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17th International Conference on Brain Informatics, BI 2024

作者： Doborjeh, Zohreh lavin, Daniel Hunter, Kirsty Heym, Nadja Heasman, Bryony Doborjeh, Maryam Kasabov, Nikola Gibson, Glen Sumich, Alexander School of Engineering Computer and Mathematical Sciences Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand School of Population Health Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand Department of Sport Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom NTU Psychology School of Social Sciences Nottingham Trent University Nottingham United Kingdom School of Software Engineering Dalian University Dalian China Institute for Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Department of Food and Nutritional Sciences The University of Reading Whiteknights Reading United Kingdom Department of Psychology and Neuroscience Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819632930

The immune and enteric systems have been implicated in psychopathology, including depressive disorders. However, the precise neurocognitive mechanisms remain unclear. The present study uses Brain-Inspired Spiking Neural Network (SNN) technique to model electroencephalographic (EEG) data as a function of gut-microbiota, inflammation, and depressive symptoms. Forty-two participants (nonclinical cohort) were assessed for Bacteroides-Prevotella (BAC) colonization in faecal samples, Interleukin 6 (IL-6) in peripheral blood, depressive symptoms (self-report of cognitive and non-cognitive symptoms) and brain function (resting state EEG). SNN models were applied to EEG data to generate connection weight values across scalp regions. Connection weights were visualized as a function of 1) depression;2) inflammation;3) gut microbiome. Lower depression and higher BAC were associated with higher frontal connection weights (cognitive depression = bilateral, non-cognitive and BAC = left hemisphere only). Lower cognitive depression and lower IL-6 were associated with higher posterior connection weights. The SNN models achieved higher accuracy for theta and alpha EEG sub-bands, highlighting their relevance in differentiating between groups. These findings emphasize the potential of SNNs in predicting parameters from brain functional connectivity data and developing targeted diagnostic and intervention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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Cardiovascular Diseases Risk Analysis using Distance-Based Similarity Measure of Neutrosophic Sets

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Neutrosophic Sets and Systems 2021年 47卷 26-37页

作者： Mustapha, Norzieha Alias, Suriana Yasin, Roliza Md Abdullah, Ilyani Broumi, Said Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Kelantan Kelantan Bukit Ilmu Machang18500 Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Laboratory of Information Processing Faculty of Science Ben M’Sik University Hassan II Casablanca Morocco

One of the highest causes of death in many countries in this modern era is cardiovascular diseases. There are a few symptoms that linked significantly to the cardiovascular diseases. The symptoms and diseases relationship can be represented by neutrosophic set values. In general, neutrosophic set gives remarkable contribution in denoising, clustering, segmentation, and classification in handling data of many real applications including in medical field. This study aims to analyse the cardiovascular disease risks by a new distance-based similarity measure motivated from intuitionistic fuzzy set theory. The proof for all the properties is presented clearly. Then, a case study is conducted by using the data on the severity level of the six symptoms found in two different patients. The neutrosophic data are analysed to determine the patients’ possibility of having any one or combination of the three types of cardiovascular diseases. A comparative study involving three common distance measures is conducted. The results show that the similarity indexes for all measures of both patients are less than 0.5. This situation can further conclude as both patients are possibly not suffering from cardiovascular diseases. © 2021, Neutrosophic Sets and Systems. All Rights Reserved.

关键词： Data handling

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LEARNING SURROGATE EQUATIONS FOR THE ANALYSIS OF AN AGENT-BASED CANCER MODEL

arXiv

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arXiv 2025年

作者： Burrage, Kevin Burrage, Pamela Kreikemeyer, Justin N. Uhrmacher, Adelinde M. Weerasinghe, Hasitha N. School of Mathematical Sciences Queensland University of Technology BrisbaneQLD Australia Department of Computer Science University of Oxford United Kingdom Modelling and Simulation Institute for Visual and Analytic Computing Faculty of Informatics and Electrical Engineering University of Rostock Rostock Germany

In this paper, we adapt a two species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction with a third species – namely immune cells. We run six different scenarios to explore the competition between cancer and immune cells and the initial concentration of the immune cells on cancer dynamics. We then use coupled equation learning to construct a population-based reaction model for each scenario. We show how they can be unified into a single surrogate population-based reaction model, whose underlying three coupled ordinary differential equations are much easier to analyse than the original agent-based model. As an example, by finding the single steady state of the cancer concentration, we are able to find a linear relationship between this concentration and the initial concentration of the immune cells. This then enables us to estimate suitable values for the competition and initial concentration to reduce the cancer substantially without performing additional complex and expensive simulations from an agent-based stochastic model. The work shows the importance of performing equation learning from agent-based stochastic data for gaining key insights about the behaviour of complex cellular dynamics. © 2025, CC BY.

