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Is Arabic text categorization a solved task? 5

Is Arabic text categorization a solved task?

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5th international conference on Intelligent Systems and Computer Vision, ISCV 2022

作者： Kah, Anoual El Zeroual, Imad Mohamed First University Faculty of Sciences Oujda Morocco Moulay Ismail University L-STI T-IDMS Faculty of Sciences and Techniques Errachidia Meknes Morocco

ISBN: (纸本)9781665495585

the amount of Arabic content on the Internet is being proliferated day by day, scoring the highest growth among the top online languages during the last two decades. Arabic is the fourth most used language on the world wide web, making producing reliable data mining applications and information retrieval engines significantly challenging. therefore, Arabic text categorization has gained the attention of different researchers from various fields. As a result, considerable research works have addressed Arabic text categorization. Some of these research works reported accuracy rates that were very close to 100%. Due to that, one can only think if Arabic text categorization is a solved task or is still under-studied? To answer this question, we screened 262 related papers based on eight former surveys and reviews, following the PRISMA-ScR guidelines. then, we focused on top-ranked results that are over 95%. In this paper, we present the outcomes of our investigation by addressing several research questions regarding the datasets, the preprocessing techniques, the dimensionality reduction methods, and the classifiers that have been used. © 2022 IEEE.

关键词： machine learning

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DeepText: Pioneering the Future of Text Classification with Innovative Deep learning Techniques

DeepText: Pioneering the Future of Text Classification with ...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Prathyakshini Jyothi Shetty Department of Information Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Nitte Karkala India Department of Computer Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Nitte Karkala India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Text classification is an extremely vital task in Natural Language Processing, and it is utilized in many different applications. As there is increase in number of complicated documents there is need for deeper comprehension of machine learning techniques which will be able to identify text in various application. Text classification can be used to organize large amounts of unstructured data. Text classification witnessed remarkable advancements through the application of deep learning techniques. these techniques can be used in various classification tasks through which the strengths and weakness could be analyzed. the study analyzes different text classification methods with wide range of datasets which is applicable for the classification tasks. the advantages and limitations of text classification techniques with different datasets are listed out in order to identify the best technique out of all the available techniques.

关键词： Deep learning Surveys Support vector machines Sentiment analysis Accuracy Text recognition Text categorization Transfer learning Feature extraction Transformers

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Forecasting Short Term Peak Loads of Distribution Transformer (DT) Using machine learning and Computational Statistics—Various Methodologies and their Pros and Cons 5th

Forecasting Short Term Peak Loads of Distribution Transforme...

引用

5th international conference and Exhibition on Smart Grids and Smart Cities, ISUW 2019

作者： Roy, Arghya India Power Corporation Limited Kolkata700091 India

ISBN: (纸本)9789811612985

machine learning (ML) is a technique that employs computational statistics to learn the pattern of data and from there it tends to predict. Over last few years, abundance of data have been made available in standard format and therefore it is compelling to use ML techniques to achieve better prediction. In several instances we have witnessed 98–99% accuracy in prediction levels using ML techniques. In Utility Industry, one of the key parameter every DISCOM would like to forecast is Peak Load. Since HT (>11 kVA) and EHT (Extra High Tension > 66 kVA) customers in the designated DISCOM region demand continuous power supply, reducing interruption hours and feeder outages are two most important factors draw attentions of Utility Service Providers. It has been found that accurate prediction of Peak Load Demand for a day ahead can remarkably reduce the above two parameters. In fact, knowing the peak load in advance can help in optimizing the load distribution at substation levels. While predicting peak load, we should consider weather (Temperature), time of the day (expressed in HH:MM format), Day type (Weekdays or Weekends) and finally kWH of a DT. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Early Flood Warning System using machine learning

