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检索条件"任意字段=Scientific Conference on Mechatronics Engineering and Computer Science"

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Multi-Modal Emotion Recognition Network with Balanced Audio-Visual Feature Extraction 5

Multi-Modal Emotion Recognition Network with Balanced Audio-...

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5th International conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Chen, Zeyu Wu, Yiming Cao, Ronghui Hunan University College of Computer Science and Electronic Engineering Changsha China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798331528911

Video and audio are ways for humans to perceive the world beyond language. It is meant to enable robots to imitate and recognize human emotional expressions. However, most current audio-visual emotion analysis models tend to extract deep features from only one modality. In contrast, the other modality plays a supporting role and cannot fully extract deep features from both modalities. This article proposes a balanced audio-visual emotion analysis model. Specifically, EfficientNet and Wav2vec 2.0 are used for visual and auditory modality feature extraction, respectively, ensuring that indepth features can be extracted from both modalities. Secondly, we use Transformer as the decision-level fusion operator, exchanging information between the two modalities. We verified our model on the RAVDESS dataset and achieved a Top1 accuracy of 88.54%, surpassing audio-visual emotion analysis models of the auxiliary type. © 2024 IEEE.

关键词： Emotion Recognition

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Three Languages Simulate Polygons and Perform on the Web Python, Fortran, C, with PHP and 'gnuplot' 5th

Three Languages Simulate Polygons and Perform on the Web Pyt...

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5th International conference on Soft Computing and its engineering Applications (IcSoftComp)

作者： Casquilho, Miguel Pacheco, Pedro Univ Lisbon Dept Chem Engn IST Lisbon Portugal CERENA Ctr Nat Resources & Environm Lisbon Portugal Univ Lisbon Dept Comp Sci & Engn IST Lisbon Portugal

ISBN: (纸本)9783031537271;9783031537288

On a web page, we display, resorting to the Monte Carlo method, the statistical behavior of the perimeter and area of random polygons inscribed in a circle. We obtain three outcomes: those variables behave surprisingly, mainly the perimeter;three languages run at very different speeds, which is expected, but notorious;and the web page is an example of what can be simply done on the Web. The web page is of free access to a user through the use of a browser, thus without any software installation and necessitating no particular power or matching operating system. This is an illustrative example making it possible to ready adaptation to a number of other problems. It combines the Web and convenient computer languages, to wit: PHP, Python, Fortran, and C, and the 'gnuplot' utility for plotting. The study has as objectives: to observe the said behaviors;to serve as a model to solve other problems;and, more generally, to call attention both to the Web as a medium for scientific computing, and to the adequacy of this in scientific publications. This study recommends computing in a web-based style, in general, which can use executable programs mostly similar to classical computing, these being the inevitable difficulty. This scarcely explored approach is promptly accessed here, and invites the cooperation of academia and industry in our technological era.

关键词： Web-based computing random generated polygons computing in science and Technology engineering education Statistical applications

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques 7

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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7th International conference on Inventive Computation Technologies, ICICT 2024

作者： Anjaneya, L.H. Maheswari, K. Revathi, N. Siva, K.P. Soudagar, Manzoore Elahi M. Dhanraj, Joshuva Arockia Bapuji Institute of Engineering And Technology Department of Electrical And Electronics Engineering Karnataka Davangere577004 India Cmr Technical Campus Department of Computer Science And Engineering Hyderabad Telangana Kandlakoya501401 India Panimalar Engineering College Department of Electronics And Communication Engineering Tamil Nadu Chennai600123 India Sri Eshwar College of Engineering Department of Computer Science And Business Systems Tamil Nadu Coimbatore641202 India Saveetha Institute of Medical And Techinical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Department of Mechatronics Engineering Chennai Tamil Nadu Padur603103 India

ISBN: (纸本)9798350359299

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score. © 2024 IEEE.

关键词： Genetic algorithms

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Enhancing MPI Communication Efficiency for Grid-Based Stencil Computations

Enhancing MPI Communication Efficiency for Grid-Based Stenci...

