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检索条件"任意字段=International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022"

共 558 条记录，以下是171-180 订阅

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data-driven Building Occupancy Prediction: An Educational Building Case Study

Data-Driven Building Occupancy Prediction: An Educational Bu...

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international conference on computing in Civil Engineering (I3CE)

作者： Mirzabeigi, Shayan Razkenari, Mohamad SUNY Coll Environm Sci & Forestry Dept Sustainable Resources Management Syracuse NY 13210 USA

ISBN: (纸本)9780784483893

Predicting building occupancy is crucial to design and operation of energy systems in buildings. The common method to model building occupancy is using static occupancy schedules, recommended by energy standards. data-driven approaches are used to provide a more dynamic and accurate prediction of occupant interactions with buildings. This study built upon previously monitored data for 44 full days in a public educational building, collected via sensors on room-level occupant counts combined with indoor environmental quality data. We developed an automated machine learning framework with pipeline for pre-processing, model selection, and prediction result analysis. The framework evaluates various machine learning algorithms, including K-nearest neighbors (KNN), support vector machine (SVM), classification and regression trees (CART), and ensemble methods. Different evaluation metrics were adopted to quantify the percentage of errors. The results of these occupancy predictions were compared with the conventional static schedules in the ASHRAE Standard 90.1 through the application of dynamic energy simulation. The output of this study informs the construction sector about the advances of data analytics for occupancy schedule prediction, facility operation, and building simulation.

关键词： data Analytics

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Prediction of Diabetes at early stages using ANN & Outlier Exposure

Prediction of Diabetes at early stages using ANN & Outlier E...

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2022 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2022

作者： Jagini, Ajay Gayatri, Nandam Nagaraju, Sangepu Rao, Vardhineni Saketh Rao, Kanjarla Sathish Sumanth, Boorla KITS Department of CSE Telangana Warangal India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

In the present scenario of medical field, Diabetes is a critical issue which do require a lot of attention and if predicted in early stages it can be cured easily. From past few decades many machine learning algorithms are being used for detection at early stages. In this research our principal attention is on prediction of diabetes for pregnant woman that can also be termed as Gestational Diabetes. This may also lead to affecting of Type 2 Diabetes. Several researchers worked hard to predict the results accurately. ANN has considerable edge over other classification algorithms. Machine Learning, Artificial Intelligence, data Analytics, and other latest domains are being engaged in prediction of several categories of diseases in the health sector. ANNs (Artificial Neural Network) made a massive impact in medical department further egresses for various data driven applications in domain of health care. It starts with disease diagnosis, image processing of infected organs and finally predicting the disease accurately. Our primary focus is finding out the effect of parameters over dataset to forecast if a specific person is suffering from diabetes or not. PIMA dataset has been considered with 20 neurons for hidden layers and acquired accuracy of 86.2% when there are no outliers in dataset. This research depicts the accuracy &effectiveness of ANN model when compared to other classification algorithms. © 2022 IEEE.

关键词： data Analytics

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data driven Modelling and Prediction Of Rainfall 3

Data Driven Modelling and Prediction Of Rainfall

引用

3rd international conference on intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Anusree, P.S. Devadhas, G.Glan Shinu, M.M. Mary Synthia Regis Prabha, D.M. Dhanoj, M. Vimal Jyothi Engineering College Electronics & Instrumentation Kerela Kannur India Vimal Jyothi Engineering College Eie Department Kerela Kannur India Noorul Islam Center for Higher Education Eee Department Kumaracoil India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The prediction of weather and is difficult because these phenomena are highly non-linear and complicated phenomena. Technology based on artificial intelligence enables knowledge processing and is utilised in predicting. Synthetic neural network (ANN) has emerged as an alluring substitute for conventional statistical techniques for anticipating the behaviour of nonlinear systems The purpose of this paper is to prevent tools to model and predict rainfall behavior form past observations based on past observation. There are two fundamentally different approaches that are used in the paper to develop a model, both based on statistical methods based on ANNs. The prediction efficiency was evaluated based on 115years of mean annual rainfall between 1901and 2015. © 2022 IEEE.

关键词： Neural networks

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AtFP: Attention-based Failure Predictor for Extreme-scale computing 13

AtFP: Attention-based Failure Predictor for Extreme-scale Co...

