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4th International Conference on Intelligent Medicine and Image Processing, IMIP 2022

作者： Muramamatsu, Chisako Nishio, Mizuho Oiwa, Mikinao Yakami, Masahiro Kubo, Takeshi Fujita, Hiroshi Faculty of Data Science Shiga University Japan Department of Radiology Kobe University Hospital Japan Department of Radiology Nagoya Medical Center Japan Preemptive Medicine and Lifestyle-Related Disease Research Center Kyoto University Hospital Japan Department of Radiology Tenri Hospital Japan Department of Electrical Electronic and Computer Engineering Faculty of Engineering Gifu University Japan

ISBN: (纸本)9781450396202

Medical image analysis systems can help radiologists in reading images accurately and promptly. The systems are expected to be robust to images obtained with any imaging systems by different vendors and at different facilities. However, because of the prevalence of diseases and difficulty of collecting samples with various characteristics, the systems may not apply well to all the images. Artificial intelligence-powered systems are expected to learn from experience and be improved continuously. In this study, we investigated whether continual training with local samples can improve system performance using mammography and lung CT databases. Different training strategies using unlabeled data are compared. © 2022 ACM.

关键词： computer aided diagnosis

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Determining Network Performance Through Bandwidth Optimization in Underwater Fiber Optic Communication 3

Determining Network Performance Through Bandwidth Optimizati...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Diwakar, Mandar P. Beemkumar, N. Yadav, Dhananjay Mishra, Varun Khandari, Harsimrat Divya, K. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Department of Mechanical Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

Information the overall performance of underwater fiber optic (UWFO) communique networks is vital for a hit deployment of this technology in an expansion of applications. That allows you to achieve most efficient community overall performance, bandwidth optimization must be hired. This paper examines distinctive bandwidth optimization techniques and how they're used to improve conversation among UWFO elements. The proposed approach utilizes adaptive modulation and coding techniques, as well as advanced error correction coding schemes, to efficiently utilize the available bandwidth and minimize signal degradation. Through extensive simulations and experiments, we demonstrate the effectiveness of our approach in improving network performance, including increased data transfer rates and reduced error rates, compared to traditional methods. Our findings have significant implications for the design and operation of underwater fiber optic communication systems, potentially enhancing their potential for a wide range of applications in marine environments. In the end, several destiny studies guidelines are diagnosed to in addition enhance the overall performance and reliability of UWFO conversation networks. © 2023 IEEE.

关键词： Communiqué Conversation Investigates Networks Optimization

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Bayesian Optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction Model 5

Bayesian Optimization with Stacked Sparse Autoencoder based ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Shaikh, Irfan Abdul Karim Krishna, P. Vamsi Biswal, Swagat Gourav Kumar, A. Sasi Baranidharan, S. Singh, Kamlesh Faculty of Business Bahrain Polytechnic Isa Town Bahrain School of Management Malla Reddy University Telangana Hyderabad India Odisha University of Tecnology and Research Department of Electrical Engineering Orissa Bhubaneswar India Institute of Engineering and Technology Srinivas University Mentor-IT-Inurture Education Solutions Pvt Ltd Bangalore Department of Cloud Technology and Data Science Karnataka Dakshina Kannada India Department of Mba and Research Center Karnataka Bangalore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9781665474672

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new Bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (BOSSAE-CPP) model. The major intention of the BOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the BOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the BO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the BOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the BOSSAE-CPP technique. © 2023 IEEE.

关键词： Electronic money

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China CDC weekly 2022年第40期4卷 885-889,I0002-I0005页

作者： Yuan Bai Mingda Xu Caifen Liu Mingwang Shen Lin Wang Linwei Tian Suoyi Tan Lei Zhang Petter Holme Xin Lu Eric H.Y.Lau Benjamin J.Cowling Zhanwei Du WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina Laboratory of Data Discovery for Health Limited(D24H) Hong Kong Science ParkHong Kong Special Administrative RegionChina China-Australia Joint Research Center for Infectious Diseases School of Public HealthXi’an Jiaotong University Health Science CenterXi’an CityShaanxi ProvinceChina Department of Genetics University of CambridgeCambridge CB23EHUK College of Systems Engineering National University of Defense TechnologyChangsha CityHunan ProvinceChina Department of Computer Science Aalto UniversityEspooFinland Center for Computational Social Science Kobe UniversityKobeJapan

Introduction:Minimizing the importation and exportation risks of coronavirus disease 2019(COVID-19)is a primary concern for sustaining the“Dynamic COVID-zero”strategy in *** estimation is essential for cities to conduct before relaxing border control ***:Informed by the daily number of passengers traveling between 367 prefectures(cities)in China,this study used a stochastic metapopulation model parameterized with COVID-19 epidemic characteristics to estimate the importation and exportation ***:Under the transmission scenario(R0=5.49),this study estimated the cumulative case incidence of Changchun City,Jilin Province as 3,233(95%confidence interval:1,480,4,986)before a lockdown on March 14,2022,which is close to the 3,168 cases reported in real life by March 16,*** a total of 367 prefectures(cities),127(35%)had high exportation risks according to the simulation and could transmit the disease to 50%of all other regions within a period from 17 to 94 *** average time until a new infection arrives in a location in 1 of the 367 prefectures(cities)ranged from 26 to 101 ***:Estimating COVID-19 importation and exportation risks is necessary for preparedness,prevention,and control measures of COVID-19—especially when new variants emerge.

