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19th International Conference on Software Technologies, ICSOFT 2024

作者： Yousfi, Ghania Mohellebi, Hassane Electrotechnics Department Faculty of Electrical Engineering and Computer Science Mouloud Mammeri University Tizi-Ouzou Algeria

ISBN: (纸本)9789897587061

In the present study we present a numerical modeling of a laminate magnetic material using an homogenization technique which exploit an inverse problem resolution. The laminate magnetic material is consisting of an alternate of magnetic and insulates layers. The equivalent magnetic reluctivity is computed for the homogenized domain by considering a non linear behavior of the magnetic layers. A finite element method is used to solve the 2D non-linear electromagnetic partial differential equation. An optimization problem is constructed and solved with the association of the 2D finite element resolution and a conjugate bi-gradient algorithm. The computation of the equivalent magnetic reluctivity is then performed for different excitation field value according to B-H curve. The comparison of the obtained B-H curves of the laminate and the homogenized domains to the theoretical B-H curve (experimental data) show a good agreement of laminate results. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Inverse problems

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Automatic Semantic Segmentation and Classification of Remote Sensing Image Data for Flood Detection Using Novel LSTM Neural Network

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SN computer science 2024年第8期5卷 992页

作者： Sonavale, Amruta Chakkaravarthy, Midhun Srinivasa Rao, Surampudi Salleh, Hishamuddin Bin M. Jadhav, Jagannath Department of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia Faculty of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia ECE Malla Reddy College of Engineering and Technology Hyderabad India Faculty of Electronics and Communication Engineering KLE College of Engineering and Technology Chikodi India

Floods are one of the main threats to human life and the property of natural disasters, especially in highly populated urban areas. Fast and accurate extraction of submerged area risky to supporting emergency planning and providing damage assessment in spatial and temporal measurements. Satellite multispectral images have limited bands, low resolution, and information that can be analyzed. The bands are merged to form a unified image that incorporates data from all bands. However, current contouring techniques are affected by chromatic aberrations. This study uses intensity-hue-saturation and higher-order statistics to enhance the spatial and spectral information of remote sensing images in combination with segmentation and classification methods. High performance can be shown by implementing edge detection technology to identify objects using structure in Remote Sensing Images (RSI). The parameters used to evaluate the implementation of the proposed edge detection method included root mean square error, correlation coefficient, structural similarity index measure, and the error associated with the mean spectral analysis. The dimensionality of multiband RSI can be reduced based on higher-order data statistics using independent component analysis. Furthermore, images can be clustered using high-resolution panchromatic RSI models. The proposed technique can convert the pixel powers into the sign powers of adjacent higher-order partitions during region segmentation. Pixel intensities can be analyzed using preprocessing techniques such as denoising, which gradually stabilizes the object’s mean value. This method is shown not to impact the original seed level. In this proposed work wiener filtering is *** process, the structural region is extracted from the preprocessing image. After detecting structural regions in the RSI, the different feature values are obtained for further classification. The propos

关键词： Adaptive median filtering Flood detection LSTM Neural network algorithm Structural object son the RSI Versatile Linear Edge Detection (VLED) algorithm

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Evolution and Prospects of Foundation Models: from Large Language Models to Large Multimodal Models

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computers, Materials & Continua 2024年第8期80卷 1753-1808页

作者： Zheyi Chen Liuchang Xu Hongting Zheng Luyao Chen Amr Tolba Liang Zhao Keping Yu Hailin Feng College of Mathematics and Computer Science Zhejiang A&F UniversityHangzhou311300China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Mathematics and Computer Science Department Faculty of ScienceMenofia UniversityShebin El KomMenoufia Governorate32511Egypt School of Computer Science Shenyang Aerospace UniversityShenyang110136China Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language *** modeling,crucial for AI development,has evolved from statistical to neural models over the last two ***,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training *** the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models *** advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal *** survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language ***,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI ***,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

关键词： Artificial intelligence large language models large multimodal models foundation models

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Employing Adbdconvolutional KNN, Diagnosis of Irregular Driver Behavior 11

Employing Adbdconvolutional KNN, Diagnosis of Irregular Driv...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Chandramma Manjunath, C.R. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665487320

Actual observation of unusual aggressive driving is essential for enhancing car safety. to enhance riding habits and behaviour in order to keep fatal crashes. The utilization of perception abnormal driving things that tend is becoming more and more common since it is essential to both the current level of driverless vehicles and the health of both passengers and motorists in vehicles. Recent developments in deep learning techniques, such as the impressive extension capacity of modern deep neural networks and the vast quantities of film clips necessary for fully retraining the data-driven models, may substantially help with this challenging problem. To conclude the study, new massive continuing to learn models are provided. These methods are motivated by the recently created and extensively used networked cnn model known as the Excessive Drive Control System in place (ADBD Net). © 2022 IEEE.

