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Application of Convolutional Neural Networks for Cervical Cancer Detection in Women's Uterus 2

Application of Convolutional Neural Networks for Cervical Ca...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Ramesh, Janjhyam Venkata Naga Visalakshi, J. Kishore, Ganjikuntla Uday Samuel, John Neerugatti, Vikram Yashika, S. Graphic Era Hill University Department of CSE Uttarakhand India CMR Institute of Technology Dept. of CSE -AIML Telngana India CMR College of Engineering & Technology Dept. of CSE-Data Science Telngana India MLR Institute of Technology Department of CSE Telngana India Dept. of CSE Banglore India

ISBN: (纸本)9798350367461

HPV infection is frequently the cause of cervical cancer, the fourth most prevalent type of cancer that mostly affects women's cervixes. Early vaccination of women may be the key to its eradication, but this effort is fraught with difficulties, particularly in middle-class or lower-income nations where access to healthcare is scarce. In this paper, a novel method for detecting cervical cancer using medical image processing techniques and Convolutional Neural Networks (CNNs) is presented. A serious health risk is cervical cancer, especially in areas with poor access to medical facilities. By using CNNs to analyze medical pictures, such as pap smears and cervical scans, the suggested approach seeks to overcome this difficulty by enabling the early identification of abnormalities in the cervical region that may be signs of cancer or precancerous lesions. data collection, preprocessing, segmentation, feature extraction, model training, and evaluation are all steps in the workflow. The CNN model shows promising performance in reliably diagnosing cervical cancer by automatic feature extraction from labelled datasets. The efficacy of the suggested strategy is confirmed by evaluation measures including area under the ROC curve, sensitivity, specificity, and accuracy. © 2024 IEEE.

关键词： Cervical cancer convolutional neural network diagnosis medical image processing pap smear

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Cybernated Watermarking Approach on Image with Cryptographic Encryption 2

Cybernated Watermarking Approach on Image with Cryptographic...

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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Sivasankari, R. Veeramakali, T. Mustafa Nawaz, S.M. Prakash, P. Umadevi, P. Alvin Ancy, A. B.S.Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering India Srm Institute of Science and Technology School of Computing Dept of Data Science and Business Systems Tamilnadu Kattankulathur India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Sri Sai Ram Engineering College Dept of Electronics and Communication Engineering India Kpr Institute of Engineering and Technology Department of S & H Coimbatore India Anna University Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350387933

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types;a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text. © 2024 IEEE.

关键词： Cryptography

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Sustainable Energy Efficient Routing Protocol using Chimp algorithm and SOM for Underwater Wireless Sensor Networks

Sustainable Energy Efficient Routing Protocol using Chimp al...

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2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Philip Paul Arunodhayam, T. Vadde, Akhil Raj Kumar, Thimmampali Prasanna Silas, Salaja Gandu, Shalem Preetham Harshith Kumar, Chamarti Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India College of Engineering and Computing George Mason University Data Analytics Engineering VA Fairfax22030 United States

ISBN: (纸本)9798350306927

Underwater Wireless Sensor Networks (UWSNs) are increasingly being used for various applications, such as environmental monitoring, underwater exploration, and oceanographic research. protocols that consume less energy for underwater wireless sensor networks (UWSNs) are still a difficult undertaking because of the harsh conditions present underwater and the restricted battery capacity of the sensor nodes. This research paper proposes an optimized multi-Hop routing protocol using Chimp Optimization and Self-Organizing Map (SOM). The proposed technique is compared with three popular routing protocols - AODV, DSDV, and DSR in terms of network lifetime and packet delivery ratio. Chimp Optimization algorithm as well as SOM are used to improve network performance. The findings indicate that the put-forward technique exceeds the performance of existing routing protocols, as it increases network lifetime, PDR and PLR while decreasing packet loss ratio. The network lifetime of the put-forward technique is 1039.42 seconds, which is higher than AODV, DSDV, and DSR. Similarly, the PDR of the proposed technique is 97.38%, which is comparatively higher than the three routing protocols. Moreover, the PLR of the proposed technique is 2.62%, which is much lower than AODV, DSDV, and DSR. The proposed technique based on Chimp Optimization and SOM can effectively optimize the multi-Hop routing protocol in underwater wireless sensor networks to achieve sustainable energy efficiency, thus providing important insights for researchers and practitioners to enhance the performance of underwater wireless sensor networks. © 2023 IEEE.

