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Development of Anomaly Detection with Variable Contexts on Refrigeration Data 28

Development of Anomaly Detection with Variable Contexts on R...

引用

28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Meyer, Melina Gergeleit, Martin Krechel, Dirk RheinMain University of Applied Sciences Dept. of Design Computer Science Media Wiesbaden Germany

ISBN: (纸本)9798350339918

Anomaly detection in time series data is a well-known research topic and of great interest to the industry. However, industrial systems are complex due to the nature of real world plants and their interrelationships in the overall system. Industrial plants often cannot be considered in isolation because their environment affects them. Thus, industrial plants have variable contexts in which they are embedded. The combination of a variable context with the information of the plant poses a challenge in modeling AI approaches for anomaly *** the field of refrigeration, machine learning approaches are still in their early stages. In live systems, simple threshold values are often still used. There are only a few published approaches to detect error situations using machine learning on such *** this work, the challenges to such a model architecture are analyzed and a dataset of real refrigeration plants is built. Then, models for detection and classification are explored and their applicability to this type of data is evaluated. Furthermore, models are combined to add context to improve the results compared to models without context information. © 2023 IEEE.

关键词： Anomaly detection

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Classification of Depression, Anxiety and Stress from Handwriting and Drawing with Stacking Models 8

Classification of Depression, Anxiety and Stress from Handwr...

引用

8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Bayrak, Semra Golgiyaz, Sedat Aykut, Murat Karadeniz Technical University Graduate School of Natural and Applied Science Dept. of Computer Engineering Trabzon61080 Turkey Bingöl University Dept. of Computer Engineering Bingöl Turkey Karadeniz Technical University Dept. of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798331540104

Recently, emotion analysis from online handwriting and drawings has become an important research topic. This study aims to determine the optimal model combination using dynamic and statistical features obtained from handwriting and drawings for detecting emotional states. The feature vectors are derived from physical (kinematic), statistical and signal processing (spectral, cepstral and frequency domain) analyses. To address the class imbalance problem, ADASYN (Adaptive Synthetic Sampling) and Tomek Links methods were applied together. In this way, the number of samples of the minority class has been increased, while over-sampled classes have been reduced. Dimensionality reduction was realized using PCA, and Gradient Boosting Classifier (GBC) feature selection. After determining the fewest and most effective features, the samples were classified using the two-level stacking ensemble method. eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), GBC, Random Forest (RF) methods are selected for base and meta models. Model and hyperparameter optimization was carried out by Optuna frameork. The performance of the models is evaluated through experiments in publicly available EMOTion recognition from HAndWriting and draWing (EMOTHAW) database. The aim was to determine the emotional states of depression, anxiety and stress with the data obtained from 7 different drawing/writing tasks of 129 people. The results obtained from accuracy, f1-score, precision and recall metrics show that the model provides remarkable performance. © 2024 IEEE.

关键词： Dimensionality reduction

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Evaluation of the Effectiveness of Vulnerable Road User Clustering in C-V2X Systems

Evaluation of the Effectiveness of Vulnerable Road User Clus...

引用

2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Rupp, Matthias Wischhof, Lars Munich University of Applied Sciences HM Dept. of Computer Science and Mathematics Munich Germany

ISBN: (纸本)9798350346473

Vulnerable Road Users (VRUs) are at special risk in road traffic. The dissemination of information about VRUs in Intelligent Transportation Systems (ITS) can help to protect VRUs from various risks. The Vulnerable Road User Awareness Message (VAM) format has been specified by ETSI as a way of distributing VRU-related information. The standard also describes a clustering mechanism that can help to reduce the number of generated messages and therefore prevent computing overload and network congestion in certain situations. This paper presents initial results on the effectiveness of the clustering approach proposed in the ETSI standard as well as its impact on position accuracy. Therefore, the clustering schemes are implemented in coupled network-and mobility simulations, modeling traffic situations with pedestrians and vehicles. In addition, the impact of one of the most relevant parameters for forming clusters, the maxClusterVelocityDifference, on clustering is assessed. The results indicate that VRU clustering can signifi-cantly reduce the number of VAM messages but also leads to a larger position error between the actual-and received position of individual VRUs. © 2023 IEEE.

