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检索条件"机构=Computer Science and Computer Engineering"

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Dynamic L-Budget Clustering of Curves 19

Dynamic L-Budget Clustering of Curves

引用

19th Scandinavian Symposium on Algorithm Theory, SWAT 2024

作者： Buchin, Kevin Buchin, Maike Gudmundsson, Joachim Plätz, Lukas Thiel, Lea Wong, Sampson Faculty of Computer Science TU Dortmund University Germany Faculty of Computer Science Ruhr University Bochum Germany Faculty of Engineering The University of Sydney Australia Department of Computer Science University of Copenhagen Denmark

ISBN: (纸本)9783959773188

A key goal of clustering is data reduction. In center-based clustering of complex objects therefore not only the number of clusters but also the complexity of the centers plays a crucial role. We propose LBudget Clustering as unifying perspective on this task, optimizing the clustering under the constraint that the summed complexity of all centers is at most L. We present algorithms for clustering planar curves under the Fréchet distance, but note that our algorithms more generally apply to objects in metric spaces if a notion of simplification of objects is applicable. A scenario in which data reduction is of particular importance is when the space is limited. Our main result is an efficient (8 + Ε)-approximation algorithm with a (1 + Ε)-resource augmentation that maintains an L-budget clustering under insertion of curves using only O(LΕ−1) space and O∗(L3 log L + L2 log(r∗/r0)) time where O∗ hides factors of Ε−1 © Kevin Buchin, Maike Buchin, Joachim Gudmundsson, Lukas Plätz, Lea Thiel, and Sampson Wong;licensed under Creative Commons License CC-BY 4.0.

关键词： Approximation algorithms

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Microstructure Estimation Using Synergistic Dual-path Hybrid Network

Microstructure Estimation Using Synergistic Dual-path Hybrid...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jiquan Cao, Jian Yang, Junqing Wang, Siqi Chen, Geng Heilongjiang University Department of Computer Science and Technology Harbin China Northwestern Polytechnical University School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350386226

White matter microstructure plays a pivotal role in the diagnosis and study of brain disorders. Deep learning-based estimation of white matter microstructural indices from diffusion MRI (dMRI) data has gained increasing research attention in recent years. To conduct effective learning in the heterogeneous space (i.e., x-space and q-space) of dMRI data, hybrid neural networks were proposed and have shown great potential. To this end, we propose a new hybrid neural network, called synergistic dual-path hybrid convolutional neural network (SDH-Net), for more effective microstructure estimation. In our SDH-Net, we propose a dual-path architecture that takes bidirectional asymmetric learning in x-space and q-space to enhance feature representation in heterogeneous domains. Firstly, 3D patches are extracted from each diffusion gradient as vertices, and then a graph is constructed based on the correlation between diffusion angles. In the x-q learning branch, an effective representation is embedded in x-space to enhance learning in q-space, while vice versa in the q-x learning branch. Extensive experiments on data from the human connectome project demonstrate that our SDH-Net outperforms the existing state-of-the-art models. © 2024 IEEE.

关键词： Convolutional neural networks

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Semantic Segmentation with Attention Dense U-Net for Lung Extraction from X-ray Images 6

Semantic Segmentation with Attention Dense U-Net for Lung Ex...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Mahmud Auvy, Akib Al Zannah, Rafiatul Mahbub-E-Elahi Sharif, Shezhan Mahmud, Washik Al Noor, Jannatun Research Group Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Deep learning and digital image processing are crucial in medical imaging research. Lung segmentation is particularly challenging, demanding accurate differentiation of complex structures, sophisticated algorithms, and deep learning models for reliable results. In our research, We used the Shen-zhen chest X-ray dataset, comprising 566 frontal chest X-rays focused on pulmonary tuberculosis. This dataset, compiled by the Guangdong Medical College and Shenzhen No. 3 People's Hospital, was released by the United States National Library of Medicine. Images, captured using a Philips DR Digital Diagnostic system, were resized from $3000 \times 3000$ pixels to $512 \times 512$ pixels for computer-aided diagnosis research. We initially used the Attention Dense U-Net to extract lungs from chest X-rays, leveraging attention mechanisms to focus on relevant features and dense blocks to improve feature reuse. Our goal is to assess its performance for chest X-ray segmentation, comparing it with other deep learning models due to limited research in this area. So, the model is compared with four other variants of U-Net architectures to segment lung pixels from an X-ray image. After implementing the approach using a benchmark dataset and comparing it to other existing architectures, we present that Attention Dense U-Net gives the best accuracy for all given parameters, with a result of accuracy: 97.48%, Dice coefficient: 94.87%, and IoU: 93.87%. © 2024 IEEE.

