Showing 1-25 of 18,417 resultsfor "Index Terms":Short-term Information
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Human Activity Recognition via Wi-Fi CSI and VAE-CNN-BiLSTM Hybrid Deep Learning
2024 International Conference on Brain Computer Interface & Healthcare Technologies (iCon-BCIHT)
Year: 2024 | Conference Paper |
Human Activity Recognition (HAR) is vital across multiple applications, such as healthcare monitoring, smart home systems, and surveillance. Recently, Wi-Fi channel state information (CSI) has gained attention as a valuable data source for HAR, owing to its non-intrusive nature and capacity to capture detailed spatial information. This paper introduces a deep learning framework that combines varia...Show More
Research on evaluation model of health state information security protection for automotive braking system based on machine learning
2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE)
Year: 2024 | Conference Paper |
The Long Short-Term Gradient (LSTG) architecture innovatively combines the time series analysis capability of Long short-term memory network (LSTM) with the efficient training strategy of Mini-batch Gradient Descent (MGD). It has brought innovation to the research of health state information security protection evaluation model of automobile brake system based on machine learning. LSTG uses LSTM t...Show More
Information Filtering Unit-Based Long Short-Term Memory Network for Industrial Soft Sensor Modeling
Xiaoqing Zheng;Yifan Zhao;Bo Peng;Ming Ge;Yaguang Kong;Song Zheng
The use of soft sensors is an alternative solution to physical sensors for real-time acquisition of key quality variables that cannot be measured online in real time. Long short-term memory (LSTM)-based methods have been improving the state-of-the-art in soft sensors. In order to mine the correlation between historical information and current input and utilize the relatively important historical i...Show More
A multi-mechanism fusion research method for entity relationship extraction modeling
2024 3rd International Conference on Electronics and Information Technology (EIT)
Year: 2024 | Conference Paper |
NLP relies heavily on entity-relationship extraction, but complicated structures provide challenges for conventional approaches. This paper proposes an advanced model integrating multi-head attention, BiLSTM, CRF, and gated attention mechanisms, leveraging the pre-trained Chinese-RoBERTa-wwm-ext model for text encoding. The Attention-Enhanced BiLSTM captures dependencies and focuses on key informa...Show More
Classifying True and Fake News Using LSTM Architecture
2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)
Year: 2024 | Conference Paper |
The explosion of knowledge available on digital channels has transformed news distribution and consumption. But this has also resulted in the proliferation of false news, so seriously impairing public debate, public confidence, and the Sustainable Development Goals on peace, justice, and solid institutions as well as on quality education and peace. Maintaining journalistic integrity and informed p...Show More
Short-term Power Load Forecasting Based on Multiple Load Characteristics and TCN-GRU Combined Model
2024 2nd International Conference on Algorithm, Image Processing and Machine Vision (AIPMV)
Year: 2024 | Conference Paper |
In response to the non-linear and non-stationary nature of load data, this paper investigates the construction of a short-term power load forecasting composite model based on MIC-CEEMDAN-TCN-GRU. Firstly, the Maximum Mutual Information Coefficient (MIC) method is employed to extract features from the high-dimensional load dataset, aiming to reduce the complexity of input features. Secondly, the Co...Show More
A BiLSTM-Based IoT Intrusion Detection System with Mutual Information and Focal Loss
2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC)
Year: 2024 | Conference Paper |
The rapid expansion of global Internet of Things (IoT) device numbers has significantly heightened the importance of securing these systems. As a core technology for IoT security protection, intrusion detection systems (IDS) have garnered significant attention from both academia and industry. This study introduces an IDS named BMF-IDS, designed to enhance IoT security. BMF-IDS leverages the advant...Show More
Enhancing Named Entity Recognition Through Neural Architectures
2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)
Year: 2024 | Conference Paper |
This study delves into advanced neural architectures for Named Entity Recognition (NER), a task pivotal in Natural Language Processing. Focused on leveraging context dependencies within sentences, our investigation explores the efficacy of incorporating memory using Long Short-Term Memory (LSTM) networks. It extends to Bi-directional LSTMs for capturing both past and future information. Additional...Show More
Unveiling Media Bias: Investigating Influences, Politics, and Solutions through Advanced Classification Techniques
2024 Second International Conference on Advances in Information Technology (ICAIT)
Year: 2024 | Conference Paper |
