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International Conference on computer and Automation engineering, ICCAE

作者： Ivan Kuric Vladimír Stenchlák Ivan Zajačko Martin Bohušík Dorota Więcek Department of Automation and Production Systems University of Žilina Žilina Slovakia Faculty of Mechanical Engineering and Computer Science University of Bielsko-Biala Bielsko-Biala Poland

This article deals with the problematics of obtaining and processing the EOG signals. This research describes the ways in which it is possible to measure and evaluate the measured bio-electrical signals of brain and muscle activity in more detail. The work also describes the analysis of the current state of this problematics and individual models of artificial intelligence are also described in the work - artificial neural networks that can be used for such advanced analysis. The experimental part of this work is performed in the MATLAB environment using the Deep Neural Network Toolbox. The experimental part of the work is focused on the analysis and processing of bio-electrical activity eye-motor muscles in the area of eye-tracking with the help of the OLIMEX EEG-SMT device. The information provided in this research can be used for improving safety and reliability and elimination of potential risks connected to an epilepsy disease and epilepsy seizures in automated production systems.

关键词： Deep learning Safety devices Production systems Voltage measurement Automation Tracking Current measurement

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Chaotic Ions Motion Optimization (CIMO) for Biological Sequences Local Alignment: COVID-19 as a Case Study 1

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3rd International Symposium on Signal and Image Processing, ISSIP 2020

作者： Issa, Mohamed Helmi, A.M. Elaziz, Mohamed Abd Bhattacharyya, Siddhartha Computer and Systems Department Faculty of Engineering Zagazig University Zagazig Egypt Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt Rajnagar Mahavidyalaya Birbhum India

ISBN: (数字)9789813369665

ISBN: (纸本)9789813369658

COVID-19 is a pandemic that broke out throughout the world and has a high mobility to transfer between humans. Developing intelligent bioinformatics tools is a mandatory to aid in the analysis of the disease. One of these tools is local aligner which aims to find the longest common subsequence between two biological sequences. Fragmented local aligner technique (FLAT) was developed based on meta-heuristic algorithms to accelerating the alignment process, especially for sequences with huge length. In this paper, the performance of ions motion optimization (IMO) algorithm for implementing FLAT was measured. The performance was poor, and a chaotic parameter was added in the exploration equations of IMO to enhance its performance for FLAT. A set of real proteins having a product length which ranges from 250,000 to 9,000,000 were used as a dataset to test the performance of IMO and its developed version. Besides, COVID-19 virus was aligned using FLAT according to IMO and chaotic IMO to verify the enhancement of IMO. All results were compared to the results founded by Smith–Waterman approach. The tests prove the superiority of chaotic IMO over IMO for implementing FLAT on all datasets. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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A polynomial quantum computing algorithm for solving the dualization problem for positive boolean functions

A polynomial quantum computing algorithm for solving the dua...

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2023 International Workshop on AI for Quantum and Quantum for AI, AIQxQIA 2023

作者： Mezzini, Mauro Gomez, Fernando Cuartero Lopez Pelayo, Fernando Paulet Gonzalez, Jose Javier de la Cruz Calvo, Hernan Indibil Pascual, Vicente Department of Education Roma Tre University Rome00185 Italy Computing Systems Department Faculty of Computer Science Engineering University of Castilla-La Mancha Albacete02071 Spain Quantum Computing Department Qsimov Quantum Computing S.L Talavera de la Reina Toledo45600 Spain

Given two positive Boolean functions ∱ : {0, 1}n → {0, 1} and g : {0, 1}n → {0, 1} expressed in their positive irredundant DNF Boolean formulas, the dualization problem consists in determining if g is the dual of ∱, that is if ∱(x1, . . ., xn) = g(x1, . . . xn) for all (x1, . . . xn) ∈ {0, 1}n. In this paper we present a quantum computing algorithm that solves the dualization problem in polynomial time with respect to the dimensions of the DNF expressions. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Boolean Functions Computational complexity Dualization Quantum Algorithm

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Biological Influence of Low-Frequency Electromagnetic Field Exposition Based on Selected Cell Death Markers

Biological Influence of Low-Frequency Electromagnetic Field ...

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ELEKTRO

作者： Robert Cichorowski Olga Kocikowska Marek Bajtoš Roman Radil Ladislav Janoušek Daria Gendosz De Carrillo Sebastian Student Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology liwice Poland Department of Engineering and Systems Biology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Department of Physiology Faculty of Medical Sciences Silesian University of Medicine Katowice Poland Department of Electromagnetic and Biomedical Engineering Faculty of Electrical Engineering and Informational Technology Žilina Slovakia Department of Histology Faculty of Medical Sciences in Zabrze Silesian University of Medicine Zabrze Poland Biotechnology Center Silesian University of Technology Gliwice Poland

ISBN: (数字)9798350372359

ISBN: (纸本)9798350372366

This work presents the results of the examination of the HeLa cell line exposure on the ELF-EMF (extremely low-frequency electromagnetic field). In particular, the relationship between ELF-EMF exposition time and cell death. The examination of cell death hallmarks were examined by estimation levels of selected proteins - FACL4 (a protein that is a part of the ferroptosis pathway) and CK18 (Cytokeratin related to necrosis and apoptosis pathways) in the proposed model of workers exposed to ELF-EMF week.

