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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Boruta-AttLSTM: A Novel Deep learning Architecture for Soil Moisture Prediction 3rd

Boruta-AttLSTM: A Novel Deep Learning Architecture for Soil ...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Koné, Bamory Ahmed Toru Bouaziz, Bassem Grati, Rima Boukadi, Khouloud Faculty of Economics and Management of Sfax University of Sfax Sfax Tunisia Higher Institute of Computer Science and Multimedia University of Sfax Sfax Tunisia College of Technological Innovation Zayed University Dubai United Arab Emirates

ISBN: (纸本)9783031463372

Water scarcity is worsening due to poor water management in irrigated areas, which directly impacts global food safety. Furthermore, effective irrigation scheduling necessitates predicting future soil moisture content, representing soil water availability. For this purpose, the current study proposes a novel data-driven architecture based on deep learning algorithms to predict soil volumetric water content. The proposed architecture combines the time-processing ability of Long Short-Term Memory with the attention mechanism’s ability to process long sequences. The suggested architecture’s resulting model is compared to a 2-layer LSTM in terms of MSE, MAE, RMSE, and R2 score. This study also examines the relationships between various climate and soil parameters and targets soil moisture. The relevance of input features is considered by the feature selection strategy using their computed shapley values. The findings of this study suggest that attention mechanisms can increase the performance and generalizability of regular LSTMs. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Soil moisture

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Exploring Text Vectorization on the Polarity Detection of Spanish Comments 3rd

Exploring Text Vectorization on the Polarity Detection of Sp...

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3rd international conference on Electronic Governance with Emerging Technologies, EGETC 2024

作者： Rios, Fernando Fernandez Martinez-Rodriguez, Jose L. Crespo-Sanchez, Melesio Rios-Alvarado, Ana B. Ortiz-Rodriguez, Fernando Guerrero-Melendez, Tania Y. UAM Reynosa-Rodhe Autonomous University of Tamaulipas Reynosa Mexico Faculty of Engineering and Science Autonomous University of Tamaulipas Victoria Mexico Faculty of Medicine Autonomous University of San Luis Potosi San Luis Potosi Mexico

ISBN: (纸本)9783031770289

The widespread use of the internet and social media has generated a vast amount of information on various topics, thanks to user comments and opinions. This information can be used as a subject of study for analysis and research. For example, comments about products, services, or government entities can be utilized to understand their public perception. Due to the immense volume of data generated, different techniques have been developed for automatic opinion recognition, also known as opinion mining. However, advancements in the Spanish language are still limited, leaving room for exploration in tasks such as text vectorization to determine opinion polarity through a supervised classification process. This paper presents a strategy for Spanish opinion mining to identify the sentiment expressed in comments posted on the social networking service X (formerly Twitter), classifying them according to their polarity (positive, negative, or neutral). In this sense, we propose a hybrid text vectorization approach, combining traditional vectorization techniques with grammatical POS tags. For the experiments, various vectorization techniques were evaluated as input for supervised and deep learning algorithms. The results demonstrate that hybrid vectorization improves classification performance for some cases compared to traditional vectorization models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Social Media Emotional Analysis

Social Media Emotional Analysis

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： Pavith P Nakshathra D.V Dhivya J Vijeta Iyer Department of Civil Engineering Kumaraguru College of Technology Coimbatore India Department of Data Science Kumaraguru College of Liberal Arts and Science Coimbatore India Department of Mathamatics Kumaraguru College Of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Individuals express an extensive array of emotions on social media: a platform that is both vibrant and perpetually evolving. To effectively comprehend social trends, how brands are perceived and the general public's attitude, it is crucial to examine these emotions. There are two primary methods for interpreting and classifying emotions derived from written content: sentiment analysis and emotion detection. Sentiment analysis allows us to ascertain whether a particular piece of information is positive, negative, or neutral; however, emotion detection delves deeper to pinpoint specific feelings such as joy, frustration, or anxiety. This distinction is significant because it provides a more nuanced understanding of public sentiment. Although both methods are valuable, they serve different purposes in the analysis of emotional expression on social media. This paper examines sentiment analysis and emotion recognition within social media posts, investigating the methods, applications and challenges involved. It reviews a range of techniques for sentiment analysis: lexicon-based approaches and machine-learning models, as well as various frameworks for classifying emotions. However, the study also addresses challenges such as detecting sarcasm and the constantly evolving nature of online language. By employing these analytical methods, researchers and organizations can acquire valuable insights into patterns of digital engagement, indications of mental health and overall user experiences. This ultimately enhances our comprehension of human emotions in the digital age, although it presents certain complexities.

