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Enhancing breast cancer histopathological image classification using attention-based high order covariance pooling

Neural Computing and Applications 2024年第36期36卷 23275-23293页

作者： Waqas, Muhammad Ahmed, Amr Maul, Tomas Liao, Iman Yi School of Computer Science Faculty of Science and Engineering University of Nottingham Malaysia Campus Selangor Dar ul Ehsan Semenyih43500 Malaysia Department of Software Engineering University of Malakand Khyber Pakhtunkhwa Lower Dir Chakdara18800 Pakistan Department of Computer Science Edge Hill University Lancashire L39 4QP United Kingdom School of Computer Science Faculty of Science University of Nottingham NottinghamNG8 1BB United Kingdom

Breast cancer, the most common cancer affecting female patients, presents serious challenges for proper detection. Although computer-aided diagnostic techniques have progressed, their accuracy and efficacy remain limited. To overcome these challenges, we introduce DHA-Net, a new deep learning system that combines an effective attention module (EAM) and a high-order pooling layer with a ResNet-18 backbone. DHA-Net is tested using three well-known breast cancer histopathology image datasets: BreakHis, BACH2018, and a closely related Kaggle-Breast cancer histopathology dataset. Our experiments show that DHA-Net not only improves on existing state-of-the-art approaches, but significantly outperforms them in classifying breast cancer images. This work emphasizes the novel combination of an EAM with high-order pooling, demonstrating DHA-Net’s potential to improve diagnostic accuracy and serve as a more effective tool for medical imaging applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Lung cancer

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Enhancing Academic Integrity in Online Assessments: Introducing an Effective Online Exam Proctoring Model using YOLO 2

Enhancing Academic Integrity in Online Assessments: Introduc...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Singh, Tripty Nair, Rekha R. Babu, Tina Duraisamy, Prakash Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Science and Engineering University of Wisconsin Green BayWI United States

The issue of cheating during exams has become a significant concern, gaining widespread attention from the public and media. This issue has been made worse by the transition of educational institutions to an online format, making it simpler to engage in academic dishonesty. To overcome this problem, developed an innovative solution known as the Online Exam Proctoring Model using You Only Look Once (YOLO) Model. With the use of cutting-edge computer vision algorithms, the proposed model makes use of data from cameras and microphones to automatically spot and prevent cheating during online exams. Face orientation, face spoofing detection, mouth tracking, audio analysis, mobile device recognition, and person counting are among the primary elements built into the model. Object detection algorithms, in conjunction with pre-trained YOLO weights, empower the model to detect mobile devices and count the number of individuals present in the exam environment. The automatic termination of exams upon detection of mobile devices, multiple persons, or no persons for 10 seconds acts as a strong deterrent against cheating. The suggested methodology created a reliable and effective tool for online examinations, guaranteeing the academic integrity and enabling impartial assessment of the skills and knowledge of learners. By implementing this solution, educational institutions can foster a secure online exam environment, promoting a culture of honesty and trust between students and educators. As navigate the evolving environment of education, the model stands as a reliable ally in upholding the credibility and authenticity of online learning experiences. © 2024 Elsevier B.V.. All rights reserved.

关键词： Object detection

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GENOCARE PROGNOSTICATOR MODEL: HOST GENETICS PREDICT SEVERITY OF INFECTIOUS DISEASE

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Scalable Computing 2025年第2期26卷 924-939页

作者： DUBEY, SHIVENDRA SINGH, SHWETA VERMA, DINESH KUMAR LODHI, SUDHEER KUMAR DUBEY, SAKSHI Department of Artificial Intelligence and Machine Learning Manipal University Rajasthan Jaipur303007 India School of Engineering and Technology Jagran Lakecity University Madhya Pradesh Bhopal462042 India Department of Computer Science and Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Gujarat Vadodara391760 India

Scientific community understanding of the variance in severity of infectious disease like COVID-19 across patients is an important area of focus. The article presents an innovative voting ensemble GenoCare Prognosticator (GCP) model that incorporates XGBoost and Random Forest classifiers, two cutting-edge machine learning approaches. A large dataset that incorporates medical covariates like gender and age along with biological WES (Whole Exome Sequencing) data was used to train these models. Five-fold stratified cross-validation was used to process the dataset in order to improve model stability and avoid overfitting. Two medical covariates and sixteen recognized candidate gene variants were among the eighteen major features on which our GCP model had been verified using data from earlier studies. Specific post-hoc clarification of the model's predictions was provided by ExplainerDashboard, a Python open-source library, to improve interpretability. Furthermore, we utilized OpenTarget and Enrichr, two bioinformatic resources, to establish connections between the discovered variations in genetics and pertinent ontologies, biological pathways, and possible drug/disease relationships. Unsupervised clustering of SHAP key feature values was included in the analysis, which revealed intricate genetic interactions that affect the severity of the disease. Our results show that although gender and age are the main factors influencing the severity of COVID-19, complex genetic interactions cause severe symptoms in a specific subset of patients. This work contributes to our comprehension of the biological variables influencing the severity of COVID-19 and offers a reliable, comprehensible model that can help recognize patients at high risk and guide individualized treatment plans. © (2025), (West University of Timisoara). All rights reserved.

