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检索条件"任意字段=International Multi-Conference in Computer Science and Computer Engineering"

共 584311 条记录，以下是4321-4330 订阅

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Advanced Insights in Nursing science: Applying ARIMA-CLSTM for Concept Analysis 3

Advanced Insights in Nursing Science: Applying ARIMA-CLSTM f...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Thirumalaikumari, T. Kumar, Vinay Somasundaram, K. Josephson, P. Joel Mavliya, Anil Kumar Anwer Basha, H. Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India B.N. College of Engineering and Technology Department of Computer Science and Engineering Lucknow India Bannari Amman Institute of Technology Department of Information Science and Engineering Tamil Nadu Sathyamangalam India Department of Ece Telangana Hyderabad India Coer College of Nursing and Parmedical Sciences Rookee UK Haridawar India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India

ISBN: (纸本)9798350381931

The phrase "nursing science"extends much beyond the theoretical underpinnings of topics like asepsis and body mechanics. This lecture takes a classical Aristotelian stance in its examination of scientific concepts, natural phenomena, scientific knowledge, and the procedures of inquiry and proof. After that, it will show how the science is useful by applying traditional approaches to nursing. Preprocessing, feature extraction, and model training must proceed without pauses. A part of preparing data is making sure it is normalized and removing stop words. The GLCM is utilized for feature extraction;however, while it provides a large amount of data, it is not optimal for picture feature extraction, image recognition, or classification. It is necessary to extract features before training an ARIMA-CLSTM model. The suggested method outperforms the state-of-the-art algorithms using the current data. A considerable improvement was noted in terms of accuracy, with the findings reaching 97.33%. © 2024 IEEE.

关键词： Nursing

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Exploring Quantum Machine Learning Algorithms for Enhanced Data Classification and Clustering in Complex Systems - Integration with Gradient Boosting and K-means for Improved Performance

Exploring Quantum Machine Learning Algorithms for Enhanced D...

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2024 international conference on Cybernation and Computation, CYBERCOM 2024

作者： Babu, M. Arun, M. Pushparani, S. Muthukrishnan, A. Poongavanam, N. Rajalakshmi Engineering Çollege Department of Information Technology Thandalam Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

This research focuses on the implementation of quantum machine learning with the classical models such as Gradient Boosting and K-means clustering for efficient classification and clustering of most complex datasets in advanced systems. Taking advantage of the large datasets that are handled by the qc, this work examines Quantum Support Vector Machines (QSVM), Quantum Boosting, and Quantum K-means. To compare the efficiency of these hybrid models three datasets - the healthcare, the financial, and the cyber-security datasets - are used. Our findings indicate that quantum optimization algorithms achieve better accuracy in clustering in comparison to the classical counterparts and are computationally faster. This work shows how quantum machine learning can lead to the enhancement of difficult data processing tasks, a major issue in the quantum computing domain, with specific reference to resources and high-dimensional data. The opportunity to integrate quantum and classical models successfully is the best basis for further developments in machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Performance of Known Ratings-Based multi-Criteria Recommender System for Housing Selection

Performance of Known Ratings-Based Multi-Criteria Recommende...

引用

2023 international conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Arif, Yunifa Miftachul Farid Muhtarom, Muhammad Nurhayati, Hani Universitas Islam Negeri Maulana Malik Ibrahim Department of Informatics Engineering Malang Indonesia

ISBN: (纸本)9798350320954

Housing developments are increasingly massive, and the lack of available information makes prospective customers experience difficulties in choosing a housing. These conditions resulted in the need for a recommendation system to assist consumers in choosing a place to live. In this study, we propose using the multi-Criteria Recommender System (MCRS) to produce the most recommended housing selection recommendations in a case study of five housing complexes in Malang Raya. The system generates recommendations based on known user rating of 14 criteria and an overall rating (R0) stored in the database. In the experimental stage, the MCRS system in this study used four different methods: cosine, adjust cosine, Pearson correlation, and spearman rank-order correlation coefficient. The test results show that the recommendation system with each similarity method can produce housing recommendations by displaying the three most relevant housing recommendations to the user. Next, we use a confusion matrix to analyze the accuracy of the recommendations generated by the four similarity methods. The results of the confusion matrix calculation show that the average accuracy value for cosine-based similarity is 63.8%, the adjusted-cosine similarity is 70.4%, the Pearson correlation is 88.7%, and the Spearman rank-order correlation coefficient is 75.57%. © 2023 IEEE.

关键词： Recommender systems

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Indonesian Batik Pattern Recognition Using Siamese Network with Triplet Loss

Indonesian Batik Pattern Recognition Using Siamese Network w...

