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5th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Zerrouki, Hayet Moussaoui, Samira Haouari, Noureddine RIIMA Laboratory University of Sciences and Technology Houari Boumedienne Department of Computer Science Algiers Algeria YouWrite Rennes France

ISBN: (纸本)9798350381450

An Internet of Vehicles (IoV) network is an evolution of Internet of Things (IoT) and VANET. In IoV, the Medium Access Control (MAC) protocol based on the standard 802.11p remains a big challenge for broadcasting safety messages. As the number of users increases, attention is given to Quality of Experience (QoE) and channel allocation. This paper proposes an efficient QoEC-based MAC scheme for IoV. The goal of this paper is to increase the network's effectiveness and the quality of service it offers. In our scheme, vehicles are organized into clusters. The cluster head is responsible for attributing slots, forwarding messages sent by their cluster members, and scheduling inter-cluster and intra-cluster communications. The efficiency of the QoEC-MAC scheme is evaluated with several simulations. The simulation results show that the proposed QoEC-MAC protocol achieves a lower collision ratio, higher throughput, and shorter delay. © 2023 IEEE.

关键词： Vehicles

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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Design of surrogate models in civil engineering by neural networks 9

Design of surrogate models in civil engineering by neural ne...

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9th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Drahy, Vojtech Marik, Radek Kalviainen, Heikki Czech Technical University in Prague Department of Computer Science Prague Czech Republic Czech Technical University in Prague Department of Telecommunication Engineering Prague Czech Republic Lappeenranta-Lahti University of Technology Lut Computer Vision and Pattern Recognition Laboratory Lappeenranta Finland

ISBN: (纸本)9798350342482

We present a task from the critical infrastructure field in materials engineering. We created a surrogate model for the bridge construction object to determine the material parameters' values. The work aims to use neural networks to conduct an initial investigation of the task and to find out the aspects of machine learning application. To reduce the computational complexity of the models, we designed specific neural networks whose architecture corresponds to the structure and characteristics of the processed data. Furthermore, we outcome also interpretability and justification of the model's decision-making. The main contribution of the work is the replacement of the unknown or too complex physical, mathematical description of material objects with a neural network model. © 2024 IEEE.

关键词： Neural network models

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Linear Time Approximation Algorithm for Column Subset Selection with Local Search 38

Linear Time Approximation Algorithm for Column Subset Select...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zou, Yuanbin Huang, Ziyun Xu, Jinhui Wang, Jianxin Feng, Qilong School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha410083 China

The Column Subset Selection (CSS) problem has been widely studied in dimensionality reduction and feature selection. The goal of the CSS problem is to output a submatrix S, consisting of k columns from an n × d input matrix A that minimizes the residual error A − SS†A2F, where S† is the Moore-Penrose inverse matrix of S. Many previous approximation algorithms have non-linear running times in both n and d, while the existing linear-time algorithms have a relatively larger approximation ratios. Additionally, the local search algorithms in existing results for solving the CSS problem are heuristic. To achieve linear running time while maintaining better approximation using a local search strategy, we propose a local search-based approximation algorithm for the CSS problem with exactly k columns selected. A key challenge in achieving linear running time with the local search strategy is how to avoid exhaustive enumerations of candidate columns for constructing swap pairs in each local search step. To address this issue, we propose a two-step mixed sampling method that reduces the number of enumerations for swap pair construction from O(dk) to k in linear time. Although the two-step mixed sampling method reduces the search space of local search strategy, bounding the residual error after swaps is a non-trivial task. To estimate the changes in residual error after swaps, we propose a matched swap pair construction method to bound the approximation loss, ensuring a constant probability of loss reduction in each local search step. In expectation, these techniques enable us to obtain the local search algorithm for the CSS problem with theoretical guarantees, where a 53(k + 1)-approximate solution can be obtained in linear running time O(ndk4 log k). Empirical experiments show that our proposed algorithm achieves better quality and time compared to previous algorithms on both small and large datasets. Moreover, it is at least 10 times faster than state-of-the-art algorithms a

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Classification and Detection of Breast Cancer Review 9

