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A Feature Fusion Attention-based Deep Learning Algorithm for Mammographic Architectural Distortion Classification

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Rehman, Khalil Ur Jianqiang, Li Yasin, Anaa Bilal, Anas Basheer, Shakila Ullah, Inam Jabbar, Muhammad Kashif Tian, Yibin Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Beijing University of Technology College of Software Engineering Beijing100124 China Hainan Normal University College of Information Science and Technology Hainan Haikou571158 China Princess Nourah Bint Abdulrahman University Department of Information Systems College of Computer and Information Science Riyadh11671 Saudi Arabia Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan

Architectural Distortion (AD) is a common abnormality in digital mammograms, alongside masses and microcalcifications. Detecting AD in dense breast tissue is particularly challenging due to its heterogeneous asymmetries and subtle presentation. Factors such as location, size, shape, texture, and variability in patterns contribute to reduced sensitivity. To address these challenges, we propose a novel feature fusion-based Vision Transformer (ViT) attention network, combined with VGG-16, to improve accuracy and efficiency in AD detection. Our approach mitigates issues related to texture fixation, background boundaries, and deep neural network limitations, enhancing the robustness of AD classification in mammograms. Experimental results demonstrate that the proposed model achieves state-of-the-art performance, outperforming eight existing deep learning models. On the PINUM dataset, it attains 0.97 sensitivity, 0.92 F1-score, 0.93 precision, 0.94 specificity, and 0.96 accuracy. On the DDSM dataset, it records 0.93 sensitivity, 0.91 F1-score, 0.94 precision, 0.92 specificity, and 0.95 accuracy. These results highlight the potential of our method for computer-aided breast cancer diagnosis, particularly in low-resource settings where access to high-end imaging technology is limited. By enabling more accurate and timely AD detection, our approach could significantly improve breast cancer screening and early intervention worldwide. © 2013 IEEE.

关键词： Deep neural networks

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The Capability of Wavelet Convolutional Neural Network for Detecting Cyber Attack of Distributed Denial of Service in Smart Grid 18

The Capability of Wavelet Convolutional Neural Network for D...

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18th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2021

作者： Monday, Happy Nkanta Li, Jian Ping Nneji, Grace Ugochi Yutra, Abel Zenebe Lemessa, Bona Debela Nahar, Saifun James, Edidiong Christopher Haq, Amin Ul University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu Sichuan 611731 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan 611731 China University of Missouri School of Computer and Information Systems St LouisMO United States

ISBN: (纸本)9781665413640

The electrical system's dependability, security, and efficiency are all improved through smart grid technologies. Its dependence on digital communication technology, on the other hand, introduces new risks and vulnerabilities that should be examined for the purpose to providing effective and trustworthy service delivery. This study presents a method for the detection of distributed denial of service (DDoS) attacks on smart grid infrastructure. Continuous wavelet transform (CWT) is used in the suggested approach to convert one-dimensional traffic data to two-dimensional time-frequency domain scalogram as the input to the wavelet convolutional neural network (WavCovNet) to detect anomalous behavior in the data by distinguishing attack features from normal patterns. Our results demonstrate that the proposed approach detects DDoS attacks with a high rate of detection and with a very low rate of false alarm. © 2021 IEEE.

关键词： Smart power grids

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A Low Self-Noise Fiber Optic Gyroscope Based on Dual-polarization Configuration for Geophysical Rotation Sensing

A Low Self-Noise Fiber Optic Gyroscope Based on Dual-polariz...

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2021 Asia Communications and Photonics Conference, ACP 2021

作者： Chen, Yanjun Cao, Yuwen Wang, Wenbo He, Yan Zhou, Tong Li, Zhengbin State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing100871 China School of Software and Microelectronics Peking University Beijing102600 China

A fiber optic gyroscope based on dual-polarization configuration_is designed and realized. The 24-hour stability test shows aself-noise level of 3 × 10−9 rad/s/√Hz within the frequency range of 0.001Hz to 1Hz. &... 详细信息

ISBN: (纸本)9781957171005

关键词： Gyroscopes

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A High-Resolution Approach for Dynamic Traffic Police Gestures Recognition Based on Spatial Context and Temporal Features Fusion