关键词： Medical imaging

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Advanced Threat Protection for File Storage 4

Advanced Threat Protection for File Storage

引用

4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Neelakantan, P. Malige, Gangappa Vnr Vjiet Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350387063

Cloud storage is widely used in various industries, but concerns about data security persist. Traditional techniques like cryptography and steganography have limitations and cannot provide sufficient security on their own. To address this issue, we propose a unique security system that combines multiple cryptographic algorithms and steganography. By using fast and secure symmetric key algorithms, such as AES and DES, along with the benefits of the asymmetric key algorithm RSA, we develop an efficient method to encrypt and decrypt data. This approach takes advantage of the strengths of each algorithm to create a strong encryption system that protects data from unauthorized access. Furthermore, incorporating splitting and merging techniques adds an extra layer of security. The LSB steganography technique is used to securely store essential key information. Implementing this comprehensive approach in the cloud environment not only enhances server security but also builds user confidence, as encrypted data is stored on a single cloud server. Overall, this sophisticated security mechanism offers enhanced data protection, making cloud storage a more reliable and secure option for various applications in different industries. © 2024 IEEE.

关键词： Cloud security

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IoT and Cloud Synergy in Healthcare: A Unified Approach for Remote Monitoring of Vital Health Metric

IoT and Cloud Synergy in Healthcare: A Unified Approach for ...

引用

2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Nailwal, Sagar Raza, Arbaz Chauhan, Kunal Singh, Gurpreet Kaur, Amanpeet Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350381887

The goal of this study is to revolutionise healthcare monitoring systems by introducing a novel method that combines cloud computing and the Internet of Things. The model prioritises the use of an advanced array of sensors to collect and analyse vital health indicators in real-time. The temperature sensor, infrared sensor, sound, pulse oximeter, and CO2 level monitor are among the important sensors (MQ-145). Sophisticated machine learning and artificial intelligence methods can detect and anticipate major health issues like asthma and cardiovascular diseases early on in the process of gathering data, which is then effortlessly transmitted to a cloud platform. In addition to guaranteeing patient comfort and delivering reliable health data, the system sets itself apart by providing continuous, non-invasive monitoring. Cloud technology integration improves data access and storage, giving medical practitioners immediate analytics to help them make better decisions. By facilitating quick medical reactions and lowering the dangers related to delayed diagnosis, this approach promises to greatly improve patient care. All things considered, our system offers a scalable, efficient, and proactive approach to health management in the digital age, marking a significant advancement in remote healthcare monitoring. © 2024 IEEE.

关键词： Electronic health record

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Network Summarization Through the Lens of Latent Semantic Analysis and Fuzzy Techniques

Network Summarization Through the Lens of Latent Semantic An...

引用

International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Vanathi, M. Lakshmanan, L. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9783031619281

Extracting text summarization involves the retention of only the most important sentences in a document. Finding the appropriate ranking order of the phrases in the document according to their relevance is a vital step in extractive text summarization. Singular value decomposition or SVD algorithm based on latent semantic analysis focuses on recognizing the sections in the document which are related in terms of their semantic nature. Fuzzy algorithms involve reasoning of the priority order of the sentences using fuzzy logic unlike the use of discrete values. While significant work has been done for extractive text summarization in English and other foreign languages, there is ample scope for improving the performance of systems when dealing with Marathi text. An analysis of the characteristics of both these techniques is conducted to compare their benefits and shortcomings. The performance of both the algorithms is evaluated on a document dataset using standard performance metrics including the ROUGE metric. Both of these approaches are objectively compared in order to determine their applicability, particularly when dealing with non-English text in general or Marathi in particular. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantics

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AI-Driven Solutions with CNN and IoT for Enhancing Visual Impairments 5

AI-Driven Solutions with CNN and IoT for Enhancing Visual Im...

引用

5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Prasad, M. Lakshmi Varshitha, Veeramalli Goud, A. Praneeth Bindu, R. Srinivasulu, Ch. Rao, S. Janardhana Institute of Aeronautical Engineering Dept. of Computer Science and Engineering Dundigal Hyderabad India

ISBN: (纸本)9798331522100

Visual impairment is a problem that affects millions of people worldwide, preventing them from walking independently and performing daily tasks. Some of the challenges that blind people face include crossing the street, reading, driving, and socializing. Despite the scientific community's continuous efforts to treat the eye, these solutions have not yet been found. Traditional aids such as canes are useful but limited. Get in the groove. The main focus of the project is to integrate the YOLOv5 object detection model with a convolutional neural network (CNN). Known for its fast and accurate target detection, YOLOv5, together with CNN, improves the physical ability to detect and classify targets in difficult areas with high accuracy. The eye uses the camera module to continuously monitor the environment and identify various objects in real time;the detected data is converted into speech by text-to-speech (TTS) converters such as Google Text- to-Speech (gTTS) and pyttsx3 This description allows users to get an instant description of their surroundings, unlike helping the blind, it improves their ability to navigate independently rather than just providing references, but there are also good ways to work with better and more reliable assistive technologies. © 2025 IEEE.

