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Custom dataset Text Classification: An Ensemble Approach with machine learning and Deep learning Models

Custom Dataset Text Classification: An Ensemble Approach wit...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Deekshitha Valluri Suneetha Manne Nikitha Tripuraneni dept of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

In academic institutions, commercial enterprises, research centers, technology-heavy businesses, and government funding agencies, maintaining consistent data is a major difficulty. For an entity, which might be anything from an object to a place or thing, most data are irregular. These days, to identify significant patterns that represent the data, entity links in a dataset are investigated by text mining or data analytics. With this knowledge, alternatives are then taken. Analytics creates data and finds patterns by turning words into numbers. In the end, better data organization results in better conclusions. However, classifying and processing each piece of data by hand is difficult. As a result, in the domain of Natural Language Processing (NLP), which looks at grammatical and lexical patterns, intelligent text processing systems have emerged. Before mining, it's imperative to examine and comprehend the nature of the data. Text categorization requires automation because of the increasing volume of data and the requirement for accuracy or precision. It is an interesting study opportunity to develop automatic classifying texts with deep learning methods to handle difficult NLP tasks with semantic constraints. Text categorization is founded on data analytics, which can facilitate information discovery. The majority of the advantages can be obtained by applying these insights to emerging applications that support decision-making, improve resources. Improved techniques for parameter optimization demonstrating effective knowledge discovery will be the focus of future research studies.

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AI4LEGAL-KGSUM 2022 - Joint Proceedings of the 3th international Workshop on Artificial Intelligence Technologies for Legal Documents and the 1st international Workshop on Knowledge Graph Summarization, co-located with the 21st international Semantic Web conference, ISWC 2022

AI4LEGAL-KGSUM 2022 - Joint Proceedings of the 3th Internati...

引用

3rd international Workshop on Artificial Intelligence Technologies for Legal Documents and the 1st international Workshop on Knowledge Graph Summarization, AI4LEGAL-KGSUM 2022

The proceedings contain 14 papers. The topics discussed include: an anonymization tool for open data publication of legal documents;building and analyzing the Brazilian legal knowledge graph;introduction of artificial intelligence in Belgian court: failures, challenges and opportunities;LawSampo portal and data service for publishing and using legislation and case law as linked open data on the semantic web;finding case law: leveraging machine learning research to enhance public access to UK judgments;evaluation of data augmentation for named entity recognition in the German legal domain;towards building a legal virtual assistant based on knowledge graphs;an Indian court decision annotated corpus and knowledge graph;and summaries of knowledge graph entities: first steps to measure the relevance of symptoms to infer diseases.

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Research on Feature learning Algorithm of Social Media based on Deep Network

Research on Feature Learning Algorithm of Social Media based...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Haoming Bai Linjiao Wu Chengdu University of Technology College of Earth Science Chengdu Sichuan China University of Shanghai for Science and Technology College of Communication and Art Design Shanghai Shanghai China

Deep learning is a branch of machine learning, which has been used to solve many problems in the fields of computer vision, speech recognition, natural language processing and so on. Deep neural networks are trained by inputting large amounts of data into them, and then they learn how to recognize patterns from these data. This process is called supervised training because the network learns from tagged examples (such as images) or other inputs (such as audio files or text documents). The social media based performed by two models such as Deep network structure feature learning and Feature learning of social media based on deep network. The suggested approach achieved the better transmission of social media by M10, DBLP and Cora of values about 0.70, 0.75 and 0.80 respectively. The problem with deep learning is that it is difficult to train these networks on new tasks without accessing a large amount of tag data for each task. In addition, there are many challenges in teaching the knowledge learned in the learning process.

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Application of water injection profile recognition based on machine learning method in F oilfield 3

Application of water injection profile recognition based on ...

引用

3rd international conference on Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field, ICMSP 2021

作者： Liu, Yiru Gu, Jianwei Xu, Zhijia Jiang, Zhen UPC Qingdao China The Eighth Oil Production Plant PetroChina Changqing Oilfield Company Limited PCOC Xi'an China CNPC Dagang Oilfield Exploration and Development Research Institute Tianjin China

ISBN: (纸本)9781665437158

Interlayer water injection profile is an important data for oilfield development and adjustment. At present, it is mainly based on field test, with high cost and few data. For the problem of water injection profile identification in F oilfield, this paper uses the dynamic and static data of reservoir to carry out the prediction of water injection profile by machine learning method. Based on the analysis of the influencing factors of water absorption profile, the sensitive parameters of 11 dimensions and their calculation methods are proposed, and the basic data sample database is constructed. XGBoost ensemble learning algorithm is used to realize small sample database prediction of longitudinal water injection profile in F oilfield. Compared with KH split water absorption method, it can more accurately grasp the change law of each water injection well's longitudinal water absorption status in the well area. The test results show that the recognition accuracy of the new identification method is 85.5% compared with the logging interpretation results. The water absorption index of each layer is predicted, and the water absorption difference between layers is clearly grasped, which points out the direction for injection production allocation. © 2021 IEEE.

