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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Ashwinkumar, V.K. Loganathan, V. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

Phishing attacks, a prevalent form of social engineering and cyber threats, exploit human vulnerabilities to deceive internet users into divulging sensitive data for deceptive intentions. Among these, URL-based phishing attacks pose significant risks to individuals and organizations by manipulating trust in URLs to steal data or inject malware. This paper seeks to address this issue by employing machine learning models and deep neural networks trained on a dataset containing both malicious and normal URLs. Extracting relevant features from URL and website content, the models are evaluated and compared based on their predictive performance. This research contributes to enhancing cybersecurity measures against URL-based phishing attacks by leveraging advanced machine learning techniques for detection and prevention. © 2024 IEEE.

关键词： Websites

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VisQAid: Visual Question Answering for Visually Impaired 6

VisQAid: Visual Question Answering for Visually Impaired

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Anjana, S. Arya, J. Jayaraj, Sandra V Raj, Sreelakshmi T Manmadhan, Sruthy Nss College of Engineering Department of Computer Science and Engineering Palakkad India

ISBN: (纸本)9798350381689

Visual Question Answering for the visually impaired is an emerging and important research area. This field centers on creating technologies so visually impaired people can interact with their surroundings and learn by asking questions about visual content. The goal is to empower them to gain greater independence through this technology.A Visual Question Answering system has been developed to assist visually impaired people in understanding visuals. Using the VizWiz dataset, the proposed model learns from pictures and questions asked by visually impaired people. By training the system with Contrastive Language-Image Pre-training on this data, it can now respond to many visual questions in plain language. The results of the project shows the performance of the Visual Question Answering (VQA) system on the VizWiz *** accuracy rate of 72.9% shows the ability to answer visual questions from those who have sight challenges. © 2024 IEEE.

关键词： Contrastive Learning

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Advancements in Heart Disease Classification: Leveraging Deep Learning Techniques for ECG Analysis 15

Advancements in Heart Disease Classification: Leveraging Dee...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Manimaran, V. Shanthi, N. Aravindhraj, N. Aatarsh, K.M. Adharshini, G. Gokul, P. Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350370249

Heart disease, a global health burden, demands early and accurate detection. While vast medical datasets exist, extracting crucial diagnostic patterns remains a challenge. This study investigates the potential of deep learning architectures to improve heart disease diagnosis accuracy. While traditional machine learning approaches have shown promise, this research delves deeper. It explores Convolutional Neural Networks (CNNs) and pre-trained models like VGG19, ResNet50, and DenseNet201 to identify patterns within medical data. The proposed framework leverages deep learning models to analyze diverse medical records, potentially including electrocardiograms (ECGs) and imaging data. These models automatically extract significant features, which subsequently build a classification model for heart disease detection. The study aims to compare the performance of deep learning models against traditional methods using metrics like accuracy, sensitivity, and specificity. By harnessing the power of deep learning for both feature extraction and classification, this research strives to contribute to the development of more robust and accurate diagnostic tools for heart disease. © 2024 IEEE.

关键词： Deep learning Densely Connected Convolutional Networks Heart disease classification Residual Neural Networks Visual Geometry Group

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Early Diabetics Prediction Using Multi Model Approaches in Machine Learning 9

Early Diabetics Prediction Using Multi Model Approaches in M...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Renugadevi, G. Sasi Kala Rani, K. Oswalt Manoj, S. Saranya, N. Goudhaman, M. Sri Krishna College of Engineering And Technology Department of Computer Science And Engineering Chennai India Dr Ngp Institute of Technology Department of Computer Science And Engineering Chennai India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365092

Diabetics is the maximum common non-transmissible disease and the deadliest disease worldwide, affecting five hundred and thirty-seven mountain individuals. Diabetics container be carried on by a variety of sources, with an unhealthy diet, high BP, abnormal cholesterol, a personal antiquity of the disease, and inactivity. Early diabetes prediction using IoT can be done by monitoring various health parameters and identifying patterns that may indicate the development of diabetes. In Proposed work, Mendeley Data Repository is a real-time data have been taken for early diabetes prediction. The Proposed hybrid Backward elimination method with Maximum Relevance, Minimum Redundancy (BE-MRMR) algorithm When integrating backward elimination and the MRMR algorithm, you can use the strengths of both strategies to identify a small but informative set of features for early diabetic prediction, potentially enhancing model interpretability and generalisation. Finally, after eliminating features, left with a subdivision of structures that subsidize most to the replica's performance. This feature given to various machine learning classifiers RF, ANN, SVM to predict early diabetics. These models produce performance matrices is calculated in terms of accuracy, specificity, precision and sensitivity. © 2024 IEEE.