关键词： Lung cancer

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Sfamnet: A Scene Flow Attention-Based Micro-Expression Network

SSRN

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SSRN 2023年

作者： Liong, Gen-Bing Liong, Sze-Teng Chan, Chee Seng See, John CISiP Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan School of Mathematical and Computer Sciences Heriot-Watt University Malaysia Putrajaya Malaysia

Tremendous progress has been made in facial Micro-Expression (ME) spotting and recognition;however, most works have focused on either spotting or recognition tasks on the 2D videos. Until recently, the estimation of the 3D motion field (a.k.a scene flow) for the ME has only become possible after the release of the multi-modal ME dataset. In this paper, we propose the first Scene Flow Attention-based Micro-expression Network, namely SFAMNet. It takes the scene flow computed using the RGB-D flow algorithm as the input and predicts the spotting confidence score and emotion labels. Specifically, SFAMNet is an attention-based end-to-end multi-stream multi-task network devised to spot and recognize the ME. Besides that, we present a data augmentation strategy to alleviate the small sample size problem during network learning. Extensive experiments are performed on three tasks: (i) ME spotting;(ii) ME recognition;and (iii) ME analysis on the multi-modal CAS(ME)3 dataset. Empirical results indicate that depth is vital in capturing the ME information and the effectiveness of the proposed approach. Our source code is publicly available at https://***/genbing99/SFAMNet . © 2023, The Authors. All rights reserved.

关键词： Face recognition

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Multi-Agent Economic Modeling Automation Technologies 9

Multi-Agent Economic Modeling Automation Technologies

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Zhukova, Aleksandra A. Pilnik, Nikolai P. Kamenev, Ivan G. Iusup-Akhunov, Batyrzhan B. Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Mathematical Modelling in Economic Social and Ecological Systems Vavilov st. 44 building 2 Moscow119333 Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Mathematical Modelling of Economic Systems Vavilov st. 44 building 2 Moscow119333 Russia Hse University Faculty of Economic Sciences Department of Applied Economics Pokrovsky Bulvar 11 Moscow109028 Russia Fsbeihe Bauman Moscow State Technical University Department of Fundamental Sciences-11 2nd Baumanskaya street 5-1 Moscow05005 Russia The Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology 9 Institutskiy per. Moscow Region Dolgoprudny141701 Russia

ISBN: (纸本)9798350311402

Complex economic models often describe the agent's decisions, i.e. choice of some controlled variables in accordance with criteria set by agents. Unlike purely statistical ones, agent-based models allow to describe processes with different modes, in which the agent's behavior changes quali-tatively when the environment (including the actions of other agents) change. The practical use of mathematical models of complex systems with a number of interacting agents is limited by complexity of their construction and possible errors when dealing with multitude of expressions. computer tools, such as the 'Ecomod' system discussed in the article, can substantially reduce the time spent on model creation and processing, and help to coordinate the work of several researchers on the model. © 2023 IEEE.

关键词： Autonomous agents

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The Optimization of the Halophilic Cellulase Production: A 3-2-1 Multilayer Perceptron Artificial Neural Network Approach 4th

The Optimization of the Halophilic Cellulase Production: A 3...

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4th Symposium on Intelligent Manufacturing and Mechatronics, SIMM2021

作者： Ahmarofi, Ahmad Afif Gunny, Ahmad Anas Nagoor Jamil, Jastini Mohd Amlus, Naimah Department of Computer Science Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Kedah Merbok08400 Malaysia School of Bioprocess Engineering Universiti Malaysia Perlis Kompleks Pusat Pengajian Jejawi 3 Perlis Arau02600 Malaysia School of Quantitative Sciences College of Arts and Sciences Universiti Utara Malaysia Kedah Sintok06010 Malaysia Universiti Utara Malaysia Information Technology Universiti Utara Malaysia Kedah Sintok06010 Malaysia

ISBN: (纸本)9789811689536

Lignocellulose is one of the bio-resources available on the earth. It could be hydrolyzed into simple sugar. The previous study found that carboxymethylcellulose (CMC), FeSO4·7H2O, and NaCl are the significant mediums that influence the production of halophilic cellulase. Despite that, an appropriate method is deemed crucial from an industrial perspective to optimize halophilic cellulose production for cost-effectiveness. In this regard, the optimum halophilic cellulose production is determined from the best-so-far parameter of the three significant mediums. A data mining process using Multilayer Perceptron (MLP) based on the Artificial Neural Networks (ANN) method is developed to optimize the parameter from a set of experimental data. A 3-2-1 MLP network was constructed to learn the experimental data. As a result, the root squared error from the MLP is 0.0118 during the validation process. Subsequently, the MLP network was considered to determine the parameter of the significant medium and the production of halophilic cellulose. Consequently, this finding provides beneficial guidance for the manufacturer in the chemical industry to achieve efficient halophilic cellulose production. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial Neural Networks Data mining Halophilic cellulose Multilayer Perceptron

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Deploying Fuzzy Rough Set and Artificial Immune System Algorithms for Air Quality Prediction 11th

Deploying Fuzzy Rough Set and Artificial Immune System Algor...

