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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Procedia Computer Science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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International Symposium on Networks, Computers and Communications (ISNCC)

作者： Dheya Mustafa Safaa M. Khabour Ahmed S. Shatnawi Eyad Taqieddin Dept. of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Dept. of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan Dept. of Software Engineering Jordan University of Science & Technology Irbid Jordan Dept. of Cyber Security School of Computing and Informatics Al-Hussein Technical University Amman Jordan

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier.

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Existence analysis of a degenerate diffusion system for heat-conducting gases

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Nonlinear Differential Equations and Applications 2021年第4期28卷 1-28页

作者： Favre, Gianluca Jüngel, Ansgar Schmeiser, Christian Zamponi, Nicola Fakultät für Mathematik Universität Wien Oskar-Morgenstern-Platz 1 Vienna 1090 Austria Institute for Analysis and Scientific Computing Vienna University of Technology Wiedner Hauptstraß e 8-10 Vienna 1040 Austria School of Business Informatics and Mathematics University of Mannheim B6 28 Mannheim 68159 Germany

The existence of global weak solutions to a parabolic energy-transport system in a bounded domain with no-flux boundary conditions is proved. The model can be derived in the diffusion limit from a kinetic equation with a linear collision operator involving a non-isothermal Maxwellian. The evolution of the local temperature is governed by a heat equation with a source term that depends on the energy of the distribution function. The limiting model consists of cross-diffusion equations with an entropy structure. The main difficulty is the nonstandard degeneracy, i.e., ellipticity is lost when the gas density or temperature vanishes. The existence proof is based on a priori estimates coming from the entropy inequality and the H^{- 1} method and on techniques from mathematical fluid dynamics (renormalized formulation, div-curl lemma). © 2021, The Author(s).

关键词： Compensated compactness Cross-diffusion Energy transport Renormalized equation Weak solutions

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Users Acceptance Online Attendance Application Using Barcode Scanner: The Case Study in al Buraimi University College (BUC) Students

Users Acceptance Online Attendance Application Using Barcode...

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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022

作者： Alsideiri, Abir Alsharida, Rawan A. Hammood, Maytham Shakir, Mohanaad Binti Cob, Zaihisma Rashid Al Shamsi, Ibrahim Muttar, Ahmed Kh. Abood, Rehab Sheker, Mustefa Buraimi Information Technology Department Oman Tikrit University Computer Science Department Tikrit Iraq Cob University of Buraimi College of Business Buraimi Oman Universiti Tenaga National College of Computing and Informatics Malaysia Applied Science University College of Administrative Sciences Manama Bahrain Al-Fallujah Secondary School for Girls General Directorate of Anbar Education Fallujah Iraq Department of the Sewage of Fallujah Ministry of Municipalities and Public Works Fallujah Iraq

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Marking students' attendance within a class session is the prime task of the subject requirements since marking the attendance will regulate the students to attend the session. Moreover, it verifies how many students are attending the class. Previously, attendance was recorded manually by pen and paper. This method takes a long time, especially if the number of students is significant and adds workload to the teacher, and sometimes the data may be prone to error. This paper presents a mobile application for student attendance to avoid these issues. This application will ease the process of attending to the students in the registration instead of the traditional ones where the teacher spends time calling students to know who is present or absent. The students will use their mobile to log in to the application and select the current class to open the camera to scan the code provided by the teacher, and automatically their attendance will be registered. This application is mainly designed for Buraimi University College lecturers and students. © 2022 IEEE.

关键词： E-learning

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Evaluation of KNN-NMF Algorithm for Recommendation Systems in E-Commerce

Evaluation of KNN-NMF Algorithm for Recommendation Systems i...

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IEEE Conference on Systems, Process & Control (ICSPC)

作者： Hew Sok Yen Samuel-Soma M. Ajibade Muhammed Basheer Jasser Bayan Issa Yoke Leng Yong Faizah Mohammed Bashir Emmanuel Falude Yakubu Aminu Dodo Department of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) School of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia Faculty of Informatics Engineering University of Aleppo Syria School of Information and Communication Technology HELP University Persiaran Cakerawala Subang Bestari Seksyen Shah Alam Malaysia Department of Decoration and Interior Design Engineering College of Engineering University of Hail Hail Kingdom of Saudi Arabia Malaysia-Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur Malaysia Architectural Engineering Department College of Engineering Najran University Najran Kingdom of Saudi Arabia

