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

作者： Vats, Satvik Kour, Harpreet Aggarwal, Ruchika Katoch, Navneet Sharma, Rishabh Gomathi, R.D. Graphic Era Hill University Computer Science and Engineering Dehradun248002 India Graphic Era Deemed to be University Dehradun248002 India Cgc Landran Chandigarh Business School of Administrations Department of Computer Applications Mohali India Chandigarh Business School of Administration Department of Computer Applications India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India Kongu Engineering College Department of English Erode Tamilnadu Perundurai India

ISBN: (纸本)9798350370249

Analysis of facial expressions for emotion detection is relevant to many areas of interest in computer science and engineering among them being, human-computer interaction (HCI), security, and health. The problem with traditional approaches that use fixed sets of features is that facial data may be highly variable and the approaches may then demonstrate poor reliability. In this study, we present a novel approach for the classification of prompted emotive voices which employs a convolutional neural network (CNN) for feature extraction and a support vector machine (SVM) for classification. CNN-SVM hybrid is a technique that combines the advantages of the CNN and the SVM to improve accuracy and reduce the level of random variation. Using the abovementioned proposed method, the system was tested on a benchmark data set to get an accuracy of 0.92 accuracy which surpasses standalone CNN and SVM results as well as other recent works on a similar problem. The findings presented here show that the proposed CNN-SVM fusion model can be used to successfully identify emotions from facial expressions, especially for real-life applications. Future work will investigate additional possibilities with superior model learning and increase the scope of the data used. © 2024 IEEE.

关键词： CNN-SVM fusion Convolutional Neural Networks deep learning Emotion detection facial expressions machine learning Support Vector Machines

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Discover Crypto-Jacker from Blockchain Using AFS Method 1

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4th International Conference on computer and Communication Technologies, IC3T 2022

作者： Subburaj, T. Shilpa, K. Sultana, Saba Suthendran, K. Karuppasamy, M. Arun Kumar, S. Jyothi Babu, A. Department of Computer Applications Rajarajeswari College of Engineering Bangalore India Department of CSE CMR Technical Campus Telangana Hyderabad India Department of Information Technology Kalasalingam Academy of Research and Education Krishnan Koil Tamilnadu Srivilliputhur India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Koil TamilNadu Srivilliputhur India Department of Computer Science and Engineering Bethesda Institute of Technology and Science Gwalior India Department of MCA Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9789811985638

ISBN: (纸本)9789811985621

The blockchain technology is used throughout the world for digital ledgers of transactions. To maintain all participant transactions on a blockchain, distributed ledger technology (DLT) is used. Blockchain technology has become a popular method of transferring huge amounts due to the pandemic situation. A hacker was able to exploit some part of a chain, smart contract, exchange, or stealing cryptocurrency, that is, a hack or a theft. Such attacks are referred to as crypto-Jack attacks. The Wormhole cryptocurrency platform was hacked in Feb 2022, resulting in a loss of $326 Millions. Many cryptocurrency Web pages are being hacked every day. Most of the attacks are focusing the financial purpose, so banking attackers are globally raised. To identify crypto-jackers and to trace out the hijackers, we proposed the new technology. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Web-based remote sensing image retrieval using multiscale and multidirectional analysis based on Contourlet and Haralick texture features

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International Journal of Intelligent Computing and Cybernetics 2021年第4期14卷 533-549页

作者： Rajakumar Krishnan Arunkumar Thangavelu P.Prabhavathy Devulapalli Sudheer Deepak Putrevu Arundhati Misra School of Computer Science and Engineering Vellore Institute of TechnologyVelloreIndia School of Information Technology and Engineering Vellore Institute of TechnologyVelloreIndia School of Computer Science and Engineering Vellore Institute of TechnologyVelloreIndiaand Space Applications Centre AhmedabadIndia

