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Sarcasm Detection in Libyan Arabic Dialects Using Natural Language Processing Techniques 4

Sarcasm Detection in Libyan Arabic Dialects Using Natural La...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Alakrot, Azalden Dogman, Aboagela Ammer, Fathi Yefren College of Medical Technology Dept. of Information Technology Yefren Libya Libyan Academy for Postgraduate Studies Dept. of Electrical and Computer Engineering Al Zentan Libya University of Zawia Dept. of Computer Science Azawia Libya

ISBN: (纸本)9798350372632

Sarcasm is often used to convey the opposite of what is actually said (positive words, negative meaning). It is a type of humor that relies on the listener or reader to understand the intended meaning of the words. Sarcasm can be difficult to detect in written text, especially on social media platforms where people often use abbreviated language and emojis to convey their messages. The sarcasm detection can be a crucial role in refining Automated Sentiment (SA). In order to enhance the effectiveness of data-driven approaches for customer service and product development. Furthermore, the existing research on sarcasm detection in Arabic is limited. Also, there is a noticeable absence of studies in particular, the sarcasm detection in Libyan Arabic dialects. This study utilizes a data-set of 5082 collected from Facebook platform, labeled by team annotators experienced in the Libyan dialects as (sarcastic or non-sarcastic), and extracting some lexical and syntactic features which used to training Support Vector Machine (SVM) model. The results show commendable performance with an accuracy of 79.15%, precision of 79.3%, recall of 79.7%, and an F1 score of 79.5%. © 2024 IEEE.

关键词： Support vector machines

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Evaluation of Machine Learning Models in Student Academic Performance Prediction 5

Evaluation of Machine Learning Models in Student Academic Pe...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Sandeepa, A.G.R. Mohottala, Sanka General Sir John Kotelawala Defence University Dept. of Computer Engineering Ratmalana Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Science Malabe Sri Lanka

ISBN: (纸本)9798331530983

This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations while for train set, it was 99.45%. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach. © 2025 IEEE.

关键词： Students

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Deep Equilibrium Models Meet Federated Learning 31

Deep Equilibrium Models Meet Federated Learning

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Gkillas, Alexandros Ampeliotis, Dimitris Berberidis, Kostas Dept. of Computer Engineering and Informatics University of Patras Greece Dept. of Digital Media and Communication Ionian University Greece

ISBN: (纸本)9789464593600

In this study the problem of Federated Learning (FL) is explored under a new perspective by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL, such as the communication overhead due to the sharing large models and the ability to incorporate heterogeneous edge devices with significantly different computation capabilities. Additionally, a weighted average fusion rule is proposed at the server-side of the FL framework to account for the different qualities of models from heterogeneous edge devices. To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning, contributing to the development of an efficient and effective FL framework. Finally, promising initial experimental results are presented, demonstrating the potential of this approach in addressing challenges of FL. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Deep learning

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Deep Learning-Based Approach for Detecting Copy-Move Forgery in Digital Images 27

Deep Learning-Based Approach for Detecting Copy-Move Forgery...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Dar, Navid Ahmad Atsya, Rani Kumar, Anuj Sharda University dept. of Computer Science and Engineering UP Greater Noida India Greater Noida Institute of Technology dept. of computer applications UP Greater Noida India

ISBN: (纸本)9798350392319

Copying and pasting a portion of the same image to hide another portion is the most common method of fraudulent image enhancement. Cloning a restricted portion of an image and pasting it once or more inside a comparable picture is known as copy-move image forgery (CMF). The procedure aims to highlight or hide a certain aspect in the finished picture, usually a person or an object. The harm brought about by the horrible deed frequently takes unexpected forms. For the automated identification of CMF, the paper proposes a Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) approach. The CASIA 2.0 dataset's, which includes both real and fake photos, was used in the study. The proposed model is simple to use, and its application shows that it is faster than previous approaches that are now regarded as state of the art. The results show that, when compared to the other currently available methods, the recommended approach achieves the highest level of accuracy (90.56%). © 2024 IEEE.

