检索结果-内蒙古大学图书馆

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

作者： Gonuguntla, Surya Reddy, M. Aditya Vardhan Sridhar, Vaidehi Saketh, Challagundla Neethu, M.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Recent advancements in technology have resulted in the creation of novel approaches for administering organ donation systems, with an objective of overcoming the restrictions of traditional centrally controlled systems. Blockchain technology is a potential replacement to the existing centralised methods for better transparency, security, and efficiency in organ donation. Previous research on organ donation systems mostly employed centralized database solutions, which have limited interoperability, a predefined set of protocols, and do not permit distributed control due to lack of security, transparency and accountability. A further significant component of the transplantation procedure is the matching of organ donor/recipient based on compatibility. Despite traditional centralised database solutions, blockchain promotes decentralized control, improving interoperability and accountability. Crucially, it facilitates the critical donor-recipient matching process, improving compatibility and possibly revolutionising transplantation operations. © 2024 IEEE.

关键词： Blockchain Technology Data Privacy Decentralization Organ Donation System Security Transparency

来源：评论

学校读者我要写书评

暂无评论

Explainable Screening and Classification of Cervical Cancer Cells with Enhanced ResNet-50 and LIME 3

Explainable Screening and Classification of Cervical Cancer ...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Chandana, Baddireddi Sree Kommana, Chakrika Madhav, G. Sethu Basa Pati, Peeta Singh, Tripty Afnaan, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350381931

High levels of recovery rates are proven to be possible at early detection of cancerous cells through various tests, meticulous monitoring, and previous findings. Pap-smear tests are widely used to obtain cervical cells as they are of low cost and due to their painless diagnosis. The use of Deep Learning techniques to create an automated screening and classification system for cervical cancer cells is becoming more and more popular. Our proposed method uses the SIPAKMED pap-smear imagine dataset and Deep Learning classification algorithms to screen for cervical cancer using ResNet-50. The experiment's findings showed that the suggested classification strategy for the ResNet50 architectural model had the highest accuracy (91.04%), outperforming both the Inception-V3 and MobileNet models. In addition, our experiment is advancing further by integrating Explainable Artificial Intelligence (XAI) into the analysis of test images generated by various models. The incorporation of LIME Explainability aims to enhance the interpretability and transparency of the diagnostic process. © 2024 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Malicious Query Detection Using Lexical Analyzer and Machine Learning 3

Malicious Query Detection Using Lexical Analyzer and Machine...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Prahalya, S.P. Samhitha, Sankepalli Sai Ratan, Vadapalli Sanjana Belwal, Meena Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350369083

SQL injection attacks continue to pose a serious threat in web security that calls for methods of creative solving. With database systems becoming more dynamic and complex, threat landscapes continuing to change, old techniques often do not work well. To address this issue, the study proposes a framework that involves combining machine learning techniques with a custom lexical analyser to predict normal and abnormal SQL queries that target MySQL data tables. This study uses six machine learning classifiers-Support vector machines, Logistic Regression, Linear Regression, Naïve Bayes, K-Nearest Neighbors and Random Forest for accurately telling apart malicious and non-malicious queries and to identify potential risk of SQL injection, contributing to overall web security. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

TweetFeel: Analyzing Emotions in the Twittersphere

TweetFeel: Analyzing Emotions in the Twittersphere

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Adithya Gokulan, S. Reddy, Mahati Varshith, M. Venugopalan, Manju Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

The majority of people on the planet now have access to the Internet for text, image, audio, and video communication. Through social media, people from many backgrounds share knowledge about current events and express their own opinions on them. Analyzing people's emotions is necessary to comprehend and identify the behavior of such vast amounts of textual data about them. The study focuses on information gathered from Twitter, one of the most widely used social media platforms, by examining both historical and real-time feeds and extracting emotions from them. The necessary English-language Twitter data is transformed into a vector of six emotions, and supervised learning methods like Logistic Regression, Naive Bayes, SVM, Decision Tree, Random Forest, Gradient Boosting, and Multi-Layer Perceptron are utilized to identify the label of one of the basic human emotions. Gradient Boosting and SVM are the top-performing models that reported the highest F1-Score of 0.88 among the other models. Sadness and joy came out to be the best-performing emotions whereas it was comparatively difficult to predict surprise emotion. © 2024 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

AI-Based Posture Correction, Real-Time Exercise Tracking and Feedback using Pose Estimation Technique 3

AI-Based Posture Correction, Real-Time Exercise Tracking and...

