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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Thomas-Hercules, Daniel Palaniappan, Ramaswamy University of Kent Ai and Data Analytics Research Group School of Computing Chatham United Kingdom

ISBN: (纸本)9798350389609

This research investigates the effectiveness of icons in memory-related tasks, focusing on two icon styles (Solid and Outline) and two semantic classifications (Resemblance and Abstract). Through the N-Back task and electroencephalogram (EEG) measurements, the study aims to provide quantitative insights into memory load and recall speed. Twelve University of Kent students participated in an experiment involving 1-Back and 2-Back tests, using four icon combinations: Solid Resemblance, Solid Abstract, Outline Resemblance, and Outline Abstract. Each participant completed eight tests, during which N-Back reaction times, accuracy, and EEG data were recorded using a 32-channel wireless brain-computer interface (BCI). Statistical analysis was performed using the Wilcoxon signed rank test to evaluate reaction times and event-related potentials (ERPs). The results revealed no significant differences in recall speed between the Solid and Outline icon styles. However, contrary to previous research favoring Resemblance icons, Abstract icons were recalled significantly faster, particularly in the Outline style. However, Outline Abstract icons exhibited a significantly smaller P300 amplitude compared to Outline Resemblance icons, indicating a higher memory load. These findings suggest that Outline Resemblance icons may offer better usability due to their lower memory load but Outline Abstract icons if recall speed is the key. The study also employed N400 analysis to further explore the semantic processing of the icon combinations. These findings underscore the need for further research into the various dimensions of icons to better understand their impact on memory performance. © 2024 IEEE.

关键词： Brain computer interface

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Neurocognitive Impact of Icon Styles and Semantics on Memory and Recall Speed: An N-Back Study Using EEG

Neurocognitive Impact of Icon Styles and Semantics on Memory...

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data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Daniel Thomas-Hercules Ramaswamy Palaniappan AI and Data Analytics Research Group School of Computing University of Kent Chatham UK

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

关键词： Wireless communication Statistical analysis Semantics Focusing Solids Particle measurements Electroencephalography Brain-computer interfaces Cognitive informatics Usability

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: Quantum Artificial Intelligence for the Automotive Industry

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KI - Künstliche Intelligenz 2024年第4期38卷 351-359页

作者： Stollenwerk, Tobias Bhattacharya, Somtapa Cattelan, Michele Ciani, Alessandro Compostella, Gabriele Headley, David Klepsch, Johannes Klusch, Matthias Leder, Markus Macaluso, Antonio Michielsen, Kristel Nabok, Dmytro Papanikolaou, Anestis Rausch, Alexander Schumann, Marco Skolik, Andrea Yarkoni, Sheir Wilhelm, Frank K. Institute for Quantum Computing Analytics (PGI-12) Forschungszentrum Jülich Jülich Germany Volkswagen Data:Lab Volkswagen Group Munich Germany Machine Learning Research Lab Volkswagen Group Munich Germany Mercedes-Benz AG Stuttgart Germany BMW AG München Germany German Research Center for Artificial Intelligence (DFKI) Saarbrüecken Germany Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich Jülich Germany Robert Bosch GmbH Corporate Research Renningen Germany

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series data and Sequential Decision Making

arXiv

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arXiv 2023年

作者： Yu, Shujian Li, Hongming Løkse, Sigurd Jenssen, Robert Príncipe, José C. The Machine Learning Group UiT - The Arctic University of Norway Tromsø Norway The Quantitative Data Analytics Group Vrije Universiteit Amsterdam Netherlands The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States The Drones and Autonomous Systems group at NORCE Norwegian Research Centre Tromsø Norway The Pioneer AI Centre Copenhagen University The Norwegian Computing Center Oslo Norway

The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be simply estimated by a kernel density estimator from given samples. We illustrate the advantages (e.g., rigorous faithfulness guarantee, lower computational complexity, higher statistical power, and much more flexibility in a wide range of applications) of our conditional CS divergence over previous proposals, such as the conditional KL divergence and the conditional maximum mean discrepancy. We also demonstrate the compelling performance of conditional CS divergence in two machine learning tasks related to time series data and sequential inference, namely time series clustering and uncertainty-guided exploration for sequential decision making. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for computing Machinery.