This research investigates the common problem of media bias, focusing on how it affects what journalists and news producers choose to cover. Media bias is when news does not follow the usual rules, going beyond personal opinions. The level and direction of bias can differ across countries because journalists cannot cover all aspects and they need to construct a concise story with selected facts. I...Show More
Extraction of Person Entities Affiliated with Telkom University using Long Short-Term Memory (LSTM) on Related News Articles
2022 5th International Conference on Information and Communications Technology (ICOIACT)
Year: 2022 | Conference Paper |
News text is text that contains information about an incident that is submitted in writing or not. In the news text there is a lot of unstructured information and makes it difficult for people to know what entities are contained in the news text. To overcome these problems, information extraction is carried out which is useful for adapting information entities in the form of structured text. In th...Show More
Wave height prediction based on CNN-LSTM
2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)
Year: 2020 | Conference Paper |
Cited by: Papers (13)
The short-term wave height forecast is of great significance to the development and utilization of energy. To improve the accuracy of short-term wave height prediction, we propose a prediction model based on convolutional neural network (CNN) and long short term memory (LSTM) network as they have excellent feature extraction ability and are very good at processing time series data. This model leve...Show More
Fault Information Mining in Avionics Components Based on Unstructured Data
2023 14th International Conference on Reliability, Maintainability and Safety (ICRMS)
Year: 2023 | Conference Paper |
To address the requirements of unstructured fault data mining and statistical analysis of avionics systems, this study excavates implicit knowledge, investigates potential knowledge associations, and realizes a deep understanding of knowledge to better interpret data. Combined with recent progress of text data extraction technology, this paper studies the entity relationship extraction and failure...Show More
Enhanced and Efficient Gated Recurrent Unit and Long Short-Term Memory Architectures for Detecting False Information
Ramakrishnan Raman;Vikram Kumar;C. P. Sanjay;Dhaval Rabadiya;Smruti Patre;R. Meenakshi
2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS)
Year: 2024 | Conference Paper |
In the digital era, the rapid dissemination of false information has emerged as a formidable challenge, undermining the credibility of online platforms and posing a threat to informed public discourse. Addressing this issue, our research introduces innovative enhancements to Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures, aimed at bolstering the efficiency and accuracy ...Show More
A Surrogate Model of Rectifiers with LSTM Neural Networks for Various Modulated Signals
2024 IEEE Asia-Pacific Microwave Conference (APMC)
Year: 2024 | Conference Paper |
This paper presents a surrogate model for rectifiers with LSTM neural networks (NNs) for various modulated signals. Rectifiers in simultaneous wireless information and power transfer (SWIPT) systems frequently receive modulated signals. The rectifier exhibits memory effects because the envelope of the signal varies over time with modulation. Although many studies have modeled rectifier behavior wi...Show More
Research on the cultivation of teachers' information ethics
2010 International Symposium on Computer, Communication, Control and Automation (3CA)
Year: 2010 | Conference Paper |
Cultivating the teacher's information ethics has double meanings, concerning teacher's own information literacy, will influence the instruction activity and students' information ethics too, it is the important component of teacher's information literacy. But teacher's information ethics education is often ignored. How to know and how to cultivate the teacher's information ethics correctly is an i...Show More
Fault prediction model of cloud platform based on long short-term memory network
2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
Year: 2022 | Conference Paper |
In the process of implementing recommendation, the time sequence of users' browsing and following page information is important data information in the recommendation algorithm, and the same user's different preferences for items at different times also have a certain impact on the recommendation results. Under the framework of the filtering model, it is proposed to integrate the long-short-term m...Show More
Navigating the Newscape: Long Short-Term Memory Models for Misinformation Detection
2024 11th International Conference on Software Defined Systems (SDS)
Year: 2024 | Conference Paper |
In an era where digital misinformation poses a severe challenge to societal trust, identifying fake news is imperative. This study employs a Long Short-Term Memory (LSTM) neural network to discern misinformation within a dataset of approximately 79,000 labeled news texts. Our LSTM model benefits from its aptitude for processing sequential data, crucial for understanding textual context. We evaluat...Show More