关键词： Roads and streets

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The metric-aware kernel-width choice for LIME 1

The metric-aware kernel-width choice for LIME

引用

Joint 1st World Conference on eXplainable Artificial Intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC

作者： Barrera-Vicen, Aurelio Paluzo-Hidalg, Eduardo Gutiérrez-Naranjo, Miguel A. Dept. Computer Science and Artificial Intelligence University of Seville Spain Dept. Applied Mathematics I University of Seville Spain

Local Interpretable Model-Agnostic Explanations (LIME) are a well-known approach to provide local interpretability to Machine Learning models. LIME uses an exponential smoothing kernel based on the kernel width value, which defines the width of the local neighbourhood. In this paper, we study the influence of the distances for these local explanations, and we explore the choice of kernel width to guarantee a fair performance comparison between the distances. © 2023 CEUR-WS. All rights reserved.

关键词： Lime

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Asynchronous Complete Visibility Algorithm for Luminous Robots on Grid 11

Asynchronous Complete Visibility Algorithm for Luminous Robo...

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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Kim, Yonghwan Katayama, Yoshiaki Wada, Koichi Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan Hosei University Dept. of Applied Informatics Tokyo Japan

ISBN: (纸本)9798350306941

An autonomous mobile robot system is a distributed system consisting of mobile computational entities (called robots) that autonomously and repeatedly perform three operations: Look, Compute, and Move. Various problems related to autonomous mobile robots, such as gathering, pattern formation, or flocking, have been extensively studied to understand the relation between each robot's capabilities and the solvability of these problems. In this study, we focus on the complete visibility problem, which involves relocating all the robots on an infinite grid plane so that each robot is visible to every other robot. We assume that each robot is a luminous robot (i.e., has a light with a constant number of colors) and opaque (not transparent). In this paper, we propose an algorithm to achieve complete visibility (i.e., every robot can observe all the other robots) when a set of robots is given. The algorithm ensures that complete visibility is achieved even when robots operate asynchronously and have no knowledge of the total number of robots on the grid plane using only two colors. © 2023 IEEE.

关键词： Visibility

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The Use of Encryption to Preserve the Privacy and Security of Electronic Health Records 2

The Use of Encryption to Preserve the Privacy and Security o...

引用

2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Naz, Naila Samar NCBA&E Dept. of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

Cloud computing is the storage of data over the Internet rather than locally on a user's device, and it is one of the most extensively used applications of computers and related technologies in today's world. Since cloud computing became publicly available, its popularity has risen. End customers may be able to obtain a better application that is more convenient, less expensive, and both effective and efficient because of cloud computing. When storing and managing sensitive data, it is critical to select a storage and management solution that provides security and privacy safeguards. Patients have long been maintained on the cloud, but the data has always been protected by ciphertext encryption. One of the most challenging challenges in a cloud computing environment is the need to protect users' online activities as well as their personal information. The healthcare system was created with the intention of addressing the demands of medical practitioners by providing a solid foundation, simple means to obtain patient information, and efficient ways to use the system. This work describes, with implementation details, an innovative approach for establishing a reliable and effective e-health cloud model employing identity-based encryption. This approach is suggested in this article. The time, power, and energy requirements for encryption and decryption of the suggested strategy were estimated and compared to those of the currently used method. It has been demonstrated that the cryptographic approach presented can reduce the time required for data decoding by up to 50%. Because the decryption process is quicker, the cryptographic procedures may be conducted with less power. © 2023 IEEE.

关键词： Cryptography

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Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques 22

Automating Neural Model Selection in Spiking Neural Networks...

引用

22nd International Learning and Technology Conference, L and T 2025

作者： Sanaullah Honda, Hirotada Roy, Kaushik Schneider, Axel Wasmuth, Joachim Jungeblut, Thorsten University of Applied Sciences and Arts Dept. of Mathematics and Engineering Bielefeld Germany Toyo University Dept. of Information Networking Tokyo Japan A&T State University Dept. of Computer Science NC United States

ISBN: (纸本)9798331532215

The selection of optimal neural models in Spiking Neural Networks (SNNs) traditionally depends on a trial-and-error approach, which is both time-consuming and sometimes tends to suboptimal selection of the neural model. This research study explores the integration of Automated Machine Learning (AutoML) techniques into SNNs to simplify the process of designing an SNN model by automatically selecting the most optimal neural model. For example, in traditional neural networks, AutoML proves that it has the potential to significantly reduce the time and effort required to identify the best-performing architectures, by automating the selection and updating of loss functions and hyper-parametric values. To this end, we present a stand-alone framework for applying AutoML in SNNs, highlighting how this approach can improve accuracy, efficiency, and reliability in the selection process of optimal neural models, as these neural models are the core principle of designing SNN architecture. The proposed method not only accelerates the development of SNN models but also enhances their performance by systematically identifying optimal configurations that might be overlooked through manual methods. Therefore, to validate our approach, the proposed architecture was tested using different well-known benchmarks with different sets of neurons, ranging from 100 to 3000 neurons, and demonstrated state-of-the-art results in image classification tasks. © 2025 IEEE.

