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A Time Series Forecasting Method Using DBN and Adam Optimization 4th

A Time Series Forecasting Method Using DBN and Adam Optimiza...

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4th European-Alliance-for-Innovation (EAI) international conference on Artificial Intelligence for Communications and Networks (AICON)

作者： Kuremoto, Takashi Furuya, Masafumi Mabu, Shingo Kobayashi, Kunikazu Nippon Inst Technol Saitama 3458501 Japan Yamaguchi Univ Yamaguchi 7558611 Japan Aichi Prefectural Univ Aichi 4801342 Japan

ISBN: (纸本)9783031291258;9783031291265

Deep Belief Net (DBN) was applied to the field of time series forecasting in our early works. In this paper, we propose to adopt Adaptive Moment Estimation (Adam) optimizationmethod to the fine-tuning process of DBN instead of the conventional Error Back-Propagation (BP) method. Meta parameters, such as the number of layers of Restricted Boltzmann machine (RBM), the number of units in each layer, the learning rate, are optimized by Random Search (RS) or Particle Swarm Optimization (PSO). Comparison experiments showed the priority of the proposed method in both cases of a benchmark dataset CATS which is an artificial time series data used in competitions for long-term forecasting, and Lorenz chaos for short-term forecasting in the sense not only prediction precision but also learning performance.

关键词： Time series forecasting Deep learning Deep Belief Net Error Back-Propagation Adam learning optimization

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Comprehensive Differentiation of Partitional Clusterings 25

Comprehensive Differentiation of Partitional Clusterings

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25th international conference on Enterprise Information Systems, ICEIS 2023

作者： Schütz, Lars Bade, Korinna Nürnberger, Andreas Köthen Germany Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany

ISBN: (纸本)9789897586484

Clustering data is a major task in machine learning. From a user’s perspective, one particular challenge in this area is the differentiation of at least two clusterings. this is especially true when users have to compare clusterings down to the smallest detail. In this paper, we focus on the identification of such clustering differences. We propose a novel clustering difference model for partitional clusterings. It allows the computational detection of differences between partitional clusterings by keeping a full description of changes in input, output, and model parameters. For this purpose, we also introduce a complete and flexible partitional clustering representation. Both the partitional clustering representation and the partitional clustering difference model can be applied to unsupervised and semi-supervised learning scenarios. Finally, we demonstrate the usefulness of the proposed partitional clustering difference model through its application to real-world use cases in planning and decision processes of the e-participation domain. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Supervised learning

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machine learning Models for Micro-bubble Image Detection in Mosquito Sprayer Quality Control:Addressing Class and Scale Imbalance 4

Machine Learning Models for Micro-bubble Image Detection in ...

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4th international conference on Big Data Analytics and Practices, IBDAP 2023

作者： Srisaeng, Suppasit Sadakorn, Pongsakorn Ploddi, Kritchavat Areechokechai, Darin Suwannik, Worasait Ministry of Public Health Thailand Department of Disease Control Thailand Kasetsart University Department of Computer Science Thailand

ISBN: (纸本)9798350300192

Introduction: Vector-borne diseases (VBDs) account for around 17% of global infectious diseases, with malaria and dengue being the most prevalent. Effective VBD prevention involves controlling mosquito populations by spraying micro-bubble (MB) insecticide. Quality control measures for MB size are essential, but few centers can assess MB size measurement. machine learning (ML) is a powerful tool for automating and enhancing image analysis tasks, including measuring MB size. In this study, we train and evaluate ML models using an MB image dataset to assess their ***: Our experimental study used an MB image dataset labeled by specialists, with images resized and randomly assigned to train, validate, and test datasets. We employed various evaluation metrics, such as recall, precision, and mean average precision (mAP). Image augmentation techniques were applied to increase the number of unqualified MBs. Pretrained models from Detectron2 (D2), MMDetection (MM), and YOLO libraries were used for training and ***: We identified severe class and scale imbalances in the dataset. Single-stage and two-stage CNN models demonstrated superior performance in MB detection compared to DCN and transformer-based models. D2 outperformed MM, and training Faster-RCNN R-50 on an augmented dataset improved performance for unqualified MB detection. Considering multiple evaluation metrics, YOLO v7 exhibited the highest accuracy and effectiveness in detecting ***: Our study revealed the effectiveness of CNN models in MB detection and the importance of selecting appropriate libraries and pretrained weights. Image augmentation improved unqualified MB detection while considering multiple evaluation metrics provided a more comprehensive understanding of model performance. Models like YOLO v7 and Faster-RCNN R-50 and X-101 are recommended for practical MB quality assessment tasks, as they demonstrate high precision and recall performance for qualified and un

关键词： Quality control

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QNotation: An Interactive Visual Tool to Lower learning Barriers in Quantum Computing 4

QNotation: An Interactive Visual Tool to Lower Learning Barr...

