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International Conference on Computer and Applications (ICCA)

作者： Hager Saleh Michael McCann Shaker El-Sappagh John G. Breslin Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt Insight Research Ireland Centre for Data Analytics University of Galway Galway Ireland Atlantic Technological University Letterkenny Ireland Department of Computing Atlantic Technological University Letterkenny Ireland Faculty of Computer Science and Engineering Galala University Suez Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt School of Engineering University of Galway Galway Ireland

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This paper presents a real-time multimodal frame-work to enhance ICU mortality prediction for heart disease patients by integrating structured data, clinical notes, and big data streaming platforms. The proposed framework comprises two components: an offline multimodal model and a real-time pipeline. In the offline component, we proposed the Transformer-FusionNet model leverages BioBERT-Transformer for clinical notes and a Recurrent Neural Network (RNN) for structured data. The model integrates outputs through a concatenation layer to make predictions and is rigorously evaluated using the MIMIC-III dataset. It demonstrates superior performance across evaluation metrics and achieves an accuracy of 91.720%, precision of 91.85%, recall of 91.720%, and F1-score of 91.693%, outperforming other single and multimodal models. The real-time pipeline component incorporates Apache Spark and Apache Kafka for ingesting, preprocessing, and streaming structured data and clinical notes. This integration enables real-time mor-tality prediction with TransformerFusionNet, addressing critical gaps in the utilization of clinical notes in healthcare analytics. The framework significantly advances multimodal deep learning applications in healthcare by combining state-of-the-art models with scalable streaming technologies, offering a practical solution for improving clinical decision support systems and patient outcomes in real-time, especially in intensive care unit settings.

关键词： Heart Accuracy Recurrent neural networks Biological system modeling Pipelines Mortality Medical services Big data Transformers Real-time systems

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Development and Testing of Mobile Assisted Language Learning Application to Improve Oral Clinical Case Presentation of Student Nurses

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2023年第15期17卷 171-188页

作者： Milton, Cynthia Subramaniam, Aruna Sridevi, S. Kumar, S. Ganesh Faculty of Allied Health Sciences Sri Ramachandra Institute of Higher Education Tamil Nadu Chennai India Faculty of Nursing Sri Ramachandra Institute of Higher Education Tamil Nadu Chennai India Sri Ramachandra Engineering and Technology Sri Ramachandra Institute of Higher Education Tamil Nadu Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India

Background: Nurses’ oral case presentation is a core clinical communication skill with distinctive linguistic qualities that require specific language training. The present paper is a user feedback analysis of a start-up Mobile Assisted Language Learning application (MALL app) developed and implemented to provide English language training to improve Oral Case Presentation (OCP) of student nurses pursuing baccalaureate degrees from a nursing college in South India. Objective: The aim was to develop and test an Android based OCP-MALL application for user acceptability and effectiveness in improving OCP performance and decreasing the perceived language difficulty. Methodology: Sixty-two student nurses from a reputed nursing college in Chennai, South India, using Android mobile phones participated in the study. All the participants were given access and instructions on the use of OCP-MALL app. A comparison of pre and post-test of perceived level of language difficulty and OCP pre and post score along with users’ acceptance were collected. Results: The users’ acceptability showed that all participants rated the clarity in reading the text with highest mean score of 8.29 and std of 1.276, simplicity in navigation – moving to next page with next highest mean score of 8.19 and std of 1.526, clarity in text application layout with a mean score of 8.14 and std of 1.497 and user friendliness of the application with a mean score of 8.13 and std of 1.393. Conclusion: This startup OCP-MALL app user satisfaction feedback proves that Indian ELT trainers can create cost-effective and need-based m-learning applications. © 2023 by the authors of this article. Published under CC-BY.

