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Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

Intelligent Automation & Soft Computing 2023年第3期35卷 3403-3413页

作者： S.Ayshwarya Lakshmi K.Anandavelu Department of Computer Science and Engineering University College of Engineering Panruti607106TamilnaduIndia Department of Mechanical Engineering MRK Institute of TechnologyCuddalore608301TamilnaduIndia

Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer *** suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer pic-ture segmentation *** time and resources are always limited,the proposed enhanced cuckoo search optimization algorithm is one of the most effec-tive strategies for dealing with global optimization difﬁ*** of the most signiﬁcant requirements is to design optimal solutions to optimize their *** is no particular technique that can answer all optimization *** proposed enhanced cuckoo search optimization method indicates a constructive precision for skin cancer over with all image segmentation in computerized *** accuracy of the proposed enhanced cuckoo search based optimization for melanoma has increased with a 23%to 29%improvement than other optimization *** total sensitivity and speciﬁcity attained in the proposed system are 99.56%and 99.73%*** proposed method outperforms by offering accuracy of 99.26%in comparisons to other conventional *** proposed enhanced optimization technique achieved 98.75%,98.96%for Dice and Jaccard coefﬁ*** model trained using the suggested measure outperforms those trained using the conventional method in the segmentation of skin cancer picture data.

关键词： Cukoo search optimization technique ﬁtness function cancer

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Enhanced Small Lesion Detection on Grape Leaves Using Optimized YOLOv7 3

Enhanced Small Lesion Detection on Grape Leaves Using Optimi...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Mahalakshmi, S. Gandhi, Jay Prabakaran, V. Rastogi, Shailesh Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur Chennai India Faculty of Engineering and Technology Parul University Department of Computer science and Engineering Vadodara India Pune India Department of Finance Pune India

ISBN: (纸本)9798331508456

The detection of small lesions on grape leaves plays a pivotal role in the early identification and management of plant diseases, potentially reducing significant agricultural losses. Traditional detection systems, including advanced versions such as YOLOv7, often overlook small target diseases due to resolution constraints and inadequate feature extraction capabilities. Addressing this, our research enhances the YOLOv7 architecture by incorporating a specialized prediction branch and leveraging an improved channel attention mechanism in conjunction with an Extended-Efficient Long-range Attention Network (E-ELAN). Firstly, a new detection head specifically designed for small targets enhances the resolution capabilities of the system. Secondly, the integration of asymmetric convolution into the E-ELAN facilitates refined multi-scale feature extraction, critical for distinguishing minute pathological changes. Further, we enhance the model's sensitivity to small-scale features through a revamped channel attention mechanism. Moreover, we replace the conventional Complete Intersection over Union (CIoU) with a Structured Intersection over Union (SIoU) in the loss function, significantly improving the model's localization accuracy. Experimental results from testing the optimized YOLOv7 on a dataset of grape leaves affected by three prevalent diseases show an average accuracy improvement of 2.7%, achieving a remarkable 93.5%. This substantiates our model's efficacy in enhancing small lesion detection, thereby contributing effectively to early disease intervention strategies in viticulture. © 2025 IEEE.

关键词： asymmetric convolution channel attention mechanism plant disease diagnostics small lesion detection YOLOv7

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An Efficient Method to Deal with Missing Values and Class Imbalance in Healthcare Data 5th

An Efficient Method to Deal with Missing Values and Class Im...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Singh, Harpreet Singh, Birmohan Kaur, Manpreet Rani, Suvita Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Sangrur Longowal India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Sangrur Longowal India