Early Flood Warning System using Machine Learning

引用

Smart Electronics and Communication (ICOSEC), international conference on

作者： P. Mainkandan Sai Dileep Golla Sai Phanindra Pavan Kumar Gatikoppu Sai ChaithanyaPuttaparthi Sai AchyuthNarahari SaikiranDavadi Department of Electronics and Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamil Nadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamil Nadu India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

this research presents a novel approach to early flood warning systems, specifically tailored for the challenging context of the Indian subcontinent. Recognizing the devastating impact of floods on communities, the project addressed the scarcity of comprehensive data in India by pioneering the extraction of information from local news reports, creating a robust dataset that sets this study apart from traditional flood forecasting models. the research study has developed a tool for predicting potential flood scenarios before they occur. By synthesizing complex data into an interactive and comprehensible format, the web application named “Early Flood Predictor” empowers individuals and governments to make informed decisions in the face of imminent threats. the objectives included innovative data collection methods, implementation and optimization of a cutting-edge machine learning model, and the development of an intuitive web application. the machine learning model, based on the Random Forest Classifier, achieved the accuracy of 98% on the dataset. the web application, hosted on Render, provides real-time flood predictions for any city in India, contributing to proactive measures and informed decision-making. the Early Flood Predictor project stands as a technological achievement with the potential to make a significant impact on flood preparedness and resource allocation, showcasing the possibilities of leveraging technology for the greater good.

关键词： Accuracy Urban areas data collection Alarm systems Real-time systems data models Floods data mining Random forests Meteorology

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Research on Optical Fiber Vibration Identification Technology Based on Big data Analysis in Optical Cable Operation and Maintenance Early Warning

Research on Optical Fiber Vibration Identification Technolog...

引用

5th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2024

作者： Xun, Sichao Shen, Yusheng Xiao, Hongyi Xu, Xingyong State Grid Jiangsu Electric Power Co. Lid. Yancheng Power Supply Branch Jiangsu Yancheng224000 China

this paper aims to develop an optical fiber vibration identification system based on big data analysis to realize the real-time monitoring and data analysis of the running state of optical cable. the goal is to reduce the impact of sudden failures on the network by accurately analyzing fiber vibration data. Big data technology is adopted to process and analyze fiber vibration data, and data-driven method is used to provide decision support, including collecting large amounts of fiber vibration data, applying data mining technology to identify vibration patterns, and using to make fault prediction with machine learning algorithms. the implementation effect of the system shows that through real-time data monitoring and analysis, the stability of the network can be effectively improved, the ledger management and query method can be improved, the network panorama presentation and real-time monitoring can be realized, and the impact of sudden faults on the network operation can be significantly reduced. through these measures, the system not only improves the reliability of the network, but also optimizes the fault management and early warning mechanism, providing strong support for the network maintenance and operation. © 2025 the Authors. Published by Elsevier B.V.

关键词： data reduction

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machine learning Based Heart Disease Prediction Using ECG Image

Machine Learning Based Heart Disease Prediction Using ECG Im...

引用

Emerging Technology (INCET), international conference for

作者： Kishor Bhangale Saurabh Kadam Sakshi Chame Hardik Saklikar Department of E&TC Pimpri Chinchwad College of Engineering and Research Ravet Pune India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In a global health context outlined by the World Health Organization (WHO), the escalating prevalence of CVDs underscores a pressing need for advanced diagnostic methodologies. CVDs remain a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for early detection. Traditional diagnostic methods face challenges in providing timely and accurate identification of heart conditions, often relying on subjective interpretation and lacking the sensitivity required for early intervention. Recognizing this critical gap, our engineering project focuses on the early detection of heart diseases using ML and DL techniques applied to ECG images. the integration of ML and DL offers a promising avenue to overcome the limitations of conventional diagnostic tools, leveraging intricate patterns within ECG data for more precise and timely detection. By addressing these challenges, our research aims to contribute to the advancement of cardiovascular health, providing a robust framework for early disease detection and improving overall patient outcomes.

关键词： Heart Sensitivity Image recognition Pressing Organizations machine learning Forestry

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pattern Clustering Approach for Activity recognition in Smart Homes 11th

Pattern Clustering Approach for Activity Recognition in Smar...

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11th international conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Sukor, Abdul Syafiq Abdull Zakaria, Ammar Kamarudin, Latifah Munirah Wahab, Mohd Nadhir Ab Perlis Malaysia Perlis Malaysia School of Computer Science Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9789811681288

In recent years, studies in activity recognition have shown an increasing amount of attention among other researchers. Activity recognition is usually performed through two steps: activity pattern clustering and classification processes. Clustering allows similar activity patterns to be grouped together while classification provides a decision-making process to infer the right activity. Although many related works have been suggested in these areas, there is some limitation as most of them are focused only on one part of these two processes. this paper presents a work that combines pattern clustering and classification into one single framework. the former uses the Self Organizing Map (SOM) to cluster activity data into groups while the latter utilizes semantic activity modelling to infer the right type of activity. Experimental results show that the combined method provides higher recognition accuracy compared to the traditional method of machine learning. Furthermore, it is more appropriate for a dynamic environment of human living. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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MLLMIE: Multiple Large Language Model Information Extraction in Ancient Medical Texts

MLLMIE: Multiple Large Language Model Information Extraction...