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2024 IEEE International conference on Computing, Semiconductor, mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Goudar, Santosh Iranagouda Nayaka, Premkumar Sj Girish, K.K. Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Lab Department of Computer Science and Engineering SurathkalBharat Mangalore India

ISBN: (纸本)9798331517892

In parallel computing, where efficiency and speed are crucial, the Message Passing Interface (MPI) is a fundamental paradigm for managing large-scale distributed memory systems. MPI is critical to complex computational tasks, particularly in grid-based computations that solve intricate numerical problems by discretizing spatial domains into structured grids. However, MPI Cartesian communicators exhibit limitations in handling these computations effectively, especially when managing large-scale data exchanges and complex stencil patterns. This paper addresses these challenges by presenting an integrated approach that combines MPI collective and Cartesian communication methods. The proposed solution simplifies data distribution, eliminates redundant interfaces, and enhances communication efficiency. Experimental results show a 43% reduction in execution time and a 40% decrease in communication overhead, with scalability improvements achieving 12.5x speedup using 64 processes. These quantitative outcomes demonstrate the advan-tages of the proposed method over conventional MPI Cartesian approaches, establishing it as a reliable framework for advancing High-Performance Computing (HPC) capabilities in grid-based applications. © 2024 IEEE.

关键词： Message passing

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Rigorous engineering of Collective Adaptive Systems Introduction to the 5^th Track Edition 12th

Rigorous Engineering of Collective Adaptive Systems Introduc...

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12th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation

作者： Wirsing, Martin De Nicola, Rocco Jaehnichen, Stefan Tribastone, Mirco Ludwig Maximilians Univ Munchen Munich Germany IMT Sch Adv Studies Lucca Lucca Italy TU Berlin Berlin Germany FZI Forschungszentrum Informat Berlin Berlin Germany

ISBN: (纸本)9783031751066;9783031751073

A collective adaptive system is made up of cooperating entities that can adjust in real time to evolving, open environments and shifting requirements. To ensure such a system meets its intended goals, rigorous engineering must employ suitable methods and tools. This introduction offers a short overview of the 5(th) edition of the track "Rigorous engineering of Collective Adaptive Systems" and briefly presents the 20 scientific contributions, organised into seven thematic sections. Large Ensembles and Collective Dynamics, Knowledge, Consciousness and Emergence, Automated Reasoning for Better Interaction, Modelling and engineering Collective Adaptive Systems, Analysing Collective Adaptive Systems.

关键词： adaptive system collective system ensemble software engineering formal method rigorous method machine learning large language model

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Continuous Tweet Stream Analysis using Apache Spark Streaming API & Kafka 15

Continuous Tweet Stream Analysis using Apache Spark Streamin...

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15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Nisha, A.G. Shivaprasad, N. Nischitha, C.B. Shashikala, B.M. Sujithra, M. Chaba, Yogesh Electronics & Communications Engineering JSS Science & Technology University Mysuru India Dept.of Electronics & Communications Engineering Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Dept.of Computer Science St.Joseph's First Grade College Mysore India Dept.of Computer Applications Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Social media has become a trend nowadays people are spending more time in social media platform like Facebook, Twitter, Instagram to share their opinions, creating new interests, ideas and generate *** provide a major platform for collecting information which helps us to analyze them for our *** such microblogging social network is twitter which allows information collaboration and quick information sharing that provides relief from rising *** has several millions of people tweeting and expressing their perspectives on a due to its distinctively brief and straightforward expressing *** enormous volume of raw data from tweets can be utilised for analysis and processing based on industrial or business *** this technical paper, the application of tweet sentiment analysis by extracting a vast number of tweets using continuous tweet stream provided by Spark and Kafka has been *** system fetches live tweets from Twitter based on the search criteria, process the tweets, by using text mining techniques and classify them based on different labels. © Grenze scientific Society, 2024.

关键词： Tweets

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Smart Trolley: A Smart Solution for Theft Prevention and Social Distancing in Shopping 7

Smart Trolley: A Smart Solution for Theft Prevention and Soc...

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7th International conference on Inventive Computation Technologies, ICICT 2024

作者： Shukla, Deepika Shah, Monika Institute of Technology Nirma University Department of Computer Science And Engineering Gujarat India

ISBN: (纸本)9798350359299

This work presents a novel smart shopping trolley integrating proximity sensors and load cells to address post-COVID-19 public health concerns. By providing real-time social distancing alerts and automatic billing, it enhances efficiency while minimizing contact points. Noteworthy features include scenario-tested distancing alerts and sensitivity to unique situations such as the presence of child in the trolley. This interdisciplinary effort merges insights from epidemiology, technology, and public health, emphasizing the transformative potential of scientific innovation. By fostering safer and more efficient public spaces, this smart trolley demonstrates the critical role of scientific advancements in safeguarding public health. © 2024 IEEE.

关键词： Crime

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An Industrial IoT Driven Approach for Sewage Gas Identification and Reporting System using Intelligent Sensors with Logical Controller 1

An Industrial IoT Driven Approach for Sewage Gas Identificat...

