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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Short Paper: AI-Driven Disaster Warning System: Integrating Predictive data with LLM for Contextualized Guideline Generation 24

Short Paper: AI-Driven Disaster Warning System: Integrating ...

引用

11th international conference on Networking, Systems and Security, NSysS 2024

作者： Faiaz, Md. Abrar Nawar, Nowshin Bangladesh University of Engineering and Technology Dhaka Bangladesh University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9798400711589

Early warning systems are the backbone of disaster management, but traditional systems often send static alerts with no personalized advice. this work investigates an AI-powered disaster warning system using predictive analytics and LLMs to design personalized recommendations over efficient response strategies. this system ensures that real-time information on context-specific and geographically relevant preparedness is tailored to the needs of varied populations. It was built using the LangChain framework and features an architectural composition with a predictive layer in disaster monitoring and an advisory layer for proactive safety guidelines. It integrates current weather conditions with advanced machine learning algorithms to enhance disaster prediction and utilizes LLMs in constructing customized actions, hence streamlining emergency messaging with enhanced clarity and relevance. Among the tested models, it turned out that the Gemini Pro LLM is the most effective, consistently generating outputs appropriate in context and accurate. Analysis under different temperature settings demonstrated the ability of the model to maintain high precision on various types of crises. the findings indicate that this approach can transform traditional early warning systems into more adaptive, user-centered frameworks, hence enhancing disaster management practices. © 2024 Copyright held by the owner/author(s).

关键词： Alarm systems

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Brain Tumour Detection from MRI Images Using Enhanced Extreme Machine learning Probabilistic Scaling 11th

Brain Tumour Detection from MRI Images Using Enhanced Extrem...

引用

11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Geetha, A. Keerthika, V. Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India

ISBN: (纸本)9789819717231

the development of aberrant cells, some of which may develop into cancer, results in a brain tumour. Magnetic resonance imaging (MRI) scans frequently reveal brain malignancies. MRI scans are used to identify the abnormal tissue growth in the brain. In several research publications, algorithms for machine learning and deep learning are used to detect brain tumours. It can be used to identify brain tumours quickly and accurately in MRI scans, which makes it simpler to treat patients. these forecasts also help the radiologist act quickly. In the suggested work, preprocessing, segmentation, feature extraction, and classification are all included. An MRI brain image’s undesirable pixels are removed using the Wiener filter during the preprocessing stage. To divide up the data, we applied the fuzzy means clustering (FCM) algorithm. In the second stage, the characteristics of the MRI’s GLCM are extracting the features from the image associated with the MRI brain image. An enhanced extreme learning machine probabilistic scaling is applied in the classification step to categorize the prevailing output image and the interrogation image. the results demonstrate how effective and reliable the suggested methodology is when compared to other recent studies. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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MSSthreD: Multitask Social Media Sentiment Analysis with Sarcasm for Public Security threat Detection 11

MSSThreD: Multitask Social Media Sentiment Analysis with Sar...

引用

11th international conference on Information Technology, Computer and Electrical engineering, ICITACEE 2024

作者： Suhaimin, Mohd Suhairi Md Hijazi, Mohd Hanafi Ahmad Moung, Ervin Gubin Politeknik Kota Bharu College Education Department of Polytechnic and Community Kelantan Kota Bharu Malaysia Universiti Malaysia Sabah Data Technology and Applications Research Group Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Sabah Malaysia Universiti Malaysia Sabah Data Technology and Applications Research Group Faculty of Computing and Informatics Sabah Malaysia

ISBN: (纸本)9798350389289

Public security threat detection is critical for maintaining safety of citizens from possible natural or non-natural disasters. Social media, with its vast and diverse content, serves as a significant source for communication and information dissemination. Early detection of threats through social media analysis can greatly benefit in prevention before the event, or response and rescue during the event. However, the noisy textual with linguistic nature of social media content presents substantial challenges for sentiment analysis. Coupling with sarcasm can reverse the polarity of sentiments, complicating threat detection efforts. To address these challenges, we introduce an application framework for public security threat detection that embeds multitask learning, incorporating sentiment analysis and sarcasm detection. this application utilized preprocessing and feature engineering techniques based on Natural Language Processing to capture the nuance of sentiment and sarcasm, with deep learning network model of Bidirectional Long Short-Term Memory and Gated Recurrent Unit combination. the sentiment filtering and identifying threat-related keywords, producing monitoring reports that highlight threats earlier than traditional web news sources, as evidenced by our case study results. this application demonstrates the practical utility and impact of the framework in enhancing public security through timely threat detection. © 2024 IEEE.

关键词： Long short-term memory

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Prediction of NPK Fertilizer in Chili Plants Using SARIMA Model 11

Prediction of NPK Fertilizer in Chili Plants Using SARIMA Mo...