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Due to growth in e-commerce, recommender systems have gained importance in providing recommendations to the user to help him or her make a choice. These systems not only assist the users in searching through the extensive product list but also increase businesses’ productivity by recommending products. The aim of this project is to suggest a hybrid recommender system which combines the aspects of collaborative and content-based filtering so as to enhance the efficiency of the recommendation system in e-commerce. In addition, it is to evaluate the performance of this model with the individual collaborative and content-based models. The study involved creating the CF and CBF models with the various algorithms like the Matrix Factorization, KNN, SVD, and the TF-IDF vectorization. The best models from CF and CBF were then combined with a Hybrid Filtering (HF) model by using weighted and switching combination to enhance the model’s effectiveness. The performance of the CF, CBF and the HF models was compared, in order to check if the proposed hybrid approach is better than the existing models. The results of the experiment show that the hybrid switching model provided better results than the CF and CBF models on their own. The hybrid filtering model had also managed to reduce the RMSE to $\mathbf{1. 0 6 5}$ and improved precision to 83.2%. This demonstrates the advantage of the proposed model over the traditional CF and CBF approaches. Our study validates that the hybrid filtering recommender system, especially the switching combination strategy, outperformed the traditional standalone models by integrating the benefits of both collaborative and content-based filtering. This provides a good starting point for future work and there are many directions in which the research can be expanded.

关键词： Productivity Machine learning algorithms Filtering Collaboration Switches Hafnium Nearest neighbor methods Electronic commerce User preference Recommender systems

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A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos

arXiv

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arXiv 2022年

作者： Lin, Zhi Lin, Junhao Zhu, Lei Fu, Huazhu Qin, Jing Wang, Liansheng Department of Computer Science School of Informatics Xiamen University Xiamen China Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Nursing The Hong Kong Polytechnic University Kowloon Hong Kong

Breast lesion detection in ultrasound is critical for breast cancer diagnosis. Existing methods mainly rely on individual 2D ultrasound images or combine unlabeled video and labeled 2D images to train models for breast lesion detection. In this paper, we first collect and annotate an ultrasound video dataset (188 videos) for breast lesion detection. Moreover, we propose a clip-level and video-level feature aggregated network (CVA-Net) for addressing breast lesion detection in ultrasound videos by aggregating video-level lesion classification features and clip-level temporal features. The clip-level temporal features encode local temporal information of ordered video frames and global temporal information of shuffled video frames. In our CVA-Net, an inter-video fusion module is devised to fuse local features from original video frames and global features from shuffled video frames, and an intra-video fusion module is devised to learn the temporal information among adjacent video frames. Moreover, we learn video-level features to classify the breast lesions of the original video as benign or malignant lesions to further enhance the final breast lesion detection performance in ultrasound videos. Experimental results on our annotated dataset demonstrate that our CVA-Net clearly outperforms state-of-the-art methods. The corresponding code and dataset are publicly available at https://***/jhl-Det/CVA-Net. Copyright © 2022, The Authors. All rights reserved.

关键词： Ultrasonic applications

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High-Rate Quantum Key Distribution Exploiting Integrated Photonics and Multi-Pixel Superconducting Nanowire Detectors

High-Rate Quantum Key Distribution Exploiting Integrated Pho...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Wei Li Likang Zhang Yichen Lu Jia Huang Lixin You Hao-Kun Mao Bingze Yan Qiong Li Qiang Zhang Feihu Xu Jian-Wei Pan Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences University of Science and Technology of China Hefei China Shanghai Research Center for Quantum Science and CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Cyberspace Science Faculty of Computing Harbin Institute of Technology Harbin China Hefei National Laboratory University of Science and Technology of China Hefei China

We report a quantum key distribution system that is able to generate key at a record high key rate of 115.8 Mb/s over 10-km fibre. This attributes to a high-efficiency multi-pixel superconducting nanowire detector, a ... 详细信息

ISBN: (纸本)9781957171258

关键词： Detectors Distributed feedback lasers Grating couplers Photon counting Quantum information Quantum key distribution

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Two-stage CNN-based framework for leukocytes classification

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Computers in Biology and Medicine 2025年 187卷 109616-109616页