关键词： Export Changchun prevention

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Fractional discrete neural networks with variable order: solvability, finite time stability and synchronization

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The European Physical Journal Special Topics 2024年 1-14页

作者： Hioual, Amel Alomari, Saleh Al-Tarawneh, Hassan Ouannas, Adel Grassi, Giuseppe Laboratory of Dynamical Systems and Control University of Oum El-Bouaghi Oum El Bouaghi Algeria Faculty of Science and Information Technology Software Engineering Jadara University Irbid Jordan Department of Data Sciences and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Department of Mathematics and Computer Science University of Oum EL-Bouaghi Oum El Bouaghi Algeria Dipartimento Ingegneria Innovazione Universita del Salento Lecce Italy

Research in the field of dynamic behaviors in neural networks with variable-order differences is currently a thriving area, marked by various significant discoveries. However, when it comes to discrete-time neural networks featuring fractional variable-order nonlocal and nonsingular kernels, there has been limited exploration. This paper stands as one of the initial contributions to this subject, focusing primarily on the topics of stability and synchronization in finite-time within discrete neural networks. The research employs the nabla ABC variable-order difference operator, with a primary approach involving the investigation of a novel Gronwall inequality using the Atangana-Baleanu difference variable-order sum operator. This analysis leads to the development of a uniqueness theorem and a criterion for the stability in finite-time of variable-order discrete neural networks. Furthermore, the requirements stemming from this type of stability and the novel Gronwall inequality serve as the foundation for establishing the conditions necessary for achieving finite-time synchronization in these networks, employing a specific control using state feedback method. Finally, the study utilizes numerical solutions to validate the obtained results.

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Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

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Context-Aware Enhancement and Transformer Network for Image-Text Retrieval

Context-Aware Enhancement and Transformer Network for Image-...

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International Conference on computer Supported Cooperative Work in Design

作者： Zheng Dong Ruijia Zhang Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Shandong Engineering Research Center of Big Data Applied Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Image-text matching is an important and challenging task in the field of multimedia analysis, aimed at bridging the semantic gap between visual content and language descriptions. Although this field has significant implications for enhancing cross-modal interaction, most previous work still faces challenges in accurately aligning images with text descriptions, especially when dealing with images containing rich semantic information. To address this issue, we propose a novel context-aware image-text matching model that extracts and summarizes visual region information aligned with multiple text descriptions from a single image. Specifically, we designed an adaptive context-aware self-attention module to extract representations of visual regions and text words. By controlling the complementary semantic relationships within each modality, our model can adaptively capture contextual information for each modality. We then introduce a Transformer-based encoder layer to extract features at multiple levels and aggregate region-level features into image-level features. Finally, fine-grained word-region alignment is conducted to match image features with corresponding text features. To evaluate the effectiveness of our method, we conducted extensive experiments on two benchmark datasets, Flickr30K and MS-COCO. The experimental results show that our model outper-forms several state-of-the-art baselines in image-text matching tasks, demonstrating the effectiveness and practicality of our model in handline cross-modal retrieval tasks.

关键词： Measurement Visualization Adaptation models Federated learning Semantics Transformers Feature extraction Faces Context modeling Cross modal retrieval

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A Review of Machine Learning Algorithms for Detection of Breast Cancer

A Review of Machine Learning Algorithms for Detection of Bre...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Abhay Tale Aditya Barhate Prateek Verma Palash Gourshettiwar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. The advancement in the applied technique of Machine Learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. This review paper provides insights into how different ML algorithms including the Support Vector Machine (SVM), Convolutional Neural Network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. These algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. The paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. This review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice

关键词： Support vector machines Machine learning algorithms Sensitivity Reviews Precision medicine Ubiquitous computing Breast cancer Vectors Convolutional neural networks Random forests

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Matching patterns with variables under hamming distance

arXiv

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arXiv 2021年

作者： Gawrychowski, Pawel Manea, Florin Siemer, Stefan University of Wroclaw Faculty of Mathematics and Computer Science Göttingen University Computer Science Department Campus-Institut Data Science Germany Göttingen University Computer Science Department Germany

A pattern α is a string of variables and terminal letters. We say that α matches a word w, consisting only of terminal letters, if w can be obtained by replacing the variables of α by terminal words. The matching problem, i.e., deciding whether a given pattern matches a given word, was heavily investigated: it is NP-complete in general, but can be solved efficiently for classes of patterns with restricted structure. In this paper, we approach this problem in a generalized setting, by considering approximate pattern matching under Hamming distance. More precisely, we are interested in what is the minimum Hamming distance between w and any word u obtained by replacing the variables of α by terminal words. Firstly, we address the class of regular patterns (in which no variable occurs twice) and propose efficient algorithms for this problem, as well as matching conditional lower bounds. We show that the problem can still be solved efficiently if we allow repeated variables, but restrict the way the different variables can be interleaved according to a locality parameter. However, as soon as we allow a variable to occur more than once and its occurrences can be interleaved arbitrarily with those of other variables, even if none of them occurs more than once, the problem becomes intractable. © 2021, CC BY.

关键词： Hamming distance

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RETRACTED ARTICLE: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications (Archives of Computational Methods in Engineering, (2024), 31, 1, (291-311), 10.1007/s11831-023-09978-x)

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Archives of Computational Methods in Engineering 2025年

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Punjab Phagwara144411 India Department of Computer Science Government Bikram College of Commerce Punjab Patiala147001 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Mohali Gharuan140413 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajaplayam India

The Editors-in-Chief have retracted this article. After publication, concerns were raised about the originality of the study reported. An investigation conducted by the Publisher confirmed that the article contains material that substantially overlaps with a previous study by different authors [1]. In particular, the research methodology, research questions, numerous formulations, and discussion are almost identical with those in [1]. The Editors-in-Chief therefore consider this article redundant. None of the authors have responded to correspondence from the Publisher about this retraction. © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2025.

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