Advancing Zero-Shot Learning With Fully Connected Weighted Bipartite Graphs in Machine Learning

Advances in bioinformatics and biomedical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 26

Published: March 22, 2024

This chapter presents a novel method for improving zero-shot learning in ML by using fully connected weighted bipartite graphs. Problems with generalizability and adaptability plague learning, that lets models identify categorize things or ideas without any explicit training. To overcome these obstacles greatly enhance machine models' ability to absorb comprehend unknown input, this investigates how linked graphs may be integrated. A thorough introduction is provided at the outset of investigation. It describes method's value field while drawing attention problems restrictions on current approaches. Anyone involved whether as researcher, practitioner, hobbyist, will find an invaluable resource. lays out theory some practical considerations

Language: Английский

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Introduction to Bioinformatics and Machine Learning

Advances in bioinformatics and biomedical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 317 - 332

Published: March 22, 2024

ML has revolutionised bioinformatics' difficult biological data analysis. Pattern recognition and process categorization help systems diagnose diseases, predict protein structures, investigate gene expression. Few-shot learning bioinformatics are effective at optimising results with limited resources, overcoming dataset access issues. advance precision medicine drug discovery while improving understanding. This chapter examines methods like supervised classification, clustering, probabilistic graphical models to find new insights. Text mining, biology, evolution, proteomics, genomics use deterministic stochastic heuristics for optimisation. Understanding modern technologies understanding implementation challenges is the study goal. highlighted show its importance. together improve our knowledge solve real problems, research in medical sciences.

Language: Английский

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Healthcare 5.0

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 235 - 256

Published: April 19, 2024

The chapter explores the transformative shift from traditional to integrated healthcare. It delves into technology, patient empowerment, interdisciplinary collaboration, data-driven decision-making, and preventive role of AI in diagnostics treatment, along with use big data for improved outcomes, resource allocation, disease surveillance, is highlighted. advocates a proactive healthcare model, emphasizing early intervention ethical considerations. virtual reality augmented reality's potential medical practices discusses impact telemedicine on accessible convenient care, especially underserved areas. This concise overview provides insights future medicine Healthcare 5.0.

Language: Английский

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Performance Evaluation of Machine Learning Models for Predicting Energy Consumption and Occupant Dissatisfaction in Buildings

Buildings, Journal Year: 2024, Volume and Issue: 15(1), P. 39 - 39

Published: Dec. 26, 2024

This study evaluates the performance of 15 machine learning models for predicting energy consumption (30–100 kWh/m2·year) and occupant dissatisfaction (Percentage Dissatisfied, PPD: 6–90%), key metrics optimizing building performance. Ten evaluation metrics, including Mean Absolute Error (MAE, average prediction error), Root Squared (RMSE, penalizing large errors), coefficient determination (R2, variance explained by model), are used. XGBoost achieves highest accuracy, with an MAE 1.55 kWh/m2·year a PPD 3.14%, alongside R2 values 0.99 0.97, respectively. While these highlight XGBoost’s superiority, its margin improvement over LightGBM (energy MAE: 2.35 kWh/m2·year, 3.89%) is context-dependent, suggesting application in high-precision scenarios. ANN excelled at predictions, achieving lowest (1.55%) Percentage (MAPE: 4.97%), demonstrating ability to model complex nonlinear relationships. modeling advantage contrasts LightGBM’s balance speed making it suitable computationally constrained tasks. In contrast, traditional like linear regression KNN exhibit high errors (e.g., 17.56 17.89%), underscoring their limitations respect capturing complexities datasets. The results indicate that advanced methods particularly effective owing intricate relationships manage high-dimensional data. Future research should validate findings diverse real-world datasets, those representing varying types climates. Hybrid combining interpretability precision ensemble or neural be explored. Additionally, integrating techniques digital twin platforms could address real-time optimization challenges, dynamic behavior time-dependent consumption.

Language: Английский

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Securing Privacy in the Metaverse

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 58 - 76

Published: April 19, 2024

This research explores the integration of privacy-preserving federated learning techniques in context biomedical image analysis within metaverse. The metaverse, a virtual shared space, has witnessed remarkable advancements various fields, including healthcare. However, ensuring confidentiality sensitive medical data poses significant challenge. study proposes novel approach to address this concern by employing learning, collaborative machine paradigm that enables model training across decentralized devices without compromising individual privacy. investigation focuses on application these enhance aiming facilitate and diagnosis. Through implementation secure authors aim strike balance between technological innovation safeguarding health information evolving landscape

Language: Английский

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Healthcare Services Enhancement in the Smart City Using 5G

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 217 - 234

Published: April 19, 2024

A smart city is a technologically modern urban area that uses different modes of electronic methods and sensors to collect specific data. The information collected can be used efficiently effectively improve the quality operations across city. Fifth generation (5G) technology for wireless mobile communication best suited services, which provide higher data rates, increased traffic capacity, ultra-low latency, high connection density. Rich healthcare sector (HCS) one core foundation block any city, will benefit from wide range vital infrastructure provided by 5G. purpose this chapter analyze effects ramifications 5G in HCS perspectives. technological setting financial advantages are also covered chapter, keeping cities mind. More on model suggested 5G-enabled

Language: Английский

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Real-World Applications of Explainable AI in Healthcare

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 158 - 178

Published: April 19, 2024

This chapter delves into the profound impact of explainable artificial intelligence (XAI) on healthcare sector, emphasizing its pivotal role in elevating diagnostics, treatment modalities, and ultimately, patient outcomes. The integration has brought about a revolutionary shift, promising unparalleled improvements care insights gleaned from extensive datasets. A core theme explores XAI as remedy to demystify intricate decision-making processes inherent AI algorithms, especially traditionally opaque deep learning neural networks. narrative unfolds journey showcasing practical applications healthcare, illuminating capacity equip professionals with actionable insights, instill trust, effectively address ethical concerns.

Language: Английский

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Federated Learning Approach to Safeguard User Privacy

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 104 - 120

Published: April 19, 2024

Current intrusion detection models based on machine learning require reliable datasets, but the public dataset updates are typically delayed after new attacks, which slows down model's update speed. Also, to train existing model, data needs be shared; hence, it lacks integrity. To address this issue, project implements a never-ending (NEL) framework for that utilizes multi-task and transfer continuously acquire knowledge from private regardless of sharing them publicly. The NEL also integrates serendipitous learning, model by identifying classifying attack categories suspected traffic attacked devices. enhances various continuous training methods with federated safeguard user privacy, ensuring is not transmitted directly.

Language: Английский

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A Review of Mental Health Analysis Through Social Media Using Machine Learning and Deep Learning Approaches

Published: May 23, 2024

Language: Английский

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