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13th international conference on Reliability, Maintainability, and Safety, ICRMS 2022

作者： Li, Longhao Znati, Taieb University of Pittsburgh Department of Computer Science PittsburghPA United States College of Information Technology United Arab Emirates University United Arab Emirates

ISBN: (数字)9781665486903

ISBN: (纸本)9781665486903

Extreme-scale computing is paving the way for unparalleled advances in scientific discovery and innovation. However, as systems scale, their propensity to failure increases significantly, making it difficult for long running applications that span a large number of computing nodes to make forward progress. Achieving high performance in extreme scale environments, while minimizing energy consumption, has emerged as a daunting challenge. Significant advances on how to deal with failure, both physical and logical, have been achieved, with varying degree of success. A key component of fault tolerance relies heavily on the ability of the scheme to predict failure accurately. Varies approaches, including intelligent methods, have been proposed to predict failures. In this paper, we propose an attention-based failure predictor (AtFP), which automatically extracts representative features from the raw event log data to predict failure. The results show that, using the same input and output layers, AtFP outperforms frequently used LSTM methods. The proposed model reduces the F1 score by 39% and the training time by 65%. © 2022 IEEE.

关键词： Fault tolerance

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data-driven Human-Like Path Planning for Autonomous Driving Based on Imitation Learning 5

Data-Driven Human-Like Path Planning for Autonomous Driving ...

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5th international conference on intelligent Autonomous systems, ICoIAS 2022

作者： Liu, Peijiang Wang, Xiao Zhang, Changzhu College of Electronics and Information Engineering Tongji University Shanghai China DominantTech Shanghai China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

In autonomous driving systems, path planning algorithms play a crucial role in ensuring vehicle safety, driving efficiency, and passenger comfort. The traditional path planning algorithms cannot meet the real-Time and passenger comfort requirements of autonomous driving, an alternative is imitation learning, a data-driven approach that mimics expert agents' driving behavior and leverages recent advances in supervised learning. We present a learning-based planner that aims to robustly drive a vehicle by mimicking human drivers' driving behavior. First, we leverage a mid-To-mid approach that allows us to reduce sample complexity. Furthermore, we introduce task losses that penalize undesirable behaviors, which is done by introducing a differentiable rasterizer that directly converts the waypoints output by the network into images. Most importantly, we add the state information of ego vehicle at the historical time to the original LSTM network input which improves the completion rate of scene tasks and the comfort of passengers. Finally, we experiment on the nuPlan dataset to verify the effectiveness of our method. © 2022 IEEE.

关键词： Motion planning

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Research on digital twin model of energy equipment 2

Research on digital twin model of energy equipment

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2nd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Zhang, Zheyu Jiang, Jinyang Cheng, Xiao Electric Power Research Institute of State Grid Chongqing Electric Power Company Energy Internet Center Chongqing China

ISBN: (纸本)9781665442763

Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, Biotechnology, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. By introducing the technology and development process of digital twinning, this paper puts forward a model for real-time analysis and monitoring combined with the establishment method of digital twinning model of transformer electromagnetic and thermal field in power system and the technologies of Internet of things, cloud computing and big data. © 2022 IEEE.

关键词： Big data

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Implementing Open-Domain Question-Answering in a College Setting: An End-to-End Methodology and a Preliminary Exploration 14

Implementing Open-Domain Question-Answering in a College Set...

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14th international conference on Computer Supported Education (CSEDU)

作者： Gonzalez-Bonorino, Augusto Lauria, Eitel J. M. Presutti, Edward Marist Coll Sch Comp Sci & Math Poughkeepsie NY 12601 USA Marist Coll Data Sci & Analyt Dept Informat Technol Poughkeepsie NY USA Marist Coll Enrollment Management Poughkeepsie NY USA

ISBN: (纸本)9789897585623

advances in Artificial Intelligence and Natural Language Processing (NLP) can be leveraged by higher-ed administrators to augment information-driven support services. But due to the incredibly rapid innovation rate in the field, it is challenging to develop and implement state-of-the-art systems in such institutions. This work describes an end-to-end methodology that educational institutions can utilize as a roadmap to implement open domain question-answering (ODQA) to develop their own intelligent assistants on their online platforms. We show that applying a retriever-reader framework composed of an information retrieval component that encodes sparse document vectors, and a reader component based on BERT-Bidirectional Encoder Representations from Transformers- fine-tuned with domain specific data, provides a robust, easy-to-implement architecture for ODQA. Experiments are carried out using variations of BERT fine-tuned with a corpus of questions and answers derived from our institution's website.

关键词： Deep Learning Natural Language Processing Transformers AI in Higher-education Open Domain Question-Answering BERT roBERTa ELECTRA Minilm Information Retrieval

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GenEthos: A Synthetic data Generation System with Bias Detection and Mitigation

GenEthos: A Synthetic Data Generation System with Bias Detec...

