One of the biggest challenges in learning from data streams is adapting the classification model to new data. Due to the evolving nature of data streams, they are subject to a phenomenon known as concept drift that ma...
One of the biggest challenges in learning from data streams is adapting the classification model to new data. Due to the evolving nature of data streams, they are subject to a phenomenon known as concept drift that makes previously learned knowledge and model outdated. Therefore, concept drift must be efficiently detected in order to adapt the classification model. While there exists a plethora of drift detectors, with different mechanisms, selecting the most suitable for a new stream is a difficult task, since apriori knowledge may not be available and changes over time can affect the performance of the detector. This paper proposes a framework that exploits statistical and temporal meta-features from sliding windows to dynamically recommend a suitable drift detector in real-time for unseen chunks of streams according to its properties using Meta-Learning. We performed experiments on 10 real-world data streams and 18 synthetic generated data streams that were subject to concept drift and class imbalance in order to evaluate the performance of the proposed framework. Experiments exposed that the proposed approach was able to enhance the concept drift detection in a variety of scenarios demonstrating robustness to class imbalance and the advantages of dynamically selecting the drift detector.
The integration of Industry 4.0 technologies in agriculture will reduce the increasing challenges of agricultural process around the globe. The real-time farm management with high degree of automation will greatly imp...
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The integration of Industry 4.0 technologies in agriculture will reduce the increasing challenges of agricultural process around the globe. The real-time farm management with high degree of automation will greatly improve productivity, agri-food supply chain efficiency and food safety. This paper describes a fully customized LoRa-based IoT system that aims for a low-cost, low power and wide range wireless sensor network targeted for smart farms. The presented system integrates already existing Programmable Logic Controllers (PLC) typically used to control multiple processes and devices such as water pumps, certain machinery, etc. along with a newly developed network of wireless LoRA sensors distributed over the farm. A Telegram bot is also included as novelty for automated user communication via this mobile phone messaging application. The network structure was deployed and tested. The developed integrated system also includes a cloud-based monitoring application to provide remote visualization and control for all the farming environment.
This paper presents FraMark, a blockchain-based framework for 5G network management that utilizes Hyperledger Fabric and fractional Non-Fungible Tokens (NFTs) to optimize resource allocation. We introduce a comprehens...
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ISBN:
(数字)9798331542825
ISBN:
(纸本)9798331542832
This paper presents FraMark, a blockchain-based framework for 5G network management that utilizes Hyperledger Fabric and fractional Non-Fungible Tokens (NFTs) to optimize resource allocation. We introduce a comprehensive system that fractionalizes digital assets to represent 5G network resources, enhancing efficiency and allocation flexibility. Our framework addresses the challenges of 5G network management by ensuring low latency, high transaction throughput, and secure resource trading. We conduct experiments to evaluate the performance of our approach, including CPU and memory utilization, latency analysis, and block size assessment. The results demonstrate the effectiveness of FraMark in improving network resource utilization and transaction efficiency, validating our framework’s claims. By leveraging blockchain technology and innovative resource slicing techniques, FraMark paves the way for dynamic and efficient 5G network management.
One of the musical activities that can be positively impacted by the Internet of Musical Things (IoMusT) is music education. However, although the IoMusT's properties hold a promising potential to enrich music lea...
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ISBN:
(数字)9798350366525
ISBN:
(纸本)9798350366532
One of the musical activities that can be positively impacted by the Internet of Musical Things (IoMusT) is music education. However, although the IoMusT's properties hold a promising potential to enrich music learning processes, the extent to which early childhood music educators and scholars have embraced this emerging type of technology and explored their potential is still very limited. To bridge this gap, we first survey the relevant literature at the confluence of the Internet of Things, music technology, and education. Then, we propose a pedagogical framework to support designing IoMusT applications for early childhood music education. The framework is based on five dimensions: embodied sense-making, nonlinearity, participatory sense-making, privacy and security, as well as accessibility and inclusiveness. Furthermore, we corroborate the framework with a set of pedagogical scenarios showing its usage. Our study aims to foster interdisciplinary research at the confluence of pedagogy and music technology in an application domain, that of early childhood music education, hitherto unexplored.
The Unmanned Aerial Vehicle (UAV) has emerged as a transformative technology application in healthcare, environmental monitoring, and search and rescue operations, with the growing complexity of their services, Dynami...
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ISBN:
(数字)9798331542603
ISBN:
(纸本)9798331542610
The Unmanned Aerial Vehicle (UAV) has emerged as a transformative technology application in healthcare, environmental monitoring, and search and rescue operations, with the growing complexity of their services, Dynamic Environments, and limited resources. This paper introduces a novel approach that integrates machine learning (ML) with game theory to revolutionize the decision-making process in UAV service selection. Our method focuses on enhancing user experience by employing ML algorithms to analyze user requests and predict the most suitable UAV services. Concurrently, game theory is integrated to evaluate and ensure efficient selection regarding delay, throughput, packet loss ratio, and residual energy within UAV *** synergy aims to benefit both users and service providers by optimizing service delivery and user satisfaction. Experimental results demonstrate the efficacy of our model, highlighting its superior performance in terms of accuracy, precision, recall, and F-score.
Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that...
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ISBN:
(数字)9798350356632
ISBN:
(纸本)9798350356649
Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer’s disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer’s disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases.
During the COVID-19 pandemic, the use of a people tracking system could have been crucial, particularly in sensitive environments, such as hospitals. DPPL Hallway Tracker is a framework that uses security camera foota...
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in the twenty-first century, seems many students are reluctant to participate in classroom matters day by day. Some days ago, a course teacher is providing a lecture, on the most important topics that will be implemen...
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Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is c...
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ISBN:
(数字)9798350384727
ISBN:
(纸本)9798350384734
Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is critical for effective treatment. Leveraging artificial intelligence, particularly novel models like Transformers, presents promising avenues for improved diagnosis. This paper explores the efficacy of a Collective Intelligence approach using AI in classifying cancerous and non-cancerous tumors, aiming to reduce classification errors and support clinical decision-making. We created five different configurations using various datasets to compare the results. The results show solid performance for the CI in the evaluated tasks, reaching up to 75.89% accuracy. The lack of images in certain classes significantly contributes to overfitting. It is suggested to explore data expansion strategies and improve consistency in image capture for future work.
Large Language Models represent a transformative technology at the forefront of artificial intelligence and natural language processing, with applications spanning diverse domains. This study conducts a comprehensive ...
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ISBN:
(数字)9798350366235
ISBN:
(纸本)9798350366242
Large Language Models represent a transformative technology at the forefront of artificial intelligence and natural language processing, with applications spanning diverse domains. This study conducts a comprehensive science mapping analysis of the LLMs research field, leveraging bibliometric techniques to uncover its thematic structure, trends, and global actors involved. Utilizing data from the Web of science, a corpus of 1303 research documents from 2010 to 2023 is analyzed, revealing a notable surge in research activity, particularly in recent years. Key thematic areas driving research within the field are identified, including chatbot, code generation, augmented reality, transformers, and machine learning paradigms. Foundational technologies such as transformers are pivotal in shaping the research landscape, while emerging themes like prompt learning hint at future directions. This study offers valuable insights for researchers, practitioners, and policymakers seeking to navigate the dynamic landscape of LLMs research and harness its full potential for societal benefit.
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