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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mary Vespa, M. Shajeena, J. Shiny, R.M. Gayathri Devi, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Diabetes is a malnutrition that results from elevated glucose levels. Diabetes eventually results in diabetic retinopathy (DR), a retinal condition that significantly impairs vision. It is an insulin-dependent difficulty that impacts ocular health. The receptive to cells at the back of the individual retina's veins gets injured, which is the cause of it. A favorable prognosis for this condition depends on its early identification. In this paper, the application of Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) on color fundus images is used for diabetic retinopathy is applied for understanding the task. The goal of the proposed DR identification method is to use deep learning to automatically identify the problem. An hybrid model that was trained and tested to distinguish between healthy and DR-affected fundi was able to achieve an accuracy of roughly 99.07% using fundus images from diabetic patients that were available in MESSIDOR. © 2025 IEEE.

关键词： Convolutional neural networks

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Method for Finding an Investment Strategy in the Case of a Sparse Covariance Matrix 17

Method for Finding an Investment Strategy in the Case of a S...

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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Gorelik, Victor Zolotova, Tatiana Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Simulation Systems and Operations Research Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

An optimality principle is proposed for making investment decisions based on efficiency and risk assessments with a sparse covariance matrix. The method is implemented as a program with a graphical interface and demon... 详细信息

ISBN: (纸本)9798350375718

关键词： Marketplaces

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Life Expectancy Prediction using machine learning Technique - KNN 3

Life Expectancy Prediction using Machine Learning Technique ...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Narooka, Preeti Vashistha, Aarushi Mitra, Priyanka Derashri, Gulisha School of Computer Science Engineering Manipal University Department of Artificial Intelligence and Machine Learning Rajasthan Jaipur303007 India Jaipur Engineering College and Research Centre Department of Computer Science and Engineering Jaipur303905 India Jaipur Engineering College and Research Centre Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350384598

In the modern world, life expectancy turns out to be the most valuable factor in determining population's health. Today, it is more crucial to predict the future because life is more precarious due to a variety of circumstances, including social, economic, environmental, educational, lifestyle, and health conditions. The machine learning algorithm KNN for life expectancy is presented in this work. The dataset gathered for this study is separated into a training set (70%) and a test set (30%), and the forecasts precision is examined. To avoid misclassification, KNN needs a larger sample size, and the best K are chosen more in accordance with cross-validation experience than with strong statistical theory. © 2024 IEEE.

关键词： Adversarial machine learning

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DP-RAE: A Dual-Phase Merging Reversible Adversarial Example for Image Privacy Protection 24

DP-RAE: A Dual-Phase Merging Reversible Adversarial Example ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Jiajie Du, Xia Zhou, Jizhe Pun, Chi-Man Xu, Qizhen Liu, Xiaoyuan School of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Computer Science Engineering Research Center of Machine Learning and Industry Intelligence Sichuan University Chengdu China Department of Computer and Information Science University of Macau China

ISBN: (纸本)9798400706868

In digital security, Reversible Adversarial Examples (RAE) blend adversarial attacks with Reversible data Hiding (RDH) within images to thwart unauthorized access. Traditional RAE methods, however, compromise attack efficiency for the sake of perturbation concealment, diminishing the protective capacity of valuable perturbations and limiting applications to white-box scenarios. This paper proposes a novel Dual-Phase merging Reversible Adversarial Example (DP-RAE) generation framework, combining a heuristic black-box attack and RDH with Grayscale Invariance (RDH-GI) technology. This dual strategy not only evaluates and harnesses the adversarial potential of past perturbations more effectively but also guarantees flawless embedding of perturbation information and complete recovery of the original image. Experimental validation reveals our method's superiority, secured an impressive 96.9% success rate and 100% recovery rate in compromising black-box models. In particular, it achieved a 90% misdirection rate against commercial models under a constrained number of queries. This marks the first successful attempt at targeted black-box reversible adversarial attacks for commercial recognition models. This achievement highlights our framework's capability to enhance security measures without sacrificing attack performance. Moreover, our attack framework is flexible, allowing the interchangeable use of different attack and RDH modules to meet advanced technological requirements. © 2024 ACM.

