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Query based biomedical document retrieval for clinical information access with the semantic similarity

Multimedia Tools and Applications 2024年第18期83卷 55305-55317页

作者： Gupta, Supriya Sharaff, Aakanksha Nagwani, Naresh Kumar Nagpur India Department of Computer Science and Engineering National Institute of Technology Raipur Raipur India

The amount of exploration done for the available medical literature is quite less and at the same time, there is less awareness of information mining in this specific field. The accessibility of immense quantity of biomedical literature has opened up additional opportunities to apply Information Retrieval and NLP methods for mining existing archives. Therefore, a query based retrieval application (QBR) based on hybrid similarity of string and semantic similarity can help medical professionals in their ongoing research. There are multiple benefits of utilizing various NLP applications, for example, information retrieval engine, and clinical diagnosis frameworks for decision support in medical field. These applications depend on the capacity to gauge Hybrid textual similarity (HTS) and N-Gram similarity. Hybrid similarity is the combination of weighting function and word embedding models providing similarity scores with optimum results. In this work, the main focus is on building of a new biomedical document retrieval model which can pull relevant literature for clinical decision support system based on the specific query. There is also an attempt to compare the statistical and NLP based approaches of query based biomedical document retrieval with the baseline systems. Analysis of the proposed method inclusive of semantic word embeddings shows promising results for both of the suggested similarities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Information retrieval

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Classification of the respiratory infection vaccination tweets using fuzzy logic and deep learning

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Multimedia Tools and Applications 2024年 1-25页

作者： Bhimavarapu, Usharani Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Respiratory diseases are contagious and immensely affect all aspects of people and spread through the air or direct contact. COVID-19 is one of the most dangerous respiratory infections, and it has exaggerated many countries. The battle to curb its spread was waged in every country, even with few or no infections. Vaccination is one of the most vital to fight against COVID-19, and it started in India in January 2021. Every country's government has created awareness programs about COVID-19 and its updates through messages and videos on social media to reduce misconceptions and panic that followed due to the outright misinformation about COVID-19 and its impacts. This study classifies the medical vaccination tweets related to COVID-19;we extracted the tweets regarding vaccination in India from 1 January 2021 to 31 December 2021. We classified the tweets into four categories: pro-vaccine, anti-vaccine, hesitancy and cognizant. We performed the text summarization using fuzzy logic and classification using the stacked ANN and compared the results using the different word embedding models. During the vaccination period, we identified that allergy is a general topic discussed by individuals in social media through quadratic discriminant analysis. The proposed model surpassed the results of the baseline models and achieved an accuracy of 96.7%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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Deep Learning-Based Trojan Detection in Network Traffic: A CNN-BiLSTM Approach 100

Deep Learning-Based Trojan Detection in Network Traffic: A C...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Hoque, Md Mozammal Alam, Khorshed Monir, Md Fahad Tarek Habib, Md University of Houston Department of Electrical and Computer Engineering United States Department of Computer Science & Engineering Bangladesh

ISBN: (纸本)9798331517786

Trojan detection from network traffic data is crucial for safeguarding networks against covert infiltration and potential data breaches. Deep learning (DL) techniques can play a pivotal role in detecting trojans from network traffic data by learning complex patterns and anomalies indicative of malicious behavior, thus enhancing detection accuracy and efficiency. This technique is pivotal in the context of data security, specifically in next-generation network architecture for its ability to effectively detect complex and evolving cyber threats, such as Trojan horse traffic, ensuring robust network protection. The research gap in detecting network Trojans lies in developing techniques capable of efficiently identifying stealthy and polymorphic variants while minimizing false positives. This study focuses on trojan detection from network traffic data, crucial for network security. Deep learning, specifically a combination of Convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks, is proposed to enhance accuracy and efficiency by learning complex patterns indicative of malicious behavior. Our proposed approach extracts features using CNNs to capture spatial dependencies and utilizes BiLSTM networks to process sequential dependencies, achieving 99% accuracy in trojan detection on real-world datasets. We validated this performance by comparing with previous approaches to ensure robustness in a common dataset. Making our work available open-source on, can enhance its accessibility and promote future research opportunities in Trojan Network Detection. After this work, we will work on detecting trojan networks from our institute's network data and will release the trojan network dataset, as there is a gap in open-source trojan network datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing photo-to-anime translation with prestyled paired datasets