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2024 international conference on computer engineering, Network and Intelligent multimedia, CENIM 2024

作者： Makmur, Stephanie Anastasia Sunjaya, Fredy Lucky, Henry Meiliana School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350368802

This paper describes Indonesian batik pattern recognition depending on a Siamese Neural Network with a triplet loss function. The fabric art considered as Indonesian, that is batik is complex and comes in a wide variety of designs and hence hard to be staged and categorized. This paper has therefore developed a method that entails the acquisition and preprocessing of batik image dataset, data augmentation to enrich the dataset, the training of the Siamese Network coupled with CNNs (VGG16, DenseNet169, ResNet152) for feature extraction and optimizing the triplet loss function. This would show the efficiency of the Siamese Network in achieving high accuracy at a limited dataset over the traditional methods in pattern recognition. From several experiments carried out, the highest accuracy was obtained by the base model DenseNet169 with an accuracy level of 84% as a Siamese Network with trainable parameters set to false, where from this result there was an increase of 13% from the previous 71% when implemented on CNN with the same parameters. By carrying out this research, it is hoped that in the future more data can be collected from several regions in Indonesia regarding the diversity of batik patterns and motifs. © 2024 IEEE.

关键词： Neural networks

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Classification of Tuberculosis Based on Thorax X-ray Images Using multi-Scale Convolutional Neural Network 7

Classification of Tuberculosis Based on Thorax X-ray Images ...

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7th international conference of computer and Informatics engineering, IC2IE 2024

作者： Sari, Indah Purnama Al-Khowarizmi Ramadhani, Fanny Satria, Andy Universitas Muhammadiyah Sumatera Utara Department of Information Technology Medan Indonesia Universitas Negeri Medan Department of Computer Science Medan Indonesia Universitas Dharmawangsa Department of Information Technology Medan Indonesia

ISBN: (纸本)9798331505530

Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis, a pathogen that most successfully infects the lungs. The most frequently used technique for diagnosing TB disease is through x-ray examination of the thorax (lungs). The X-ray results can be seen image/visually by the doctor to see whether there are characteristics and patterns of TB disease in the patient. This research focuses on Artificial Intelligence (AI) to help doctors and provide efficient alternative solutions in diagnosing patients, whether the patient is suffering from TB or not more quickly. This research was designed using a multi-Scale Convolutional Neural Network (CNN) to classify tuberculosis based on chest x-ray images. The data used is an X-ray image of the thorax which is used as input for the image processing process. The dataset collected amounted to 1400 data consisting of 2 classes, namely normal lungs and lungs of tuberculosis sufferers. The CNN model consists of 3 convolution layers measuring 3x3, 3 pooling layers (Maxpool) measuring 2x2 and 1 fully-connected layer that uses softmax activation. The filters used in each convolution layer are 128. This research uses the Adam Optimizer algorithm. The dataset is divided into 1120 data in scenario 1 and 978 data in scenario 2 for training and 280 data in scenario 1 and 422 data in scenario 2 for testing. In the training process, the epoch value of 20 was used to obtain an accuracy value of 100% in all scenarios. In the testing stage, an accuracy value of 99.29% was obtained in scenario 1 and 97.67% in scenario 2. © 2024 IEEE.

关键词： Convolutional neural networks

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A Novel Technique for Detecting Concealed Malicious URLs within the Tor Network 8

A Novel Technique for Detecting Concealed Malicious URLs wit...

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8th international conference on Inventive Systems and Control, ICISC 202

作者： Kanimozhi, P. Felcia Jerlin, I. Kumar, T. Ananth Ananth, Christo Mathusoothanan, K. Preethi, E. Ifet College of Engineering Computer Science and Engineering Villupuram India Grace College of Engineering Department of Computer Science and Engineering Tamilnadu Thoothukudi India Samarkand State University Uzbekistan Holycross Engineering College Department of Computer Science and Engineering Tamilnadu Thoothukudi India

ISBN: (纸本)9798350386578

Onion URLs lead to the dark web, a mysterious and secretive internet space with many websites. This paper proposes a novel content-based classification of. onion URLs. Given the concerns surrounding the dark web's anonymity, the project aims to classify these hidden services as either 'benign' or 'potentially harmful. ' The method involves scraping. onion websites for text and metadata, followed by natural language processing and machine learning analysis of word frequencies, sentiment, and topic modeling. A labeled dataset with strict criteria is utilized to train and fine-tune classification models. This project assists dark web users in identifying legitimate and risky services and contributes to discussions on content analysis and classification in anonymized online environments. With onion URLs mostly hidden and unindexed, the research advances privacy and security in this unique digital space. By automating the classification of. onion URLs, a valuable tool is provided, contributing to content analysis, online anonymity, and internet safety discussions. The problem statement seeks a Proof of Concept (PoC) to list active TOR-hosted hidden server URLs (.onion). © 2024 IEEE.

关键词： Anonymity

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Not What I was Trained for - Out-of-Distribution-Tests for Interactive AIs 15th

Not What I was Trained for - Out-of-Distribution-Tests for I...

