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Automatic lung cancer detection using hybrid particle snake swarm optimization with optimized mask RCNN

Multimedia Tools and Applications 2024年第31期83卷 76807-76831页

作者： Sudha, R. Maheswari, K. M. Uma Department of Computer Science and Engineering School of computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India Department of Computing Technologies School of Computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India

As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that is crucial to a person's survival. The first diagnosis of the malignant nodules is typically made using chest radiography (X-rays) and computed tomography (CT) scans;however, the potential presence of benign nodules results in incorrect conclusions. The early phases of both benign and malignant nodules exhibit striking similarities. In this paper, a novel deep learning-based model is proposed for the precise diagnosis of malignant nodules. The proposed approach consists of two stages namely, pre-processing and lung nodule detection. Initially, the Lung CT scan images are collected from the dataset. Then, to remove the noise present in the input image, we apply an adaptive median filter. Then, to enhance the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied. After pre-processing, the image is given to the optimized mask RCNN classifier to detect the malignant and benign nodules. To enhance the performance of the Mask RCNN classifier, the hyper-parameters are optimally selected using hybrid particle snake swarm optimization (PS2OA). The proposed PS2OA is a hybridization of particle swarm optimization (PSO) and snake swarm optimization (SSO). The performance of the proposed approach is analyzed based on different metrics and effectiveness compared with state-of-the-art works. The proposed approach attained the maximum accuracy of 97.67%. This work aimed at assisting radiologists to detect and diagnose small-size pulmonary nodules more accurately. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Federated learning for Healthcare 5.0: a comprehensive survey, taxonomy, challenges, and solutions

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Soft computing 2025年第2期29卷 673-700页

作者： Amin, Muhammad Sadiq Ahmad, Shabir Loh, Woong-Kee School of Electrical and Computer Science National University of Science and Technology Islamabad Pakistan School of Computing Gachon University Seongnam13120 Korea Republic of

The advent of Healthcare 5.0 heralds a groundbreaking revolution in digital healthcare, superseding the achievements of its predecessor, Healthcare 4.0. Integrating cutting-edge technologies such as the Internet of Medical Things (IoMT), smart wearables, and the extraordinary capabilities of Artificial Intelligence (AI), Healthcare 5.0 envisions a unified framework that grants seamless access to health records, fosters interconnectedness among individuals, resources, and institutions, and empowers intelligent responses to medical concerns. However, the realization of Healthcare 5.0 faces a significant challenge in the form of high-speed data transmission using smart devices. Conventional AI approaches relying on centralized data processing raise compelling concerns surrounding information privacy and scalability within the Healthcare 5.0 context. Amidst this backdrop, federated learning emerges as a beacon of hope, offering a decentralized AI paradigm that facilitates on-device machine learning without compromising end-user privacy through centralized data export. Safeguarding data integrity, federated learning holds the key to unlocking the full potential of Healthcare 5.0. In this pioneering study, we conduct an extensive survey, exploring the transformative implications of federated learning within the realm of Healthcare 5.0. By shedding light on recent advancements tailored to this paradigm, we delve into the fundamental concepts of resource-awareness, privacy preservation, incentivization, and personalization, all within the framework of federated learning. Moreover, we meticulously scrutinize key parameters including security, sparsification, quantization, robustness, scalability, and privacy, providing an authentic evaluation of the current progress in federated learning for Healthcare 5.0. This comprehensive survey serves as an indispensable cornerstone for the evolution of Healthcare 5.0, offering invaluable insights into its unique requirements and untapp

关键词： Electronic health record

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A survey of acoustic eavesdropping attacks:Principle,methods,and progress

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High-Confidence computing 2024年第4期4卷 116-128页

作者： Yiwei Chen Wenhao Li Xiuzhen Cheng Pengfei Hu Institute of Intelligent Computing the School of Computer Science and TechnologyShandong UniversityQingdao 266237China

In today’s information age,eavesdropping has been one of the most serious privacy threats in information security,such as exodus spyware(Rudie et al.,2021)and pegasus spyware(Anatolyevich,2020).And the main one of them is acoustic *** eavesdropping(George and Sagayarajan,2023)is a technology that uses microphones,sensors,or other devices to collect and process sound signals and convert them into readable *** much research has been done in this area,there is still a lack of comprehensive investigation into the timeliness of this technology,given the continuous advancement of technology and the rapid development of eavesdropping *** this article,we have given a selective overview of acoustic eavesdropping,focusing on the methods of acoustic *** specifically,we divide acoustic eavesdropping into three categories:motion sensor-based acoustic eavesdropping,optical sensor-based acoustic eavesdropping,and RFbased acoustic *** these three representative frameworks,we review the results of acoustic eavesdropping according to the type of equipment they use and the physical principles of ***,we also introduce several important but challenging applications of these acoustic eavesdropping *** addition,we compared the systems that meet the requirements of acoustic eavesdropping in real-world scenarios from multiple perspectives,including whether they are nonintrusive,whether they can achieve unconstrained word eavesdropping,and whether they use machine learning,*** general template of our article is as follows:firstly,we systematically review and classify the existing eavesdropping technologies,elaborate on their working mechanisms,and give corresponding ***,these eavesdropping methods were compared and analyzed,and each method’s effectiveness and technical difficulty were evaluated from multiple *** addition to an assessment of the current state of the field,we dis