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Multimedia Tools and Applications 2024年第41期83卷 89393-89414页

作者： Chang, Chuan-Wang Dharmawan, Pratamagusta Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan

Animation is a widespread artistic expression that holds a special place in people's hearts. Traditionally, animation creation has relied heavily on manual techniques, demanding skilled drawing abilities and a significant amount of time. For instance, many Japanese anime films draw inspiration from real-world settings, requiring access to relevant references and artists capable of translating them into anime visuals. Consequently, the development of technology that automatically converts images into anime holds great significance. Numerous methods for style transfer have been developed using unsupervised learning and have achieved impressive results. However, unsupervised learning methods suffer when the image contains multiple styles within itself because they learn the style of the image globally. To solve this problem, we propose splitting these styles within the image into multiple classes: sky, buildings, greenery, water, and other objects. Then, we style these separated classes using existing image-to-image translation models. Finally, we train a pix2pix model to learn image style transfer in a paired manner. The experimental results show that the images are effectively styled into the resulting anime-styled image domain with comparable results to existing unsupervised learning GAN-based methods. The proposed method can effectively transfer the style from real-world photos into the anime-styled image domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Unsupervised learning

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A Service Level Agreement Aware Online Algorithm for Virtual Machine Migration

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computers, Materials & Continua 2023年第1期74卷 279-291页

作者： Iftikhar Ahmad Ambreen Shahnaz Muhammad Asfand-e-Yar Wajeeha Khalil Yasmin Bano Department of Computer Science and Information Technology University of Engineering and TechnologyPeshawar25120Pakistan Department of Computer Science Shaheed Benazir Bhutto Women UniversityPeshawar25000Pakistan Center of Excellence in Artificial Intelligence Department of Computer ScienceBahria UniversityIslamabadPakistan

The demand for cloud computing has increased manifold in the recent *** specifically,on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing *** cloud service provider fulfills different user requirements using virtualization-where a single physical machine can host multiple *** virtualmachine potentially represents a different user environment such as operating system,programming environment,and ***,these cloud services use a large amount of electrical energy and produce greenhouse *** reduce the electricity cost and greenhouse gases,energy efficient algorithms must be *** specific area where energy efficient algorithms are required is virtual machine *** virtualmachine consolidation,the objective is to utilize the minimumpossible number of hosts to accommodate the required virtual machines,keeping in mind the service level agreement *** research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized *** online algorithm is analyzed using a competitive analysis *** addition,an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark *** proposed online algorithm consumed 25%less energy and performed 43%fewer migrations than the benchmark algorithms.

关键词： Cloud computing green computing online algorithms virtual machine migration

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An Extended SDN Architecture for Video-on-Demand Caching

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Mobile Networks and Applications 2024年第6期29卷 2004-2021页

作者： Chiang, Wei-Kuo Li, Tsung-Ying Department of Computer Science & Information Engineering National Chung Cheng University Chiayi621 Taiwan

Owing to the variety of ways to view the Internet and the changes in user behavior on the Internet, network traffic has been explosively growing in recent years. Users can watch high-quality videos on the Internet;it is a critical issue to reduce network traffic and increase the user's quality of experience (QoE). Therefore, there have been in-network caching services that cache the content that had been fetched by the user in a proxy server. Meanwhile, the software-defined network (SDN) has been developed to implement the network function through the virtualization function. Programmers can implement customized network functions using the SDN architecture. In this paper, we proposed an Extended SDN Cache service architecture (ESC). The ESC decomposes the function of inspecting incoming traffic, making cache decisions, and caching content to three network entities. This design can reduce the load of a single network entity. To reduce the load of the SDN controller, we utilize an extended OpenFlow switch named the DPI (deep packet inspection) switch, which can inspect the incoming traffic. The ESC designed a mechanism that can cache the different parts of a video in distinct cache nodes. The distributed content storage mechanism can increase the cache capability and the system's flexibility. We use the M/M/1 queuing model to analyze the average queuing delay time and compare the ESC queuing delay time with the C-flow and the OpenCache. The numerical analysis results show that the ESC queuing delay is shorter than the other two. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Queueing theory

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Enhancing Deep Learning Models for Sentiment Analysis Integrating Texts and Emojis: A Comprehensive Survey 10

Enhancing Deep Learning Models for Sentiment Analysis Integr...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Suresh Kumar, S. Gayathri, D. Shugavaneshwar, R. Department of Computer Science and Engineering Tamilnadu Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350353068

Sentiment analysis is used to get meaningful insights from data that is being retrieved from various resources or social media platforms. Sentiment analysis plays an important role in making crucial decisions that can improve the existing status significantly. This survey addresses and compares various machine learning models and deep learning models that have been recently employed for efficient sentiment analysis. In general, people use both texts and emojis to express their emotions. Considering the above fact, the proposed system addresses reviews that integrate both texts and emojis. The convolution neural network (CNN) is trained with a dataset that contains a cluster of texts and emojis. However, the texts and emojis are handled separately by processing them independently for efficient results. © 2024 IEEE.