ISBN: (纸本)9789819773701

The present era belongs to the age of digital devices, where everything is going to be digitized. This makes the massive production of data at faster rates and brings Big Data to light. The arrival of big data has influenced and reformed many domains like Healthcare, Bioinformatics, Agriculture, etc. Healthcare big data has gained the attention of various researchers as it aids in the early diagnosis and prediction of diseases. Generally, real-world healthcare data contains missingness and imbalance distribution of classes which makes classification a challenging problem. Therefore, an efficient method is introduced to deal with the problems of data incompleteness and class imbalance in healthcare data. A class mean and standard deviation-based imputation strategy is proposed to impute the missing values within the region of a particular class. Further, an Improved Elephant Herding Optimization (IEHO) algorithm is utilized to apply the optimal weights for improving the imbalanced learning in healthcare data. A new fitness function is proposed which considers the data missingness and class imbalance along with the feature weighting. The optimized k-Nearest Neighbor (k-NN) learning algorithm is used to perform the classification. The performance of the proposed method is evaluated on four healthcare datasets: Pima diabetes, Wisconsin Breast Cancer (Original), Hepatitis, and HCC Survival. A comparative analysis with state-of-the-art methods is performed based on four-performance metrics: accuracy, balanced accuracy, g-mean, and f-score. The proposed method obtained higher classification accuracy for Wisconsin Breast Cancer (Original), Hepatitis, and HCC Survival datasets than state-of-the-art methods. Experimental results validate the effectiveness of the proposed method in dealing with the missing values and class imbalance problems in healthcare data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An Optimal Weighted Ensemble of 3D CNNs for Early Diagnosis of Alzheimer’s Disease

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SN computer science 2024年第2期5卷 252页

作者： Dharwada, Sriram Tembhurne, Jitendra Diwan, Tausif Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur 441108 India

Alzheimer disease (AD) is a chronic neurological disorder in which the loss of brain cells causes dementia. Early and accurate diagnosis of AD will lead to better treatment of the disease before irreversible brain damage has been occurred. This paper proposes the classification of Alzheimer's disease using 3D structural Magnetic Resonance Imaging (sMRI) images through 3D convolutional neural networks (CNNs). Most existing methods utilizing 3D subject-level CNNs for Alzheimer's disease classification design a single model which relies on a very large training dataset for improved generalization. Herein, we address this issue through 3D transfer learning which makes use of knowledge gained from a pre-trained task. We train 3D versions of five classical 2D image classification architectures—ResNet, ResNeXt, SE-ResNet, SE-ResNeXt, and SE-Net—by initializing each model with pre-trained weights from their 2D counterparts, and combine their predictions through a weighted average method. The weights assigned to each model of the ensemble are optimized to achieve a performance better than any single 3D CNN model. With a relatively smaller training dataset, the proposed model obtains 97.27%, 82.33%, 90.41%, 84.22%, 84.26%, and 77.1% accuracies for the Alzheimer’s disease (AD) versus cognitively normal (CN), early mild cognitive impairment (EMCI) versus CN, late mild cognitive impairment (LMCI) versus CN, EMCI versus AD, LMCI versus AD, and EMCI versus LMCI classification tasks, outperforming current state-of-the-art methods, and indicating the effectiveness of our proposed model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Alzheimer’s disease Convolutional neural networks Ensemble learning Transfer learning

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Addressing challenges in accurate brain tumor classification in MRI: a transfer learning approach with EfficientNetB3 and comprehensive model evaluation

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Multimedia Tools and Applications 2024年 1-31页

作者： Das, Surajit Goswami, Rajat Subhra Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Arunachal Pradesh Jote791113 India

