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Existence and stability for a nonlinear hybrid differential equation of fractional order via regular Mittag–Leffler kernel

Existence and stability for a nonlinear hybrid differential ...

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作者： Slimane, Ibrahim Dahmani, Zoubir Nieto, Juan J. Abdeljawad, Thabet Faculty of Exact Sciences and Informatics UMAB Abdelhamid Ibn Badis University of Mostaganem Mostaganem Algeria Department of Statistics Mathematical Analysis and Optimization Institute of Mathematics University of Santiago de Compostela Santiago de Compostela Spain Department of Mathematics and General Sciences Prince Sultan University Riyadh Saudi Arabia Department of Medical Research China Medical University Taichung Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

This paper deals with a nonlinear hybrid differential equation written using a fractional derivative with a Mittag–Leffler kernel. Firstly, we establish the existence of solutions to the studied problem by using the Banach contraction theorem. Then, by means of the Dhage fixed-point principle, we discuss the existence of mild solutions. Finally, we study the Ulam–Hyers stability of the introduced fractional hybrid problem. © 2021 John Wiley & Sons, Ltd.

关键词： Nonlinear equations

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A Cloud-based Distributed Computing Approach for Extracting Molecular Descriptors 22

A Cloud-based Distributed Computing Approach for Extracting ...

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Proceedings of the 6th International Conference on Algorithms, Computing and Systems

作者： Christos Didachos Dionisis Panagiotis Kintos Manolis Fousteris Vassilis C. Gerogiannis Le Hoang Son Andreas Kanavos Department of Computer Engineering & Informatics University of Patras Greece Department of Pharmacy Laboratory of Medicinal Chemistry University of Patras Greece Department of Digital Systems University of Thessaly Greece VNU Information Technology Institute Vietnam National University Vietnam Department of Informatics Ionian University Greece

ISBN: (纸本)9781450397407

Feature extraction is an initial and essential part for the development of accurate predictive machine learning classifiers. In the research field of drug discovery and development, the usage of molecular descriptors, which can be defined as mathematical representations of molecules’ chemical properties, is a challenging task not only for machine learning studies but even for "classical wet lab" approaches. However, a high diversity of these descriptors is required in order to exploit all the available knowledge and, consequently, to maximize the potentially predictive power of approaches that could be applied for the discovery of new bioactive compounds against one or more molecular targets. Furthermore, the representation and normalization of these information is considered a rather time-consuming process. Herein, we present an approach that employs the power of cloud and distributing computing for the extraction, processing and representation of big datasets, leading to the generation of molecular descriptors in a reasonable time frame.

关键词： Coiled Computing Performance Computational Drug Design Dask Molecular Descriptors Chemical Big Data

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Chaotic American zebra search optimization algorithm for benchmark challenges 15

Chaotic American zebra search optimization algorithm for ben...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Kamboj, Vikram Kumar Krishna, Ardhala Bala Verma, Chaman Raboaca, Maria Simona Filote, Constantin Stepanova, Maria Schulich School of Engineering University of Calgary AB Canada Malla Reddy Engineering College for Women Dept. of Electrcical and Electronics Engineering Hyderabad India Eötvös Loránd University Faculty of Informatics Dept. of Media and Edu. Informatics Budapest Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava720229 Romania Bauman Moscow State Technical University Engineering Business and Management Faculty Russia

ISBN: (纸本)9798350321388

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges. © 2023 IEEE.

关键词： Mapping

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Advancing Fetal Surveillance with Physiological Sensing: Detecting Hypoxia in Fetal Sheep

Advancing Fetal Surveillance with Physiological Sensing: Det...

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IEEE SENSORS

作者： Weitao Tang Nhi Tran Nasim Katebi Reza Sameni Gari D. Clifford David Walker Vaishnavi Horlali Callum Taylor Robert Galinsky Faezeh Marzbanrad Electrical and Computer System Engineering Monash University Melbourne Australia The Ritchie Centre Hudson Institute of Medical Research Melbourne Australia Dept. of Biomedical Informatics Emory University Atlanta USA Dept. of Biomedical Engineering Georgia Institute of Technology Emory University Atlanta USA Department of Obstetrics and Gynaecology Monash University Melbourne Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