关键词： Sentiment analysis

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Learning Group Actions on Latent Representations 38

Learning Group Actions on Latent Representations

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jin, Yinzhu Shrivastava, Aman Fletcher, P. Thomas Department of Computer Science University of Virginia United States Department of Electrical and Computer Engineering Department of Computer Science University of Virginia United States

In this work, we introduce a new approach to model group actions in autoencoders. Diverging from prior research in this domain, we propose to learn the group actions on the latent space rather than strictly on the data space. This adaptation enhances the versatility of our model, enabling it to learn a broader range of scenarios prevalent in the real world, where groups can act on latent factors. Our method allows a wide flexibility in the encoder and decoder architectures and does not require group-specific layers. In addition, we show that our model theoretically serves as a superset of methods that learn group actions on the data space. We test our approach on five image datasets with diverse groups acting on them and demonstrate superior performance to recently proposed methods for modeling group actions. © 2024 Neural information processing systems foundation. All rights reserved.

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Training Speech Recognition Model with Speech Synthesis and Text Discriminator

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Journal of Information science and engineering 2024年第2期40卷 359-373页

作者： Lin, Hou-An Chen, Chia-Ping Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung804 Taiwan

In this paper, we build neural-network model-based automatic speech recognition (ASR) systems incrementally for performance improvement. First, we add an adversarial text discriminator module to train the speech recognition model to correct typos in recognition results. Experiments show that the character error rate (CER) and word error rate (WER) of the ASR system achieved 12.3% and 31.4%. Second, we insert a pre-trained speech synthesis (text-to-speech, TTS) module to the ASR model. When we exploit a pre-trained TTS in ASR training, the CER and WER are reduced from 12.6% and 31.7% to 10.8% and 24.4%, demonstrating that pre-trained TTS can improve ASR. Finally, we include both pre-trained TTS and text discriminator in ASR training. The performance of this ASR system is further improved, achieving the CER and WER of 9.9% and 22.7% respectively. On Formosa Speech Recognition Challenge task using Taibun Hàn-jı¯ transcription, the proposed method also achieves better CER than a system based on hybrid DNN-HMM chain model. © 2024 Institute of Information science. All rights reserved.

关键词： Speech recognition

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An Ensemble Machine Learning-Based Approach for Detecting Malicious Websites Using URL Features 3rd

An Ensemble Machine Learning-Based Approach for Detecting Ma...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Uddin, Khandaker Mohammad Mohi Islam, Md. Ashraful Hasan, Md. Nahid Ahmad, Kawsar Haque, Mir Aminul Department of Computer Science andEngineering Southeast University Dhaka1208 Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka1205 Bangladesh Department of Computer Science and Engineering Jahangirnagar Univerisity Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

The Internet has transformed into a hub for a wide array of illegal activities, ranging from annoying spam ads to financial scams, all thanks to advancements in modern technology. With the constant enhancements in network technology, data traffic on the Internet is ballooning in both volume and scope. Consequently, the complexity and prevalence of cyber threats and attacks are on the rise, with websites becoming the prime targets for hackers. These intruders, or hackers, embed harmful content into web pages with the intent of carrying out nefarious activities. They employ various tactics, such as hacking into computers or trying to harvest information through compromised or malicious websites. Some of these tactics involve injecting malicious software into web links. Therefore, it has become more critical than ever to identify and block such websites before the average user can stumble upon them. In this paper, we propose a method that utilizes machine learning techniques to detect fraudulent websites. We evaluate the prediction accuracy of different machine learning classification methods in this study. We trained our model using various algorithms, including Naive Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (K-NN), Decision Trees, Random Forests, and AdaBoost. Ultimately, we employed an ensemble approach and achieved an outstanding accuracy rate of 99.98%, surpassing other methods in effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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