Purpose-Extracting suitable features to represent an image based on its content is a very tedious *** in remote sensing we have high-resolution images with a variety of objects on the Earth’s *** distance metric is used to measure the similarity between query and database *** low distance obtained image is indexed at the top as high relevant information to the ***/methodology/approach-This paper aims to develop an automatic feature extraction system for remote sensing image *** texture features based on Contourlet transform are fused with statistical features extracted from the QuadTree(QT)decomposition are developed as feature set to represent the input *** extracted features will retrieve similar images from the large image datasets using an image-based query through the web-based user ***-The developed retrieval system performance has been analyzed using precision and recall and F1 *** proposed feature vector gives better performance with 0.69 precision for the top 50 relevant retrieved results over other existing multiscale-based feature extraction ***/value-The main contribution of this paper is developing a texture feature vector in a multiscale domain by combining the Haralick texture properties in the Contourlet domain and Statistical features using QT *** features required to represent the image is 207 which is very less dimension compare to other texture *** performance shows superior than the other state of art methods.

关键词： Image retrieval Remote sensing Contourlet Texture features Web-based search CBIR Multiscale texture

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Histopathological Image Classification of Breast Cancer using EfficientNet 3

Histopathological Image Classification of Breast Cancer usin...

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3rd International Conference for Emerging Technology, INCET 2022

作者： Venkatesh Sheela, Rachel K Nagaraju, Y Sahu, Divya Anwesh Bangalore University University Visvesvaraya College of Engineering Department of Computer Science and Engineering Bangalore560 001 India National Institute of Technology Departmnet of Computer Applications Tiruchirappalli620015 India

ISBN: (纸本)9781665494991

Deep learning algorithms help achieve promising results in diagnosis of cancer patient. However, obtaining patient confidential data and medical images of cancer patient from medical hospitals and diagnosis centre is challenging thus preventing the deep learning usage in analysis of medical data of patient. Among woman, Breast cancer is prevailing cancer and it has become common health problem. The classification of Breast cancer focuses on identifying breast tumours sub-classes. Deep learning is among the current trends in computer science research and it enhances neural networks. The layers in neural networks are more, deep which allows higher abstraction levels and help to get accurate analysis of data. Google's recently proposed architecture-EfficientNet, has less parameters which results in faster convergence for classification using ImageNet which showed that EfficientNet outperformed prevailing architectures such as DenseNet and ResNet. This research work include comparing the performance of EfficientNetV2-B0 architecture with DenseNet and ResNet architectures and few others for multiple class tumour classification on BreakHis dataset, which contains around 8000 histopathology images of breast that vary in magnification. This research work offers a methods that is based on transfer learning that improves existing architecture in multi-class classification by relying on pretrained DCNN trained on ImageNet dataset. Pretrained weights of EfficientNetV2-B0 are transferred as initial weights and the model is fine tuned in order to identify different benign and malignant tissue samples in tumor cells and classify them into multiclass. The model proposed has been tested with optimised hyperparameters irrespective of magnification of the images and was able to achieve accuracy upto 98%. The results surpass earlier studies in terms of accuracy across the performance metrics defined for CAD systems of breast cancer based on histological images. © 2022 IEEE.

关键词： Image classification

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System for Predicting Heart Disease using Sophisticated Machine Learning Techniques

System for Predicting Heart Disease using Sophisticated Mach...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Pandey, Trilok Nath Bhusan Dash, Bibhuti Barik, Lalbihari De, Utpal Chandra Tripathy, Abinash Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Kiit Deemed to Be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798350308167

Cardiovascular illness stands as a significant global health issue, and timely identification of this condition holds paramount importance to ensure effective intervention. In this paper, we introduce a fused model for anticipating heart ailments. This amalgamation incorporates four distinct algorithms: LightGBM, XGBoost, Multilayer Perceptron, and AdaBoost. Through an assessment on the Cleveland dataset, AdaBoost emerged as the most accurate with a precision of 0.985. Subsequently, XGBoost followed closely with an accuracy of 0.980, succeeded by LightGBM at 0.956, and Multilayer Perceptron at 0.936. To harness the collective power of these models, we employed a weighted bagging technique that accords weights to predictions based on their individual accuracy rates. The resultant composite model attained an accuracy of 0.96. This outcome is slightly lower than the model with the highest accuracy, as it involves a weighted mean calculation across model predictions. This study indicates the potential utility of the proposed methodology in cardiac disease prediction, leveraging the ensemble approach to potentially outperform standalone models. © 2023 IEEE.

