Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis

Antibiotic-resistant bacteria have proliferated at an alarming rate as a result of the extensive use of antibiotics and the paucity of new medication research. The possibility that an antibiotic-resistant bacterial infection would progress to sepsis is one of the major collateral problems affecting people with this condition. 31,000 lives were lost due to sepsis in England with costs about two billion pounds annually. This research aims to develop and evaluate several classification approaches to improve predicting sepsis and reduce the tendency of underdiagnosis in computer-aided predictive tools. This research employs medical data sets for patients diagnosed with sepsis, it analyses the efficacy of ensemble machine learning techniques compared to non ensemble machine learning techniques and the significance of data balancing and Conditional Tabular Generative Adversarial Nets for data augmentation in producing reliable diagnosis. The average F Score obtained by the non-ensemble models trained in this paper is 0.83 compared to the ensemble techniques average of 0.94. Nonensemble techniques, such as Decision Tree, achieved an F score of 0.90, an AUC of 0.90 and an accuracy of 90%. Histogram-based Gradient Boosting Classification Tree achieved an F score of 0.96, an AUC of 0.96 and an accuracy of 95%, surpassing the other models tested. Additionally, when compared to the current state of the art sepsis prediction models, the models developed in this study demonstrated higher average performance in all metrics, indicating reduced bias and improved robustness through data balancing and Conditional Tabular Generative Adversarial Nets for data augmentation. The study revealed that data balancing and augmentation on the ensemble machine learning algorithms boost the efficacy of clinical predictive models and can help clinics decide which data types are most important when examining patients and diagnosing sepsis early through intelligent human-machine interface

Performance Evaluation of Ensemble Deep Learning Algorithms for Prediction of Pandemic Disease

For optimal healthcare management and counter-measures, it is essential to monitor and predict severe disease at the right time, before it becomes pandemic. In this research work, the most recent pandemic is considered as an example, as the viral coronavirus (COVID-19) prognosis is crucial to learn from. The severe COVID-19 threat has had a substantial influence on the global health security scene, forcing the creation of cutting-edge computer models to imorive monitoring, control, and mitigation measures. The research study aims to develop a generalized model assessing the healthcare parameters at a personalized and community dimensions and predicting the severity of the disease before becoming pandemic. To achieve this aim, this paper has systematically evaluated the outcomes of different experiments utilizing the ResNet, DenseNet, and ensemble models using a variety of performance criteria. The ensemble model consistently demonstrated superior performance across all metrics, exhibiting an accuracy and f1-score of 97%. In comparison, the DenseNet model earned an accurancy and f1-score of 93%, while the ResNet model earned an accurancy and f1-score of 88%. All models in this paper demonstrated promising accuracy and the potential to ain in COVID-19 prediction. Chest x-ray images were employed to experiment the computational models of accurately predicting the disease. Such experiment allows us to have a better understanding of the advantages and disadvantages of various computer models for predicting sever disease, which will help create more precise and effective prediction systems fr medical condition. The achieve result highlights the efficacy of ensemble techniques for exploiting the synergistic benefits of multiple models. The knowledge gained from this study aims to go beyond the theoretical sphere and expand its influence into the real world of hospital administration

Scalable Machine Learning Model for Highway CCTV Feed Real-Time Car Accident and Damage Detection

This study investigates the potential advantages of employing computer vision algorithms to enhance real-time accident detection and response on highways using CCTV feed. Traditional techniques rely on retrospective data, which can decrease response times and precision. Computer vision algorithms have the potential to enhance detection speed and precision, resulting in quicker emergency response and monitoring of traffic flow. The primary objective of this study is to identify the advantages of utilising computer vision algorithms and the data gathered through them to enhance road safety measures and reduce the occurrence of accidents. This study is anticipated to result in quicker emergency response times, the identification of areas where statistically more accidents are likely to occur, and the use of collected data for research purposes, which can lead to enhanced road safety measures. Using computer vision algorithms for accident detection and response has the potential to reduce the human and monetary costs associated with traffic accidents

An Innovative Approach Based on Machine Learning to Evaluate the Risk Factors Importance in Diagnosing Keratoconus

Background and objective: Keratoconus is a non-inflammatory corneal condition affecting both eyes and is present in one out of every 2,000 people worldwide. The cornea deforms into a conical shape and thins, resulting in high-order aberrations and gradual vision loss. Risk factor analysis in the degradation of keratoconus is under-researched. Methods: This research work investigates and uses effective machine learning models to gain insight into how much the risk factors of a patient contribute towards the progressive stages of keratoconus, as well as how significant these factors are in the creation of an accurate prediction model. This research demonstrates the value of machine learning approaches on a clinical dataset. This research paper employs several machine learning algorithms to classify the patients' stage of keratoconus using clinical information, such as measurements of the cornea's topography, elevation, and pachymetry taken using pentacam equipment at Sydney's Vision Eye Institute Chatswood. Results: Eight different machine learning techniques were investigated over three variations of a dataset and achieved an average accuracy of 68, 80, and 90% for the risk factor, pentacam, and cumulative datasets, respectively. The results show a significant increase in accuracy and a 97% increase in AUC upon addition of risk factor data compared to the models trained on pentacam data alone. The machine learning methods shown in this paper outperform those in current research. Conclusions: This research highlights the importance of machine learning methods and risk factor data in the diagnosis of keratoconus and highlights the patient's primary optical aid as the strongest risk factor. The goal of this research is to support the work of the ophthalmologists in diagnosing keratoconus and provide better care for the patient

