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Big data analytics in healthcare: Applications, examples and challenges 4th

Big data analytics in healthcare: Applications, examples and...

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4th international conference on Computational Methods in Science and technology, ICCMST 2024

作者： Kaur, A. Goyal, S. Batra, N. Gupta, N. Ambala Haryana Mullana India Computer Science and Engineering Department CGC-College of Engineering Landran Mohali India

ISBN: (纸本)9781032911571

The healthcare sector is evolving at an exponential rate due to the speed of technological disruption and digital innovation. The size of healthcare data is increasing exponentially, making it more challenging to locate any kind of relevant information. Big data is currently changing how data is delivered traditionally by employing big data analytics techniques to produce insightful knowledge. In the healthcare sector, big data analytics offers numerous advantages, including the ability to identify serious illnesses early on and provide more effective care to the appropriate patient at the appropriate moment, thereby enhancing the standard of care. The quick analysis and integration of massive amounts of different types of data generated by various clinical settings, hospitals, other social media platforms, and medical data lakes is made possible by big data analytics tools. Many challenges must be addressed by the current health data analytics systems, which offer technical means for gathering, combining, processing, analysing, visualising, and interpreting data. This article delves into the intriguing topic of healthcare big data analytics. Due to a dearth of comprehensive study, this topic has not gotten enough attention in the past. This article covers the unique characteristics of big data, big data analysis tools, and the steps the healthcare business takes from data collection to data distribution. © 2025 the Author(s).

关键词： Electronic health record

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A comprehensive analysis and sensitivity model for emotion detection in text 1st

A comprehensive analysis and sensitivity model for emotion d...

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1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Singh, Pancham Asthana, Amita Solanki, Beerbal Tomar, Swati Kansal, Mrignainy Tyagi, Gagan Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad India Department of Electronics and Communication Engineering Ajay Kumar Garg Engineering College Ghaziabad India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India Technical Architect Tech Mahindra India

ISBN: (纸本)9781032874036

People use a variety of communication channels, including text, voice, and gestures, to convey a wide range of emotions in their daily interactions. The problem of automatically identifying these emotions from textual information has important practical ramifications. This study focuses on natural language processing (NLP) methods for textual emotion identification, particularly in Twitter tweets. The study uses machine learning, sentiment analysis, tokenization, and other techniques with a focus on using APIs such as the Twitter API. With an emphasis on the use of unsupervised learning algorithms and data expansion methodologies, the focal issue examines relevant work in sentiment analysis on social media. The suggested approach tackles issues including tokenization, sentiment analysis, and noise reduction in order to categorize tweets into distinct moods. The front-end development using Flask provides a user-friendly interface to visualize real-time Twitter data based on user-defined keywords. The study achieved an overall accuracy rate of 92% in classifying human moods across a diverse dataset. © 2025 the Author(s).

关键词： Tweets

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Visual Decoding using a Learnable wavelet-based Spatial-Spectral-Temporal EEG Embedding 13

Visual Decoding using a Learnable Wavelet-based Spatial-Spec...

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13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Choi, Yebin Kim, Jun-Mo Choi, WooHyeok Ji, Chang-Hoon Oh, Ji-Hye Kam, Tae-Eui Dept. Human-Centered Artificial Intelligence Sangmyung University Seoul Korea Republic of Dept. Artificial Intelligence Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Visual decoding seeks to identify or reconstruct visual stimuli perceived by individuals based on neural activity. Although functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) have achieved remarkable success in visual decoding, their high costs, limited portability, and real-time processing challenges necessitate alternative approaches. Electroencephalography (EEG) provides a promising solution due to its cost-effectiveness, high temporal resolution, and suitability for real-time applications. However, conventional EEG-based encoders often rely on simplistic architectures, which limits their ability to fully capture the spatial, spectral, and temporal (SST) features of EEG signals, leading to suboptimal performance. In this study, we propose a novel brain decoding framework utilizing a state-of-the-art EEG encoder specifically designed to capture the SST characteristics of EEG signals. The proposed framework was evaluated on the THINGS-EEG dataset. It achieved a mean top-1 accuracy of 19.7% and a top-5 accuracy of 50.7% in zero-shot retrieval tasks, outperforming conventional EEG encoders. These results demonstrate the potential of our method in advancing EEG-based visual decoding task. © 2025 IEEE.

关键词： Electroencephalography

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2nd international conference on Next-Generation Computing and information Systems, ICNGCIS 2023

2nd International Conference on Next-Generation Computing an...

