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Vision transformer-convolution for breast cancer classification using mammography images: A comparative study

International Journal of Hybrid Intelligent Systems 2024年第2期20卷 67-83页

作者： Abimouloud, Mouhamed Laid Bensid, Khaled Elleuch, Mohamed Aiadi, Oussama Kherallah, Monji National Engineering School of Sfax University of Sfax Laboratory of Electrical Engineering (LAGE) University of KASDI Merbah Ouargla National School of Computer Science (ENSI) University of Manouba Artifcial Intelligence and Information Technology Laboratory (LINATI) Kasdi Merbah University Faculty of Sciences University of Sfax Advanced Technologies for Environment and Smart Cities (ATES Unit) University of Sfax

Breast cancer is a significant global health concern, highlighting the critical importance of early detection for effective treatment of women's health. While convolutional networks (CNNs) have been the best for analysing medical images, recent interest has emerged in leveraging vision transformers (ViTs) for medical data analysis. This study aimed to conduct a comprehensive comparison of three systems a self-attention transformer (VIT), a compact convolution transformer (CCT), and a tokenlearner (TVIT) for binary classification of mammography images into benign and cancerous tissue. Thorough experiments were performed using the DDSM dataset, which consists of 5970 benign and 7158 malignant images. The performance accuracy of the proposed models was evaluated, yielding results of 99.81% for VIT, 99.92% for CCT, and 99.05% for TVIT. Additionally, the study compared these results with the current state-of-the-art performance metrics. The findings demonstrate how convolution-attention mechanisms can effectively contribute to the development of robust computer-aided systems for diagnosing breast cancer. Notably, the proposed approach achieves high-performance results while also minimizing the computational resources required and reducing decision time. © 2024 - IOS Press. All rights reserved.

关键词： Breast cancer (BC) computer-aided diagnosis (CAD) convolutional networks (CNN) DDSM mammography vision transformers (VIT)

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A Two-Layered Machine Learning Approach for Anti-Malware Sustainability 9

A Two-Layered Machine Learning Approach for Anti-Malware Sus...

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Alrammal, Muath Naveed, Munir Sallam, Suzan Tsaramirsis, Georgios Higher Colleges of Technology Faculty of Computer Information Science Abu Dhabi United Arab Emirates Khawarizmi International College General Education Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9789380544441

The need for sophisticated security solutions for mobile devices is an urgent and crucial requirement for the sustainable use of smart phones. The number and scale of sophisticated attacks on smart devices is ever-increasing. There are several limitations and challenges to counter such attacks, for example, the security solutions must be light-weight and sustainable to counter such attacks. Also, the limited capacity of computing resources in mobile devices pose serious challenges for security solutions as they must keep scanning for malware in real-time constraints. The sustainability of such solutions is another issue because of the influx of new malware definitions and patterns. In this work, a security model for android devices is presented using a two layered approach. In the first layer, Monte-Carlo simulations and reinforcement learning techniques work together to extract the malware pattern and the second layer filters the false flags to detect malware. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Reinforcement learning

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Studying the Effects of Prolonged Thermal Stress Aiming to Induce Artificial Aging on DRAM Retention-Based Physical Unclonable Functions

Studying the Effects of Prolonged Thermal Stress Aiming to I...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Nikolaos Athanasios Anagnostopoulos Nico Mexis Yufan Fan Stephanie Senjuty Bartsch Tanja Schaier Stefan Katzenbeisser Tolga Arul Faculty of Computer Science and Mathematics University of Passau Passau Germany Department of Electrical Engineering and Information Technology Technical University of Darmstadt Darmstadt Germany Department of Computer Science Technical University of Darmstadt Darmstadt Germany

This work studies the effects of thermal stress on Dynamic Random-Access Memory (DRAM) retention-based Physical Unclonable Functions (PUFs) based on Commercial Off-The-Shelf (COTS) Single-Board computer (SBC) modules. Our results show that prolonged thermal stress, in general, leads to a decrease in the entropy of the responses of this PUF (as the number of bit flips is reduced) and a loss of its reliability (as responses taken before and after the stress has been applied to this PUF do not adequately match each other). Nevertheless, we note that as time passes after we stop applying thermal stress to the relevant PUF devices, their responses start to resemble more and more those that they produced before thermal stress was applied to them. As prolonged thermal stress has been utilised in order to induce artificial aging on electronics, our work not only studies the potential effects of aging on such electronics as DRAMs in general and DRAM PUFs in particular, but also sheds some doubt on the ability of this method to induce permanent effects that would truly resemble the effects of aging.

