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1.
Big Data ; 12(2): 155-172, 2024 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-37289808

RESUMEN

Diabetic foot ulcer (DFU) is a problem worldwide, and prevention is crucial. The image segmentation analysis of DFU identification plays a significant role. This will produce different segmentation of the same idea, incomplete, imprecise, and other problems. To address these issues, a method of image segmentation analysis of DFU through internet of things with the technique of virtual sensing for semantically similar objects, the analysis of four levels of range segmentation (region-based, edge-based, image-based, and computer-aided design-based range segmentation) for deeper segmentation of images is implemented. In this study, the multimodal is compressed with the object co-segmentation for semantical segmentation. The result is predicting the better validity and reliability assessment. The experimental results demonstrate that the proposed model can efficiently perform segmentation analysis, with a lower error rate, than the existing methodologies. The findings on the multiple-image dataset show that DFU obtains an average segmentation score of 90.85% and 89.03% correspondingly in two types of labeled ratios before DFU with virtual sensing and after DFU without virtual sensing (i.e., 25% and 30%), which is an increase of 10.91% and 12.22% over the previous best results. In live DFU studies, our proposed system improved by 59.1% compared with existing deep segmentation-based techniques and its average image smart segmentation improvements over its contemporaries are 15.06%, 23.94%, and 45.41%, respectively. Proposed range-based segmentation achieves interobserver reliability by 73.9% on the positive test namely likelihood ratio test set with only a 0.25 million parameters at the pace of labeled data.


Asunto(s)
Diabetes Mellitus , Pie Diabético , Internet de las Cosas , Humanos , Pie Diabético/diagnóstico por imagen , Reproducibilidad de los Resultados , Internet
2.
Rev. bras. med. esporte ; 29: e2022_0152, 2023. tab, graf
Artículo en Inglés | LILACS | ID: biblio-1394837

RESUMEN

ABSTRACT Introduction: In today's rapid development of science and technology, digital network data mining technology is developing as fast as the expansion of the frontiers of science and technology allows, with a very broad application level, covering most of the civilized environment. However, there is still much to explore in the application of sports training. Objective: Analyze the feasibility of data mining based on the digital network of sports training, maximizing athletes' training. Methods: This paper uses the experimental analysis of human FFT, combined with BP artificial intelligence network and deep data mining technology, to design a new sports training environment. The controlled test of this model was designed to compare advanced athletic training modalities with traditional modalities, comparing the athletes' explosive power, endurance, and fitness. Results: After 30 days of physical training, the athletic strength of athletes with advanced fitness increased by 15.33%, endurance increased by 15.85%, and fitness increased by 14.23%. Conclusion: The algorithm designed in this paper positively impacts maximizing athletes' training. It may have a favorable impact on training outcomes, as well as increase the athlete's interest in the sport. Level of evidence II; Therapeutic studies - investigating treatment outcomes.


RESUMO Introdução: No rápido desenvolvimento atual de ciência e tecnologia, a tecnologia de mineração de dados de rede digital desenvolve-se tão rápido quanto a expansão das fronteiras da ciência e tecnologia permitem, com um nível de aplicação muito amplo, cobrindo a maior parte do ambiente civilizado. No entanto, ainda há muito para explorar da aplicação no treinamento esportivo. Objetivo: Análise de viabilidade da mineração de dados com base na rede digital da formação esportiva, maximizar o treinamento dos atletas. Métodos: Este trabalho utiliza a análise experimental da FFT humana, combinada com a rede de inteligência artificial da BP e tecnologia de mineração profunda de dados, para projetar um novo ambiente de treinamento esportivo. O teste controlado deste modelo foi projetado para comparar modalidades avançadas de treinamento atlético com as modalidades tradicionais, comparando o poder explosivo, resistência e condição física do atleta. Resultados: Após 30 dias de treinamento físico, a força atlética dos esportistas com aptidão física avançada aumentou 15,33%, a resistência aumentou 15,85%, e o condicionamento físico aumentou 14,23%. Conclusão: O algoritmo desenhado neste artigo tem um impacto positivo na maximização do treinamento dos atletas. Pode ter um impacto favorável nos resultados do treinamento, bem como aumentar o interesse do atleta pelo esporte. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.