关键词： Acoustic eavesdropping Attack scenarios and threat models Acoustic side-channel attacks

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Music genre classification using convolution temporal pooling network

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Multimedia Tools and Applications 2024年 1-16页

作者： T. M, Vijayameenakshi T. R, Swapna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Music genre classification is one of the most interesting topics in digital music. Classifying genres is basically subjective, and different listeners may perceive genres in various ways. Furthermore, it might be difficult to classify some songs accurately since they belong to numerous genres. Genres are incredibly wide and ill-defined categories, which makes them problematic. Thus, genre-based measures are inherently inaccurate and coarse. Moreover, not every piece of music cleanly fits into a particular genre. Many papers based on deep neural networks perform sound recognition and classification with input images of audio, which do not affect the time–frequency representation of a signal. The traditional method adds waveform augmentation to the audio signal, thereby increasing the network's training speed. This paper considers music genre classification with the convolution temporal pooling framework and explores the impact of adding the SpecAugment method to augment the spectrogram itself. The augmented spectrogram is then fed into a convolutional temporal pooling network. In this model, the temporal and pooling layers identify the genre pattern and classify the songs based on the genre. It also predicts these duplication that will occur in the given sample. We apply this model to the GTZAN dataset, a widely used dataset for music genre classification. This method improves the identification of Rock and Pop song and also eliminates the replication of the songs. The trained model reports an accuracy of 0.75 for training a 30-s audio file. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional temporal pooling network Genre classification Music genre classification SpecAugment Spectrogram

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A Reinforcement Learning Based Approach to Partition Testing

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计算机科学技术学报（英文版） 2025年第1期40卷 99-118页

作者：孙昌爱肖明俊代贺鹏刘淮 School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computing Technologies Swinburne University of Technology Hawthorn Australia

Partition testing is one of the most fundamental and popularly used software testing *** first di-vides the input domain of the program under test into a set of disjoint partitions,and then creates test cases based on these *** by the theory of software cybernetics,some strategies have been proposed to dynamically se-lect partitions based on the feedback information gained during *** basic intuition of these strategies is to assign higher probabilities to those partitions with higher fault-detection potentials,which are judged and updated mainly ac-cording to the previous test *** a feedback-driven mechanism can be considered as a learning process—it makes decisions based on the observations acquired in the test ***,advanced learning techniques could be leveraged to empower the smart partition selection,with the purpose of further improving the effectiveness and efficiency of partition *** this paper,we particularly leverage reinforcement learning to enhance the state-of-the-art adaptive partition testing *** algorithms,namely RLAPT_Q and RLAPT_S,have been developed to implement the proposed *** studies have been conducted to evaluate the performance of the proposed approach based on seven object programs with 26 *** experimental results show that our approach outperforms the existing partition testing techniques in terms of the fault-detection capability as well as the overall testing *** study demonstrates the applicability and effectiveness of reinforcement learning in advancing the performance of software testing.

关键词： partition testing reinforcement learning temporal-difference learning intelligent software engineering

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Geometric-like imaginarity:Quantification and state conversion

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science China(Physics,Mechanics & Astronomy) 2025年第2期68卷 13-22页

作者： Meng-Li Guo Bo Li Shao-Ming Fei School of Science East China University of TechnologyNanchang 330006China School of Computer and Computing Science Hangzhou City University Hangzhou 310015China School of Mathematical Sciences Capital Normal UniversityBeijing 100048China

From the perspective of resource-theoretic approach,this study explores the quantification of imaginary in quantum *** propose a well defined measure of imaginarity,the geometric-like measure of *** with the usual geometric imaginarity measure,this geometric-like measure of imaginarity exhibits smaller decay difference under quantum noisy channels and higher *** applications,we show that both the optimal probability of state transformations from a pure state to an arbitrary mixed state via real operations,and the maximal probability of stochastic-approximate state transformations from a pure state to an arbitrary mixed state via real operations with a given fidelity f,are given by the geometric-like measure of imaginarity.

关键词： quantum resource theory imaginarity geometric-like channels state transformations

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Survey on leveraging pre-trained generative adversarial networks for image editing and restoration

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science China(Information sciences) 2023年第5期66卷 28-55页

作者： Ming LIU Yuxiang WEI Xiaohe WU Wangmeng ZUO Lei ZHANG School of Computer Science and Technology Harbin Institute of Technology Department of Computing Hong Kong Polytechnic University

Generative adversarial networks(GANs) have drawn enormous attention due to their simple yet efective training mechanism and superior image generation quality. With the ability to generate photorealistic high-resolution(e.g., 1024 × 1024) images, recent GAN models have greatly narrowed the gaps between the generated images and the real ones. Therefore, many recent studies show emerging interest to take advantage of pre-trained GAN models by exploiting the well-disentangled latent space and the learned GAN priors. In this study, we briefly review recent progress on leveraging pre-trained large-scale GAN models from three aspects, i.e.,(1) the training of large-scale generative adversarial networks,(2) exploring and understanding the pre-trained GAN models, and(3) leveraging these models for subsequent tasks like image restoration and editing.

关键词： survey generative adversarial networks pre-trained models image editing image restoration

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A hybrid memory architecture supporting fine-grained data migration

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Frontiers of computer science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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