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23rd International Conference on control Systems and computer science Technologies, CSCS 2021

作者： Craciunescu, Mihai Baicu, Diana Mocanu, Stefan Dobre, Cristian Faculty of Automatic Control and Computer Science Department of Automatic Control and Applied Informatics Bucharest Romania Rinftech Bucharest Romania

ISBN: (纸本)9781665439398

In this paper, a method of calculating the occupancy of a shelf will be presented. A vision pillar composed of two RGB cameras and two ToF depth cameras will be used to scan a shelf and determine the percentage of emptiness for the products. Since the cameras will scan a small section of the shelf at the time, a stitching method will be applied on both depth and RGB stream. In order to identify categories of products, along with their spatial delimitation within a shelf, a segmentation neural network is used. By combining the segmentation output with the depth information, the distance from the pillar to the product can be determined. Since this type of information does not help a human operator or a supervisor in understanding the state of the shelf or in making an appropriate decision, such as restocking, a method for computing the shelf occupancy is proposed. Optimization must be done to the processing algorithms to enable them to run on an embedded platform, which will allow for the implementation of the proposed architecture on a platform deployed in a production environment. All the computation is done on board of the mobile robot without transmitting the data to an external server. © 2021 IEEE.

关键词： Cameras

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Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

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computers, Materials & Continua 2022年第8期72卷 2467-2482页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Shady Y.El-Mashad Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Mechanical Engineering Department Qassim UniversityBuraidah51452Saudi Arabia Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt

The sample’s hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing ***(HGB)is a critical component of the human body because it transports oxygen from the lungs to the body’s tissues and returns carbon dioxide from the tissues to the *** the HGB level is a critical step in any blood analysis *** often indicate whether a person is anemic or polycythemia *** ensemble models by combining two or more base machine learning(ML)models can help create a more improved *** purpose of this work is to present a weighted average ensemble model for predicting hemoglobin *** optimization method is utilized to get the ensemble’s optimum *** optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search(SCSFS).The proposed SCSFS ensemble is compared toDecision Tree,Multilayer perceptron(MLP),Support Vector Regression(SVR)and Random Forest Regressors as model-based approaches and the average ensemble *** SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.

关键词： Sine cosine optimization metaheuristics optimization hemoglobin estimation weight average ensemble

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation

Out-of-distribution Object Detection through Bayesian Uncert...

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International Conference on Information Fusion

作者： Tianhao Zhang Shenglin Wang Nidhal Bouaynaya Radu Calinescu Lyudmila Mihaylova Automatic Control and Systems Engineering The University of Sheffield UK Department of Electrical and Computer Engineering Rowan University USA Department of Computer Science University of York UK

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset.

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Domain-specific data characteristics: A study on meaning of stylometric sub-concepts and in-class imbalance

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Procedia computer science 2024年 246卷 4421-4430页

作者： Urszula Stańczyk Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

In the context of data imbalance probably the most investigated problem is imbalance of classes, as learning from the data with this characteristic makes detection of existing patterns for all classes more difficult. However, other problems related to imbalance also exists and the paper addresses such cases where classes are balanced, but there is in-class imbalance. Such imbalance can be caused by uneven representation of sub-concepts. When there is a noticeable difference between the numbers of samples belonging to sub-concepts, this can turn the under-represented sub-concepts into disjuncts. Data irregularities of this type can hinder recognition, therefore actions are typically taken to restore balance. In the investigations described, the issue was studied in the stylometric domain and various classifiers were applied to the data that was balanced, then imbalanced, and finally with restored balance. The experiments show that the specifics of the domain of application can put its own mark on the data which is difficult to overcome by standard processing such as under- or oversampling. Observed dependence on a learner and dataset makes the issue even more complex and layered, and shows the need for deeper studies.

关键词： Attribute domain Data irregularities Sub-concept Disjunct Pattern recognition Stylometry Authorship attribution

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Observations of data characteristics and irregularities through domain-oriented transformations of attributes

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Procedia computer science 2024年 246卷 2694-2703页

作者： Urszula Stańczyk Grzegorz Baron Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper presents research dedicated to observations of relations between attribute properties and discretisation. In the investigations described, the gradually increasing sets of features were discretised by selected approaches, and several variants of data were constructed. The continuous, partially discrete, and completely translated datasets were explored by the chosen classifiers and their performance studied in the context of a number of discretised attributes, discretisation procedures, and the way of processing of features and datasets. The stylometric problem of authorship attribution was the machine learning task under study. The experimental results enable to observe closer the specificity of style-markers employed as characteristic features, and indicate conditions for efficient recognition of authorship. They can be extended to other application domains with similar characteristics.

