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Machine Learning for Energy Load Prediction and its Interpretation 11

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Machine Learning for Energy Load Prediction and its Interpre...

11th IEEE International Conference on Intelligent Systems, IS 2022

作者： Charytanowicz, Malgorzata Olwert, Anna Radziszewska, Weronika Jarnicka, Jolanta Gajowniczek, Krzysztof Zabkowski, Tomasz Brozyna, Jacek Mentel, Grzegorz Matejko, Grzegorz Research Institute Pas Center for Methods of Data Analysis Systems Warsaw Poland Research Institute Pas Department of Computer Science Systems Warsaw Poland Research Institute Pas Center for Computer Modelling Systems Warsaw Poland Warsaw University of Life Sciences Institute of Information Technology Warsaw Poland Rzeszow University of Technology Department of Quantitative Methods Rzeszów Poland Polskie Towarzystwo Cyfrowe Lublin Poland

ISBN: (纸本)9781665456562

This paper refers to real-life data on energy consumption from three companies, connected to a medium voltage grid. The companies use single-, two-, or three-zone tariffs and the general characteristic of their activity is comparable. The input dataset contains hourly electricity consumption recorded from January 2020 to December 2020. The independent variables used for the study include weather, time-dependent factors, and aggregated energy factors. We perform the prediction of consumption using three methods: naive, multiple regression, and extreme gradient boosting regression. The performance of the methods is compared based on the MSE, RMSE, MAE, and MAPE metrics. The study shows that the scores achieved for the extreme gradient boosting method are higher than for the multiple regression. To gain the interpretability of the energy load forecasting and feature analysis the Shapley additive explanations method is used for the best model. As a result, a ranking of the most important factors is created. It shows the importance of aggregated energy factors and weather conditions while placing daylight and season at the bottom of the ranking. © 2022 IEEE.

关键词： Energy utilization

VisioPhysioENet: Multimodal Engagement Detection using Visual and Physiological Signals

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arXiv 2024年

作者： Singh, Alakhsimar Verma, Nischay Goyal, Kanav Singh, Amritpal Kumar, Puneet Li, Xiaobai Computer Science and Engineering Department National Institute of Technology Jalandhar India Center for Machine Vision and Signal Analysis University of Oulu Finland The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

This paper presents VisioPhysioENet, a novel multimodal system that leverages visual and physiological signals to detect learner engagement. It employs a two-level approach for extracting both visual and physiological features. For visual feature extraction, Dlib is used to detect facial landmarks, while OpenCV provides additional estimations. The face_recognition library, built on Dlib, is used to identify the facial region of interest specifically for physiological signal extraction. Physiological signals are then extracted using the plane-orthogonal-to-skin method to assess cardiovascular activity. These features are integrated using advanced machine learning classifiers, enhancing the detection of various levels of engagement. We thoroughly tested VisioPhysioENet on the DAiSEE dataset. It achieved an accuracy of 63.09%. This shows it can better identify different levels of engagement compared to many existing methods. It performed 8.6% better than the only other model that uses both physiological and visual features. Copyright © 2024, The Authors. All rights reserved.

关键词： Physiological models

The necessity of depth for artificial neural networks to approximate certain classes of smooth and bounded functions without the curse of dimensionality

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arXiv 2023年

作者： Gonon, Lukas Graeber, Robin Jentzen, Arnulf Faculty of Natural Sciences Department of Mathematics Imperial College London United Kingdom Institute for Analysis and Numerics Faculty of Mathematics and Computer Science University of Münster Germany School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

In this article we study high-dimensional approximation capacities of shallow and deep artificial neural networks (ANNs) with the rectified linear unit (ReLU) activation. In particular, it is a key contribution of this work to reveal that for all a, b ∈ with b−a ≥ 7 we have that the functions (Formula Presented) as well as the functions (formula Presented) can neither be approximated without the curse of dimensionality by means of shallow ANNs nor insufficiently deep ANNs with ReLU activation but can be approximated without the curse of dimensionality by sufficiently deep ANNs with ReLU activation. More specifically, we prove that in the case of shallow ReLU ANNs or deep ReLU ANNs with a fixed number of hidden layers and with the size of scalar real parameters of the approximating ANNs growing at most polynomially in the dimension d ∈ N we have that the number of ANN parameters must grow at least exponentially in the dimension d ∈ N while in the case of deep ReLU ANNs with the number of hidden layers growing in the dimension d ∈ N we have that the number of scalar real parameters of the approximating ANNs grows at most polynomially in both the inverse of the prescribed approximation accuracy Ε > 0 and the dimension d ∈ , even if the absolute values of the ANN parameters are assumed to be uniformly bounded by one. We thus show that the product functions and the sine of the product functions are polynomially tractable approximation problems among the approximating class of deep ReLU ANNs with the number of hidden layers being allowed to grow in the dimension d ∈ . We establish the above outlined statement not only for the product functions and the sine of the product functions but also for other classes of target functions, in particular, for classes of uniformly globally bounded C∞-functions with compact support on any [a, b]d with a ∈ , b ∈ (a, ∞). Roughly speaking, in this work we lay open that simple approximation problems such as approximating the sine or cosine

