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检索条件"任意字段=2024 IEEE/ACS 21st International Conference on Computer Systems and Applications, AICCSA 2024"

共 150 条记录，以下是11-20 订阅

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Classification of Cancer Pathology Reports Using Rule-Based Approaches: A Review 21

Classification of Cancer Pathology Reports Using Rule-Based ...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Ouchene, Hiba Mezzi, Melyara Oukid, Lamia Benblidia, Nadjia Bentayeb, Fadila LRDSI Laboratory Department of Computer Sciences Faculty of Sciences University Blida 1 B.P 270 Route de Soumaa Blida Algeria ERIC Laboratory University of Lyon Lyon 2 Bron France

ISBN: (纸本)9798331518240

Cancer pathology reports are integral to cancer diagnosis and treatment. The growing volume of these reports necessitates automated classification, prompting a shift towards rule-based Natural Language Processing (NLP) systems due to their capacity for more contextualized processing. This review examines these rule-based approaches, highlighting the key features extracted from the pathology reports, and evaluating the benefits they offer. Despite certain limitations, rule-based methods demonstrate considerable promise in the classification of cancer pathology reports. We conclude by identifying future research opportunities aimed at addressing these limitations to further enhance the benefits of the classification process. © 2024 ieee.

关键词： Lung cancer

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Enhancing SimpLex, a Lexical Simplification Architecture 21

Enhancing SimpLex, a Lexical Simplification Architecture

引用

2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Camara, Mohamed Ellouze, Samira Jaoua, Maher Gargouri, Bilel University of Sfax Miracl Faculty of Economics and Management Dept. of Computer Science Sfax Tunisia

ISBN: (纸本)9798331518240

Lexical simplification (LS) is the process of replacing complex words in a sentence with simpler alternatives to improve readability and understandability. However, LS is a challenging task because the target population may have varying literacy levels, resulting in different needs. This study aims to enhance the SimpLex architecture, a LS framework proposed in the literature. Several techniques have been explored to achieve this goal. First, we added new features and trained two additional machine learning models: LightGBM and XGBoost. Additionally, multiple transformer models were examined, including DistilBERT, RoBERTa Large, Facebook BART, and Microsoft DeBERTa. The results demonstrated the effectiveness of LightGBM and XGBoost in predicting complex words. Furthermore, DistilBERT and RoBERTa Large achieved higher SARI scores and lower perplexity compared to the other pre-Trained models, except BERT, which had a slightly lower perplexity than the aforementioned models. © 2024 ieee.

关键词： Adversarial machine learning

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Are my Answers Medically Accurate? Answers Reliability Regarding Questions over Healthcare Knowledge Graphs 21

Are my Answers Medically Accurate? Answers Reliability Regar...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Baazouzi, Wiem Kachroudi, Marouen Faiz, Sami National School of Computer Science University of Manouba RIADI Laboratory Manouba2010 Tunisia ESPRIT School of Engineering El Ghazala2080 Tunisia Université de Tunis El Manar Faculté des Sciences de Tunis Informatique Programmation Algorithmique et Heuristique LR11ES14 Tunis2092 Tunisia Université de Tunis El Manar Ecole Nationale d'Ingénieurs de Tunis Laboratoire de Teledetection et Systemes d'Information A Reference Spatiale 99/UR/11-11 Tunis2092 Tunisia

ISBN: (纸本)9798331518240

In recent years, there has been a growing interest in the digitalization of medical records and health data. This makes it possible to build sophisticated systems that facilitate rapid data retrieval, processing and analysis. An example of such systems is Question-Answer ones. It can help domain experts efficiently retrieve the required data by asking questions in natural language. Question-Answering systems based on Knowledge Graphs have found applications in various fields. Our work aims to build a question-Answering chatbot for electronic medical records. Thus, healthcare professionals can ask questions in natural language, and the chatbot will iteratively build and execute appropriate SPARQL queries over a given Knowledge Graph containing digital medical records to return relevant data. © 2024 ieee.

关键词： Electronic health record

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Improving User Experience for AI Chatbots through LLM Models 21

Improving User Experience for AI Chatbots through LLM Models

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Khennouche, Feriel Elmir, Youssef Djebari, Nabil Boubchir, Larbi École Supérieure en Sciences et Technologies de l'Informatique et du Numérique Laboratoire LITAN Amizour Bejaia06300 Algeria SGRE-lab Bechar Algeria Targa Ouzemour Bejaia06000 Algeria University of Paris 8 LIASD Research Laboratory Saint-Denis93526 France

ISBN: (纸本)9798331518240

This paper introduces an innovative strategy for enhancing the user experience via an AI-powered FAQ chatbot for the Ecole Supérieure en Sciences et Technologies de l'Informatique et du Numérique (EstIN). By combining multiple Large Language Models (LLMs) such as BART, GPT, and BERT, the standard static FAQ system is converted into a dynamic, interactive interface. Our hybrid approach aims to expose the unique strengths of each model through a multi-phase training process. Initial experimentations have revealed that BERT outperforms other models in handling EstIN's FAQ dataset, providing better context comprehension and response relevance. We intend to additionally refine the chatbot by studying the effect of different model training sequences. The expected outcome is a scalable and adaptable chatbot that significantly improves user engagement. © 2024 ieee.