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Discriminative Vector Learning with Application to Single Channel Speech Separation

Discriminative Vector Learning with Application to Single Ch...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ha Minh Tan Kai-Wen Liang Jia-Ching Wang Dept. of Computer Science and Information Engineering National Central University Taiwan Faculty of Information Technology Nguyen Tat Thanh University Vietnam

In this paper, we introduce a discriminative vector learning method and apply it to single-channel speech separation. First, speech samples are transformed into discriminative vectors using two backbone networks. These vectors are easily separated by simple clustering algorithms. Among them, vectors with lower similarity are separated into different clusters, while vectors in the same cluster have higher similarity. This property is very important in image segmentation, audio separation, and data clustering problems. In our work, we design the network architecture to improve the discriminativeness of vectors through learning, taking this task as spectrogram segmentation. Experiments show that our method significantly improves performance compared to other deep clustering methods for speech separation.

关键词： Learning systems Image segmentation Clustering methods Clustering algorithms Signal processing algorithms Network architecture Task analysis

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Task Offloading Strategy with Emergency Handling and Blockchain Security in SDN-Empowered and Fog-Assisted Healthcare IoT

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Tsinghua science and technology 2022年第4期27卷 760-776页

作者： Junyu Ren Jinze Li Huaxing Liu Tuanfa Qin School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510640China Guangxi University Xingjian College of Science and Liberal Arts Nanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronics and InformationGuangxi UniversityNanning 530004China Faculty of Earth Sciences&Geography Trinity CollegeUniversity of CambridgeCambridgeCB21TNUK

With the rapid advancement of the Internet of Things(IoT),the typical application of wireless body area networks(WBANs)based smart healthcare has drawn wide attention from all sectors of *** alleviate the pressing challenges,such as resource limitations,low-latency service provision,mass data processing,rigid security demands,and the lack of a central entity,the advanced solutions of fog computing,software-defined networking(SDN)and blockchain are leveraged in this *** the basis of these solutions,a task offloading strategy with a centralized low-latency,secure and reliable decision-making algorithm having powerful emergency handling capacity(LSRDM-EH)is designed to facilitate the resource-constrained edge devices for task ***,to well ensure the security of the entire network,a comprehensive blockchain-based two-layer and multidimensional security strategy is ***,to tackle the inherent time-inefficiency problem of blockchain,we propose a blockchain sharding scheme to reduce system time *** simulation has been conducted to validate the performance of the proposed measures,and numerical results verify the superiority of our methods with lower time-latency,higher reliability and security.

关键词： wireless body area networks(WBANs) healthcare IoT software-defined networking(SDN) fog computing blockchain task offloading blockchain sharding

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Breast Cancer Segmentation in Mammograms using Antlion Optimization and CNN/GRU Architectures

Breast Cancer Segmentation in Mammograms using Antlion Optim...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Radhia Khdhir Salwa Othmen Aymen Belghith Jamilah Alamri Department of Computer Science College of Science and Arts in Gurayat Jouf University Gurayat Saudi Arabia Department of Information Technology Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Computers and Information Technology Department College of Science and Arts in Turaif Northern Border University Arar Saudi Arabia Computer Science Department College of Informatics and Computing Saudi Electronic University Riyadh Saudi Arabia

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Early and accurate diagnosis of breast cancer is crucial for successful treatment and improved patient outcomes. This paper proposes a novel hybrid approach for breast cancer classification in mammographic images that combines the powerful optimization capabilities of Antlion Optimization (ALO) with the feature extraction and learning potential of a deep learning (DL) model. To enhance breast cancer detection, we introduce a novel preprocessing approach that combines a Gaussian filter with Residual Pixel Removal (RPR). This initial step aims to reduce noise and highlight relevant features in the images. We then extract textural features using the Gray Level Cooccurrence Matrix (GLCM), providing valuable insights into the spatial distribution of intensities. Finally, we leverage the power of Gated Recurrent Unit (GRU) networks for classification, enabling the model to effectively learn complex relationships within the extracted features and achieve accurate cancer detection. Our proposed ALO-based segmentation approach, incorporating either Convolutional Neural Networks (CNN) or GRU architectures, achieves superior performance compared to existing solutions in breast cancer segmentation. Extensive simulations demonstrate significant improvements in accuracy, precision, recall, and F1-score. Notably, even without utilizing the computationally intensive GRU architecture, our CNNbased model exhibits near-optimal results, making it a valuable option for time-sensitive applications.