Classification and Detection of Breast Cancer Review

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9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Flayeh, Azhar Kassem Douik, Ali Thajeal, Salam University of Sousse NOCCS Laboratory Dept. National Engineering School of Sousse Sousse Tunisia University of Technology Department of Computer Science Baghdad Iraq

ISBN: (纸本)9798350350845

The Cancer is the second-leading cause of death for women aged 20-59 worldwide and very few men. Compared to other cancers, breast cancer kills more people. According to ***, 13% of American women are at risk of having this cancer, and approximately 80% of them advance, which decreases recovery and treatment success. Deep learning methods for BC detection have been successful thanks to AI. This improves early diagnosis, increasing patient survival. This publication provides a detailed review of the deep learning-based BC diagnosis literature. It aims to help practitioners and researchers understand this domain's issues and trends. This article reviews looks at deep learning methods for breast cancer detection. Next, we examine and synthesize the latest AI-based breast cancer diagnostic studies using multiple breast DL modalities. We also provide a complete overview of breast-cancer imaging datasets, emphasizing their importance in AI-driven algorithms and deep learning model training. The investigation found that the CNN is the most widely used and accurate BC diagnosis model. Also, accuracy measures are the key way to evaluate such models. To provide a complete reference for breast cancer imaging researchers from the details of the researchers works. we can say that The performance of breast cancer detection is influenced by three factors: (1) the efficacy of the CAD system, (2) the characteristics of the population under analysis, and (3) the proficiency of the radiologists utilizing the system. CAD can assist in detecting microcalcifications, which may serve as potential indicators of © 2024 IEEE.

关键词： Magnetic resonance

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DARNet: Dual Attention Refinement Network with Spatiotemporal Construction for Auditory Attention Detection 38

DARNet: Dual Attention Refinement Network with Spatiotempora...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yan, Sheng Fan, Cunhang Zhang, Hongyu Yang, Xiaoke Tao, Jianhua Lv, Zhao Anhui Province Key Laboratory of Multimodal Cognitive Computations School of Computer Science and Technology Anhui University China Department of Automation Tsinghua University China

At a cocktail party, humans exhibit an impressive ability to direct their attention. The auditory attention detection (AAD) approach seeks to identify the attended speaker by analyzing brain signals, such as EEG signals. However, current AAD algorithms overlook the spatial distribution information within EEG signals and lack the ability to capture long-range latent dependencies, limiting the model's ability to decode brain activity. To address these issues, this paper proposes a dual attention refinement network with spatiotemporal construction for AAD, named DARNet, which consists of the spatiotemporal construction module, dual attention refinement module, and feature fusion & classifier module. Specifically, the spatiotemporal construction module aims to construct more expressive spatiotemporal feature representations, by capturing the spatial distribution characteristics of EEG signals. The dual attention refinement module aims to extract different levels of temporal patterns in EEG signals and enhance the model's ability to capture long-range latent dependencies. The feature fusion & classifier module aims to aggregate temporal patterns and dependencies from different levels and obtain the final classification results. The experimental results indicate that DARNet achieved excellent classification performance, particularly under short decision windows. While maintaining excellent classification performance, DARNet significantly reduces the number of required parameters. Compared to the state-of-the-art models, DARNet reduces the parameter count by 91%. Code is available at: https://***/fchest/***. © 2024 Neural information processing systems foundation. All rights reserved.

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Enhancing photovoltaic energy forecasting:a progressive approach using wavelet packet decomposition

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Clean Energy 2024年第3期8卷 95-108页

作者： Khaled Ferkous Mawloud Guermoui Abderahmane Bellaour Tayeb boulmaiz Nadjem Bailek LMTESE Laboratory of Materials Technology of Energy Systems and EnvironmentUniversitéde Ghardaia47000Algeria Renewable Energy Applied Research Unit(URAER) Ghardaia47000Algeria Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer ScienceFaculty of Sciences and TechnologyAhmed Draia University of AdrarAdrar 01000Algeria

Accurate photovoltaic(PV)energy forecasting plays a crucial role in the efficient operation of PV power *** study presents a novel hybrid machine-learning(ML)model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour *** proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting *** evaluations demonstrate the superiority of the proposed approach over conventional ML methods,including Gaussian process regression,extreme learning machines,artificial neural networks and support vector machines,across all *** proposed model exhibits lower normalized root mean square error(nRMSE)(2.116%)and root mean square error(RMSE)(208.233 kW)values,along with a higher coefficient of determination(R^(2))of 99.881%.Furthermore,the exceptional performance of the model is maintained even when tested with various prediction ***,as the forecast horizon extends from 1.5 to 5.5 hours,the prediction accuracy decreases,evident by the increase in the RMSE(710.839 kW)and nRMSE(7.276%),and a decrease in R2(98.462%).Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior *** study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model,suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy,thereby contributing to more efficient grid management.