关键词： Generative adversarial networks

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Judging the Psychological Impact of News Articles on Rehab Patients: A Deep Learning Approach

Judging the Psychological Impact of News Articles on Rehab P...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Khan, Amit Majumdar, Dipankar Mondal, Bikromadittya Rcc Institute of Information Technology Dept. of Information Technology Kolkata India Rcc Institute of Information Technology Dept. of Computer Sc & Engineering Kolkata India B.P. Poddar Institute of Mgmt & Tech. Dept. of Computer Sc & Engineering Kolkata India

ISBN: (纸本)9798350390759

Sentiment detection and analysis from review comments of products, blogs and other social media communication is a common practice. Lot of research work have been proposed and established in the domain. While the above may prove to be effective for sales and marketing challenges but the same effort for the sentiment analysis of news articles is scarce. But surprisingly, this can definitely establish as a potentially efficient instrument for medical domain judging the suitability of a news article for individuals with disturbed health condition like depression, heart diseases or mental retardation having risk of committing suicides. To address the same, this article presents a novel approach harnessing the potential of Machine Learning (ML) and Deep Learning (DL) to detect the sentiment of a news article so as to measure its impact on the individual readers. The proposed work uses various supervised ML algorithms such as SVM, Naïve Bayes', Logistic Regression, Random Forest and Ensemble Method for assessing the inherent sentiment of news articles. Additionally, DL based Transformer model specifically BERT has also been employed to enhance the performance. Obtained results with various performance metrics clearly depict our model's excellent performance. © 2024 IEEE.

关键词： Deep learning

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Phase Fraction Calculation in Multiphase Steel Microstructures Through Semantic Segmentation 10

Phase Fraction Calculation in Multiphase Steel Microstructur...

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Swain, Bishal Ranjan Cheoi, Kyung Joo Ko, Jaepil Kumoh National Institute of Technology Dept. of Computer & Ai Convergence Engineering Gumi Korea Republic of Chungbuk National University Dept. of Computer Science Cheonju Korea Republic of

ISBN: (纸本)9798350394924

The characterization of a material is defined by its transformation kinetics which is measured using the fraction of volume transformed. In common situations, a polycrystalline parent phase is transformed into various sub-phase constituent regions based on the desired material property. Characterization of such multiphase regions include accurate identification of phase and calculation of the phase fraction. To this end, we propose using semantic segmentation to identify and calculate the phase fraction in steel microstructures. We assign each pixel in an image to accomplish both phase identification and segmentation which we perform using enhanced ELU-Net coupled with improved combined loss function. Though scalable experiments we demonstrate more accurate phase fraction calculation via superior semantic segmentation by achieving an overall 8.6% performance increase over baseline with mean phase fraction error of 5.69%. © 2024 IEEE.

关键词： Semantic Segmentation

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Short-term Wind Power Ramp Forecasting Using Sequential Approach

Short-term Wind Power Ramp Forecasting Using Sequential Appr...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Gopalakrishnan, Leechita Barrera, Julian L Cardenas Fatima, N. Sabiyath B.S. Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India University of New Brunswick Dept. of Electrical and Computer Engineering Fredericton Canada

