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Development of a Secure Cloud-based Breast Cancer Diagnosis System 22

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Development of a Secure Cloud-based Breast Cancer Diagnosis ...

6th International Conference on Cloud and Big Data Computing, ICCBDC 2022

作者： Amin, Fazal-E. Hussain, Muhammad Ali, Zulfiqar Busaleh, Mariam Al Sultan, Sarah A. Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh11541 Saudi Arabia School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Department of Radiology College of Medicine King Khalid University Hospital King Saud University Riyadh Saudi Arabia

ISBN: (纸本)9781450396578

Breast cancer is one of the most common cancers that cause death in women. Breast cancer was the most common cancer in 2020, with 2.26 million new cases. Cancer mortalities can be reduced by early detection and treatment. There are two components of early detection: early diagnosis and screening. Our work is related to early diagnosis. Early diagnosis helps to start treatment early, which is more effective and less expensive. The lives of cancer patients can be improved significantly by early detection and avoiding treatment delays. This paper presents the design and development of a secure cloud-based breast cancer diagnosis system. The proposed system uses using convolutional neural network for breast mass classification. Fragile zero watermarking will be implemented to secure the transmission of mammographs. The proposed system will enable women living in remote areas to get the facility for the routine initial screening near their homes without making their identity and medical data vulnerable. It will reduce the cost and time by avoiding frequent visits of the patients to specialized health care centers. On the other hand, it will help enhance the quality of service at specialized healthcare centers by reducing the number of patients in the initial screening. © 2022 ACM.

关键词： Deep learning

Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation

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arXiv 2024年

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © 2024, CC BY-NC-SA.

关键词： Semantic Segmentation

Analyzing and Comparing Results for Multiple Machine Learning Models 4

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Analyzing and Comparing Results for Multiple Machine Learnin...

2023 4th International Conference on computer engineering and Intelligent Control, ICCEIC 2023

作者： Ng, Jefferson Li, Yijian Ji, Xuxiao Yang, Can Wan, Jiaqiao Lu, Xingyu University of California Department of Mechanical Engineering Irvine92697 United States Courant Institute of Mathematical Sciences New York University NY10012 United States University of Waterloo School of Computer Science WaterlooCAN2L 3G1 United States Oregon State University Department of Electrical Engineering and Computer Science Corvallis97331 United States Zhengzhou University Software College Zhengzhou450000 China United World College Changshu China Jiangsu215500 China

ISBN: (纸本)9798350308877

In the contemporary landscape of atmospheric sciences, the ability to recognize specific weather phenomena accurately and efficiently has become paramount, largely due to the exponential growth in sensor-generated data. This paper undertakes an in-depth analysis of advanced algorithms, namely YOLOV8, ResNet50, and Convolutional Neural Networks (CNN), for the purpose of weather pattern identification and classification. Building on the foundation of deep learning, the YOLOV8's real-time object detection capabilities are leveraged to discern intricate weather patterns in diverse datasets, from meteorological stations to satellite imagery. On the other hand, Resnet 50 which is another CNN based model was also tested along with CNN for comparison. An exhaustive evaluation of these algorithms covers various metrics, including accuracy, precision, computational efficiency, and real-world applicability. Special emphasis is placed on the crucial stages of data preprocessing, feature extraction, and model tuning, highlighting their impact on the algorithms' overall performance. Our findings suggest that, when appropriately optimized, both YOLOV8 and CNN exhibit exceptional capabilities in discerning and classifying intricate weather patterns, whereas ResNet50 exhibits comparatively less performance. © 2023 IEEE.

关键词： Convolutional neural networks

Precoding Optimization for Rate Splitting Enabled Internet of Underwater Things over Optical Wireless Underwater Turbulent Channels

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IEEE Internet of Things Journal 2025年

作者： Xing, Fangyuan Tong, Fei Liu, Xiaonan Wang, Zhenduo Leung, Victor C. M. Southeast University School of Cyber Science and Engineering Nanjing211189 China School of Natural and Computing Science University of Aberdeen AberdeenAB24 3FX United Kingdom Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

