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Deep image features sensing with multilevel fusion for complex convolution neural networks & cross domain benchmarks
No full textEfficient image retrieval from a variety of datasets is crucial in today's digital world. Visual properties are represented using primitive image signatures in Content Based Image Retrieval (CBIR). Feature vectors are employed to classify images into predefined categories. This research presents a unique feature identification technique based on suppression to locate interest points by computing productive sum of pixel derivatives by computing the differentials for corner scores. Scale space interpolation is applied to define interest points by combining color features from spatially ordered L2 normalized coefficients with shape and object information. Object based feature vectors are formed using high variance coefficients to reduce the complexity and are converted into bag-of-visual-words (BoVW) for effective retrieval and ranking. The presented method encompass feature vectors for information synthesis and improves the discriminating strength of the retrieval system by extracting deep image features including primitive, spatial, and overlayed using multilayer fusion of Convolutional Neural Networks(CNNs). Extensive experimentation is performed on standard image datasets benchmarks, including ALOT, Cifar-10, Corel-10k, Tropical Fruits, and Zubud. These datasets cover wide range of categories including shape, color, texture, spatial, and complicated objects. Experimental results demonstrate considerable improvements in precision and recall rates, average retrieval precision and recall, and mean average precision and recall rates across various image semantic groups within versatile datasets. The integration of traditional feature extraction methods fusion with multilevel CNN advances image sensing and retrieval systems, promising more accurate and efficient image retrieval solutions
Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review
No full textAccurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear relationships within large datasets. This study presents a systematic literature review (SLR) of deep learning applications for solar PV forecasting, addressing a gap in the existing literature, which often focuses on traditional ML or broader renewable energy applications. This review specifically aims to identify the DL architectures employed, preprocessing and feature engineering techniques used, the input features leveraged, evaluation metrics applied, and the persistent challenges in this field. Through a rigorous analysis of 26 selected papers from an initial set of 155 articles retrieved from the Web of Science database, we found that Long Short-Term Memory (LSTM) networks were the most frequently used algorithm (appearing in 32.69% of the papers), closely followed by Convolutional Neural Networks (CNNs) at 28.85%. Furthermore, Wavelet Transform (WT) was found to be the most prominent data decomposition technique, while Pearson Correlation was the most used for feature selection. We also found that ambient temperature, pressure, and humidity are the most common input features. Our systematic evaluation provides critical insights into state-of-the-art DL-based solar forecasting and identifies key areas for upcoming research. Future research should prioritize the development of more robust and interpretable models, as well as explore the integration of multi-source data to further enhance forecasting accuracy. Such advancements are crucial for the effective integration of solar energy into future power grids
Association between blood cortisol levels and numerical rating scale in prehospital pain assessment
No full textBackground
Nowadays, there is no correlation between levels of cortisol and pain in the prehospital setting. The aim of this work was to determine the ability of prehospital cortisol levels to correlate to pain. Cortisol levels were compared with those of the numerical rating scale (NRS).
Methods
This is a prospective observational study looking at adult patients with acute disease managed by Emergency Medical Services (EMS) and transferred to the emergency department of two tertiary care hospitals. Epidemiological variables, vital signs, and prehospital blood analysis data were collected. A total of 1516 patients were included, the median age was 67 years (IQR: 51–79; range: 18–103) with 42.7% of females. The primary outcome was pain evaluation by NRS, which was categorized as pain-free (0 points), mild (1–3), moderate (4–6), or severe (≥7). Analysis of variance, correlation, and classification capacity in the form area under the curve of the receiver operating characteristic (AUC) curve were used to prospectively evaluate the association of cortisol with NRS.
Results
The median NRS and cortisol level are 1 point (IQR: 0–4) and 282 nmol/L (IQR: 143–433). There are 584 pain-free patients (38.5%), 525 mild (34.6%), 244 moderate (16.1%), and 163 severe pain (10.8%). Cortisol levels in each NRS category result in p < 0.001. The correlation coefficient between the cortisol level and NRS is 0.87 (p < 0.001). The AUC of cortisol to classify patients into each NRS category is 0.882 (95% CI: 0.853–0.910), 0.496 (95% CI: 0.446–0.545), 0.837 (95% CI: 0.803–0.872), and 0.981 (95% CI: 0.970–0.991) for the pain-free, mild, moderate, and severe categories, respectively.
