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Machine Learning Approaches for Survival Prediction in Covid-19 Patients: A Comparative Analysis

Machine Learning Approaches for Survival Prediction in Covid...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Islam, Md. Muhiminul Shuvo, M.I.R. Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350350289

The COVID-19 pandemic has underscored the significance of accurately predicting patient survival in order to promptly administer efficient medical care. The augmentation of biological and healthcare service data volume has been found to enhance the precision of disease prognosis, survival prediction, and other clinical assessments. Numerous biological characteristics serve as the underlying factors contributing to the etiology of numerous diseases. Therefore, it is imperative to have precise medical data that possesses appropriate characteristics in order to facilitate an analysis that exhibits exceptional clinical accuracy. In order to effectively analyze data, it is imperative to employ a machine learning model that is both exact and accurate in predicting sickness or survival outcomes. An expeditious and accurate assessment of the disease's magnitude is crucial during a particular phase of a pandemic, such as the Covid-19 outbreak. The primary aim of this research is to employ machine learning methodologies in order to forecast the survival outcomes of individuals diagnosed with COVID-19. This will be accomplished by using a publicly available dataset comprising various medical attributes pertaining to 383,499 COVID-19 patients, which was collected and made accessible by the Directorate General of Epidemiology, Secretariat of Health in Mexico. Various machine learning techniques, including Regression methods, Artificial Neural Networks, Random Forest Classifier, Support Vector Machine, AdaBoost, and XGBoost, are employed on the dataset that has undergone diverse preprocessing procedures. The experimental findings demonstrated that the system yielded numerous advantages in comparison to previous efforts in the same field. © 2024 IEEE.

关键词： COVID-19

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Optimizing Job Search: A Multi-Model Approach with DRNN-BES, Collaborative Filtering, and LLM-NER Resume Analysis 3

Optimizing Job Search: A Multi-Model Approach with DRNN-BES,...

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Jayakumar, Naveenkumar Patil, Netra Pokal, Roshankumar S. Dev, Rudra Pratap Nair, Sidharth Vellore Institute of Technology School of Computer Science and Engineering Vellore India College of Engineering Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

This research introduces an innovative job recommendation platform designed to optimize the job search process through advanced technology integration. The platform features a sophisticated Resume Analyzer leveraging NLP and ML to meticulously extract candidate information. Coupled with a Recommendation System that employs mixture models such as DRNN-BES and collaborative filtering, it delivers personalized job suggestions tailored to individual profiles. Additionally, the system utilizes state-of-the-art web scraping technologies to continuously update its database with diverse and current job listings from various online sources, ensuring a wide range of opportunities are accessible. This integration of technologies not only refines job-matching precision but also significantly improves the efficiency of connecting job seekers with appropriate opportunities. Initial experimental results underscore the enhanced effectiveness and accuracy of our recommendation system, particularly when incorporating user behavioral data into the collaborative filtering algorithm. This paper demonstrates the transformative potential of integrating AI into job seeking, offering a scalable, unbiased, and time-efficient approach to modern employment challenges. © 2024 IEEE.

关键词： Collaborative filtering

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Unravel News: An Efficient Summarization Approach

Unravel News: An Efficient Summarization Approach

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Saha, Ankan Al Shafi, Abdullah Khulna University of Engineering and Technology Dept. Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798331529765

The process of automatically creating a concise and informative summary of a news piece is known as news summarizing. This can be a useful tool for people who want to quickly get the gist of a news story without having to read the entire article. Extractive and abstractive summarization are the two basic methods used to summarize news. The process of extractive summarization entails locating and separating the key phrases from the source text. In this paper, sentence weighting is used for extractive summarization. The words in the headlines are given ten times more emphasis than the other words in the news article. The final summarization is done by rearranging the sentences as per the sequence in the main news article. The paper concludes by discussing the future of news summarization, which is an active area of research. © 2024 IEEE.

关键词： Abstractive Summary Extractive Summary ROUGE Sentence Rank Algorithm Stemming Text Rank Algorithm

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A Fusion-Based Approach to Multiclass Retinal Disease Classification: Transfer Learning Strategy with CBAM Integration 2

A Fusion-Based Approach to Multiclass Retinal Disease Classi...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Nath, Bijoy Deb Afroge, Shyla Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

Eye diseases pose significant global public health challenges, often resulting to irreversible blindness if untreated. Early, precise diagnosis of these disorders is critical. In this paper, we address this crucial issue using transfer learning methods for the categorization of multiple eye disorders. The proposed architecture features a fusion-based technique that combines EfficientNetB0 and MobileNetV3Large convolutional neural networks (CNNs) for feature extraction and incorporates Convolutional Block Attention Module (CBAM), which dynamically focuses on significant regions of the images, enhancing the model's discriminative capability. Our proposed model achieves outstanding performance on two separate datasets: 97.85% accuracy on the Eye Disease Classification (EDC) dataset and 91.96% on the Ocular Disease Intelligent Recognition (ODIR) dataset, surpassing existing state-of-the-art approaches in ocular disease classification. By developing the proposed model, we intend to contribute to the creation of more effective diagnostic tools and eventually enhance patient outcomes in ophthalmology. © 2024 IEEE.