关键词： computer simulation languages

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Utilizing Multi-step Loss for Single Image Reflection Removal 21

Utilizing Multi-Step Loss for Single Image Reflection Remova...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Elnenaey, Abdelrahman Torki, Marwan Alexandria University Faculty of Engineering Department of Computer and Systems Engineering Alexandria Egypt Knights Lab Egypt

ISBN: (纸本)9798331518240

Image reflection removal is crucial for restoring image quality. Distorted images can negatively impact tasks like object detection and image segmentation. In this paper, we present a novel approach for image reflection removal using a single image. Instead of focusing on model architecture, we introduce a new training technique that can be generalized to image-to-image problems, with input and output being similar in nature. This technique is embodied in our multi-step loss mechanism, which has proven effective in the reflection removal task. Additionally, we address the scarcity of reflection removal training data by synthesizing a high-quality, non-linear synthetic dataset called RefGAN using Pix2Pix GAN. This dataset significantly enhances the model's ability to learn better patterns for reflection removal. We also utilize a ranged depth map, extracted from the depth estimation of the ambient image, as an auxiliary feature, leveraging its property of lacking depth estimations for reflections. Our approach demonstrates superior performance on the SIR2 benchmark and other real-world datasets, proving its effectiveness by out-performing other state-of-the-art models. © 2024 ieee.

关键词： Image denoising

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Lectures on AI in Healthcare, an Interdisciplinary Learning Approach 21

Lectures on AI in Healthcare, an Interdisciplinary Learning ...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Jagusch, Benjamin Hensel, Simon Lux, Thomas Niederrhein University of Applied Sciences Faculty of Health Care Krefeld Germany Niederrhein University of Applied Sciences Faculty of Computer Sciences Krefeld Germany

ISBN: (纸本)9798331518240

Teaching students about Artificial Intelligence (AI) in healthcare presents significant challenges for both educators and students. This study examines the lecture AI in Healthcare that was conducted at the Niederrhein University of Applied Sciences as part of the master's programme Health Care. The lecture is intended for students from both the Faculty of Health Care and the Faculty of computer Sciences. It aims to provide them with an understanding of the fundamental concepts of AI and the practical usage in healthcare through exercises and interdisciplinary group work. Despite the positive feedback on the relevance of AI and ML, students identified several challenges. These challenges include the lack of fundamental knowledge about healthcare specific datasets, fundamental AI concepts, and also difficulties in the context of the interdisciplinary collaboration. These issues were provided by the implementation of a Teaching Analysis Poll (TAP) halfway through the lecture. This study shows the necessity for preparatory courses and the provision of resources. This could bridge knowledge gaps and enhance the practical relevance of the lecture within the curriculum. Additionally, the use of visual programming tools like Orange Data Mining proved beneficial, especially for nontechnical students. The findings suggest, that by addressing these gaps and refining instructional methods, the learning experience can be improved and the students can be better prepared for the complexities of the healthcare industry. © 2024 ieee.

关键词： students

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Malicious Intent Detection Framework for Social Networks 21

Malicious Intent Detection Framework for Social Networks

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Fausak, Andrew R. Tunc, Cihan Fausak, Andrew T. Computer Science & Engineering University of North Texas DentonTX United States

ISBN: (纸本)9798331518240

Many people have online social accounts (OSAs) in online communities (OCs) such as Facebook (Meta), Twitter (X), Instagram (Meta), Mastodon, and Nostr. OCs enable quick and easy interaction with friends, family, and even online communities to share information about. However, there is also a dark side to OCs, where users with malicious intent join OC platforms with the purpose of criminal activities such as spreading fake news/information, cyberbullying, propaganda, phishing, stealing, and unjust enrichment. These criminal activities are especially concerning when harming minors. Detection and mitigation are needed to protect and help OCs by detecting, stopping, and preventing such criminals from harming others. To answer this challenge, we propose the first steps of a framework for analyzing and identifying malicious intent in OCs, which we refer to as the malicious intent detection framework (MIDF). MIDF is an extensible proof-of-concept that uses machine learning techniques to enable detection and mitigation. The framework will first be used to detect malicious users using only relationships. Then it can be leveraged to create a suite of malicious intent vector detection models, including phishing, propaganda, scams, cyberbullying, racism, spam, and bots for open-source online social networks, such as Mastodon, and Nostr, © 2024 ieee.