关键词： AdaBoost Ensemble model Heart Disease LightGBM Machine Learning Multilayer Perceptron XGBoost

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Risk Navigation in Finance: A Deep Dive into Silicon Valley Bank and Credit Suisse Strategies 3

Risk Navigation in Finance: A Deep Dive into Silicon Valley ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Gostu, Hrishikumar Parmar, Manish Naik, Tanvi Jagtap, Nalini D y Patil International University School of Computer Science Engineering and Applications Pune Akurdi India D y Patil International University School of Interdisciplinary Studies Pune Akurdi India Dr D y Patil Institute of Engineering Management and Research India

ISBN: (纸本)9798350381931

This study explores the various intricate issues surrounding the decline of Silicon Valley Bank (SVB) and its resulting implications on the world market. Using sophisticated analytical methods, the research analyses some possible causes for the collapse of SVB. Secondly, it uncovers the intricate connection between corporate governance and the general performance of *** continuous risk analysis is focused on risk management as the centrality of the issue. The study emphasizes the critical importance of soundness and survival for financial institutions, especially in light of the dynamic nature of the market. Through the examination of these dimensions, the study seeks to shed light on the factors that led to the downfall of SVB and generate knowledge on more general issues related to preserving institutional stability and resilience. © 2024 IEEE.

关键词： Risk assessment

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LightGBM Model for Credit Card Fraud Discovery 1

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6th International Conference on Microelectronics, Electromagnetics, and Telecommunications, ICMEET 2021

作者： Muttipati, Appala Srinuvasu Viswanadham, Sangeeta Dharavathu, Radha Nema, Jayalakshmi Department of Computer Science and Applications KL Deemed to be University Andhra Pradesh Vaddeswaram Guntur India Department of Computer Science and Engineering Anil Neerukonda Institute of Technology and Sciences Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Raghu Institute of Technology Andhra Pradesh Visakhapatnam India

ISBN: (数字)9789811685507

ISBN: (纸本)9789811685491

The exponential growth of e-commerce and online-based payment options has created an empirical universe of financial fraud, with credit card fraud being the most prevalent. For several years, many researchers have developed a variety of data mining-based methods to address this issue. To detect credit card fraud, there has recently been a lot of interest in using machine learning algorithms instead of data mining techniques. In the digital space of financial transactions, on-going work is being conducted to put in a conceptual difference between fraud identification and predicting likely fraudulent opportunities. This paper extends the fraud detection technique and proposes a LightGBM-based detection algorithm. The dataset is a credit card dataset for credit card transactions in Europe. Our approach outperformed other traditional approaches such as random forest, AdaBoost, and XGBoost in this experiment. Furthermore, it demonstrates the value of feature engineering in terms of feature selection and performance tuning. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Adaptive boosting

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Artificial intelligence propels lung cancer screening:innovations and the challenges of explainability and reproducibility

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Signal Transduction and Targeted Therapy 2025年第2期10卷 492-494页

作者： Mario Mascalchi Chiara Marzi Stefano Diciotti Experimental and Clinical Biomedical Sciences“Mario Serio” University of FlorenceFirenzeItaly Department of Statistics Computer ScienceApplications“G.Parenti”University of FirenzeFirenzeItaly Department of Electrical Electronicand Information Engineering“Guglielmo Marconi”-DEIUniversity of BolognaCesenaItaly Alma Mater Research Institute for Human-Centered Artificial Intelligence University of BolognaBolognaItaly

