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Pneumonia Detection using Machine Learning Techniques 8

Pneumonia Detection using Machine Learning Techniques

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Rameez, Mohd Senthil Kumar, K. Logashree, M.J. Srm Institute of Science and Technology Ktr Dept. Of Computing Technologies Chennai India

ISBN: (纸本)9798350376425

Pneumonia continues to be a major contributor to global mortality rates, particularly affecting children under five and older adults. Swift and precise identification is essential for successful treatment and better patient results. Although conventional diagnostic approaches are reliable, they often require significant time and depend on the presence of skilled radiologists. In recent times, machine learning methodologies have emerged as robust tools for automating pneumonia detection from chest X-rays, offering the potential to boost diagnostic precision and efficiency. This study investigates the creation and implementation of various machine learning models, with a focus on deep learning techniques like Convolutional Neural Networks (CNNs), for identifying pneumonia. We execute and evaluate different model structures, including ResNet, DenseNet, and custom CNN designs, assessing their efficacy using a comprehensive dataset of chest X-ray images. Our findings underscore the effectiveness of machine learning in pneumonia detection, showcasing these models' capacity to substantially enhance diagnostic procedures. We examine the advantages and limitations of each method and consider the feasibility of incorporating these models into clinical settings. This investigation contributes to the expanding evidence supporting AI-driven diagnostic tools, especially in resource-limited environments, and sets the stage for improved pneumonia management on a global scale. © 2024 IEEE.

关键词： Convolutional neural networks

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Vehicle Damage Identification using Deep Learning Techniques

Vehicle Damage Identification using Deep Learning Techniques

引用

2024 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2024

作者： Mishra, Siddhant Kamal, Divya Kumar K, Senthil Srm Institute of Science and Technology Ktr Dept. of Computing Technologies Chennai India

ISBN: (纸本)9798350348460

In this innovative study, a cutting-edge approach harnessing YOLOv8 and ResNet-50 is proposed for an advanced vehicle damage detection system. Integrating damage segmentation and a meticulous repairing management system, this solution aims to revolutionize workshop operations. Through the utilization of sophisticated algorithms, the system accurately identifies and categorizes vehicular damages, facilitating precise and efficient repair strategies. Moreover, this comprehensive system incorporates an intelligent airbag recommendation feature, optimizing safety measures based on the detected damages. The integration of an accident dashboard further enhances the system's functionality, providing real-time insights into accident patterns and trends. This holistic approach not only streamlines workshop workflows but also prioritizes safety and precision in vehicular repair processes, marking a significant advancement in vehicle damage detection technology. © 2024 IEEE.

关键词： Artificial Intelligence data Augmentation Deep Learning Image Processing ResNet-50 Segmenting Sustainable solution Transportation U-Net

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An Analysis of Human Factors in Malware Attacks

An Analysis of Human Factors in Malware Attacks

引用

2024 International Conference on Computer and Applications, ICCA 2024

作者： Alotaibi, Jawza Al Ja'am, Jihad M. Hoda, Mohammed Oryx Universal College in Partnership with Liverpool John Moores University Dept. of Computing and Data Science Doha Qatar Kuwait College of Science and Technology Dept. of computer Science and Engineering Doha Kuwait

ISBN: (纸本)9798350367560

This research study investigates human responses to malware attacks, focusing on attacker motivations, individual reactions, and key factors influencing user vulnerability and resilience. Using a structured survey questionnaire, the study gathered data from 104 participants with diverse demographics across various occupations. The data was analyzed using SPSS statistical software and Python-based machine learning algorithms. The survey included demographic information and responses to cybersecurity-related questions about human behavior and experience. Machine learning techniques identified ten critical factors as significant predictors of vulnerability to malware attacks. The prediction model indicated that 55 out of 104 participants were vulnerable, with higher vulnerability observed among female participants and those in non-IT professions. This study contributes valuable insights into the human factors influencing cybersecurity resilience, providing a foundation for developing effective strategies to mitigate malware attack vulnerability. © 2024 IEEE.

关键词： Cyber attacks

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A Study on Viability of Cryptocurrency Adoption in Qatar: A Thematic Coding Approach of Fifty Reviews

A Study on Viability of Cryptocurrency Adoption in Qatar: A ...

