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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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UML and NFR-framework based method for the analysis of the requirements of an information system

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International Journal of Information Technology (Singapore) 2023年第1期15卷 411-422页

作者： Arif, Mohd. Mohammad, Chaudhary Wali Sadiq, Mohd. Computer Science and Technology Research Group Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi 110025 India Software Engineering Lab Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi 110025 India

software requirements are the expectation of stakeholders which are identified and modeled by various requirements elicitation and modeling techniques like traditional methods, goal oriented methods, and unified modeling language (UML), etc. The identified software requirements are broadly classified into functional requirements (FRs) and non-functional requirements (NFRs). Based on our literature review, we found that most of the focus in the area of software engineering is on the analysis of FRs as compared to NFRs. For the successful development of any information system both FRs and NFRs should be considered equally during the elicitation and modeling process;and ignoring NFRs may lead to failure of software. Therefore, to address this issue, we developed a method for the analysis of both FRs and NFRs so that a successful software product can be developed by a software company. In the proposed method, FRs are modeled by UML use-case diagram, class-diagram, and activity diagram. The NFRs are analyzed using NFR-framework;and NFR propagation rules have been derived from the requirements of an information system. In this framework, fuzzy based approach has been used to analyze the contribution links of softgoal interdependency graph. The applicability of the proposed method is discussed by using FRs and NFRs of library information system. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Functional requirements Library information system Non-functional requirements software requirements UML

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A Web-Based Vaccine Distribution System for Covid-19 Using Vaxallot 4th

A Web-Based Vaccine Distribution System for Covid-19 Using V...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Valarmathi, B. Srinivasa Gupta, N. Prakash, G. BarathyKolappan, A. Padmavathy, N. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Database Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Systems Engineer Tata Consultancy Services Tamil Nadu Chennai India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031488900

Vaxallot seeks to implement a system to distribute vaccines across high-risk groups accounting for various parameters and prove to be superior to what conventional systems are capable of today. It is a Python flask-based tool backed by infrastructure and data resources from the Covid India central repository;all it needs is a single channel input and a single parameter of the value produced, and the algorithm will take care of the rest. Since it’s Python-based and has an active integration with google sheets, live value updating could be possible for the real-time output of the distribution. The novelty of the proposed mechanism is the unique priority index, a score that accounts for an array of factors associated with the pandemic and is computed for regions in question here;this makes way for better distribution of vaccines. The application has an exclusive segment centered on handling excess units, if any. Moreover, since the application is developed to suit the needs of dynamic demographics, any region can roll out this application for purposes they desire to serve the masses. Since it isn’t bound by a coronavirus, it can be used by the healthcare industry as they deem fit. © 2024, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Coronavirus

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A Framework Design of ML Classifier Algorithm for Retrieve the Information about the Drugs and its Quality 4

A Framework Design of ML Classifier Algorithm for Retrieve t...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Meenakshi, M. Ruban, S. Nandhini, T.J. Loganayagi, S. Sayed, Raad Muhammed Hassan, Enas Al-Saidi, Imad Al-Din Hussein Saveetha University Saveetha School of Engineering Chennai Thandalam602117 India St Aloysius Deemed to Be University Department of Software Technology India Saveetha University Saveetha School of Engineering-SIMATS Department of Computer Science and Engineering chennai India Department of Computer Science and Engineering chennai tamil nadu thandalam602105 India Collage of Nursing National University of Science and Tqechnology dhi qar64001 Iraq Al-Farahidi University Baghdad Iraq Al-kunooze University College Computer Engineer Department basra Iraq

ISBN: (纸本)9798350360165

Every day, pharmaceutical firms have to do extensive product analyses. The same product is frequently registered more than once in several systems, each using unique properties. For these businesses, precise and high-quality information is crucial, especially considering the nature of the pharmaceuticals these goods contain. The hypothesis of this research project is that machine learning provides a workable way to effectively combine different data sources and match records pertaining to the same product. It is no longer feasible to match records by hand because of the extraordinarily large volume that has to be processed. In a large data context, this paper presents a framework for matching pharmacological records using machine learning approaches. The framework trains machine learning by utilising specified criteria for record matching. These models are then tested by forecasting matches between records that deviate from these preset guidelines. Lastly, by creating a huge variety of record combinations and forecasting matches based on them, the system mimics the production environment. The findings show that although the training datasets produce good results - the top model has an average accuracy of about 85% - the production environment has its own set of difficulties. In spite of this, matches that depart from established norms are effectively predicted by the framework. These results highlight how machine learning can be used to perform record matching tasks, as manual processing at this size is impossible. © 2024 IEEE.

