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arXiv 2023年

作者： Barenholz, Daniël Montali, Marco Polyvyanyy, Artem Reijers, Hajo A. Rivkin, Andrey van der Werf, Jan Martijn E.M. Department of Information and Computing Sciences Utrecht University Princetonplein 5 Utrecht3584 CC Netherlands Faculty of Computer Science Free University of Bozen-Bolzano piazza Domenicani 3 Bolzano39100 Italy The University of Melbourne VIC3010 Australia Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads 321 Kgs. Lyngby2800 Denmark

A process discovery algorithm aims to construct a model from data generated by historical system executions such that the model describes the system well. Consequently, one desired property of a process discovery algorithm is rediscoverability, which ensures that the algorithm can construct a model that is behaviorally equivalent to the original system. A system often simultaneously executes multiple processes that interact through object manipulations. This paper presents a framework for developing process discovery algorithms for constructing models that describe interacting processes based on typed Jackson Nets that use identifiers to refer to the objects they manipulate. Typed Jackson Nets enjoy the reconstructability property which states that the composition of the processes and the interactions of a decomposed typed Jackson Net yields a model that is bisimilar to the original system. We exploit this property to demonstrate that if a process discovery algorithm ensures rediscoverability, the system of interacting processes is rediscoverable. © 2023, CC BY.

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Tuberculosis Detection from Chest Radiographs with Pre-trained Deep Learning Scheme: A Study 31

Tuberculosis Detection from Chest Radiographs with Pre-train...

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31st International Conference on Computer Theory and Applications, ICCTA 2021

作者： Kadry, Seifedine Rajinikanth, Venkatesan Chandrasekar, A. Nandhini, A. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India St. Joseph's College of Engineering Omr Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9781665478540

In human, the abnormality in lung causes a severe respiratory problem and breathing difficulties. Tuberculosis (TB) is one of the common lung abnormality caused due a bacterium named Mycobacterium tuberculosis. TB infection will cause harsh breathing issues and untreated TB will lead to death. Detection of the TB using Chest X-ray is one of the common techniques and in this research work;TB detection using Deep-Learning-Technique (DLT) is demonstrated. The various phases of this research involve;(i) Image collection and resizing, (ii) Deep-Feature (DF) extraction with chosen technique, (iii) Classification using SoftMax classifier and (iv) Performance evaluation with existing technique. The proposed work employs a 5-fold cross validation and the best value is considered as the result. The outcome of this study confirms that, the classification accuracy achieved with ResNetl8 and K-Nearest Neighbor (ResNet+KNN) offered better outcome >97% compared to other DLT of this study. © 2021 IEEE.

关键词： Deep learning

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Patient Clustering to Improve Process Mining for Disease Trajectory Analysis Using Indonesia Health Insurance Dataset

Patient Clustering to Improve Process Mining for Disease Tra...

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International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Angelina Prima Kurniati Gede Agung Ary Wisudiawan Guntur Prabawa Kusuma Siti Saadah Nurul Aida Osman Zulhelmy Wan Nur Syahidah Binti Wan Firdaus Hafidz School of Computing Telkom University Bandung Indonesia School of Applied Science Telkom University Bandung Indonesia Department of Computer & Information Sciences Universiti Teknologi Petronas Perak Malaysia Faculty of Ecnomoy and Business Universitas Islam Riau Riau Indonesia Yusoff Centre for Mathematical Sciences Universiti Malaysia Pahang Pahang Malaysia Health Policy and Management Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350385106

ISBN: (纸本)9798350385113

Process mining is a process-oriented data analytics approach that has been applied in many domains, including healthcare. Healthcare process mining aims to improve health care services through treatment enhancement and disease traj ectory analysis, among others. One of the common challenges in process mining for healthcare is the high variability in the healthcare data. This paper proposes clustering as a method to improve process mining for disease trajectory analysis. The proposed method is applied in a case study coming from a real dataset of Indonesia health insurance, consisting of records of patient treatments and diagnoses of Indonesia health insurance participants. Clustering was done to group patient profiles into clusters based on their similarities. This approach comes from a prior assumption that similar patients might have similar sequent of treatments. This research contributes to two directions: a technical contribution to improve process mining projects in handling high variability data using categorization and a research contribution to promote Indonesia health insurance dataset as a real-world healthcare dataset.