引用

2024 International Conference on Computer and Applications, ICCA 2024

作者： Al-Nassr, Asma Nasser M. N. Alazab, Moutaz Al-Siddiqi, Essa H. M. Oryx Universal College in Partnership with Liverpool John Moores University in Qatar Dept. of Computing and Data Science Doha Qatar

ISBN: (纸本)9798350367560

This study investigates the factors driving cryptocurrency adoption in Qatar, with a focus on creating a comprehensive taxonomy to guide policymakers and stakeholders in this emerging financial landscape. Given Qatar's pivotal role in the Gulf Cooperation Council (GCC) and the broader Middle East and North Africa (MENA) region, the paper is particularly significant. By examining cryptocurrency adoption through the lens of Islamic finance principles and regulatory considerations, the study offers valuable insights into this evolving financial domain. The research employs a thematic coding analysis of 50 literature sources, covering aspects such as regulatory frameworks, Sharia compliance, socio-cultural factors, and technological advancements, ensuring a well-rounded understanding of the topic. The findings reveal a complex interplay of factors influencing cryptocurrency adoption in Qatar, including the need for Sharia-compliant models, robust regulatory frameworks, and the impact of cultural and religious perspectives on financial innovation. Additionally, the study highlights the importance of addressing privacy and security concerns in this context. The research emphasizes the necessity of a balanced approach that tackles regulatory challenges, ensures Sharia compliance, and leverages technological advancements to facilitate successful cryptocurrency adoption. This study significantly contributes to the understanding of cryptocurrency adoption in the Islamic world and provides a strong foundation for future research and policy development in Qatar and similar economies. © 2024 IEEE.

关键词： Decentralized finance

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Cirrhosis Prediction in Chronic Liver Disease Patients Using Machine Learning Techniques 3

Cirrhosis Prediction in Chronic Liver Disease Patients Using...

引用

3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Athish, V.P. Sree Nandha, S.S. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Dept. of Data Science and Business Systems Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350322842

Liver failure is a serious medical condition that can have life-threatening consequences. The liver is responsible for filtering blood and performing many other important functions. With the rise of machine learning in healthcare, there is an opportunity to use past datasets to predict liver failure early on. The main focus of this research work is to explore the application of supervised machine learning algorithms in predicting liver failure. The study involved pre-processing techniques such as univariate and bivariate analysis, and data visualization to gain better insights into the features of the dataset. Using these techniques, a multi-class classification model was built with machine learning algorithms, and the performance metrics of accuracy, F1 score, and recall were used to compare the algorithms. The results show that machine learning algorithms can accurately predict liver failure and can be an effective tool in healthcare. This study demonstrates the potential of machine learning in predicting liver failure and can contribute to the development of future models in the field. The results of this research study indicates that the proposed algorithm achieved an accuracy of 94.48%. The proposed work will be beneficial to researchers in this field and inspire them to build more advanced and accurate models. © 2023 IEEE.

关键词： Support vector machines

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Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification 38

Unified Mechanism-Specific Amplification by Subsampling and ...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Schuchardt, Jan Stoian, Mihail Kosmala, Arthur Günnemann, Stephan Dept. of Computer Science Munich Data Science Institute Technical University of Munich Germany Dept. of Engineering University of Technology Nuremberg Germany

Amplification by subsampling is one of the main primitives in machine learning with differential privacy (DP): Training a model on random batches instead of complete datasets results in stronger privacy. This is traditionally formalized via mechanism-agnostic subsampling guarantees that express the privacy parameters of a subsampled mechanism as a function of the original mechanism's privacy parameters. We propose the first general framework for deriving mechanism-specific guarantees, which leverage additional information beyond these parameters to more tightly characterize the subsampled mechanism's privacy. Such guarantees are of particular importance for privacy accounting, i.e., tracking privacy over multiple iterations. Overall, our framework based on conditional optimal transport lets us derive existing and novel guarantees for approximate DP, accounting with Rényi DP, and accounting with dominating pairs in a unified, principled manner. As an application, we analyze how subsampling affects the privacy of groups of multiple users. Our tight mechanism-specific bounds outperform tight mechanism-agnostic bounds and classic group privacy results. © 2024 Neural information processing systems foundation. All rights reserved.

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Comprehensive Methods for Secure Medical Image Transfer: Encryption and Standardization Techniques

Comprehensive Methods for Secure Medical Image Transfer: Enc...