关键词： Deep neural networks

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Automated facial expression recognition using novel textural transformation

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9435-9449页

作者： Tuncer, Turker Dogan, Sengul Subasi, Abdulhamit Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey Institute of Biomedicine Faculty of Medicine University of Turku Turku Finland Department of Computer Science College of Engineering Effat University Jeddah21478 Saudi Arabia

Facial expressions demonstrate the important information about our emotions and show the real intentions. In this study, a novel texture transformation method using graph structures is presented for facial expression recognition. Our proposed method consists of five steps. First the face image is segmented and resized. Then the proposed graph-based texture transformation is used as feature extractor. The exemplar feature extraction is performed using the proposed deep graph texture transformation. The extracted features are concatenated to obtain one dimensional feature set. This feature set is subjected to maximum pooling and principle component analysis methods to reduce the number of features. These reduced features are fed to classifiers and we have obtained the highest classification accuracy of 97.09% and 99.25% for JAFFE and TFEID datasets respectively Moreover, we have used CK + dataset to obtain comparison results and our textural transformation based model yielded 100% classification accuracy on the CK + dataset. The proposed method has the potential to be employed for security applications like counter terrorism, day care, residential security, ATM machine and voter verification. © 2023, The Author(s).

关键词： Feature extraction

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French-Fulfulde Textless and Cascading Speech Translation: Towards a Dual Architecture 1st

French-Fulfulde Textless and Cascading Speech Translation: T...

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1st EAI International Conference on Safe, Secure, Ethical, Responsible Technologies and Emerging Applications, SAFER-TEA 2023

作者： Diane Carole, Tala Metalom Blaise Omer, Yenke Jean Louis, Fendji Kedieng Ebongue Department of Mathematics and Computer Science Faculty of Science University of Ngaoundere Ngaoundere Cameroon Department of Computer Engineering University Institute of Technology University of Ngaoundere Ngaoundere Cameroon

ISBN: (纸本)9783031563959

Speech-to-Speech translation is attracting increasing attention from researchers due to its potential of easing the communication. It can be leveraged to develop voice user interfaces for important services, such as agriculture in low-literate communities where poorly resourced languages such as Fulfulde are used. If an earlier technique, such as cascading speech, requires textual data, a recent and textless one, such as direct speech-to-speech, only considers speech corpus, playing a crucial role in considering oral languages. In this work, a general approach for a dual architecture is proposed integrating direct speech-to-speech and speech-to-speech translation using automatic speech recognition, machine translation, and text-to-speech translation. Beyond proposing an architecture, this paper focuses on an important step in cascading speech translation, namely automatic speech recognition (speech to text translation). Automatic speech recognition for the Fulfulde language, using the Kaldi toolkit, allowed obtaining an average Word Error Rate of 28.91% for S2T with the monophone acoustic model and a WER of 26.58% for S2T with the triphone acoustic model. The dataset used is based on agricultural words recorded by natives of northern Cameroon. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Agriculture

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Enhanced Form-Based SPARQL Builder: Effortless Access to Temporal Medical Data for Users Without Technology Knowledge 17

Enhanced Form-Based SPARQL Builder: Effortless Access to Tem...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Vcelak, Petr Mika, Ondrej Kryl, Martin Kleckova, Jana University of West Bohemia Faculty of Applied Sciences Department of Computer Science and Engineering Plzen Czech Republic

ISBN: (纸本)9798331507398

Accessing complex medical data, especially temporal information, presents a significant challenge for non-technical users, including healthcare professionals not versed in technology or query languages like SPARQL. This paper introduces an enhanced form-based SPARQL query builder that offers effortless access to temporal medical data. The described system simplifies constructing SPARQL queries into an intuitive, form-driven interface that abstracts and hides the technical complexities. Novel enhancement is primarily the user's ability to filter and order data with column constraints. By focusing on usability, the system empowers users to retrieve temporal medical information without needing in-depth knowledge of database systems or SPARQL syntax. We evaluate the system's usability and performance by testing it with non-technical healthcare professionals. Results demonstrate that users can easily navigate the interface, significantly reduce the time spent querying the database, and effectively retrieve data. This approach benefits from FAIR data principles and enhances data reusability in healthcare settings for reaching accuracy and precision for follow-up analysis in evidence-based research, facilitating better decision-making that can improve future patient outcomes. © 2024 IEEE.

关键词： Spatio-temporal data

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Traffic Information Extraction And Estimation In Developing Countries 9

Traffic Information Extraction And Estimation In Developing ...

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9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Minh, Quang Tran Phan, Trong Nhan Bao, Thu Le Thi Faculty of Computer Science and Engineering Department of Information Systems Ho Chi Minh city Viet Nam

ISBN: (纸本)9798331505042

This study aims to investigate and propose solutions to traffic information extraction used for state estimation based on traffic data collected from crowd-source. Concretely, a framework to traffic data collection such as voice and image/videos and the AI techniques to analyze such data to estimate traffic state will be proposed. This traffic information such as congested locations, reason caused such a traffic condition, routing suggestion will then be disseminated to commuters, which helps to eliminate traffic congestions. We have conducted evaluations using our system prototype and the results show the effectiveness of the proposed solutions revealing that the proposed approaches can be applied to real-world applications, especially in developing countries. © 2024 IEEE.

关键词： Data collection

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Blockchain-Based Trust Model for Inter-Domain Routing

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computers, Materials & Continua 2025年第3期82卷 4821-4839页

作者： Qiong Yang Li Ma Sami Ullah Shanshan Tu Hisham Alasmary Muhammad Waqas Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia School of Computing andMathematical Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerthWA 6027Australia

Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain *** research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP *** this paper,we propose a blockchain-based trust model for inter-domain *** model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the *** BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective *** incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing *** forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing *** use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust *** with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.

关键词： Inter-domain routing BGP security blockchain trust model trust mechanisms trust evaluation

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