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Optimizing Classification of Congestive Heart Failure Using Feature Weight Importance Correlation 1st

Optimizing Classification of Congestive Heart Failure Using ...

引用

1st MICCAI Meets Africa Workshop

作者： Oronti, Iyabosola B. Haleem, Muhammad S. Pecchia, Leandro Popham, Thomas Univ Warwick Coventry CV4 7AL England Queen Mary Univ London 327 Mile End Rd London E1 4NS England Univ Campus Bio Medico Roma Via Alvaro Portillo 21 I-00128 Rome Italy

ISBN: (纸本)9783031791024;9783031791031

In this work, a novel method for selecting the optimal set of input features for classifying the presence of congestive heart failure (CHF) using a supervised machine learning approach is presented. A random forest classifier (RFC) was utilized to carry out the binary classification task and two different models were explored. We employed the embedded RFC feature importance attribute for the first model, and a multi-classifier technique which integrates the feature weight importance correlation (F-WIC) method was adopted for the second model. Our results show that the second model using the F-WIC method offers superior performance (100% accuracy) and provides a generalized approach to feature engineering for machine learning models irrespective of the algorithm used. This work offers a novel method for selecting the optimal set of input features for classifying the presence of congestive heart failure (CHF) using a machine learning approach.

关键词： machine learning feature engineering congestive heart failure

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Racial Disparity in Breast Cancer Prognosis 33rd

Racial Disparity in Breast Cancer Prognosis

引用

33rd International Conference on Software and Data Engineering

作者： Owrang, M. Mehdi O. Horestani, Fariba Jafari Amer Univ Washington DC 20016 USA

ISBN: (纸本)9783031752001;9783031752018

In this work, we looked at the significance of the race factor in breast cancer prognosis, using Association rules data mining technique. We utilized XLMiner data mining tool for our experiments. The data used is the National Cancer Institute's SEER Public-Use Data. Several experiments were conducted based on the prognostic factors including those of Age, Behavior code, Stage of cancer, Grade, and Marital status with respect to Race. Our discovered association rules indicate that Japanese patients have better survival rate than White patients and White patients have better survival rate than Black patients. The racial disparity in breast cancer prognosis is shown to be statistically significant.

关键词： Data Mining Association Rules Breast Cancer Prognosis Prognostic Tool Hypothesis Testing

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The Role of Actor's Creative Self-efficacy in AI Enabled Value Creation 5th

The Role of Actor's Creative Self-efficacy in AI Enabled Val...

引用

5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Hongisto, Tuomas Vilko, Jyri LUT Univ LUT Sch Engn Sci Kauppalankatu 13 Kouvola 45100 Finland

ISBN: (纸本)9783031807596;9783031807602

Researching creativity remains crucial despite the growing use of AI, as human creativity drives innovation, problem-solving, and value creation in ways that AI alone cannot replicate. This study examines the impact of Solution-Focused (SF) coaching on individuals' creative self-efficacy (CSE) within AI-enabled environments. Core findings indicate that SF coaching significantly enhances CSE by increasing participants' awareness of their creative potential and cognitive abilities related to creativity. The study reveals that a clear coaching structure, personalized tools, and the quality of the coach-client relationship and interaction are essential for maximizing the benefits of SF coaching. These findings underscore the importance of fostering human creativity alongside AI to achieve dynamic and human-centric value creation in modern organizations.

关键词： AI Coaching Collaborative innovations Creativity Creative Self-Efficacy Dialogue Digital transformation Industry 5.0 Innovation Organizational creativity Self-Efficacy Solution-Focused coaching Value creation

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CRNet: Convolutive Recurrent Network for Suspect Face Identification 4th

CRNet: Convolutive Recurrent Network for Suspect Face Identi...