关键词： Students

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IP SafeGuard - An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Daffodil Smart City Birulia Department of Software Engineering Dhaka1216 Bangladesh Oryx Universal College with Liverpool John Moores University School of Computing and Data Sciences Cybersecurity Department Doha34110 Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems Al-Salt19385 Jordan Department of Computer Science and Engineering Dhaka Bangladesh Amman Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

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Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

arXiv

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arXiv 2023年

作者： Ezugwu, Absalom E. Oyelade, Olaide N. Ikotun, Abiodun M. Agushaka, Jeffery O. Ho, Yuh-Shan Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Department of Computer Science Faculty of Physical Sciences Ahmadu Bello University Zaria Nigeria Trend Research Centre Asia University No. 500 Lioufeng Road Wufeng Taichung41354 Taiwan

The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many technological transformation agendas currently being propagated by many nations, for which the already yielded benefits are outstanding. From a regional perspective, several studies have shown that machine learning technology can help address some of Africa's most pervasive problems, such as poverty alleviation, improving education, delivering quality healthcare services, and addressing sustainability challenges like food security and climate change. In this state-of-the-art paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 89% were articles with at least 482 citations published in 903 journals during the past three decades. Furthermore, the collated documents were retrieved from the science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent. © 2023, CC BY.

关键词： Image recognition

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DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting

arXiv

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arXiv 2023年

作者： Ye, Maoyuan Zhang, Jing Zhao, Shanshan Liu, Juhua Liu, Tongliang Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China School of Computer Science Faculty of Engineering The University of Sydney Australia JD Explore Academy *** China College of Computing & Data Science Nanyang Technological University Singapore

End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although Transformer-based methods eliminate the heuristic post-processing, they still suffer from the synergy issue between the sub-tasks and low training efficiency. Besides, they overlook the exploring on multilingual text spotting which requires an extra script identification task. In this paper, we present DeepSolo++, a simple DETR-like baseline that lets a single Decoder with Explicit Points Solo for text detection, recognition, and script identification simultaneously. Technically, for each text instance, we represent the character sequence as ordered points and model them with learnable explicit point queries. After passing a single decoder, the point queries have encoded requisite text semantics and locations, thus can be further decoded to the center line, boundary, script, and confidence of text via very simple prediction heads in parallel. Furthermore, we show the surprisingly good extensibility of our method, in terms of character class, language type, and task. On the one hand, our method not only performs well in English scenes but also masters the transcription with complex font structure and a thousand-level character classes, such as Chinese. On the other hand, our DeepSolo++ achieves better performance on the additionally introduced script identification task with a simpler training pipeline compared with previous methods. Extensive experiments on public benchmarks demonstrate that our simple approach achieves better training efficiency compared with Transformer-based models and outperforms the previous state-of-the-art. For example, on ICDAR 2019 ReCTS for Chinese text, our method boosts the 1-NED metric to a new record of 78.3%. On ICDAR 2019 MLT, DeepSolo++ achieves absolute 5.5% H-mean and 8.0% AP improvements on joint detection an

关键词： Semantics

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Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review

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Informatics in Medicine Unlocked 2023年 38卷

作者： Kanyongo, Wellington Ezugwu, Absalom E. Department of Computer Science Faculty of Science Engineering Bindura University of Science Education Bindura Zimbabwe School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Pietermaritzburg Campus Pietermaritzburg 3201 South Africa Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom 2520 South Africa