Enhancement Techniques for Improving Facial Recognition Performance in Convolutional Neural Networks

The advent of convolutional neural networks (CNNs) to the development of face recognition system has been a game changer in the field of computer vision and pattern recognition. This research work uses a pre trained MobileNet-V1 model to develop an effective CNN model capable of high performance. We also tackle several common facial recognition challenges which include occlusions, illumination variations, make-ups, pose variation and ageing through the use of several improvement techniques. The techniques include adopting a less computationally costly approach, transfer learning and hyper-parameter finetuning. The Top-1 accuracy 70.6% and Top-5 accuracy 89.5% of the base MobileNet-V1 model has been improved using these techniques to achieve training accuracy of 95% and accuracies of 96.4%, 98.0% and 99.1% on the Pins face recognition dataset, FaceScrub data-set and LFW data-set, respectively. The work done so far illustrates the need for further research into improvement techniques for convolutional neural networks

Effective Machine Learning Based Techniques for Predicting Depression

Depression is a global disorder with serious consequences. With more depression-related data and improved machine learning, it may be possible to build intelligent systems that can detect depression early on. This research uses the burns depression checklist as the gold standard for diagnosing depression and the support vector machine, decision tree, and light gradient boosting method as algorithms to create models capable of diagnosing depression on a dataset of 604 surveyed participants. This research demonstrates the efficiency of machine learning algorithms within the field of mental health. Research into the efficacy of algorithms such as AdaBoost, Gradient Boosting and Bagging for depression detection is extensive. This paper serves to increase the body of knowledge by training insufficiently researched algorithms on a commonly used depression detection dataset with the goal of reaching or surpassing the level of performance seen in current research. This research has found the the decision tree classifier to be the best tool for predicting depression with an accuracy and AUC rating of 95.66% with an AUC of 0.942, while that of the support vector machine classifier and the light gradient boosting classifier are 91.48% with an AUC of 0.942 and 94.58% and 0.942, respectively. The techniques presented in this paper perform better than those being used in current machine learning research. This study may help in determining what attributes are most crucial in diagnosis of depressed individuals as well as improve the health of the general populace