Brain tumors pose significant health risks because of uncontrolled and abnormal cell growth, potentially leading to the devastating of certain organs and even death, mostly in adult populations. So, early and precise brain tumor classification (BTC) is essential, as a timely diagnosis directly impacts the efficacy of treatment and the survival rates of patients. Magnetic Resonance Imaging (MRI) is the preferred diagnostic tool for BTC due to its superior image quality, yet manual classification methods have the disadvantage of being time-consuming and labour-intensive. The maturity of deep learning (DL) in modern times has brought remarkable progress in automating complex processes across multiple domains, including transfer learning (TL) in medical imaging. In our study, we propose an approach to partially automate the BTC task through the application of transfer learning on MRI data. After applying image pre-processing techniques like cropping, enhancement and Augmentation, we tested six architectures - EfficientNetB6, EfficientNetB3, VGG19, NASNetLarge, DenseNet169 and DenseNet201. Our experiments on a comprehensive publicly available kaggle dataset show that the EfficientNetB3 model achieved the best performance, with a training accuracy of 100% and test accuracy of 99.62%, outperforming all other architectures. EfficientNetB6 demonstrated a similar accuracy of 99.62%, while DenseNet201 and NASNetLarge performed well, with accuracies of 99.24% and 99.08%, respectively. VGG19, although a popular baseline model, achieved a lower accuracy of 94.96%. These findings suggest that EfficientNetB3, alongside other advanced architectures, offers a robust and accurate solution for brain tumor classification in MRI, paving the way for more reliable and efficient diagnostic tools in clinical settings. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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Exploring the Adoption and Innovation of Digital Twins in Healthcare 16

Exploring the Adoption and Innovation of Digital Twins in He...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： John, Thomas Nath, Keshab Guravaiah, Koppala Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam686635 India Department of Computer Science and Engineering Bhattadev University Assam India

Digital Twins replicate the real situations and the outcomes, helping organizations to make better decisions. Digital Twins find valuable in different domains such as manufacturing, automotive, healthcare, and etc. In healthcare, the report is transferred to a digital environment after the patient had a physical examination. This aids in a proper diagnosis and appropriate therapy for the patient, which is one of the most crucial aspects of medical care. Robots that assist in surgical decision-making have benefited from digital twins. There were different studies that have been made for the use of Digital Twins in the field of Healthcare. The surveys conducted in the subject of digital twins are identified in this research together with their benefits and limitations. In this paper, a comprehensive overview of the Literature reviews done on Digital Twins especially in healthcare space. This paper details about the different technologies as well as the operations using Digital Twins with respect to healthcare. The paper explains the different applications, challenges, and issues of Digital Twins. This study examines the real-world applications of digital twins in healthcare research. The overall goal of this paper is to look for the future of the field of Digital Twins and its possible setbacks and opportunities in the field of healthcare. © 2025 Elsevier B.V.. All rights reserved.

关键词： Surgery

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A Review of Modern Computing Paradigms: Cloud, Fog, and Edge

A Review of Modern Computing Paradigms: Cloud, Fog, and Edge

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sharma, Neha Choudhary, Monika Srivastava, Prakash Prabha, Chander Sharma, Himanshu Cgc Department of Computer Science and Engineering Punjab Landran India Department of Computer Science & Engineering Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331509828

In the current competitive environment, businesses are looking at how to lower their IT expenses while simultaneously improving their productivity and flexibility to respond quickly to changes in their operational procedures. One of the most widely accepted information technology-based services is cloud computing, which has garnered interest from both the academic and business communities as well as the general public-characteristics such as pay-per-use and subscription options ease of use, elasticity, and low entry costs. Low entry cost in fog, cloud, and edge computing is achieved by utilizing pay-as-you-go pricing models, leveraging open-source technologies, and optimizing resource allocation. By emphasizing how this technology differs from the rest of the current technology and concentrating on its numerous benefits and areas of application-which provide proof of its growing popularity-this paper presents the development of modern computer paradigms and associated areas of research interest. A discussion of each paradigm is done by clearly defining its main ideas and how they relate to the others. The paper presents a comparative assessment of Cloud, Edge, and Fog computing indicating their diverse areas of applicability. © 2025 IEEE.

