph represents the effectiveness of ant colony optimization along with different classifiers. We have also made a table that represents the accuracy of the prediction for all the different classifiers while using the algorithm.

关键词： Fault diagnosis Ant colony optimization software algorithms predictive models Prediction algorithms Feature extraction software

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A Basic Iterative Non-Sign Average Gradient Adversarial Attack on Brain Tumor Image

A Basic Iterative Non-Sign Average Gradient Adversarial Atta...

引用

software engineering and Computer Science (CSECS), international conference on

作者： Jiaxuan Wei Jiawei Tang Shifa Liu Weilong Chen School of Information Science & Engineering Lanzhou University Lanzhou China

As one of the major diseases that pose a threat to human health, brain tumors' diagnosis plays a vital role in the intervention and rehabilitation process. Classification models based on deep neural networks (DNNs) have been proven effective in aiding doctors to diagnose brain tumors through MRI images. However, the injection of tiny perturbations to the images can have a significant impact on the diagnosis, indicating the vulnerability of DNNs. therefore, research on adversarial attack methods are highly significant in the medical diagnosis field. In this paper, we combined the non-sign gradient attack method and average gradient to propose a non-sign average gradient attack method(ABINM). By performing attack experiments on brain tumor MRI images under three different classification models, it was verified that the ABINM method can achieve higher rate of attack success and the generated adversarial sample shows stronger transferability on white-box and black-box testing compared to the baseline gradient-based adversarial attack methods.

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Robust predictive Position Control of Drug Delivery Mechanism

Robust Predictive Position Control of Drug Delivery Mechanis...

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), international conference on

作者： Yao Xie Panpan Xun Guangzu Zhou Renhao Liu Han Su Northwest Institute of Mechanical & Electrical Engineering Xianyang China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

A new robust predictive control strategy is proposed for the position control of the drug delivery mechanism. Firstly, based on the dynamic model of the drug delivery mechanism, a generalized predictive position controller is designed. then, on the basis of the designed generalized predictive position controller, the sliding mode compensation structure is added, which not only ensures the fast dynamic response of the original generalized predictive controller, but also suppresses the influence of the disturbance on the system. the simulation model of the control system of the drug delivery mechanism is built and the simulation analysis is carried out in the case of no drug and drug respectively. the results show that compared with PID control, the proposed control strategy has higher position control accuracy and stronger robustness.

关键词： Drugs Accuracy System dynamics Simulation Position control Signal processing algorithms predictive models Drug delivery Robustness predictive control

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Forecasting Short-Term Load Profiles in Distribution Networks Using Machine Learning

Forecasting Short-Term Load Profiles in Distribution Network...

引用

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

作者： Sorokin Aleksandr Maltsev Roman Yablokov Andrey Titov Vladislav Electric Systems Department Ivanovo State Power Engeneering University (ISPU) Ivanovo Russia Electric Power Systems Automatic Control Department Ivanovo State Power Engeneering University (ISPU) Ivanovo Russia

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

the article is devoted to the development of a machine learning model for forecasting short-term load profiles of various consumers at balance demarcation points in 6(10) kV distribution networks. the analysis, processing of initial data, training of the machine learning model, and interpretation of research results were carried out in the MATLAB software suite using the Statistics and Machine Learning Toolbox module. the assessment of the impact of forecasting errors on the operational regime of the electrical network was performed through automated modeling of distribution network operating modes using the real-time simulation software and hardware complex RTDS (Real-Time Digital Simulator).

关键词： Accuracy Machine learning Distribution networks predictive models Mathematical models software Real-time systems Steady-state Forecasting Load modeling

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19th EAI international conference on Computer Science and Education in Computer Science, CSECS 2023

19th EAI International Conference on Computer Science and Ed...

引用

19th EAI international conference on Computer Science and Education in Computer Science, CSECS 2023

ISBN: (纸本)9783031446672

the proceedings contain 32 papers. the special focus in this conference is on Computer Science and Education in Computer Science. the topics include: software Reuse Approach Based on Review and Analysis of Reuse Risks from Projects Uploaded to Github;AI-Enabled Infrared thermography: Machine Learning Approaches in Detecting Peripheral Arterial Disease;the Impact of Virtual and Augmented Reality on the Development of Motor Skills and Coordination in Children with Special Educational Needs;Deep Learning models for Vaccinology: Predicting T-cell Epitopes in C57BL/6 Mice;Changes in Patterns of Infectivity and Mortality with SARS-CoV-2 Omicron Variant in Bulgaria;data Representations and Ensemble Deep Learning Networks for Functional Neuroimaging Datasets;a Methodology for engineering Design and Simulation of a Satellite Horn Antenna;Optimized FPGA Implementation of an Artificial Neural Network Using a Single Neuron;on the p-Rank of the Incidence Matrix of a Projective Hjelmslev Plane;an Approach to Environmental Study from Observations and Sensing Towards a Digital Twin;the Application of Data Analytics for Understanding Patterns of Mergers and Acquisitions and CEO Characteristics in and between Crisis Times;interdependencies Between Cryptocurrency Markets, Precious Metals and Energy Resources;exploring Banking Stability through Diverse Parameters and Mathematical models;an Integrated Framework for Assessing Data and Business Analytics Skills for the Job Market;assessing the Resilience of the Banking System in the Republic of Bulgaria Using Mathematical models;the Ecosystem of Computer Science Education in Bulgarian Primary School – State of the Art;using Extract, Transform, and Load Framework and Data Visualization Tools to Enhance Career Services for Analytics Master’s Program Student.

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Hybrid Transfer Learning Approach for Emotion Analysis of Occluded Facial Expressions 6th

Hybrid Transfer Learning Approach for Emotion Analysis of ...

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6th international conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023

作者： Pamod, Dilshan Charles, Joseph Hewarathna, Ashen Iranga Vigneshwaran, Palanisamy Lekamge, Sugeeswari thuseethan, Selvarajah Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Erlangen Germany Department of Software Engineering Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka School of Information Technology Deakin University Geelong Australia

ISBN: (纸本)9783031530814

the ability to recognise and interpret emotional expressions is crucial since emotions play a significant role in our daily lives. Emotions are multifaceted phenomena that affect our behavior, perception, and cognition. As a result, numerous machine-learning and deep-learning algorithms for emotion analysis have been studied in previous works. Finding emotion in an obscured face, such as one covered by a scarf or hidden in shadow, is considerably harder than in a complete face, though. this study explores the effectiveness of deep learning models in occluded facial emotion analysis through a transfer learning approach. the performance of two individual pre-trained models, MobileNetV2 and EfficientNetB3, is compared alongside a hybrid model that combines both approaches. this comparison is conducted using the FER-2013 dataset. the dataset consists of 35,887 images and categorizes emotions into seven emotional categories. the results indicate that the hybrid model attained the highest accuracy, with a score of 93.04% for faces occluded at the top and 92.63% for faces occluded at the bottom. Additionally, the study suggests that top-occluded faces displayed more pronounced emotional expressions in comparison to bottom-occluded faces. Overall, these findings imply that hybrid architecture, which was developed as a state-of-the-art model in the study, proves to be effective for analyzing emotions in facial expressions that are partially obscured. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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