关键词： Proteins Biological system modeling Estimation Electromagnetic fields

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Domain adaptive FL for edge-enabled privacy-preserving MRI analysis

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Alexandria engineering Journal 2025年 128卷 324-339页

作者： Iqbal, Saeed Choudhry, Imran Arshad Ullah, Inam Kaur, Kuljeet Choi, Bong Jun Hassan, Mohammad Mehedi College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan Department of Computer Engineering Gachon University Seongnam13120 Korea Republic of Electrical Engineering Department École de technologie supérieure MontréalQCH3C1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India School of Computer Science and Engineering Soongsil University Seoul Korea Republic of Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Data heterogeneity, privacy leakage challenges, the ineffectiveness of conventional collaborative learning techniques, and unresolved managing non-IID data distributions are some of the major obstacles to implementing artificial intelligence (AI) in healthcare. Federated learning (FL) frameworks frequently have trouble distinguishing between privacy protection and model accuracy, especially when used for delicate medical imaging applications. This study presents a novel framework that synergizes federated learning (FL) with edge computing to address these issues while safeguarding patient privacy. Our proposed Domain Adaptive Federated (DAD) learning approach effectively mitigates both inter-client and intra-client data heterogeneity, enabling collaborative model training across diverse medical imaging modalities (MRI, CT, PET) through cross-domain adaptation. Experimental evaluations on MRI brain segmentation datasets demonstrate the superior performance of DAD compared to traditional FL methods, as evidenced by significant improvements in F1-score (96.3), sensitivity (96.0), specificity (97.1), and AUC (96.7). This enhanced accuracy and robustness in handling heterogeneous and privacy-sensitive data render DAD an ideal candidate for privacy-preserving AI in consumer healthcare. By pioneering innovative strategies for collaborative model training and data privacy, this research contributes to the emerging field of edge intelligence, paving the way for improved patient outcomes while adhering to stringent confidentiality and ethical mandates. © 2025

关键词： Federated learning

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Applications of Neural Networks in Engineer-to-order Environment 15

Applications of Neural Networks in Engineer-to-order Environ...

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15th CIRP Conference on Intelligent Computation in Manufacturing engineering, ICME 2021

作者： Ziegler, Cedric C. Dobhan, Alexander Heusinger, Moritz University of Applied Sciences Würzburg-Schweinfurt Faculty of Business and Engineering Konrad-Zuse-Straße 2 Schweinfurt97421 Germany University of Applied Sciences Würzburg-Schweinfurt Faculty of Computer Science and Business Information Systems Sanderheinrichsleitenweg 20 Wuerzburg97074 Germany

Machine tool selection and quotation costing have a low level of automation in today's engineer-to-order environments. The decision-making process is based on imprecise human judgment even if all final product characteristics are known. To improve precision and save time, we propose to apply artificial neural networks that are trained on data of already produced products. In particular, we address the selection of several grinding wheels to produce a milling cutter, which is a multiple-criteria decision, using multi-label classification. The quotation costing is a single output regression problem. In both tasks, our results on real-world data show high accuracy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Neural networks

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Low-Cost, High-Performance Algorithm and Hardware Architecture for SAO Mode Decision

Low-Cost, High-Performance Algorithm and Hardware Architectu...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Bingjing Hou Chang Liu Yuejun Zhang Leilei Huang Minge Jing Yibo Fan State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Institute of Microelectronic Circuits and Systems East China Normal University Shanghai China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Sample adaptive offset (SAO) is applied for reducing sample distortion and attenuating ringing artifacts in both HEVC and VVC standards. The rate-distortion optimization process is used to select the best SAO parameter. It accounts for the majority of SAO complexity and determines the SAO filtering performance. Therefore, the rate-distortion cost calculation should be well handled to achieve both low cost and high coding performance. In this paper, we propose a simplified and optimized rate estimation algorithm based on the characteristics of SAO parameter syntax and entropy coding process. And we propose a hierarchical flow for rate-distortion cost calculation to further reduce the computational cost and storage cost for SAO mode decision hardware. The results show that the proposed algorithm achieves an average YUV loss of -0.10%, and the proposed SAO mode decision hardware architecture can be implemented by only 21.8k gates using TSMC 65nm process under 400 MHz.

