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Using geostatistics to fight neglected tropical diseases

Significance 2024年第4期21卷 14-18页

作者： Purkiss, Jana Clark, Freya N. Giorgi, Emanuele Centre for Health Informatics Computing and Statistics (CHICAS) Lancaster University Medical School

Jana Purkiss, Freya N. Clark and Emanuele Giorgi describe a spatial solution to controlling diseases such as leprosy, dengue and rabies that affect around a billion people worldwide

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Data-Driven Analysis of Irregular Respiratory Signals Derived from the Chest-Wearable Respeck Monitor 24

Data-Driven Analysis of Irregular Respiratory Signals Derive...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Arvind, D.K. Futera, Filip Centre for Speckled Computing School of Informatics University of Edinburgh Scotland United Kingdom

ISBN: (纸本)9798331518622

The respiratory rate and the respiratory flow/effort are important vital signs monitored during the care of patients in the hospital and during remote management of subjects with chronic lung diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and congenital airways malformation. The Respeck is a wireless device worn as a plaster on the chest which monitors continuously respiratory signals, such as the respiratory rate (breaths/minute), and the respiratory flow/effort (amplitude over time). The regularity of the respiratory signal, or rather the nature and frequency of occurrence of irregular breathing episodes can be used as one of the characteristics of the subjects' clinical status. This paper develops computational methods for classifying episodes of rapid-shallow breathing based on approximating the tidal volume using features such as: the area under the respiratory signal curve, peak respiratory flow and the respiratory rate. Results are presented for a data-driven approach to the analysis of a Respeck dataset of respiratory flow signals gathered from 137 asthmatic subjects. © 2024 IEEE.

关键词： Lung cancer

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Use of gamification and game-based learning in educating Generation Alpha: A systematic literature review

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Educational Technology and Society 2024年第2期27卷 114-132页

作者： Fernando, Pumudu A. Premadasa, H. K. Salinda Department of Computing Informatics Institute of Technology Sri Lanka Centre for Computer Studies Sabaragamuwa University Sri Lanka

Children born after 2010 are labelled as members of Generation Alpha, who currently pursue their primary education. Gamification and game-based learning methodologies have gained popularity in the global education sector in the recent past. The goal of this study is to investigate the present state of adopting gamification and game-based learning for primary education student cohorts, with the aid of recent peer-reviewed research publications. By employing a systematic mapping design, reviewed paper results are categorized and further evaluated in relation to the attributes such as type of gamification, game mechanics and elements used, evaluation context, type of experimental outcome, gamified academic subject area, and type of application. With the aid of review results, this study proposes a mapping of gamification learning mechanics, suited to address Generation Alpha traits. It also recommends future research directions, which include investigating the realistic learning preferences of Generation Alpha, the need for designing novel educational game elements and adaptive gamification learning strategies, and the importance of long-term studies with diverse learner samples. The findings of this study will support prospective stakeholders, such as researchers, educators, parents, and educational policymakers, to utilize gamification approaches effectively in educating Generation Alpha. © (2023), (International Forum of Educational Technology and Society). All Rights Reserved.

关键词： Adaptive learning Game-based learning Gamification Generation Alpha Primary education

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A View into the Future of Academic Publishing 46

A View into the Future of Academic Publishing

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46th ICT and Electronics Convention, MIPRO 2023

作者： Sojat, Zorislav Skala, Karolj Ruder Bošković Institute Centre for Informatics and Computing Zagreb Croatia