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Shree, Manu Mohapatra, Amar Kumar Vishwakarma, Virendra P. Suwanwiwat, Hemmaphan Lee, Ickjai Indira Gandhi Delhi Tech Univ Women Dept Informat Technol New Delhi 110006 India Guru Gobind Singh Indraprastha Univ New Delhi 110078 India Deakin Univ Victoria 3216 Australia James Cook Univ Cairns 4878 Australia

ISBN: (纸本)9783031837890;9783031837906

Identifying suspects in critical situations-particularly when they are wearing scarves, masks, or are in environments with light obstructions and concealed facial expressions-poses significant challenges. To address these issues, a method known as the Convolutive Recurrent Network (CRNet) for suspect face identification is proposed. CRNet utilises deep neural networks, specifically the Residual Network-50, leveraging a transfer learning approach for efficient feature extraction. In addition, Bidirectional Long Short-Term Memory (BiLSTM) layers are employed to capture spatial and recurrent features, with BiLSTM layers serving as the core component of the model. CRNet is designed to overcome the limitations of current models in managing complex situations, such as scarves, spectacles, high illumination, and varied expressions. CRNet fills this gap by integrating mechanisms that provide flexibility for ambiguous features and variable lighting conditions. Experimental and comparative analysis demonstrates that CRNet significantly outperforms existing methods, providing notable improvements in both accuracy and reliability. This approach introduces a rapid feature-learning method for precise suspect identification by integrating spatial dependencies, enhancing versatility across various computer vision domains. The model's potential impact on criminal investigations is substantial due to its fast bidirectional feature processing. Experimental results demonstrate the robustness and adaptability of CRNet, achieving accuracy rates of 97.46% on the Extended Cohn-Kanade dataset, 98.08% on the Augmented Reality dataset, and 99.58% on the Extended Yale B dataset-substantially surpassing the baseline accuracy of 46.00%.

关键词： Residual Network-50 Bidirectional Long Short-Term Memory Suspect Face Recognition Facial Recognition Feature Extraction

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Femoral Head Surface Segmentation Using Ultrasound Images as Input Source: A Local Phase and Rigid Filtering Approach

Femoral Head Surface Segmentation Using Ultrasound Images as...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： de Avila-Armenta, Eduardo Celaya-Padilla, Jose Maria Adamson, Robert B. A. Moreno-Chavez, Gamaliel Martinez-Torteya, Antonio Soto-Murillo, Manuel A. Galvan-Tejada, Jorge I. Galvan-Tejada, Carlos E. Acosta-Cruz, Erika Moreno-Terrazas, Osvaldo Cid-Baez, Miguel A. Jacome-Cadena, Diana L. Univ Autonoma Zacatecas Unidad Acad Ingn Elect Jardin Juarez 147Ctr Zacatecas 98000 Zacatecas Mexico Dalhousie Univ Elect & Comp Engn Dept Halifax NS B3H 4R2 Canada Univ Monterrey Escuela Ingn & Tecnol Ave Ignacio Morones Prieto 4500 PteJesus M Garza San Pedro Garza 66238 Nuevo Leon Mexico Univ Autonoma Coahuila Dept Biotecnol Fac Ciencias Quim Saltillo Coahuila Mexico OM Sports Med Saltillo Coahuila Mexico Univ Autonoma Zacatecas Modulo Salud Integral Area Ciencias Salud Jardin Juarez 147Ctr Zacatecas 98000 Zacatecas Mexico Univ Autonoma Zacatecas Unidad Acad Med Humana & Ciencias Salud Jardin Juarez 147Ctr Zacatecas 98000 Zacatecas Mexico

ISBN: (纸本)9783031834349;9783031834356

Hip arthroscopy, a minimally invasive procedure for diagnosing and treating hip disorders, is gaining traction due to technological advancements. However, surgeons face challenges in accurately visualizing bone surfaces during the procedure, hindering their ability to effectively guide surgical interventions. computer-Assisted Orthopedic Surgery (CAOS) systems, which incorporate imaging modalities like ultrasound (US), offer a promising solution to address these limitations. This research contributes to the field of CAOS by proposing a novel algorithm for femoral head bone surface segmentation in US images. Leveraging local phase features for image denoising and a rigid object filtering process, the algorithm overcomes inherent noise and simplifies segmentation tasks. The proposed method achieves promising results, demonstrated by metrics like accuracy (0.7978) and low Hamming Loss (0.2022). Additionally, metrics like F-score (0.8269) and recall (0.8377) indicate robust bone surface delineation. While the absolute y-centroid difference is 32.125 pixels, the percentual error remains low at 4.4010%.