关键词： Ophthalmology

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Deep Learning Model for Weather Prediction 1

Deep Learning Model for Weather Prediction

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Singh, Shubhanshi Tyagi, Neha Amity School of Engineering and Technology Dept. Computer Science and Engineering Noida India

ISBN: (纸本)9798350353648

Weather forecasting predicts atmospheric conditions at a particular time and place. It is an important task with a wide range of applications, including agriculture, transportation, and disaster prevention. Traditional weather forecasting methods rely on physical models of the atmosphere, which can be complex and computationally expensive. In recent years, deep learning techniques are effective for weather forecasting. Deep learning models can learn complex patterns from data, which can be used to predict future weather conditions accurately. This project aims to create a deep-learning model for forecasting weather. A huge dataset of historical meteorological data, including radar, satellite, and surface measurements, will be used to train the model. Numerous meteorological factors, such as temperature, precipitation, and wind speed, will be predicted by the model. We will compare the deep learning model's performance with conventional weather forecasting techniques. Creating a deep learning model that is more accurate and effective than current approaches is the project's main objective. The initiative may result in improved weather forecasts that are prompt and accurate, better agricultural, transportation, and disaster prevention decision-making, and less money lost because of weather-related incidents. Possible obstacles for the project are gathering and purifying a sizable dataset of meteorological data, using this data to train a deep learning model, and then comparing the model's output with more conventional techniques. © 2024 IEEE.

关键词： Wind forecasting

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Image Segmentation based Approach for Skin Disease Detection and Classification using Machine Learning Algorithms 2

Image Segmentation based Approach for Skin Disease Detection...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Sany, Nazmul Hasan Chandra Shill, Pintu Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350317558

Skin disorders are ubiquitous, stemming from causes including fungus, germs, allergies, and viruses. Although laser and photonics medical technologies have revolutionised rapid and precise diagnosis, their exorbitant prices restrict accessibility. To address this, this paper describes an image processing strategy for dermatological screening and procedure that comprises obtaining digital photographs of damaged skin patches and applying image processing to detect the disease type. Moreover, when conducted manually by medical specialists, identifying skin illnesses is time-intensive and subjective. As a consequence, both patients and physicians want automated skin disease prediction, which makes the treatment plan faster. This cost-effective approach depends on a basic setup: a camera and a computer. Operating on colour photos, a solution combines resizing for feature extraction using a pertained convolutional neural network, followed by classification using a multiclass SVM. The technology efficiently recognises four separate skin illnesses with an astonishing 90.8% accuracy, giving users complete findings spanning disease kind, extent, and severity. © 2024 IEEE.

关键词： Decision Tree Image Processing K-NN Machine Learning Skin Disorder SVM

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Efficiency Redefined: The Document Scanner App for Precise Entity Identification with Customized NER 3

Efficiency Redefined: The Document Scanner App for Precise E...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Javid, Sajid Saim, Mohammad Islam, Saiful Indraprastha Institute of Information Technology Delhi Dept. of Computer Science & Engineering Delhi India University of Cincinnati Dept. of Computer Science & Engineering CincinnatiOH United States Zakir Husain College of Engineering & Technology Aligarh Muslim University Dept. of Computer Engineering Aligarh India

ISBN: (纸本)9798331518592

In the era of rapid advancements in technology, the efficient digitization of paper-based documents remains a crucial challenge across various domains. The traditional approach to document scanning often struggles with accurate extraction of critical information from unstructured or diverse document formats. This paper presents the development of a web-based document scanner application that leverages Named Entity Recognition (NER) to extract entities from scanned business cards efficiently. The study aims to address the challenge of digitizing and organizing physical documents, with a focus on precise entity extraction. The methodology involves creating a custom dataset of scanned business cards, preprocessing the data, and training an NER model using machine learning techniques. The application is built using the Flask framework and integrates OpenCV for text extraction and SpaCy for NER. The key findings include the model's high F1 score in identifying entities such as names, organizations, designations, emails, and contact details. The application streamlines document digitization by automatically extracting and tagging relevant metadata, minimizing the need for manual data entry. This research contributes to enhancing document management and demonstrates broader applicability for other document types apart from business cards. The implications highlight improvements in workflow automation, data accuracy, and the potential for integrating with larger document management systems. © 2024 IEEE.

