The purpose of this research is to study various aspects of the usage of cloud computing in organizational structure and provides details on keywords, countries of origin, authors of these articles and publications. This study employs a visual way to assess articles about the usage of cloud computing in organizational structure. VOSviewer was used to locate and provide a summary of Web of Science papers from 1800 to the present. Publication source, publication organization, authors, funding sources, nation, citations of articles, citations of country, and citations of publication source were noted and examined. Citation, authorship, co-citation, and network of keyword co-occurrence bibliometric maps were created. 4449 cited references and 393 articles were analyzed. The United States dominates the citations (1196) and publications (54, 13.7
Suicide is a critical issue in contemporary society, giving rise to significant societal and economic ramifications. To mitigate these adverse effects, it is imperative to implement effective suicide prevention strategies. In this context, deep neural network (DNN) algorithms have gained prominence and are increasingly applied across various healthcare domains. In our research, we examined the efficacy of employing DNNs for predicting suicide attempts. Our study involved a descriptive-analytical, cross-sectional investigation that harnessed a DNN algorithm, specifically a Sequential model comprising four Dense layers, to analyze suicide-related data. We identified several crucial risk factors, such as a history of psychiatric hospitalization, the day of the week, and occupation, for predicting suicide attempts. Leveraging the DNN algorithm on a dataset encompassing 1453 individuals with a suicide history, we framed the problem as a binary classification task with suicide history as the target variable. To enhance model performance, we applied preprocessing techniques, including class balancing, and constructed a DNN model with a Sequential architecture featuring four Dense layers. It is worth emphasizing that the DNN algorithm’s effectiveness hinges on the quality and quantity of available data and the model’s architectural choices. Nevertheless, our study demonstrated satisfactory overall performance of the DNN algorithm. For instance, the model achieved an f1-score of 91
Poland syndrome is a medical condition known by partial or complete absence of the pectoralis muscle. Since this condition has a low prevalence in the society, its assessment and examination remain as an active research area. The aim of this study was to assess the thermal behavior of the pectoralis major, deltoid, trapezius, and latissimus dorsi after an upper body strength training plan in a person with Poland Syndrome type II. This study was conducted in two patients, a participant with Poland Syndrome type II and a clinically healthy participant. A four-week training plan was developed and implemented. The focus was on the upper body, especially the chest and back. Thermographic images were taken weekly in the anterior and posterior upper body. Temperature differences in four regions of interest of the upper body (pectoralis major, deltoid, trapezius, and latissimus dorsi) were assessed. After the analysis, the deltoid muscle of the participant with Poland syndrome showed a temperature behavior that differed from the rest of the regions. In addition, the temperature variation in the healthy person was higher. Temperature effects showed differences in the main muscles of the upper body over four weeks of training, which could be due to the presence of Poland Syndrome. According to results, the monitoring of muscle activation by infrared thermography may be a tool that allows early detection of muscle disorders; however, it is necessary to continue the research, considering the possibility of expanding the sample.
Plants and trees play a crucial role to maintain life on earth, as it is the initial component of the food chain and hence they are inevitable for life sustainability. Human activities have resulted in increased plant diseases and endangered the habitats essential for the survival of a large number of plant species. Conservation of plant species and its disease control has become an imperative and emergency need to recover, restore, and maintain the plant diversity of natural habitats. Rapid developments in computer vision and mobile communication devices can greatly benefit the automation of plant species recognition, plant disease identification, and disease control. The proposed model is a combined system for disease detection and species identification by using images of healthy and unhealthy leaves. The model is trained and validated in an integrated dataset created by combining the standard datasets of species identification and disease detection of medicinal leaves. Proposed work studies the performance of two different hybrid deep learning architectures. First one makes use of Convolutional Siamese Network (CSN) a twin CNN model which requires only a small number of training samples. Second uses CNN followed by ensemble classifier configuration. Two types of base CNN models used are simple basic model (SM) and pre-trained model VGG16. The experimentation has shown that using a multi-learners model, CNN accuracy can be improved with fewer training images, without data augmentation, with less feature dimensionality. On an average 13.4
With the increase in the number of mobile money users and the rise of mobile money transactions over time, underlying security flaws have been noted in mobile money services operations. The purpose of this study is to investigate the primary security weaknesses in Rwanda’s mobile money services, the challenges, as well as the risks involved. We collected data through administering an online questionnaire, filled by 30 registered mobile money (MM) users. We interviewed a registered MM agent. The collected data were analyzed using Python where there is a 93
