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Distributed computing and High Performance computing (DCHPC), International Conference on

作者： Mahmood Fazlali Mina Mirhosseini Mahyar Shahsavari Alex Shafarenko Mashallah Mashinchi Department of Computer Science Cyber Security and Computing Research Group University of Hertfordshire Hertfordshire UK Department of Computer and Data Sciences Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran AI Department Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen The Netherlands Department of Statistics Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran

An important issue in data mining and information retrieval is the problem of multiple similarity or n-similarity. This problem entails finding a group of n data points with the highest similarity within a large dataset. Exact methods to solve this problem exist but come with high time and space complexities. Additionally, various metaheuristic algorithms have been proposed, including genetic algorithms, gravitational search algorithms, particle swarm optimization, imperialist competitive algorithms, and fuzzy imperialist competitive algorithms. These metaheuristics are capable of finding near-optimal solutions within a reasonable timeframe, although there is no guarantee of achieving exact results. In this paper, we employ a parallelization technique using CUDA to expedite the exact method. We conduct experiments on textual datasets to identify a group of n textual documents with the highest similarity to each other. The experimental results demonstrate that the proposed parallel exact method significantly reduces execution time compared to the best sequential approach and CPU multi-core implementation. Furthermore, it is evident that the proposed method requires less memory space than the exact method.

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TransformerFusionNet: A Real-Time Multimodal Framework for ICU Heart Failure Mortality Prediction Using Big data Streaming

TransformerFusionNet: A Real-Time Multimodal Framework for I...

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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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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...

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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

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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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Formation of Research Area for Malaysian Urban data Centre and Waste Heat Recovery

Formation of Research Area for Malaysian Urban Data Centre a...

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Digital Transformation and Intelligence (ICDI), International Conference on

作者： Marini Othman Siti Sarah Maidin Azizah Suliman Nur Zakri Zakaria Dua' Nassar Abbas M. Al-Ghaili Faculty of Data Science and Information Technology INTI International University Nilai Malaysia Independent Researcher Sepang Malaysia Faculty of Computer & Information Science Princess Nourah bint Abdulrahman University Riyadh KSA Institute of Informatics and Computing in Energy (IICE) Universiti Tenaga Nasional (UNITEN)

ISBN: (纸本)9798350397017

The realization that the energy consumption due to digital application will continue to rise significantly while climate change has persistently demand efficiency in the energy use, has triggered data centers to become more accountable in shouldering the task to operate sustainably. Green strategy for data centers, among others, should look at improving treatment of waste heat generated by their operation and cooling process. While intensive efforts are taking place in Scandinavian countries, many countries are yet to follow in their steps. A case study involving three urban data centres in Malaysia is conducted to gain insight on their potential in implementing waste heat recovery for utilization by relevant systems within their vicinity. It is found that the data centers have attained favourable in term of safety, security and reliability. However, there are a lot to be desired from the aspect of energy efficiency. A way forward suggests for further study to explore adoption and innovation of waste heat recycling models to improve the performance of Malaysian urban data centers in alignment to improve use and reuse of energy whilst making them more climate-neutral.

关键词： data centers Technological innovation Urban areas Water heating Energy efficiency data models Recycling Climate change

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