关键词： Femoral head segmentation computer-Assisted Orthopedic Surgery (CAOS) systems Ultrasound imaging Local phase features Rigid object filtering

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Urban Governance Models in Smart Cities: Case of Quito-Ecuador

Urban Governance Models in Smart Cities: Case of Quito-Ecuad...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Villao, Datzania Rodrigues Soares, Jakson Renner Lois Gonzalez, Ruben Camilo Univ Santiago Compostela Santiago De Compostela Spain Univ Estatal Peninsula Santa Elena La Libertad Ecuador Univ A Coruna La Coruna Spain

ISBN: (纸本)9783031834349;9783031834356

In recent years, the urbanization of cities has accelerated around the world. However, cities face many challenges in several areas, such as having sufficient access to fresh water, cleaner energy, smart buildings that understand the needs of their occupants and the ability to transport efficiently, keeping the city greener. All these problems lead cities to analyze alternatives to solve these problems. One of these alternatives is precisely to become smart cities, which in turn would lead them to be more sustainable and developed. One of the main dimensions of a smart city is the governance model that is the role of the interaction and relationship of the actors involved such as the State, community, and private companies in the planning and implementation of a smart city. The research had as a purpose to identify the urban governance model of Quito, the capital of Ecuador. The research was qualitative in nature applying a documentary review that got enough information to identify the characteristics of the urban model for Quito. As a result, the urban governance identified for Quito was the welfare-oriented governance model because the main actor is the local government, the key governance tool is the network with different government level specially with national government, the public services are provided by public companies belong to the Quito Municipality and the level of citizen participation in decision-making is weak.

关键词： Urban Governance Smart Cities Welfare-Oriented Model

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Ontological Approach to Describing the Interaction of information Processes in Intelligent Systems 2nd

Ontological Approach to Describing the Interaction of Inform...

引用

2nd International Conference on information Technologies and Their Applications

作者： Ivashenko, Valerian Shunkevich, Daniil Belarusian State Univ Informat & Radielectron Minsk BELARUS

ISBN: (纸本)9783031734199;9783031734205

The paper considers an ontological approach to describing the interaction of information processes in intelligent systems. A hierarchical family of ontologies is proposed that investigate both fundamental aspects of representing temporal entities in knowledge bases of intelligent systems and higher-level aspects related to describing the interaction of information processes. The hierarchy of such processes from the processes performed within the hardware platform to the processes of problem solving within a distributed system of agents is considered. Approaches to synchronization of such processes are considered. Special attention is paid to the ontology of spatio-temporal entities, which specifies the semantics of concepts describing changes in various entities in time, such as events, situations, cause-and-effect relations, and so on. Of the three levels of information processes, the paper discusses in the most detail the lower level related to the interpretation of information processes within the hardware platform. Two approaches to organizing of the such interpretation are considered.

关键词： Ontology information Process Intelligent System Dynamic Subject Domain Multi-agent Systems OSTIS Technology SC-code

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Revolutionizing Crop Leaf Disease Detection: A Novel Ensemble Learning Framework Using Customized EfficientNets 4th

Revolutionizing Crop Leaf Disease Detection: A Novel Ensembl...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Jannat, Nahrin Hasan, S. M. Mahedy Zibran, Minhaz F. Rajshahi Univ Engn & Technol Rajshahi Bangladesh Idaho State Univ Pocatello ID USA