关键词： Forecasting

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Efficient Internet Community Analysis using Integrated ADB-CB for Fake Account Detection 1

Efficient Internet Community Analysis using Integrated ADB-C...

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1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Bhaskar, S.V. Roja Ramani, D. Srujana, Raja Vinayaka, S. Pyati Prathyusha, Thammineni Santosh Krishna, B.V. New Horizon College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350365405

Detecting fraudulent accounts in Internet communities is a difficult undertaking due to fraudsters' clever methods and the large amount of user data involved. Concerns about security and authenticity in online social networks (OSNs) have grown as a result of their widespread incorporation into daily life. The rise in popularity of OSNs has attracted unscrupulous actors looking to exploit personal data and spread misleading information. While academics have investigated different ways for detecting aberrant behaviors and bogus accounts, current systems frequently suffer from inadequate feature selection and suboptimal classification algorithms. In this research, we propose ADB- CB, a unique algorithm aimed to improve the detection of phony Instagram accounts. To increase accuracy and reliability, we use four feature selection and dimension reduction algorithms, including AdaBoost, CatBoost, and ExtraTree classifiers. The experimental findings show that our strategy works well for identifying between authentic and phony Instagram profiles. © 2024 IEEE.

关键词： Fake detection

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An Enhanced Analysis of Cloud-Based Deep Learning Model in Smart Healthcare Management 2

An Enhanced Analysis of Cloud-Based Deep Learning Model in S...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Khanna, Tejaswi Mathur, Sonali Ims Engineering College Department of Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350371055

Cloud-based Deep learning of models has been widely applied in intelligent healthcare management to deliver improved diagnostics and analytics for advanced patient outcomes. The aim of this research is to assess the cloud based Totally Deep system's performance more thoroughly and familiarize oneself with the model across a range of healthcare scenarios. The model can be assessed based on its capacity to predict outcomes largely from an affected person's clinical history and records, as well as its ability to accurately diagnose conditions unique to each affected person. Accuracy, time efficiency, and scalability could also be used to evaluate the version's performance. The findings of this study will help to enhance more intelligent predictive healthcare analytics for improved patient outcomes in real-world packages. © 2024 IEEE.

关键词： Broadly Cloud-Based Evaluation Healthcare Knowledge

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Double Enhanced Solution Quality Boosted RIME Algorithm with Crisscross Operations for Breast Cancer Image Segmentation

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Journal of Bionic engineering 2024年第6期21卷 3151-3178页

作者： Mengjun Sun Yi Chen Ali Asghar Heidari Lei Liu Huiling Chen Qiuxiang He Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehran***Iran College of Computer Science Sichuan UniversityChengdu610065China Department of Pathology The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China

The persistently high incidence of breast cancer emphasizes the need for precise detection in its ***-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical *** Threshold Image Segmentation(MTIS)is widely favored due to its stability and straightforward *** when dealing with sophisticated anatomical structures,high-level thresholding is a crucial technique in identifying fine *** enhance the accuracy of complex breast cancer image segmentation,this paper proposes an improved version of RIME optimizer EECRIME,denoted as the double Enhanced solution quality Crisscross RIME *** original RIME initially conducts an efficient optimization to target promising *** double-enhanced solution quality(EESQ)mechanism is proposed for thorough exploitation without falling into local *** contrast,the crisscross operations perform a further local exploration of the generated feasible *** performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark ***,an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma(IDC)histology *** results demonstrate that the developed model significantly surpasses its competitors,establishing it as a practical approach for complex medical image processing.