Conclusions
Cortisol levels show similar pain evaluation as NRS, with high-correlation for NRS pain categories, except for mild-pain. Therefore, cortisol evaluation via the EMS could provide information regarding pain status
Tecnologia da Informação e Comunicação: Estratégias para otimizar os serviços hospitalares
No full textAs premissas que motivaram e originaram a realização da presente tese de doutorado, cujo título, Tecnologia da Informação e Comunicação: estratégias para otimizar os serviços hospitalares, estão associadas pelo interesse de identificar se os sistemas operacionais da Tecnologia da Informação e Comunicação do Hospital Santo Antônio, no município de Alenquer/PA, geram ações estratégicas que possam otimizar os serviços ofertados. Frente às inovações tecnológicas, o crescimento do uso da Tecnologia da Informação e Comunicação, tornou-se uma estratégia fundamental não só no mundo da gestão empresarial, mas também se expandiu nas instituições na área da saúde, com o objetivo de oferecer a otimização dos serviços e assistência, a diminuição de custos e o desenvolvimento de estratégias de uma gestão de qualidade. O desenvolvimento do trabalho teve embasamento na pesquisa bibliográfica descritiva, com o enforque qualitativo no qual buscou-se fontes que listaram produções de autores que abordaram assuntos ligados ao tema da pesquisa. E para uma investigação mais objetiva e precisa do objeto de estudo, adotou-se a pesquisa de campo, com enforque qualitativo e quantitativo. Os resultados obtidos por meio das pesquisas realizadas e apresentada, são concludentes ao objetivo que identificou que ações da Tecnologia da Informação e Comunicação, são estratégias na otimização dos serviços hospitalares e que a instituição procura fazer inovações na área tecnológica para melhor servir aos pacientes com rapidez, eficiência e humanização, além de proporcionar otimização e assistência, diminui custos, restrições de falhas, confiança, seguridade nos acessos das informações e estratégias para uma gestão de qualidade. Considerando que as estratégias geram fortalecimento e credibilidade na organização, além de uma visão holística sobre o funcionamento interno do hospital e ainda prever a sustentabilidade da instituição
Novel transfer learning approach for hand drawn mathematical geometric shapes classification
No full textHand-drawn mathematical geometric shapes are geometric figures, such as circles, triangles, squares, and polygons, sketched manually using pen and paper or digital tools. These shapes are fundamental in mathematics education and geometric problem-solving, serving as intuitive visual aids for understanding complex concepts and theories. Recognizing hand-drawn shapes accurately enables more efficient digitization of handwritten notes, enhances educational tools, and improves user interaction with mathematical software. This research proposes an innovative machine learning algorithm for the automatic classification of mathematical geometric shapes to identify and interpret these shapes from handwritten input, facilitating seamless integration with digital systems. We utilized a benchmark dataset of mathematical shapes based on a total of 20,000 images with eight classes circle, kite, parallelogram, square, rectangle, rhombus, trapezoid, and triangle. We introduced a novel machine-learning algorithm CnN-RFc that uses convolution neural networks (CNN) for spatial feature extraction and the random forest classifier for probabilistic feature extraction from image data. Experimental results illustrate that using the CnN-RFc method, the Light Gradient Boosting Machine (LGBM) algorithm surpasses state-of-the-art approaches with high accuracy scores of 98% for hand-drawn shape classification. Applications of the proposed mathematical geometric shape classification algorithm span various domains, including education, where it enhances interactive learning platforms and provides instant feedback to students
Client engagement solution for post implementation issues in software industry using blockchain
No full textIn the rapidly advanced and evolving information technology industry, adequate client engagement plays a critical role as it is very important to understand the client’s concerns, and requirements, have the records, authorizations, and go-ahead of previously agreed requirements, and provide the feasible solution accordingly. Previously multiple solutions have been proposed to enhance the efficiency of client engagement, but they lack traceability, trust, transparency, and conflict in agreements of previous contracts. Due to the lack of these shortcomings, the client requirement is getting delayed which is causing client escalations, integrity issues, project failure, and penalties. In this study, we proposed the UniferCollab framework to overcome the issues of collaboration between various teams, transparency, the record of client authorizations, and the go-ahead on previous developments by implementing blockchain technology. We store the data on the permissible network in the proposed approach. It allows us to compile all the requirements and information shared by clients on permissible blockchain to secure a large amount of data which enhances the traceability of all the requirements. All the authorizations from the client generate push notifications for any changes in their current system executed through smart contracts. It removes the ambiguity between various development teams if the client has only shared the requirement with one team. The data is stored in the decentralized network from where information is gathered which resolves the traceability, transparency, and trust issues. Lastly, evaluations involved a total of 800 hypertext transfer protocol (HTTP) requests tested using Postman with blockchain block sizes ranging from 0.568 KB to 550 KB and an average size increase of 280 KB was observed as new blocks were added. The longest chain in the network was observed during 800 repetitions of blockchain operations. Latency analysis revealed that delays in processing HTTP requests were influenced by decentralized node processing, local machine response times, and internet bandwidth through various experiments. Results show that the proposed framework resolves all client engagement issues in implementation between all stakeholders which enhances trust, and transparency improves client experience and helps us manage disputes effectively
Association between blood cortisol levels and numerical rating scale in prehospital pain assessment
No full textBackground
Nowadays, there is no correlation between levels of cortisol and pain in the prehospital setting. The aim of this work was to determine the ability of prehospital cortisol levels to correlate to pain. Cortisol levels were compared with those of the numerical rating scale (NRS).