RESUMEN Introducción: En el rápido desarrollo actual de la ciencia y la tecnología, la tecnología de extracción de datos de redes digitales se desarrolla tan rápido como lo permiten las fronteras en expansión de la ciencia y la tecnología, con un nivel de aplicación muy amplio que abarca la mayor parte del entorno civilizado. Sin embargo, aún queda mucho por explorar de la aplicación en el entrenamiento deportivo. Objetivo: Análisis de viabilidad de la minería de datos basada en la red digital de entrenamiento deportivo, maximizar la formación de los atletas. Métodos: Este trabajo utiliza el análisis experimental de la FFT humana, combinado con la red de inteligencia artificial BP y la tecnología de minería de datos profunda, para diseñar un nuevo entorno de entrenamiento deportivo. La prueba controlada de este modelo se diseñó para comparar las modalidades de entrenamiento atlético avanzado con las modalidades tradicionales, comparando la potencia explosiva, la resistencia y la forma física del atleta. Resultados: Después de 30 días de entrenamiento físico, la fuerza atlética de los atletas con un estado físico avanzado aumentó en un 15,33%, la resistencia aumentó en un 15,85% y el estado físico aumentó en un 14,23%. Conclusión: El algoritmo diseñado en este trabajo tiene un impacto positivo en la maximización del entrenamiento de los atletas. Puede tener un impacto favorable en los resultados del entrenamiento, así como aumentar el interés del atleta por el deporte. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.


Asunto(s)
Humanos , Inteligencia Artificial , Aptitud Física/fisiología , Redes Neurales de la Computación , Rendimiento Atlético/fisiología , Atletas
3.
Comput Intell Neurosci ; 2022: 1874436, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35990150

RESUMEN

The smart city is an emerging concept that is based on the integration of various electronic devices and citizens that enhance the flow of information. IoT is an integral part for next generation wireless network infrastructure for acting as an interface of collecting data and controlling delivery of message which are using in smart cities. In this paper, an IoT-oriented relay assisted MIMO for beyond the fifth-generation wireless network system is proposed. The proposed system provides higher capacity and lower BER. The proposed system's BER results are compared with various combinations of transmission and receiving antennas at source, relay, and destination. It is found from BER performance that the developed scheme with relay does provide 1-17 dB gain with respect to direct connection. It is also found from mathematical analysis and simulation results that this scheme provides 3 to 9 b/s/Hz improvement in performance of capacity at 5 to 10 dB by adding a different combination of STBC and VBLAST. Simulation results are also presented to demonstrate the diversity and multiplexing gain that is a key to providing high data rates with reliable communication with many interferences for the IoT system. This system can also be used for massive antennas-based IoT system by raising the number of transmitting and receiving antennas with proposed encoding and decoding techniques explained in this paper.


Asunto(s)
Electrónica , Ciudades , Simulación por Computador
4.
Comput Intell Neurosci ; 2022: 7988894, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35602647

RESUMEN

Social media is one of the most revolutionary innovations in computer science that facilitates connecting people in the world to share information, ideas, and thoughts. In recent years, social media has demonstrated tremendous growth, which has affected individuals, businesses, communities, and economies. The focus of the present study is to identify the impact of social media on geopolitics and economic growth. The study is based on a systematic review of previous literature on the subject. It has been revealed through the findings that social media impacts geopolitics by decreasing the level of censorship and increasing the spread of news or information, while it also enables the politicians to influence individuals over online social networks through the great level of access. On the other hand, it has been identified that social media has both positive and negative impacts on geopolitics and economic growth. Social media is able to unite diverse groups and individuals spread across the planet dedicated to specific issues. The formation of communities and the ability of social media to unite groups show how social media could contribute positively to geopolitics and economic growth. But it decreases the productivity level of the individuals; on the other hand, it does contain the potential to create a participatory economy, which can be beneficial for a particular country. Some argue that social sharing has encouraged people to use computers and mobile phones to express their concerns on social issues without actually having to engage actively with campaigns in real life. Their support is limited to pressing the "Like" button or sharing content. This study performs a thorough study selection exercise and a quality assessment to ensure that the present study is valuable to academia and the relevant stakeholders, especially the experts of computer science who can develop the smartest artificial intelligence and cognitive computing tools that can help mitigate those risks of social media for the geopolitically volatile, uncertain, complex, and ambiguous world and ensure smooth economic growth.


Asunto(s)
Medios de Comunicación Sociales , Inteligencia Artificial , Cognición , Comercio , Desarrollo Económico , Humanos
5.
Comput Intell Neurosci ; 2022: 6447769, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35548099

RESUMEN

Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.


Asunto(s)
Encéfalo , Imagen por Resonancia Magnética , Algoritmos , Encéfalo/diagnóstico por imagen , Imagen por Resonancia Magnética/métodos , Máquina de Vectores de Soporte
6.
J Healthc Eng ; 2022: 2950699, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35251564

RESUMEN

Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud computing. Connected healthcare is receiving extraordinary contemplation from the industry, government, and the healthcare communities. In this study, several studies published in the last 6 years, from 2016 to 2021, have been selected. The selection process is represented through the Prisma flow chart. It has been identified that these increasing challenges of healthcare can be overcome by the implication of AI, ML, DL, Edge AI, IoMT, 6G, and cloud computing. Still, limited areas have implemented these latest advancements and also experienced improvements in the outcomes. These implications have shown successful results not only in resolving the issues from the perspective of the patient but also from the perspective of healthcare professionals. It has been recommended that the different models that have been proposed in several studies must be validated further and implemented in different domains, to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively.