ISBN: (纸本)9798331533311

As wind energy becomes a key player in the global transition to renewable energy, its variability presents significant challenges. Accurate wind ramp forecasting is crucial for managing such rapid fluctuations in wind power output, ensuring grid stability, and enhancing the reliability of renewable energy integration. This paper presents a short-term wind ramp forecasting model using a sequential approach: initially forecasting wind power through the integration of Variational Mode Decomposition (VMD) and the XGBoost algorithm, and subsequently detecting wind ramps using the Definition Based Sign Indicator (DSI) method. The historical wind speed and power data for Turkey was obtained from Kaggle. The dataset comprises readings from a wind turbine's SCADA system in Turkey, with measurements recorded at 10minute intervals. Variational Mode Decomposition (VMD) was applied to deconstruct and restore the historical wind power of the wind field, resulting in the formation of three training datasets. Wind power projections for the subsequent 8 hours, at 10 -minute intervals, are generated using historical wind power, wind speed, and wind direction as inputs. The DSI algorithm is employed to identify ramps. To assess the predictive capability of the proposed model, two algorithms, SVR and XGBoost, were employed for forecasting and the resulting error is minimal. The proposed model, VMD + XGBOOST is also compared without VMD to evaluate the performance. Taking mean square error (MSE), root mean square error (RMSE) mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) as evaluation indicators, the results show that the forecasted accuracy of wind power is significantly improved after VMD processing. The performance of XGBOOST is better than SVR in the two evaluation indicators. Precision, Recall, and F1-score are calculated to evaluate the accuracy of the predicted wind ramps. © 2024 IEEE.

关键词： Wind forecasting

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Enhanced Realtime Sign Language Detection and Multilingual Translation System

Enhanced Realtime Sign Language Detection and Multilingual T...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Kakkar, Madhur Daniel, John Sudha, D. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331538538

Sign language is the communication medium that is utilized by the hearing-impaired community. The medium is visual and it uses hand signs and symbols for communication. Over 300 different types of sign languages present in the world today. Sign language uses gestures and hand movements for the majority of the words and finger spellings for the rest. Nonverbal speakers face a lot of challenges while interacting with verbal speakers. Even non-verbal speakers who speak different sign languages face difficulties communicating with each other. This project proposes an enhanced real-time system which detects gestures and finger spellings of multiple sign languages, and translates them into multiple spoken languages. The proposed system aims to utilize CNN network for finger spelling recognition and Long Short-Term Memory (LSTM) network for gesture interpretation. The system will use computer vision for accurate gesture detection and Google Translator API for recognizing signs which will be translated into multiple spoken languages. Initial tests with isolated datasets indicate promising accuracy for both finger spelling and gesture recognition. The goal of the completed system is to help aid communication between verbal and non-verbal speakers and to provide an efficient mode of communication between the two communities. © 2025 IEEE.

关键词： Interactive computer systems

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Federated Digital Twin 27

Federated Digital Twin

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27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Vergara, Christian Bahsoon, Rami Theodoropoulos, Georgios Yanez, Wendy Tziritas, Nikos School of Computer Science University of Birmingham Birmingham United Kingdom Southern University of Science and Technology Dept. of Computer Science and Engineering Shenzhen China University of Thessaly Dept. of Informatics and Telecommunications Lamia Greece

ISBN: (纸本)9798350337846

Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence;and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems. © 2023 IEEE.

关键词： Metadata

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Decentralized Query Solving Educational Website 2

Decentralized Query Solving Educational Website

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2nd International Conference on informatics, ICI 2023

作者： Jadhav, Swati Bhavik, Manasi Barde, Rohan Bhosale, Vaibhavi Bura, Rishita Amune, Amruta Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350343830

With the blockchain technology's quick advancement and the rising need for the Internet's partial decentralization, the use of underlying technology built on the blockchain has drawn a lot of attention. This research paper presents the design and implementation of a decentralized query-solving educational website, leveraging blockchain technology and a stack of complementary technologies including Solidity, ThirdWeb, React, ***, MetaMask, and Hardhat. The proposed platform aims to revolutionize the traditional online learning experience by addressing challenges related to data integrity, trustworthiness of content, and query-solving efficiency. This study delve into the technical aspects of implementing a blockchain-based query-solving educational website, including the design considerations, data structure, consensus mechanisms, and privacy enhancements. The research paper highlights the technical considerations and challenges encountered during the implementation process, including the design of smart contracts, integration of ThirdWeb technologies, development of the user interface, and integration with MetaMask for secure authentication and transaction handling. © 2023 IEEE.

关键词： Smart contract

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