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2nd international conference on Next-Generation Computing and information Systems, ICNGCIS 2023

ISBN: (纸本)9781032738659

The proceedings contain papers. The special focus in this conference is on . The topics include: Impact of artificial intelligence on high-penetration renewable physical infrastructure;collaborative attacks and defense;building a safe and secure metaverse;distributed-computing based versatile healthcare services framework for diagnostic markers;supervised context-aware Latent Dirichlet allocation-based drug recommendation model;combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment;Time series analysis of vegetation change using remote sensing, GIS and FB prophet;advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution;Towards a serverless computing and edge-intelligence architecture for the Personal-Internet-of-Things (PIoT);IOT and developed deep learning based road accident detection system and societal knowledge management;Healthcare CAD model for hierarchical processing of surgical navigation system;applicability of eye tracking technology in virtual keyboard for human-computer interactions;a digital twin framework for smart contract-based DeFi applications in the metaverse: Towards interoperability, service scaleup & resilience;a novel approach to glass identification using ensemble learning for forensics;deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification;Multi-class instance segmentation for the detection of cervical cancer cells using modified mask RCNN;dilated convolution model for lightweight neural network;artificial intelligence based Monte Carlo model for epidemic forecasting for societal aspect;efficient data visualization through novel recurrent neural network-based dimension reduction;An optimized CNN-based model for pneumonia detection;EEG-based emotion recognition: Leveraging CNNs for precision;A seven layer DNN approach for social-media multilevel image-based text classification.

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Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model 31

Enhancing Rumor Detection Methods with Propagation Structure...

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31st international conference on Computational Linguistics, COLING 2025

作者： Cui, Chaoqun Li, Siyuan Ma, Kunkun Jia, Caiyan School of Computer Science and Technology & Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798891761964

Pretrained Language Models (PLMs) have excelled in various Natural Language processing tasks, benefiting from large-scale pretraining and self-attention mechanism's ability to capture long-range dependencies. However, their performance on social media application tasks like rumor detection remains suboptimal. We attribute this to mismatches between pretraining corpora and social texts, inadequate handling of unique social symbols, and pretraining tasks ill-suited for modeling user engagements implicit in propagation structures. To address these issues, we propose a continue pretraining strategy called Post Engagement Prediction (PEP) to infuse information from propagation structures into PLMs. PEP makes models to predict root, branch, and parent relations between posts, capturing interactions of stance and sentiment crucial for rumor detection. We also curate and release large-scale Twitter corpus: TwitterCorpus (269GB text), and two unlabeled claim conversation datasets with propagation structures (UTwitter and UWeibo). Utilizing these resources and PEP strategy, we train a Twitter-tailored PLM called SoLM. Extensive experiments demonstrate PEP significantly boosts rumor detection performance across universal and social media PLMs, even in few-shot scenarios. On benchmark datasets, PEP enhances baseline models by 1.0-3.7% accuracy, even enabling it to outperform current state-of-the-art methods on multiple datasets. SoLM alone, without high-level modules, also achieves competitive results, highlighting the strategy's effectiveness in learning discriminative post interaction features. © 2025 Association for Computational Linguistics.

关键词： Tweets

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Improving Stress Detection with Synthetic Datasets: GPT-4o-Mini and Transformer Model Evaluation 17th

Improving Stress Detection with Synthetic Datasets: GPT-4o-...

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17th Asian conference on Intelligent information and Database Systems, ACIIDS 2025

作者： Badran, Nabeel Le, John Le, Thanh Uchiya, Takahiro School of Computing and Information Technology University of Wollongong Northfields Avenue WollongongNSW2522 Australia School of Business Faculty of Business and Law University of Wollongong Wollongong Australia Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya Japan

ISBN: (纸本)9789819660070

The rise of mental health issues as a public health concern necessitates efficient detection methods. Social media is now an integral part of daily life, where users frequently share stress-related experiences. This study investigates synthetic data generation techniques to expand datasets and improve the accuracy of stress analysis in social media posts using machine learning models. Using the OpenAI GPT-4o-mini model through its API, we implement two text augmentation techniques, paraphrasing and inspirational augmentation, generating diverse datasets with original, paraphrased, and inspirationally augmented texts. Evaluations on BERT and DistilBERT show that the combined dataset led to the best result, with BERT achieving an accuracy of 84.74%, compared to 73.94% with the baseline. Our experimental evaluation, conducted on real-world user posts, demonstrates that synthetic datasets significantly improve stress analysis accuracy, highlighting its potential for mental health detection in social media. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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An Important and Successful Multi-image Stereography-Based Secret Communication System for Military Applications 12th

An Important and Successful Multi-image Stereography-Based S...