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kaushik, M. Kandala, Mahita Vignesh, G.S. Vithyatharshana, N. Palaniswamy, Suja Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331528201

Physical activity is an essential component of physical and mental health-being. Although physical activity offers numerous advantages, maintaining proper posture could be difficult, especially when it comes to complex movements or poses. This task is even challenging for the ones who exercise at home, those who have disabilities, or social anxiety. In order to deal with this issue, this work proposes an AI gym tracker app powered by advanced pose estimation technology through OpenCV and Mediapipe which are two popular frameworks being used in computer vision to access and solve several real-life problems through pose-estimation. This paper describes an AI-powered gym tracker app built with MediaPipe and OpenCV. It uses real-time pose estimation and joint angle calculation to provide feedback on exercise form as well as expanded rep count. This application employs a unique method by using dynamic angle thresholds and joint distance computations to provide precise feedback about user posture. It demonstrates the application's effectiveness in assisting users in maintaining proper form and tracking their workout progress by demonstrating a counter. Future work is to broaden its functionality to embrace more exercises and to further add other forms of decision feedback system, thus exploring the possible incorporation of this application into various fitness apps which would in-turn enhance the overall experience of the users. © 2024 IEEE.

关键词： mHealth

来源：评论

学校读者我要写书评

暂无评论

Improving Disaster Response with U-Net and Attention Mechanism in Earthquake Damage Assessment Using Remote Sensing Data 6th

Improving Disaster Response with U-Net and Attention Mechani...

引用

6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Anvitha, Vemireddy Maheswari, V. Uma Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819734412

The significance of assessing building damage for post-disaster rescue and reconstruction is acknowledged, and a novel approach is introduced to address the challenges associated with detecting and classifying damage levels. Unlike many existing studies that primarily focus on binary classification, the proposed neural network combines localization and classification of damaged buildings simultaneously. The key component of this network comprises a variety of attention U-Nets with different backbones, which leverage attention mechanisms to prioritize relevant features and channels while minimizing the impact of irrelevant ones. Training is conducted using the extensive xBD dataset, dedicated to advancing building damage assessment, and results are assessed based on balanced F1-scores. Notably, the U-Net with an attention mechanism exhibits the best individual performance, achieving an impressive F1-score of 0.752 and overall score of *** method’s effectiveness is further validated on the xView2 dataset, a comprehensive building damage assessment dataset, demonstrating precise damage scale classification and building segmentation simultaneously. This breakthrough promises to significantly enhance post-disaster rescue efforts, offering robust and transferable solutions for improved disaster response. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Disasters

来源：评论

学校读者我要写书评

暂无评论

A Detailed Analysis of Machine Learning Models to Predict Water Potability 15

A Detailed Analysis of Machine Learning Models to Predict Wa...

引用

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

作者： Sreekumar, Vishnu Ihsan, Faraz Reghuram, Surya Sarath, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Water is one of the most indispensable things in our life. However, getting clean drinking water has become more difficult for a lot of people lately. This research paper uses machine learning techniques to tackle the crucial problem of predicting the potability of water. Reliability in water potability assessment is critical, as concerns about water quality and its effects on public health are becoming more widespread worldwide. We present a novel predictive model to assess the potability of water sources by utilizing an extensive dataset that includes various water quality parameters. In order to examine intricate relationships within the dataset, the study uses a cutting-edge machine learning framework that integrates algorithms like Random Forest, KNN, SVM, Decision tree, and Gradient boosting. To extract pertinent information, feature engineering is used, and model performance is rigorously tuned using hyper parameters. To determine the best method for predicting water potability, a comparative analysis of several models is carried out. The proposed model is validated on different datasets, which guarantees its robustness and generalizability in a range of environmental conditions. The outcomes show that the predictions of water potability have a high degree of sensitivity, specificity, and accuracy. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Vehicle Detection in Unmanned Aerial Imagery Through Advance You Only Look Once Architectures 15

Vehicle Detection in Unmanned Aerial Imagery Through Advance...

引用

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

作者： Nishitha, U. Lokesh, V. Kaushik, Tata Singh, Rimjhim Padam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

The detection of vehicles by unmanned aerial vehicles (UAVs) is gaining substantial attention in the field of traffic control. In aerial images, vehicles have a unique perspective and take up less pixels than other objects in the broader data set.. This study uses the YOLOv8 (You Only Look Once version 8) deep learning architecture to propose an enhanced method of vehicle recognition in aerial photos. This work goes beyond the conventional YOLOv8 implementation by introducing new methods for data augmentation to enhance the efficiency and the model's resiliency. To build a diverse and enriched dataset, the suggested methodology makes use of cutting-edge techniques such image cropping, contrast correction, and brightness improvement. Training the YOLOv8 model on this enhanced dataset produces encouraging results;on a different test set with ground truth annotations, a noteworthy achievement of 7 0 % accuracy in vehicle detection was made. This accomplishment highlights the effectiveness of our strategy and shows a notable increase in accuracy over earlier approaches. The study demonstrates the potential of YOLOv8 in tackling the difficulties of aerial vehicle detection and highlights the crucial role that data augmentation plays in improving model performance and resilience. © 2024 IEEE.

