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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Babu, Tina Naik, Pramod Kumar Nair, Rekha R Janhavi, P.G. Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Dayananda Sagar University Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350354171

This project focuses on developing a deep learning algorithm to classify eye diseases from images. The dataset comprises images depicting various eye conditions, including glaucoma, cataracts, normal eyes, and diabetic retinopathy. We adopt a comprehensive approach by utilizing pre-trained architectures such as ResNet-18, GoogleNet, AlexNet, VGG19, and ResNet-50 as the backbone of our models, fine-tuning each on our dataset. During the training process, we employed data augmentation techniques to enhance the dataset, improving the models' generalization ability across diverse eye conditions. The evaluation of the models involves a combination of cross-entropy loss and accuracy metrics to assess performance. Notably, the individually trained models exhibited varying accuracies, with ResNet-18 achieving the highest accuracy of 92.7% on the validation set. Additionally, we conducted an analysis of model performance using metrics such as precision, recall, and F1-score, further enhancing our understanding of the models' capabilities. The visualization of results is facilitated through the presentation of confusion matrices, providing insights into the models' classification behavior across different eye diseases. This study opens up a promising new path for improving ophthalmology screening and diagnosis by demonstrating the prospective benefits of deep learning in automating the classification of eye diseases. © 2024 IEEE.

关键词： Ophthalmology

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A Comprehensive Exploration of Storage Attacks in Information-Centric Networking Environments 10

A Comprehensive Exploration of Storage Attacks in Informatio...

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10th International Conference on advanced Computing and Communication Systems, ICACCS 2024

作者： Sethu, S. Manikandan, A. Vels Institute of Science Technology & Advanced Studie Department of Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

To help users overcome the limitations of current IP networks, Data Networking (NDN) and Content-Centric Network (CCN) architectures have been created to move communication from host-centric to content-centric. NDN is essentially a network for the dissemination of content. In NDN, a user's request for material will be the only reason for contact. The descriptive capabilities of NDN technology leverage the globally unique names that encryption and content-based security provide. As part of its design, it upholds content validity and reliability. We examine several storage risks that NDN caches face, including content poisoning attacks, cache pollution attacks, and man-in-the-middle attacks. © 2024 IEEE.

关键词： Internet protocols

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A Robust Convolutional Neural Network Architecture for Automated Pneumonia Detection from Chest X-ray Images 3

A Robust Convolutional Neural Network Architecture for Autom...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Babu, Tina Naik, Pramod Kumar Nair, Rekha R Pallavi, K. Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350354171

Pneumonia is a significant global health concern, especially for vulnerable populations such as children under five. Timely and accurate diagnosis is critical for effective treatment and improved patient outcomes. This project proposes a robust solution leveraging Convolutional Neural Networks (CNNs) for automated pneumonia detection from chest X-ray images. The model, trained on a meticulously preprocessed dataset of 5,216 chest X-ray images, demonstrated exceptional performance, achieving a training accuracy of 98.16% and a validation accuracy of 98.45% after 50 epochs. The architecture comprises five Conv2D layers with ReLU activation, followed by max-pooling layers, and a fully connected artificial neural network for classification. The model's success in distinguishing between normal and pneumonia-infected cases underscores its potential as a valuable tool for healthcare professionals, offering a reliable and efficient means of pneumonia identification. The model's high accuracy and efficiency make it a valuable asset, potentially reducing the workload on radiologists and enabling quicker diagnoses, ultimately leading to more timely interventions and improved patient outcomes. Comprehensive evaluation, including testing on a separate dataset and real-world image inferences, validates the robustness of the proposed solution. © 2024 IEEE.

关键词： Convolutional neural networks

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Improved Deepmaxoutmodel for Disease Prediction in Healthcare IoT Cloud System 3

Improved Deepmaxoutmodel for Disease Prediction in Healthcar...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Preetha, P. Packialatha, A. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350374643

The Internet of Medical Things IoMT has grown rapidly due to the growing popularity of wearable technology and its applications in health monitoring systems. The IoMT significantly reduces the death rate by facilitating early sickness identification. Predicting cardiac disease is among the most significant issues with clinical dataset analysis. Finding the key components of heart disease prediction is the goal of the suggested investigation, which makes use of machine learning techniques. Despite several studies on the issue, the accuracy of the findings on the diagnosis of heart illness is low. The purpose of this paper is to provide an innovative framework for healthcare monitoring that includes the following phases. Data preprocessing, feature extraction, acquisition of data, and prediction are the primary stages. The process of acquiring data is at the IoTs layer. The medical data is considered in this case (benchmark datasets of different illnesses). The data pre-processing operations of data cleaning and data filtering at the cloud layer are applied to the data obtained from the IoT sensors (considering the dataset), where the data normalization process will take place. Subsequently, statistical features and raw features are extracted. The multi-layered Deepmaxout model will predict the diseases during in the prediction phase. Lastly, the evaluation of the suggested approach is compared to existing methods using several types of metrics. © 2024 IEEE.

关键词： Prediction models

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Recognition of Deep Fake Voice Acoustic using Ensemble Bagging Model 5

Recognition of Deep Fake Voice Acoustic using Ensemble Baggi...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Basha, Shaik Abdul Yunus Priya, K.Ulaga Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350379945

As artificial speech generates technological evolution;fake voice information has become an increasingly prevalent avenue for fraud. Numerous investigations have been carried out into machine learning techniques, signal processing techniques, and the Automatic Speaker Verification System to address such issues. Since there are increasing methods to create fake information, techniques must be developed to determine when audio recordings employed as electronic proof are corrupted. Such recordings contain the potential to be manipulated. Hence, this research work identified the data regarding audio systems as either fake or real by i) extracting features from audio file metadata using oversampling techniques and ii) Developing an Ensemble Bagging Model appropriate for analyzing input audio signal followed by classification of fake (fraudulent) and real (authentic) audio. Our experimental results reveal that the ensemble bagging model attained an accuracy of 99.5%, precision of 99%, 99.6% recall, and 99.9% Fl-measure in the prediction of the audio system compared with traditional approaches. © 2024 IEEE.