作者： Khan, Siraj Sajjad, Muhammad Escorcia-Gutierrez, José Dhahbi, Sami Hijji, Mohammad Muhammad, Khan Department of Computer Science Islamia College University Peshawar25120 Pakistan Department of Computational Science and Electronics Universidad de la Costa CUC Barranquilla080002 Colombia Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Leukocytes are pivotal markers in health, crucial for diagnosing diseases like malaria and viral infections. Peripheral blood smear tests provide pathologists with vital insights into various medical conditions. Manual leukocyte counting is challenging and error-prone due to their complex structure. Accurate segmentation and classification of leukocytes remain challenging, impacting both accuracy and efficiency in blood microscopic image analysis. To overcome these limitations, we propose a robust two-stage CNN framework that integrates YOLOv8 for precise segmentation and MobileNetV3 for effective classification. Initially, WBCs are segmented using YOLOv8m-seg, extracting ROIs for subsequent analysis. Then, features from segmented ROIs are used to train MobileNetV3, classifying WBCs into lymphocytes, monocytes, basophils, eosinophils, and neutrophils. This framework significantly advances leukocyte categorization, enhancing diagnostic performance and patient outcomes. The proposed technique achieved impressive accuracy rates of 99.56 %, 99.19 % and 98.89 % during segmentation and 99.28 %, 99.63 % and 98.49 % during classification on Raabin-WBC, PBC and LISC datasets, respectively, outperforming state-of-the-art methods. © 2024 Elsevier Ltd

关键词： White blood cells Blood smear images Image segmentation Deep learning MobileNetV3 YOLOv8 Image classification

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DAFNI: A computational platform to support infrastructure systems research

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Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2023年第3期176卷 108-116页

作者： Matthews, Brian Hall, Jim Batty, Michael Blainey, Simon Cassidy, Nigel Choudhary, Ruchi Coca, Daniel Hallett, Stephen Harou, Julien J James, Phil Lomax, Nik Oliver, Peter Sivakumar, Aruna Tryfonas, Theodoros Varga, Liz Scientific Computing Department Science and Technology Facilities Council Didcot United Kingdom School of Geography and the Environment University of Oxford Oxford United Kingdom Centre for Advanced Spatial Analysis University College London London United Kingdom Transportation Research Group Faculty of Engineering and Physical Sciences University of Southampton Southampton United Kingdom Department of Civil Engineering University of Birmingham Birmingham United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Centre for Environmental and Agricultural Informatics Cranfield University Cranfield United Kingdom Department of Mechanical Aerospace and Civil Engineering University of Manchester Manchester United Kingdom School of Engineering Newcastle University Newcastle United Kingdom School of Geography University of Leeds Leeds United Kingdom Department of Civil and Environmental Engineering Imperial College London London United Kingdom Department of Civil Engineering University of Bristol Bristol United Kingdom Department of Civil Environmental and Geomatic Engineering University College London London United Kingdom

Research into the engineering of infrastructure systems is increasingly data intensive. Researchers build computational models to explore scenarios such as investigating the merits of infrastructure plans, analysing historical data to inform system operations or assessing the impacts of infrastructure on the environment. Models are more complex, at higher resolution and with larger coverage. Researchers also require a 'multi-systems' approach to explore interactions between systems, such as energy and water with urban development, and across scales, from buildings and streets to regions or nations. Consequently, researchers need enhanced computational resources to support cross-institutional collaboration and sharing at scale. The Data and Analytics Facility for National Infrastructure (DAFNI) is an emerging computational platform for infrastructure systems research. It provides high-throughput compute resources so larger data sets can be used, with a data repository to upload data and share these with collaborators. Users' models can also be uploaded and executed using modern containerisation techniques, giving platform independence, scaling and sharing. Further, models can be combined into workflows, supporting multi-systems modelling and generating visualisations to present results. DAFNI forms a central resource accessible to all infrastructure systems researchers in the UK, supporting collaboration and providing a legacy, keeping data and models available beyond the lifetime of a project. © 2023 Emerald Publishing Limited: All rights reserved.

关键词： Urban growth

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Audio-Driven Talking Face Video Generation with Dynamic Convolution Kernels

arXiv

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arXiv 2022年

作者： Ye, Zipeng Xia, Mengfei Yi, Ran Zhang, Juyong Lai, Yu-Kun Huang, Xuwei Zhang, Guoxin Liu, Yong-Jin MOE-Key Laboratory of Pervasive Computing The Department of Computer Science and Technology Tsinghua University Beijing China The Department of Computer Science and Engineering Shanghai Jiao Tong University China The School of Mathematical Sciences University of Science and Technology China School of Computer Science and Informatics Cardiff University United Kingdom Kwai Inc.

In this paper, we present a dynamic convolution kernel (DCK) strategy for convolutional neural networks. Using a fully convolutional network with the proposed DCKs, high-quality talking-face video can be generated from multi-modal sources (i.e., unmatched audio and video) in real time, and our trained model is robust to different identities, head postures, and input audios. Our proposed DCKs are specially designed for audio-driven talking face video generation, leading to a simple yet effective end-to-end system. We also provide a theoretical analysis to interpret why DCKs work. Experimental results show that our method can generate high-quality talking-face video with background at 60 fps. Comparison and evaluation between our method and the state-of-the-art methods demonstrate the superiority of our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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