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

作者： Menon, Gokulraj Peter, Amal P. Jomon, Ebin Varghese, Joe Anly Antony, M. Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350384369

Our project pioneers a transformative platform, marrying artificial intelligence with fashion customization. Users engage with an intuitive interface, seamlessly translating their style visions into unique apparel designs. Leveraging machine learning, the system analyzes content and styles images, intelligently transferring selected stylistic elements to create a harmonious blend. The output showcases personalized, stylized objects each a reflection of the user's chosen style fused with the original content subset. This endeavor not only redefines fashion personalization but also marks a distinctive application of AI in creative expression. We invite collaboration and support as we shape a future where technology reimagines the landscape of personalized apparel design. © 2024 IEEE.

关键词： Economic and social effects

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Designing a Comprehensive Analysis Framework for University Student Stress

Designing a Comprehensive Analysis Framework for University ...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Lertnimitphan, Phattharakit Sumpaongen, Thanaporn Jiramongkonphat, Thananat Songmuang, Pokpong Kongkachandra, Rachada Thammasat University Data Science And Innovation College Of Interdisciplinary Studies Bangkok Thailand Thammasat University Department Of Computer Science Bangkok Thailand

ISBN: (纸本)9798350370096

In the dynamic landscape of higher education, there is a growing need to comprehend and address student stress. This demand has led to the development of a comprehensive Computational Analysis Framework for University Student Stress. The framework aims to develop the field by introducing innovative tools and systems for data collection, preparation, exploratory data analysis, principal component analysis, and clustering. Unlike existing methodologies, our approach pioneers a systematic and technologically advanced solution to analyze stress levels and coping strategies among university students. © 2024 IEEE.

关键词： K-means clustering

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Transforming Heart Disease Detection with BERT: Novel Architectures and Fine-Tuning Techniques

Transforming Heart Disease Detection with BERT: Novel Archit...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Javeed, Muhammad Usman Ali, Muhammad Shafqat Iqbal, Asma Azhar, Muhammad Aslam, Shafqat Maria Shabbir, Ifra Department of Computer Science Comsats University Islamabad Sahiwal Pakistan Department of Applied Data Science Hong Kong Shue Yan University Hong Kong

ISBN: (纸本)9798331510503

The prediction of heart disease is crucial for effective prevention and treatment. However, extracting clinical information such as CAD, smoking, hypertension, hyperlipidemia, obesity, and family history of CAD from unstructured electronic health records (EHRs) poses significant challenges to clinicians. This research introduces a novel approach that leverages an ensemble of transfer learning algorithms combined with a multi-head attention mechanism to automatically extract heart disease risk factors from EHRs. Various deep learning models, including BERT, BioBERT, BioClinical BERT, RoBERTa, and XLNet, were initially trained on medical data sets and subsequently fine-tuned on the i2b2 clinical data set. Individual models delivered strong results, with RoBERTa achieving the highest accuracy of 95. 27% and an F1 score of 94. 94%. BioBERT, BioClinical BERT, XLNet, and BERT also performed well, with precision ranging from 94. 73% to 95. 03%. However, the proposed ensemble model with multi-head attention outperformed all, achieving an accuracy of 96.35% and the F1-score of 95.76%. These findings highlight the superior ability of the ensemble model to capture complex inter-dependencies between heart disease risk factors, making it a robust tool for clinical prediction. © 2024 IEEE.

关键词： Electronic health record

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Non-Invasive Muzzle Matching for Cattle Identification Using Deep Learning

Non-Invasive Muzzle Matching for Cattle Identification Using...

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Sanjel, Arun Khanal, Bikram Rivas, Pablo Speegle, Greg School of Engineering and Computer Science Baylor University Department of Computer Science United States

ISBN: (纸本)9798350327595

Accurate cattle identification is an essential but complicated issue in the field of livestock management. Traditional identifying methods can involve invasive procedures, posing ethical difficulties and compromising animal welfare. This paper addresses this pressing issue by proposing a deep learning-based methodology for non-invasive cattle identification through muzzle matching. Our approach leverages a comprehensive dataset of 4923 cleaned and cropped muzzle images from 268 distinct cattle breeds. The model showcases exceptional performance with a training accuracy of 98.88 % and a test accuracy of 100 %. Importantly, our methodology avoids invasive procedures and exhibits adaptability, effectively handling introducing new animals into the system. This versatility ensures the model's reliability across diverse operational scenarios, making it a suitable candidate for insurance fraud prevention and animal trading applications. The paper also highlights critical future research directions, including expanding the dataset to encompass a broader range of cattle breeds and muzzle variations and the potential integration with other identification modalities. © 2023 IEEE.

关键词： Adaptability Cattle recognition Deep Learning Muzzle point Non-invasive Techniques

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Enhancing breast cancer histopathological image classification using attention-based high order covariance pooling

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Neural Computing and Applications 2024年第36期36卷 23275-23293页

作者： Waqas, Muhammad Ahmed, Amr Maul, Tomas Liao, Iman Yi School of Computer Science Faculty of Science and Engineering University of Nottingham Malaysia Campus Selangor Dar ul Ehsan Semenyih43500 Malaysia Department of Software Engineering University of Malakand Khyber Pakhtunkhwa Lower Dir Chakdara18800 Pakistan Department of Computer Science Edge Hill University Lancashire L39 4QP United Kingdom School of Computer Science Faculty of Science University of Nottingham NottinghamNG8 1BB United Kingdom