关键词： Sentiment analysis Emotion recognition Social networking (online) Reviews User-generated content Organizations Mental health Market research Complexity theory Telecommunication computing

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Expression of Concern for: Applying Deep learning Strategies for Optimizing Real-Time data Analysis in machine learning

Expression of Concern for: Applying Deep Learning Strategies...

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

作者： Varun Mishra Sudhanshu Dev Akhilendra Pratap Singh Gautham Krishna Geetha B Rutuja S. Kadam Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Civil Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

Deep mastering is becoming increasingly essential for fact analysis in machine learning. It has been used to remedy various applications, from natural language processing to photograph recognition, and has made goodsized achievements in many fields. In this paper, we discover a way to use deep getting-to-know for actual-time data evaluation optimization in gadget learning. Mainly, we present a deep getting-to-know framework for real-time statistics analysis optimization, including an expansion of deep gaining knowledge of fashions and techniques. We speak about the capability of actual-time statistics evaluation optimization with deep getting-to-know and numerous improvement techniques for making the optimization method extra green. Eventually, we offer an empirical evaluation of the proposed framework in machine studying packages, demonstrating the usefulness of presenting a deep learning-based totally technique to real-time statistics analysis optimization. We finish the paper with a dialogue of destiny directions for deep learning research for actual-time information evaluation optimization.

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3rd international conference on Science of Cyber Security, SciSec 2021

3rd International Conference on Science of Cyber Security, S...

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3rd international conference on Science of Cyber Security, SciSec 2021

ISBN: (纸本)9783030891367

The proceedings contain 18 papers. The special focus in this conference is on Science of Cyber Security. The topics include: An Event-Based Parameter Switching Method for Controlling Cybersecurity Dynamics;dismantling Interdependent Networks Based on Supra-Laplacian Energy;simulations of Event-Based Cyber Dynamics via Adversarial machine learning;stochastic Simulation Techniques for Inference and Sensitivity Analysis of Bayesian Attack Graphs;multi-granularity Mobile Encrypted Traffic Classification Based on Fusion Features;Caps-LSTM: A Novel Hierarchical Encrypted VPN Network Traffic Identification Using CapsNet and LSTM;SARR: A Cybersecurity Metrics and Quantification Framework (Keynote);a data-Free Approach for Targeted Universal Adversarial Perturbation;a Multi-level Elastic Encryption Protection Model;preface;DWT-DQFT-Based Color Image Blind Watermark with QR Decomposition;using Chinese Natural Language to Configure Authorization Policies in Attribute-Based Access Control System;protecting data Privacy in Federated learning Combining Differential Privacy and Weak Encryption;a New Method for Inferring Ground-Truth Labels and Malware Detector Effectiveness Metrics;botnet Detection Based on Multilateral Attribute Graph;mining Trojan Detection Based on Multi-dimensional Static Features.

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A Novel Multi-view Fuzzy Clustering Algorithm Based on Fuzzy C-Means 1

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3rd international conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Lin, Ze Chen, Yixiang National Engineering Research Center of Trustworthy Embedded Software East China Normal University Shanghai200062 China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Fuzzy c-means (i.e., FCM) is a representative clustering method that is widely used in machine learning and pattern recognition. It can describe the degree of fuzziness of objects to clusters using memberships, but it cannot assess the degree of fuzziness of the clustering results. Motivated by such issues, in this paper, we propose a multi-view fuzzy clustering method based on FCM and a fuzzy assessment is introduced to assess the fuzziness of clustering results. Specifically, we constructed a multi-view FCM to obtain a reasonable membership matrix by fully integrating multi-view information. Based on this, we propose a fuzzy assessment method to evaluate the fuzziness of the clustering results and mark the objects between the different clusters (i.e., the object has the feature of multiple clusters). The experimental result shows that the proposed multi-view fuzzy clustering method achieves better performance than several related clustering methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： pattern recognition

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State-of-the-Art Methods for Fine-Grained Emotion Detection from Malayalam Text using Deep learning: A Survey

State-of-the-Art Methods for Fine-Grained Emotion Detection ...

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international conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

作者： K Anuja P C Reghu Raj Remesh Babu K R Dept. of Computer Science and Engineering Govt. Engineering College Palakkad A. P. J. Abdul Kalam Technological University Kerala India

Social media has changed the way people express their opinions, views, and feedback about anything in their minds. This large volume of sentiment-rich data needs to be examined properly in order to comprehend the mood, attitude, or sentiment underlying those words. Computers can process, interpret, and analyze vast volumes of natural data using natural language processing (NLP), which helps them to comprehend human languages. Now all organizations are looking for more fine-grained sentiment analysis to plan their business strategy. This paper investigates state-of-the-art textual data analysis using NLP techniques. The increased use of Indian languages in communicating platforms is a challenging task for opinion mining. In this paper, we survey the state-of-the-art methods based on deep learning and fuzzy rules for sentiment analysis.