A High-Resolution Approach for Dynamic Traffic Police Gestur...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Jian He Shengsheng Jiang Xin Wei Cheng Zhang Ruihai Dong Beijing Engineering Research Center for IOT Software and Systems Beijing University of Technology Beijing China Faculty of Information Technology Beijing University of Technology Beijing China School of Computer Science University College Dublin Dublin Ireland

According to the requirement of recognizing traffic police gestures for driver assistance systems and intelligent vehicles, a universal model for dynamic traffic police gesture recognition is firstly introduced, of which can accurately present the spatial context (such as the relative lengths of skeletons, the angles between each skeleton w. r. t. gravity, and part features) of the traffic police gestures. Secondly, an architecture which can respectively extract spatial context and temporal features of dynamic traffic police gesture is proposed. Meanwhile, deep neural network and LSTM are introduced to build a high-resolution traffic police gestures recognizer (namely HRTPGR). At last, the open Police Gesture Dataset is used to train and test TPGR, and the experimental results show that the TPGR achieves a state-of-the-art accuracy with 98.7% for dynamic traffic police gestures recognition, and has strong anti-interference ability to light, background and gesture shape changes.

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Automating the Training and Deployment of Models in MLOps by Integrating systems with Machine Learning

arXiv

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arXiv 2024年

作者： Liang, Penghao Song, Bo Zhan, Xiaoan Chen, Zhou Yuan, Jiaqiang Information Systems Northeastern University San JoseCA United States Computer Science Northeastern University BostonMA United States Electrical Engineering New York University NY United States Software Engineering Zheiiang University Hangzhou China Information Studies Trine University PhoenixAZ United States

This article introduces the importance of machine learning in real-world applications and explores the rise of MLOps (Machine Learning Operations) and its importance for solving challenges such as model deployment and performance monitoring. By reviewing the evolution of MLOps and its relationship to traditional software development methods, the paper proposes ways to integrate the system into machine learning to solve the problems faced by existing MLOps and improve productivity. This paper focuses on the importance of automated model training, and the method to ensure the transparency and repeatability of the training process through version control system. In addition, the challenges of integrating machine learning components into traditional CI/CD pipelines are discussed, and solutions such as versioning environments and containerization are proposed. Finally, the paper emphasizes the importance of continuous monitoring and feedback loops after model deployment to maintain model performance and reliability. Using case studies and best practices from Netflix, the article presents key strategies and lessons learned for successful implementation of MLOps practices, providing valuable references for other organizations to build and optimize their own MLOps practices. Copyright © 2024, The Authors. All rights reserved.

关键词： Pipelines

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AI in Education: Improving Quality for Both Centralized and Decentralized Frameworks

AI in Education: Improving Quality for Both Centralized and ...

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IEEE Education engineering (EDUCON)

作者： Nisha Thorakkattu Madathil Saed Alrabaee Mousa Al-kfairy Rafat Damseh Abdelkader N Belkacem Department of Information Systems and Security College of IT United Arab Emirates University UAE College of Technological Innovation Zayed University Dubai United Arab Emirates Department of Computer Science and Software Engineering College of IT United Arab Emirates University UAE Department of Computer Engineering and Networks College of IT United Arab Emirates University UAE

Education is essential for achieving many Sustainable Development Goals (SDGs). Therefore, the education system focuses on empowering more educated people and improving the quality of the education system. One of the latest technologies to enhance the quality of education is Artificial Intelligence (AI)-based Machine Learning (ML). As a result, ML has a significant influence on the education system. ML is currently widely applied in the education system for various tasks, such as creating models by monitoring student performance and activities that accurately predict student outcomes, their engagement in learning activities, decision-making, problem-solving capabilities, etc. In this research, we provide a survey of machine learning frameworks for both distributed (clusters of schools and universities) and centralized (university or school) educational institutions to predict the quality of students' learning outcomes and find solutions to improve the quality of their education system. Additionally, this work explores the application of ML in teaching and learning for further improvements in the learning environment for centralized and distributed education systems.

