Glioblastoma is one of the most aggressive and deadliest types of brain cancer, with low survival rates compared to other types of cancer. Analysis of Magnetic Resonance Imaging (MRI) scans is one of the most effectiv...
详细信息
Air pollution, a major global concern resulting in numerous annual fatalities, has been associated with various health disorders. This study focuses on understanding the impact of residing in heavily polluted cities o...
Air pollution, a major global concern resulting in numerous annual fatalities, has been associated with various health disorders. This study focuses on understanding the impact of residing in heavily polluted cities on mental and behavioral patterns, specifically exploring the potential link between air pollution and autism. Supervised classification algorithms, including logistic regression, random forest, decision tree, and AdaBoost, were employed to predict the risk of autism in individuals residing in severely polluted countries by 2030. The research aims to identify the relationship between genetic variations in adults with Autism Spectrum Disorder (ASD) and harmful air pollutants, investigate gender differences in autism frequency, and determine the neurotoxicant posing the greatest danger for neurodegeneration and its impact on autistic individuals. This study successfully employed supervised classification models to uncover hidden patterns between air pollution and autism risk. The binary prediction model achieved an average accuracy of 70%, with the AdaBoost algorithm demonstrating the highest accuracy at 73% in predicting autism prevalence. Increased PM2.5 concentrations correlated with higher autism risk compared to other neurotoxicants. These findings underscore the importance of sustainable practices and pollution reduction to safeguard human health.
ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transforma...
详细信息
In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated comment moderation systems greatly aid human moderatorsby ...
The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph c...
详细信息
Businesses are using data analytics more and more in the age of digital transformation to inform strategic choices and maximize operational effectiveness. The goal of this research study is to improve decision-making ...
详细信息
ISBN:
(数字)9798350387490
ISBN:
(纸本)9798350387506
Businesses are using data analytics more and more in the age of digital transformation to inform strategic choices and maximize operational effectiveness. The goal of this research study is to improve decision-making processes in corporate operations via the integration of modern data analytics approaches. Through the analysis of case studies from a range of sectors, we demonstrate how essential data analytics is for seeing patterns, projecting results, and gaining useful knowledge. As we explore data collecting, processing, and analysis techniques, highlighting the significance of data integrity and quality. The use of artificial intelligence (AI) technologies and machine learning algorithms to automate decision-making, lower human error, and speed up company procedures is also covered in the article. The essential elements-organizational culture, technology infrastructure, and workforce competencies, among others-that make up effective data analytics efforts via a thorough literature research and practical analysis. Based on the results, companies who use data analytics well may enhance their agility in adapting to market changes and get a competitive advantage. data privacy, security, and prejudice are among the ethical issues and concerns related to data analytics that are finally covered in this study. Businesses may handle these obstacles and optimize the advantages of data- driven decision-making by following the recommendations given.
From a dataset, one can construct different machine learning (ML) models with different parameters and/or inductive biases. Although these models give similar prediction performances when tested on data that are curre...
详细信息
Erasure Codes are widely implemented in distributed storage systems to achieve fault tolerance with high storage efficiency. Reed-Solomon code is commonly deployed in data centers due to its optimal storage efficiency...
详细信息
This article examines the current state of the intelligent building monitoring system created for the University of Santiago de Compostela (USC) in the framework of the OPERE project and proposes a modification based ...
详细信息
ISBN:
(数字)9798350366488
ISBN:
(纸本)9798350366495
This article examines the current state of the intelligent building monitoring system created for the University of Santiago de Compostela (USC) in the framework of the OPERE project and proposes a modification based on the Fog Computing paradigm. The study is developed in the context of the European regulations for the energy efficiency of facilities and the reduction of greenhouse gases. The current system implements the data processing in DADIS modules, developed by this research group for the acquisition and flexible transmission of information. These modules provide the information to the monitoring system which offers functionalities such as energy consumption dashboards, configurable operation schedules and ad hoc data visualization. However, the limitations of the current system include the difficulty in scaling the processing of the acquired information and database queries. The described upgrade proposes the extensive use of MQTT to standardize communications and allow the development of stand-alone applications to scale processing. The same architecture facilitates the incorporation of Big data infrastructures that would solve even more complex query problems than those addressed in this scenario.
An anomaly-based intrusion detection system(A-IDS)provides a critical aspect in a modern computing infrastructure since new types of attacks can be *** prevalently utilizes several machine learning algorithms(ML)for d...
详细信息
An anomaly-based intrusion detection system(A-IDS)provides a critical aspect in a modern computing infrastructure since new types of attacks can be *** prevalently utilizes several machine learning algorithms(ML)for detecting and classifying network *** date,lots of algorithms have been proposed to improve the detection performance of A-IDS,either using individual or ensemble *** particular,ensemble learners have shown remarkable performance over individual learners in many applications,including in cybersecurity ***,most existing works still suffer from unsatisfactory results due to improper ensemble *** aim of this study is to emphasize the effectiveness of stacking ensemble-based model for A-IDS,where deep learning(e.g.,deep neural network[DNN])is used as base learner *** effectiveness of the proposed model and base DNN model are benchmarked empirically in terms of several performance metrics,i.e.,Matthew’s correlation coefficient,accuracy,and false alarm *** results indicate that the proposed model is superior to the base DNN model as well as other existing ML algorithms found in the literature.
暂无评论