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10th International Conference on Engineering and Emerging Technologies, ICEET 2024

作者： Azam, Muhamad Ali, Rashid Muqaddas, Ranya Ashraf, Muhammad Zahoor, Ammara The Superior Universty Department of Computer Science and Information Technology Lahore5400 Pakistan The Superior University Department of Computer Science and Information Technology Lahore5400 Pakistan The Superior Universty Information Engineering Technology Department Lahore5400 Pakistan Gulab Devi Teaching Hospital Information Technology Department Lahore5400 Pakistan

ISBN: (纸本)9798331532895

In this research paper, we examine which of a number of machine learning classifiers are able to predict diabetes in a dataset of 100,000 cases where features include gender, age, state, binary health indicators, smoking, body mass index (BMI), and glucose level, one of which was known to be diabetic. Extensive exploratory data analysis was undertaken to explore the distribution and the relationships between features of the data set and these predictions. (Prajapati, (2023, July).We then evaluated the model output using metrics like as the Accuracy, AUC-ROC, Precision-Recall curves. Support Vector Machine (SVM), Voting Classifier (VC), and Stacking Classifier (SC). The output of these models was then evaluated with the help of measures using metrics like accuracy, AUC-ROC, and Precision-Recall curves. (Hasan, 2020). The highest accuracy of 98.75% was achieved by the voting classifier (VC), Random Forest (RF) was second with 97.18%, and XGBoost was 3rd with 97.15%. The results of this research can be applied in diabetes prediction, with an important conclusion on how ensemble methods could improve the accuracy indicators for healthcare applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Paper Defects Recognition Based on Deformable Convolution

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Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry 2022年第2期54卷 37-50页

作者： Qu, Yun-Hui Tang, Wei Feng, Bo Computer Teaching and Research Section Xi’an Medical University China Department of Electric and Control Engineering Shaanxi University of Science & Technology China

There are some problems in traditional paper defects classification, such as the poor generalization performance, less types of recognition, and insufficient recognition accuracy. The deep learning method provides a new scheme for paper defects classification. However, convolutional neural network has strict requirements for the size of the input image. This requires that in the process of practical engineering application, for the collected paper defect images to be classified, the area containing paper defect must be segmented during preprocessing, and then the size of the paper defect area must be adjusted to meet the input requirements of the adopted classifier. To solve the above problems, the two-stage target detection network Faster R-CNN (Region-Convolutional Neural Network) was used in paper defects recognition to solve the problem of the size requirements of the input image;In addition, the deformable convolution layer was added after the traditional convolution layer to learn the characteristics of paper defects more efficiently and accurately, so as to improve the accuracy and accuracy of paper defects recognition and classification;Finally, the deformable RoI (Region-of-Interest) pooling layer was used to replace the RoI pooling layer of classic Faster R-CNN to locate and classify the paper defects area more accurately. Experiments show that the proposed algorithm has a further improvement in accuracy and scalability compared with the previous algorithm. © 2022 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved.

关键词： Convolution

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Spoken language-based automatic cognitive assessment of stroke survivors

Language and Health

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Language and Health 2024年第1期2卷 32-38页

作者： Mirheidari, Bahman Bell, Simon M. Harkness, Kirsty Blackburn, Daniel Christensen, Heidi University of Sheffield Department of Computer Science Sheffield United Kingdom Sheffield Biomedical Research Centre University of Sheffield and Sheffield Teaching Hospitals NHS Foundation Trust Department of Neuroscience Sheffield United Kingdom

Stroke survivors (SSs) often experience cognitive decline following their initial stroke, necessitating repeat post-stroke cognitive assessments. Current methods of assessment, such as the pen-and-paper-based Montreal Cognitive Assessment (MoCA), is time-consuming and often reliant on seeing skilled clinicians in person. This is at a time when patients have a lot of often diverse rehabilitation needs. To address these challenges, our paper introduces the first system of its kind to be used for this cohort. CognoSpeak is an automated cognitive assessment system that people can use initially on the ward immediately post-stroke (baseline) and subsequently at home (follow-ups). CognoSpeak assesses cognitive decline by asking users to engage with a virtual agent by answering questions and completing clinically-motivated tasks and cognitive tests. The system then uses AI to extract and process speech, language, and interactional cues for cognitive decline. The system was originally developed for dementia;here, we show that it can successfully predict MoCA scores (regression) and identify cognitive decline predicated on a MoCA-based threshold (classification) in the stroke survivor cohort. We explore an extensive set of acoustic- and text-based features as well as different machine learning models. Leveraging a unique dataset of 55 SS CognoSpeak interactions, our findings show excellent performance for both regression and classification style prediction with the best regression result (Normalised Root Mean Squared Error (N-RMSE)) of 0.092. In addition, we show that direct classification of the MoCA score cutoff of 26 yields an F1-score of 0.74 (Specificity: 0.73, Sensitivity: 0.75) using a Logistic Regression Classifier. This demonstrates the first evidence of the system's robustness and clinical potential. © 2024 The Authors