With the booming development of the underwater Internet of Things, underwater devices and data volume are explosively increasing. Thanks to the advantages of high efficiency and low complexity, the emerging rate splitting multiple access (RSMA) can be integrated with underwater optical wireless communication (UOWC), which can help improve resource utilization and system performance. However, the oceanic characteristics are distinctive and pose great challenges for RSMA-enabled UOWC networks. Precoding design is crucial for benefitting from RSMA-enabled systems, and thus, we explore the precoding optimization for RSMA-enabled UOWC networks over underwater turbulent channels. Specifically, the RSMA-enabled UOWC network is modeled, where a combined turbulent channel involving the effects of absorption, scattering, and underwater turbulence is considered, and a message transmission scheme with rate splitting design is analyzed. A precoding problem is formulated to maximize the ergodic sum rate (ESR) under the constraint of total transmitted power. Since the accurate expression of ESR is a non-closed form and intractable, we first propose a consecutive Fenton-Wilkinson (CFW)-based ergodic rate approximation approach to solve this formulation problem. Fenton-Wilkinson moment matching is consecutively employed to achieve a closed-form and low-complexity expression of the ergodic rate, as only Fenton-Wilkinson moment matching method can offer closed-form solutions for underlying parameters of the approximating log-normal distributions. For further solving the non-convex precoding problem, the successive convex approximation (SCA) approach is adopted, which is particularly applicable for resource-limited underwater environments. The effectiveness of the proposed CFW approach and precoding strategy is evaluated by extensive simulation results for various user deployments and different network loads. © 2025 IEEE.

关键词： Normal distribution

Ethical Aspects of ChatGPT in software engineering Research

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arXiv 2023年

作者： Akbar, Muhammad Azeem Khan, Arif Ali Liang, Peng Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta15210 Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu90014 Finland School of Computer Science Wuhan University Wuhan430072 China

ChatGPT can improve software engineering (SE) research practices by offering efficient, accessible information analysis and synthesis based on natural language interactions. However, ChatGPT could bring ethical challenges, encompassing plagiarism, privacy, data security, and the risk of generating biased or potentially detrimental data. This research aims to fill the given gap by elaborating on the key elements: motivators, demotivators, and ethical principles of using ChatGPT in SE research. To achieve this objective, we conducted a literature survey, identified the mentioned elements, and presented their relationships by developing a taxonomy. Further, the identified literature-based elements (motivators, demotivators, and ethical principles) were empirically evaluated by conducting a comprehensive questionnaire-based survey involving SE researchers. Additionally, we employed Interpretive Structure Modeling (ISM) approach to analyze the relationships between the ethical principles of using ChatGPT in SE research and develop a level based decision model. We further conducted a Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis to create a cluster-based decision model. These models aim to help SE researchers devise effective strategies for ethically integrating ChatGPT into SE research by following the identified principles through adopting the motivators and addressing the demotivators. The findings of this study will establish a benchmark for incorporating ChatGPT services in SE research with an emphasis on ethical considerations. Impact Statement—This paper establishes the impact of employing ChatGPT in SE research while carefully addressing ethical challenges like privacy, data security, and bias. By developing a taxonomy through a comprehensive literature survey and an extensive questionnaire-based survey, we have created a guideline based on the identified motivators, demotivators, and ethical principles. Using ISM and MICMAC approa

关键词： Taxonomies

Quality Evaluation in Guavas using Deep Learning Architectures: An Experimental Review 4

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Quality Evaluation in Guavas using Deep Learning Architectur...

4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Choudhury, Tanupriya Mahdi, Hussain Falih Agarwal, Archit Chakraborty, Arnav Arunachalaeshwaran, V R Sarkar, Tanmay Tomar, Ravi Uttarakhand Dehradun2480007 India University of Diyala Department of Computer and Software Engineering Baqubah Iraq School of Computing National University of Singapore Kent Ridge119007 Singapore School of Computer Science Lambton College ONL4Z3E9 Canada Malda Polytechnic Department of Food Processing West Bengal Malda732102 India

ISBN: (纸本)9781665468350

Determining the quality of fruit can be very challenging without the proper understanding. There are many factors that can result in determining the quality and therefore the actual taste of any given fruit. In this paper we attempt to determine the quality of the guava fruit on what are considered the three most important aspects of flavour by experts, those being, Colour, Shape and texture. All three of these aspects will be determined on a scale of 1-5. In order to produce these results, we are using an exceptional deep learning architecture and a primary dataset curated by a panel of experts who photographed the fruits in six-hour intervals for many days. We have compared the results over multiple deep learning architectures to find the most accurate results. This paper shows the importance of deep learning as a potential future tool for assessing the quality of fruits. © 2022 IEEE.