ISBN: (纸本)9789532331042

The exponential growth of academic knowledge production in present times, compared to historical evolution, has led to the Open Access movement as a means of sharing knowledge with the community. However, this development also gave rise to unexpected predatory behaviour on the side of many publishers, turning the publishing system upside-down. The present-day challenges faced by academic publishing call for modern information science-based technologies such as Distributed Ledger and Distributed Database, as well as Virtual Money, to democratise the process. This article proposes the DAP initiative and project, which aims to provide a free, democratic, and trustworthy open access publishing platform with Virtual Money, the DAP Ergions, for recompensation of work, and recognition and ranking of high-quality work and all DAP users. The DAP internal economy and the spread towards external economy using the Ergions provides a generic opportunity to all academicians, citizen scientists, and the global population to be involved in the progress of gathering new human knowledge. This initiative is critical in our times, given the significant lowering of trustworthiness of published work due to predatory publishing practices and the complete lack of transparency in the review and work ranking process. Besides, the democratisation of academic publishing will ensure that retired academicians, citizen scientists, and the global population can contribute their intellectual potential in solving the huge social and ecological problems caused by human behaviour and activities. © 2023 MIPRO Croatian Society.

关键词： Distributed ledger

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Comparative Analysis Between K-Nearest Neighbor (KNN) and Deep Learning Classifiers for Emotion Classification in Virtual Reality Using Electrodermography (EDG) and Heart Rate 4th

Comparative Analysis Between K-Nearest Neighbor (KNN) and De...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Bulagang, Aaron Frederick Mountstephens, James Teo, Jason Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Evolutionary Computing Laboratory Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia

ISBN: (纸本)9789819729760

Virtual Reality as a stimulus to evoke emotion in emotion recognition studies has been emerging in recent years, the objective of this research is to do a comparative analysis between classifiers such as Deep learning and KNN with Heart Rate and electrodermography signals. The difference in classifiers applied plays an important part when it comes to generating the result in accuracy. In this research, 30 participants volunteered in the experiment of using VR as stimuli while their HR and EDG signals were recorded. The recorded signals are then classified using KNN and Deep Learning classifier. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Heart

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User Authentication with Keystroke Dynamics: Performance Evaluation in Neural Network 6

User Authentication with Keystroke Dynamics: Performance Eva...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Kek, Xin-Jin Leau, Yu-Beng Tan, Soo Fun Cybersecurity Research Lab Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia

ISBN: (纸本)9798350389692

In the current context of research, it investigates user authentication through keystroke dynamics using the neural network model which focuses on Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long Short-Term (LSTM). As of above, four distinct experiments were conducted: (1) evaluating 3 different algorithms, (2) layering configuration for CNN and GRU fusion, (3) number of filters and neurons as hyperparameters, (4) assessing combinations of events counts and classes. In justification, the final outcome demonstrates an achieved accuracy of 87%. Based on the experiments that were carried out, optimal hyperparameters were identified: window size of 40, 256 for the number of filters and neurons and 2500 as the optimal number of events with 30 as the number of classes. These findings underscore a significance of considering diverse combinations of a number of events and classes by providing insights into the determination of optimal hyperparameters for keystroke dynamics in performance evaluation. © 2024 IEEE.

关键词： Convolutional neural networks

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Benchmark DPC++ code and performance portability on heterogeneous architectures 46

Benchmark DPC++ code and performance portability on heteroge...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Mijic, Nenad Davidovic, Davor Rüer Bošković Institute Centre for Informatics and Computing Zagreb Croatia

ISBN: (纸本)9789532331042

Source code portability is becoming increasingly important in the development of new solutions in HPC due to the wide diversification of hardware and heterogeneity of systems. With Intel's oneAPI suite of programming tools and the Data Parallel C++ compiler, a single source code containing both host and device code can leverage hardware architectures from different vendors. Using the compiler's interoperability, it can be linked to existing libraries such as MPI to run the program on a distributed memory system. In this paper we benchmark and analyze the performance that can be achieved with the Intel DPC++ compiler, using the distributed Cholesky QR2 algorithm as an example and comparing it with the native CUDA and C++ implementation. The analysis shows that the performance degradation when using SYCL is negligible when a smaller number of nodes are used, but with the cost that some additional self-made optimizations are required in SYCL code. © 2023 MIPRO Croatian Society.

