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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in engineering & Sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

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computer Systems Science & engineering 2023年第7期46卷 125-135页

作者： S.Navaneethan P.Siva Satya Sreedhar S.Padmakala C.Senthilkumar Department of Electronics and Communication Engineering Saveetha Engineering CollegeChennai602105India Department of Information Technology Seshadri Rao Gudlavalleru Engineering CollegeKrishna521356India Department of Computer Science Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Communication Engineering Saveetha School of EngineeringSIMATSChennai602105India

The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so *** pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size,occlusion of eyelids,and *** Convolutional Neural Networks(DCNN)are being used in pupil recognition systems and have shown promising results in terms of *** improve accuracy and cope with larger datasets,this research work proposes BOC(BAT Optimized CNN)-IrisNet,which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil *** proposed method is based on very deep architecture and many tricks from recently developed popular *** results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight,easy to implement,and of cutting-edge ***,the region-based black box method for determining pupil center coordinates was *** proposed architecture was tested using various IRIS databases,including the CASIA(Chinese academy of the scientific research institute of automation)Iris V4 dataset,which has 99.5%sensitivity and 99.75%accuracy,and the IIT(Indian Institute of Technology)Delhi dataset,which has 99.35%specificity and MMU(Multimedia University)99.45%accuracy,which is higher than the existing architectures.

关键词： BAT algorithm IRIS datasets DCNN pupil detection black box method

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Streamlining plant disease diagnosis with convolutional neural networks and edge devices

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Neural Computing and Applications 2024年第29期36卷 18445-18477页

作者： Ahamed, Md. Faysal Salam, Abdus Nahiduzzaman, Md. Abdullah-Al-Wadud, M. Islam, S. M. Riazul Department of Computer Science and Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical Engineering Qatar University Doha Qatar Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Natural and Computing Sciences University of Aberdeen AberdeenAB24 3FX United Kingdom

In addressing labor-intensive process of manual plant disease detection, this article introduces an innovative solution—the lightweight parallel depthwise separable convolutional neural network (PDSCNN) coupled with a hybrid ridge regression extreme learning machine classifier. This combined approach automates plant disease classification, significantly expediting the learning process and reducing computational complexity. The PDSCNN model excels in feature capture, boasting a mere 9 layers and 0.053 million parameters, resulting in exceptional computational efficiency. On top of that, the classification performances of traditional ELM increased by introducing a hybrid ridge ELM model. Its accelerated operations, reduced memory usage, and optimized parallel processing render it lightweight and ideal for edge devices. The proposed framework exhibits remarkable efficacy, showcasing a precision of 95.76%, a recall of 89.29%, an F1-score of 91.25%, an accuracy of 94.22%, and an AUC of 99.72%. Moreover, training and testing times clock in at just 0.2314 and 0.0259 s, respectively. A comparative analysis against state-of-the-art transfer learning methods underscores its real-time analytical prowess. To demonstrate real-world applicability, we successfully validated our model on the Jetson Nano hardware, making it suitable for resource-constrained settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Computational efficiency

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Decentralized Energy Using IoT and Cloud Computing for Enhancing Grid Resilience Through Microgrid Integration 6

Decentralized Energy Using IoT and Cloud Computing for Enhan...

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6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Solainayagi, P. Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379297

Climate change, cyberattacks, and renewable energy integration threaten the contemporary electrical system. This paper proposes integrating decentralized energy production, the Internet of Things (IoT), microgrids, and cloud computing to improve grid resilience. Solar panels, wind turbines, and small power plants can reduce the grid's need for centralized electricity. Microgrids may function independently or alongside the main grid during outages or crises. The IoT allows real-time energy asset and grid infrastructure monitoring, control, and optimization. IoT sensors, smart meters, and actuators gather and analyze data for proactive maintenance, problem detection, and load control. Cloud computing powers this resilient grid ecosystem with scalable, cost-effective data storage, processing, and analytics. Cloud solutions enable predictive maintenance, demand response, and grid optimization by processing massive volumes of IoT data. Using decentralized energy production, IoT, and cloud computing, grid operators may improve resilience. First, they identify and react to grid abnormalities in real time, limiting downtime and cascading failures. Second, they may optimize energy output and reduce carbon emissions by integrating renewable energy sources better. Third, sophisticated cybersecurity and predictive threat analysis may improve grid security. © 2024 IEEE.