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11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Lasisi, Ayodele Ghazali, Rozaida Ismail, Lokman Hakim Husaini, Noor Aida Department of Mathematical Sciences Faculty of Science Augustine University Ilara-Epe Lagos106101 Nigeria Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Parit Raja86400 Malaysia Department of Design Engineering and Architecture Faculty of Civil and Environmental Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Parit Raja86400 Malaysia

ISBN: (纸本)9789811681288

The widespread of air pollution due to emissions of highly detrimental concentrates on human life needs paramount attention. Devising air prediction strategy that accurately analyze air quality level through gathering of useful information allow for relevant organizations to disseminate promptly control measures. This paper proposes the use of artificial immune system (AIS) algorithms consisting of Immunos algorithms (Immunos-1, Immunos-2, Immunos-99), CLONal selection ALGorithm (CLONALG), clonal selection classification algorithm (CSCA), artificial immune recognition system algorithms (AIRS1, AIRS2, Parallel AIRS2) for air quality prediction. The fuzzy rough set selects pertinent data features summarizing interpretations of the data. Comparative simulations reveal that the Parallel AIRS2 produced superlative results to other algorithms with detection rate of 96.40%. Effective prediction performance can be generated with AIS algorithms having highest detection rates and lowest false alarm rates. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Hybrid Data Augmentation for Citation Function Classification

Hybrid Data Augmentation for Citation Function Classificatio...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yang Zhang Yufei Wang Quan Z. Sheng Adnan Mahmood Wei Emma Zhang Rongying Zhao Faculty of Science and Engineering School of Computing Macquarie University Sydney Australia School of Information Management Wuhan University Wuhan China School of Computer and Mathematical Sciences The University of Adelaide Adelaide Australia

The citation function generally signifies the purpose or reason underlying a citation within a scholarly paper or a research article. Automatic citation function classification is, therefore, a task in computational linguistics and information science that can facilitate further applications in reference research, citation recommendation, and evaluation of research activities. By taking into account the state of the art, we identify two major constraints pertinent to the data of the citation function classification task, i.e., data imbalance and data sparsity. On the one hand, the natural distribution of different types of citations in one scientific literature is uneven leading to data imbalance in the real scenario. On the other hand, the citation function data is generally labeled by an expert which takes huge human effort resulting in a limited data scale. To this end, in this paper, we propose HybridDA, a two-stage model based on GPT-2 data argumentation and data retrieval to synthesize more high-quality annotated citation function data in a bid to solve both data imbalance and data sparsity problems. We conduct experiments on imbalance setting and low resource setting with our proposed approach. The experimental results on both of these settings demonstrate that our proposed model can achieve competitive performance in contrast to the other baseline models.

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Parallel Image Encryption Algorithm Using Partitioned Cellular Automata on Graphic Processor Unit

Parallel Image Encryption Algorithm Using Partitioned Cellul...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Mahmood Fazlali Amirhossein Alihoseini Ebrahim Zarei Myasar Tabani Seyedali Pourmoafi Cybersecurity and Computing Systems Research Group University of Hertfordshire Hatfiled UK Dept. of data and Computer Sciensce Faculty of Mathematical sciences Shahid Beheshti University Tehran Iran Dep. of Computer Science Khansar Campus University Of Isfahan Isfahan Iran

Image encryption is a reliable method for securely transmitting images over a network. The time required to encrypt and decrypt an image in online applications is also very important. Although cellular automata cryptography is an appropriate technique for parallelizing and accelerating cryptographic methods, its capacity cannot be demonstrated only in multi-core platforms. Thus, it is needed to parallelize cellular automata cryptography on Graphic Processor Units (GPUs) in order to significantly decrease the encryption/decryption time. In this paper, we propose a new parallel algorithm for two-dimensional cellular automata cryptography that is implemented on GPU. The proposed algorithm uses multiple threads at once to accelerate the bit-level permutation and substitution operations by taking into account the capacity of cellular automata in parallel processing. According to the study experimental findings, the proposed algorithm performs faster on GPU compared to a multicore platform while maintaining the same level of security in comparison to the serial algorithm.

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An Interpretable Model for Large-Scale Smart Contract Vulnerability Detection

SSRN

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SSRN 2023年

作者： Feng, Xia Liu, Hai-Yang Wang, Liang-Min Zhu, Hui-Juan Sheng, Victor S. Faculty of Data Science City University of Macao China School of Mathematical Sciences Jiangsu University Zhenjiang China School of Cyber Science and Engineering Southeast University Nanjing China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Department of Computer Science Texas Tech University LubbockTX United States

Smart contracts hold billions of dollars in digital currency, and their security vulnerabilities have drawn a lot of attention in recent years. Traditional methods for detecting smart contract vulnerabilities rely primarily on symbol execution, which makes them time-consuming with high false positive rates. Recently, deep learning approaches have alleviated these issues but still face several major limitations, such as lacking interpretability and susceptibility to evasion techniques. In this paper, we propose a feature selection method for uplifting modeling. The fundamental concept of this method is a feature selection algorithm, utilizing interpretation outcomes to select critical features thereby reducing the scales of features. The learning speed could be accelerated significantly because of the reduction of the feature size. Experiment shows that our proposed model performs well in six types of vulnerability detection. The accuracy of each is higher than 93\% and the average detection time of each smart contract is less than 1 ms. Notably, through our proposed feature selection algorithm, the training time of each type of vulnerabilities is reduced by nearly 80\% compared with its original. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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