关键词： Water absorption

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Predictive maintenance project implementation based on data-driven & data mining

Predictive maintenance project implementation based on data-...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Zineb Znaidi Moulay El Houssine Ech-Chhibat Azeddine Khiat Laila AIT El Maalem Laboratory M2S21 EN SET MOHAMMEDIA UNIVERSITY HASSAN II CASABLANCA MORROCO Laboratory SSDIA EN SET MOHAMMEDIA UNIVERSITY HASSAN II CASABLANCA MORROCO

Today's global economy is experiencing a sharper slowdown than ever before. With a higher risk of deterioration or disappearance of manufacturing companies than usual. As a result, manufacturers must face this situation by adopting strong strategies, including equipment management and cost optimization in connection. This is why predictive maintenance is an important pillar in achieving these objectives. Except that predictive maintenance requires a budget and time for the proper implementation, such investment can only be accepted by investors if it is sure that the expected results will contribute effectively to the reduction of maintenance costs. To do this, it is necessary to assess the risks that could impact this project's success, mainly the data reliability and the machine learning model performance. The aptitude to predict the need for maintenance of a system in a perfect way at a specific time is one of the main challenges in this scope. This paper proposes a methodology for data management in the case of predictive maintenance project implementation. It starts by introducing the project study phase for cost & benefit evaluation based on data-driven. Then it presents the predictive concept based on data mining & machine learning tools for optimal model building, as well for the project performance follow up a monitoring approach is proposed based on the continuous improvement concept.

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SAM based Computational Analysis of Root Mean Square Error for SPES Using LR, RF & ARIMA machine learning Models

SAM based Computational Analysis of Root Mean Square Error f...

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Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), international conference on

作者： Rudresh Jani Palak Patel Siddharth Joshi Soham Vyas Department of Electrical Engineering Pandit Deendayal Energy University Gandhinagar Gujarat India Department of Electrical Engineering School of Energy Technology Pandit Deendayal Energy University Gandhinagar India Non executive consultant APA Enviornment Pvt. Ltd. Stafford United Kingdom Department of Computer Engineering School of Technology Pandit Deendayal Energy University Gandhinagar Gujarat India

Renewable energy is crucial in addressing various global challenges like climate change, global warming, human development index, etc. To address these challenges, wind and solar energy are the mature technologies to harness green power. Integrating machine learning into the renewable energy sector can lead to more efficient, reliable, and sustainable energy systems. The work proposed for the solar photovoltaic energy system. The solar photovoltaic system of 500kW AC is simulated in the System Advisor Model (SAM). The data for annual energy generation is fetched to form the dataset. This dataset from SAM contains the hourly energy generation data for a particular year. The comparative analysis is proposed after mining the dataset. The machine learning algorithms are Linear Regression, Random Forest and ARIMA. To evaluate the performance of these algorithms, the statistical measure, namely Root Mean Square Error (RMSE), was used. The comparative analysis shows the effectiveness of the proposed work and its dataset for the machine learning algorithms.

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Investigating & Uncovering the Future of Smart Residential Automation: A Historical Comprehensive Survey and machine learning Insights

Investigating & Uncovering the Future of Smart Residential A...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Khalid Aziz Sakshi Dua Prabal Gupta SCA Lovely Professional University Phagwara Punjab India Department of ECE SCSE Lovely Professional University Phagwara Punjab India