关键词： Windmill

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A Voltage-supportive Controller for Ultra-fast Electric Vehicle Chargers in Islanded DC Microgrids

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Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 896-906页

作者： Noushin Poursafar Seyedfoad Taghizadeh M.Jahangir Hossain Masoud Karimi-Ghartemani School of Engineering Macquarie UniversitySydneyNSW 2109Australia School of Electrical and Data Engineering University of Technology SydneySydneyNSW 2007Australia Department of Electrical and Computer Engineering Mississippi State UniversityMississippiMS 39762USA IEEE

This paper presents a controller for fast and ultrafast electric vehicle(EV)charging *** affecting the charging efficiency,the proposed controller enables the charger to provide support to the interconnection voltage to counter and damp its *** solutions are either hardware-based such as using supercapacitors and flywheels which increase the cost and bulkiness of the charging station,or software-based such as P/V droop methods which are still unable to provide a robust and strong voltage *** paper proposes an emulated supercapacitor concept in the control system of the ultra-fast EV charger in an islanded DC ***,it converts the EV from a static load to a bus voltage supportive load,leading to reduced bus voltage oscillations during single and multiple ultra-fast EV charging operations,and rides through and provides supports during extreme external *** analysis and design guidelines of the proposed controller are presented,and its effectiveness and improved performance compared with conventional techniques are shown for different case studies.

关键词： Electric vehicle(EV) charging station ultrafast charging DC microgrid voltage support capacitor emulation

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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection 7

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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7th International Congress on Ambient Intelligence, Software engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Jiménez-Delgado, E. Quesada-López, C. Méndez-Porras, A. Lara-Petitdemange, A. Alfaro-Velasco, J. Graduate Program in Computer Science and Informatics University of Costa Rica Costa Rica Technological Institute of Costa Rica Computer Engineering Department Costa Rica School of Computer Science and Informatics University of Costa Rica Costa Rica

ISBN: (纸本)9798350364538

In this study, two deep learning models for automatic tattoo detection were analyzed;a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures. © 2024 IEEE.

关键词： Convolution

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SkinSage - Lesion Diagnosis Using Deep Learning Techniques 14

SkinSage - Lesion Diagnosis Using Deep Learning Techniques

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Geetika Bhardwaj, Paarth Nimish Bhargava, Vaibhav Singh, Shivendra Amity School of Engineering and Technology Department of Computer Science and Engineering Noida201301 India

ISBN: (纸本)9798350344837

The alarming rise in instances of skin cancer in recent years, one of the most prevalent malignancies worldwide, emphasises how important early and accurate identification is. The SkinSage project, which combines the precision of deep learning models and technologies. Earlier, dermatologists relied on eye exams to spot skin lesions. But with datasets like HAM10000 and digital photography, it became easy to record a variety of skin conditions. SkinSage uses cutting-edge neural architectures like CNN, ResNet, DenseNet, and InceptionV3 to recognise patterns and nuances that are much above the capabilities of the human eye. In addition to being a computational marvel, the SkinSage project also exemplifies accessibility by making its analytical capabilities available via a smartphone application in future. High-quality skin lesion diagnostics will be accessible to everyone thanks to this platform, regardless of location or degree of training. With the confluence of these developments, SkinSage becomes more than just a tool for diagnosing skin cancer;it signals a paradigm shift that has the potential to democratise early detection and save countless lives in the future. © 2024 IEEE.

