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

共 3244 条记录，以下是181-190 订阅

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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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ProductGuard: Empowering Informed Choices for Safe Living

ProductGuard: Empowering Informed Choices for Safe Living

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Prof. Sandyarani Vadlamudi K. S. Shashikala Suyash Ganesh Patil Rajath U Shetty Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bangalore Karnataka

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In a landscape where consumer decisions significantly influence personal health and well being, the necessity for informed choices regarding product safety is paramount. This project introduces ProductGuard, a mobile application designed to empower users in making conscientious and informed decisions about skincare and personal care products. The project integrates four core components aimed at assisting users in evaluating product safety, comparing ingredients, providing personalized recommendations based on skin type, and offering expert guidance through a chatbot interface. The first component employs advanced image analysis technology utilizing a dataset extracted from the Environmental Working Group (EWG) website, enabling users to capture product ingredient images and receive detailed safety analyses. The second component allows comparative analysis by uploading images of two product ingredient lists, swiftly determining the safer option. Leveraging deep learning models and MobileNetV2, the third component performs skin type classification based on facial images, ensuring accurate categorization into dry, oily, normal, or sensitive skin, further providing tailored product recommendations. Lastly, a chatbot powered by ChatGPT serves as the fourth component, furnishing users with real-time guidance on skincare, ingredient queries, and safe living practices. Developed using React Native, Flask integration, and Python backend, this application amalgamates cutting-edge technology with user-centric design for seamless navigation and accessibility across various devices. ProductGuard surpasses existing systems by offering a holistic approach to skincare analysis, integrating advanced image recognition, personalized recommendations, and real-time expert assistance, thereby empowering users to make informed and safer skincare choices.

关键词： Deep learning Image analysis Accuracy Chatbots Skin Real-time systems User experience Reliability Product safety Usability

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862953

The proceedings contain 28 papers. The special focus in this conference is on Advancements in Smart Computing and Information Security. The topics include: Prediction of Anti-Cholinesterase Inhibitor for Alzheimer Disease Using machine learning Techniques;scrutinize Search Engine Optimization Strategies with Artificial Intelligence to Rank a Website;a Study on machine learning Algorithms: Forecasting Crop Prices;improving Student Stress Analysis: Novel Methods for Collecting, Preprocessing, and Fusing Features;extraction of Normal and Abnormal Region in Colposcopy Image to Support Cervical Cancer Clinical Decision;optimizing Pricing Strategies: A Comprehensive Framework Using Bayesian Inference and Game Theory;hybrid Ensemble Gradient Boosting Algorithm to Predict Diabetes Health Care Analytics;a Review on machine learning Algorithms for Real-Time Traffic Management;hyperparameter-Tuned Intention mining for Mental Health Diagnosis Using Logistic Regression;machine learning for Accessible and Precise Assessment in Smart Monitoring Systems;an Efficient Model for Academic Performance Prediction of the University Students;comprehensive Comparative Study on data mining and machine learning Approaches for Fraud Detection in Financial Services;prediction of Stock Price and Detection of Stock Market Trends Using Adaptive learning Techniques;asthma Prediction Using Fuzzification and machine learning Based Ensemble Classifier;detection and Analysis of Features of Optic Nerve Head Using Retinal Fundus Images of an Eye for a Priori Prediction of Glaucoma;a Hybrid Deep learning Framework for Uncertain Supply Chains: An Optimization Approach;a Comprehensive Review of machine learning Techniques in Recommender System for E-Commerce Platform;Ethics of AI in the Educational Sector - Navigating the Moral Landscape;international Law and AI Interface.