关键词： Image segmentation Accuracy Noise computer architecture Logic gates Feature extraction Breast cancer

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Detection of Toxic Content on Social Networking Platforms Using Fine Tuned ULMFiT Model

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Intelligent Automation & Soft Computing 2023年第1期35卷 15-30页

作者： Hafsa Naveed Abid Sohail Jasni Mohamad Zain Noman Saleem Rao Faizan Ali Shahid Anwar Department of Software Engineering Faculty of ScienceUniversity of LahorePakistan Department of Computer Science COMSATS University IslamabadLahore CampusPakistan Institute for Big Data Analyics and Artifcial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversii Tekoogi MARA40450Shah AlamSelangorMalaysia TechnoGenics SMC PVT LTD LahorePakistan Department of Computer and Infomation Science Universiti Teknologi PETRONASBandar Ser IskandarTronohPerakMalaysia Depatment of Information Engineering Technology National Skils University IslamabadSector H-8/1Faiz AhmedFaiz RoadIslamabadPakistan

Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meaning they are available in many different *** problem for these types of websites is to handle meaningless and irrelevant *** this paper we have worked on the Quora insincere questions(questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers)dataset in order to identify user insincere questions,so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the ***,a research was carried out with recurrent neural network and pretrained glove word embeddings,that achieved the F1 score of *** proposed study has used a pre-trained ULMFiT *** model has outperformed the previous model with an F1 score of 0.91,which is much higher than the previous studies.

关键词： Machine learning text mining quora mining artificial intelligence natural language processing

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An Analysis of Life Tech Related IPRs : A comparison study on Pharmaceutical vs. Non-Pharmaceutical Patents 4

An Analysis of Life Tech Related IPRs : A comparison study o...

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4th IEEE Global Conference on Life sciences and Technologies, LifeTech 2022

作者： Mitsumori, Yaeko Kato, Hiroshi Kamijo, Koichi Chega Osaka University Osaka Suita Japan The Faculty of Law Nihon University Tokyo Japan International Professional University of Technology in Tokyo Department of Information and Computer Science Tokyo Japan

ISBN: (纸本)9781665419048

The COVID-19 pandemic has led to a dramatic loss of human life worldwide and presents an unprecedented challenge to global public health. Since the deadly virus spread out across the world rapidly, several countries experienced a collapse in their medical care systems. Not only developing countries, but developed countries as well have been suffering from a lack of medicines, hospital beds, doctors/nurses, and medical machines and equipment. Under these circumstances, managing the intellectual property rights (IPRs) of vaccines, medicines, and medical machines and equipment has become a big issue. Since developing a new product in the medical industry takes a long time and requires a huge amount of investment, the IPRs of vaccines, medicines, medical machines and equipment are highly protected. However, under national emergencies, such as the COVID-19 pandemic, how should the IPRs of these products be dealt with? This study analyzed how to handle medical IPRs, focusing on the medical machines and equipment industry. For this purpose, this study first provided an overview of the current status of the COVID-19 pandemic worldwide as well as the medical machines and equipment industry. Next, this study examined patent regulations and governmental policies, along with citizens' and industries' movements to "not to exercise"IPRs. This article presents potential solutions and/or has implications for the post-COVID-19 era and the "New Normal"era with COVID-19. © 2022 IEEE.

关键词： Vaccines

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Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method 12th

Understanding the Influence of Multiple Factors on the Sprea...

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12th International Conference on Health information science, HIS 2023

作者： Xu, Zhenkai Lin, Shaofu Huang, Zhisheng Fu, Yu Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Vrije University Amsterdam Amsterdam Netherlands Clinical Research Center for Mental Disorders Shanghai Pudong New Area Mental Health Center Tongji University School of Medicine China and Deep Blue Technology Group Shanghai China

ISBN: (纸本)9789819971077

The Omicron variant of SARS-CoV-2, emerging in November 2021, has rapidly spread worldwide due to its high transmissibility and ability to evade vaccines. It is still not fully under control, and there is a need to enhance our scientific understanding of the Omicron variant. Investigating the influencing factors and the correlated characteristics of the transmission of the Omicron variant remains an important issue in COVID-19 prevention and control. This study utilized data from various sources to investigate Omicron’s transmission factors. Focusing on populous countries like China, France, and the US, a multiple regression model was optimized through the Gauss-Newton method to reveal links between daily Omicron cases and variables like climate, population, healthcare, and vaccination and etc. Results showed vaccination rates, healthcare facility numbers, and population density as pivotal factors influencing transmission. Higher vaccination rates and more healthcare facilities correlated with lower Omicron transmission, while dense population areas experienced higher spread. These findings hold significance for guiding public health decisions and shaping vaccination strategies amidst the Omicron variant’s ongoing impact. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： COVID-19

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Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category)

Model of Visualization and Analytics for Open Data (Case: El...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Ripto Mukti Wibowo Bahjat Fakieh Muhammad Sher Ramzan Ahmed Saeed Alzahrani Muazzam Siddiqui Bander Alzahrani Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Luddy School of Informatics Computing and Engineering Indiana University Bloomington

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities.

关键词： Technological innovation Data analysis Pandemics Voting Data visualization Big Data Data models

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