关键词： Deep learning

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Optimized Convolutional Neural Network at the IoT edge for image detection using pruning and quantization

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Multimedia Tools and Applications 2025年第9期84卷 5435-5455页

作者： Naveen, Soumyalatha Kounte, Manjunath R Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India School of Electronics and Communication Engineering REVA University Bangalore India

Most real-time computer vision applications heavily rely on Convolutional Neural Network (CNN) based models, for image classification and recognition. Due to the computationally and memory-intensive nature of the CNN model, it’s challenging to deploy on resource-constrained Internet of Things (IoT) devices to enable Edge intelligence for real-time decision-making. Edge intelligence requires minimum inference latency, memory footprint, and energy-efficient model. This work aims to develop an energy-efficient deep learning accelerator using a 3-stage pipeline: Training, Weight-pruning, and Quantization to reduce the model size and optimize the resources. First, we employ YOLOv3, a CNN architecture to detect objects in an image on the trained data. In addition, a sparse network of YOLO has been created by using pruning, which helps to improve the network’s performance and efficiency by reducing the computational requirements. Finally, we utilize 8-bit quantization to reduce the precision of the weights and activations, in a neural network. The evaluation of our proposed model shows that combining pruning and 8-bit quantization improves the efficiency and performance of the model. While pruning shows a decline of 80.39% in model parameters. The combination of 8-bit quantization results in an improvement in inference latency by 22.72% compared to existing SQuantization approach and a reduction of energy consumption by 29.41%. © The Author(s) 2024.

关键词： Convolutional neural networks

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Python-Based Free and Open-Source Web Framework Implements Data Privacy in Cloud Computing 4th

Python-Based Free and Open-Source Web Framework Implements D...

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4th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2023

作者： Veeresh, V. Parvathy, L. Rama Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9789819965854

Data sharing turn into a remarkably striking service delivered by cloud computing architecture due to its suitability and reduced cost. As a probable practice for understanding finely secured distribution of data, using a technique called Attribute-Based Encryption (ABE) with a different variety of users. In real time, ABE technique has its own drawbacks like high computation overhead and less security in cloud data storage with fine-grained data files. Processing tasks could be transferred to remote computing resources utilizing cloud computing. Cyber insurance is a realistic way of passing on cyber security risks, but data security can or cannot be improved depending on the environment's key characteristics. This paper focused on one income insurer, client participants, and insured volunteers, focusing on two different cybersecurity aspects and their impact on the standard form of contracts. Since cyber security is linked, an entity's degree of security is determined not just by its own effort and effort but also by the efforts of others operating within the same system. As such, effective resource utilization could be performed, reducing manufacturer costs, but the only drawback of cloud services is that data will be secured and reasonably priced in the cloud. Hence, before going to the cloud, all content must be encrypted. Consumers of a collision protection information-sharing system are given secure private keys so they may add or remove consumers. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Digital storage

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Comprehensive Review and Meta-Analysis of Machine Learning Applications in Screening for Diabetic Retinopathy Analysis

Comprehensive Review and Meta-Analysis of Machine Learning A...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Siddiqui, Ahmad Talha Kaur, Harleen Naaz, Sameena Tanveer, Safdar School of Engineering & Technology Department of Computer Science & Engineering New Delhi110062 India

ISBN: (纸本)9798350348460

The leading cause: diabetic retinopathy global blindness, affects 10% to 24% of individuals with type 1 or type 2 diabetes in primary care. Early detection using deep learning methods is critical for timely intervention and preserving vision. Our comprehensive review, including a meta-analysis, evaluates the effectiveness of these algorithms in DR detection. This study extensively assessed machine learning's diagnostic accuracy in identifying diabetic retinopathy across diverse cases using color fundus images, aiming to pinpoint the most advanced ML strategy available. We extensively reviewed relevant literature from January 2015 to December 2022, utilizing EMBASE, PubMed, Google Scholar, and Scopus. Following PRISMA guidelines, we focused on machine learning-based study designs. Two authors independently assessed articles for inclusion based on predefined criteria, and data were collected using a standardized form. The meta-analysis reveals strong performance of machine learning in detecting diabetic retinopathy in color fundus photos, indicating readiness for clinical use. However, caution is advised due to methodological limitations in some earlier studies, such as lack of external validation and potential biases in participant selection © 2024 IEEE.

关键词： diabetic retinopathy feature extraction machine learning

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