A Novel Short-Term Load Forecasting Model Based on Bidirectional Long Short-Term Memory and Kolmogorov-Arnold Network
2024 China Automation Congress (CAC)
Year: 2024 | Conference Paper |
With the development of modern power systems and the widespread adoption of smart grids, accurate short-term load forecasting has become increasingly important. Although Bidirectional Long Short-Term Memory (BILSTM) networks excel at capturing bidirectional dependencies in time series data, they exhibit limitations when dealing with highly nonlinear data and often lack interpretability. This paper...Show More
Long Short-Term Memory and Synthetic Minority Over Sampling Technique-Based Network Traffic Classification
Yirga Yayeh Munaye;Atinkut Molla;Yenework Belayneh;Bizuayehu Simegnew
2024 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)
Year: 2024 | Conference Paper |
The swift expansion of network data and the growing complexity of cyberattacks demand sophisticated techniques for precise and prompt categorization of network traffic. The capacity to accurately categorize network traffic as BENIGN or malicious/attack is critical in the field of cybersecurity. The goal of this study was to create a system for classifying network traffic by combining the Synthetic...Show More
Detection Method for False Data Injection Attacks Based on STCN-BiLSTM
2024 11th International Forum on Electrical Engineering and Automation (IFEEA)
Year: 2024 | Conference Paper |
To address the difficulties in feature extraction from long time-series data and the lack of interpretability in deep learning-based FDIA detection schemes, this paper proposes an FDIA detection method based on Spatiotemporal Convolution-Bidirectional Long Short-Term Memory (STCN-BiLSTM) networks. To prevent the loss of sequential information, the data in the dataset is first divided into two stre...Show More
A Bi-LSTM Algorithm for Digital Library Information Resource Classification and Retrieval
2024 International Conference on Interactive Intelligent Systems and Techniques (IIST)
Year: 2024 | Conference Paper |
Information retrieval technology is one of the core technologies for information resource retrieval in digital libraries. The popularization of digital information resources provides a broader foundation for the classification and retrieval of information resources in digital libraries. Therefore, digital libraries need to adopt more advanced and efficient algorithms and search technologies to opt...Show More
Analysing LSTM and Transformer Models for Chatbot Development: A Comparative Study
Shivank Solanki;Sudeep Batra;Mrinal Pandey;Monika Goyal;Mamta Arora
2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)
Year: 2024 | Conference Paper |
In today’s digital landscape, the development of chatbots plays a pivotal role in enhancing user engagement across various applications. This paper conducts a comparative study between Long Short-Term Memory (LSTM) and Transformer models for chatbot development, focusing on their efficacy in generating contextually coherent responses. LSTM, known for its sequential data processing capabilities, is...Show More
Fusion of CNN and LSTM Models for Enhanced Gait Swing Phase Identification
2024 Global Conference on Communications and Information Technologies (GCCIT)
Year: 2024 | Conference Paper |
This paper presents a CNN-LSTM hybrid model intended for precise recognition of gait swing phases, explicitly focusing on initial swing, mid swing, and terminal swing. Gait analysis plays an important part in different fields including medical care and biomechanics, offering experiences in motion functions and supporting rehabilitation techniques. The proposed model incorporates a innovative appro...Show More
The use of ALSTM-FCN for tobacco planting extraction from time-series Sentinel-1A Sar images
2022 29th International Conference on Geoinformatics
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Spatial information on tobacco planting is crucial to many agricultural applications regarding tobacco production and management. This paper presents a deep learning model, i.e., Attention Long Short-Term Memory Fully Convolutional Network (ALSTM-FCN), to extract tobacco planting areas using time-series Sentinel-1A (S1A) SAR images. Using the ALSTM-FCN model, high-level temporal and spatial image ...Show More
Advanced Extractive Summarization of Indonesian Texts Using LSTM Models
Muhammad Faisal;Shafira Halmahera;Vivin Octavia Cahyani;Abdul Aziz;Ashri Shabrina Afrah;Supriyono
2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)
Year: 2024 | Conference Paper |
In the current digital era, the volume of available information continues to increase, especially in the form of online articles. This surge presents a challenge for readers to quickly grasp the core information. This study aims to develop an advanced extractive summarization model for Indonesian texts using Long Short-Term Memory (LSTM) networks. The research process involves several key steps: p...Show More
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