关键词： Differential privacy

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Improved top-down attention phasmatodea population spiking neural network based multi crop recommendation framework

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Multimedia Tools and Applications 2025年第10期84卷 8071-8105页

作者： R, Chetan Ashoka, D.V. B, Ajay Prakash Department of Computer Science and Engineering JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Information Science JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Information Science and Engineering JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Artificial Intelligence and Machine Learning Dr. Ambedkar Institute of Technology Visvesvaraya Technological University Karnataka India

Modern agriculture refers to the contemporary practices, technologies and methods used in food production to meet the growing global demand for agricultural products. With increasing concerns about environmental degradation, soil health and resource depletion, there is an insistent need for agricultural practices that are both productive and sustainable. Multicropping mitigate the negative impacts of monoculture and reduce the reliance on synthetic inputs. This research introduces an innovative approach for multi-crop recommendation through the utilization of an integrated framework that combines advanced optimization techniques and neural networks like Enhanced top-down attention Spiking Neural Network (ETSNN). The proposed framework involves two main phases: the first phase focuses on classifying soils suitable for specific agro-ecological zones using an enhanced top-down attention spiking neural network. The second phase predicts multi-crop yields across different districts based on various environmental parameters using an Improved Phasmatodea population Evolutionary Algorithm (IPEA). Before classifying the crops feature selection takes place for the dataset by Enhanced Sea horse Optimization (ESO) method. The outputs from both phases are harmonized using a stacking-based fusion method. The developed user interface accepts real-time input for location-specific soil properties and provides crop recommendations that take into account both the input data and climate parameters. The proposed method attains 99.7% accuracy, 99.2% precision, 99% recall and 99.9% F1-score respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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DROPEX: Disaster Rescue Operations and Probing using EXpert drones 8

DROPEX: Disaster Rescue Operations and Probing using EXpert ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kannan, Kausthub Awati, Aditya N. Rao, Smruthi S. Malagi, Vindhya P. Dayananda Sagar College of Engineering Department of Computer Science And Design Bangalore India Dayananda Sagar College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9798331505462

Disasters, both natural and man-made, pose significant risks to human life and infrastructure, necessitating swift and efficient search and rescue (SAR) operations. Traditional SAR methods often struggle to access hazardous areas, resulting in delayed responses and increased risk to rescuers. These methods are unable to run automated simultaneous rescues which lead the operations to face challenges such as difficulty in quickly assessing damage, locating survivors, and delivering aid. This paper address the problem by proposing a autonomous swarm of drones framework which improves the response time as well as increases accessibility zone of the rescue operation. The proposed framework DROPEX, is an autonomous UAV (Unmanned Aerial Vehicle) which employs a dual-dome architecture with surveillance drones to detect individuals in distress and payload drones to deliver aid. This framework ensures rapid deployment, accurate navigation, and efficient data transmission in disaster-stricken areas while minimizing the need for manual intervention. The drones are able to recognise the victims in need using object detection models such as YOLO and Detection Transformer (DETR) with thermal vision. By using Long Range Wide Area Network (LoRaWAN) and object detection models, the drones are able to reduce the response time and increase accessibility. The main focus is on creating a robust, scalable, and economical system to enhance the speed, efficiency, and effectiveness of disaster rescue operations. © 2024 IEEE.

关键词： Drones

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ClinicCare Manager: An Efficient Electronic Health Record (EHR) and Healthcare Management System Using Spring Boot and React 5

ClinicCare Manager: An Efficient Electronic Health Record (E...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bindu, S. Ashok, K. Krishnamurthy, Keerthana Chethan, C. Jatinkrishna, Gajula Srinivas Gagan, V. New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India New Horizon College of Engineering Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518097

Healthcare institutions often face challenges in managing patient data, optimizing clinic workflows, and ensuring timely reporting. Smaller clinics, in particular, may lack the resources to streamline services for both patients and healthcare providers. This work introduces a platform that automates patient data analysis and report generation to address these needs. Built using React, Spring Boot, and PostgreSQL, the application provides efficient, end-to-end service with robust data handling and fast processing. Performance metrics show efficient resource utilization, with CPU usage rarely exceeding 3 % and heap memory remaining well within the 2048 MB limit. Compared to other health care applications, this platform offers greater scalability and responsiveness. Additionally, the framework supports secure authentication and role-based access, making it a reliable and scalable solution that meets the expanding demands of health care environments. © 2024 IEEE.