Non-adherence to prescribed medication is a major public health concern that escalates the risk of morbidity and death as well as incurring extra expenses associated with hospitalisation. According to the World Health Organization (WHO), only 50% of people suffering from chronic diseases follow the treatment recommendations despite the counsel provided to patients on the importance of medication adherence (MA). Early detection of non-communicable disease (NCD) patients poorly adhering to recommended medications using analytics based on machine learning (ML) may improve the outcomes of NCD patients positively. This paper presents a systematic review of literature involving the application of ML in evaluating MA amongst NCD patients. The articles considered in this study were extracted from Web of science, Google Scholar, PubMed, and IEEE Explore. Twenty-five articles in total met the criteria for inclusion. These were articles that utilised ML techniques to analyse MA in NCDs, with patients suffering from diabetes (n = 8), hypertension (n = 3), cardiovascular disease (CVD) and statin adherence (n = 6), cancer (n = 3), respiratory diseases (n = 2), and other NCD conditions (n = 3). The proportion of days covered (PDC) was typically used to evaluate MA. It emerged from the study that for MA to be considered high, the adherence threshold should be at least 75% of the PDC, a universally accepted threshold. In MA analytics research and practice, a PDC ≥80% threshold is typically regarded as a high level of adherence to prescription medication. Logistic regression (LR) (n = 12), random forest (RF) (n = 11), support vector machine (SVM) (n = 7), neural net (n = 6), ensemble learning (n = 6), MLPs (n = 4), XGBoost (n = 3), Bayesian network (BN) (n = 3), and gradient boosting (n = 3) were the most frequently applied ML techniques in the analytics of MA amongst NCD patients. It should be underscored that leveraging standard ML, deep learning (DL), and ensemble learning has eno

关键词： Cancer Cardiovascular disease (CVD) Chronic disease Deep learning Diabetes Hypertension Machine learning Medication adherence NCD patients Non-communicable disease Respiratory disease

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Automatic Sinhala News Classification Approach for News Platforms 7

Automatic Sinhala News Classification Approach for News Plat...

引用

7th IEEE International Conference on Engineering Technologies and Applied sciences, ICETAS 2020

作者： Kirindage, Gayashan Godewithana, Navod Sri Lanka Institute of Information Technology Faculty of Computing Department of Data Science Colombo Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Software Engineering Colombo Sri Lanka

ISBN: (纸本)9780738105048

Because of generating various news articles in large scale, online sources moved into an automatic categorization mechanism. This research has been conducted using LDA topic modeling approach and using other classification algorithms to establish a news categorization solution. Sinhala news websites have only few news categories and do not have any relationships or hierarchies between the categories. Therefore, some users require to search manually and find the necessary articles which are in those categories. Purpose of this study is to build a news categorization model with categorization hierarchies for Sinhala news articles. The goals of the models are to identify the most suitable news category for a related news article and develop hierarchies using generated news categories and assign the news articles according to the hierarchical structure. The final experiments and evaluations show that the solution performs well to solve the automatic categorization problem in Sinhala news platforms. © 2020 IEEE.

关键词： Machine learning

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Emotion Classification Through EEG Signals: The EmoSense Model

Emotion Classification Through EEG Signals: The EmoSense Mod...

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computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Yi Ken Chuah Chean Khim Toa Sim Kuan Goh Chow-Khuen Chan Hiroki Takada School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Selangor Malaysia School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Biomedical Engineering Faculty of Engineering Universiti Malaya Malaysia Department of Human & Artificial Intelligent Systems Graduate School of Engineering University of Fukui Fukui Japan

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

In the Emotion research, there has been extensive work on emotion classification using electroencephalogram (EEG) signals. Despite most studies having achieved high accuracy, there is a potential for improvement in the model, particularly in training time reduction to optimize the modeling efficiency. Hence, our study aims to develop an efficient EEG-based emotion classification model that achieves high accuracy with a reduced number of training epochs. The proposed model, named EmoSense, employs a hybrid deep learning neural network architecture, which comprises a combination of Convolutional Neural Network (CNN), Deep Autoencoder (DAE), Bidirectional Long Short-Term Memory (BiLSTM), and an Attention mechanism. EEG features are first extracted using Differential Entropy (DE) on the EEG channels, which are then structured to form a four-dimensional (4D) representation of features with temporal, spatial, and frequency information. Subsequently, the extracted features will be used as input to the proposed model for training. Experimental evaluation of the publicly available DEAP dataset demonstrated that the proposed model achieved an accuracy of 94.11% with only 35 epochs. This performance is comparable with the result of the Cascaded Convolutional Recurrent Neural Networks model developed by Meng et al., which reported an accuracy of 94.64% with 100 epochs. The primary reason for the comparison with Meng et al. is that our overall process, including feature extraction and formation, is similar to their study, with the only difference being the deep learning architecture. Our finding indicates that the EmoSense model is not only able to achieve comparable accuracy but also reduces the number of training epochs, demonstrating the potential to learn the EEG features in a shorter time and lowering the computational cost. This study will benefit EEG-based emotion recognition to be more accessible and practical for real-world applications.