引用

machine learning and Natural Language Processing (MLNLP), international conference on

作者： Feilong Wang Donghui Shi Xinyi Cui School of Electronics and Information Engineering Anhui Jianzhu University Mass Spectrometry Key Technology R&D and Clinical Application of Anhui Province Jointly Constructed Discipline Key Experiments Hefei China School of Electronics and Information Engineering Anhui Jianzhu University Hefei China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

Ancient medical texts, which reflect traditional cultural practices, can play an important role in the promotion of medical traditions. Nevertheless, the complexity of extracting information from classical Chinese medical texts and the potential inconsistencies and hallucinations in large language models during information extraction present significant challenges. In domains such as medicine, where the precision of information extraction is of paramount importance, the paper augmented existing methodologies for information extraction from large language models and proposed a novel approach, designated as Multiple Large Language Model Information Extraction (MLLMIE). the method initially defined a pattern layer to explicitly delineate the concepts, terms, and their interrelationships within the text. Subsequently, it employed prompt engineering to extract information from multiple models, thereby obtaining candidate medical ancient text knowledge results. Finally, the model validation layer was employed to filter the candidate results, thereby obtaining the final medical ancient text knowledge. the method attains F1 scores of 92.68% for named entity recognition and 94.91% for relation extraction in ancient medical texts, thereby demonstrating high-quality medical ancient text information extraction. this method effectively enhances the accuracy of ancient medical text information extraction and facilitates the discovery and application of ancient medical text knowledge.

关键词： Knowledge engineering Large language models Instruments Named entity recognition machine learning Information retrieval Information filters data mining Cultural differences Prompt engineering

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Visual Question Answering Model Based on CAM and GCN 5

Visual Question Answering Model Based on CAM and GCN

引用

5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Wen, Ping Li, Ma Zhen, Zhang Ze, Wang Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

Visual Question Answering (VQA) is a challenging problem that needs to combine concepts from computer vision and natural language processing. In recent years, researchers have proposed many methods for this typical multimodal problem. Most existing methods use a two-stream strategy, i.e., compute image and question features separately and fuse them using various techniques, rarely relying on higher-level image representations, to capture semantic and spatial relationships. Based on the above problems, a visual question answering model (CAM-GCN) based on Cooperative Attention Mechanism (CAM) and Graph Convolutional Network (GCN) is proposed. First, the graph learning module and the concept of graph convolution are combined to learn the problem-specific graph representation of the input image and capture the interactive image representation of the specific problem. Image region dependence, and finally, continue to optimize the fused features through feature enhancement. the test results on the VQA v2 dataset show that the CAM-GCN model achieves better classification results than the current representative models. © 2022 ACM.

关键词： Semantics

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Multimodal Dialogue Generation Based on Transformer and Collaborative Attention 5

Multimodal Dialogue Generation Based on Transformer and Coll...

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Guan, Wei Zhang, Zhen Ma, Li Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

In view of the fact that the current multimodal dialogue generation models are based on a single image for question-and-answer dialogue generation, the image information cannot be deeply integrated into the sentences, resulting in the inability to generate semantically coherent, informative visual contextual dialogue responses, which further limits the application of multimodal dialogue generation models in real scenarios. this paper proposes a Deep Collaborative Attention Model (DCAN) method for multimodal dialogue generation tasks. First, the method globally encode the dialogue context and its corresponding visual context information respectively;second, to guide the simultaneous learning of interactions between image and text multimodal representations, after the visual context features are fused with the dialogue context features through the collaborative attention mechanism, the hadamard product is used to fully fuse the multimodal features again to improve the network performance;finally, the fused features are fed into a transformer-based decoder to generate coherent, informative responses. in order to solve the problem of continuous dialogue in multimodal dialogue, the method of this paper uses the OpenVidial2.0 data set to conduct experiments. the results show that the responses generated by this model have higher correlation and diversity than existing comparison models, and it can effectively integrate visual context information. © 2022 ACM.

关键词： Image enhancement

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