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11th International Conference on Web information systems and Technologies, WEBIST 2015

作者： Walha, Afef Ghozzi, Faiza Gargouri, Faïez Multimedia InfoRmation Systems and Advanced Computing Laboratory Sfax Tunisia Institute of Computer Science and Multimedia Sfax University Sfax Tunisia

ISBN: (纸本)9789897581069

Considered as a rich source of information, social networking sites have been created lot of buzz because people share and discuss their opinions freely. Sentiment analysis is used for knowing voice or response of crowd for products, services, organizations, individuals, events, etc. Due to their importance, people opinions are analyzed in several domains including information retrieval, semantic web, text mining. These researches define new classification techniques to assign positive or negative opinion. Decisional systems like WeBhouse, known by their data-consuming must be enriched by this kind of pertinent opinions to give better help to decision makers. Nevertheless, cleaning and transformation processes recognized by several approaches as a key of WeBhouse development, don't deal with sentiment analysis. To fulfill this gap, we propose a new analysis algorithm which determines user's sentiment score of a post shared on the social network Facebook. This algorithm analyzes user's opinion depending on opinion terms and emoticons included in his comments. This algorithm is integrated in transformation process of ETL approach.

关键词： Sentiment analysis

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24th KES International Conference on Knowledge-Based and Intelligent information and Engineering systems, KES 2020

作者： Ammar, Randa Ben Ayed, Yassine Ben Multimedia Information System and Advanced Computing Laboratory Sfax University Tunisia

Alzheimer's disease (AD) is the most leading symptom of neurodegenerative dementia;AD is defined now as one of the most costly chronic diseases. For that automatic diagnosis and control of Alzheimer's disease may have a significant effect on society along with patient well-being. Language disorder is regarded to be among the most common symptoms of AD, as a direct and natural result of cognitive impairment. Hence, the diagnosis of Alzheimer's disease using speech-based features is gaining growing attention. The aim of this study is to extract linguistic features following a proposed taxonomy of language impairment of AD patients. Obtained results indicate that the proposed taxonomy of the linguistic features extracted from the speech samples can be used to differentiate between Alzheimer's disease patients and the healthy control group. Support Vector Machine (SVM) classifier obtained classification accuracy over 90 percent. © 2020 The Authors. Published by Elsevier B.V.

关键词： Taxonomies

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Detection of semantic relationships between terms with a new statistical method

Detection of semantic relationships between terms with a new...

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10th International Conference on Web information systems and Technologies, WEBIST 2014

作者： Ksentini, Nesrine Tmar, Mohamed Gargouri, Faïez MIRACL: Multimedia InfoRmation Systems and Advanced Computing Laboratory University of Sfax Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia

ISBN: (纸本)9789897580246

Semantic relatedness between terms plays an important role in many applications, such as information retrieval, in order to disambiguate document content. This latter is generally studied among pairs of terms and is usually presented in a non-linear way. This paper presents a new statistical method for detecting relationships between terms called Least Square Mehod which defines these relations linear and between a set of terms. The evaluation of the proposed method has led to optimal results with low error rate and meaningful relationships. Experimental results show that the use of these relationships in query expansion process improves the retrieval results.

关键词： information retrieval

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Medical case-based retrieval by using a language model: MIRACL at imageCLEF 2012

Medical case-based retrieval by using a language model: MIRA...

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2012 Cross Language Evaluation Forum Conference, CLEF 2012

作者： Majdoubi, Jihen Loukil, Hatem Tmar, Mohamed Gargouri, Faiez Multimedia InfoRmation system and Advanced Computing Laboratory Higher Institute of Information Technologie and Multimedia University of sfax Tunisia

This paper reports the experiment results of the MIRACL team in participating in the medical case retrieval task of ImageCLEF 2012. In this paper, we propose our contribution for conceptual indexing of medical article... 详细信息

关键词： Indexing (of information)

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A new approach for TV program identification based on video grammar 09

A new approach for TV program identification based on video ...

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7th International Conference on Advances in Mobile computing and multimedia, MoMM2009

作者： Zlitni, Tarek Mahdi, Walid Ben Abdallah, Hanène Mir at cl Multimedia Information Systems and Advanced Computing Laboratory Higher Institute of Computer Science and Multimedia BP 3021 Sfax 69042 Tunisia

ISBN: (纸本)9781605586595

In this paper, we propose a new approach to identify programs in TV streams. In the first step of our approach, we construct a reference catalogue for video grammars of visual jingles. In the second step, we identify programs in TV streams by examining the similarity of the video signal to the visual grammars in the catalogue. After presenting our approach, we report the results of its experimental evaluation on several streams extracted from different channels and composed of several programs. Copyright 2009 ACM.

关键词： Indexing (of information)

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Evaluation of acoustic features for early diagnosis of Alzheimer disease 19th

Evaluation of acoustic features for early diagnosis of Alzhe...

