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Investigation of the hyperchaos and control in the fractional order financial system with profit margin

Partial Differential Equations in Applied Mathematics 2024年 9卷

作者： Johansyah, Muhamad Deni Sambas, Aceng Qureshi, Sania Zheng, Song Abed-Elhameed, Tarek M. Vaidyanathan, Sundarapandian Sulaiman, Ibrahim Mohammed Department of Mathematics Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu 22200 Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Tasikmalaya 46196 Indonesia Department of Computer Science and Mathematics Lebanese American University Beirut 13-5053 Lebanon Department of Mathematics Near East University Mersin 99138 Turkey Department of Basic Sciences and Related Studies Mehran University of Engineering and Technology Jamshoro 72060 Pakistan School of Data Science Zhejiang University of Finance and Economics Hangzhou 310018 China Institute of Mathematics and Interdisciplinary Sciences Zhejiang University of Finance and Economics 310018 China Department of Mathematics Faculty of Science Assiut University Assiut 71516 Egypt Centre for Control Systems Vel Tech University Avadi Tamil Nadu Chennai 600062 India Institute of Strategic Industrial Decision Modelling School of Quantitative Sciences Universiti Utara Malaysia Sintok Bukit Kayu Hitam Kedah 06010 Malaysia Faculty of Education and Arts Sohar University Sohar 311 Oman

This research study proposes a novel hyperchaotic finance system with profit margin and then utilizes the Adomian Decomposition Method (ADM) to tackle the solution of the associated Caputo-derivative based fractional-order hyperchaotic finance system with profit margin. Numerical simulations and analyses, such as the evaluation of Lyapunov exponents (LE), the creation of bifurcation diagrams (BD), the complexity analysis (CA), and the 0-1 test, are used to get a full picture of the system. The results of our study demonstrate the occurrence of periodic, chaotic, and hyperchaotic dynamics in the system. Furthermore, the overall criteria for the existence and the uniqueness of the exact solutions for Caputo fractional-order models are presented. In addition, we present a control methodology for the fractional-order hyperchaotic financial system utilizing the Laplace transform and linear feedback control. Significantly, our research showcases a noteworthy correlation between the analytical results and numerical simulations, emphasizing the soundness and effectiveness of our methodology. © 2024 The Author(s)

关键词： Chaotic system Financial system Fractional order Hyperchaos

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An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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A Secure Authentication Mechanism for Wireless Sensor Networks

A Secure Authentication Mechanism for Wireless Sensor Networ...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Rifaqat Ali Preeti Chandrakar Mohammad S. Obaidat Kuei-Fang Hsiao Arup Kumar Pal SK Hafizul Islam Department of Mathematics and Scientific Computing National Institute of Technology Hamirpur Himachal Pradesh India Department of Computer Science and Engineering National Institute of Technology Raipur Chhattisgarh India College of Computing and Informatics University of Sharjah Sharjah UAE KAIST University of Jordan Amman Jordan University of Science and Technology Beijing China Amity University Noida UP India Department of Information Systems University of Sharjah Sharjah UAE Department of Information Management Ming Chuan University Taiwan Department of Computer Science and Engineering Indian Institute of Technology (ISM) Dhanbad Jharkhand India Department of Computer Science and Engineering Indian Institute of Information Technology Kalyani West Bengal India

ISBN: (数字)9781728165448

ISBN: (纸本)9781728165455

In Wireless Sensor Networks (WSNs), the sensor nodes are often used to collect the data and send it to the gateway node. This real data are the most important and sensitive. If someone gets this data illegally, then there is no privacy. Thus, user authentication is most precious for securing this data over an insecure channel. To keep this important matter in mind, we have developed a secure authentication mechanism for WSNs. The proposed method is proved through BAN logic, which guarantees mutual authentication and session key agreement securely. The informal security analysis is also executed for the proposed mechanism to show that it resists various wicked attacks. Apart from this, the performance evaluation is also carried out along with other existing schemes in regards of communication and computation costs.

关键词： Wireless sensor networks Authentication Logic gates Password Coplanar waveguides Computational modeling Security

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SoK: A modularized approach to study the security of automatic speech recognition systems

arXiv

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arXiv 2021年

作者： Chen, Yuxuan Zhang, Jiangshan Yuan, Xuejing Zhang, Shengzhi Chen, Kai Wang, XiaoFeng Guo, Shanqing Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University China School of Cyber Science and Technology Shandong University China SKLOIS Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Department of Computer Science Metropolitan College Boston University United States School of Informatics Computing and Engineering Indiana University Bloomington United States

With the wide use of Automatic Speech Recognition (ASR) in applications such as human machine interaction, simultaneous interpretation, audio transcription, etc., its security protection becomes increasingly important. Although recent studies have brought to light the weaknesses of popular ASR systems that enable out-of-band signal attack, adversarial attack, etc., and further proposed various remedies (signal smoothing, adversarial training, etc.), a systematic understanding of ASR security (both attacks and defenses) is still missing, especially on how realistic such threats are and how general existing protection could be. In this paper, we present our systematization of knowledge for ASR security and provide a comprehensive taxonomy for existing work based on a modularized workflow. More importantly, we align the research in this domain with that on security in Image Recognition System (IRS), which has been extensively studied, using the domain knowledge in the latter to help understand where we stand in the former. Generally, both IRS and ASR are perceptual systems. Their similarities allow us to systematically study existing literature in ASR security based on the spectrum of attacks and defense solutions proposed for IRS, and pinpoint the directions of more advanced attacks and the directions potentially leading to more effective protection in ASR. In contrast, their differences, especially the complexity of ASR compared with IRS, help us learn unique challenges and opportunities in ASR security. Particularly, our experimental study shows that transfer attacks across ASR models is feasible, even in the absence of knowledge about models (even their types) and training data. © 2021, CC BY.

