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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 3245 条记录，以下是181-190 订阅

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Review on the Challenges and Future Directions of Deep learning-Based Techniques for Advance Prediction of Cardiac Attack 1

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

作者： Kumar, Shrawan Thakur, Bharti Yogananda School of AI Computer and Data Sciences Shoolini University H.P. Solan India

ISBN: (数字)9783031317231

ISBN: (纸本)9783031317224

One of the challenging medical areas that makes it difficult to predict an event before it occurs is a cardiac attack or syndrome. Insufficient oxygen intake will result in improper electric heart rhythm balance, changes in irregular heartbeats over time, and imbalance in all bodily functions throughout the day, which will ultimately cause an attack of heart valves and blood flow. Since the disease has a very slow response time, early-stage prediction is necessary. The main goal of the study is to create an expert system that uses multi-variate feature predictors and Deep learning (DL) classification algorithms to handle independent and multi-class variables to predict the incidence of a cardiac attack for a patient at the current stage or in the future. The research aims to create a framework model using cutting-edge algorithmic techniques that will motivate medical professionals to find early warning signs of cardiac attack so they can find ways to save patients. Around fifty reviews are essential for designing the architecture of the expert system design for the prediction of a cardiac attack according to a thorough literature re- view on different algorithms and their effectiveness in predicting cardiac attacks. This knowledge review proposes may lead to addressing early cardiac attacks that accept the selected features from different feature predictors and forms a fuzzification of selected features along with rule sets and knowledge-base from experts using the DL and data mining algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Expert systems

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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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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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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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A Study on machine learning Algorithms: Forecasting Crop Prices 3rd

A Study on Machine Learning Algorithms: Forecasting Crop Pri...

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

作者： Hirpara, Jignesh Doshi, Milan Patel, Amit Choudhury, Koushik Marwadi University Rajkot-Morbi Road Gujarat Rajkot360003 India Department of C.S. & I.T Atmiya University Rajkot Gujarat India

ISBN: (纸本)9783031862984

In this era, with the technology used in every field, the agriculture market also generates a huge amount of revenue every day. Computer technology plays a vital role in managing and finding meaningful information about it. To manage large datasets, a field that integrates data mining, machine learning, mathematics, computer applications, and artificial intelligence is used. Crop price forecasting is a demanding and interesting method for the agricultural sector because it is dependent on future crop production worldwide. The maximum crop price for the current session or the next year is something that the entire agriculture community is interested in knowing. Currently, crop price analysis dominates the study instead of agriculture crop amount forecast. In comparison to other nations, crop yield in India is quite uneven when measured by the agriculture fraternity. It is insufficient as a base rate for any chosen crop price if an appropriate MSP is not provided. There may be a decrease in Indian poverty when these farmers receive or determine fair crop prices. A vast amount of commodity data is produced in agriculture today. A significant amount of commodity data is produced by the agriculture sector, but sadly, most of this data is not utilized to uncover hidden information. machine learning algorithms are used to make a machine think like a human, comprehend the data sets that are currently available, and compare them to similar events that have happened in the past. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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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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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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