关键词： Adversarial machine learning

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Enabling Collaborative and Green Generative AI Inference in Edge Networks

Enabling Collaborative and Green Generative AI Inference in ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Tian, Meng Liu, Zhicheng Qiu, Chao Wang, Xiaofei Niyato, Dusit Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin China Nanyang Technological University College of Computing and Data Science Singapore Shenzhen University College of Computer Science and Software Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350351255

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm's ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content. © 2024 IEEE.

关键词： Generative adversarial networks

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LLM-based Methodology for Expanding 3D Metadata in Point Cloud 27

LLM-based Methodology for Expanding 3D Metadata in Point Clo...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Kim, Geon-Woo Kim, Joo-Chang Department of AI Computer Science and Engineering Kyonggi University Suwon16227 Korea Republic of Contents Convergence Software Research Institute Kyonggi University Suwon16227 Korea Republic of

ISBN: (纸本)9791188428137

This paper proposes a method for expanding the metadata of three-dimensional point cloud data using Large Language Models (LLMs). Currently, point cloud data plays a crucial role in various fields such as autonomous driving and medical image reconstruction, necessitating the expansion of metadata for efficient processing. Traditionally, metadata construction has relied on manual input, which is prone to errors. In this study, we propose a method that utilizes LLMs, particularly the Llama 3.1 model, to extract the center points of each class in the point cloud data and expand the metadata by adding these center points to the annotation files. By using center points, computational costs are reduced, and the performance of segmentation and detection models based on this data is improved. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Data assimilation

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Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection 25

Bridging Machine Learning and Formal Concept Analysis for Ef...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Samara, Ghassan Almatarneh, Sattam Al-Oraiqat, Karim A. Alrawashdeh, Hazim Drieieva, Hanna Elrashidi, Ali Hasan, Yassin M. Y. Zarqa University Faculty of Information Technology Department of Computer Science Jordan Central Ukrainian National Technical University Department of Cybersecurity and Software Kropyvnytskyi Ukraine Zarqa University Faculty of Information Technology Department of Data Science and Artificial Intelligence Jordan Zarqa University Faculty of Information Technology Department of Software Engineering Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Assiut University Electrical Engineering Department Egypt

ISBN: (纸本)9798331540012

Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude"low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering"of crowded zones into hierarchical formal concepts and these latter's "bottom-up parsing"to finally identify the dense zones. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction of Antimicrobial Peptides Using Deep Neural Networks

Prediction of Antimicrobial Peptides Using Deep Neural Netwo...

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16th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2023

作者： Gülsüm Söylemez, Ümmü Yousef, Malik Bakir-Gungor, Burcu Department of Software Engineering Faculty of Engineering Muş Alparslan University Muş Turkey Department of Information Systems Zefat Academic College Zefat13206 Israel Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri Turkey

Antimicrobial peptides (AMPs) are crucial elements of the innate immune system;and they are effective against bacteria that cause several diseases. These peptides are investigated as potential alternatives of antibiotics to treat infections. Since wet lab experiments are expensive and time-consuming, computational methods become crucial in this field. In this study, we suggest a computational technique for AMP prediction using deep neural networks (DNN). We trained a DNN classifier using physicochemical features that include a sequential model;and evaluated the model with 10-fold cross-validation on a benchmark dataset. We compared our method with other machine learning approaches and demonstrated that the method we developed generates higher performance (accuracy: 92%, precision: 92%, recall: 93%, f1: 93%, AUC: 98%). In our experiments, we have realized that there is a strong positive correlation between the ‘Normalized Hydrophobic Moment’ feature and ‘Angle Subtended by the Hydrophobic Residues’ feature;and strong negative correlations between ‘Normalized Hydrophobicity’ feature and ‘Disordered Conformation Propensity’ feature, and between ‘Amphilicity Index’ - ‘Disordered Conformation Propensity’ features. We believe that the approach we proposed could guide further experimental studies and could facilitate the prediction of other types of AMPs having anticancer, antivirus, antiparasitic activities. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Deep neural networks