Breast cancer, the most common cancer affecting female patients, presents serious challenges for proper detection. Although computer-aided diagnostic techniques have progressed, their accuracy and efficacy remain limited. To overcome these challenges, we introduce DHA-Net, a new deep learning system that combines an effective attention module (EAM) and a high-order pooling layer with a ResNet-18 backbone. DHA-Net is tested using three well-known breast cancer histopathology image datasets: BreakHis, BACH2018, and a closely related Kaggle-Breast cancer histopathology dataset. Our experiments show that DHA-Net not only improves on existing state-of-the-art approaches, but significantly outperforms them in classifying breast cancer images. This work emphasizes the novel combination of an EAM with high-order pooling, demonstrating DHA-Net’s potential to improve diagnostic accuracy and serve as a more effective tool for medical imaging applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Lung cancer

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Image Segmentation and Feature Extraction for Gastric Cancer Detection 3

Image Segmentation and Feature Extraction for Gastric Cancer...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Yamuna, S. Vinothiyalakshmi, P. Sri Venkateswara College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350381931

Gastric cancer is a prevalent and life-threatening disease that necessitates early detection for effective *** recent years, image segmentation techniques have shown promise in aiding the detection of gastric cancer. This study introduces a novel approach for detecting gastric cancer by utilizing image segmentation and feature extraction *** primary objective is to accurately determine the presence of cancer in the stomach based on medical images. The proposed method involves segmenting the stomach region from the input image using advanced segmentation algorithms. Subsequently, relevant features are extracted from the segmented region to differentiate between cancerous and non-cancerous tissues. The experimental results validate the effectiveness of the proposed approach in accurately detecting gastric cancer. If implemented, this method has the potential to assist medical professionals in making timely and accurate diagnoses, ultimately leading to improved patient outcomes. © 2024 IEEE.

关键词： Feature extraction

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Prediction of Vehicle Carbon Emission Using Machine Learning 5

Prediction of Vehicle Carbon Emission Using Machine Learning

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

作者： Ashok, K. Rithishbrahma, Palem New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350379945

The automotive industry is a major contributor to carbon emissions, posing a threat to the environment and human health. This research explores the potential of machine learning for predicting vehicle CO2 emissions. It utilizes a comprehensive dataset encompassing diverse factors like vehicle type, engine specifications, driving patterns, and environmental conditions. Feature engineering and model training with various algorithms like linear regression, decision trees, and neural networks are employed. The findings demonstrate the effectiveness of machine learning in predicting emissions with high accuracy, offering valuable insights into influencing factors. The developed models empower the automotive industry to manage emissions more efficiently, contributing to climate change mitigation and a more sustainable future. This study highlights the potential of machine learning in addressing critical environmental challenges. © 2024 IEEE.

关键词： Zero-carbon

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Trade Ease with Machine Learning and AWS

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IEEE Access 2023年 11卷 25893-25905页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research. © 2013 IEEE.

关键词： Deep neural networks

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OTP-Based Software-Defined Cloud Architecture for Secure Dynamic Routing

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computers, Materials & Continua 2022年第4期71卷 1035-1049页

作者： Tae Woo Kim Yi Pan Jong Hyuk Park Department of Computer Science and Engineering Seoul National University of Science and Technology(Seoultech)Seoul01811Korea Department of Computer Science Georgia State UniversityAtlanta30303USA

In the current era,anyone can freely access the Internet thanks to the development of information and communication *** cloud is attracting attention due to its ability to meet continuous user demands for ***,Cloud is effective for systems with large data flow such as the Internet of Things(IoT)systems and Smart ***,the use of traditional networking technology in the cloud causes network traffic overload and network security ***,the cloud requires efficient networking technology to solve the existing *** this paper,we propose one-time password-based software-defined cloud architecture for secure dynamic routing to mitigating the above-mention *** proposed cloud architecture provides a secure data path through dynamic routing using One-Time Internet Protocol(OTIP)algorithm between each *** the network side,we use software-defined technology to provide efficient network management and data *** introduce a software-defined cloud architecture that applies OTIP algorithms for secure dynamic *** conduct a comparative analysis between general IP communication and proposed OTIP communication *** evaluates the performance of OTIP ***,we examine the proposed software-defined cloud architecture,including how to apply OTIP in secure dynamic routing according to the results of the comparative analysis.

关键词： Secure routing dynamic routing one-time password softwaredefined network software-defined perimeter cloud computing

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A Multithreaded-BFS Algorithm for Optimizing the Graph Computation in Multicore Processing System 5

A Multithreaded-BFS Algorithm for Optimizing the Graph Compu...

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5th International Conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Juneja, Kapil Khurana, Dhiraj Department of Computer Science Engineering Bennett University Uttar Pradesh Greater Noida India Department of Computer Science Engineering Maharshi Dayanand University Haryana Rohtak India

Graphs are the most complex data structure that involves heavy mathematical computations. Various real-time applications use the graph at storage, processing, object characterization, and behavior specification stages. The connectivity analysis, coverage building, and routing are the critical applications of graph evaluation. Various associated applications need the exploration of a graph with a practical and real-time outcome. The processing of larger graphs is a big challenge for the researcher. In this paper, a hybrid graph-based computation optimization method is designed to optimize the performance of graph processing in multicore processors. The BFS algorithm is applied over the multiple processors to optimize the functional response. The multithreaded-BFS is implemented to reduce the time of graph processing and computation. The proposed algorithm is implemented in the OpenMP environment. The experimentation is done on two-core and four-core processors. The experiment is conducted on a 100 to 1000-node network. The traversing and graph coloring algorithms are implemented for the analysis. The comparative analysis is conducted on single-core and multi-core processors with DFS and BFS-based algorithms. The analysis results identified that the proposed algorithm reduced the processing time effectively. © 2024 The Authors. Published by Elsevier B.V.

关键词： Digital storage

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