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1st International conference on Innovative Sustainable Technologies for Energy, mechatronics and Smart Systems, ISTEMS 2024

作者： Rajasubha, J. Ramya, R. Juliet, A. Hency Janakiraman, V. Devi, S. Rukmani Karuppaiah, S. Dhanalakshmi College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Data Analytics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Based on a real-time sewage gas level detection and monitoring system, this system seeks to offer intelligent solutions for the monitoring of toxic sewage gases. An alarm is issued to the observer who is looking at the circumstances from a distance whenever a specific threshold is crossed. Following this, the data is transmitted together with different gas ppm levels, indicating if the worker can clean or operate securely in that region. The suggested solution incorporates both the IoT platform and the remotely located IoT monitoring devices. This necessitates finding the appropriate threshold values for septic plants and facilities, as well as calibrating gas sensors for use in industry. The gear is meant to provide the sewage worker advance notification whenever the concentration of toxic gaseous substances grows over time, ensuring their safety. The presence of gas, temperature, and other factors in sewage may be detected using a variety of sensors. By notifying sewage workers as well as cleaners via SMS whenever the threshold value drops below the observed levels, this technology helps save people from harmful infections, which is a social good. A number of harmful gas concentrations are graphed out for real-time monitoring, and this is how it accomplishes its job. In order to assess the efficacy of the suggested model, this paper cross-validates it with the Conventional Sewage Gas Detection (CSGD) based on GPRS functioning. The goal of the cross-validation is to create an IoT based Sewage Gas Detection (IoTSGD) system that can monitor and analyze toxic gas levels in real-time and notify workers. Firebase receives the measured gas levels. If the levels rise over a certain point, it will notify the authorized individuals working remotely through their linked mobile devices. An SMS message can alert the authorities if the sewage system is likely to overflow. For the purpose of monitoring, an android app was created and connected with the planned system. © 2024 IEE

关键词： Internet of things

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A Framework for Ontology-Based engineering Systems: Advances and Open Questions About Knowledge Capture and Use in Aerospace Manufacturing 5th

A Framework for Ontology-Based Engineering Systems: Advances...

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5th International conference on Innovative Intelligent Industrial Production and Logistics

作者： Mas, Fernando Arista, Rebeca Skrzek, Murillo Oliva, Manuel Morales-Palma, Domingo Szejka, Anderson Luis Univ Seville Seville Spain Airbus SAS Toulouse France Pontif Catholic Univ Parana PUCPR Curitiba Parana Brazil Airbus Def & Space Seville Spain CT Engn Grp Madrid Spain

ISBN: (纸本)9783031807749;9783031807756

Modern Ontology-Based engineering (OBE) systems, built over the foundations of Knowledge-Based engineering (KBE), leverage ontology-based technologies and the semantic web to capture and formalise knowledge holistically. This comprehensive knowledge repository can subsequently feed design and decision support tools with robust generative features. However, the integration and reuse of knowledge poses challenges for which it is necessary to develop appropriate methods and tools capable of handling diverse types of knowledge from both human experience and databases. The objective of this paper is to discuss the current limitations of OBE systems to address knowledge integration and reuse, based on the research and experiences of the authors in the aerospace manufacturing industry. This aims to provide a set of Research Questions (RQ) and the author's position to them, to serve as future research work and topics of discussion for the scientific community.

关键词： Ontology-Based engineering (OBE) Knowledge-Based engineering (KBE) Models for Manufacturing (MfM) Ontologies

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Enhancing Driver Injury Severity Prediction Using Optimized Oversampling and Feature Selection Techniques 9

Enhancing Driver Injury Severity Prediction Using Optimized ...

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9th International conference on computer science and engineering, UBMK 2024

作者： Kiliç, Vahide Nida Mutlu, Gizen Saraç, Esra Aci, Çiǧdem Özen, Murat Adana Alparslan Türkes Science and Technology University Adana Turkey Mersin University Mersin Turkey Mersin University Mersin Turkey

ISBN: (纸本)9798350365887

Globally, it is widely acknowledged that driver errors are responsible for approximately 90% of traffic accidents. Identifying the potential risk factors affecting driver injuries' severity is crucial for informing better decision-making and selecting more effective countermeasures. This study uses a new and extensive dataset to predict driver injury severity. We employ the SMOTE-ENN technique to address the class imbalance accident datasets, which simultaneously applies both oversampling and undersampling to create a balanced and robust dataset. Additionally, significant features are selected using Relief-F, Extra Tree, and Recursive Feature Elimination methods. Driver injury severities are then classified using three different machine learning algorithms. The results demonstrate that the Random Forest algorithm achieved an F1 score of 93% using a reduced set of features. © 2024 IEEE.

关键词： Machine learning

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