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IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR

作者： Marcell Szá ntó Gergő Strasser Lá szló Szá sz Lehel Dé nes-Fazakas Gyö rgy Eigner Gá bor Kerté sz Levente Ková cs Physiological Controls Research Center &#x00D3 buda University Budap&#x00E9 st Software Engineering Institute John von Neumann Faculty of Informatics &#x00D3 buda University Budapest Biomatics and Applied Artificial Intelligence Institute John von Neumann Faculty of Informatics &#x00D3 Applied Informatics and Applied Mathematics Doctoral School &#x00D3 E&#x00F6 tv&#x00F6 s L&#x00F3 r&#x00E1 nd Research Network Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9781665479332

ISBN: (纸本)9781665479349

Changes in blood sugar levels are significantly affected by dietary carbohydrate intake, which clearly affects insulin therapy. However, currently available insulin pumps do not perform continuous blood glucose monitoring, and the patient should do so. The quality of the treatment can be further improved if the phenomenon of eating can be recognized based on the characteristic movements, and then by sending an automatic signal of the event to the insulin pump, it can be taken into account that an increase in blood sugar levels is expected. In our work, we focused on this task and, using 4 different tools, collected data from more than 8 people on their hand movements during their meals, on which we performed various data manipulations. Finally, using the aggregation of the obtained data set, we tested different machine learning techniques, from which we will select the optimal algorithm that can recognize the motion characteristic of carbohydrate intake independently of the person with the smallest error. The article presents the tools used for the measurement, the operations performed on the data, as well as the tried and tested machine learning methods and their comparative analysis. Our goal is to improve the quality of life of people with diabetes by increasing the effectiveness of base bolus therapy.

关键词： Medical treatment Insulin pumps Gesture recognition Data collection Diabetes Character recognition Biosensors

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A Framework for the Dynamic Generation of Workflows for Network Management

A Framework for the Dynamic Generation of Workflows for Netw...

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IFIP/IEEE International Symposium on Integrated Network Management

作者： Andrzej Jasinski Yuansong Qiao Enda Fallon Ronan Flynn Software Research Institute Athlone Institute of Technology Athlone Ireland Faculty of Engineering & Informatics Athlone Institute of Technology Athlone Ireland

A demonstration of the application of dynamically generated workflows to the management of a network environment is presented. A proposed workflow management framework (WMF) uses dynamically generated workflows to control the managed environment. Exception detection and handling in the workflow generation produce recommendations for mitigating incidents that might occur. The key features of the proposed WMF are its ease of implementation, flexibility and scalability. These features facilitate a quick response by the definition of new recommendations in the form of a workflow that can be executed in a controlled environment. The effectiveness of these recommendations can be assessed through feedback from the managed environment.

关键词： Scalability

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Training Artificial Neural Networks by Coordinate Search Algorithm

Training Artificial Neural Networks by Coordinate Search Alg...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Ehsan Rokhsatyazdi Shahryar Rahnamayan Sevil Zanjani Miyandoab Azam Asilian Bidgoli H.R. Tizhoosh Nature-Inspired Computational Intelligence (NICI) Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Engineering Brock University St. Catharines ON Canada Faculty of Science Wilfrid Laurier University Waterloo ON Canada Department of Artificial Intelligence and Informatics Mayo Clinic Rhazes Lab Rochester MN USA

Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously; for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such

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John Hollerton: a life in industrial analytical spectroscopy, part 1

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Spectroscopy Europe 2022年第8期34卷 32-35页

作者： Davies, Antony N. Cashyap, Mohan Hollerton, John SERC Sustainable Environment Research Centre Faculty of Computing Engineering and Science University of South Wales United Kingdom MASS Informatics Harpenden United Kingdom Hollerton Scientific Software Consultancy St Albans United Kingdom

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Representing Process Characteristics to Increase Confidence in Assurance Case Arguments 14th

Representing Process Characteristics to Increase Confidence ...

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14th International Conference on Dependability of Computer Systems, DepCoS-RELCOMEX 2019

作者： Jarzębowicz, Aleksander Markiewicz, Szymon Department of Software Engineering Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Narutowicza 11/12 Gdańsk80-233 Poland

ISBN: (纸本)9783030195007

An assurance case is a structured, evidence-based argument demonstrating that a safety or other quality objective of a high integrity system is assured. Assurance cases are required or recommended in many industry domains as a means to convince the regulatory bodies to allow commissioning of such system. To be convincing, an argument should address all potential doubts and thus cover numerous additional issues, including the processes that led to development of the considered system. It is however not obvious, which elements of processes (and which characteristics of them) should be documented and how to include them in the argument without making it too large and complex. In this paper we provide description structures for essential process elements. The structures were developed on the basis of literature search and reviews of publicly available assurance cases. We also show how to include such information within the overall assurance case in a way that reduces the complexity and allows to distinguish process-related elements from the primary argument. © 2020, Springer Nature Switzerland AG.