Sistema SCADA UNAH para eficiencia energética

A SCADA system is a specialized software application to run on computers and field devices for monitoring and control of any system or distant installations. Therefore, the following is a proposal for the implementation of a SCADA system in the UNAH, which has as a main objective the reduction of electricity consumption costs, since the university currently has high energy consumption. In addition, the university has a fiber optic ring that is present on campus (where the entity responsible for managing the network is the Executive Director of Technology Management EDTM, DEGT in Spanish), which reduces the initial investment needed, and is a vital reason for conducting this project. The SCADA system will enable the user to monitor and control installed devices (data acquisition card, PC, PLC, and various motion and capacitive sensors), where these are interconnected to the network via a VLAN through a Switch and with the other buildings through fiber optic ring, which has its main server. It is important to mention that the same research led us to perform a REAL SCADA prototype of low cost, to demonstrate the performance of this type system and prove that it can be applied to the UNAH. The prototype was developed using a Data Acquisition Card, Project Board, and electronic devices, Performing the real-world data acquisition and control of these physical variables through remote computers via a wireless communication network, for simulation of a network LAN TCP / IP. The design and prototype were presented orally at the Research Seminar class and specifically to DEGT, where development and implementation of SCADA were explained, which must start with a pilot Building for the design and development of the system to carry all UNAH. The investment plan for the 25 buildings with high consumption of electrical power is 3,903,743.75 LPS, where funds could be acquired as follows, 30% own funds or from external sources (1,171,123.13 LPS), and 70% funded by one or more financial institutions (2,732,620.63 LPS). These investments include furniture, software and hardware only, these costs can vary considerably, because much of the network equipment already exist in university. The DEGT manages these resources, so it is important that, the detail of these must given by them and is itself which defines the final cost. The conditions in which the project arises, allows us to recover the investment in eighteen months. As shown in parallel the relationship between benefit and cost of project implementation, because the benefit is very profitable for more than five years. See Fig.8 Due to the magnitude of the project, it is extremely important to hire a professional engineer to work in DEGT full time, with extensive knowledge of electricity, software, automated systems and networks. Granting sufficient authority to coordinate and manage the implementation and development of this project and other energy saving projects in buildings of the National Autonomous University of Honduras. If the complete project is implemented, the economic savings that the UNAH would have is more than a million Lempiras annually. Key words: SCADA; Automation; Networks and Communication Protocols; Remote Control; Computer Systems for Automation and Control. DOI: http://dx.doi.org/10.5377/rct.v0i6.514 Revista Ciencia y Tecnología, No. 6, Segunda época, junio 2010: 97-109Un sistema SCADA es una aplicación de software especializada para funcionar sobre computadoras y dispositivos de campo, para el monitoreo y control de cualquier sistema o instalación a distancia; es por esta razón que se presenta una propuesta para la implementación de un sistema SCADA en la UNAH, la que tiene como principal objetivo reducir los costos en la factura por consumo de energía eléctrica, ya que la universidad actualmente cuenta con un alto consumo energético. Además la misma universidad dispone de un anillo de fibra óptica que está presente en los predios universitarios, (donde el ente responsable del manejo de dicha red es la Dirección Ejecutiva de Gestión de Tecnología DEGT) el cual reduce los costos de manera importante; es una razón de vital importancia para llevar a cabo este proyecto. El sistema SCADA, nos permitirá monitorear y controlar los dispositivos instalados (Tarjeta de adquisición de datos, PC, PLC, y los diferentes sensores de movimiento y capacitivos), donde éstos a su vez se interconectan a la Red VLAN por medio de un Switch, y con los demás edificios a través del anillo de fibra óptica, el cual tiene su servidor principal. Es importante hacer mención que la misma Investigación nos llevó a realizar un prototipo de SCADA REAL de bajo costo, para lograr evidenciar el funcionamiento de este tipo de sistema y demostrar que es posible aplicarlo a la UNAH. El Prototipo fue desarrollado utilizando una tarjeta de Adquisición de Datos, Proyect Board, y dispositivos electrónicos, realizando la toma de datos del mundo real y el control de estas variables físicas a través de computadoras remotas por medio de una red de comunicación Wireless, simulando una Red LAN TCP/IP. El proyecto y el prototipo fueron presentados oralmente en la clase de Seminario de investigación y de forma muy especial también a la DEGT, donde se formuló la implementación del SCADA arrancando con un edificio piloto para la elaboración y desarrollo del sistema para llevarlo a toda la UNAH. El plan de inversión para los 25 edificios de alto consumo de energía eléctrica de la UNAH, es de L. 3 903,743.75, donde los fondos podrían ser adquiridos de la 1 siguiente forma: 30 % fondos propios o provenientes de fuentes externas, L.1 171,123.13 y un 70 % financiado por una o varias entidades financieras 1 L.2 732,620.63. Estas inversiones incluyen mobiliario, software y hardware 1 únicamente. Los costos pueden variar considerando, que mucho equipo de Red ya lo posee la DEGT, por ello es importante que el detalle de estos mismos los brinde la DEGT y sea ella misma la que defina el costo final. En las condiciones en que se plantea el proyecto, nos permite recuperar la inversión en un año y seis meses como se muestra de forma paralela la relación que existe entre el beneficio y el costo de la implementación del proyecto, ya que el beneficio es muy rentable para más de cinco años. Ver Fig.8 Debido a la magnitud del proyecto es sumamente importante contratar a un profesional de la ingeniería que labore en la DEGT con dedicación exclusiva, con amplios conocimientos de electricidad, software, autómatas y Redes, otorgándole facultades suficientes para coordinar y gestionar la implementación y desarrollo de este proyecto y de los restantes proyectos de ahorro energético en los edificios de la UNAH. De implementarse el proyecto de forma completa, el ahorro económico que tendría la UNAH seria de más de un millón de lempiras anualmente con tan sólo disminuir una hora de iluminación diaria. Palabras Clave: SCADA; Automatización; Redes y Protocolos de comunicación; Control Remoto; Informática de Sistemas de Automatización y Control. DOI: http://dx.doi.org/10.5377/rct.v0i6.514 Revista Ciencia y Tecnología, No. 6, Segunda época, junio 2010: 97-10

Ciencia y tecnología (No. 6 segunda época jun 2010)

Publicación Bianual de la Dirección de Investigación Científica de la Universidad Nacional Autónoma de Honduras.Contenidos: La consulta a los pueblos originarios a la luz del Convenio 169 de la Organización Internacional del Trabajo / Andy Guillermo de Jesús Javalois Cruz 3. Ecos del silencio: el clamor de las poblaciones indígenas y afro descendientes de Honduras 43. La tortura: punto de vista de los derechos humanos, punto de vista del derecho internacional humanitario / Reina Isabel Savoff Ortega 55. El desempleo y subempleo juvenil asociado a la baja calificación técnica de los jóvenes egresados de secundaria. Estudio de caso del Distrito Central período 1990-2006 / Merlin Ivania Padilla Contreras 80. Sistema SCADA UNAH para eficiencia energética / Dennis A. Rivera, José Gabriel Zorto Aguilera 97. Desapariciones forzadas: una violación al derecho internacional humanitario y derecho internacional de derechos humanos / Ilda Lilian Cartagena Santos 110