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Adaptive pelican optimization with optimized mask RCNN for automatic lung cancer detection

Multimedia Tools and Applications 2025年 1-21页

作者： Sudha, R. Maheswari, K.M. Uma Department of Computer Science and Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu India

Lung cancer is a prevalent and deadly disease worldwide, necessitating accurate and timely detection methods for effective treatment. Deep learning-based approaches have emerged as promising solutions for automated medical image analysis. This study proposes an enhanced Mask R-CNN framework tailored specifically for the automatic detection and severity analysis of lung cancer from CT images. The proposed approach consists of three stages namely, pre-processing, lung nodule detection, and segmentation using enhanced Mask R-CNN and severity analysis. Our framework employs a deep convolutional neural network architecture trained on a comprehensive dataset of annotated lung images. By incorporating a region-based convolutional neural network (R-CNN) with a mask prediction branch, our model accurately localizes lung tumors while providing precise pixel-level segmentation masks. To enhance the performance of mask RCNN, the parameter present in the classifier is optimally selected using the adaptive pelican optimization (APO) algorithm. The proposed framework detects lung tumors and provides a comprehensive severity analysis, enabling clinicians to assess cancer stage and progression accurately. Evaluation on a benchmark dataset demonstrates superior detection accuracy and robustness compared to existing methods. Our enhanced Mask R-CNN approach shows promise as a valuable tool for early diagnosis and severity assessment of lung cancer, potentially improving patient outcomes and healthcare efficiency. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Lung cancer

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

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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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Manhattan-based k-member clustering and enhanced rabbit optimization algorithm for k-anonymization in social network

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Multimedia Tools and Applications 2024年第30期83卷 74739-74756页

作者： Sivasankari, K. Maheswari, K. M. Uma Department of computer science and engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of computing technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

Online social networks are becoming more and more popular, according to recent trends. The user's primary concern is the secure preservation of their data and privacy. A well-known method for preventing individual identity in publicly accessible datasets is K-anonymization. Existing anonymity techniques' primary drawback is that they're susceptible to similarity and attribute disclosure attacks. Furthermore, a large amount of data has been lost in the given database. To address this limitation, the proposed model integrated an anonymizing algorithm with Manhattan-based k-member clustering and an enhanced rabbit optimization algorithm (MKCAEROA). The Manhattan distance-based k member (MKCA) clustering algorithm is employed in this case to cluster the initial database. The resulting clusters are then further optimized, and the original database is made anonymous, using the enhanced rabbit optimization algorithm (EROA). In the suggested model, EROA is included to satisfy the specified anonymity constraints while concurrently minimizing the clustering error rate and the generated information loss. When compared to existing approaches, the experimental results demonstrate that the suggested method provides the highest clustering accuracy with the least amount of information loss. The MATLAB workbench will be used to implement the suggested model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Database systems

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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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NeurDB: an AI-powered autonomous data system

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science China(Information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

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Intelligent Automation & Soft computing 2023年第8期37卷 1895-1907页

作者： S.Priya R.S.Ponmagal Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia

Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion *** address such threats towards cloud services,numerous techniques exist that mitigate the service threats according to different *** rule-based approaches are unsuitable for new threats,whereas trust-based systems estimate trust value based on behavior,flow,and other ***,the methods suffer from mitigating intrusion attacks at a higher *** article presents a novel Multi Fractal Trust Evaluation Model(MFTEM)to overcome these *** method involves analyzing service growth,network growth,and quality of service *** process estimates the user’s trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support(TSS).Also,the user’s trust in supporting network stream by computing Trusted Network Support(TNS).Similarly,the user’s trust in achieving higher throughput is analyzed by computing Trusted QoS Support(TQS).Using all these measures,the method adds the Trust User Score(TUS)value to decide on the clearance of user *** proposed MFTEM model improves intrusion detection accuracy with higher performance.

关键词： Intrusion detection cloud systems trusted service support trusted network support trust user score trusted QoS support

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A novel feature selection approach with integrated feature sensitivity and feature correlation for improved prediction of heart disease

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Journal of Ambient Intelligence and Humanized computing 2023年第9期14卷 12005-12019页

作者： Saranya, G. Pravin, A. Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai600119 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai603203 India

This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The proposed methodology is implemented using the Cleveland heart disease dataset which comprises a total of 120 heart disease patient records. Data imputation was utilized for missing values, and min–max normalization was utilized for data transformation. We attempted to construct an RF-based classifier for coronary heart disease in this paper by combining feature sensitivity and correlation analysis. The sensitivity-based feature selection process ranks features according to their value in assessing CHD risk, and the feature correlation analysis phase analyses if there are any correlations between features. The heart disease prediction accuracy of 81.16% was obtained using the proposed RF-FSFCA technique by omitting five features (sex, hemoglobin, TD, CRF, and cirrhosis). When compared to the Naïve Bayes, decision tree, regression analysis, and support vector machine models, the proposed model offered a higher accuracy of 86.141% without omitting any features. It also offered sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) scores of 87.321%, 87.364%, 91.23, and 91.02 respectively. Experiment findings demonstrated that the proposed RF-FSFC approach significantly improves prediction accuracy as compared to other approaches that do not use the integrated Feature selection method. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sensitivity analysis

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Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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computer Modeling in Engineering & sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

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Real-Time Speech Enhancement Based on Convolutional Recurrent Neural Network

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Intelligent Automation & Soft computing 2023年第2期35卷 1987-2001页

作者： S.Girirajan A.Pandian Department of Computer Science and Engineering School of ComputingSRM Institute of Science and EngineeringKattankulathurTamil NaduIndia

Speech enhancement is the task of taking a noisy speech input and pro-ducing an enhanced speech *** recent years,the need for speech enhance-ment has been increased due to challenges that occurred in various applications such as hearing aids,Automatic Speech Recognition(ASR),and mobile speech communication *** of the Speech Enhancement research work has been carried out for English,Chinese,and other European *** a few research works involve speech enhancement in Indian regional *** this paper,we propose a two-fold architecture to perform speech enhancement for Tamil speech signal based on convolutional recurrent neural network(CRN)that addresses the speech enhancement in a real-time single channel or track of sound created by the *** theﬁrst stage mask based long short-term mem-ory(LSTM)is used for noise suppression along with loss function and in the sec-ond stage,Convolutional Encoder-Decoder(CED)is used for speech *** proposed model is evaluated on various speaker and noisy environments like Babble noise,car noise,and white Gaussian *** proposed CRN model improves speech quality by 0.1 points when compared with the LSTM base model and also CRN requires fewer parameters for *** performance of the pro-posed model is outstanding even in low Signal to Noise Ratio(SNR).

关键词： Speech enhancement convolutional encoder-decoder long short-term memory noise suppression speech restoration

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