关键词： Ethanol

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Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data

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Computers in Biology and Medicine 2024年 178卷 108704-108704页

作者： Taberna, Gaia Amaranta Samogin, Jessica Zhao, Mingqi Marino, Marco Guarnieri, Roberto Cuartas Morales, Ernesto Ganzetti, Marco Liu, Quanying Mantini, Dante Movement Control and Neuroplasticity Research Group KU Leuven Leuven3001 Belgium Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou730000 China Department of General Psychology University of Padova Padova35131 Italy Dirección Académica Universidad Nacional de Colombia Sede de La Paz La Paz202017 Colombia pRED Data & Analytics Roche Innovation Center Basel F. Hoffmann–La Roche Ltd Basel4070 Switzerland Department of Biomedical Engineering Southern University of Science and Technology Shenzhen518055 China KU Leuven Brain Institute KU Leuven Leuven3000 Belgium

Introduction: High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets. Findings: Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software. Conclusions: NET is freely available (https://***/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows. © 2024

关键词： Electroencephalography

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Keywords-oriented data Augmentation for Chinese 6

Keywords-oriented Data Augmentation for Chinese

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Yuan, Fang Hong, Xianbin Yuan, Cheng Fei, Xiang Guan, Sheng-Uei Liu, Dawei Wang, Wei Ai Group Enc Digital Technology Co. Ltd Shanghai China Research Institute of Big Data Analytics Xi'an Jiaotong-Liverpool University Suzhou China Ai Group Beijing Livedata Technology Co. Ltd Beijing China School of Computing Electronics and Maths Coventry University Coventry United Kingdom Cyber Technology Institute De Montfort University Leicester United Kingdom

ISBN: (纸本)9781728186351

In natural language processing tasks, data is very important, but data collection is not cheap. Large volume data can well serve a series of tasks, especially for deep learning tasks. data augmentation methods are solutions to data problems, which can work well on rising data quality and quantity, such as generating text without meaning changing and expanding the diversity of data distribution. A user-friendly method of the data augmentation is to sample words in a text then augmenting them. The sampling method is often implemented by a random probability. Although the performance of this solution has been proved over the past few years, random sampling is not the best choice for the data augmentation as it has a chance of randomly introducing some noise into initial data, like stop words. The generated data could interfere with the subsequent tasks and drop the accuracy of the tasks' solutions. Hence, this paper aims to introduce a novel data augmentation method that could avoid involving such noisy data. The strategy is keywords-oriented data augmentation for Chinese (KDA). The KDA proposed in this paper indicates a method of extracting keywords based on category labels, and an augmenting method based on the keywords. In contrast to randomness, the proposed technique firstly selects the key information data, then expands the selected data. The experimental section is compared with another two typical data augmentation techniques on three Chinese data sets for text classification tasks. The result shows that the KDA technique has a better performance in the data augmentation task than the compared two. © 2020 IEEE.

关键词： Classification (of information)

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Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem

arXiv

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arXiv 2022年

作者： Tahir, Barzan Hussein Rashid, Tarik A. Rauf, Hafiz Tayyab Bacanin, Nebojsa Chhabra, Amit Vimal, S. Yaseen, Zaher Mundher Department of Computer Science and Engineering University of Kurdistan Helwer Erbil Iraq Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia USQ's Advanced Data Analytics Research Group School of Mathematics Physics and Computing University of Southern Queensland QLD4350 Australia New era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq

Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced Fitness Dependent Optimizer. Correspondingly, various standard estimations are used to prove the stability

关键词： Decision making

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Big POI data integration with linked data technologies 22

Big POI data integration with linked data technologies

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22nd International Conference on Extending database Technology, EDBT 2019

作者： Athanasiou, Spiros Giannopoulos, Giorgos Graux, Damien Karagiannakis, Nikos Lehmann, Jens Ngomo, Axel-Cyrille Ngonga Patroumpas, Kostas Sherif, Mohamed Ahmed Skoutas, Dimitrios Information Management Systems Institute Athena Research Center Greece Enterprise Information Systems Department Fraunhofer IAIS Germany Smart Data Analytics Group University of Bonn Germany Computing Center Universität Leipzig Germany Data Science Group Universität Paderborn Germany

ISBN: (纸本)9783893180813

Point of Interest (POI) data constitutes the cornerstone in many modern applications. From navigation to social networks, tourism, and logistics, we use POI data to search, communicate, decide and plan our actions. POIs are semantically diverse and spatiotemporally evolving entities, having geographical, temporal, and thematic relations. Currently, integrating POI datasets to increase their coverage, timeliness, accuracy and value is a resource-intensive and mostly manual process, with no specialized software available to address the specific challenges of this task. In this paper, we present an integrated toolkit for transforming, linking, fusing and enriching POI data, and extracting additional value from them. In particular, we demonstrate how Linked data technologies can address the limitations, gaps and challenges of the current landscape in Big POI data integration. We have built a prototype application that enables users to define, manage and execute scalable POI data integration workflows built on top of state-of-the-art software for geospatial Linked data. This application abstracts and hides away the underlying complexity, automates quality-assured integration, scales efficiently for world-scale integration tasks, and lowers the entry barrier for end-users. Validated against real-world POI datasets in several application domains, our system has shown great potential to address the requirements and needs of cross-sector, cross-border and cross-lingual integration of Big POI data. © 2019 Copyright held by the owner/author(s).

关键词： data integration

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