关键词： Energy efficiency

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inQuery- Online AI Application Formatting and Management System

inQuery- Online AI Application Formatting and Management Sys...

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computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Aishwarya Singh Smriti Tejjus S. Bhat Uditi Sharma Yash Sharma CSE (Data Science) ABES Engineering College Ghaziabad India Dept. of CSE (Data Science) ABES Engineering College Ghaziabad India

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

Written applications, with standardized fields like date, subject, sender and receiver, serve various purposes such as leave requests, misconduct appeals and suggestions. Surprisingly, even in today’s tech-driven world, these applications often remain paper-based, leading to repetitive tasks like leave requests and complaints, which can be time-consuming and inefficient. Managing paper applications also poses challenges like misplacement and physical storage *** methodology addresses this by designing the application’s UI in Figma, exporting assets to Android Studio and creating a new project. In Android Studio, we implement the UI and functionality using these design assets, integrating Firebase services for authentication, real-time database and storage. Artificial Intelligence will be used for formatting the applications. We then test the application and ensure ongoing maintenance and *** observations reveal that many institutions still rely on traditional application methods. We recommend institutions adopt online application writing and management systems, as demonstrated by our Android app, “inQuery: An Application for Applications,” simplifying and enhancing this process.

关键词： Generative AI Education Receivers Writing Real-time systems Filling Safety

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Emergency vs Non-Emergency Vehicle Classification: Enhancing Intelligent Traffic Management Systems

Emergency vs Non-Emergency Vehicle Classification: Enhancing...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Madan, S. Shetty, Kishan Kodipalli, Ashwini Rao, Trupthi Kumaraswamy, S. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept of Artificial Intelligence and Data Science Karnataka Bengaluru India Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350300826

Due to its dense population, India frequently experiences traffic congestion, which puts lives in danger by trapping vehicles like ambulances, police cars, and fire trucks which run on road for emergency purpose. It becomes crucial to give these vehicles priority and allow for their easy passage. However, it becomes challenging or even impossible for traffic police to effectively handle such circumstances. Therefore, there is a requirement for an automated system that can locate emergency vehicles in high-traffic areas, alert the controller, or drive itself to direct other vehicles to make room. This study suggests an automated method for identifying emergency vehicles from CCTV footage that makes use of deep convolutional neural networks (CNN). The objective is to effectively identify and classify emergency vehicles in real-time, leveraging the power of advanced object detection techniques. With an accuracy of 91.73% and a loss of 0.2120, the suggested technique outperformed existing optimizers in accurately recognizing and classifying emergency vehicles. © 2023 IEEE.

关键词： Object detection

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Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images 4

Enhanced Vision Transformer with Lion Optimizer for Accurate...

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4th International Conference on Technological Advancements in computational sciences, ICTACS 2024

作者： Bhatnagar, Roheet Hassan, Esraa Saber, Abeer Shams, Mahmoud Y. Elbedwehy, Samar Manipal University Jaipur Department of Computer Science and Engineering Rajasthan India Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial Intelligence Information Technology Dept. Damietta34517 Egypt Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Data Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350387490

Monkeypox is a contagious viral infection caused by the monkeypox virus (Mpoxvirus). Characterized by skin lesions, fever, respiratory difficulties, swollen lymph nodes, and neurological complications, MPX can be fatal. Recent outbreaks have spread the disease across Europe, Australia, the United States, and Africa. Traditionally diagnosed through PCR testing of skin lesions, this process poses significant risks to medical personnel due to potential exposure to the highly contagious virus. However, the usage artificial intelligence (AI) is revolutionizing diagnostics, providing a safer and more efficient approach to MPX detection. In this paper, a new AI technique based on vision transformer and lion optimizer for classifying Monkeypox Skin Images. The presented model used for classifying the dataset and results reached accuracy of 99.6%. © 2024 IEEE.