关键词： Cognitive decline assessment Post-stroke Rehabilitation Speech Technology

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Assessing Software Practitioners’ Work Engagement and Job Satisfaction in a Large Software Company—What We Have Learned

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SN computer science 2025年第3期6卷 1-24页

作者： Cerqueira, Lidiany Nunes, Lourene Guerra, Renan Malheiros, Viviane Freire, Sávio Carneiro, Glauco Leite, Julio Cesar Spínola, Rodrigo Macedo Santos, José Amancio Mendonça, Manoel Department of Computer Science Federal University of Bahia Bahia Salvador 40170-115 Brazil Data Processing Federal Service DF Brasília 70836-900 Brazil Teaching Department Federal Institute of Ceará Ceará Morada Nova 62.940-000 Brazil Department of Computer Science Federal University of Sergipe Sergipe São Cristóvão 610101 Brazil Department of Computer Science Virginia Commonwealth University Richmond 23284 VA United States Department of Technology State University of Feira de Santana Bahia Feira de Santana 44036-900 Brazil

Social aspects of software practitioners strongly influence software engineering outcomes. For instance, software companies seek to measure how factors like work engagement and job satisfaction impact employee productivity and software quality. Work engagement represents a positive, fulfilling, work-related mental state, while job satisfaction reflects how content professionals are with their roles. Our study aims to highlight the social aspects of software practitioners considering a large organization. We investigate the work engagement and job satisfaction of software practitioners working remotely at a large software organization in the public sector. We surveyed 891 software practitioners and analyzed their responses qualitatively and quantitatively. The survey participants indicated strong work engagement. They perceived their teams as effective, but there is room for improving social aspects, such as communication within the teams, promoting discussions about career development, and consistent feedback. Most professionals are satisfied with their teams, though a small minority expressed concerns that may influence their willingness to recommend their teams. Our findings provide relevant information about the engagement and satisfaction of employees within this particular type of organization and expand the knowledge base on the subject, supporting new research efforts in the area. © The Author(s) 2025.

关键词： Human factors Job satisfaction Productivity Remote work Work engagement

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A Novel Deep Learning Model Watermark Algorithm with Strong Anti-pruning Robustness

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第4期9卷 2516-2527页

作者： Sun, Xiuyan Gu, Chengling Tang, Linlin Kong, Lingping Public Education Department of Laiwu Vocational and Technical College China Teaching Affairs Office of Laiwu Vocational and Technical China Shenzhen518067 China Faulty of Computer Science VSB-Technical University of Ostrava Ostrava708 00 Czech Republic

Deep learning has developed rapidly in recent years and has achieved sur-prising results in multiple fields. A trained model consumes a considerable amount of computational resources from the trainer and has certain value. Therefore, copyright protection for deep learning models is an issue that cannot be ignored. This article pro-poses a framework consisting of three neural networks: Encoder, Decoder and Adversary, and uses three steps: graphic watermark embedding, model watermark embedding, and extraction reinforcement to complete the training of neural network. In order to verify the effectiveness of the framework, this article sets up three experiments: effectiveness verification, uniqueness verification, and robustness verification. The experimental results indicate that the model embedded with watermarks in this framework has certain robustness against some attacks against watermarks. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Deep reinforcement learning

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Towards Developing Digital Twin Enabled Multi-Agent Systems 11th

Towards Developing Digital Twin Enabled Multi-Agent Systems

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11th InternationalWorkshop on EngineeringMulti-Agent Systems, EMAS 2023

作者： Mariani, Stefano Picone, Marco Ricci, Alessandro Department of Sciences and Methods of Engineering University of Modena and Reggio Emilia Reggio Emilia Italy Department of Computer Science and Engineering University of Bologna Cesena Italy

ISBN: (纸本)9783031485381

The Multi-Agent Systems (MASs) literature provides abstractions, techniques, and development platforms to design and implement the virtual environment within which agents operate. However, coupling such an environment with a physical counterpart is still cumbersome, as existing approaches deal with the issue in an ad-hoc way, without general purpose abstractions and methods. Recently, a new paradigm could complement the agent-oriented one to deal with digitalisation of physical environments in a more principled and interoperable way: the Digital Twin (DT). In this paper, we propose a first principled integration between MAS and DTs for MAS environment engineering. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multi agent systems

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Designing a Model for Predicting Asthma in Adolescent Using Map Reduce and Federated Learning

Designing a Model for Predicting Asthma in Adolescent Using ...