关键词： Speech recognition

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Weighted multi-level deep learning analysis and framework for processing breast cancer WSIs

arXiv

引用

arXiv 2021年

作者： Bokor, Peter Hudec, Lukas Fabian, Ondrej Benesova, Wanda Slovak University of Technology Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Ilkovicova 2 Bratislava84216 Slovakia Institute for Clinical and Experimental Medicine Clinical and Transplant Pathology Centre Videnska 1958/9 Prague 4140 21 Czech Republic Charles University Thomayer University Hospital 3rd Faculty of Medicine Department of Pathology and Molecular medicine Videnska 800 Prague 4140 59 Czech Republic

Prevention and early diagnosis of breast cancer (BC) is an essential prerequisite for the selection of proper treatment. The substantial pressure due to the increase of demand for faster and more precise diagnostic results drives for automatic solutions. In the past decade, deep learning techniques have demonstrated their power over several domains, and computer-Aided (CAD) diagnostic became one of them. However, when it comes to the analysis of Whole Slide Images (WSIs), most of the existing works compute predictions from levels independently. This is, however, in contrast to the histopathologist expert approach who requires to see a global architecture of tissue structures important in BC classification. We present a deep learning-based solution and framework for processing WSIs based on a novel approach utilizing the advantages of image levels. We apply the weighing of information extracted from several levels into the final classification of the malignancy. Our results demonstrate the profitability of global information with an increase of accuracy from 72.2% to 84.8%. © 2021, CC BY.

关键词： Medical imaging

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K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

arXiv

引用

arXiv 2021年

作者： Saifullah, Shoffan Dreżewski, Rafal Khaliduzzaman, Alin Tolentino, Lean Karlo Ilyos, Rabbimov Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Jl. Babarsari 2 Yogyakarta55281 Indonesia Institute of Computer Science AGH University of Science and Technology Al. Mickiewicza 30 Kraków30-059 Poland Faculty of Agricultural Engineering and Technology Sylhet Agricultural University Sylhet3100 Bangladesh Department of Electronics Engineering Technological University of the Philippines Manila1000 Philippines Faculty of Digital Technologies Samarkand State University Samarkand Uzbekistan

The quality of the hatching process influences the success of the hatch rate besides the inherent egg factors. Eliminating infertile or dead eggs and monitoring embryonic growth are very important factors in efficient hatchery practices. This process aims to sort eggs that only have embryos to remain in the incubator until the end of the hatching process. This process aims to sort eggs with embryos to remain hatched until the end. Maximum checking is done the first week in the hatching period. This study aims to detect the presence of embryos in eggs. Detection of the existence of embryos is processed using segmentation. Egg images are segmented using the K-means algorithm based on Lab color images. The results of the image acquisition are converted into Lab color space images. The results of Lab color space images are processed using K-means for each color. The K-means process uses cluster k=3, where this cluster divides the image into three parts: background, eggs, and yolk. Egg yolks are part of eggs that have embryonic characteristics. This study applies the concept of color in the initial segmentation and grayscale in the final stages. The initial phase results show that the image segmentation results using k-means clustering based on Lab color space provide a grouping of three parts. At the grayscale image processing stage, the results of color image segmentation are processed with grayscaling, image enhancement, and morphology. Thus, it seems clear that the yolk segmented shows the presence of egg embryos. Based on this process and results, the initial stages of the embryo detection process used K-means segmentation based on Lab color space. The evaluation uses MSE and MSSIM, with values of 0.0486 and 0.9979;this can be used as a reference that the results obtained can detect embryos in egg yolk. This protocol could be used in a nondestructive quantitative study on embryos and their morphology in a precision poultry production system in the future. © 2021, CC

关键词： Image segmentation

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Predicting extreme events from data using deep machine learning: when and where

arXiv

引用

arXiv 2022年

作者： Jiang, Junjie Huang, Zi-Gang Grebogi, Celso Lai, Ying-Cheng The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi’an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Shaanxi Xi’an China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King’s College University of Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University TempeAZ85287 United States

We develop a deep convolutional neural network (DCNN) based framework for model-free prediction of the occurrence of extreme events both in time ("when") and in space ("where") in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the 2D complex Ginzburg-Landau equation and empirical wind speed data of the North Atlantic ocean to demonstrate and validate our machine-learning based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme event on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world. © 2022, CC BY.