关键词： COVID-19

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Social Network-Based Folk Culture Propagation in the Digital Age:Analyzing Dissemination Mechanisms and Influential Factors

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Tsinghua science and Technology 2025年第2期30卷 894-907页

作者： Zhaojin Li Fugao Jiang Weiyi Zhong Chao Yan Department of Exercise Science and Sports Studies Qufu Normal UniversityQufu 273100China School of Engineering Qufu Normal UniversityRizhao 276826China School of Computer Science Qufu Normal UniversityRizhao 276826China

The rapid development of the internet has ushered the real world into a“media-centric”digital era where virtually everything serves as a *** the new attributes of interactivity,immediacy,and personalization facilitated by online communication,folklore has found a broad avenue for *** these,online social networks have become a vital channel for propagating *** using social network theory,we devise a comprehensive approach known as ***,we utilize embedding techniques to capture users’low-level and high-level social ***,by applying an automatic weight assignment mechanism based on the embedding representations,multi-level social relationships are aggregated to assess the likelihood of a social interaction between any two *** experiments demonstrate the ability to classify different social *** addition,we delve into the potential directions of folklore evolution,thus laying a theoretical foundation for future folklore communication.

关键词： folklore culture dissemination embedding representations social relationships influential factors

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Research on Internet of Medical Things: Systematic Review, Research Trends and Challenges

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Recent Advances in computer science and Communications 2024年第6期17卷 72-82页

作者： Anand, Dinesh Kaur, Avinash Singh, Manpreet School of Computer Applications Lovely Professional University Punjab Phagwara India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Applications Guru Nanak Dev Engineering College Punjab Ludhiana India

Introduction: Remote data exchange operations in healthcare are observed, consult-ed, monitored and treated by the Internet of Medical Things (IoMT). It is an extension of the Internet of Things (IoT). Method: At the growing stage of IoT, IoMT is speedily drawing researchers’ interest due to its extensive use in healthcare systems. Smaller and lower-priced wireless devices with various communication protocols have led to the formation of IoMT. Healthcare data is exchanged through wireless communication with IoMT. The margining of IoMT and healthcare can yield multiple benefits in terms of: better quality of life, care services and developing solution/s at low cost. In this article, a systematic literature review has been conducted on IoMT. Results: Authors have thoroughly investigated the different versions of healthcare 1.0, 2.0, 3.0 and 4.0 as proposed by the healthcare industry. Furthermore, the taxonomy of IoMT has been designed and compared with existing surveys. Conclusion: This survey is unique and stands different from the point of view of existing sur-veys. It supports the future of IoMT researchers to bring new insight to their researches. © 2024 Bentham science Publishers.

关键词： Electronic data interchange

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Beamforming and Reflection Design for Short Packet ISAC With Non-Ideal RIS: An A3C-Based Approach

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8379-8384页

作者： Mahmoudi, Behrad Khonsari, Ahmad Zeinali, Farshad Mili, Mohammad Robat Mashhadi, Mahdi Boloursaz Xiao, Pei University of Tehran Department of Electrical and Computer Engineering Tehran Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran School of Computer Science in the Institute for Research in Fundamental Sciences Tehran Iran United Kingdom Institute for Advanced Innovative Solutions Tehran Iran

Integrated sensing and communication (ISAC) is a promising solution to mitigate the increasing congestion of the wireless spectrum. In this paper, we investigate the short packet communication regime within an ISAC system assisted by a reconfigurable intelligent surface (RIS) to meet the low latency ultra-reliable requirements in the next-generation wireless networks. We consider a non-ideal RIS model that captures effects of the phase-dependent amplitude variations in the reflection coefficients, and we have incorporated the near-field model into the channels between the RIS and the users or targets. In this setup, we jointly design the transmit beamforming and the RIS phase shifts to maximize the sum rate while satisfying the sensing signal-to-noise ratio (SNR) requirement. The system simultaneously carries out multitarget sensing and multi-user short packet communications with the help of the RIS. Considering the non-convex and dynamic nature of the resulting optimization problem, we propose an asynchronous advantage actor-critic (A3C) based method for beamforming and reflection design in this setup. Numerical results demonstrate the superiority of the proposed scheme over the benchmarks. © 1967-2012 IEEE.