关键词： Convolutional neural networks

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Skin Cancer Detection: A Hybrid Approach Combining CNNs and Explainable AI 2

Skin Cancer Detection: A Hybrid Approach Combining CNNs and ...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kumar, M. Kiran Vaishnavi, J. Anjibabu, T. Jayasri, V. Lakireddy Bali Reddy College of Engineering Computer Science Engineering Department Mylavaram India

ISBN: (纸本)9798331505745

One of the most common and potentially deadly types of cancer is skin cancer, for which prompt and precise detection is essential to successful treatment and better patient outcomes. Conventional diagnostic techniques mostly rely on dermatologists' manual examinations, which can be laborious, subjective, and prone to mistakes, particularly in environments with limited resources. Furthermore, because of their similar visual features, the variety of skin cancer types from vascular lesions to melanoma and actinic keratosis presents further difficulties. In order to overcome these constraints, this study suggests an advanced deep learning-based method for automated skin cancer detection that makes use of Explainable Artificial Intelligence (XAI) and Convolutional Neural Networks (CNN). We utilized the Synthetic Minority Oversampling Technique (SMOTE) to rectify class imbalances and guarantee a well-balanced dataset after performing significant data augmentation strategies to increase data diversity and improve model robustness. This improved dataset was used to train a CNN model using convolutional operations, which demonstrated its capacity to generalize complex patterns in the data with an amazing accuracy of 95%. The DenseNet121 model then achieved an accuracy of 78%. Furthermore, the model's approach to decision-making was made interpretable through the use of XAI techniques like Grad-CAM, which improved understanding of its predictions and promoted confidence among medical experts. This method is a viable way to help dermatologists with early diagnosis and promote AI-driven healthcare solutions because it not only showed great accuracy in identifying skin cancer b © 2025 IEEE.

关键词： Convolutional neural networks

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Improved Fake News Detection by Combining Sentence Transformers, Variational Autoencoders, and Topic Modelling: the VAE-Topic Model Fusion Method 5

Improved Fake News Detection by Combining Sentence Transform...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Tufchi, Shivani Yadav, Ashima Ahmed, Tanveer Bennett University Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

In this study, we present a pioneering method aimed at discerning the veracity of news content by leveraging state- of-the-art embedding techniques. Recognizing the importance of capturing semantic nuances, we employ sentence transformers to generate embeddings that encapsulate the essence of the news content. This process results in high-dimensional data, which, although rich in information, can be computationally intensive for subsequent analysis. To address this, we introduce a Variational Autoencoder (VAE) to achieve an effective dimensionality reduction while preserving critical information. However, given the intricate and hierarchical nature of news content, we integrate a topic modeling approach known as Pachinko Allocation Model (PAM) into our framework. PAM offers a more refined data representation by capturing the latent hierarchical relationships between various news topics, adding an extra layer of semantic understanding to our method. The enhanced data representations from the VAE and PAM processes are con- catenated, creating a comprehensive feature set. This combined feature set is then used to train multiple classifiers to segregate real news from fake counterparts. Through this comprehensive numerical investigation, we have achieved an accuracy of 96.80% and an F1 score of 97% using the transformer-based sentence paraphrase- multilingual architecture. Our study's findings are not only significant in terms of accuracy but also underscore the potential of combining multiple advanced techniques to tackle the fake news challenge. © 2024 IEEE.

关键词： Autoencoders Fake news Machine Learning Models Topic Modelling Transformers Embeddings

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"Online Assignment Plagiarism Checker with Ocr

"Online Assignment Plagiarism Checker with Ocr

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

作者： Chaudhari, Sachin Shende, Ayush R. Zade, Tejas G. Bhakare, Umesh N. Dhutraj, Pranil S. Ghugal, Mrunal P. Jhulelal Institute of Technology Computer Science & Engineering Nagpur India

ISBN: (纸本)9798350348460

The chapter addresses plagiarism, highlighting its detrimental effects on education and the state of the economy in the country. It draws attention to several types of plagiarism, such as paraphrased writing, keyword searches, and verbatim. One way for detecting plagiarism is to use WordNet for sentence transformation and overlap identification. The paragraph also discusses a plagiarism detector program that lets users upload assignment files, establish login passwords, and compare content with the source web pages. The subject of optical character recognition (OCR) in computer vision is covered in the second section. It talks about how accurately OCR reads printed text and how some scanning faults can make some fields unreadable. The passage illustrates the use of text recognition by major international tech companies such as Apple. © 2024 IEEE.

关键词： and Google Lens and online programs Apple assignment errors Google iOS 15 LBP Live Text login Optical Character Recognition precision registration SVM WordNet

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