In a recent study published in Nature Medicine,Wang,Shao,and colleagues successfully addressed two critical issues of lung cancer(LC)screening with low-dose computed tomography(LDCT)whose widespread implementation,despite its capacity to decrease LC mortality,remains challenging:(1)the difficulty in accurately distinguishing malignant nodules from the far more common benign nodules detected on LDCT,and(2)the insufficient coverage of LC screening in resource-limited areas.1 To perform nodule risk stratification,Wang et *** and validated a multi-step,multidimensional artificial intelligence(AI)-based system(Fig.1)and introduced a data-driven Chinese Lung Nodules Reporting and Data System(C-Lung-RADS).1 A Lung-RADS system was developed in the US to stratify lung nodules into categories of increasing risk of LC and to provide corresponding management recommendations.

关键词： screening low dosecomputedtomography explainability reproducibility nodule risk stratificatio noduleriskstratification malignant nodules artificialintelligence

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Dragonfly Algorithm and Variants for Feature Selection: A Review

Dragonfly Algorithm and Variants for Feature Selection: A Re...

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Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS), International Conference on

作者： P T Prinson Angelina Geetha Department of Computer Applications Hindustan Institute of Technology and Science Chennai Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai

Ultimately, the key to successfully analysing real-world problems is to be diligent and thorough. It is important to carefully consider all available data, remove any redundant or unnecessary information, and approach the analysis with an open mind. By doing so, it is possible to gain valuable insights and make informed decisions that can help solve complex real-world problems. By removing redundant, irrelevant, and noisy data, feature selection (FS), a difficult machine learning problem, attempts to lessen the number of features while maintaining a decent degree of classification accuracy. Feature selection is intended to reduce features. This immediately reduces the search space and aids machine learning algorithms in using only the most significant features. A review of Dragonfly algorithms and variants is presented in this paper, emphasizing their main characteristics. Eventually, the conclusion concentrates on the present research on Dragonfly Algorithm, emphasizing its limitations with suggested solutions that lead to future directions

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Hybrid CNN-LSTM Model for Enhanced Weather Forecasting: Leveraging Spatial and Temporal Dependencies 4

Hybrid CNN-LSTM Model for Enhanced Weather Forecasting: Leve...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vallileka, N. Rajkumar, G.Vinoth Krishnan, R. Santhana Shankar, S. Vijay Francis Raj, J. Relin Karthikeyan, M. Saravana Psna College of Engineering and Technology Department of Computer Applications Dindigul India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (纸本)9798331523923

Accurate weather forecasting is critical for decision-making in agriculture, disaster management, urban planning, and numerous other domains. Traditional forecasting methods often struggle to capture the intricate spatial and temporal dependencies in weather data. This work proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to address these challenges. The CNN component efficiently extracts spatial features from satellite images, such as cloud formations and precipitation zones, while the LSTM network models the temporal dependencies inherent in meteorological data, such as temperature, humidity, and wind speed. The model is trained on a dataset comprising historical weather records and satellite imagery, spanning several years. PyTorch, a dynamic deep learning framework, is used to implement and train the hybrid architecture. Data preprocessing involves normalization, image augmentation, and sequence structuring to prepare inputs for the model. The CNN layers, built with convolutional and pooling operations, extract spatial features, which are combined with temporal weather data and passed to the LSTM network. The LSTM layers capture long-term dependencies and trends in weather patterns. Fully connected layers map the learned features to final predictions, including temperature, rainfall, and other key weather parameters. The hybrid model is evaluated using metrics such as Root Mean Square Error (RMSE) for numerical predictions and accuracy for classification tasks. Results indicate that the proposed hybrid CNN-LSTM model outperforms standalone models and traditional statistical approaches, demonstrating its ability to leverage spatial and temporal dependencies for accurate weather forecasting. This work highlights the potential of hybrid deep learning models in advancing weather forecasting capabilities, offering a scalable and reliable solution for real-world applications such as e

关键词： Convolutional neural networks

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