关键词： Beamforming

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Multi-criteria Rank Based Geo-distributed Load Balancing for Cloud Computing 2

Multi-criteria Rank Based Geo-distributed Load Balancing for...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Darius, Preethi S. H. Krishna Sowjanya, K. Majumder, Pratham Manju, V.N. Saha, Sanchari Suneetha, J. Mitra, Paramita Cmrit Department of Compter Science and Engineering Bengaluru India University Department of Information Science and Engineering Bangalore India School of Computer Science and Engineering Reva University Bengaluru India

ISBN: (纸本)9798350328202

Efficient delivery of cloud computing services is subject to the efficiency of the service broker policies and the load balancing algorithms. The service broker distributes the load coming from the user base across the data centers of the Cloud Service Providers(CSPs). Selecting the appropriate data center to process the user requests is highly crucial in order to meet the required Quality-of-Service(QoS) parameters. A novel service broker policy, Multi-criteria Rank Based geo-distributed Load Balancing(MCRBgLB) algorithm is proposed that distributes the load to the data centers based on the rank that is dynamically calculated for each data center. The performance of the proposed algorithm is compared with the baseline algorithms, Closest Data Center Algorithm (CDCA) and Optimized Response Time Algorithm (ORTA) in Cloud Analyst simulation toolkit. The proposed algorithm outperforms the baseline algorithms in terms of cost by up to 27% and processing times by up to 63% in various simulation scenarios. © 2024 IEEE.

关键词： Quality of service

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An intelligent active probing and trace-back scheme for IoT anomaly detection

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Digital Communications and Networks 2024年第1期10卷 168-181页

作者： Luying Wang Lingyi Chen Neal N.Xiong Anfeng Liu Tian Wang Mianxiong Dong School of Computer Science and Engineering Central South UniversityChangsha 410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA Artificial Intelligence and Future Networks Beijing Normal University&UICZhuhai 519087China Department of Information and Electronic Engineering Muroran Institute of TechnologyMuroran 050-8585Japan

Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data *** Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33%respectively,while the accuracy of trust identification is improved by 20%.

关键词： Anomaly detection Internet of things Integrating data collection Mobile edge users Intelligent

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ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba 16

ColorMamba: Towards High-quality NIR-to-RGB Spectral Transla...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Zhai, Huiyu Jin, Guang Yang, Xingxing Kang, Guosheng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Department of Computer Science Hong Kong Baptist University Hong Kong

Translating NIR to the visible spectrum is challenging due to cross-domain complexities. Current models struggle to balance a broad receptive field with computational efficiency, limiting practical use. Although the Selective Structured State Space Model, especially the improved version, Mamba, excels in generative tasks by capturing long-range dependencies with linear complexity, its default approach of converting 2D images into 1D sequences neglects local context. In this work, we propose a simple but effective backbone, dubbed ColorMamba, which first introduces Mamba into spectral translation tasks. To explore global long-range dependencies and local context for efficient spectral translation, we introduce learnable padding tokens to enhance the distinction of image boundaries and prevent potential confusion within the sequence model. Furthermore, local convolutional enhancement and agent attention are designed to improve the vanilla Mamba. Moreover, we exploit the HSV color to provide multi-scale guidance in the reconstruction process for more accurate spectral translation. Extensive experiments show that our ColorMamba achieves a 1.02 improvement in terms of PSNR compared with the state-of-the-art method. Our code is available at https://***/AlexYangxx/ColorMamba/. © 2024 H. Zhai1, G. Jin1, X. Yang2 & G. Kang1.

关键词： RGB color model

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Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques

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Annals of Data science 2024年第1期11卷 217-242页

作者： Das Guptta, Sumitra Shahriar, Khandaker Tayef Alqahtani, Hamed Alsalman, Dheyaaldin Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & ampTechnology Chittagong4349 Bangladesh Unit of Cybersecurity Department of Computer Science Center of Artificial Intelligence King Khalid University Abha Saudi Arabia School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

In this paper, we mainly present a machine learning based approach to detect real-time phishing websites by taking into account URL and hyperlink based hybrid features to achieve high accuracy without relying on any third-party systems. In phishing, the attackers typically try to deceive internet users by masking a webpage as an official genuine webpage to steal sensitive information such as usernames, passwords, social security numbers, credit card information, etc. Anti-phishing solutions like blacklist or whitelist, heuristic, and visual similarity based methods cannot detect zero-hour phishing attacks or brand-new websites. Moreover, earlier approaches are complex and unsuitable for real-time environments due to the dependency on third-party sources, such as a search engine. Hence, detecting recently developed phishing websites in a real-time environment is a great challenge in the domain of cybersecurity. To overcome these problems, this paper proposes a hybrid feature based anti-phishing strategy that extracts features from URL and hyperlink information of client-side only. We also develop a new dataset for the purpose of conducting experiments using popular machine learning classification techniques. Our experimental result shows that the proposed phishing detection approach is more effective having higher detection accuracy of 99.17% with the XG Boost technique than traditional approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Search engines

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