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arXiv 2021年

作者： Grbčić, Luka Družeta, Siniša Mauša, Goran Lipić, Tomislav Lušić, Darija Vukić Alvir, Marta Lučin, Ivana Sikirica, Ante Davidović, Davor Travaš, Vanja Kalafatović, Daniela Pikelj, Kristina Fajković, Hana Holjević, Toni Kranjčević, Lado Department of Fluid Mechanics and Computational Engineering Faculty of Engineering University of Rijeka Vukovarska 58 Rijeka51000 Croatia Department of Computer Engineering Faculty of Engineering University of Rijeka Vukovarska 58 Rijeka51000 Croatia Center for Advanced Computing and Modelling University of Rijeka Radmile Matejčić 2 Rijeka51000 Croatia Department of Hydrotechnics and Geotechnics Faculty of Civil Engineering University of Rijeka Radmile Matejčić 3 Rijeka51000 Croatia Department of Environmental Health Faculty of Medicine University of Rijeka Braće Branchetta 20/1 Rijeka51000 Croatia Department of Environmental Health Teaching Institute of Public Health of Primorje-Gorski Kotar County Krešimirova 52a Rijeka51000 Croatia Department of Biotechnology University of Rijeka Radmile Matejčić 2 Rijeka51000 Croatia Laboratory for Machine Learning and Knowledge Representations Rudjer Boskovic Institute Biljenička cesta 54 Zagreb10000 Croatia Center for Informatics and Computing Ruđer Bošković Institute Biljenička cesta 54 Zagreb10000 Croatia Department of Geology Faculty of Science University of Zagreb Horvatovac 102a Zagreb10000 Croatia

Coastal water quality management is a public health concern, as poor coastal water quality can potentially harbor pathogens that are dangerous to human health. Tourism-oriented countries need to actively monitor the condition of coastal water at tourist popular sites during the summer season. In this study, routine monitoring data of Escherichia Coli and enterococci across 15 public beaches in the city of Rijeka, Croatia, were used to build machine learning models for predicting their levels based on environmental parameters as well as to investigate their dynamics and relationships with environmental stressors. Gradient Boosting algorithms (Catboost, Xgboost), Random Forests, Support Vector Regression and Artificial Neural Networks were trained with routine monitoring measurements from all sampling sites and used to predict E. Coli and enterococci values based on environmental features. The evaluation of stability and generalizability with 10-fold cross validation analysis of the machine learning models, showed that the Catboost algorithm performed best with R2 values of 0.71 and 0.68 for predicting E. Coli and enterococci, respectively, compared to other evaluated ML algorithms including Xgboost, Random Forests, Support Vector Regression and Artificial Neural Networks. We also use the SHapley Additive exPlanations technique to identify and interpret which features have the most predictive power. The results show that site salinity measured is the most important feature for forecasting both E. Coli and enterococci levels. Finally, the spatial and temporal predictive accuracy of both ML models were examined at sites with the historically lowest coastal water quality. The spatial *** and enterococci models achieved strong R2 values of 0.85 and 0.83, while the temporal models achieved R2 values of 0.74 and 0.67. The temporal model also achieved moderate R2 values of 0.44 and 0.46 at a site with historically high coastal water quality. © 2021, CC BY-NC-ND.

关键词： Machine learning

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The Internet of Food Things: Adding value to the digitalisation of the UK food supply chain

The Internet of Food Things: Adding value to the digitalisat...

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Living in the Internet of Things, IoT 2019

作者： Brewer, Steve Pearson, Simon Bidaut, Luc Frey, Jeremy G. Parr, Gerard Zisman, Andrea LIAT University of Lincoln Lincoln United Kingdom School of Computer Science University of Lincoln Lincoln United Kingdom Chemistry University of Southampton United Kingdom School of Computing Sciences University of East Anglia Norwich United Kingdom Faculty of Science Technology Engineering and Mathematics School of Computing and Communications Open University Milton Keynes United Kingdom

ISBN: (纸本)9781839531255

The Internet of Things (IoT) is beginning to have a pronounced impact on the digitalised food manufacturing chain. Whilst the food chain is becoming at once more complex and more critical, the application of IoT has begun to be recognised as a key component of this transformation. However, exactly what the IoT is, or is not, is both evolving and provoking a lively debate. New clarifiers are being added to the term such as the Internet of Industrial Things and the Autonomous Internet of Things to ostensibly help clarify the terminology. For the purposes of the Internet of Food Things Network Plus, our conceptual and physical platform is predicated on the idea of a secure and trustworthy network based on open standards, where-possible commodity devices, and underpinned by societally ratified regulatory arrangements. © 2019 Institution of engineering and Technology. All rights reserved.