关键词： Aerial photography

来源：评论

学校读者我要写书评

暂无评论

SMOTE-Based deep network with adaptive boosted sooty for the detection and classification of type 2 diabetes mellitus

引用

Multimedia Tools and Applications 2024年 1-26页

作者： Immadisetty, Phani Kumar Rajabhushanam, C. Computer Science Engineering Bharath Institute of Science and Technology BIST Selaiyur Tamil Nadu Chennai600073 India School of Computing Bharath Institute of Science and Technology BIST Selaiyur Tamil Nadu Chennai600073 India

Type 2 diabetes (T2D) is a prolonged disease caused by abnormal rise in glucose levels due to poor insulin production in the pancreas. However, the detection and classification of this type of disease is very challenging and requires effective techniques for learning the T2D features. Therefore, this study proposes the use of a novel hybridized deep learning-based technique to automatically detect and categorize T2D by effectively learning disease attributes. First, missing value imputation and a normalization-based pre-processing phase are introduced to improve the quality of the data. The Adaptive Boosted Sooty Tern Optimization (Adap-BSTO) approach is then used to select the best features while minimizing complexity. After that, the Synthetic Minority Oversampling Technique (SMOTE) is used to verify that the database classes are evenly distributed. Finally, the Deep Convolutional Attention-based Bidirectional Recurrent Neural Network (DCA-BiRNN) technique is proposed to detect and classify the presence and absence of T2D disease accurately. The proposed study is instigated via the Python platform, and two publicly available PIMA Indian and HFD databases are utilized in this study. Accuracy, NPV, kappa score, Mathew's correlation coefficient (MCC), false discovery rate (FDR), and time complexity are among the assessment metrics examined and compared to prior research. For the PIMA Indian dataset, the proposed method obtains an overall accuracy of 99.6%, FDR of 0.0038, kappa of 99.24%, and NPV of 99.6%. For the HFD dataset, the proposed method acquires an overall accuracy of 99.5%, FDR of 0.0052, kappa of 99%, and NPV of 99.4%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Village Ranking: Leveraging Cluster Analysis and Machine Learning 5

Enhancing Village Ranking: Leveraging Cluster Analysis and M...

引用

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

作者： Karthikeya, Moturi Madhan, S. Sha, Akhbar Megha, R. Dhanush Krishna, R. Gopakumar, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

The research presented in this paper addresses the critical challenge of ranking Indian villages based on their potential for socioeconomic growth, inspired by the objectives of the Shyam Prasad Mukerji Rurban Mission (SPMRM). Leveraging a novel algorithm known as ClusterRank, an extension of the PageRank algorithm, we endeavor to provide a timely, efficient, and accurate solution for village ranking. The ClusterRank algorithm, an extension of the well-known PageRank algorithm, serves as the cornerstone of our approach. It excels in its ability to efficiently and accurately rank villages, outperforming alternative ranking methods. Notably, our findings reveal the superior convergence speed of the ClusterRank algorithm, affirming its capacity to produce rapid results. To assess the efficacy of ClusterRank, we employ a range of statistical ranking coefficients, including the Spearman, Kendall, and Point Biserial correlation coefficients. Our analysis demonstrates a strong positive correlation, with a Spearman correlation coefficient of 0.89, between the rankings generated by ClusterRank and the ground truth rankings provided by SPMRM. This correlation underscores the effectiveness of ClusterRank in identifying villages with high growth potential and highlights its alignment with the objectives of the SPMRM. This research presents a compelling case for the adoption of the ClusterRank algorithm by SPMRM as a valuable tool in the identification of rural areas poised for rurban development. Such adoption not only promises substantial time and resource savings but also directs the focus of SPMRM towards the implementation of targeted interventions and initiatives to uplift rural communities. Moreover, our supplementary analyses using Kendall and Point Biserial correlation coefficients further validate the robustness of the ClusterRank algorithm. © 2024 The Authors. Published by Elsevier B.V.

关键词： Cluster analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：