关键词： Interoperability

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Multiple Adaptive Derivative Passive Image Processing Approach to Unsharp Masking Restoration for CNN on Low Light Images 4th

Multiple Adaptive Derivative Passive Image Processing Approa...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Pailus, Rayner Henry Alfred, Rayner Faculty of Computing and Informatics Creative Advanced Machine Intelligence Research Centre Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9789819729760

Most researchers thought face detection challenges are distinguishing faces against the background images and coping with multiple face poses whereby every face will only have an eye, half of a nose and half of a mouth when a face is taken from one side profile image. The actual challenge of face detection is when there is no light or luminant to shine on the face when the image is taken. This challenge is also known as low light illumination. These days, CNNs have been used as the basis of deep learning frameworks in various research works. This paper compares our restoration process against existing non-CNN-based and CNN-based methods. It evaluates whether Retinex and Bilateral theory still can be applied in enhancing and solving face detection in low light. When an image goes through multiple adaptive changes, multiple times as input and output, it is no longer appropriate to call it an output but rather a derivative. Thus, our proposed output is labelled as Multiple Adaptive Derivative and this paper evaluates the performance of the proposed Multiple Adaptive Derivative Passive Image Processing (MADPIP) restoration and enhancement processes for images taken in low light using Logarithmic, CLAHE and Unsharp Masking. Apart from contrast and edge quality, MADPIP’s integrity, image entropy, visual edge and time processing are also measured. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Enhancing Low Light Image Classification Using MADPIP Approach 4th

Enhancing Low Light Image Classification Using MADPIP Approa...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Pailus, Rayner Henry Alfred, Rayner Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9789819729760

Many researchers are interested in finding solutions for face recognition against low light illumination, many succeeded using Histogram Equalization, Retinex Theory, Illumination map estimation model, Frequency domain and Deep learning methods. Nevertheless, not all these mentioned methods can improve all types of face detection and face recognition challenges. Many researchers of face recognition agree and believe that a better-quality input image used for face detection, feature extraction, feature selection, training and testing will produce better detection and recognition. Thus, this paper will evaluate other established and published algorithms and our proposed method called Multiple Adaptive Derivative Passive Image Processing (MADPIP). Our solution does not use only one method but multiple and does not have a single output but multiple outputs. When an image goes through multiple adaptive changes multiple times, it is no longer appropriate to call it an output but rather a multiple-processed output or a multiple adaptive derivative. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification enhancement Convolutional neural network Image restoration Low light images

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A Robust Multiple Adaptive Derivative Face Recognition System on Pose and Illumination 4th

A Robust Multiple Adaptive Derivative Face Recognition Syste...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Pailus, Rayner Henry Alfred, Rayner Faculty of Computing and Informatics Creative Advanced Machine Intelligence Research Centre Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9789819729760

Computer system using CNN visions has proven to be flawed as it fails to capture all the different angles and positions of the image. The main problem with CNN is that it has is no proper system in localizing a person’s head without depending on the person image appearance. CNN based models can memorise images that are augmented and linked together after trained them with supervised positive and negative from the ground truth annotated images marked with landmarks but does not conduct face localization. Thus, the objective of this paper is to investigate, design a face recognition system that is able to recognise a person regardless of his or her head poses. This paper’s task is to investigate a proper system that will be able to be classify face localization. First, MAD Passive Image Processing (MADPIP) is used to perform restoring and enhancing all images. Second, MAD Face Detection (MADFD) is used to process face image with the optimization of Zhu Ramanan (OZR) algorithm to locate landmarks position accurately. Then, MAD Localization and Landmarking (MADLL) is used to build OZR model by performing landmarking classification. Next, the Optimized Zhu Ramanan (OZR) is designed to improve Zhu Ramanan (ZR) processing time by replacing HOG with OZR and finally all the extracted features are normalized. The results showed that the performance of the proposed Multiple adaptive derivative Face Recognition System (MADFRS)is acceptable in which the proposed MADFRS approach proposed a solution with accuracy percentage of 99.80% which is the third highest for LFW Dataset Benchmark. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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