ISBN: (纸本)9783031821493;9783031821509

The presence of crop leaf diseases poses a persistent and significant threat to agricultural productivity and food security, especially in Bangladesh, where agriculture plays a pivotal role in the economy. Developing efficient methodologies for timely crop leaf disease detection and management becomes paramount. Nonetheless, our study addressed the hurdles in detecting crop leaf diseases, with a special emphasis on two datasets that included corn, and potato rather than relying on a single dataset. While existing studies often rely on straightforward transfer learning (TL) techniques, our research aimed to enhance the performance of TL by systematically incorporating various EfficientNets versions and customizing their architecture with different layers. In addition, our research made a notable contribution by introducing a novel model selection method for ensemble learning that extended beyond traditional accuracy metrics to address misclassifications and class-specific gaps. We devised a customized approach incorporating misclassification counts and Hamming Loss, redefining the model selection process. Furthermore, we identified the most suitable EfficientNets models for each dataset and leveraged the Gradient Class Activation Map (Grad-CAM) for decision visualization of the model. Consequently, our research effectively addressed agricultural challenges and paved the path toward more robust and precise crop leaf disease detection.

关键词： Crop Leaf Disease EfficientNet Misclassification Hamming Loss Ensemble Learning Grad-CAM

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Predictive Analytics in Weather Forecasting Using Machine Learning and Deep Learning 4th

Predictive Analytics in Weather Forecasting Using Machine Le...

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4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Pantola, Deepika Gupta, Madhuri Agarwal, Mahim Bohra, Rupal Rawat, Kritika Bennett Univ Sch Comp Sci Engn & Technol Greater Noida India

ISBN: (纸本)9783031734762;9783031734779

Climate and weather patterns have a significant influence on many aspects of our lives, including infrastructure planning, emergency preparedness, agriculture, and electricity use. It is not just a scientific endeavor but also a practical imperative to comprehend and accurately predict temperature swings in the context of Helsinki, which is a city known for its dramatic seasonal adjustments. Major weather forecasting centers use numerical weather prediction models that operate on many supercomputers to solve difficult nonlinear mathematical equations simultaneously. These models offer medium-range weather forecasts, with a grid length of 10-20 km, every 6 h to 18h. Nonetheless, agriculture and emergency preparations frequently rely on higher-resolution regional forecasting models to provide them with more precise short- to medium-range projections. This study aims to conduct time series analysis and weather forecasting for Helsinki using facts from 2015 to 2019, utilizing models such as ARIMA, SARIMAX, LSTM, and GARCH. The experimental results based on MAE, MSE, RMSE, R2 score and MAPE, show that the SARIMAX has better performance, which makes it the preferred choice for handling the temporal and geographical intricacies of Helsinki's weather patterns.

关键词： ARIMA weather forecasting LSTM time series SARIMAX

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Fuzzy Theory in Credit Scoring: A Literature Review

Fuzzy Theory in Credit Scoring: A Literature Review

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Moreno Salazar, Fabian Leandro Figueroa-Garcia, Juan Carlos Univ Distrital Francisco Jose Caldas Bogota Colombia

ISBN: (纸本)9783031834349;9783031834356

The second Basel accord was published in 2004 by the Basel Committee on Banking Supervision which allows financial institutions under surveillance to measure their own credit risk to define its own credit rating. Since then, credit scoring (CS) is a well-studied topic that relies on different classical statistics and machine learning algorithms to classify applicants as good/bad based on the probability of default of their own payments. This paper presents a literature review on the use of fuzzy theory in credit scoring models where the main goal is to show the main applications of fuzzy theory in credit scoring and its advantages for modeling imprecise information. This research is significant as it provides a comprehensive overview of the current state of fuzzy theory applications in credit scoring models, the most relevant authors and the trends in research. By synthesizing recent advancements in this area, it serves as a valuable resource for researchers and practitioners aiming to enhance model interpretability and mitigate risk, particularly in the context of financial crises. The insights offered herein will facilitate a deeper understanding of how fuzzy logic can improve decision-making processes in credit risk assessment.

关键词： Fuzzy Credit scoring Review

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