关键词： Electronic health record

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A Review of Vehicular Adhoc Network Routing and Congestion Control

A Review of Vehicular Adhoc Network Routing and Congestion C...

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2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Kori, Kaveri Sridevi, H. Sharnbasva University Department of Computer Science and Engineering Karnataka Kalaburagi585103 India Sharnbasva University Department of Artificial Intelligence and Machine Learning Karnataka Kalaburagi585103 India

ISBN: (数字)9798350363289

ISBN: (纸本)9798350363289

In VANET, the efficiency of the RSUs positioned beside the roadways is crucial for both transportation and service provision. Direct communication between vehicles and infrastructure is supported by the network (V2V and V2I, correspondingly). The architecture of a VANET should satisfy several requirements to function properly and enable dependable real-time communication between vehicles while considering their low-power radio transmission of units, dynamic topology, and high mobility. Numerous routing protocols are suggested and assessed for VANETs. There are very few testbeds worldwide since they need a large count of wireless devices to test routing protocols with genuine VANETs and MANETs prototypes. By examining 25 publications, this survey aims to create a brief summary of VANET routing. In the current work, it offers an analysis of reliable routing models based on optimization algorithms. The best outcomes will be displayed once the performance metrics of the works that were examined are examined. © 2024 IEEE.

关键词： Congestion control (communication)

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Accurate Detection of COVID-19 and Pneumonia from Chest X-Rays and CT Images Using DNN 2

Accurate Detection of COVID-19 and Pneumonia from Chest X-Ra...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sekar, K. Suganya Devi, K. Murali Baskaran, V. Dheepa, T. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai Thandalam India National Institute of Technology Silchar Department of Computer Science and Engineering Silchar India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai Thandalam India Kongunadu College of Engineering and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350382952

X-rays play a crucial role in computed tomography (CT) imaging, enabling the visualization of internal structures within the body. It is particularly valuable in assessing pulmonary injuries in COVID-19 patients. Global health is facing a serious crisis as a result of such disease. Most affected persons could be saved if COVID-19 is diagnosed in time. However, COVID-19 can sometimes be misdiagnosed as other lung diseases, potentially leading to severe consequences such as death or significant injury, especially if the virus spreads rapidly throughout the thoracic cells. This research proposes a multi-classification deep neural network(DNN) to identify COVID-19 and pneumonia using a combination of CX-R and CT scan image datasets. The combined dataset will improve classification performance. The proposed DNN model achieves significant results in detecting COVID-19 and pneumonia. The proposed model provides an accuracy of 98.75%, a recall value of 98.75%, a precision value of 98.45%, F1-score of 98.43%, and an AUC of 99.60% on the above datasets. © 2024 IEEE.

关键词： Coronavirus

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Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER

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Intelligent Automation & Soft Computing 2023年第3期35卷 3545-3555页

作者： C.P.Thamil Selvi P.Muneeshwari K.Selvasheela D.Prasanna Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and TechnologyCoimbatore641009TamilnaduIndia Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Chennai602105Tamil NaduIndia Department of Computer Science and Engineering Sreesakthi Engineering CollegeKaramadai641104Tamil NaduIndia Department of Computer Science and Engineering Mahendra Engineering CollegeNamakkal637503Tamil NaduIndia

The term sentiment analysis deals with sentiment classiﬁcation based on the review made by the user in a social *** sentiment classiﬁcation accuracy is evaluated using various selection methods,especially those that deal with algorithm *** this work,every sentiment received through user expressions is ranked in order to categorise sentiments as informative and *** order to do so,the work focus on Query Expansion Ranking(QER)algorithm that takes user text as input and process for sentiment analysis andﬁnally produces the results as informative or *** challenge is to convert non-informative into informative using the concepts of classiﬁers like Bayes multinomial,entropy modelling along with the traditional sentimental analysis algorithm like Support Vector machine(SVM)and decision *** work also addresses simulated annealing along with QER to classify data based on sentiment *** the input volume is very fast,the work also addresses the concept of big data for information retrieval and *** result com-parison shows that the QER algorithm proved to be versatile when compared with the result of *** work uses Twitter user comments for evaluating senti-ment analysis.

关键词： Sentiments QER SVM bayes multinomial big data analysis simulated annealing

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