关键词： Rime optimization algorithm Double-enhanced solution quality mechanism Crisscross optimization algorithm Image segmentation Breast cancer

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Envisioning the interactive convergence of Generative AI and Facial Expression Recognition 9

Envisioning the interactive convergence of Generative AI and...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Florance, G. Nirmala, M. Khan, Jumainah Saifudeen Hisham, K.M. New Horizon College of Engineering Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

This paper explores and envisions a collaboration between Facial Expression Recognition (FER) and Generative Artificial Intelligence (GenAI) to create an advanced human-computer interaction (HCI) experience. Deep learning techniques are harnessed to interpret human emotions from facial expressions, using a structured approach that includes pre-processing, feature extraction, and classification. The data acquired from the FER stage informs GenAI systems - such as Generative Pre-trained Transformers (GPTs) - to produce contextually relevant and emotionally resonant responses. The proposed system leverages a sophisticated camera setup to capture facial data with high accuracy under diverse lighting conditions, ensuring robust and reliable emotion inference. Virtual avatars complete the multimodal experience by generating corresponding nonverbal cues. Special attention is given to creating a dynamic feedback loop for continuous learning and adaptation, leading to increasingly effective human-computer dialogues. Ethical implications, such as user privacy, data security, and bias mitigation, are central to the design and deployment of this system, underscoring the commitment to ethical standards and user empowerment. This paper aims to demonstrate how the convergence of FER and GenAI can revolutionize HCI, paving the way for enhanced empathetic and intelligent computing interfaces. © 2024 IEEE.

关键词： Intelligent systems

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Automated Insurance Claims Using Block Chain 10

Automated Insurance Claims Using Block Chain

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

作者： Sagar, Jukanti. Varun Siddharth, Poshala. Sai, B. Nikhil Seshadri, U. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350384369

This paper proposes an advanced system for automated insurance claims processing, integrating Natural Language Processing (NLP) and blockchain technology. Unlike existing systems that rely on rule-based automation and centralized databases, our proposed system enhances efficiency, data security, and fraud detection. NLP is employed to extract nuanced information from claims, while blockchain ensures decentralized and immutable record-keeping. The objectives of this project encompass efficiency optimization, heightened data security, cost reduction, trust fortification, and streamlined processes through the convergence of Natural Language Processing (NLP) and blockchain technology. By automating data extraction, minimizing human errors, and reducing operational costs, this innovative system aims to revolutionize the insurance claims process, ultimately fostering greater trust and transparency. © 2024 IEEE.

关键词： Cost reduction

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A Deep Learning Framework for Hourly Bitcoin Price Prediction Using Bi-LSTM and Sentiment Analysis of Twitter Data

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SN computer science 2024年第6期5卷 767页

作者： Patel, Raj Chauhan, Jaya Tiwari, Naveen Kumar Upaddhyay, Vipin Bajpai, Abhishek Computer Science and Engineering Rajkiya Engineering College Kannauj Electronics Engineering Harcourt Butler Technical University Kanpur

Cryptocurrencies like bitcoin predictions hold significant importance due to their impact on various stakeholders and aspects of the financial landscape. Investors heavily rely on the predictions to make effective and efficient conclusions about buying, selling, or holding Bitcoin as part of their investment portfolios. Accurate forecasts enable effective risk management, allowing individuals and institutions to confidently navigate the volatile cryptocurrency market. In this paradigm, our emphasis is on the prediction of the next hour’s bitcoin price. In most of the model, we give the bitcoin price as input but here along with the bitcoin price we are giving coin price and sentiments as input and it predicts the next hour’s output price. In this work, we have used bidirectional LSTM with preprocessed data. We have performed data cleansing and sentiment analysis as part of the data pretreatment step. Recent 90-days data have been selected for the prediction which is then fed to a bidirectional LSTM model. It is a specialized version of recurrent neural network (RNN) architecture that is often used in sequence-to-sequence tasks, including time series prediction. It can be applied to Bitcoin price prediction by leveraging its ability to capture dependencies in both past and future data. The source code of the project is available at: https://***/rajpatel4835/Forecasting-Bitcoin-Price. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bidirectional LSTM Bitcoin (BTC) Recurrent neural network (RNN) Sentiment analysis

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