引用

2024 International Conference on science, Technology, Engineering and Management, ICSTEM 2024

作者： Priyadharshini, K. Nandi, Hitesh Parmar, Harsh Dept. of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350376913

The primary goal of the process is to securely transfer medical images. This involves utilizing MRI datasets for processing and applying resizing and filtering techniques such as Conv2D and Gaussian blur to standardize the images. Additionally, an attack model is developed to enhance information security, starting with a PGD attack to access model weights and followed by a DAG attack to increase loss functions. Encryption is then performed using a Chaos-based encryption model to create a chaotic map and the Henon map for image conversion. A randomly generated key encryption method is utilized to generate a key for secure transfer. The encrypted image is displayed, and in the receiver section, the process is reversed for decryption by inputting the key generated in the encryption model. Finally, the original medical image is retrieved along with parameters such as MSE, MAE, RMSE, and F2 score to assess the accuracy of the recovered image and ensure its correctness. © 2024 IEEE.

关键词： Cryptography

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Customer Churn Prediction using Machine Learning 14

Customer Churn Prediction using Machine Learning

引用

14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Upadhyay, Samiksha Rajalakshmi, M. School of Computing SRM Institute of Science and Technology Dept. of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350335095

One significant problem that businesses face is customer attrition. It has become crucial for corporate operations and growth to prevent customer churn and work to keep clients. It is challenging to effectively estimate customer turnover because the majority of the existing projections use a single prediction model. Concentrating on the results of predictions of the models of machine learning, this study proposes a combination of estimating model for customer turnover and performs practical research on the model's efficacy. The combined prediction model outperforms the single customer churn prediction model in terms of accuracy and predictive impact, according to the findings of the predictions. It can also more naturally express the fundamental traits of the churn consumers. © 2023 IEEE.

关键词： Forecasting

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A Steganographic Method Using a Secure and Effective Pattern-Based Approach 2

A Steganographic Method Using a Secure and Effective Pattern...

引用

2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Vatsa, Arjit Agarwal, Sanskar Ranjani, M. School of Computing SRM Institute of Science and Technology Dept. of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350347982

Information is now widely available thanks to the development of the Internet and communication technologies. By itself, the abundance of knowledge creates a risk. The risk of intentional interference and eavesdropping in electronic communication is rising. In this day and age, privacy and security are top priorities. Two techniques have been developed in this context: cryptography and steganography. Steganography hides the presence of the message, whereas cryptography scrambles it to make it illegible. Steganographic techniques might be frequency-domain or spatial-domain (changing the pixel values to conceal data) (modifying the image's frequency coefficients). The vulnerability of spatial domain techniques to steganalysis is greater than that of frequency-domain methods. In this project, we provide an original pattern-based steganography method with an enhanced Caesar cypher for cryptography. This method chooses four patterns to conceal once the data is first encrypted using the extended Caesar cypher, the message in the picture. An additional degree of protection is added by securing the secret message with an AES-encrypted password. This technique is unnoticeable to visual assaults, histogram attacks, and statistical attacks since the changes it produces are not evident in the final image. This method offers fresh insight into a steganographic process that is more reliable, effective, and adaptable. © 2023 IEEE.

关键词： Cryptography

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Deep Transfer Learning Based Multi-Classification of Dental Diseases X-Ray Images 4

Deep Transfer Learning Based Multi-Classification of Dental ...

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4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Narmadha, R.P. Karthik, B. Mithiloshini, B. Saranya, R. KIT-KalaignarKarunanidhi Institute of Technology Dept. Artificial intelligence and data science Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

This research work proposes a deep transfer learning model for multi-classification purposes within dental diseases classed by their X-ray images. The paper elaborates typical challenges posed by dentistry, which faces many diagnosis issues;the decisive process of proper classification into types of diseases requires being fine and efficient. Most state-of-the-art methodologies developed through CNNs and DNNs have been found to yield fair results, but they both suffer from overfitting and are time-consuming processes. The new model uses the ResNet50 to get over those limitations. Dental X-ray pictorials are to be grouped into four distinct categories using the proposed architecture: cavities, fillings, impacted teeth, and implants. The system proposed should classify these four categories in X-ray images with an accuracy greater than 92%. Therefore, the proposed system will automatically identify areas requiring clinical treatment. Expected outcome is a strong and dependable tool for enhanced dental disease diagnosis by better accuracy, efficiency, and performance in the evvaluation of X-ray pictures. © 2024 IEEE.

关键词： Convolutional neural networks

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