关键词： Optical character recognition

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A Machine Learning Approach to Identifying Employees at Risk of Workplace Injury 2

A Machine Learning Approach to Identifying Employees at Risk...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Pramanik, Bablu Modak, Santanu Brainware University Dept. of Computer Science & Engineering India Bengal College of Engineering and Technology Dept. of Information Technology India

ISBN: (纸本)9798350360523

Workplace injuries are a critical concern, with millions occurring annually, leading to substantial human and economic costs. This study focuses on a qualitative analysis based on statistics from 2013 to 2017, with a specific emphasis on fatal and non-fatal workplace accidents within the European Union (EU) and Romania. The primary objective is to assess Romania's workplace safety status in the context of the EU and to propose strategic measures for improvement. To achieve this goal, four key indicators and statistical datasets are utilized, sourced from the National Institute of Statistics (NIS) of Romania, the Eurostat database of the European Commission, and the Romanian Labor Inspection. These indicators include the rate of incidence frequency index for occupational accidents, the average duration index, the frequency index for fatal accidents, and the severity index, enabling a comprehensive evaluation of accident frequency and severity. The rate of incidence, measuring injuries per 100,000 workers, is a pivotal indicator. Additionally, the study calculates the frequency index of non-fatal accidents (injuries per 1,000 employees) and the fatal accident frequency index (injuries per 1,000 workers). Statistical findings are rigorously validated through ANOVA analysis and T-tests. Following data evaluation, the study offers strategic recommendations informed by national and European strategies, including the National Occupational Safety and Health (OSH) Strategy for 2017-2020 and the "EU Strategic Framework on Health and Safety at Work"spanning 2014 to 2020. These recommendations aim to guide efforts for workplace accident control and prevention in Romania and the broader European *** summary, this study's core objective is to comprehensively analyze workplace accidents in Romania and the EU, employing various indicators and statistical data. Its primary aim is to assess the current status and propose evidence-based strategies for improvement, aligning with

关键词： Machine learning

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Enhancing Mortality Prediction in ICU: A Deep Learning Framework for Multivariate Time Series Classification

Enhancing Mortality Prediction in ICU: A Deep Learning Frame...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Sathia Bhama, Ponsy R K Sarah R, Roshina Shobia, T.R. Vinothini, R. Anna University Dept. of Computer Technology MIT Campus Chennai India Sri Sairam Institute of Technology Dept. of Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331541170

Relying on human diagnosis is potentially prone to errors, time-consuming and leading to treatment mistakes. Early Intensive Care Unit (ICU) mortality prediction is important for determining which patients are most likely to die and for offering appropriate interventions that will preserve their lives. However, conventional diagnostic approaches often face challenges in fully harnessing the extensive information contained within Electronic Health Record (EHR) data, particularly in capturing the intricate temporal dynamics of patient health. In this research paper, a hybrid model leverages the power of Temporal Convolutional Networks (TCN) in conjunction with Bidirectional Gated Recurrent Units (BiGRU) to effectively capture the complex temporal dependencies present in ICU time series data, before constructing the model, the data is pre-processed by cutting-edge Generative Adversarial Network (GAN)-based imputation techniques to handle missing values within the EHR data. Through this comprehensive framework, the goal is to significantly enhance mortality prediction accuracy, empower clinicians with actionable insights into patient prognosis, and ultimately improve patient outcomes in critical care settings. © 2024 IEEE.

关键词： Electronic health record

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Real-Time Stress Detection Using Facial Images and Convolutional Neural Networks

Real-Time Stress Detection Using Facial Images and Convoluti...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Sindu, M. Ashwin, T. Shyam Sunder, V. Shivasankaran, S. Poongothai, P. Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Mathematics Tamil Nadu India

ISBN: (纸本)9798331535476

Stress is one of the prevailing issues in today's world, deeply impacting the actual well-being of individuals. In this paper, we introduce an innovative method to detect stress that deviates from the mainstream approaches, which rely solely on physiologic signals and self-reporting. Our non-invasive, efficient, and scalable real-time system, built on the facial image data processed by CNNs, makes real-time human stress detection possible. The system captures facial expressions through video or static images, which it then processes within a CNN model to identify stress-related features. CNNs are particularly innovative in that they are adept.at automatically extracting and learning hierarchical features from extremely complex, unstructured data like facial images. Facial expression datasets were used to train and test the proposed model in different stress conditions. The model achieved a very good accuracy level in differentiating stressed from non-stressed states, thus putting it on par with professionals. Real-time testing of the system's performance was done on edge devices such as smartphones to prove its potential for use in real-life applications related to mobile health. As facial image data is utilised by the system, thereby killing the need for wearable sensors or invasive physiological monitoring, the approach would be more accessible for detecting stress levels. This research showed that CNN models developed based on facial images can transform stress monitoring. Its implications are wide, applicable in health care, workplace settings, and even self-manageable personal stress. In future work, the model should be generalised for diverse populations, the system's real-time capabilities enhanced, and multimodal data further integrated to increase detection accuracy and reliability. © 2024 IEEE.

关键词： Convolutional neural networks

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