关键词： Surveys Learning systems Federated learning Education Predictive models Problem-solving Task analysis

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AutoDebloater: Automated Android App Debloating

AutoDebloater: Automated Android App Debloating

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IEEE International Conference on Automated software engineering (ASE)

作者： Jiakun Liu Xing Hu Ferdian Thung Shahar Maoz Eran Toch Debin Gao David Lo School of Computing and Information Systems Singapore Management University Singapore School of Software Technology Zhejiang University China School of Computer Science Tel Aviv University Israel Department of Industrial Engineering Tel Aviv University Israel

Android applications are getting bigger with an increasing number of features. However, not all the features are needed by a specific user. The unnecessary features can increase the attack surface and cost additional resources (e.g., storage and memory). Therefore, it is important to remove unnecessary features from Android applications. However, it is difficult for the end users to fully explore the apps to identify the unnecessary features, and there is no off-the-shelf tool available to assist users to debloat the apps by themselves. In this work, we propose AutoDebloater to debloat Android applications automatically for end users. AutoDebloater is a web application that can be accessed by end-users through a web browser. In particular, AutoDebloater can automatically explore an app and identify the transitions between activities. Then, AutoDebloater will present the Activity Transition Graph to users and ask them to select the activities they do not want to keep. Finally, AutoDebloater will remove the activities that are selected by users from the app. We conducted a user study on five Android apps downloaded from three categories (i.e., Finance, Tools, and Navigation) in Google Play and F-Droid. The results show that users are satisfied with AutoDebloater in terms of the stability of the debloated apps and the ability of AutoDebloater to identify features that are never noticed before. The tool is available at http://***. The code is available at https://***/jiakun-liu/autodebloater/ and the demonstration video can be found at https://***/Gmz0-p2n9D4.

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Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement

Machine Learning in Higher Education using RF and SVM for Pe...

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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati Taruna Anand D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Ambala India Department of Humanities and Social Sciences Graphic Era (Deemed to be) University Dehradun India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions.

关键词： Support vector machines Radio frequency Training Accuracy Education Symbols Feature extraction Tokenization Integrated circuit modeling Principal component analysis

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Towards An Efficient Searching Approach of ROS Message by Knowledge Graph

Towards An Efficient Searching Approach of ROS Message by Kn...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Sun Bo Xinjun Mao Shuo Yang Long Chen College of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The Robot Operating System (ROS) has become the most popular robot development framework in the last few years, which has loosely coupled structure and provides remote communications between different component nodes. The ROS messages are critical to bridge the communication channels and clearly define the data structures. The developers can use the standardized or user-customized ROS message types to construct a communication channel between two component nodes uniquely. However, it becomes increasingly difficult for developers to find the required ROS message type from thousands of diverse ROS message types in ROS-based robotic software development. Finding the proper ROS message type is a non-trivial task because developers may hardly know the exact names of required ROS messages but only has a rough knowledge of the task domain features. To tackle this challenge, we construct a novel ROS Message Knowledge Graph (RMKG) with 4543 entities and 14320 relationships, including all ROS message types and message packages. We take the shortest path algorithm to search ROS message in RMKG by searching with ROS message feature or ROS message package and visualize the subgraph structure of the search results. Moreover, we develop a ROS message package library that supports fuzzy queries to find the required message package. A comprehensive evaluation of RMKG shows the high accuracy of our knowledge construction approach. A user study indicates that RMKG is promising in helping developers find suitable ROS message types for robotics software development tasks. An effect evaluation of message package fuzzy query shows the good effects of our fuzzy query method under different situations.

关键词： Bridges Operating systems Conferences Communication channels Data structures software Libraries

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Discovering Complex Correlations Among Multiple IoT Devices in Smart Environments

Discovering Complex Correlations Among Multiple IoT Devices ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Andrew D'Angelo Chenglong Fu Xiaojiang Du Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Air Force Research Laboratory Rome NY USA

The ubiquity of the Internet of Things (IoT) in a vast range of consumer applications is unparalleled. Unfortunately, despite the benefits of IoT, its widespread integration comes with significant security challenges. Considering IoT devices' capability to interact with the physical environment, there is an urgent need for effective anomaly detection. The state-of-the-art anomaly detection method, HAWatcher, models the normal behaviors of smart homes with inter-device correlations and demonstrates great results. Nonetheless, it is limited to capturing only simple one-to-one correlations between two events or states, which undermines its capability to detect anomalies in more complicated environments. To address this issue, we present a novel correlation discovering method to mine complex two-to-one correlations in such complicated IoT-enabled environments. We conduct experiments over two weeks on four smart home testbeds and obtain 70 two-to-one correlations. The correlations are applied to 9 anomaly scenarios, which show significant improvements in detecting anomalies over one-to-one correlations.

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