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12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Ravi, C.N. Suhasini, T.S. Narasimharao, Jonnadula Mahendar, A. Amruthraj, B. Venkateswara Reddy, R. Department of Computer Science and Engineering CMR Engineering College Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819601387

A significant factor in data transfer is computer science. Security considerations make it difficult to transmit sensitive data or have conversations via the internet. Typically, cryptography is used to transmit text-based underground messages and conceal in sequence. These days, there are numerous methods for concealing information on any media. A technique like this is steganography. The creation of a covert communication system that uses multi-image steganography will enable safe communication among the dispatcher and the recipient free from hacker intervention. Image steganography is the main technique for information hiding. It involves integrating the cipher text within a picture called the cover picture that is very difficult for strangers to view through an unassisted eye. Within a cover image, the concealed data may take the shape of any kind of text, picture, music, or even video. The secret code is split up into several pieces and etched into several cover images, according to the conceptual notion of multi-image steganography. Thus, in order to make it extremely difficult for the hackers to hide the data, we suggested two image steganography concepts. In order to create a safe and protected system, this learning suggest by means of the Advanced Encryption Standard (AES) approach of cryptography with the Least Significant Bit (LSB) method of steganography. Here, a symmetric key—which is used by both the sender and the recipient to encrypt and decode data—is used by both parties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereo image processing

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Incentivizing Crowdsensing for DT-Enabled Metaverse 20th

Incentivizing Crowdsensing for DT-Enabled Metaverse

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20th IFIP WG 10.3 international conference on Network and Parallel Computing, NPC 2024

作者： Ye, Dongdong Huang, Xumin Wu, Yuan Kang, Jiawen Zhong, Weifeng Niyato, Dusit School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China Department of Computer and Information Science University of Macau Taipa China Collge of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819628292

Digital Twin (DT) has been proposed as a key enabling technology to produce a digital clone of the physical world and facilitate the convergence of the physical and virtual worlds for building a real-world Metaverse. For physical-virtual synchronization, crowdsensing is applied to employ Internet of Things (IoT) devices to sense physical objects and collect status information for timely updating the associated DTs. However, an incentive mechanism is necessitated to stimulate the devices to perform sensing tasks while meeting task requirements such as Age of information (AoI) requirements. Thus, we adopt a contract theoretic approach to design an incentive mechanism for the devices that are classified into several types and the type parameters are unknown to others. The contract consists of a set of contract items with each contract item representing a device type, which specifies the required data updating frequency and monetary reward. We formulate an optimal contract design problem subject to feasible constraints including the average AoI requirement for each device type. Furthermore, we solve the problem by transforming it into a convex optimization problem that can be directly tackled by the existing methods. Finally, we provide numerical results to demonstrate the effectiveness and efficiency of our scheme. © IFIP international Federation for information processing 2025.

关键词： Convex optimization

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Inside and Inside: Efficient Anomaly Detection by Fully Capturing the Detailed Dynamics

Inside and Inside: Efficient Anomaly Detection by Fully Capt...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ziyu Tang Xiren Zhou Ao Chen Shikang Liu Chuyang Wei Huanhuan Chen School of Computer Science and Technology University of Science and Technology of China Hefei China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Anomaly detection in sequential signals is gaining prominence, especially with limited training data and timeliness requirements. Fully extracting the data-inside changing information, we propose a novel wavelet-Enhanced Reservoir Computing framework (WE-Res). Our framework uses Discrete wavelet Transform (DWT) to decompose signals for multi-level detail and trend extraction recursively. Each level is fitted using an Echo State Network (ESN) respectively, extracting its inside dynamic features into fitted models. Integrating these dynamic features creates a "multi-level dynamic feature" that enhances signal representation, aiding in distinguishing between normal and anomalous patterns. We further introduce a dual-objective optimization to refine ESNs’ reservoirs, increasing the fitting accuracy and improving category discrimination among the captured features. Validation on real-world data confirms our method’s effectiveness, especially in data-limited scenarios and less training time compared to recent baselines.

关键词： Training Accuracy Fitting Training data Signal processing Feature extraction Discrete wavelet transforms Signal representation Speech processing Anomaly detection

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Empirical wavelet Transform Grounded Poignant Ground Target Recognition and Classification by Seismic Signal processing 1

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3rd international conference on Machine Intelligence and Smart Systems, MISS 2023

作者： Mittal, Aman Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (数字)9783031317231

ISBN: (纸本)9783031317224

Humans have had the ability to recognise items for hundreds of years, possibly even since they first appeared on Earth. Humans use their senses of sight, smell, hearing, taste, and touch to determine the identities of things in their immediate surroundings. Transmission of sensory organ impulses to the brain allows for the latter's processing and interpretation of the data. That's why repetition is so important for learning. The learned knowledge is put to use in a wide variety of ways, from the mundane to the crucial, such as in the areas of security, surveillance, traffic monitoring, etc. Due to the short range of the human senses and the potential dangers of working in some environments, this approach has spatial and temporal constraints. Seismic signal processing employs the wavelet transform for detection and classification. However, picking the mother wavelet is tricky because it depends on how well it fits the original signal. As a result, the empirical wavelet transform (EWT) is investigated in this research paper as a potential adaptive wavelet transform for detection and classification. In the same way as EWT does, the mother wavelet is chosen mechanically with reference to the input signal. This study shows that EWT-based detection and classification methods perform better than STFT-based methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Seismic response

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