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Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model

computers, Materials & Continua 2022年第6期71卷 5799-5815页

作者： Ahmed S.Almasoud Siwar Ben Haj Hassine Fahd N.Al-Wesabi Mohamed K.Nour Anwer Mustafa Hilal Mesfer Al Duhayyim Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Science and Arts at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Due to the advanced developments of the Internet and information technologies,a massive quantity of electronic data in the biomedical sector has been exponentially *** handle the huge amount of biomedical data,automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative ***,multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated *** paper presents a Deep Learning based Attention Long Short Term Memory(DLALSTM)Model for Multi-document Biomedical Text *** proposed DL-ALSTM model initially performs data preprocessing to convert the available medical data into a compatible format for further ***,the DL-ALSTM model gets executed to summarize the contents from the multiple biomedical *** order to tune the summarization performance of the DL-ALSTM model,chaotic glowworm swarm optimization(CGSO)algorithm is *** experimentation analysis is performed to ensure the betterment of the DL-ALSTM model and the results are investigated using the PubMed *** comparative result analysis is carried out to showcase the efficiency of the proposed DL-ALSTM model with the recently presented models.

关键词： Biomedical text summarization healthcare deep learning lstm parameter tuning

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Factors influencing Romanian teachers' choice of IT training to avoid issues raised by online education: A data mining approach 17

Factors influencing Romanian teachers' choice of IT training...

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17th International Conference on Development and Application systems, DAS 2024

作者： Simionescu, Corina Bărîlă, Adina-Luminița Danubianu, Mirela Grădinaru, Bogdănel Constantin Doctoral School of Applied Sciences and Engineering Computers and information technology Ștefan cel Mare University Suceava Romania Faculty of Electrical Engineering and Computer Science Ștefan cel Mare University Suceava Romania

ISBN: (纸本)9798350349290

Raising the students' academic performance and improving the overall quality of education is an important goal of our society. With the exploding development of information technologies, Educational Data Mining has become a growing activity that uses the essence of Data Mining concepts to help institutions learn useful information on various academic issues. Romanian schools are facing several problems and inequalities, many of which were accentuated during the Covid-19 pandemic, when, due to restrictions on physical proximity, classes were held entirely or partly online. This paper aims to presents an experimental study on modelling teachers' attitudes towards attending IT professional development courses to deliver quality online lessons. A real data set collected through a questionnaire answered by 956 teachers is used. The final goal was that, after the best-performing model was built, the features that are significant in this approach would be analyzed. These features are influential factors in teachers' choices. We initially considered four classification techniques, after which, we refined the optimization process for Random Forest, which proved, from the first experiments, the best performance. ©2024 IEEE.

关键词： Data mining

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LLaMA-Based Models for Aspect-Based Sentiment Analysis 14

LLaMA-Based Models for Aspect-Based Sentiment Analysis

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14th Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis, WASSA 2024

作者： Šmíd, Jakub Přibáň, Pavel Král, Pavel Department of Computer Science and Engineering University of West Bohemia Faculty of Applied Sciences Czech Republic NTIS – New Technologies for the Information Society Univerzitní 2732/8 Pilsen301 00 Czech Republic

ISBN: (纸本)9798891761568

While large language models (LLMs) show promise for various tasks, their performance in compound aspect-based sentiment analysis (ABSA) tasks lags behind fine-tuned models. However, the potential of LLMs fine-tuned for ABSA remains unexplored. This paper examines the capabilities of open-source LLMs fine-tuned for ABSA, focusing on LLaMA-based models. We evaluate the performance across four tasks and eight English datasets, finding that the fine-tuned Orca 2 model surpasses state-of-the-art results in all tasks. However, all models struggle in zero-shot and few-shot scenarios compared to fully fine-tuned ones. Additionally, we conduct error analysis to identify challenges faced by fine-tuned models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Dynamic Reconfigurable Photovoltaic Array Architectures a Review 4th

Dynamic Reconfigurable Photovoltaic Array Architectures a Re...