This paper presents a comprehensive review of the various technologies employed in the realm of smart home automation. The evolution of home environments into interconnected ecosystems has been fueled by cutting-edge technologies, and this review seeks to shed light on the prominent roles played by Bluetooth, Wi-Fi (wireless Fidelity), Internet of Things (IOT (internet of things)), gesture recognition, and machine learning. The study delves into the distinctive attributes, applications, and implications of each technology in the context of smart home automation. It explores the functionalities and advantages of Bluetooth-based smart home systems, which allow seamless communication and control of devices within short distances. Likewise, Wi-Fi (wireless Fidelity)-based systems provide wider connectivity and remote control, enhancing the convenience and accessibility of smart home features. IOT (internet of things)-based solutions are examined for their capability to interconnect and synchronize a multitude of devices and sensors, creating a cohesive ecosystem that responds to user needs. Gesture recognition technology is highlighted for its intuitive and contactless interaction with smart home devices, adding a layer of convenience and futuristic appeal. machine learning's pivotal role in smart home automation is thoroughly discussed, showcasing its capacity to predict, adapt, and enhance user's experiences. From activity recognition to energy management, machine learning's ability to analyze data and make informed decisions elevates the overall intelligence and responsiveness of smart homes. The review critically evaluates the challenges, trends, and prospects of these technologies, emphasizing factors such as data security, interoperability, and user experience. By providing an overarching panorama of the technologies, this paper envisions a future where smart homes evolve into indispensable components of daily life, seamlessly integrating these technologies to off

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A Visionary Analysis on Ways and Techniques Used for Identification of Citrus Plant Disease

A Visionary Analysis on Ways and Techniques Used for Identif...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Arundhati Bora Parismita Sarma Dept. of Information Technology Gauhati University Guwahati India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This review paper discusses the identification of different leaf diseases in plants with specific reference to the lemon group. In recent times, it has been seen that leaf diseases can be identified very accurately using different machine learning and deep learning methods from the digital images of the infected leaves. The accuracy varies depending on the tagged sample size, variation of the samples, and method used. In this paper, the authors have analyzed almost forty (40) research papers and prepared a number of tables for easy understanding of techniques used and their performance in terms of percentage of accuracy. Segmentation of leaves is performed in most techniques in the image pre-processing phase. Analysis is carried out on the given tables from different perspectives and a future way of working is formulated from this survey of research.

关键词： Deep learning Surveys Plant diseases Accuracy Reviews Computational modeling Transfer learning Neural networks data models Object recognition

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Prediction Of Breast Cancer Analysis Using Cnn

Prediction Of Breast Cancer Analysis Using Cnn

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： M. Brindha S. Ruba Sree V. Praveen A. Vasanth Dept of Biomedical Engineering KSR Institute for Engineering and Technology Namakkal Tamil Nadu India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Convolutional neural networks (CNNs), a recent invention in machine learning, have shown remarkable success in bioinformatics, particularly in medical imaging. Mammography classification and pathology are regarded as a crucial obstacle to the widespread identification of breast cancer. The mammography assessment process is time consuming, tedious, expensive, and incredibly error prone. This work proposes an end-to-end computer aided diagnostic system called YOLO, which transforms DICOM format to pictures without erasing any data in order to get around this problem. Without the need for human interaction, the output picture is delivered to identify full-field digital mammograms and makes the distinction between benign and malignant tumors. YOLO topologies are employed in this article to compare their respective performances for bulk detection and classification in mammograms. Using features from classification and data mining is a quick and easy approach to group results.

关键词： YOLO Location awareness machine learning algorithms Neural networks Transforms Breast cancer Mammography

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Incorporating text and emoticon features in sarcasm and irony detection: A deep learning-based approach

Incorporating text and emoticon features in sarcasm and iron...

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data Science and Computer Application (ICDSCA), IEEE international conference on

作者： Xinjun Ma Tianning Li Zhida Jin College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China College of Computer Science and Engineering Northwest Normal University Lanzhou China

Emotions play a crucial role in social media interactions, and sarcasm and irony often convey complex emotions that can be challenging to detect accurately. This study focuses on automating the recognition of sarcasm and irony in Chinese through various computational methods using machine learning algorithms. This study utilizes both text and emoticon/emoji feature to improve detection accuracy. The study employs a combination of linguistic rules, word embeddings, and neural network models for detecting irony in Chinese microblog comments. The results highlight the advantages of deep learning techniques and pre-trained word embeddings in enhancing the accuracy of irony detection. Furthermore, the study demonstrates the viability of the proposed models by focusing on microblog posts related to social events. These contextualized experiments show promising results and pave the way for future studies to explore other domains and data sources. Overall, this study has made significant contributions to the field of irony recognition research and laid the groundwork for more complex models in the future, with practical implications for enhancing sentiment analysis in various fields.

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