关键词： Fruits

Beyond Accuracy: Evaluating certainty of AI models for brain tumour detection

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computers in Biology and Medicine 2025年 193卷 110375-110375页

作者： Nisa, Zaib Un Bhatti, Sohail Masood Jaffar, Arfan Mazhar, Tehseen Shahzad, Tariq Ghadi, Yazeed Yasin Almogren, Ahmad Hamam, Habib Department of Computer Science and Information Technology The Superior University Lahore54600 Pakistan Lahore54600 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh11633 Saudi Arabia School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon University of Ha'il Ha'il55476 Saudi Arabia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada

Brain tumors pose a severe health risk, often leading to fatal outcomes if not detected early. While most studies focus on improving classification accuracy, this research emphasizes prediction certainty, quantified through loss values. Traditional metrics like accuracy and precision do not capture confidence in predictions, which is critical for medical applications. This study establishes a correlation between lower loss values and higher prediction certainty, ensuring more reliable tumor classification. We evaluate CNN, ResNet50, XceptionNet, and a Proposed Model (VGG19 with customized classification layers) using accuracy, precision, recall, and loss. Results show that while accuracy remains comparable across models, the Proposed Model achieves the best performance (96.95 % accuracy, 0.087 loss), outperforming others in both precision and recall. These findings demonstrate that certainty-aware AI models are essential for reliable clinical decision-making. This study highlights the potential of AI to bridge the shortage of medical professionals by integrating reliable diagnostic tools in healthcare. AI-powered systems can enhance early detection and improve patient outcomes, reinforcing the need for certainty-driven AI adoption in medical imaging. © 2025

关键词： Electrotherapeutics

Rethinking Label-specific Features for Label Distribution Learning

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arXiv 2025年

作者： Xu, Suping Dai, Chuyi Shang, Lin Shao, Changbin Yang, Xibei Pedrycz, Witold Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Department of Computer Science and Technology Nanjing University Nanjing210023 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China

Label distribution learning (LDL) is an emerging learning paradigm designed to capture the relative importance of labels for each instance. Label-specific features (LSFs), constructed by LIFT, have proven effective for learning tasks with label ambiguity by leveraging clustering-based prototypes for each label to re-characterize instances. However, directly introducing LIFT into LDL tasks can be suboptimal, as the prototypes it collects primarily reflect intra-cluster relationships while neglecting interactions among distinct clusters. Additionally, constructing LSFs using multi-perspective information, rather than relying solely on Euclidean distance, provides a more robust and comprehensive representation of instances, mitigating noise and bias that may arise from a single distance perspective. To address these limitations, we introduce Structural Anchor Points (SAPs) to capture inter-cluster interactions. This leads to a novel LSFs construction strategy, LIFT-SAP, which enhances LIFT by integrating both distance and direction information of each instance relative to SAPs. Furthermore, we propose a novel LDL algorithm, Label Distribution Learning via Label-specifIc FeaTure with SAPs (LDL-LIFT-SAP), which unifies multiple label description degrees predicted from different LSF spaces into a cohesive label distribution. Extensive experiments on 15 real-world datasets demonstrate the effectiveness of LIFT-SAP over LIFT, as well as the superiority of LDL-LIFT-SAP compared to seven other well-established algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Clustering algorithms

GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment

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arXiv 2024年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

The burgeoning size of Large Language Models (LLMs) has led to enhanced capabilities in generating responses, albeit at the expense of increased inference times and elevated resource demands. Existing methods of acceleration, predominantly hinged on knowledge distillation, generally necessitate fine-tuning of considerably large models, posing a challenge for average users. Furthermore, present techniques for expediting inference and reducing costs operate independently. To address these issues, we introduce a novel and intuitive Guidance-based Knowledge Transfer (GKT) framework. This approach leverages a larger LLM as a "teacher" to create guidance prompts, paired with a smaller "student" model to finalize responses. Remarkably, GKT requires no fine-tuning and doesn’t necessitate the teacher and student models to have the same vocabulary, allowing for extensive batch generation to accelerate the process while ensuring user customization. GKT can be seamlessly integrated into cloud-edge collaboration architectures, and is versatile enough for plug-and-play application across various models. When utilizing ChatGPT as teacher model and Llama2-70B as the student model, we can achieve 95.00% of ChatGPT’s performance at 52% of the cost. The results highlight substantial enhancements in accuracy and processing speed on the GSM8K and CSQA datasets, surpassing the performance of using either the student or teacher models in isolation. Our code is publicly available at https://***/Zoeyyao27/GKT © 2024, CC BY.

关键词： Distillation