Accurate detection of hypoxia during fetal monitoring is critical for timely intervention and prevention of brain injury. This study investigated hypoxia detection in fetal sheep by developing a classifier to differentiate between baseline, during acute hypoxia (umbilical cord occlusion; UCO), and post-hypoxia recovery (post-UCO), using physiological signals and an XGBoost machine learning model. A multi-modal approach integrating electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) signals was compared to a model based on the ECG signal only. We included recordings from 14 fetal sheep, each of which underwent acute hypoxia via a single UCO procedure. Performance of hypoxia detection was measured by Marco F1 score (F1 m ), Accuracy (Acc), and standard deviation of validation error (SDE). Our multi-modal model achieved F1 m = 0.88, Acc = 0.90, while the ECG-based approach achieved F1 m = 0.82, Acc = 0.85. These results indicate that the multi-modal approach was superior to the ECG-only model for overall classification. While ECG and multimodal models correctly classified over 92% and 97% of acute hypoxia intervals, respectively, the classification of post- UCO intervals proved more challenging. The complexity of interpreting physiological signals underscores the need to consider temporal dynamics and physiological context in fetal monitoring.

关键词： Accuracy Machine learning Electrocardiography Hypoxia Brain modeling Feature extraction Electromyography Electroencephalography Complexity theory Biomedical monitoring

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MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTERING 12

MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Maximum principle

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A Framework of Artificial Intelligence for the Manufacturing and Image Classification system 5

A Framework of Artificial Intelligence for the Manufacturing...

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5th International Conference on Contemporary Computing and informatics, IC3I 2022

作者： Shetty, Pallavi Joshi, Kapil Raman, Ramakrishnan Rao, K. Naga Venkateshwara Kumar, A. Vijaya Tiwari, Mohit Department of MCA Karnataka India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Maharashtra Pune India St. Martin's Engineering College Department of ECE Telangana Secunderabad India School of Arts and Science Vinayaka Mission's Research Foundation Department of Computer Science AV Campus Tamil Nadu Chennai India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Rohtak RoadPaschim Vihar Delhi India

ISBN: (纸本)9798350398267

Artificial intelligence (AI) has been successfully employed in industries for decades, beginning with the invention of expert systems in the 1960s and continuing through the present ubiquity of deep learning. Data-driven AI solutions have grown increasingly common as a means of supporting ever-more complicated industrial processes owing to the accessibility of affordable computer and storage infrastructure. Despite recent optimism, implementing AI to smart industrial applications still offers major difficulties. The present paper gives an executive summary of AI methodologies with an emphasis on deep learning before detailing unresolved issues in AI safety, data privacy, and data quality - all of which are necessary for completely automated commercial AI systems. © 2022 IEEE.

关键词： Image classification

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A Novel Approach to IoT based Plant Health Monitoring System in Smart Agriculture 5

A Novel Approach to IoT based Plant Health Monitoring System...

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5th International Conference on Contemporary Computing and informatics, IC3I 2022

作者： Mudholkar, Megha Mudholkar, Pankaj Dornadula, Venkata Harshavardha Reddy Sreenivasulu, K. Joshi, Kapil Pant, Bhasker Thakur Institute of Management Studies Career Development & Research Department of MCA Maharashtra Mumbai India Marwadi University Department of Computer Applications Gujarat Rajkot India IT & ITES Startups Mentoring Society India KLEF Deemed to Be University Department of Biotechnology Andhra Pradesh Guntur India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

The study and practice of recognizing and resolving physiological and biologic processes that prevent plants from reaching their full hereditary potential for applications such as food, decorations, lumber, or other uses is known as plant health care. One of most crucial tasks in any setting based on agriculture is crop management. The establishment of a programmed for monitoring plant health is covered in this study. which will evaluate certain environmental factors that impact plants, such as temperature, humidity, and light intensity. Restore soil moisture as well. The Arduino Uno development boards transmit all of this data to the Ubidot IoT (Iot) cloud platform. If there is a discrepancy between the number of sensor nodes in the data and the actual number, the user's smartphone will be notified. © 2022 IEEE.

关键词： Crops

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Autonomous Vehicle Drift With a Soft Actor-critic Reinforcement Learning Agent 20

Autonomous Vehicle Drift With a Soft Actor-critic Reinforcem...

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20th IEEE Jubilee World Symposium on Applied Machine Intelligence and informatics, SAMI 2022

作者： Hunor Toth, Szilard Janos Viharos, Zsolt Bardos, Adam Institute of Computer Science and Control Research Laboratory on Engineering Management Intelligence Intelligent Processes Research Group Budapest Hungary John von Neumann University Faculty of Economics Kecskemét Hungary Budapest University of Technology and Economics Department of Automotive Technologies Budapest Hungary