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2022 international conference on computing, Communication, Security and intelligent systems, IC3SIS 2022

作者： Gujar, Shubham Shah, Tanishka Honawale, Dewen Bhosale, Vedant Khan, Faizan Verma, Devika Ranjan, Rakesh Vishwakarma Institute of Information Technology Pune India Faculty San Jose State University CA United States

ISBN: (数字)9781665468831

ISBN: (纸本)9781665468831

data-driven models function admirably in solving real-world problems. However, obtaining relevant data is difficult. Also, sometimes more diverse data is needed to identify the limitations of trained Machine Learning models. Creating such data samples based on earlier known metadata is a common practice. However, this process can induce bias in the dataset unknowingly. Generative Adversarial Networks (GAN) based data generation models generate more data based on initial data distribution. Thus, data generation models may reflect bias in the generated synthetic data. In this study, the authors have proposed an interactive synthetic data generation Graphical User Interface (GUI) tool. The tool is equipped with Bias detection and mitigation algorithms which will notify users about the pre-existing bias and provide methods to mitigate it. Similarly, this tool can be used to evaluate synthetic data generated using GAN-based models against fairness metrics. The authors have found that Learning Fair Representation (LFR) bias mitigation method has performed 62% 17.5% better than Prejudice remover and Disparate impact remover for German Credit Adult original datasets. These results were concluded based on bias detection metrics such as Statistical Parity Difference (SPD) and Disparate Impact (DI). The proposed data generation tool used with LFR method can reduced SPD metric by 93% on original German Credit data. The authors conclude that both original and synthetic datasets had a bias. Therefore, the fairness level of any dataset should be checked vigilantly. © 2022 IEEE.

关键词： Generative adversarial networks

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advances in machine learning for the innovation economy: in the shadow of war 10

Advances in machine learning for the innovation economy: in ...

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10th international conference on Monitoring, Modeling and Management of Emergent Economy, M3E2-MLPEED 2022

作者： Danylchuk, Hanna B. Semerikov, Serhiy O. The Bohdan Khmelnytsky National University of Cherkasy 81 Shevchenko Blvd. Cherkasy18031 Ukraine Kryvyi Rih State Pedagogical University 54 Gagarin Ave. Kryvyi Rih50086 Ukraine Kryvyi Rih National University 11 Vitalii Matusevych Str. Kryvyi Rih50027 Ukraine Institute for Digitalisation of Education of the NAES of Ukraine 9 M. Berlynskoho Str. Kyiv04060 Ukraine Zhytomyr Polytechnic State University 103 Chudnivsyka Str. Zhytomyr10005 Ukraine Academy of Cognitive and Natural Sciences 54 Gagarin Ave. Kryvyi Rih50086 Ukraine

This preface introduces the selected and revised papers presented at the 10th international conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2022), held online in Ukraine, on November 17-18, 2022. The conference aimed to bring together researchers, practitioners, and students from various fields to exchange ideas, share experiences, and discuss challenges and opportunities in applying computational intelligence and data science for the innovation economy. The innovation economy is a term that describes the emerging paradigm of economic development that is driven by knowledge, creativity, and innovation. It requires new approaches and methods for solving complex problems, discovering new opportunities, and creating value in various domains of science, business, and society. Computational intelligence and data science are two key disciplines that can provide such approaches and methods by exploiting the power of data, algorithms, models, and systems to enable intelligent decision making, learning, adaptation, optimization, and discovery. The papers in this proceedings cover a wide range of topics related to computational intelligence and data science for the innovation economy. They include theoretical foundations, novel techniques, and innovative applications. The papers were selected and revised based on the feedback from the program committee members and reviewers who ensured their high quality. We would like to thank all the authors who submitted their papers to M3E2 2022. We also appreciate the keynote speakers who shared their insights and visions on the current trends and future directions of computational intelligence and data science for the innovation economy. We acknowledge the support of our sponsors, partners, and organizers who made this conference possible despite the challenging circumstances caused by the ongoing war in Ukraine. Finally, we thank all the participants who attended the conference online and contributed to its su

关键词： Neural networks

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High-Security data Sharing on Edge Server using Internet of Things: A Review 1

High-Security Data Sharing on Edge Server using Internet of ...

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1st international conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2022

作者： Singh, Vikram Sabherwal, Suruchi JSSATE Department of Computer Science and Engineering Noida India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

With the advancement of technology in our daily lives, many new challenges related to security on cloud-based servers have emerged. IoT (Internet of Things) based systems are used to connect several electronic gadgets to the internet. Nowadays, Cloud computing keeps the IoT data secured, safe and accessible. Further, it also reduces the burden of intelligent gadgets. We also use edge servers nearby and place them beside the electronic gadgets. Edge nodes are the term used frequently, which could be either a collection of edge servers or a set of computing resources. Limited solutions are available for the same to date. In this paper, we investigate the capability of cloud-based advances for the IoT exhaustively by considering both the mechanical and normalization perspectives. We surveyed around 25 papers considering the present-day IoT network scene, just as their impacts on utilizing edge servers and related actual gadgets. © 2022 IEEE.

关键词： Cryptography

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