Methods
This is a prospective observational study looking at adult patients with acute disease managed by Emergency Medical Services (EMS) and transferred to the emergency department of two tertiary care hospitals. Epidemiological variables, vital signs, and prehospital blood analysis data were collected. A total of 1516 patients were included, the median age was 67 years (IQR: 51–79; range: 18–103) with 42.7% of females. The primary outcome was pain evaluation by NRS, which was categorized as pain-free (0 points), mild (1–3), moderate (4–6), or severe (≥7). Analysis of variance, correlation, and classification capacity in the form area under the curve of the receiver operating characteristic (AUC) curve were used to prospectively evaluate the association of cortisol with NRS.
Results
The median NRS and cortisol level are 1 point (IQR: 0–4) and 282 nmol/L (IQR: 143–433). There are 584 pain-free patients (38.5%), 525 mild (34.6%), 244 moderate (16.1%), and 163 severe pain (10.8%). Cortisol levels in each NRS category result in p < 0.001. The correlation coefficient between the cortisol level and NRS is 0.87 (p < 0.001). The AUC of cortisol to classify patients into each NRS category is 0.882 (95% CI: 0.853–0.910), 0.496 (95% CI: 0.446–0.545), 0.837 (95% CI: 0.803–0.872), and 0.981 (95% CI: 0.970–0.991) for the pain-free, mild, moderate, and severe categories, respectively.
Conclusions
Cortisol levels show similar pain evaluation as NRS, with high-correlation for NRS pain categories, except for mild-pain. Therefore, cortisol evaluation via the EMS could provide information regarding pain status
Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals
No full textEpileptic seizures are neurological events characterized by sudden and excessive electrical discharges in the brain, leading to disruptions in brain function. Epileptic seizures can lead to life-threatening situations such as status epilepticus, which is characterized by prolonged or recurrent seizures and may lead to respiratory distress, aspiration pneumonia, and cardiac arrhythmias. Therefore, there is a need for an automated approach that can efficiently diagnose epileptic seizures at an early stage. The primary objective of this study is to develop a highly accurate approach for the early diagnosis of epileptic seizures. We use electroencephalography (EEG) signal data based on different brain activities to conduct experiments for epileptic seizure detection. For this purpose, a novel transfer learning technique called random forest-gated recurrent unit (RFGR) is proposed. The EEG brain activity signal data is fed into the RFGR model to generate a new feature set. The newly generated features are based on the class prediction probabilities extracted by the RFGR and are utilized to train models. Extensive experiments are carried out to investigate the performance of the proposed approach. Results demonstrate that the RFGR, when used with the random forest model, outperforms state-of-the-art techniques, achieving a high accuracy of 99.00 %. Additionally, explainable artificial intelligence analysis is utilized to provide transparent and understandable explanations of the decision-making processes of the proposed approach
O uso de estudo de caso mediado por TIC na Graduação de Teologia da Uninter Chapecó
No full textO presente projeto de pesquisa com o tema “O uso de estudo de caso mediado por TIC na Graduação de Teologia da Uninter Chapecó” tem como objetivo geral analisar como o uso de estudo de caso mediado por TIC na Graduação de Teologia da Uninter Chapecó contribui para a aprendizagem significativa, sendo este um requisito para a conclusão do Mestrado em Educação, com Especialização em Tecnologias na Educação, da Universidad Internacional Iberoamericana (UNIB). A pesquisa é não experimental, básica, transversal e foi desenvolvida a partir de um enfoque metodológico qualitativo, sendo o tipo de estudo qualitativo, exploratório e descritivo. Para a pesquisa bibliográfica fez-se uma busca de produção acadêmica nas bases de dados Google Acadêmico, Repositório Uninter e no Catálogo de Teses e Dissertações da Capes, com recorte temporal de 2013 a 2022. Para a pesquisa empírica, o instrumento escolhido para a coleta de dados e informações foi o Google Formulário, que contém um Roteiro de Perguntas multitemático original enviado aos