关键词： Process mining Data analysis Clustering methods Insurance Medical treatment Medical services Robustness

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Detection of Diabetic Macular Edema in Retinal OCT Images with Firefly-Algorithm Selected Features 31

Detection of Diabetic Macular Edema in Retinal OCT Images wi...

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31st International Conference on Computer Theory and Applications, ICCTA 2021

作者： Kadry, Seifedine Rajinikanth, Venkatesan Jerry, Jiny Mariam Gifta, R. Carolin Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India St. Joseph's College of Engineering Omr Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9781665478540

In modern hospitals, computer algorithms are widely implemented to diagnose the disease in patients using the medical data collected using a recommended protocol. Recently, the diabetes is human group is gradually rising and the early stage can be predicted by screening the eye. Diabetic-Macular-Edema (DME) is one of the harsh diseases and the untreated disease will initiate various eye abnormalities including the vision loss. In clinics, the retinal image recorded with a chosen imaging system is considered to inspect the disease. In this work, Optical Coherence Tomography (OCT) images are considered for the assessment of the DME in the OCT images. The proposed scheme consist the following stages;(i) Image collection and resizing, (ii) Retinal section extraction using joint thresholding and segmentation, (iii) Feature extraction and dominant feature selection with Firefly-Algorithm, (iv) Binary classification and validation. In this work, 500 numbers of Normal/DME class images with pixel dimension 256 × 256 ×1 pixels are considered and a binary classification with a 5-fold cross validation is implemented. The proposed technique helped to get a detection accuracy of 95% with K-Nearest Neighbors (KNN) classifier. © 2021 IEEE.

关键词： Optical tomography

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Automatic Detection of Breast Cancer in Thermal Images using Optimal Handcrafted Features 31

Automatic Detection of Breast Cancer in Thermal Images using...

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31st International Conference on Computer Theory and Applications, ICCTA 2021

作者： Kadry, Seifedine Rajinikanth, Venkatesan Dasari, Susmitha Suvathi, T. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India St. Joseph's College of Engineering Omr Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9781665478540

Breast Cancer (BC) is one of the harsh illnesses which widely distress the women population and premature recognition is always preferred to reduce its harshness. BC is normally detected in hospitals using a chosen non-invasive medical imaging technique. The proposed work aims to develop and execute the machine learning supported automatic detection of the BC using breast Thermal Images (TI). In TI, examination of thermal silhouette helps to get the necessary information about the normal/cancer infected section. The developed system consist the following phases;(i) Image collection and pre-processing, (ii) Image enhancement and handcrafted feature extraction, (iii) Feature selection with Bat Algorithm, and (iv) Binary classification and validation. In this work, a 5-fold cross validation is employed to classify the test images and the detected accuracy of Fine KNN is better (>92%) compared to the alternatives. The proposed work is tested using benchmark breast TI and the outcome of this research confirms its clinical significance. © 2021 IEEE.

关键词： Image enhancement

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Nonlinear Impulsive Control Design for Biologically Grounded NSM Tumor Growth Model Using Exact Linearized Mapping

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IFAC-PapersOnLine 2023年第2期56卷 7228-7233页

作者： Zehor Belkhatir Soulaimane Berkane Larry Norton Allen Tannenbaum School of Electronics and Computer Science University of Southampton University Road Southampton SO17 1BJ UK Faculty of Computing Engineering and Media De Montfort University The Gateway Leicester LE1 9BH UK Department of Computer Science and Engineering University of Quebec in Outaouais Gatineau QC Canada Department of Medicine Memorial Sloan Kettering Cancer Center NY 10065 USA Department of Computer Science and Department of Applied Mathematics and Statistics Stony Brook University Stony Brook NY 11794 USA

This paper investigates the problem of personalized chemotherapy scheduling using a system-modeling approach based on a nonlinear impulsive control technique. In particular, this work deals with the so-called Norton-Simon-Massagué (NSM) model, a minimal biologically grounded model for controlled tumor growth, which has been accepted in clinical oncology for breast cancer treatment. In this paper, we continue this line of research, to advance the concept of closed-loop control a step further towards widespread clinical use. In the present work, the problem of chemotherapy drug scheduling is approached using an impulsive control strategy combined with a state-space observer. The technical challenges of the nonlinearity of the controlled system and the impulsive nature of control input are handled via the utilization of a novel mapping that transforms the control problem into a “simplified” linear discrete-time control problem. Theoretical guarantees regarding the sign and invertibility of the proposed transformation are demonstrated. Then, an observer-based state feedback controller is proposed for chemotherapy drug scheduling as a proof-of-concept control strategy. Simulation results showing the performance of the designed closed-loop controlled chemotherapy schedules in reducing and eradicating tumor volume are provided using model's parameters derived using real tumor data and Doxirubicin drug.