ISBN: (纸本)9781665497046

Self-driving vehicles have become a more and more important field in recent years. Supported by the techniques of Artificial Intelligence (AI), the current tendency of positive results in applications is making it a promising area to focus further research. Additionally, Reinforcement Learning (RL) is already proved to be an efficient approach for complex problems, e.g. robots, industrial systems and also games (like chess, Go), *** is a driving technique at handling limits where the driver intentionally oversteers, with loss of traction, while driving the vehicle through the entirety of a corner. It is a very challenging control task and often results in an accident when it occurs on public roads, consequently, the efficient control of this motion is especially important in the safety of autonomous *** paper reports novel research results whose main goal is to develop a self-driving agent for drift motion control based on vehicle simulation by Matlab/Simulink. Longitudinal and lateral velocity together with the yaw rate formed the state representation of the vehicle. The agent action space consists of two continuous actuator values: pedal ratio and roadwheel angle. The goal of the agent is twofold: first, it has to jump into a drifting state, second, it has to keep the vehicle in *** simulation results show that the proposed Soft Actor-Critic (SAC) RL agent is capable of learning to approach a pre-determined drift equilibrium from cornering and staying in this drift situation as well. For the training, the solution excluded using any kind of prior data, it only works with information gained from the simulation model, which is a remarkable difference from the actual state-of-the-art RL-based solutions. © 2022 IEEE.

关键词： Training Roads Simulation Reinforcement learning Mathematical models Data models Artificial intelligence

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BookChain: A Secure Library Book Storing and Sharing in academic Institutions using Blockchain technology

BookChain: A Secure Library Book Storing and Sharing in Acad...

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International Conference on computer Communication and informatics (ICCCI)

作者： P. Chinnasamy Anupriya Elumalai Ramesh Kumar Ayyasamy S.P. Kavya S. Dhanasekaran Ajmeera Kiran Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India School of Computer Science and Engineering VIT – AP University Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Department of Information Technology Kalasalingam Academy of Research and Education Deemed to be University Srivilliputtur Tamilnadu India

By exploiting wireless operators, contemporary bookcrossing allows people to share books conveniently and hastens the spread of knowledge. Nevertheless, the widespread use of smartphone bookcrossing has been seriously hampered by the lack of tracking, resulting in missing and disappearance of textbooks, additionally, it leads to organizational inefficiency. Hence, we suggest using the emerging blockchain technology to exploit the advantages of its immutability and encrypted smart contract in order to overcome these difficulties. In this research, we propose a BookChain, a blockchain-based intra-campus book-sharing system that is verifiable and effective. BookChain securely records all of the book’s interacting entities on blockchain because every consumer could retrace the book’s renting activity, hence lowering the risk of a book being misplaced. Additionally, BookChain promotes the use of smart contracts to streamline the distribution of books with very little human interference, maximizing productivity. The experimental findings demonstrate the efficiency and affordability of the suggested system even with a large number of concurrent participants.

关键词： Wireless communication Productivity Smart contracts Force Interference Decentralized applications Libraries

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Automated AI-Driven Detection of Mastoiditis in Temporal Bone via CT Scan and MRI Imaging: A Review

Automated AI-Driven Detection of Mastoiditis in Temporal Bon...

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Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： Chitra Bhole Chaitanya Nemade Chandrakant Shetty Atharv Patil Pranav Yadav Dept. Computer Engineering K.J. Somaiya Institute of Technology Sion-Mumbai India Radiology Department K.J. Somaiya Medical College & Research Center Sion-Mumbai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Recent advances in using artificial intelligence (AI) in medical imaging toward the detection of mastoiditis have shown very promising applications. Therefore, the comprehensive review on the state-of-art approaches related to the application of CT and MRI imaging modalities for automated mastoiditis detection is provided. We review methodologies that go from conventional machine learning approaches such as SVM and Random Forests, to more novel and advanced deep learning architectures, such as U-Net, ResNet, or ViT, to image temporal bones. The review systematically assesses particular techniques: transfer learning using pre-trained models, attention mechanisms focusing feature extraction, and multi-modal fusion approaches combining the data found in CT and MRI. Window level adjustments, skull stripping, adaptive histogram equalization are subjected to critical review regarding the pre-processing methods. Its review ranges over performance metrics across the studies; the current systems achieve sensitivity rates of 85-95% and specificity of 88-97% within the controlled environment. We discuss some of the key challenges such as limited availability of dataset, variance in imaging protocols, and the need for robust clinical validation. Recent evidence from AI-driven systems is promising in the support of the process for clinical diagnosis, especially when using ensemble approaches. There could be several architectures that would work in combination to enhance the robustness and efficacy of clinical diagnosis.

关键词： Deep learning Accuracy Reviews Magnetic resonance imaging Computed tomography Transfer learning Imaging Bones Robustness Clinical diagnosis

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