关键词： Phishing

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Prediction of High Heating Value Using ANN Technique Aftermath Natural Disasters 21

Prediction of High Heating Value Using ANN Technique Afterma...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Thakur, Disha Kumar, Sanjay Sharma, Bhisham Dhaou, Imed Ben UIT Himachal Pradesh University Department of Civil Engineering Shimla India UIT Himachal Pradesh University Department of Electrical Engineering Shimla India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Centre for Research Impact & Outcome Punjab 140401 India Hekma School of Engineering Computing and Design Dar Al-Hekma University Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798331518240

Natural disasters, particularly earthquakes and floods, can generate substantial waste, presenting challenges for waste management and energy recovery. This study explores the application of machine learning, specifically Artificial Neural Networks (ANN), to optimize Waste-to-Energy (WTE) conversion processes for disaster-generated Municipal Solid Waste (MSW). Using data from Sundernagar City, India, we constructed and evaluated multiple ANN models (collectively named ANNSNAGAR) to estimate High Heating Value, HHV, based on ultimate/elemental analysis of MSW. The models were rigorously assessed using statistical error metrics. Our best-performing network, with a 9-50-1 architecture (ANNMS), achieved a mean absolute percentage error (MAPE) of 1.47%, demonstrating high predictive accuracy. © 2024 ieee.

关键词： Artificial Neural Network Energy Energy Content Machine learning Municipal Solid Waste

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Leveraging Service Supply Dynamics in Senselife: Building an Explainable Recommender System for Tailored Frailty Prevention 21

Leveraging Service Supply Dynamics in Senselife: Building an...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Frikha, Ghassen Lorca, Xavier Pingaud, Herve Taweel, Adel Bortolaso, Christophe Borgiel, Katarzyna Lamine, Elyes IMT Mines Albi Industrial Engineering Center University of Toulouse Albi France Champollion National University Institute University of Toulouse CNRS-LGC Albi France Birzeit University Department of Computer Science Birzeit Palestine Research and Innovation Division Berger-Levrault Labège France ISIS Champollion National University Institute University of Toulouse Castres France

ISBN: (纸本)9798331518240

The growing elderly population in developed countries highlights the critical need for preventing frailty, which poses significant challenges to health systems due to increased risks of severe health issues. This paper introduces Senselife, a framework that provides explainable service recommendations specifically tailored for frailty prevention. It begins by outlining the medical context and challenges associated with aging, followed by an overview of existing recommender systems with similar objectives. We detail the integration of three key resources-ROR, RNA, and Data Laregion-within the Senselife framework to represent service supply. The paper explains how service supply is structured and the transformation of available data for use within our recommender engine. We introduce the concept of operational activities derived from the ROR and leverage the capabilities of LLMs to incorporate RNA data into Senselife. Additionally, we illustrate how these services are ultimately compiled into recommended service packages. Finally, the paper concludes by summarizing key findings and suggesting potential directions for future research. © 2024 ieee.

关键词： Electronic health record

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Enhancing Four-Class Motor Imagery Detection Through Advanced Feature Extraction Techniques 21

Enhancing Four-Class Motor Imagery Detection Through Advance...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Souissi, Jihen Karmani, Sourour Belwafi, Kais Djemal, Ridha National Engineering School of Sfax Sfax University ATMS Laboratory ENIS Tunisia Higher Institute of Applied Sciences and Technology of Sousse University of Sousse University of Monastir Tunisia Laboratory of Electronics and Microelectronics FSM Tunisia University of Sharjah Computer Engineering Department CCI Sharjah United Arab Emirates Sfax University National Engineering School of Sfax ATMS Lab. Electrical Engineering Department United Arab Emirates

ISBN: (纸本)9798331518240

Brain-computer interface (BCI) technology has great potential in control, communication, and neurological diagnostics by interpreting EEG signals. This paper introduces a novel method for extracting distinguishing features from EEG signals using a Common Spatial Patterns (CSP) model, enhanced with a Phase Space Reconstruction model and followed by an autoregressive (AR) model. Initially, EEG signals are denoised using an IIR Butterworth filter before feature extraction. Two classifiers, Linear Discriminant Analysis (LDA) and Multi-Layer Perceptron (MLP), are employed to differentiate features across four motor imagery (MI) tasks. For a four-class MI task, combining spatial and temporal-frequency domain features, LDA achieves an accuracy of 67.40%, while MLP reaches 64.78%. LDA yields precision and recall scores of 67.40% and 67.20%, respectively, for the F1-score, whereas MLP records precision, recall, and F1-scores of 65.36%, 64.78%, and 64.28%, respectively. The proposed architecture is evaluated using the BCI Competition IV set 2a dataset, proving its effectiveness in EEG signal classification for BCI applications. © 2024 ieee.

关键词： Frequency domain analysis

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