关键词： Training Emotion recognition Accuracy Attention mechanisms Computational modeling Brain modeling Feature extraction Electroencephalography Convolutional neural networks Tuning

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data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria

arXiv

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arXiv 2023年

作者： Li, Yijun Guo, Mengzhuo Kadziński, Milosz Zhang, Qingpeng School of Data Science City University of Hong Kong 83 Tat Chee Avenue Kowloon Tong Kowloon Hong Kong Business School Sichuan University No.24 South Section 1 Yihuan Road Chengdu610065 China Institute of Computing Science Poznan University of Technology Piotrowo 2 Poznań60-965 Poland Musketeers Foundation Institute of Data Science LKS Faculty of Medicine The University of Hong Kong Hong Kong

We present novel preference learning approaches for sorting problems with multiple temporal criteria. They leverage an additive value function as the basic preference model, adapted for accommodating time series data. Given assignment examples concerning reference alternatives, we learn such a model using convex quadratic programming. It is characterized by fixed-time discount factors and operates within a regularization framework. This approach enables the consideration of temporal interdependencies between timestamps while mitigating the risk of overfitting. To enhance scalability and accommodate learnable time discount factors, we introduce a novel monotonic Recurrent Neural Network (mRNN). It captures the evolving dynamics of preferences over time, while upholding critical properties inherent to MCS problems. These include criteria monotonicity, preference independence, and the natural ordering of classes. The proposed mRNN can describe the preference dynamics by depicting piecewise linear marginal value functions and personalized time discount factors along with time. Thus, it effectively combines the interpretability of traditional sorting methods with the predictive potential offered by deep preference learning models. We comprehensively assess the proposed models on synthetic data scenarios and a real-case study centered on classifying valuable users within a mobile gaming app based on their historical in-game behavioral sequences. Empirical findings underscore the notable performance improvements achieved by the proposed models when compared to a spectrum of baseline methods, spanning machine learning, deep learning, and conventional multiple criteria sorting approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Sorting

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National Telecommunications and Information Administration: Comments from Researchers at Boston University and the University of Chicago

SSRN

引用

SSRN 2023年

作者： Canetti, Ran Cohen, Aloni Conley, Chris Crovella, Mark Dogan, Stacey Gaboardi, Marco Hartzog, Woodrow Van Loo, Rory Robertson, Christopher T. Silbaugh, Katharine B. Department of Computer Science United States Technology Law Clinic Boston University School of Law United States Boston University School of Law United States Boston University United States Affiliated Faculty Boston University Faculty of Computing and Data Sciences United States Boston University School of Public Health United States

These comments were composed by an interdisciplinary group of legal, computer science, and data science faculty and researchers at Boston University and the University of Chicago. This group collaborates on research projects that grapple with the legal, policy, and ethical implications of the use of algorithms and digital innovation in general, and more specifically regarding the use of online platforms, machine learning algorithms for classification, prediction, and decision making, and generative AI. Specific areas of expertise include the functionality and impact of recommendation systems;the development of Privacy Enhancing Technologies (PETs) and their relationship to privacy and data security laws;legal regulation of platforms under privacy, intellectual property, and antitrust laws;the science of monitoring and measuring the behavior of large deployed systems and networks;and programming languages and the science of rigorously specifying and verifying properties of algorithms and information systems. © 2023, The Authors. All rights reserved.

关键词： Decision making

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