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4th International Conference on Electronic Engineering and Renewable Energy systems, ICEERE 2024

作者： Bouselham, Loubna Hajji, Bekkay Rabhi, Abdelhamid Mellit, Adel Benhaddou, Mohammed Energy Embedded System and Information Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco National School of Architecture Oujda Morocco Modeling Information and Systems Laboratory University of Picardie Jules Verne 3 Amiens France Renewable Energy Laboratory Faculty of Sciences and Technology Jijel University Jijel Algeria

ISBN: (纸本)9789819799749

Mismatch losses significantly reduce the output power of photovoltaic (PV) arrays. Among available mismatch mitigate techniques, the dynamic reconfigurable photovoltaic array (DRPVA) architectures can be considered as a promising technique to compensate the power losses. In contrast to fixed photovoltaic architectures, the DRPVA architectures modify dynamically the interconnections between the PV modules of the same PV array (PVA) according to the operating conditions. This paper presents the different approaches of DRPVA architectures proposed in the literature. These approaches are classified according to the reconfiguration strategy adopted and compared in terms of power enhancement, topology used, and additional material required. Through this study, the main constraints limiting the DRPVA efficiency are determined and discussed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dynamic reconfigurable PV array Mismatch losses Partial shading Power enhancement Total-cross-tied

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UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detection 14

UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detec...

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14th Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis, WASSA 2024

ISBN: (纸本)9798891761568

This paper presents our system built for the WASSA-2024 Cross-lingual Emotion Detection Shared Task. The task consists of two subtasks: first, to assess an emotion label from six possible classes for a given tweet in one of five languages, and second, to predict words triggering the detected emotions in binary and numerical formats. Our proposed approach revolves around fine-tuning quantized large language models, specifically Orca 2, with low-rank adapters (LoRA) and multilingual Transformer-based models, such as XLM-R and mT5. We enhance performance through machine translation for both subtasks and trigger word switching for the second subtask. The system achieves excellent performance, ranking 1st in numerical trigger words detection, 3rd in binary trigger words detection, and 7th in emotion detection. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A new class of generalized quasi-variational inequalities with applications to Oseen problems under nonsmooth boundary conditions

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Science China Mathematics 2024年第2期67卷 315-338页

作者： Shengda Zeng Akhtar A.Khan Stanislaw Migórski Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal UniversityYulin 537000China Faculty of Mathematics and Computer Science Jagiellonian University in Krak´owKrak´ow 30348Poland Center for Applied and Computational Mathematics School of Mathematical SciencesRochester Institute of TechnologyRochesterNY 14623USA College of Sciences Beibu Gulf UniversityQinzhou 535000China

In this paper, we study a generalized quasi-variational inequality (GQVI for short) with twomultivalued operators and two bifunctions in a Banach space setting. A coupling of the Tychonov fixedpoint principle and the Katutani-Ky Fan theorem for multivalued maps is employed to prove a new existencetheorem for the GQVI. We also study a nonlinear optimal control problem driven by the GQVI and givesufficient conditions ensuring the existence of an optimal control. Finally, we illustrate the applicability of thetheoretical results in the study of a complicated Oseen problem for non-Newtonian fluids with a nonmonotone andmultivalued slip boundary condition (i.e., a generalized friction constitutive law), a generalized leak boundarycondition, a unilateral contact condition of Signorini’s type and an implicit obstacle effect, in which themultivalued slip boundary condition is described by the generalized Clarke subgradient, and the leak boundarycondition is formulated by the convex subdifferential operator for a convex superpotential.

关键词： generalized quasi-variational inequality existence theorem optimal control Kakutani-Ky Fan theorem Oseen problem non-Newtonian fluid nonmonotone slip boundary condition

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning 14

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Novianty, Astri Putro, Budi Laksono Komala Sari, Siska Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia Indonesia University of Education Department of Computer Science Education Bandung Indonesia Telkom University School of Applied System Diploma Program of Information System Bandung Indonesia

ISBN: (纸本)9798350335095

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice. © 2023 IEEE.