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19th International Conference on Intelligent systems Design and Applications, ISDA 2019

作者： Ammar, Randa Ben Ayed, Yassine Ben Multimedia Information System and Advanced Computing Laboratory Sfax University Sfax Tunisia

ISBN: (纸本)9783030493417

Automatic diagnosis and monitoring of Alzheimer’s Disease (AD) can have a significant impact on society as well as the well-being of patients. It is known that Alzheimer’s disease (AD) influences the language abilities of affected peoples. Hence, the diagnosis of Alzheimer’s disease using speech-based features is gaining growing attention. The purpose of this article is extracting a set of acoustic features from Dementia Bank conversations from subjects with and without Alzheimer’s disease. Extracted features will be trained with Machine Learning (ML) algorithms to testing the detection accuracy. Obtained results indicate that the proposed features extracted from the speech samples can be used to distinguish between the patients with Alzheimer’s disease and the healthy control group. Classification accuracy over 90% was obtained with Support Vector Machine (SVM) classifier. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Diagnosis

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A Deep Neural Network Model for Predicting User Behavior on Facebook

A Deep Neural Network Model for Predicting User Behavior on ...

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2019 International Joint Conference on Neural Networks, IJCNN 2019

作者： Ameur, Hanen Jamoussi, Salma Hamadou, Abdelmajid Ben Multimedia InfoRmation System and Advanced Computing Laboratory University of Sfax Sfax Tunisia

ISBN: (纸本)9781728119854

On Facebook, the social media site, liking, commenting and sharing the posts or status of other users are usually considered to be the key mechanisms for exchanging opinions about different topics. Due to the non-availability of data and security constraints, only few research studies have analyzed such behavior. In this paper, we introduced a novel deep neural network model for user behavior prediction (like and comment). We presented an embedding representation method for the textual content of comments and posts based on the contextual recursive auto-encoders model. The users were represented using a deep joint auto-encoders model to fuse the users' like and comment information, and train the users' combined embedding representation. Then, the user behaviors towards a given post were embedded into the same feature space of users and posts, using the joint auto-encoders model. Thereafter, we used a fully connected layer for behavior prediction. To train and evaluate the effectiveness of the proposed method, we also constructed a large dataset collected from Facebook. The experimental results show that the proposed method could achieve better results than the previous alternative methods. © 2019 IEEE.

关键词： Embeddings

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Understanding Learner Engagement in a Virtual Learning Environment 18th

Understanding Learner Engagement in a Virtual Learning Envir...

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Joint Conferences on 18th International Conference on Intelligent systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired computing , NaBIC 2018

作者： Hlioui, Fedia Aloui, Nadia Gargouri, Faiez Multimedia Information System and Advanced Computing Laboratory University of Sfax Sfax Tunisia

ISBN: (纸本)9783030166595

The past few years has seen the rapid growth of educational data mining approaches for the analysis of data obtained from the virtual learning environments (VLE). However, due to the open and online characteristics of VLEs, vast majority of learners may enroll and drop a course freely, resulting in high dropout rates problem. One of the key elements in reducing dropout rates is the accurate and prompt identification of learners’ engagement level and providing individualized assistance. In this respect, this paper proposes a survival modeling technique to study various factors’ impact on attrition over the Open University in UK. We aim to perceive the learning from a psychological engagement perspective, which is necessary to gain a better understanding of learner motivation and subsequent knowledge and skill acquisition. In this way, we provide an innovative process that may help the tutor to interfere with weak learner at the appropriate time, such as dialog prompts, or learning resources to enhance the learning efficiency. It can help developers to evaluate the VLE effectively and expand system function for future development trend. © 2020, Springer Nature Switzerland AG.

关键词： Data mining

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Improving Speech Emotion Recognition Using Data Augmentation and Balancing Techniques 22

Improving Speech Emotion Recognition Using Data Augmentation...

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22nd International Conference on Cyberworlds, CW 2023

作者： Barhoumi, Chawki Ayed, Yassine Ben National School of Electronics and Telecommunications of Sfax Stax Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory Sfax Tunisia

ISBN: (纸本)9798350315653

Speech Emotion Recognition (SER) is a challenging task due to the complexity and variability of human emotions. In this paper, we propose an innovative approach to improve SER performance on the EMODB dataset. Our approach employs data augmentation techniques, such as noise addition and spectrogram shift, as well as balancing techniques, including random oversampling. We also extract five different features from the dataset samples: MFCC, Chroma, Mel Spectrogram, ZCR, and RMS. We compare the performance of four different classifiers MLP, SVM, KNN, and CNN with and without the use of our proposed approach. Our results demonstrate that the proposed approach significantly enhances the accuracy of speech emotion recognition compared to the approach without data augmentation and balancing techniques. Our experiments reveal that the proposed approach achieves higher accuracy and F1-score compared to other approaches, with MLP and CNN models achieving 100% accuracy. These findings highlight the effectiveness of data augmentation and balancing techniques in improving the performance of speech emotion recognition. Moreover, our approach holds great potential for application in various real-life scenarios, including mental health monitoring, human-robot interaction, and speech-based virtual assistants. © 2023 IEEE.

关键词： Support vector machines

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computing Power in the Sky: Digital Twin-assisted Collaborative computing with Multi-UAV Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

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