关键词： Fog computing

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A Novel Blockchain-Assisted Deep Learning Model For Secure Edge Intelligence in IoT Networks

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Journal of The Institution of Engineers (India): Series C 2024年 1-14页

作者： Sekhar, G. Chandra Aruna, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & ampD Institute of Science and Technology Tamilnadu Chennai India

The Internet is being connected to billions of mobile and Internet of Things (IoT) devices, generating zillions of Bytes of data at the network edge. This is in addition to developments in mobile computing and the Internet of Things (IoT). In the context of edge computing, applications are placed closer to data sources such as IoT sensors, local edge devices, and edge servers. Data are transmitted from edge devices to dedicated computer infrastructures for a range of activities in any edge computing environment. For the reason that data are regularly sent from IoT edge devices to centralized virtualization technologies, it is more vulnerable to security vulnerabilities and complexity measures. Finally, to preserve the privacy of the IoT devices, the performance of the proposed algorithms is evaluated using datasets in a large-scale network prototyped system. According to the data, the suggested strategy is both more effective and secure than the alternatives. © The Institution of Engineers (India) 2024.

关键词： Internet of things

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Designing Of Hybrid Intrusion Detection System With Combined Approach Of LightGBM-AE And Double DQN 27

Designing Of Hybrid Intrusion Detection System With Combined...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Sinha, Ditipriya Srivastava, Arpita Singh, Om National Institute of Technology Patna Department of Computer Science and Engineering Patna India Indian Institute of Engineering Science and Technology Shibpur Department of Information Technology Shibpur India

ISBN: (纸本)9798350392319

Cyber-attacks are a growing threat to individuals, businesses, and governments. Data science helps improve security by analyzing network traffic and system activities. Intrusion detection systems (IDSs) are essential for enhancing security and resilience, but IDS datasets face challenges in obtaining quality training data and accurately predicting cyberattacks. To tackle these issues, this paper designs a novel hybrid intrusion detection system that detects malicious behavior based on signature and anomaly. It utilizes advanced techniques like one hot encoding, min-max normalization for data preprocessing, modified GAMO for data oversampling, information gain, and modified grey wolf optimizer (GWO)based bi-phase approach for feature selection, stacking of LightGBM with autoencoder for signature-based detection, and finally double deep Q-network (Double DQN) for anomaly-based detection to create robust and efficient hybrid IDS model. The proposed model achieves 94.33% and 88.90% accuracy utilizing signature-based and anomaly-based IDS, respectively, outperforming state-of-the-art 'MTH-HIDS' in accuracy, precision, recall, and f1-score. © 2024 IEEE.

关键词： Network intrusion

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HSPBCI: a robust framework for secure healthcare data management in blockchain-based IoT systems

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Multimedia Tools and Applications 2024年 1-25页

作者： Gupta, Sangeeta Chithaluru, Premkumar Stephan, Thompson Nafisa, Shaik Kumar, Sandeep Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad500075 India Department of Information Technology Mahatma Gandhi Institute of Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India Centre of Research Impact and Outreach Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Abstract: Online monitoring and counseling have helped patients and made digital health management possible. However, the vulnerability of medical data gathered via devices, especially in the Internet of Things (IoT) context, highlights the necessity of strong security protocols. IoT is essential, but there are still difficulties in managing vital data over a heterogeneous network. To reduce potential health hazards, it is essential to ensure that IoT sensors do not negatively impact human health. Maintaining the privacy of patient records is crucial to avoid unauthorized access or financial gain by third parties. This paper introduces the Healthcare System using Private Blockchain-based Cloud IoT (HSPBCI) services, providing a comprehensive framework to address these issues. The framework's main goals are to speed up block-based transaction encryption and decryption times and grant authorized access to patient records. The proposed HSPBCI model restricts access to patient records to those holding the appropriate key, ensuring secure block-based access. Nodes are randomly deployed in cloud locations to generate data, which is then processed by the cloud blockchain network to filter out unscrupulous users and service providers. The results show that the proposed HSPBCI model, with up to 50 nodes, has encryption and decryption times that are over five times faster than existing models. With an improvement of a factor of O(n) over the existing O(n^2), the proposed model's transaction processing and runtimes for different characteristics and nodes range from 0.15 ns to 0.44 ns and 1 ms to 470 ms, respectively, compared to 0.18 ns to 0.53 ns and 2 ms to 570 ms in existing works. In the context of blockchain-based IoT applications, this research advances the field of secure healthcare data management. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

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