关键词： Chemical activation

Inverse Markov Process Based Constrained Dynamic Graph Layout

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Journal of computer science & Technology 2021年第3期36卷 707-718页

作者： Shi-Ying Sheng Sheng-Tao Chen Xiao-Ju Dong Chun-Yuan Wu Xiao-Ru Yuan ParisTech Elite Institute of Technology Shanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Machine Perception(Ministry of Education) National Engineering Laboratory for Big Data Analysis and ApplicationPeking UniversityBeijing 100080China

In online dynamic graph drawing,constraints over nodes and node pairs help preserve a coherent mental map in a sequence of *** the constraints is challenging due to the requirements of both preserving mental map and satisfying the visual aesthetics of a graph *** existing algorithms basically depend on local changes but fail to do proper evaluations on the global propagation when setting *** solve this problem,we introduce a heuristic model derived from PageRank which simulates the node movement as an inverse Markov process hence to give a global analysis of the layout's change,according to which different constraints can be *** constraints,along with stress function,generate layouts maintaining spatial positions and shapes of relatively stable substructures between adjacent *** demonstrate that our method preserves both structure and position similarity to help users track graph changes visually.

关键词： graph drawing data stream dynamic graph layout

Enhancing Aspect-based Sentiment analysis with ParsBERT in Persian Language

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arXiv 2025年

作者： Ariai, Farid Mahmoudi, Maryam Tayefeh Moeini, Ali Department of Computer Engineering University of Tehran Kish International Campus Kish Iran Data Analysis & Processing Research Group IT Research Faculty ICT Research Institute Tehran Iran School of Engineering Science College of Engineering University of Tehran Tehran Iran

In the era of pervasive internet use and the dominance of social networks, researchers face significant challenges in Persian text mining including the scarcity of adequate datasets in Persian and the inefficiency of existing language models. This paper specifically tackles these challenges, aiming to amplify the efficiency of language models tailored to the Persian language. Focusing on enhancing the effectiveness of sentiment analysis, our approach employs an aspect-based methodology utilizing the ParsBERT model, augmented with a relevant lexicon. The study centers on sentiment analysis of user opinions extracted from the Persian website 'Digikala.' The experimental results not only highlight the proposed method's superior semantic capabilities but also showcase its efficiency gains with an accuracy of 88.2% and an F1 score of 61.7. The importance of enhancing language models in this context lies in their pivotal role in extracting nuanced sentiments from user-generated content, ultimately advancing the field of sentiment analysis in Persian text mining by increasing efficiency and accuracy. © 2025, CC BY.

关键词： Latent semantic analysis

Strong Multimodal Representation Learner through Cross-domain Distillation for Alzheimer’s Disease Classification

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Strong Multimodal Representation Learner through Cross-domai...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yulan Dai Beiji Zou Xiaoyan Kui Qinsong Li Wei Zhao Jun Liu Miguel Bordallo López School of Computer Science and Engineering Central South University Changsha China Big Data Institute Central South University Changsha China Department of Radiology The Second Xiangya Hospital Changsha China Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Vision-language foundational models have achieved commendable results on related tasks. However, their application to medical tasks is still limited due to issues arising from data biases. Currently, leveraging existing foundational models to improve medical tasks remains a challenge. To this end, this paper proposes a strong multimodal representation learning method based on cross-domain distillation handling structural Magnetic Resonance Imaging (sMRI), Positron Emission Computed Tomograph (PET) images, and mini-mental state examination (MMSE) score for Alzheimer’s disease (AD) classification. Specifically, we establish a text-to-image cross-domain distillation learning framework, enabling a text encoder pre-trained on general visual recognition tasks to guide the training of sMRI and PET image feature extractors. Simultaneously, positional encoding is used to extract the magnitude features of MMSE scores. Based on the multimodal representations extracted from sMRI, PET images, and MMSE scores, we perform a self-attention operation equipped with a gating mechanism for multimodal feature fusion. This mechanism controls the contribution of each modality representation to the classification decision, dynamically strengthening or weakening specific modality representations and helping construct stronger fused features for AD classification. Our method undergoes 5-fold cross-validation on the widely used ADNI dataset, and comparative experimental results demonstrate that our method achieves advanced performance in two AD-related binary classification tasks.