关键词： Support vector machines

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Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

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computers, Materials & Continua 2023年第3期74卷 5607-5623页

作者： Abdallah Elhigazi Abdallah Mosab Hamdan Shukor Abd Razak Fuad A.Ghalib Muzaffar Hamzah Suleman Khan Siddiq Ahmed Babikir Ali Mutaz H.H.Khairi Sayeed Salih Faculty of Engineering Future UniversityKhartoum 10553Sudan Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Information Assurance and Security Research School of ComputingUniversiti Teknologi MalaysiaJohor81310Malaysia School of Psychology and Computer Science University of Central LancashirePreston PR12HEUK Deaptement of Management Information Systems College of Administrative ScienceApplied Science UniversityAl Eker623Bahrain Deaptement of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh11461Saudi Arabia

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 *** to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate ***,the security design of the wireless structure is vulnerable to versatile *** example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false *** security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world *** paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks *** proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the *** scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in *** proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related *** experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

关键词： 802.11 media access control(MAC) wireless local area network(WLAN) artificial neural network denial-of-service(DoS)

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Sustainable Energy Production’s Spatial Determination Framework, Based on Multi Criteria Decision Making and Geographic information system Under Neutrosophic Environment: A Case Study in Egypt

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Neutrosophic Sets and systems 2021年 42卷 136-156页

作者： El-hosiny, Amany Mohamed El-Ghareeb, Haitham Shabana, Bahaa Taher AbouElfetouh, Ahmed Department of Information System Sadat Academy for Management Sciences Tanta Egypt Faculty of Computer & Information Sciences Mansoura Egypt Computer Science Department Misr Higher Institute of Commerce and Computer Mansoura Egypt

The development of wind energy projects (WEP) have been encouraged, since the last decade. Therefore, WEP grows exponentially, which makes wind energy the trend of energy production for many countries. The success of wind energy project relies on the choice of the appropriate site for wind power plant, often decided by the application of Multi Criteria Decision Making (MCDM). The MCDM methodologies for location selection have a range of shortcomings: (1) the incomplete use of knowledge, (2) the lack of evidence in the decision-making process;and (3) the problem of ignoring the interaction between parameters. This paper presents a new framework for the location selection of wind power stations, based on the incorporating of geographic information system (GIS) and analytical network process (ANP) through neutrosophic environment to cover MCDM's shortcoming. First, an assessment model is built for wind farm site selection. Then, in the specialist committee decision, the bipolar neutrosophic set is used to express missing knowledge. In addition, we take the relationship problem into account by collecting the opinions of experts. Finally, the GIS is used to determine the wind farm potential zones. The suggested framework for the identification of wind farm sites is validated by the use of a case study from Egypt. © 2021. All Rights Reserved.

关键词： Geographic information systems

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When Left Behind in Multi-User Virtual Reality: Clues to Indicate the Absence of Users

When Left Behind in Multi-User Virtual Reality: Clues to Ind...

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2022 International Conference on Cyberworlds, CW 2022

作者： Wolfel, Matthias Basing, Lara-Marie Deuchler, Jonas Karlsruhe University of Applied Sciences Faculty of Computer Science and Business Information Systems Germany University of Hohenheim Faculty of Business Economics and Social Sciences Germany

ISBN: (数字)9781665468145

ISBN: (纸本)9781665468145

Recently, with the increasing popularity of immersive virtual reality, synchronous communication via avatars in virtual worlds has become more popular. In contrast to asynchronous communication - like social media platforms - people expect others to be present during embodied, virtual interactions. However, for a variety of reasons, this is not always the *** some communication media provide a good indicator that the other is still present, such as live images of the conversation partner in video conferencing, not all media do provide this information naturally, e.g., when the interlocutor is silent on the phone. In immersive multi-user virtual reality, a wrong and misleading signal may even be provided when an avatar is still shown while its owner is absent. In many situations, an assumed presence of the other who is actually no longer there can lead to confusion. In the presented empirical study, we show that users in shared immersive environments prefer an indicator when a person is absent and that the preferred way of visualization depends on the use case. © 2022 IEEE.

关键词： Virtual reality

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