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4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Norrie, Samantha Estey, Anthony University of Victoria Canada

ISBN: (纸本)9798350343236

One of the first challenges that new learners of Quantum Computing face is learning how to use and interpret quantum circuits represented in circuit, Dirac, and matrix notation. It is necessary for learners to understand the different notations used in Quantum Computing in order to ensure that they have a robust comprehension of the field and can make use of a variety of resources. Depending on their technical background, some learners may struggle with certain notations more than others. We present QNotation, a visual tool that helps learners explore the aforementioned notations. Circuit, Dirac, and matrix notation. this allows them to be able to identify the differences and similarities between the different notations. QNotation was built to be used at multiple stages throughout the Quantum computer learning journey. While one may start using QNotation to help them become familiar with the different notations used in Quantum Computing, the tool can also be used as a visual quantum circuit debugging tool. In addition to QNotation being a tool that can be used by learners, it can also be used by instructors to support the teachings of quantum computing concepts and building blocks, including entanglement and Quantum Fourier Transform. © 2023 IEEE.

关键词： Matrix algebra

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Engineering Brain Technology Based on Artificial Intelligence and Federated learning 4

Engineering Brain Technology Based on Artificial Intelligenc...

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Li, Qiang Zhao, Feng Tao, Jun Zhou, Wei Yu, Chengchen State Grid Information & Telecommunication Group Co. Ltd Beijing China Anhui Jiyuan Software Co. Ltd Hefei China

the digital wave has given birth to the arrival of smart power and smart cities, a large number of sensor systems deployed for traditional smart power sites have high fragmentation, large and scattered data collection, difficult system integration, and unsatisfactory application effects. A key technical solution for engineering brain based on artificial intelligence and joint learning in intelligent power plant power has been proposed. At the same time, under the premise of data not leaving islands, achieve a learning mode from the model side to multiple data sides, achieving the effect of data being available but not available. the experimental results indicate that: Although federated data governance technology increases model training time due to encrypted data transmission, with the increase of data silos, it can gradually approach data aggregation mode in testing accuracy, greatly improving the problem of data silos. the key technology solutions for the power of engineering brain based on artificial intelligence and federated learning have been demonstrated in practice. the use of digital twin technology to improve automation collection capabilities, the use of artificial intelligence technology to upgrade automation collection devices, and the use of federated learning technology to enhance the intelligence level of engineering power can effectively improve the overall level of intelligent power. In terms of engineering safety supervision, quality monitoring cost control and other aspects can provide reference for similar projects. © 2024 the Authors.

关键词： Artificial intelligence collaborative learning data remains on the island engineering brain intelligent power station

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application of hybrid neural network in data-driven flow field simulation 2

Application of hybrid neural network in data-driven flow fie...

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2nd international conference on Digital Society and Intelligent Systems, DSInS 2022

作者： Zhang, Xiaowei Dong, Wentao Wang, Wenshi Zhou, Ziyu Dong, Yucai The 15th Research Institute of China Electronics Technology Group Corporation Beijing100083 China

ISBN: (纸本)9781510663244

Due to the strong nonlinearity of navier stokes equation, it is difficult to solve the hydrodynamics simulation problem. As a century problem, it is still a major difficulty in the academic community. With the improvement of computer ability and the development of data platform, some new changes have taken place in the research direction and content of turbulence model. the data-driven machine learning method is different from the traditional approximate equation solving method in physics, and shows its application potential in highly complex flow fields. In this study, convolution cyclic hybrid neural network is used to predict the complex flow field, and the generated confrontation network is used to generate the simulated flow field. © 2023 SPIE.

关键词： Computational fluid dynamics

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FedCLUS: Federated Clustering from Distributed Homogeneous Data 4th

FedCLUS: Federated Clustering from Distributed Homogeneous D...

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4th international conference on Soft Computing and its Engineering applications (icSoftComp)

作者： Gupta, Sargam Sharma, Shachi South Asian Univ Dept Comp Sci New Delhi India

ISBN: (纸本)9783031276088;9783031276095

An emerging trend in the field of machine learning presently is Federated learning (FL) that enables to build a global model without sharing private data distributed among multiple data owners. Extensive research conducted on FL is mostly restricted to deep learning methods utilizing labeled data. However, voluminous big data generated today is unlabeled. the paper presents a federated clustering method, called FedCLUS, that allows to learn clusters from distributed homogeneous data without sharing it. the distinguishing characteristic of the FedCLUS method is its ability to retain, merge and discard original clusters sent by data owners. Also, FedCLUS can identify any number of arbitrary shaped clusters that are robust to outliers. the feasibility and performance of the FedCLUS is tested on many diverse datasets.