关键词： short photovoltaic power forecasting wavelet packet decomposition sub-series reconstruction machine learning in energy forecasting sustainable power stations renewable energy

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Multimodal Pain Assessment Βased on Physiological Biosignals: The Impact of Demographic Factors on Perception and Sensitivity 11

Multimodal Pain Assessment Βased on Physiological Biosignal...

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11th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Pavlidou, Elisavet Tsiknakis, Manolis Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Heraklion71410 Greece Computational BioMedicine Laboratory Institute of Computer Science Foundation for Research & Technology-Hellas Vassilika Vouton Heraklion70013 Greece

ISBN: (纸本)9789897587436

Pain is a multidimensional and highly personalized sensation that affects individuals’ physical and emotional state. Visual analog scales, numeric rate indicators, and various questionnaires, all relying on patient-reported outcome measurements, are considered the "gold" standard methods for assessing the severity of pain. Nevertheless, self-report tools require cognitive, linguistic, and social abilities, which may manifest variations in certain populations such as neonates, individuals with intellectual disabilities, and those affected by dementia. The purpose of this study is to automate the process through multimodal physiological-data-driven machine-learning models in order to gain deeper insights into pain sensation. We developed a pipeline using electrocardiogram (ECG), galvanic skin response (GSR), and electromyogram (EMG), along with demographic information from the BioVid dataset. The Pan & Tompkins algorithm was applied for ECG signal processing, while statistical analysis was used for feature extraction across all signals. Our study achieved 82.83% accuracy in the SVM classification task of baseline (BL) vs the highest level of pain (PA4) for females aged 20-35. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Sensory perception

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Multivariate Time Series Forecasting Using Recurrent Neural Network for a Complex System

Multivariate Time Series Forecasting Using Recurrent Neural ...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： El Abassi, Rida Idrais, Jaafar El Moudene, Yassine Sabour, Abderrahim IMI Laboratory Department of Mathematics Ibn Zohr University Agadir Morocco Department of Computer Science Ibn Zohr University Agadir Morocco

ISBN: (纸本)9783031262531

A complex system is a combination of interacting parameters, having the capacity to generate a new type of collective behavior through self-organization. This work focuses on the automatic processing of non-preprocessed multivariate time series obtained from a complex system, in particular the critical cases process of the SARS-Cov2 pandemic in Morocco. We have established a multivariate neural network which the target variable is the critical cases process, the system uses three input parameters, the active cases, the dead cases and the critical cases. We adopt the average error percentage (MAPE) to measure the performance of the model for which we found MAPE = 14.61% as the best performance depending on the tests performed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Time series

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On heterogeneity or sub-classes aspect in construction of stylometric input datasets 26

On heterogeneity or sub-classes aspect in construction of st...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Stanczyk, Urszula Baron, Grzegorz Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Stylometric analysis of texts relies on learning characteristic traits of writing styles for authors. Once these patterns are discovered, they can be compared to the ones present in other text samples, to recognise their authorship. This recognition can be compromised if input datasets are prepared without taking into consideration possible stratification of the input space, leading to specific grouping of datapoints, or sub-classes within distinguished classes. The paper shows research dedicated to construction of various structures of input datasets, and combinations of such structures between train and test sets. In the research the influence of different stratification forms on the performance of selected popular classification systems was observed. To minimise the number of influencing factors, a task of authorship attribution was performed as binary classification with balanced classes. Stylometric descriptors exploited belonged to lexical and syntactic group, giving frequencies of occurrence for chosen style-markers. It resulted in real-valued attributes and these values were explored without applying discretisation, in order to avoid the possible bias of this procedure on observations. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022)

关键词： Knowledge based systems

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