关键词： Representation learning Training visualization Text recognition Text to image Positrons Feature extraction Transformers Alzheimer's disease Biomedical imaging

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning

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Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

Computing and data Analytics (ICCDA), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R S. J Kavitha Cibi. A Subitha G. Sutherlin College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai India Data & Analytics / Visualization - Delivery Virtusa Consulting Services Private Limited Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector.

关键词： Measurement Instruments Zero shot learning Malware Mobile security Hazards Reliability Protection Smart phones Research and development

Dielectric and electromagnetic interference shielding properties of nano-silicon carbide/montmorillonite and graphene nanoplatelets filled polyvinylidene fluoride/poly (3,4-ethylenedioxythiophene)-block-poly(ethylene glycol) blend nanocomposites (vol 140, e53510, 2023)

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JOURNAL OF APPLIED POLYMER science 2023年第15期140卷 e53510-e53510页

作者： Thanasekaran, G. Ahamed, M. B. Deshmukh, K. Kadlec, J. Department of Physics B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai India Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Investigation (lead) Writing - original draft (lead) New Technologies - Research Center University of West Bohemia Plzeň Czech Republic Contribution: Conceptualization (lead) Formal analysis (equal) Investigation (equal) Project administration (equal) Resources (equal) Validation (equal) Writing - review & editing (equal) Contribution: Data curation (equal) Investigation (equal) Methodology (equal) Resources (equal) Visualization (equal)

Herein, hybrid polymer nanocomposite films comprising polyvinylidene fluoride (PVDF) and poly(3,4-ethylenedioxythiophene)-block-poly(ethylene glycol) (PEDOT-block-PEG) as matrices and silicon carbide nanoparticles (SiC NPs), montmorillonite (MMT) nanoclay, and graphene nanoplatelets (GNPs) as nanofillers were fabricated by solution casting. The structural, morphological, and thermal characteristics of the nanocomposite films were assessed using Fourier transform infrared spectroscopy (FTIR), x-ray diffraction (XRD), scanning electron microscope (SEM), thermogravimetric analyzer (TGA), and differential scanning calorimetry (DSC). The SEM results show the homogeneous distribution of nanofillers within the polymer matrix. The dielectric behavior and electromagnetic interference shielding efficiency (EMI SE) of the nanocomposites were also analyzed. The maximum dielectric constant (ε) and dielectric loss (tan δ) obtained are 47.77 and 12.08, respectively, at lower frequency (50 Hz, 150°C). The maximum EMI SE was observed to be 16.8 dB in the Ku-band region (12–18 GHz) for the nanocomposites with 20 wt% SiC, 8 wt% MMT, and 2 wt% GNPs loading. The EMI shielding was found to be absorption dominant and originated from the formation of a conductive network between hybrid nanofillers within the PVDF/PEDOT-block-PEG blend thereby improving the EMI SE values. These results suggest that PVDF/PEDOT-block-PEG/SiC/MMT/GNPs nanocomposites can be used as an efficient EMI shielding material.

关键词： dielectric properties EMI shielding morpholog PEDOT-block-PEG PVDF

An Overview on Machine Learning Methods for Partial Differential Equations: From Physics Informed Neural Networks to Deep Operator Learning

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arXiv 2024年

作者： Gonon, Lukas Jentzen, Arnulf Kuckuck, Benno Liang, Siyu Riekert, Adrian von Wurstemberger, Philippe Department of Mathematics Imperial College London United Kingdom School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China Applied Mathematics: Institute for Analysis and Numerics Faculty of Mathematics and Computer Science University of Münster Germany School of Mathematics and Statistics Nanjing University of Science and Technology Nanjing China School of Data Science The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China Mathematisches Institut Ludwig-Maximilians-Universität München Germany Risklab Department of Mathematics ETH Zurich Switzerland

The approximation of solutions of partial differential equations (PDEs) with numerical algorithms is a central topic in applied mathematics. For many decades, various types of methods for this purpose have been developed and extensively studied. One class of methods which has received a lot of attention in recent years are machine learning-based methods, which typically involve the training of artificial neural networks (ANNs) by means of stochastic gradient descent type optimization methods. While approximation methods for PDEs using ANNs have first been proposed in the 1990s they have only gained wide popularity in the last decade with the rise of deep learning. This article aims to provide an introduction to some of these methods and the mathematical theory on which they are based. We discuss methods such as physics-informed neural networks (PINNs) and deep BSDE methods and consider several operator learning approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Partial differential equations