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ekpo, Raphael Henshaw Chukwudi Osamor, Victor Azeta, Ambrose A. Akindeji, Kayode Orelaja, Adeyinka Elizabeth Covenant University Department of Computer and Information Sciences Km 10 Idiroko Road Ogun State Ota Nigeria Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia Durban University of Technology Faculty of Engineering and the Built Environment Department of Electrical Power Engineering South Africa Department of Computer Science and Quantitative Methods ClarksvilleTN United States

ISBN: (纸本)9798350358155

This study aims at designing Map Reduce and federated learning-based asthma prediction model for adolescent to provide answers to the problems associated with the existing asthma prediction model for adolescent. This study leveraged on two diverse datasets: the Nigerian Hospital Asthma dataset and the National Survey of Children Health dataset for benchmarking purposes. Symmetrical uncertainty and normalization interaction were employed for feature selection. The model was trained using Federated Artificial Intelligence (AI) Technology Enabler (FATE) and complemented with XGBoost model and one central server for federated algorithm averaging. The study was implemented with python programming language on Google Collaboratory environment. The results of the analysis showed considerable high accuracy of 0.98, precision (0.98%), recall (0.98%) and F1-socre (0.99%) for the asthmatic class and precision of 0.98%, recall of 0.98%, as well as F1-score of 0.99% for the non-asthmatic class. The implemented model was benchmark with the existing asthma prediction models in the literature. Simulated attack was also performed using implemented model with and without Map Reduce. This study concludes that the researchers' model has the potentials to outperform some of the existing machine learning models on asthma prediction in adolescent, thereby facilitating collaborative sharing of the model with other hospitals, protect the patient information from leakage, meeting the criteria of healthcare data protection regulation as well as beneficial for enhancing the security of the dataset. © 2024 IEEE.

关键词： Federated learning

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An informed deep learning model of the Omicron wave and the impact of vaccination

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computers in Biology and Medicine 2025年 191卷 109968-109968页

作者： Shamsara, Elham König, Florian Pfeifer, Nico Methods in Medical Informatics Department of Computer Science University of Tübingen Tübingen72076 Germany

The Omicron (B.1.1.529) variant of SARS-CoV-2 emerged in November 2021 and has since evolved into multiple lineages. Understanding its transmission, vaccine efficacy, and potential for reinfection is crucial. This study examines the dynamics of Omicron in Germany, France, and Italy by employing Physics-Informed Neural Networks to estimate the temporal parameters influencing its spread. We validated the performance of our model using the Root Mean Squared Percent Error (RMSPE). Our analysis revealed significant correlations between specific viral mutations—S371F, T376A, D405N, and R408S—and increased transmission rates in all three countries. These mutations, prevalent in the Omicron BA.2 and BA.3 sublineages, are linked to immune evasion and heightened transmissibility. © 2025 The Author(s)

关键词： COVID-19

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Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

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computer Systems science & Engineering 2023年第9期46卷 3423-3438页

作者： Ibrahim M.Alwayle Badriyya B.Al-onazi Jaber S.Alzahrani Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Mahmoud Othman Abdelwahed Motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran55461Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityNajran24211Saudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Arabic is one of the most spoken languages across the ***,there are fewer studies concerning Sentiment Analysis(SA)in *** recent years,the detected sentiments and emotions expressed in tweets have received significant *** substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic *** common models are available:Machine Learning and lexicon-based approaches to address emotion classification *** this motivation,the current research article develops a teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification(TLBOML-ERC)model for Sentiment Analysis on tweets made in the Arabic *** presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic *** attain this,the proposed TLBOMLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding *** addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in Arabic *** improve the efficacy of the DAE model,the teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the *** proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets *** obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.

关键词： Arabic language Twitter data machine learning teaching and learning-based optimization sentiment analysis emotion classification

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The Development and Testing of a Model for Converting American Sign Language (ASL) Sentences Using Natural Language Processing Techniques

The Development and Testing of a Model for Converting Americ...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Kaewprom, Alongkorn Suriyapattanapong, Aritat Sanmano, Smith Songmuang, Pokpong Malakul, Sivakorn Suksanareewittaya School Bangkok Thailand Streesmutprakan School Bangkok Thailand Chitrala School Bangkok Thailand Thammasat University Department Of Computer Science Pathum Thani Thailand The Institute For The Promotion Of Teaching Science And Technology Bangkok Thailand

ISBN: (纸本)9798350370096

This research addresses the global prevalence of people with hearing disabilities, recognizing the increasing numbers within this demographic. The communication challenges encountered by these individuals drive the development and evaluation of a language translation model. The primary goal is to design a model specifically tailored for translating sign language sentences into English, considering the unique word order in sign language. The study involves comparing eight models that utilize various techniques. Evaluation will be conducted through the bleu score, with 100 randomly selected sentences in each of the 10 rounds. Consistent inputs will be used across rounds to calculate the bleu score, offering a comprehensive assessment of model performance. © 2024 IEEE.

关键词： Natural language processing systems

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