关键词： Controlled tumor growth Nonlinear impulsive control Norton-Simon-Massagué (NSM) model Exact linearized transformation observer-based state feedback

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Min orderings and list homomorphism dichotomies for signed and unsigned graphs

arXiv

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arXiv 2022年

作者： Bok, Jan Brewster, Richard C. Hell, Pavol Jedličková, Nikola Rafiey, Arash Computer Science Institute Faculty of Mathematics and Physics Charles University Prague Czech Republic Department of Mathematics and Statistics Thompson Rivers University Canada School of Computing Science Simon Fraser University Canada Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Prague Czech Republic Mathematics and Computer Science Indiana State University Terre HauteIN United States

The CSP dichotomy conjecture has been recently established, but a number of other dichotomy questions remain open, including the dichotomy classification of list homomorphism problems for signed graphs. Signed graphs arise naturally in many contexts, including for instance nowhere-zero flows for graphs embedded in non-orientable surfaces. For a fixed signed graph Hb, the list homomorphism problem asks whether an input signed graph Ĝ with lists L(v) ⊆ V (Ĥ ), v ∈ V (Ĝ), admits a homomorphism f to Ĥ with all f(v) ∈ L(v), v ∈ V (Ĝ). Usually, a dichotomy classification is easier to obtain for list homomorphisms than for homomorphisms, but in the context of signed graphs a structural classification of the complexity of list homomorphism problems has not even been conjectured, even though the classification of the complexity of homomorphism problems is known. Kim and Siggers have conjectured a structural classification in the special case of "weakly balanced" signed graphs. We confirm their conjecture for reflexive and irreflexive signed graphs;this generalizes previous results on weakly balanced signed trees, and weakly balanced separable signed graphs [2,1]. In the reflexive case, the result was first presented in [18], with the proof using some of our results included in this paper. In fact, here we present our full proof, as an alternative to the proof in [18]. In particular, we provide direct polynomial algorithms where previously algorithms relied on general dichotomy theorems. The irreflexive results are new, and their proof depends on first deriving a theorem on extensions of min orderings of (unsigned) bipartite graphs, which is interesting on its own. In both cases the dichotomy classification depends on a result linking the absence of certain structures called chains and invertible pairs to the existence of a so-called special min ordering. The structures are used to prove the NP-completeness and the ordering is used to design polynomial algorithms. We also des

关键词： Trees (mathematics)

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A novel binary modified beluga whale optimization algorithm using ring crossover and probabilistic state mutation for enhanced bladder cancer diagnosis

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Informatics in Medicine Unlocked 2024年 50卷

作者： Gharaibeh, Hasan Alawad, Noor Aldeen Nasayreh, Ahmad Al Mamlook, Rabia Emhamed Makhadmeh, Sharif Naser Bashkami, Ayah Abualigah, Laith Ezugwu, Absalom E. Computer Science Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan Department Medical Laboratory Sciences – Faculty of Science Al Balqa Applied University Salt 19117 Jordan Department of Cyber Security and Cloud Computing Faculty of Information Technology Applied Science Private University Amman 11937 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman P.O. Box 346 United Arab Emirates

Bladder cancer (BC) remains a significant global health challenge, requiring the development of accurate predictive models for diagnosis. In this study, a new Binary Modified White Whale Optimization (B-MBWO) algorithm is proposed to address the BC problem. The proposed method utilizes circular transitivity optimization and the Probabilistic State Mutation Algorithm (PSMA) to enhance its optimization performance. The new method is called the BBWORCPS algorithm. High-dimensional and complex medical datasets pose challenges to the original optimization algorithms in addressing the BC problem, motivating the proposed modifications to the original Beluga Whale Optimization algorithm. These enhancements, including quantum-inspired mutation and circular crossing, aim to improve solution space exploration and enhance the algorithm's effectiveness in handling intricate feature spaces. Through comprehensive experiments on BC datasets, the superiority of the BBWORCPS algorithm in terms of feature selection accuracy and computational efficiency is demonstrated compared to existing optimization methods. The obtained findings suggest that BBWORCPS offers a promising approach for developing more precise and reliable predictive models for bladder cancer analysis. © 2024 The Author(s)