关键词： Classification Deep Learning Diagnosis Monkeypox

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Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Leveraging XAI and Breakthrough Machine Learning Techniques ...

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IEEE Region 10 Symposium

作者： Abhijna MB Akul P Ashwini Kodipalli Dept. of Information Science and Engineering Global Academy of Technology Dept. of Computer Science and Engineering Global Academy of Technology Dept. of Artificial Intelligence and Data Science Global Academy of Technology

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence.

关键词： Logistic regression Accuracy Explainable AI Medical treatment Nearest neighbor methods Boosting Brain modeling Planning Random forests Regression tree analysis

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Grey Wolf Optimizer and Deep Neural Network based Feature Selection and Classification in Medical Image Analysis 18

Grey Wolf Optimizer and Deep Neural Network based Feature Se...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chandrashekhar, Katha Leela, P. Madhavi, K. Reddy Avanija, J. Tangudu, Naresh Indur Institute of Engineering and Technology Computer Science and Engineering Siddipet India Mohan Babu University School of Computing Dept. of Data Science Tirupati India Mohan Babu University School of Computing Tirupati India Aditya Institute of Technology and Management Tekkali India

ISBN: (纸本)9789380544519

Accurate and efficient medical image analysis is required for modern healthcare applications, facilitating timely disease diagnosis and treatment. Considering the importance of optimization algorithms and deep learning methodologies, this work presents a novel approach, Grey Wolf Optimizer-Assisted Deep Neural Networks (GWO-DNN), for feature selection and classification in medical image analysis. The proposed framework utilizes the power of the Grey Wolf Optimizer to intelligently select the most relevant features from complex medical images. The selected features are then integrated into Deep Neural Networks, to create accurate models for medical image classification tasks. This integrated approach is designed to improve diagnostic accuracy and enhance patient care. The experimentation and evaluation, reveal that the proposed GWO-DNN based model for image analysis is more efficient than the existing methods. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep Neural Networks (DNN) Disease Classification Feature Selection Grey Wolf Optimization (GWO) Medical Image Analysis

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern 13

Classification of Awareness in Disorders of Consciousness Us...

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13th International Winter Conference on Brain-Computer Interface, BCI 2025

作者： Lee, Chaewon Lee, Sunho Annen, Jitka Jeong, Ji-Hoon Massimini, Marcello Casarotto, Silvia Boly, Melanie Bodart, Olivier Thibaut, Aurore Rosanova, Mario Laureys, Steven Gosseries, Olivia Lee, Minji Dept. Data Science The Catholic University of Korea Bucheon Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent GhentB9000 Belgium Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical and Clinical Sciences "L. Sacco" University of Milan Milan Italy Wisconsin Institute for Sleep and Consciousness Dept. Psychiatry University of Wisconsin Madison United States Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (纸本)9798331521929

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future. © 2025 IEEE.

关键词： Electroencephalography

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Detection and Classification of DDoS Attacks in Cloud data Using Hybrid LSTM and RNN for Feature Selection

Detection and Classification of DDoS Attacks in Cloud Data U...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： R.A. Karthika P. Sriramya A. Rohini Dept. of Computational Intelligence SRMIST Chennai Dept. of Artificial Intelligence and Data Science SIMATS Saveetha School of Engineering Chennai Dept. of CSE Presidency University Bengaluru

Cyber threats are the main source of concern in the sector it is deployed. DDoS attacks are at the very top of the list. The rapid growth of cloud migration also expands the attack surface. Different forms of DDoS attacks exist, such as DoS and DDoS etc., There are several existing machine learning algorithms to detect DDoS attacks. There are also many hybrids of machine learning algorithms which are introduced for detecting DDoS attacks to increase the accurateness of detection. Here we use hybrid Long Short-Term Memory Network (LSTM) and Recurrent Neural Network (RNN) for feature selection and deliver an improved detection system. The proposed exploratory model of Multi-layer Perceptron (MLP) classifiers uses deep neural network algorithms. The final outcome shows higher accuracy and precision by dealing with a huge dataset that was gathered. The accuracy and precision of 98.85% and 92 % respectively for grouping in structure.

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