关键词： Federated clustering Homogeneous data Performance analysis Unsupervised federated learning

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Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel 4

Semisupervised Anomaly Detection using Support Vector Regres...

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4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Tscharke, Kilian Issel, Sebastian Debus, Pascal Institute for Applied and Integrated Security Quantum Security Technologies Fraunhofer Garching near Munich Germany

ISBN: (纸本)9798350343236

Anomaly detection, an important task in data analysis, involves identifying observations or events that deviate in some way from the rest of the data. machine learning techniques have shown success in automating this process by detecting hidden patterns and deviations in large-scale data. the potential of quantum computing for machine learning has been widely recognized, leading to extensive research efforts to develop suitable quantum machine learning (QML) algorithms. In particular, the search for QML algorithms for near-term NISQ devices is in full swing. However, NISQ devices pose additional challenges due to their limited qubit coherence times, low number of qubits, and high error rates. Kernel methods based on quantum kernel estimation have emerged as a promising approach to QML on NISQ devices, offering theoretical guarantees, versatility, and compatibility with NISQ constraints. Especially support vector machines (SVM) utilizing quantum kernel estimation have shown success in various supervised learning tasks. However, in the context of anomaly detection, semisupervised learning is of great relevance, and yet there is limited research published in this area. this paper introduces an approach to semisupervised anomaly detection based on the reconstruction loss of a support vector regression (SVR) with quantum kernel. this novel model is an alternative to the variational quantum and quantum kernel one-class classifiers, and is compared to a quantum autoencoder as quantum baseline and a SVR with radial-basis-function (RBF) kernel as well as a classical autoencoder as classical baselines. the models are benchmarked extensively on 10 real-world anomaly detection data sets and one toy data set and it is shown that our SVR model with quantum kernel performs better than the SVR with RBF kernel as well as all other models, achieving highest mean AUC over all data sets. In addition, our QSVR outperforms the Quantum autoencoder on 9 out of 11 data sets. © 2023 IEEE.

关键词： learning systems

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BeRedy (Period Tracker & PCOS Diagnosis) 4

BeRedy (Period Tracker & PCOS Diagnosis)

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4th international conference on Communication Systems, Computing and IT applications, CSCITA 2023

作者： Karia, Ankita Poojary, Anushree Tiwari, Ashita Sequeira, Lian Sokhi, Manjot Kaur Department of Computer Engineering St. Francis Institute of Technology Mumbai India

ISBN: (纸本)9781665459877

We're on a mission to help all menstruating individuals understand their menstrual cycle and make it less of a taboo topic. Menstruating individuals often face a range of challenges related to their menstrual cycle, including difficulty keeping track of their cycles, finding appropriate sanitary products, and managing symptoms such as cramps. In addition, many individuals may feel anxious, stressed, or confused about menstruation, and may not have access to accurate information or resources to help them manage their menstrual health. BeRedy is a one-stop solution to all your menstrual problems. We plan to develop a system that solves the many issues that menstruating individuals have, through our easy-to-operate system. We believe that menstrual health is a fundamental human need. that's why we have created our system "BeRedy" so that one can identify the problems in their period cycle and check whether the symptoms are of PCOD/PCOS, if one is diagnosed with the disease our system will suggest various exercises and changes in their diet plan that they can make which will help to overcome it. Our system also recommends support groups that will help people get through the mental distress that they might go through because of PCOS/PCOD. With this system, menstruating individuals can track their period cycle and take precautionary measures like carrying sanitary napkins, and medications according to their needs, our system also gets insightful articles, and helpful tips on how to be healthy and happy with your periods with the help of our blogs, users can also share their period-experiences like their first-period experience or any other experience they have with others anonymously and read others' experiences. Another useful feature of our system is the ability to purchase menstrual products from trustworthy websites. this ensures that users are able to access high-quality products that are safe and effective. Our system also includes a chatbot that can answer common q

关键词： machine learning

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4th international conference on computer Science and Educational Informatization, CSEI 2022

4th International Conference on Computer Science and Educati...

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4th international conference on computer Science and Educational Informatization, CSEI 2022

ISBN: (纸本)9781839537882

the proceedings contain 40 papers. the topics discussed include: practice-based study on design of promoting rural culture and tourism on the basis of computer VR technology;study on application of simulation software in teaching of design on fire protection of buildings;empirical study on the satisfaction with the classroom teaching of the course of dietary nutrients and food hygiene;research on mathematical expressions matching algorithms for computer algebra systems;a summary and current analysis of research on precision teaching in China;a summary of personalized learning research;application of PBL in computer teaching of secondary vocational education;research on the prediction model of the college student performance based on the multi-attention mechanism;an empirical study of pedagogical content knowledge of middle school English teachers in Yunnan ethnic minority areas;and analysis of influencing factors of middle school students' learning based on C5.0 decision tree.

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