关键词： Safety engineering

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Correction: Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions

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Discover Sustainability 2025年第1期6卷 1-2页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Khan, Muhammad Amir Rehman, Ateeq Ur Saeed, Mamoon M. Hamam, Habib Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Pakistan School of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Computer Science School Education Department Government of Punjab Layyah Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia School of Computing Gachon University Seongnam Republic of Korea Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Sana’a Yemen Faculty of Engineering Uni de Moncton Moncton Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia

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Weighted Density for The Win: Accurate Subspace Density Clustering

Weighted Density for The Win: Accurate Subspace Density Clus...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Maixuan Peng Yuyang Wu Yang Lu Mengke Li Yiqun Zhang Yiu-Ming Cheung School of Computer Science and Technology Guangdong University of Technology Guangzhou China Faculty of Science Wilfrid Laurier University Brantford Canada Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

k-clustering typically struggles with the detection of irregular-distributed clusters due to the natural bias, while density clustering usually cannot well-adapt to different datasets and clustering tasks as it is not an oriented optimization process. This paper, therefore, proposes to perform density clustering in dynamically learned subspaces. To exploit the irregular-distributed clusters obtained by density clustering for the subspace determination, we design a new strategy to appropriately evaluate the importance of attributes. It turns out that the proposed Weighted Density-based Subspace Clustering (WDSC) algorithm inherits the unbiased merits of density clustering, and also upgrades the unlearning density clustering to be learnable under the subspace learning paradigm of k-clustering. A comprehensive evaluation including significance tests, ablation studies, qualitative comparisons, etc., shows the superiority of WDSC.

关键词： Accuracy Heuristic algorithms Signal processing algorithms Clustering algorithms Signal processing Acoustics Speech processing Optimization

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A Scientific Knowledge Dissemination and Pandemic Spread Interplay Model

SSRN

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SSRN 2023年

作者： Zhang, Yi Nekovee, Maziar Tang, Wanjie Ni, Ting Wu, Yue Faculty of Economics and Business Administration Yibin University Yibin644007 China School of Engineering and Informatics University of Sussex BrightonBN19RH United Kingdom Mental Health Centre Sichuan University Chengdu610064 China School of Environment and Civil Engineering Chengdu University of Technology Chengdu610000 China School of Computer and Software Engineering Xihua University Chengdu610039 China

The provision of scientific knowledge is an important pandemic control strategy as it increases the public's sense of responsibility and security and changes their behaviors. It is necessary to understand the interactive relationship between scientific knowledge and the pandemic spread. Based on the classic Brusselator model, this paper developed an interplay model considering scientific knowledge and pandemic spread. Through the theoretical analysis of the model, the possible situations of the equilibrium points are obtained, and a quantitative discriminant method that can judge whether the system is stable is proposed. From the simulation, a phase diagram was drawn depicting the scientific knowledge and pandemic spread under both stable and unstable conditions to verify the theoretical proofs and analyze the results. According to the results, several effective suggestions are proposed to control the spread of disease and this research is expanding the Brusselator model in the pandemic field, providing scientific strategies for pandemic management. © 2023, The Authors. All rights reserved.

关键词： Disease control

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Fingerprint Matching for Noisy and Distorted Patterns Using a Siamese Network With ResNet50 and Multihead Attention

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IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Kumar Lehana, Parveen Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii Ali Alzubi, Ahmad University of Jammu DSP Laboratory Department of Electronics Jammu180006 India University of Jammu Department of Computer Science and IT Jammu180006 India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab Rajpura140401 India Manipal University Jaipur Department of Computer Science & Communication Engineering Jaipur303007 India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa42-200 Poland Faculty of Informatics and Software Engineering National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv03056 Ukraine King Saud University Community College Department of Computer Science Riyadh11451 Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

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A Mathematical Model for Capacity Loss Optimization of Electrical Vehicles Iron-Phosphate-Based Supercharged Batteries Using Bees Algorithm

A Mathematical Model for Capacity Loss Optimization of Elect...

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International Middle-East Power System Conference, MEPCON

作者： D. Mourad A. Yousef M. Sammany Z. Shawa A. Steef A. Atalla Department of Electr. Eng Faculty of Eng University of PSAU Al-Kharj KSA Department of Electr. Tech Faculty of Tech and Indl. Edu University of Helwan Cairo Egypt Department of Electrical Matareya College of Technology Cairo Egypt Department of Pharmacy Practice Faculty of Pharmacy Heliopolis University for Sustainable Development Cairo Egypt Department of Software Engineering Faculty of Information Engineering University of Aleppo Aleppo Syria Faculty of Informatics Engineering Idlib Syria Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (纸本)9781665463645

The recent orientation towards using Electrical Vehicles (EVs), as an alternative to fossil-fuelled- powered vehicles, led to increasing the interest in producing super charged batteries, which is the critical component of EVs and the key of its development and rapid spread. Iron-Phosphate-Based Supercharged Battery (IP-BSBs) has proved its efficiency as a competitor to lead and lithium batteries. Now, it became necessary to increase its efficiency, by the optimum design, to appropriately fit its correspondent vehicle. However, conventional calibration models used to obtain the optimal design parameters often lead to a dramatic waste of time, effort, and resources (cost), without any guarantee to reach the optimal solution. In this paper, a mathematical model is proposed to optimize the capacity loss of IP-BSBs under real manufacturing conditions. The proposed model was solved using meta-heuristic search algorithm represented by Bees Algorithm (BA). Simulation results have shown the precision of our model and the possibility of obtaining an optimal design of IP-BSBs compared to its counterparts of widely existing types.

关键词： Solid modeling Simulation Metaheuristics Whales Voltage Rats Pareto optimization

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