关键词： Attribute domain Discretisation Data representation Data irregularities Pattern recognition Stylometry Authorship attribution

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Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

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computers, Materials & Continua 2022年第6期71卷 4627-4641页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Mohamed Saber Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Electronics and Communications Engineering Faculty of EngineeringDelta University for Science and TechnologyMansoura11152Egypt

Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state *** the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various *** Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of *** may be used to select the optimum EEG channels for use in Brain-computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be *** enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the ***(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.

关键词： Signals metaheuristics optimization feature selection multilayer perceptron support vector machines

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Improving fNIRS Signal Quality Using Smoothing Filtering

Improving fNIRS Signal Quality Using Smoothing Filtering

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2023 Progress in Applied Electrical engineering, PAEE 2023

作者： Kawala-Sterniuk, Aleksandra Mikolajewski, Dariusz Leiva, Luis A. Ruotsalo, Tuukka Lysiak, Adam Grochowicz, Barbara Jacek Gorzenariczyk, Edward Luckiewicz, Adrian Wieczorek, Anna Pelc, Mariusz Automatic Control and Informatics Opole University of Technology Faculty of Electrical Engineering Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Luxembourg Luxembourg Department of Computer Science Luxembourg Luxembourg University of Helsinki Denmark University of Copenhagen Finland Institute of Philosophy Kazimiez Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared. © 2023 IEEE.

关键词： Information filtering

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Maintenance of High Fuzzy Utility Itemsets Using the Pre-Large-Itemset Concept and Tree Structure 23

Maintenance of High Fuzzy Utility Itemsets Using the Pre-Lar...

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10th Multidisciplinary International Social Networks Conference, MISNC 2023

作者： Hong, Tzung-Pei Hung, Wei-Teng Huang, Wei-Ming Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-Sen University Library and Information Center National University of Kaohsiung Taiwan Department of Electrical and Control China Steel Inc. China

ISBN: (纸本)9798400708176

Utility mining has recently attracted much attention in real-world applications because it fits actual situations. Fuzzy utility mining approaches can discover important high-utility patterns in linguistic terms. Data, however, usually come intermittently, and users may want to know currently updated mining results instead of only the ones at the previous stage. Thus, maintaining correct knowledge is very important to this requirement. In the past, we used the fast-updated (FUP) approach and tree structures to manage the maintenance problem of fuzzy utility mining for incremental databases. In this work, we adopt the pre-large-itemset concept to speed up the tree-based maintenance of high fuzzy utility itemsets. The proposed method uses the header table and the pre-large threshold to reduce database scans for efficiency improvement. From the experimental results, the proposed approach performs better than the FUP-tree-based and batching method. © 2023 ACM.

关键词： Maintenance

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Fractional-order binary bat algorithm for feature selection on high-dimensional microarray data

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7453-7467页

作者： Esfandiari, Ahmad Farivar, Faezeh Khaloozadeh, Hamid Department of Computer and Mechatronics Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Systems and Control Faculty of Electrical Engineering K. N. Toosi University of Technology Seyed-Khandan Shariati Ave. Tehran16317-14191 Iran

High-dimensional microarray data suffer from the confounding effects of irrelevant, redundant and noisy genes on the scalability and efficiency of classification algorithms. In order for an effective dimensionality reduction and the selection of informative genes, this paper introduces a novel approach using fractional calculus concepts. This study proposes a modified version of binary the bat algorithm named fractional-order binary bat algorithm (FBBA) able to control the convergence process using more historical memory of bat behaviors. The gene selection technique contains a two-stage hybrid filter/wrapper method which employs a new correlation-based feature clustering (CFC) algorithm in the filter stage and the FBBA in the wrapper stage. The CFC-FBBA is evaluated on ten microarray gene expression datasets by employing the support vector machine classifier with a k-fold Monte Carlo cross validation data partitioning model. Furthermore, the results show that the CFC-FBBA, while minimizing the size of the gene subset, achieves the highest classification accuracy in most cases compared to several state-of-art hybrid techniques. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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Optimizing Polynomial-Time Solutions to a Network Weighted Vertex Cover Game

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IEEE/CAA Journal of automatica Sinica 2023年第2期10卷 512-523页

作者： Jie Chen Kaiyi Luo Changbing Tang Zhao Zhang Xiang Li the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004 Yuxi Middle School Taizhou 317399China IEEE the College of Physics and Electronics Information Engineering Zhejiang Normal UniversityJinhua 321004China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004China the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433 the Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 201210China

Weighted vertex cover(WVC)is one of the most important combinatorial optimization *** this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted *** first model the WVC problem as a general game on weighted *** the framework of a game,we newly define several cover states to describe the WVC ***,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the ***,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial ***,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the ***,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.

关键词： Game-based asynchronous algorithm(GAA) game optimization polynomial time strict Nash equilibrium(SNE) weighted vertex cover(WVC)

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