关键词： Internet of things

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Heralded nonlocal quantum gates for distributed quantum computation in a decoherence-free subspace

arXiv

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arXiv 2023年

作者： Su, Wanhua Qin, Wei Miranowicz, Adam Li, Tao Nori, Franco MIIT Key Laboratory of Semiconductor Microstructure and Quantum Sensing School of Physics Nanjing University of Science and Technology Nanjing210094 China Center for Joint Quantum Studies Department of Physics School of Science Tianjin University Tianjin300350 China Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Wakoshi Saitama351-0198 Japan Institute of Spintronics and Quantum Information Faculty of Physics and Astronomy Adam Mickiewicz University PoznanPL-61-614 Poland Engineering Research Center of Semiconductor Device Optoelectronic Hybrid Integration in Jiangsu Province Nanjing210094 China Quantum Information Physics Theory Research Team Quantum Computing Center RIKEN Saitama Wako-shi351-0198 Japan Physics Department The University of Michigan Ann ArborMI48109-1040 United States

We propose a heralded protocol for implementing nontrivial quantum gates on two stationary qubits coupled to spatially separated cavities. By dynamically controlling the evolution of the composite system, nonlocal two-qubit quantum (e.g., CPHASE and CNOT) gates can be achieved without real excitations of either cavity modes or atoms. The success of our protocol is conditioned on projecting an auxiliary atom onto a postselected state, which simultaneously removes various detrimental effects of dissipation on the gate fidelity. In principle, the success probability of the gate can approach unity as the single-atom cooperativity becomes sufficiently large. Furthermore, we show its application for implementing single- and two-qubit gates within a decoherence-free subspace that is immune to a collective dephasing noise. This faithful, heralded, and nonlocal protocol could, therefore, be useful for distributed quantum computation and scalable quantum networks. © 2023, CC BY.

关键词： Qubits

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RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

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Soft computing 2023年第2期28卷 455-455页

作者： Wang, Shui-Hua Satapathy, Suresh Chandra Xie, Man-Xia Zhang, Yu-Dong School of Computer Science and Technology Henan Polytechnic University Jiaozuo People’s Republic of China School of Computing and Mathematical Sciences University of Leicester Leicester UK Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia School of Computer Engineering KIIT Deemed to University Bhubaneswar India Department of Infection Diseases The Fourth People’s Hospital of Huai’an Huai’an China

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Perturbations and Phase Transitions in Swarm Optimization Algorithms

arXiv

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arXiv 2025年

作者： Vantuch, Tomáš Zelinka, Ivan Adamatzky, Andrew Marwan, Norbert Center ENET Technical University of Ostrava Czech Republic Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Faculty of Electrical & Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science Technical University of Ostrava Czech Republic Unconventional Computing Lab UWE Bristol United Kingdom Leibniz Association Potsdam Germany

Natural systems often exhibit chaotic behavior in their space-time evolution. Systems transiting between chaos and order manifest a potential to compute, as shown with cellular automata and artificial neural networks. We demonstrate that swarm optimization algorithms also exhibit transitions from chaos, analogous to a motion of gas molecules, when particles explore solution space disorderly, to order, when particles follow a leader, similar to molecules propagating along diffusion gradients in liquid solutions of reagents. We analyze these ‘phase-like’ transitions in swarm optimization algorithms using recurrence quantification analysis and Lempel-Ziv complexity estimation. We demonstrate that converging iterations of the optimization algorithms are statistically different from non-converging ones in a view of applied chaos, complexity and predictability estimating indicators. An identification of a key factor responsible for the intensity of their phase transition is the main contribution of this paper. We examined an optimization as a process with three variable factors – an algorithm, number generator and optimization function. More than 9.000 executions of the optimization algorithm revealed that the nature of an applied algorithm itself is the main source of the phase transitions. Some of the algorithms exhibit larger transition-shifting behavior while others perform rather transition-steady computing. These findings might be important for future extensions of these algorithms. © 2025, CC BY-NC-ND.

关键词： Cellular automata

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Pressure Vessel Design Simulation Using Hybrid Harmony Search Algorithm

Pressure Vessel Design Simulation Using Hybrid Harmony Searc...

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作者： Alaa A.Alomoush Mohammed I.Younis Khalid S.Aloufi AbdulRahman A.Alsewari Kamal Z.Zamli Faculty of Computing College of Computing and Applied SciencesUniviersiti Malaysia Pahang Department of Computer Engineering College of Engineering University of Baghdad College of Computer Science and Engineering Taibah University IBM Centre of Excellence Faculty of Computing College of Computing and Applied SciencesUniviersiti Malaysia Pahang

Recently the development of optimization algorithm is rapidly increased. Among several optimization algorithms, Harmony Search(HS) has been recently proposed for solving engineering optimization problems. The HS has some weaknesses such as parameters selection and falling in local optima. Many variants proposed to solve these problems. This paper presents successful hybrid algorithms with high performance to solve the pressure vessel design simulation. The hybrid algorithms consist of wellknown variants of HS and an opposition-based learning technique. The hybrid algorithm improved the HS exploration and avoiding falling in local optima, which lead the algorithm to provide significant results.