引用

11th international conference on ICT for Smart Society, ICISS 2024

作者： Pratama, Rian Putra Fakhurroja, Hanif Bangkit, Harry Pramesti, Dita Research Center for Smart Mechatronics National Research and Inovation Agency Bandung Indonesia School of Industrial Engineering Telkom University National Research and Inovation Agency Bandung Indonesia School of Industrial Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350389654

Agriculture is one of the key sectors in the Indonesian economy, contributing approximately 14% to the national Gross Domestic Product. Chili plants are one of the main agricultural commodities in the country. Fertilization is crucial in chili plant cultivation to enhance yield and production quality. While proper fertilization can increase plant growth and productivity, excessive doses can lead to plant toxicity. therefore, an accurate prediction model is needed to estimate the nutritional requirements of chili plants. this study aims to develop a prediction model for the nutritional needs of chili plants using a machine learning approach. the data used come from the Nutrient and Water Supply Monitoring System for chili plant growth and yield based on 10 T. the dataset includes chili plants treated with various doses of Nitrogen (N), Phosphorus (P), and Potassium (K) fertilizers monitored by NPK sensors. the machine learning method applied is the Seasonal Autoregressive Integrated Moving Average (SARIMA), which can predict time series data. the model was evaluated using the metrics mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). the SARIMA model (1,1,1)(1,1,0)[12J showed the best evaluation results, with an average MAE of 0.783, RMSE of 6.377, and MAPE of 0. 005. the SARIMA model can help farmers optimize NPK fertilizer practices to increase productivity and efficiency in the cultivation of chili plants. © 2024 IEEE.

关键词： Prediction models

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Power IoT Comprehensive Perception Architecture Solution 11

Power IoT Comprehensive Perception Architecture Solution

引用

11th China international conference on Electricity Distribution, CICED 2024

作者： Shi, Shaozhou He, Qiwei Song, Xiangping Nr Electric Co. Ltd. No.18 Xinfeng Road Jiangning Nanjing211106 China

ISBN: (纸本)9798350368345

the power Internet of things has the characteristics of comprehensive state perception, efficient information processing, convenient and flexible application, including four layers of perception layer, network layer, platform layer and application layer. the perception layer is the key level of the power Internet of things, which is the data source and the foundation of the Internet of things. this paper focuses on three different application scenarios of the operational perception, auxiliary perception and patrol perception of the power Internet of things perception layer. It adopts wired private network, LoRa wireless private network and 5G wireless communication technology, adopts hierarchical and partitioned star network architecture, and relies on the advanced technology of total perception to achieve the effect of condition monitoring, live detection and automatic inspection. Improve O&M security, reliability, and lean management. © 2024 IEEE.

关键词： intelligent perception intelligent power grid Internet of things (IoT) Network architecture Power IoT

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Dermatological Disease Detection Employing Transfer learning 11

Dermatological Disease Detection Employing Transfer Learning

引用

11th international conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2023

作者： Mathur, Siddharth Jain, Tarun Cse Manipal University Jaipur Jaipur India

ISBN: (纸本)9781665475129

Dermatology has long been regarded as one of science's most difficult terrains, owing to its unpredictability and difficult diagnoses. People in most developing countries find it unnecessarily expensive, preventing people from seeking early diagnosis. With the widespread use of smartphones in developing countries, new avenues for low-cost diagnosis have opened. Images captured by a smartphone's camera can be further processed to assess the disease's name and severity. the proposed system employs computer vision and deep learning techniques to aid the diagnosis of skin diseases. the images are first pre-processed with computer vision to achieve uniformity and remove any unwanted elements that could impede output efficiency. the deep learning section is concerned with data processing and the generation of results. the developed model was effective at classifying skin diseases into 19 categories. Pre-trained models like Xception, Inception V3, Inception V2, and MobileNet were employed to achieve a maximum F1 score of 92.307% from the Xception model © 2023 IEEE.

关键词： Diagnosis

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Fast Contingency Filtering Using Machine learning for Power System Planning 11

Fast Contingency Filtering Using Machine Learning for Power ...

引用

11th IEEE international conference on Smart Energy Grid engineering, SEGE 2023

作者： Alvarez, David L. Nour, Georges Abdul Gaha, Mohamed Cote, Alain Université du Québec À Trois-Rivières Department of Industrial Engineering Trois-Rivières Canada Data Science and High Performance Computing Varennes Canada Asset Sustainability Varennes Canada

ISBN: (纸本)9798350340716

Modern power systems assess reliability N -k and k = 2, 3, 4,⋖ criteria to guarantee the secure, sustainable and optimal operation of power networks. However, performing these studies with traditional methods is computation-intensive and time-prohibitive for the long-term planning of large networks. In this paper, we present a Machine learning (ML) model for rapid contingency filtering in order to evaluate problematic N - k contingency scenarios. Our proposed model is trained with data sets stochastically created and labeled with AC load flows considering load forecasting. their inputs are the time and N - quad k status of network equipment. the performance of the proposed ML model was evaluated on the IEEE 39-Bus System for a planning period of 10yr with a time resolution of 1h. the performance obtained was an accuracy greater than 95% with a time acceleration of approx. 2500x. this result makes the proposed model suitable for supporting decision making during the planning of power systems. © 2023 IEEE.