关键词： Video coding Costs Rate-distortion Estimation computer architecture Syntactics Logic gates Hardware Entropy coding Standards

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A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade

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Social sciences and Humanities Open 2025年 11卷

作者： Kovari, Attila Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Computer Engineering University of Dunaújváros Dunaújváros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John von Neumann University Kecskemét Hungary

This review examines the current state of integration and impact of AI-enhanced collaborative learning in the higher education sector. Given the rapid advances in technology, AI has enormous potential for application in educational settings, with benefits in terms of personalizing learning, better engaging learners and improving learning outcomes. Artificial intelligence tools, in particular machine learning, natural language processing and recommender algorithms, facilitate collaborative learning by enabling personalized learning through feedback and group work. Furthermore, this review concludes that predictive analytics and multimodal approaches supported by artificial intelligence have been shown to enhance student engagement and motivation, while personalized learning systems and recommender algorithms ensure the effectiveness of collaborative learning environments. It also identifies two other critical issues: good task design and effective emotional engagement and social presence in AI-based environments. In addition, it highlights some of the problems and ethical considerations arising from the integration of AI like transparency, data protection, and a balance between full automation and human touch. This review aims to integrate the current state and future opportunities of AI-enhanced collaborative learning within a higher education context to inform educators, researchers, and policy makers in pursuit of improving teaching and learning practices. © 2025 The Author

关键词： AI-Powered collaborative learning Educational outcomes Higher education Personalized learning Social presence Student engagement

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Ensemble Learning for Fetal Health Classification

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computer systems science & engineering 2023年第10期47卷 823-842页

作者： Mesfer Al Duhayyim Sidra Abbas Abdullah Al Hejaili Natalia Kryvinska Ahmad Almadhor Huma Mughal Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz University Al-Kharj16273Saudi Arabia Department of Computer Science COMSATS UniversityIslamabad53000Pakistan Faculty of Computers&Information Technology Computer Science DepartmentUniversity of TabukTabuk71491Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Information Systems Department Faculty of Management Comenius University in BratislavaOdbojárov 1082005 Bratislava 25Slovakia Department of Computer Science Kinnaird College for WomenLahore54000Pakistan

Cardiotocography(CTG)represents the fetus’s health inside the womb during ***,assessment of its readings can be a highly subjective process depending on the expertise of the *** signals from fetal monitors acquire parameters(i.e.,fetal heart rate,contractions,acceleration).Objective:This paper aims to classify the CTG readings containing imbalanced healthy,suspected,and pathological fetus ***:We perform two sets of ***,we employ five classifiers:Random Forest(RF),Adaptive Boosting(AdaBoost),Categorical Boosting(CatBoost),Extreme Gradient Boosting(XGBoost),and Light Gradient Boosting Machine(LGBM)without over-sampling to classify CTG readings into three categories:healthy,suspected,and ***,we employ an ensemble of the above-described classifiers with the *** use a random over-sampling technique to balance CTG records to train the ensemble *** use 3602 CTG readings to train the ensemble classifiers and 1201 records to evaluate *** outcomes of these classifiers are then fed into the soft voting classifier to obtain the most accurate ***:Each classifier evaluates accuracy,Precision,Recall,F1-scores,and Area Under the Receiver Operating Curve(AUROC)*** reveal that the XGBoost,LGBM,and CatBoost classifiers yielded 99%***:Using ensemble classifiers over a balanced CTG dataset improves the detection accuracy compared to the previous studies and our first experiment.A soft voting classifier then eliminates the weakness of one individual classifier to yield superior performance of the overall model.

关键词： Fetal health cardiotocography(CTG) ensemble learning adaptive boosting(AdaBoost) voting classifier

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Disentangling Local and Global Information for Light Field Depth Estimation

Disentangling Local and Global Information for Light Field D...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Yang, Xueting Deng, Junli Chen, Rongshan Cong, Ruixuan Ke, Wei Sheng, Hao Communication University of China School of Information and Communication Engineering Beijing100024 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

Accurate depth estimation from light field images is essential for various applications. Deep learning-based techniques have shown great potential in addressing this problem while still face challenges such as sensitivity to occlusions and difficulties in handling untextured areas. To overcome these limitations, we propose a novel approach that utilizes both local and global features in the cost volume for depth estimation. Specifically, our hybrid cost volume network consists of two complementary sub-modules: a 2D ContextNet for global context information and a matching cost volume for local feature information. We also introduce an occlusion-aware loss that accounts for occlusion areas to improve depth estimation quality. We demonstrate the effectiveness of our approach on the UrbanLF and HCInew datasets, showing significant improvements over existing methods, especially in occluded and untextured regions. Our method disentangles local feature and global semantic information explicitly, reducing the occlusion and untextured area reconstruction error and improving the accuracy of depth estimation. © 2023 IEEE.

关键词： Semantics

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