alunos matriculados no Módulo B 2022 de Teologia Bíblica Interconfessional, do Centro Universitário Internacional UNINTER, polo de apoio presencial Chapecó. Traz como contribuição uma reflexão sobre a pesquisa-avaliação de uma experiência educacional ou formativa já implementada na Graduação de Teologia supracitada. Os principais resultados encontrados ao categorizar o tema/problema da pesquisa estão relacionados à educação e foram abordadas teorias de aprendizagem, metodologias ativas, tecnologias de informação e comunicação e Teologia. Com base nessa análise: Compreendemos que a utilização do estudo de caso na Graduação de Teologia Bíblica Interconfessional, com a mediação das tecnologias de informação e comunicação, é uma estratégia eficaz para melhorar a formação acadêmica dos sujeitos aprendizes. Essa estratégia promove a interação e colaboração entre eles, a reflexão crítica sobre situações complexas, a aprendizagem autônoma e personalizada, além de possibilitar o acesso a uma aprendizagem mais conectiva. Essa compreensão representa uma contribuição significativa para a área de Teologia, pois ajuda a melhorar a qualidade da aprendizagem dos sujeitos aprendizes; Identificamos que a integração das tecnologias de informação e comunicação traz vantagens para a aprendizagem colaborativa em Teologia. Essas vantagens incluem o acesso a informações, comunicação, flexibilidade, estímulo à criatividade e feedback imediato. Essa identificação pode ser considerada como uma contribuição relevante para a melhoria da qualidade da formação teológica; e, Conhecemos dois avanços significativos relacionados à formação continuada de Teologia no contexto brasileiro: a educação a distância e a interdisciplinaridade. Esses avanços favorecem a formação de teólogos e teólogas mais preparados e engajados em questões relevantes para a vida acadêmica, em sociedade e na igreja. O conhecimento adquirido é relevante para o tema geral da formação em Teologia no Brasil. Concluímos recomendando que haja investigações futuras com o aprofundamento na interdisciplinaridade da Teologia com as ciências humanas e na educação continuada em Teologia no Brasil. Além disso, seria interessante explorar como esses temas estão relacionados ao uso das tecnologias da informação e comunicação. Recomendamos que outros pesquisadores considerem essa abordagem para compreender como a Teologia pode se beneficiar dessas ferramentas para promover uma aprendizagem mais efetiva e abrangente
Novel hybrid transfer neural network for wheat crop growth stages recognition using field images
No full textWheat is one of the world’s most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential for precision farming. Determining wheat growth stages accurately is crucial for increasing the efficiency of agricultural yield in wheat farming. Preliminary research identified obstacles in distinguishing between these stages, negatively impacting crop yields. To address this, this study introduces an innovative approach, MobDenNet, based on data collection and real-time wheat crop stage recognition. The data collection utilized a diverse image dataset covering seven growth phases ‘Crown Root’, ‘Tillering’, ‘Mid Vegetative’, ‘Booting’, ‘Heading’, ‘Anthesis’, and ‘Milking’, comprising 4496 images. The collected image dataset underwent rigorous preprocessing and advanced data augmentation to refine and minimize biases. This study employed deep and transfer learning models, including MobileNetV2, DenseNet-121, NASNet-Large, InceptionV3, and a convolutional neural network (CNN) for performance comparison. Experimental evaluations demonstrated that the transfer model MobileNetV2 achieved 95% accuracy, DenseNet-121 achieved 94% accuracy, NASNet-Large achieved 76% accuracy, InceptionV3 achieved 74% accuracy, and the CNN achieved 68% accuracy. The proposed novel hybrid approach, MobDenNet, that synergistically merges the architectures of MobileNetV2 and DenseNet-121 neural networks, yields highly accurate results with precision, recall, and an F1 score of 99%. We validated the robustness of the proposed approach using the k-fold cross-validation. The proposed research ensures the detection of growth stages with great promise for boosting agricultural productivity and management practices, empowering farmers to optimize resource distribution and make informed decisions