关键词： Machine learning

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TPMARL-MSS thought-Perception Multi-Agent Reinforcement learning Meeting Scheduling System 25

TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Lear...

引用

25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhang, Jinsong Mao, Xiaoxuan Li, Lu Wu, Hanqian Joint Graduate School Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration of Ministry of Education Nanjing China

ISBN: (纸本)9798350330014

In today's evolving society, the increasing complexity and frequency of meetings necessitate advanced scheduling systems. Traditional methods are constrained by rigid prede-fined strategies, lack intelligent negotiation mechanisms, and often compromise user privacy. Addressing these challenges, we introduce the thought-Perception Multi-Agent Reinforcement learning Meeting Scheduling System (TPMARL-MSS). Unlike conventional systems, TPMARL-MSS autonomously learns and refines its strategies through continuous feedback. It features automated negotiation and adaptive decision-making, offering a more nuanced scheduling approach. Importantly, our thought-Perception module protect privacy, allowing the agent to deduce preferences from participant behavior without revealing personal data. Evaluations on the real-world dataset shows that TPMARL-MSS surpasses traditional methods in efficiency and schedule quality, highlighting its practical applicability. © 2023 IEEE.

关键词： Decision making

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Multi-Modal Deep learning Architecture for Accurate Driver Behavior Recognition in automated Driving Systems 18

Multi-Modal Deep Learning Architecture for Accurate Driver B...

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18th international conference on intelligent Systems and Knowledge engineering, ISKE 2023

作者： Alhaek, Fares Liang, Weichao Rajeh, Taha M. Javed, Muhammad Hafeez Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798350318401

automated driving systems and advanced driver assistance systems require accurate and reliable driver behavior recognition models to ensure safe and efficient operation. the complexity of background contents in real-world driving scenarios presents a significant challenge. Moreover, the distinction between different driving behaviors can be extremely subtle. Minor variations in body movements, gestures, or vehicle control, which are challenging to discern accurately, may indicate distinct behaviors. To address the above issues, this paper proposes a novel multi-model deep learning architecture (MMDLA) for driver behavior recognition by integrating multiple modalities, including RGB frames, depth information, and skeleton data, to provide a more comprehensive representation of driver behavior. the proposed model consists of two stages. In the first stage, we use local and global feature extraction to extract features from RGB and Depth frames. Additionally, we construct a graph from 3D skeleton data and feed it to a graph convolutional network to extract its features. In the second stage, we perform ensemble learning and fuse the diversity of multiple features together. Experiments on the Drive&Act dataset shows that our model achieves superior performance, outperforming existing models on both hierarchical and fine-grained activity recognition tasks. © 2023 IEEE.

关键词： Advanced driver assistance systems

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Short Term Gas Load Prediction Based on CNN-LSTM Hybrid Model 11

Short Term Gas Load Prediction Based on CNN-LSTM Hybrid Mode...

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11th international conference on intelligent Computing and Wireless Optical Communications, ICWOC 2023

作者： Su, Chang Deng, Shengnan He, Zhiqiang Tan, Jian Liu, Guojin PetroChina Southwest Oil & Gasfield Company Chongqing Gas Field Chongqing China Chongqing University School of Microelectronics and Communication Engineering Chongqing China

ISBN: (纸本)9798350321791

Load prediction play a significant role in the field of gas development. In order to effectively mine historical data information and improve the accuracy of short-term gas load, this paper focuses on the gas load based on the time series nonlinear characteristics. A Convolutional Neural Networks (CNN) and Long Short-Term Memory Network (LSTM) are combined to predict the short term load of gas with the advantages of the two types of network. Firstly, CNN is used to extract the important local spatial features of gas load information with 2 layers convolutional network, then, the latent features of gas load extracted by CNN are feed into the LSTM, which can maintain a good memory of the time series with a long time span, and can fully consider the time correlation of data. Finally, the prediction value of the mixed model is generated according to the deep features of the training data. the simulation result investigates that compared with the CNN, LSTM, BiLSTM and Seq2Seq neural network model, the CNN-LSTM network model performs better to extract the spatiotemporal feature, which can predict short term gas load more accurately. © 2023 IEEE.

关键词： Forecasting

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