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Comparative Analysis of Sorting Algorithms: A Review 11

Comparative Analysis of Sorting Algorithms: A Review

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11th International Conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Ala'anzy, Mohammed Alaa Mazhit, Zhanar Ala'anzy, Alaa Fadhil Algarni, Abdulmohsen Akhmedov, Ramis Bauyrzhan, Abdiyev SDU University Department of Computer Science Almaty Kazakhstan Al-Hikma University College Department of Business Administration Baghdad Iraq King Khalid University Department of Computer Science Abha61421 Saudi Arabia Eurasian National University Department of Computer and Software Engineering Astana Kazakhstan

ISBN: (纸本)9798331518127

In the realm of computer science, sorting algorithms play a pivotal role in optimising data organisation and retrieval processes across various applications. This paper presents a comprehensive examination and comparative analysis of the most commonly used sorting algorithms, focusing on their time, space complexity, and efficiency. By scrutinising both traditional methods like bubble sort, selection sort, and insertion sort, and advanced techniques such as merge sort, quick sort, heap sort, and radix sort, this study sheds light on the performance metrics of each algorithm. Through meticulous implementation and evaluation across datasets of varying magnitudes, ranging from 100 to 100,000 integers, the research provides valuable insights into the strengths and weaknesses of these sorting techniques. The outcomes of this study offer practical guidance for professionals in data engineering and software development, aiding in the informed selection of sorting algorithms tailored to specific application needs. Utilising a rigorous experimental setup with the pseudocode, the study meticulously measures the time complexity of each algorithm, recording CPU time in seconds. The analysis reveals significant performance differentials among the algorithms, showcasing the efficiency of quick sort, shell sort, and heap sort on small datasets, while highlighting the scalability of merge sort, radix sort, and quick sort as dataset sizes increase. This research contributes to the advancement of algorithmic knowledge and provides a valuable resource for practitioners seeking to optimise sorting processes in their computational tasks. © 2024 IEEE.

关键词： software design

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Effects of un targeted Adversarial Attacks on Deep Learning Methods 15

Effects of un targeted Adversarial Attacks on Deep Learning ...

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15th International Conference on Information Security and Cryptography, ISCTURKEY 2022

作者： Degirmenci, Elif Ozcelik, Ilker Yazici, Ahmet Eskisehir Osmangazi University Computer Engineering Department Eskisehir Turkey Software Engineering Department Eskisehir Osmangazi University Eskisehir Turkey

ISBN: (数字)9781665456036

ISBN: (纸本)9781665456036

The increasing connectivity of smart systems opens up new security vulnerabilities. Since smart systems are used in various sectors such as healthcare, smart cities, and the intelligent industries, security becomes a fundamental concern. For this reason, security measurements are often added to security systems, such as authorization, authentication, encryption, Intrusion Prevention System (IPS) and Intrusion Detection Systems (IDS). However, these systems are still vulnerable to attacks such as DoS, DDoS. Recently, Deep Learning methods have been used to detect these attacks as early as possible. This method, however, has its own vulnerabilities to adversarial attacks. This paper presents three types of adversarial effects on the deep learning models analysis. In this study we use the open dataset CICIDS2017. Initially, DDoS attacks are detected using deep learning methods. Then, adversarial examples are created using adversarial attack methods. The untargeted adversarial attacks are used in this study, these being;FGSM, PGD and BIM. The results show that all adversarial attacks are effective on Deep Learning models. However, the results show that attacks from PGD and BIM are more effective. In addition, the deep learning models training is evaluated with K-fold cross-validation. The results show that, deep learning models that occur during each fold could be get different accuracy results against adversarial attacks. Adversarial attacks may be used in a K-fold cross validation process as a parameter for best model selection. © 2022 IEEE.

关键词： Intrusion detection

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