引用

15th ACMSIGCHI conference on engineering Interactive Computing Systems (EICS)

作者： Severing, Benedikt Werger, Ole Hesenius, Marc Univ Duisburg Essen Inst Software Engn Schutzenbahn 70 D-45127 Essen Germany

ISBN: (纸本)9783031592348;9783031592355

Recent advances in AI demonstrate its capacities to not only automate more and more everyday tasks, but also make direct Human-AI-Interaction possible. When using AI models in production, we might face situations where the AI is confronted with input data that is very different from the data it was trained on. Such situations are called Out-of-Distribution situations and can result in misleading AI inferences. We argue that identification, handling, and prevention of Out-of-Distribution situations is key for creating production-ready interactive AI components. In this paper, we test the robustness of state-of-the-art AI/ML approaches in Out-of-Distribution situations and propose a research agenda to gather a deeper understanding of how to identify, handle, and prevent such situations in interactive applications.

关键词： Human-computer Interaction Software engineering Artificial Intelligence Machine Learning Testing Test Automation Quality Assurance Out-of-Distribution Concept Shift

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The LDRML-index: a multi-dimensional learned index based on local dimensionality reduction 4

The LDRML-index: a multi-dimensional learned index based on ...

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4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Li, Jianrong Xu, Haixia Zhou, Xia Qiu, Zhida Cheng, Sikai College of Computer and Information Engineering Hubei Normal University Huangshi China

ISBN: (纸本)9781510686106

In a complex data environment, many databases need to have efficient access to multi-dimensional datasets. Therefore, it is important to construct a multi-dimensional index that can effectively support the retrieval of highly multi-dimensional datasets. We propose a new multi-dimensional learned index called LDRML-index, which consists of a dimensionality reduction module and a learned index module. The first module contains a local dimension reduction (LDR) component and a Z-address calculation component that processes the data to make it applicable to later module. The second module includes a learned index component and a dynamic learned index framework, enabling the learned index to better adapt to the data distribution and improve query efficiency. The experimental results show that LDRML-index can effectively reduce space consumption and improve query performance. © 2024 SPIE.

关键词： Dimensionality reduction

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Emotion Recognition from Speech using Telugu Language and Deep Learning Models 1

Emotion Recognition from Speech using Telugu Language and De...

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1st IEEE international conference on Communications and computer science, InCCCS 2024

作者： Aditya, S. Satwik, Vaka R. Krishna, Vamsi V. Vimal, C. Palaniswamy, Suja Pati, Peeta Basa Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350358858

This research involves the use of deep learning techniques to detect emotions, in Telugu speech signals, a research domain that has received little attention. Amrita speech emotion dataset-1 (ASED-1) consisting of 550 signals representing five categories. Surprise, anger, happiness, neutrality, and sadness. We explore how well various deep learning models can recognize emotions from speech. Our approach involves training models such as Convolutional Neural Networks (CNNs) Long Short-Term Memory (LSTM) networks and multi-Layer Perceptron (MLPs) on a portion of the data and evaluating their performance on a test set. The MLP model achieved an accuracy rate of 84% although this was constrained by the size of the dataset. The CNN model showed results with decreasing trends in both training and validation loss indicating learning and generalization abilities. Similarly, the AlexNet architecture demonstrated a decline in both training and validation loss suggesting a fit without overfitting. Additionally, we included a machine learning classifier at the layer. Observed different accuracies across various models. The RNN model achieved an encouraging accuracy rate on the test data showing potential for emotion classification tasks but also room for improvement, through complex architectures or larger datasets. This research paper adds to the knowledge, in the field of emotion recognition, in Telugu speech by emphasizing the difficulties and possibilities of using deep learning techniques in this domain. © 2024 IEEE.

关键词： Emotion Recognition

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COVID-19 Detection from Chest X-Ray Images Using CNN Models and Deep Learning 6th

COVID-19 Detection from Chest X-Ray Images Using CNN Models ...

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6th international conference on Intelligent Computing and Optimization, ICO 2023

作者： Moin, Nafisha Binte Sultana, Shamima Al Munem, Abdullah Imam, Omar Tawhid Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1341 Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram4349 Bangladesh

ISBN: (纸本)9783031501579

The dangerous disease and ailment known as COVID-19 has caused a global pandemic and immeasurable damage to people all around the globe. Chest X-Rays are being used to identify COVID-19 in current times due to their low cost and efficiency. In this paper, we developed Convolutional Neural Network models to detect COVID-19 from Chest X-Ray images. CNN models from the Keras library such as VGG16, VGG19, Xception, ResNet101, ResNet152, Inception, InceptionResNet, MobileNetV2, DenseNet201, NASNetLarge, and EfficientNetB3 have been used to perform experimentations. The CNN models used ImageNet as its pre-trained weights for transfer learning. Additionally, a multi-layered self-designed model has been implemented as well to see the performance. A comparative analysis has been completed in order to find the best-performing CNN model for COVID-19 detection in Chest X-Ray images. From the experiments, we found that the proposed CNN gave the best results. Additionally, it has been observed that MobileNetV2, Inception, ResNet101, and VGG16 give the highest accuracy over 99%, while the lowest accuracy is found by EfficientNetB3 at only around 50%. The self-designed multi-layered model gives a training accuracy of 97.22% and a validation accuracy of 96.42%. A significant increase in accuracy and excellent performance has been seen from the CNN models and the proposed framework. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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