关键词： Audio recordings

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Multimodal Deep Learning with XAI for Transparent and Accurate Doctor Prescription Recognition 2

Multimodal Deep Learning with XAI for Transparent and Accura...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Sunil Reddy, J. Norbu, Tenzin Sungrab Kumar, Pavan Ajjanakatt, Praveen Vitthal Harsha, Reddyam Ananthanagu, U. Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798331534950

This paper presents a deep learning framework to automate recognition of medical prescriptions by using handwritten content, feature extraction from CNNs like VGG16, Alex Net, and MobileNetV2, and sequential data utilizing LSTMs. A multi-modal approach captures even the spatial and temporal details in a prescription, which assists in an accurate interpretation of complex handwriting. Specifically, key pre- processing like grayscale conversion, normalization and edge detection improving the quality of the image, reducing noise and bringing forth the prominent features that aid in the improvement of recognition accuracy and also implementation of XAI concepts to further enhance the interpretation. This model reduces transcriptions, making it easier for the patients as well as the practitioners to understand the handwriting through the computer screen. The method therefore assists in safer patient-centered healthcare by automating prescription processing thus triggering clearer communication towards more informed healthcare decisions. © 2024 IEEE.

关键词： Deep learning

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A Survey on Artificial Intelligence based Photovoltaic Cell Defect Detection Techniques 7

A Survey on Artificial Intelligence based Photovoltaic Cell ...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Pandey, Harshita Dhavale, Sunita Defence Institue of Advanced Technology Department of Computer Science and Engineering Pune411025 India

ISBN: (纸本)9798331527822

With the increase in demand for solar energy in the form of photovoltaic (PV) panels have become an important component in sustainable energy applications. The visible defects (cracks, black spots, corrosion, etc.) in PV cells can occur during manufacturing times or while sustaining under external environmental conditions like rain, extreme temperature, humidity, etc. As a result, timely fault analysis in solar panels is essential in order to enhance both the lifespan and energy efficiency performance. The Electrolumince (EL) imaging technique is one of the popular technique used to scan PV modules in order to identify these critical defects like cracks. However, traditional PV fault detection methods rely on manual inspection and hence tedious, prone to human errors. This survey paper discusses the different methodologies and techniques using artificial intelligence (AI), Deep learning (DL), and computer vision (CV) for quick, reliable, and efficient crack detection in PV panels using EL imaging analysis. Convolutional Neural Networks (CNNs), Residual Neural Networks (ResNet), and Visual Geometry Group (VGG) have demonstrated significant improvements in defect detection accuracy. This survey paper also discusses the standard datasets available in this domain and a comparative analysis of proposed techniques in the literature. © 2024 IEEE.

关键词： Cracks

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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Detection of lung and colon cancer from histopathological images using deep learning 4th

Detection of lung and colon cancer from histopathological im...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Yadav, Vikas Singh, Jagrati Department of Computer Science and Engineering Centre for Advanced Studies Uttar Pradesh Lucknow India Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women Delhi India

ISBN: (纸本)9781032911571

Lung cancer is the most life-threatening cancer worldwide, emphasizing the critical importance of its timely detection and treatment for patient recovery. Colon cancer is also a very hazardous cancer if not treated timely because of its diagnosis at later stage. The cornerstone of diagnosis for these cancers relies on histopathological images obtained from biopsied tissues in potentially affected lung and colon regions. However, accurately and efficiently diagnosing specific types of lung and colon cancer presents challenges, consuming valuable time in the process. Diagnostic modalities have also evolved significantly, with the integration of advanced imaging techniques, liquid biopsy, and molecular profiling contributing to more accurate and personalized diagnosis. Additionally, advancements in radiomics and artificial intelligence have shown promise in improving the efficiency and precision of these types of cancer detection. In response to these challenges, Convolutional networks (CNNs) have emerged as a promising solution. In this paper, CNN model is used for prediction of above-stated two types of cancers which has demonstrated remarkable results, achieving accuracy of 99.27%. © 2025 the Author(s).

关键词： Lung cancer

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Faithful geometric measures for genuine tripartite entanglement

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Physical Review A 2024年第1期110卷 L010402-L010402页

作者： Xiaozhen Ge Lijun Liu Yong Wang Yu Xiang Guofeng Zhang Li Li Shuming Cheng Department of Control Science and Engineering Department of Applied Mathematics The Department of Mathematics and Computer Science State Key Laboratory for Mesoscopic Physics School of Physics Frontiers Science Center for Nano-optoelectronics and Collaborative Innovation Center of Quantum Matter Shanghai Institute of Intelligent Science and Technology Institute for Advanced Study

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation Ei|jkα≤Ej|ikα+Ek|ijα holds for all subadditive bipartite entanglement measure E, all permutations under parties i,j,k, all α∈[0,1], and all pure tripartite states. Then, we rigorously prove that the nonobtuse triangle area, enclosed by side Eα with 0<α≤1/2, is a measure for genuine tripartite entanglement. Finally, it is significantly strengthened for qubits that given a set of subadditive and nonsubadditive measures, some state is always found to violate the triangle relation for any α>1, and the triangle area is not a measure for any α>1/2. Our results pave the way to study discrete and continuous multipartite entanglement within a unified framework.

关键词： Entanglement measures Quantum correlations in quantum information Quantum correlations, foundations & formalism Resource theories

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