Asunto(s)
Inteligencia Artificial , Internet de las Cosas , Ciudades , Nube Computacional , Atención a la Salud , Humanos
7.
J Healthc Eng ; 2022: 9957888, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35126961

RESUMEN

Nowadays, technology has been evolving rapidly. Due to the consequent impact of smart technologies, it becomes a ubiquitous part of life. These technologies have led to the emergence of smart cities that are geographic areas driven by advanced information and communication technologies. In the context of smart cities, IoT, blockchain, and fog computing have been found as the significant drivers of smart initiates. In this recognition, the present study is focused on delineating the impact and potential of blockchain, IoT, and fog computing on healthcare services in the context of smart cities. In pursuit of this objective, the study has conducted a systematic review of literature that is most relevant to the topic of the paper. In order to select the most relevant and credible articles, the researcher has used PRISMA and AMSTAR that have culminated in the 10 most relevant articles for the present study. The findings revealed that IoT, blockchain, and fog computing had become drivers of efficiency in the healthcare services in smart cities. Among the three technologies, IoT has been found to be widely incorporated. However, it is found to be lacking in terms of cost efficiency, data privacy, and interoperability of data. In this recognition, blockchain technology and fog computing have been found to be more relevant to the healthcare sector in smart cities. Blockchain has been presented as a promising technology for ensuring the protection of private data, creating a decentralized database, and improving the interoperability of data while fog computing has been presented as the promising technology for low-cost remote monitoring, reducing latency and increasing efficiency.


Asunto(s)
Cadena de Bloques , Ciudades , Atención a la Salud , Servicios de Salud , Humanos , Privacidad
8.
J Healthc Eng ; 2021: 5528622, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33884157

RESUMEN

Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs). The paper investigates the proposed system that uses various convolutional neural network (CNN) architectures to automatically detect breast cancer, comparing the results with those from machine learning (ML) algorithms. All architectures were guided by a big dataset of about 275,000, 50 × 50-pixel RGB image patches. Validation tests were done for quantitative results using the performance measures for every methodology. The proposed system is found to be successful, achieving results with 87% accuracy, which could reduce human mistakes in the diagnosis process. Moreover, our proposed system achieves accuracy higher than the 78% accuracy of machine learning (ML) algorithms. The proposed system therefore improves accuracy by 9% above results from machine learning (ML) algorithms.


Asunto(s)
Neoplasias de la Mama , Algoritmos , Mama/diagnóstico por imagen , Neoplasias de la Mama/diagnóstico por imagen , Femenino , Humanos , Aprendizaje Automático , Redes Neurales de la Computación
9.
J Healthc Eng ; 2020: 8857346, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-33204404

RESUMEN

COVID-19 presents an urgent global challenge because of its contagious nature, frequently changing characteristics, and the lack of a vaccine or effective medicines. A model for measuring and preventing the continued spread of COVID-19 is urgently required to provide smart health care services. This requires using advanced intelligent computing such as artificial intelligence, machine learning, deep learning, cognitive computing, cloud computing, fog computing, and edge computing. This paper proposes a model for predicting COVID-19 using the SIR and machine learning for smart health care and the well-being of the citizens of KSA. Knowing the number of susceptible, infected, and recovered cases each day is critical for mathematical modeling to be able to identify the behavioral effects of the pandemic. It forecasts the situation for the upcoming 700 days. The proposed system predicts whether COVID-19 will spread in the population or die out in the long run. Mathematical analysis and simulation results are presented here as a means to forecast the progress of the outbreak and its possible end for three types of scenarios: "no actions," "lockdown," and "new medicines." The effect of interventions like lockdown and new medicines is compared with the "no actions" scenario. The lockdown case delays the peak point by decreasing the infection and affects the area equality rule of the infected curves. On the other side, new medicines have a significant impact on infected curve by decreasing the number of infected people about time. Available forecast data on COVID-19 using simulations predict that the highest level of cases might occur between 15 and 30 November 2020. Simulation data suggest that the virus might be fully under control only after June 2021. The reproductive rate shows that measures such as government lockdowns and isolation of individuals are not enough to stop the pandemic. This study recommends that authorities should, as soon as possible, apply a strict long-term containment strategy to reduce the epidemic size successfully.


Asunto(s)
COVID-19/prevención & control , Aprendizaje Automático , Modelos Biológicos , Pandemias/prevención & control , Algoritmos , Número Básico de Reproducción/estadística & datos numéricos , Ingeniería Biomédica , COVID-19/epidemiología , Simulación por Computador , Atención a la Salud , Susceptibilidad a Enfermedades/epidemiología , Femenino , Predicción , Humanos , Masculino , Pandemias/estadística & datos numéricos , Distanciamiento Físico , Cuarentena , SARS-CoV-2 , Arabia Saudita/epidemiología , Procesos Estocásticos
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