关键词： Forecasting

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Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies

arXiv

引用

arXiv 2021年

作者： Shoeibi, Afshin Ghassemi, Navid Khodatars, Marjane Moridian, Parisa Alizadehsani, Roohallah Zare, Assef Khosravi, Abbas Subasi, Abdulhamit Rajendra Acharya, U. Manuel Gorriz, J. K. N. Toosi University of Technology Tehran Iran Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Deakin University VIC3217 Australia Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute of Biomedicine Faculty of Medicine University of Turku Turku20520 Finland Department of Computer Science College of Engineering Effat University Jeddah21478 Saudi Arabia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore599489 Singapore the Department of Biomedical Engineering School of Science and Technology SUSS University Singapore599491 Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Psychiatry University of Cambridge United Kingdom

Epileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.74 © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

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Evaluation of General Large Language Models in Understanding Clinical Concepts Extracted from Adult Critical Care Electronic Health Record Notes

SSRN

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SSRN 2024年

作者： Liu, Darren Ding, Cheng Bold, Delgersuren Bouvier, Monique Lu, Jiaying Shickel, Benjamin Jabaley, Craig S. Zhang, Wenhui Park, Soojin Young, Michael J. Wainwright, Mark S. Clermont, Gilles Rashidi, Parisa Rosenthal, Eric S. Dimisko, Laurie Xiao, Ran Yoon, Joo Heung Yang, Carl Hu, Xiao Nell Hodgson Woodruff School of Nursing Emory University AtlantaGA United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Computer Science Emory University AtlantaGA United States College of Medicine University of Florida GainesvilleFL United States Department of Anesthesiology Emory University School of Medicine AtlantaGA United States Emory Critical Care Center AtlantaGA United States Division of Hospital and Critical Care Neurology United States Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School BostonMA United States Division of Pediatric Neurology Department of Neurology University of Washington SeattleWA United States Department of Critical Care Medicine University of Pittsburgh PittsburghPA United States Department of Biomedical Engineering University of Florida GainesvilleFL United States Department of Neurology Massachusetts General Hospital BostonMA United States Division of Pulmonary Allergy Critical Care and Sleep Medicine Department of Medicine University of Pittsburgh PittsburghPA United States

Objective: The field of healthcare has increasingly turned its focus towards Large Language Models (LLMs) due to their remarkable performance in various applications. While LLMs have shown promise in standardized medical exams, their performance in actual clinical applications has been underexplored. We sought to evaluate the performance of LLMs in the complex clinical context of adult critical care medicine using systematic and comprehensible analytic methods, including clinician annotation and ***: We investigated the performance of three widely available general LLMs (GPT-4, GPT-3.5, and text-davinci-003) in understanding and processing real-world clinical notes. Text from 150 clinical notes was mapped with MetaMap to standardized medical concepts aligned with the Unified Medical Language System (UMLS) and then adjudicated by 9 clinicians. Each LLM's proficiency was evaluated by identifying the temporality and negation of these concepts using a range of different prompts for an in-depth analysis. The performance of fine-tuned LLaMA 2 was compared with other LLMs in zero-shots. The performance of LLMs was also tested using 6 different qualitative performance ***: We developed a dataset featuring 2,288 clinical concepts annotated and adjudicated by 9 multidisciplinary clinicians. In 3 different tasks, GPT-4 showed overall superior performance compared to other LLMs. In comparing different prompting strategies, GPT-4 demonstrated consistently high performance across all prompts. This finding indicates that such an advanced model does not require extensive prompt engineering to achieve optimal results. Across all 6 dimensions of qualitative assessment, GPT-4 also showed superior performance, reaching 98.4% overall comprehensibility across all tasks. The GPT family models have demonstrated approximately 18 times faster than human experts while incurring only a quarter of the ***: A comprehensive qualitative performance evaluat

关键词： Natural language processing systems

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Packaging research artefacts with RO-Crate

arXiv

引用

arXiv 2021年

作者： Soiland-Reyes, Stian Sefton, Peter Crosas, Mercè Castro, Leyla Jael Coppens, Frederik Fernández, José M. Garijo, Daniel Grüning, Björn Rosa, Marco La Leo, Simone Carragáin, Eoghan Ó. Portier, Marc Trisovic, Ana Groth, Paul Goble, Carole Department of Computer Science University of Manchester United Kingdom Informatics Institute University of Amsterdam Netherlands Faculty of Science University Technology Sydney Australia Institute for Quantitative Social Science Harvard University CambridgeMA United States ZB MED Information Centre for Life Sciences Cologne Germany VIB-UGent Center for Plant Systems Biology Gent Belgium Barcelona Supercomputing Center Barcelona Spain Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Bioinformatics Group Department of Computer Science Albert-Ludwigs-University Freiburg Freiburg Germany PARADISEC Melbourne Australia Italy University College Cork Ireland Vlaams Instituut voor de Zee Oostende Belgium Institute for Quantitative Social Science Harvard University CambridgeMA United States Informatics Institute University of Amsterdam Netherlands Department of Computer Science University of Manchester United Kingdom

An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on *** annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying"just enough" Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. © 2021, CC BY.

关键词： Metadata

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