关键词： Deep learning Computers Sentiment analysis Emotion recognition data analysis Social networking (online) Mood

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machine learning Based Impostor Detection By Invariant Features From Nir Finger Vein Imaging

Machine Learning Based Impostor Detection By Invariant Featu...

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Innovations in Computer Science & Software Engineering (ICONICS), international conference on

作者： Akhtar Rasool Faisal Rehman Nadeem Sarfaraz Hanan Sharif Rashid Khan Abdul Manan Khan Department of Computer Science & Information Technology Lahore Leads University Lahore Pakistan

Biometrics mostly used as personal identification accordingly securing a client against the unapproved utilization of his or her identity. Acquiring biometric information is getting to be simpler. Smartphones and other advanced technologies exist from which biometric data can collect easily without the knowledge of others. Finger vein authentication is a method for biometric verification that depends on a vein pattern, which is located beneath the human finger’s skin. Veins are covered with skin that cannot be copied by others. In this research, our focus is on these invariant features of finger veins. We have collected invariant features from various recent features extraction techniques and then classified with state-of-the-art machine learning classifiers. For this purpose, we have used publicly available finger vein image databases. The performance has been evaluated by various evaluation metrics and comparative analysis of various machine learning classifiers has been presented to describe the performance of these classifiers on the said data set.

关键词： Measurement Biometrics (access control) Veins Imaging machine learning Feature extraction Skin

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A Comparative Study of Performance Metrics of data mining Algorithms on Medical data 3rd

A Comparative Study of Performance Metrics of Data Mining Al...

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3rd international conference on Communications and Cyber Physical Engineering, ICCCE 2020

作者： Suragala, Ashok Venkateswarlu, P. China Raju, M. JNTUK-UCEV Vizianagaram India JNTUK-UCEV Vizianagaram India JNTUK-UCEV Vizianagaram India

ISBN: (纸本)9789811579608

Computers have brought about significant technological improvements leading to the creation of enormous volumes of data, particularly in health care systems. The availability of vast amounts of data contributed to a greater need for data mining techniques to produce useful knowledge. Accurate analyzes of medical data are gaining early detection of illness and patient care with the increase of data in biomedical and health care communities. The data mining is one of the major approaches for developing sophisticated algorithms for classification of data. Some have castigated data mining for not meeting all of the humanistic statistics specifications [5]. Classification of diseases is that one of the main applications of data mining and many important attempts have been made in recent years to improve the accuracy of the diagnosis of diseases through data mining. We used four prominent data mining algorithms such as Naive Bayes Classifier, K-Nearest Neighbors (KNN) Classifier, Artificial Neural Networks (ANN) and Support Vector machine (SVM) algorithms to develop predictive models using that the ILPD (Indian Liver Patient data Set) from the UCI machine learning repository. For performance comparison purposes, we used the 10-fold cross validation method to calculate the estimation of six predictive models. We find that the support vector machine delivers the best results in a 74.82 percent accuracy classification and 56.55 percent accuracy of the Naive Bayes performed the worst. The performance metrics of classifiers were analyzed on medical dataset further sections below. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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data-driven Business Intelligence Platform for Smart Retail Stores 3

Data-driven Business Intelligence Platform for Smart Retail ...

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3rd international conference on Advancements in Computing, ICAC 2021

作者： Eheliyagoda, D.R.M.R.R.D.R.S. Liyanage, T.K.G. Jayasooriya, D.C. Nilmini, D.P.Y.C.A. Nawinna, Dasuni Attanayaka, Buddhima Sliit Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9781665408622

The following research paper presents the design and development of a data-driven decision support platform for the effective management of contemporary retail stores in Sri Lanka. This research has four core components, as a solution to the identified shortcomings. These components are Customer Relationship Management (CRM), Supplier Relationship Management (SRM), Price and Demand estimation, and Branch and Employee Performance Monitoring and Rating. The developed system has features such as product replenishment levels, decrease capital movement, reduced material wastage, better item assortment, provide supplier service efficiency, improve employee and branch-level efficiency, and elevated client *** decision support system used machine learning (ML) technologies such as LSTM (Long short-Term memory) and ARIMA (Autoregressive integrated moving average) models, Regression, Classification, and Associate Rule mining Algorithms as key technologies. data were obtained from websites such as Kaggle and other free platforms for the analysis of datasets. The resulting platform was able to perform with an accuracy of over 90% for all four core components with the tested data sets. The system presented would be particularly beneficial for the top management in retail stores to make effective and efficient decisions based on predictions and analyzes provided by the system. © 2021 IEEE.

关键词： Sales

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