关键词： Binary modified beluga whale optimization Bladder cancer Feature selection Machine learning Predictive modeling

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Nonfragile Observer-Based Event-Triggered Fuzzy Tracking Control for Fast-Sampling Singularly Perturbed Systems with Dual-Layer Switching Mechanism and Cyber-Attacks

SSRN

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SSRN 2023年

作者： Guo, Fang Luo, Mengzhuo Cheng, Jun Katib, Iyad Shi, Kaibo College of Science Guilin University of Technology Guangxi Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Applied Statistics Guilin541004 China College of Mathematics and Statistics Guangxi Normal University Guangxi Guilin541006 China Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia School of Information Science and Engineering Chengdu University Sichuan Chengdu610106 China

This paper investigates the problem of nonfragile observer-based tracking control for a class of fuzzy fast-sampling singularly perturbed systems (SPSs) with sensor saturation, which subject to event-triggered scheme and random cyber-attacks. The switching topology of the fast-sampling SPSs is dominated by a dual-layer switching mechanism, in this case, the Markov process in the underlying system is nonhomogeneous and its time-varying transition probabilities (TPs) are governed by a persistent dwell-time switching signal. First, in order to avoid asynchronous phenomenon of premise variable between the fuzzy fast-sampling SPSs and the fuzzy controller under network environments, the non-parallel distributed compensation (non-PDC) technique is applied to improve the design flexibility and reduce the conservativeness of the results. Furthermore, a novel fuzzy tracking control design scheme is proposed, in which integrating the fuzzy nonfragile observer and reference model signal into the synthetic design of the tracking controller can be expected to reduce the tracking error. Second, by constructing a well-designed time-delay and mode-dependent Lyapunov function, some sufficient conditions are derived such that the closed-loop system is mean-square exponential stable and with extended dissipative tracking performance. Finally, the effectiveness and applicability of the proposed control protocol are illustrated by two simulation examples. © 2023, The Authors. All rights reserved.

关键词： Closed loop systems

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Artificial intelligent approach for flow stability in Eyring Powell hybrid nanofluids via porous shrinking sheets

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Journal of Molecular Liquids 2025年 422卷

作者： Fatima, Nahid Kouki, Marouan Khan, Muhammad Imran Amjad, Arslan Bin Asghar, Zaheer Zeeshan, Ahmad Ijaz, Nouman Department of Mathematics and Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Information System Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Mathematics and Statistics International Islamic University Islamabad 44000 Pakistan Centre for Mathematical Sciences Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Department of Physics and Applied Mathematics Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Department of Mathematics College of Science Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Department of Mathematics and Statistics Punjab Group of Colleges G.T. Road Jada Jhelum49600 Pakistan

This study aims to analyze flow stability using a computational intelligence technique that integrates a Levenberg-Marquardt approach utilizing artificial intelligence implemented through a backpropagation neural network. AI-based LMS-BPNN approach is utilized to examine the flow behavior of Eyring Powell hybrid nanofluid over a porous shrinking sheet. By applying skillful transformations, a set of ordinary differential equations is obtained from the partial differential equations that describe the hybrid nanofluid. Scenarios 1–5 illustrate how the built-in MATLAB function, bvp4c, solves ordinary differential equations systems by carefully modifying input parameters to get the initial or reference solution. The numerical data was split into three groups: 10 % for testing, 10 % for validation, and 80 % for training. The LMS-BPNN was used to find approximate solutions for these scenarios. To verify the effectiveness and reliability of LMS-BPNN, we employed regression analysis, error metrics, and correlation index (R)-based fitness curves. In each scenario, the temperature and velocity profiles gradually approached the boundary conditions as expected. The flow performance outcomes for the dual solution (Cfx and Nux) are also analyzed using the LMS-BPNN method. The perturbation scheme is applied to the boundary layer problem to obtain the eigenvalues problem. The unsteady solution f(η,τ) converges to steady solution fo(η) for τ→∞ when γ≥0. However, an unsteady solution f(η,τ) diverges to a steady solution fo(η) for τ→∞ when γ © 2025 Elsevier B.V.

关键词： Eigenvalues and eigenfunctions

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