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RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

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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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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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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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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Detecting Bogus Information Attack in Vehicular Ad Hoc Network: A Context-Aware Approach 16

Detecting Bogus Information Attack in Vehicular Ad Hoc Netwo...

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16th International Learning and Technology Conference, L and T 2019

作者： Ghaleb, Fuad A. Zainal, Anazida Maroof, Mohd Aizaini Rassam, Murad A. Saeed, Faisal Cybersecurity X Lap Information Assurance and Security Research Group School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor81310 Malaysia Department of Computer and Electronic Engineering Sana'a Community College Sana'a5695 Yemen Faculty of Engineering and Information Technology Taiz University Taiz6803 Yemen College of Computer Science and Engineering University of Taibah Medina Saudi Arabia

Most of Vehicular Ad Hoc Network's (VANET) and Fly Ad Hoc Network (FANET) applications, protocols, and service rely on cooperativeness among network participants. Vehicles, could be unmanned aerial vehicles (e.g. drones), share their sensors' information to improve network performance and provide safety, traffic efficiency, and entertainments. The accuracy and reliability of this information are vital for VANET performance. However, misbehaving (or faulty) vehicles, which exploit the cooperativeness feature by sharing bogus information, can disrupt the fundamental operations of any potential application and cause loss of people lives and properties. Unfortunately, existing detection approaches cannot effectively thwart such attacks. The highly dynamic vehicular context has been vastly neglected as well as they rely on predefined and static security perimeters to differentiate between false and correct information. In this paper, a context-aware detection approach is proposed to improve the detection accuracy and reduce the false alarm rate. Firstly, the features that well represent the vehicular context has been extracted. Then, an online unsupervised learning method namely the hierarchical clustering algorithm is used to classify the received messages into either genuine or spurious. Finally, Bayesian-based hypothesis testing has been used to confirm the validity of the classification. Results show that the proposed detection approach is promising in effectively detect bogus information attack and improve applications performance. © 2019 The Authors. Published by Elsevier B.V.

关键词： Vehicular ad hoc networks

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Corrigendum to "Analysis of current state, gaps, and opportunities for technologies in the Malaysian oil palm estates and palm oil mills towards net-zero emissions"

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Heliyon 2024年第22期10卷 e39654页

作者： Jaya Prasanth Rajakal Foo Yuen Ng Anna Zulkifli Bing Shen How Jaka Sunarso Denny K S Ng Viknesh Andiappan Biomass Waste-to-Wealth Special Interest Group Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Kuching 93350 Malaysia. School of Engineering and Technology Sunway University Petaling Jaya 47500 Malaysia. Malaysian Palm Oil Council Level 25 PJX HM Shah Tower 16A Persiaran Barat PJS 52 46200 Petaling Jaya Selangor Malaysia.

[This corrects the article DOI: 10.1016/***.2024.e30768.].

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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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Design and Development of Smart Irrigation and Water Management System for Conventional Farming

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Journal of Physics: Conference Series 2021年第1期1844卷

作者： M A Z M Rafique F S Tay Y L Then Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus. Jalan Simpang Tiga 93350 Kuching Sarawak Malaysia

The intention of this project is to develop a smart irrigation and water management system for conventional farming. The project is conducted mainly to improve the irrigation scheduling and also to solve the over watering and under watering issues in traditional irrigation system. These problems can be solved by implementing soil moisture sensor as a smart component in the irrigation system. Smart irrigation system with the implementation of sensory-based system will be able to provide a proper irrigation scheduling, by monitoring the soil and weather condition of the farm. In this project, the sensory system consists of soil moisture sensor, temperature sensor and light intensity sensor, which basically used to monitor soil moisture level, temperature level, and light intensity level at the separate test area. Arduino Mega 2560 microcontroller will process the data from these sensors and a proper irrigation scheduling will be developed based on the data collected. Type of irrigation system that been used in this project is a sprinkler system because it has high uniformity of water distribution to the plant, which able to spread water efficiency and further optimize the water usage during irrigation process. Ultrasonic sensor is also implemented in the system to measure the amount of water used in each irrigation process performed. An offline data storage will be implemented in this project using a micro SD card module, which all the essential information such as sensory system readings and the amount of water used will be recorded and stored into a micro SD card. Thus, it allows user to monitor their farm's condition, and also gives a better view on what is really happening at their farm.

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Energy efficient algorithms based on VM consolidation for cloud computing: Comparisons and evaluations

arXiv

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arXiv 2020年

作者： Zhou, Qiheng Xu, Minxian Gill, Sukhpal Singh Gao, Chengxi Tian, Wenhong Xu, Chengzhong Buyya, Rajkumar Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom School of Software and Information Engineering University of Electronic Science and Technology of China China Faculty of Science and Technology Macao University China School of Computing and Information Systems University of Melbourne Australia

Cloud computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidation-based energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. This makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. The experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Cloud computing

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