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031863042

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031863011

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862892

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Enhancing Smart Healthcare Services Through Deep learning-Based Medical data Analysis

Enhancing Smart Healthcare Services Through Deep Learning-Ba...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Sheik Jamil Ahmed Saira Banu Atham Hajar Kadhim Deepak Gupta Aaied Eqab Muraay Majed M. K. Mohamed Faizal Department of CSE Madanapalle Institute of Technology & Science Andhra Pradesh India Presidency School of Computer Science and Engineering Presidency University Bangalore Karnataka India Department of Medical Laboratory Techniques Uruk University Baghdad Iraq Department of Mechanical Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Medical Engineering Al Kitab University Kirkuk Iraq Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In order to enhance patient care and medical decision-making, smart healthcare services are progressively utilizing cutting-edge technologies. Deep learning (DL), one of these technologies, has become a potent instrument for evaluating enormous volumes of medical data, allowing for improved clinical procedures and more precise diagnosis. This study focuses on enhancing smart healthcare services through deep learning-based medical data analysis, with a particular emphasis on Named Entity recognition (NER) and Relation Extraction (RE) tasks from clinical records. The study is implemented using Python software on the MIMIC-III dataset, utilizing an optimized Particle Swarm Optimization (PSO)-BioBERT model. The model achieved remarkable performance in both NER and RE tasks, with accuracy rates of 99.2% for NER and 99.5% for RE, and F1-scores of 98.1% and 98.8%, respectively. Compared to traditional models like Bi-LSTM and BERT, the PSO-BioBERT model significantly outperformed them, with the proposed model exceeding Bi-LSTM by 6.2% in accuracy and BERT by 12.12%. The findings highlight that the PSO-BioBERT model provides substantial improvements in extracting and classifying medical entities and their relationships from clinical data. These developments revolutionize smart healthcare services by making it possible to analyze unstructured medical information more accurately and consistently. The study emphasizes the effectiveness of deep learning models, particularly PSO-BioBERT, in improving data-driven decision-making in healthcare, which could ultimately enhance patient care outcomes and healthcare system efficiency. Future work could focus on integrating these models with real-time clinical systems for broader applications, including predictive analytics and personalized treatment plans. This approach holds promise for revolutionizing how healthcare data is utilized to improve overall patient care.

关键词： Deep learning data analysis Accuracy Decision making Medical services Software Real-time systems data mining Medical diagnostic imaging Python

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Autoencoder-Lstm Framework for Accurate Foreign Exchange Volatility Prediction

Autoencoder-Lstm Framework for Accurate Foreign Exchange Vol...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Parijat Saurabh Malik Bader Alazzam Nagarajan S D. Harikishan Reddy Sakar Fatah Sulaiman B Kiran Bala Department of Economics Mahatma Gandhi Kashi Vidyapith Varanasi India Faculty of Information Technology Jadara University Irbid Jordan School of Management Studies Bannari Amman Institute of Technology Erode Tamilnadu India Department of Management Studies Sri Venkateswara College of Engineering (Autonomous) Andhra Pradesh India Department of Business Administration College of Administrative and Financial Sciences Knowledge University Erbil Iraq Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Accurately predicting foreign exchange volatility is crucial for financial institutions, traders, and policymakers. This makes it an extremely complex and dynamic market, hence failing traditional methods of prediction for capturing intricate patterns present in time-series data. Advanced machine learning models, have shown great potential in addressing this challenge. We propose an Autoencoder-Lstm model that leverages the strengths of both autoencoder for feature extraction and LSTM networks for time-series forecasting. These networks have gated mechanisms controlling the flow of information, particularly suitable for financial forecasting in their ability to extract long-term dependencies and trends which improve the forecast's accuracy as far as selective memory in prediction of volatility goes. The autoencoder simplifies input data by extracting important features and dropping out unnecessary noise. After extracting features from the dataset the network processes them efficiently while recognizing past time series connections. The research is implemented using Python software, for model development. The framework is evaluated using real-world foreign exchange data, incorporating both raw price features and derived features such as returns and volatility measures. The model is trained and tested on a dataset of daily forex rates, achieving an impressive prediction accuracy of 99.2%. Performance metrics, including RMSE (0.012), MAE (0.008), and MSE (0.00014), underscore the model's ability to minimize prediction errors, significantly outperforming traditional models. The results demonstrate the Autoencoder-Lstm’s effectiveness in predicting exchange rate volatility, making it a valuable tool for financial forecasting. This model's high accuracy and robustness further establish its potential for broader applications in other time-series prediction tasks.

关键词： Accuracy Autoencoders Time series analysis machine learning Predictive models Feature extraction Software Robustness data mining Forecasting

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862922

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