关键词： Neural network models

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Software Defect Predictor and Classifier Tool Using Machine Learning Techniques

Software Defect Predictor and Classifier Tool Using Machine ...

引用

2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Shankar, Sahana P. Pushp, Divyansh Makadia, Nemi Dubey, Raghav Nayak, Anusha Shashikant Chaudhari, Shilpa Ramaiah University of Applied Sciences Dept. of Computer Science and Engineering Karnataka Bangalore India M S Ramaiah Institute of Technology Dept. of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

The Software testing is critical for ensuring the quality of software, but it can consume more than half of a software project's total expenses. This research can help software development teams identify potential bugs early on, leading to efficient and effective software creation. To improve the efficiency of software testing, this report presents research on software defect prediction using a hybrid machine learning algorithm that combines supervised and unsupervised learning techniques. The study used publicly available datasets to train and evaluate various machine learning models, including an ensemble learning model. A comparative analysis was conducted among KNN, Random Forest, Adaboost, Multilayer Perceptron, CNN, and Gaussian Naïve Bayes on different datasets. Feature selection was performed using various methods, including Recurrent Feature Elimination (RFE), SelectKBest, and Correlation, implemented by a tool created to select any feature selection algorithm and compare the results for various combinations with the above mentioned classifiers. The results achieved by the model are showed in detail for each of the algorithms on the selected datasets. The main objectives of the research are to create a tool to apply various machine learning techniques to the datasets and determine the most effective ones, as well as to explore different feature selection methods for improving the accuracy of defect prediction. © 2023 IEEE.

关键词： Software testing

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Multilingual Sentiment Analysis using DistilBert

Multilingual Sentiment Analysis using DistilBert

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Gupta, Nishi Bhattarai, Mandip Chavan, Abhishek Pimpri Chinchwad College of Engineering Dept. of Applied Science and Humanities Maharastra Pune India Pimpri Chinchwad College of Engineering Dept. of Computer Engineering Maharastra Pune India

ISBN: (纸本)9798331517953

In today's virtual age, studying feelings and sentiments through textual content and emojis is essential. This research article explores a way to use natural language processing (NLP) techniques to interpret feelings from textual content and emoji content. We used the distilbert-base-uncased-finetuned-sst2english version, a small variant model of BERT, to categorize feelings as positive or negative. Our experiments confirmed that the version executed extraordinarily properly throughout more than one language, with especially great effects on Indonesian textual content. Our assessment confirmed that the version has an excellent accuracy of91.50% and an F1 rating of 91.44% for Indonesian, highlighting its wonderful overall performance in this language. It additionally plays properly in English, French, German, Arabic, and Italian, even though overall performance may also range through language. These effects reveal the version's effectiveness for duties, social media monitoring, client comments evaluation, and inventory marketplace fashion assessment. We finish with a conclusion about the results of those findings. © 2024 IEEE.

关键词： Natural language processing systems

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Anomaly Detection in Supermarket Refrigeration Systems using Transformer Models: A Comparative Study

Anomaly Detection in Supermarket Refrigeration Systems using...

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2024 Lock-in or log out? Wie digitale Souveranitat gelingt, INFORMATIK 2024

作者： Meyer, Melina Gergeleit, Martin Krechel, Dirk RheinMain University of Applied Sciences Dept. Design Computer Science Media Kurt-Schumacher-Ring 18 Wiesbaden65197 Germany

ISBN: (纸本)9783885797463

This study investigates anomaly detection methods in supermarket refrigeration systems. A transformer-based model is introduced in this field and compared with LSTM autoencoders. The models are trained and evaluated using preprocessed refrigeration data, with parameters optimized for accuracy, recall, precision and F1 score. Our findings aim to enhance system monitoring and maintenance strategies, ultimately improving reliability, energy efficiency, and operational excellence in supermarket refrigeration technology. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Retail stores

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