关键词： Pressure Vessel Design Harmony Search Algorithm Optimization algorithm hybrid algorithm opposition-based learning Computational Intelligence

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Retraction Note: Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor

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Discover Applied sciences 2024年第1期7卷 1-1页

作者： Jawarneh, Malik Arias-Gonzáles, José Luis Gandhmal, Dattatray P. Malik, Rami Qays Rane, Kantilal Pitambar Omarov, Batyrkhan Mahapatra, Cosmena Shabaz, Mohammad Faculty of Computing Sciences Gulf College Muscat Oman University of British Columbia Arequipa Peru Department of Computer Science and Engineering N. B N. B. Navale Sinhgad College of Engineering Solapur India Medical Instrumentation Techniques Engineering Department College of Engineering and Technologies Al-Mustaqbal University Hillah Iraq Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai India Al-Farabi Kazakh National University Almaty Kazakhstan Vivekananda Institute of Professional Studies New Delhi India Arba Minch University Arba Minch Ethiopia

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Nucleus segmentation in pap smear images using image processing techniques

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AIP Conference Proceedings 2024年第1期2934卷

作者： Mohammad Alslatie Hiam Alquran Wan Azani Mustafa Mohammed Mansour Naser Yasmin Mohd Yacob The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman 11855 Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University 21163 Irbid Jordan Department of Biomedical Engineering Jordan University of Science and Technology 22110 Irbid Jordan Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Campus Pauh Putra 02600 Arau Perlis Malaysia Advanced Computing (AdvComp) Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Campus Pauh Putra 02600 Arau Perlis Malaysia Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Campus Pauh Putra 02600 Arau Perlis Malaysia

Cervical cancer is caused by the growth of abnormal cells in the cervix’s lining. Human papillomavirus (HPV), a sexually transmitted infection, plays a role in most cervical cancers. Therefore, cervical cancer can be avoided by having regular screenings and being vaccinated against HPV infection. The term pap-smear refers to human cell samples stained using the Papanicolaou method. The Papanicolaou method is used to detect precancerous cell changes before they become invasive cancer. The smear cell image is composed of a nucleus and cytoplasm. Cancer prevalence is determined by the shape and structure of the nucleus. Therefore, the segmentation of the nucleus is an essential step in detecting cancer. However, overlapping, poor contrast, uneven staining, and other factors make cervical nucleus segmentation difficult. This paper proposes a new segmentation method for the cervical nucleus using digital image processing. Our proposed method used a median filter to remove noise and a non-linear contrast stretching to enhance the Pap smear images. Then, we used Bradley thresholding for the segmented cervical nucleus. The main impact of this paper will assist doctors in diagnosing cervical cancer based on Pap smear images and increase the accuracy percentages compared to the conventional method.

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Corrigendum to “Molecular subtypes classification of breast cancer in DCE-MRI using deep features” [Expert Syst. Appl. 236 (2024) 121371]

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Expert Systems with Applications 2025年 270卷

作者： Ali M. Hasan Noor K.N. Al-Waely Hadeel K. Aljobouri Hamid A. Jalab Rabha W. Ibrahim Farid Meziane College of Medicine Al-Nahrain University Baghdad Iraq Biomedical Engineering Department College of Engineering Al-Nahrain University Iraq Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nile Street 64001 Thi-Qar Iraq Department of Computer Science and Mathematics Lebanese American University Beirut 1102 2801 Lebanon Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia/Mersin 10 Turkey Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

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mdx: A Cloud Platform for Supporting Data science and Cross-Disciplinary Research Collaborations

arXiv

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arXiv 2022年

作者： Suzumura, Toyotaro Sugiki, Akiyoshi Takizawa, Hiroyuki Imakura, Akira Nakamura, Hiroshi Taura, Kenjiro Kudoh, Tomohiro Hanawa, Toshihiro Sekiya, Yuji Kobayashi, Hiroki Matsushima, Shin Kuga, Yohei Nakamura, Ryo Jiang, Renhe Kawase, Junya Hanai, Masatoshi Miyazaki, Hiroshi Ishizaki, Tsutomu Shimotoku, Daisuke Miyamoto, Daisuke Aida, Kento Takefusa, Atsuko Kurimoto, Takashi Sasayama, Koji Kitagawa, Naoya Fujiwara, Ikki Tanimura, Yusuke Aoki, Takayuki Endo, Toshio Ohshima, Satoshi Fukazawa, Keiichiro Date, Susumu Uchibayashi, Toshihiro Information Technology Center The University of Tokyo Japan Information Initiative Center Hokkaido University Japan Cyberscience Center Tohoku University Japan Faculty of Engineering Information and Systems University of Tsukuba Japan Division for Information and Communication Systems The University of Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Japan Center for Cloud Research and Development National Institute of Informatics Japan Research and Development Center for Academic Networks National Institute of Informatics Japan Research Center for Open Science and Data Platform National Institute of Informatics Japan Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Japan Global Scientific Information and Computing Center Tokyo Institute of Technology Japan Information Technology Center Nagoya University Japan Academic Center for Computing and Media Studies Kyoto University Japan Cybermedia Center Osaka University Japan Research Institute for Information Technology Kyushu University Japan

The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform’s virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an the overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans. © 2022, CC BY.

关键词： Internet of things

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