Sistema experto de apoyo para el diagnóstico y tratamiento de la neumonía en cerdos

Through an expert system, it is possible to diagnose in a fast and accurate manner symptoms of pneumonia that a pig can have, as well as the level of seriousness of the illness. This can be achieved with the aid and knowledge of a veterinarian in charge of this area.  Developing an expert system to aid the decision making could treat this illness that is very common among pigs. In order to develop this application it is necessary to have certain rules or an inference engine, which allows the expert system to make decisions.  The web applications have had a great impact on society. Having an expert system is like having a tool with a lot of information stored, which in the end can help the user give an accurate diagnosis and treatment of the illness, minimizing production losses.Por medio de un sistema experto es posible diagnosticar rápida y eficazmente según los síntomas que tenga el cerdo, el nivel de gravedad de la neumonía,  esto con ayuda del conocimiento que provea el veterinario encargado en esta área y desarrollando así un sistema experto que ayude a la toma de decisiones para tratar a tiempo esta enfermedad tan común que sufre  la especie porcina. Para el desarrollo de la aplicación es necesario tener una serie de reglas o motor de inferencia que es el que permite la toma de decisiones del sistema. Los aplicativos web han tenido gran impacto en la sociedad porque con ellos se tiene a la mano todo tipo de información; al tener un sistema experto almacenado allí permitirá al usuario acceder a una herramienta experta, quien lo apoyara en un correcto diagnóstico y tratamiento para la enfermedad; minimizando perdidas en la producción

In-Vitro Biological Tissue State Monitoring based on Impedance Spectroscopy

The relationship between post-mortem state and changes of biological tissue impedance has been investigated to serve as a basis for developing an in-vitro measurement method for monitoring the freshness of meat. The main challenges thereby are the reproducible measurement of the impedance of biological tissues and the classification method of their type and state. In order to realize reproducible tissue bio-impedance measurements, a suitable sensor taking into account the anisotropy of the biological tissue has been developed. It consists of cylindrical penetrating multi electrodes realizing good contacts between electrodes and the tissue. Experimental measurements have been carried out with different tissues and for a long period of time in order to monitor the state degradation with time. Measured results have been evaluated by means of the modified Fricke-Cole-Cole model. Results are reproducible and correspond to the expected behavior due to aging. An appropriate method for feature extraction and classification has been proposed using model parameters as features as input for classification using neural networks and fuzzy logic. A Multilayer Perceptron neural network (MLP) has been proposed for muscle type computing and the age computing and respectively freshness state of the meat. The designed neural network is able to generalize and to correctly classify new testing data with a high performance index of recognition. It reaches successful results of test equal to 100% for 972 created inputs for each muscle. An investigation of the influence of noise on the classification algorithm shows, that the MLP neural network has the ability to correctly classify the noisy testing inputs especially when the parameter noise is less than 0.6%. The success of classification is 100% for the muscles Longissimus Dorsi (LD) of beef, Semi-Membraneous (SM) of beef and Longissimus Dorsi (LD) of veal and 92.3% for the muscle Rectus Abdominis (RA) of veal. Fuzzy logic provides a successful alternative for easy classification. Using the Gaussian membership functions for the muscle type detection and trapezoidal member function for the classifiers related to the freshness detection, fuzzy logic realized an easy method of classification and generalizes correctly the inputs to the corresponding classes with a high level of recognition equal to 100% for meat type detection and with high accuracy for freshness computing equal to 84.62% for the muscle LD beef, 92.31 % for the muscle RA beef, 100 % for the muscle SM veal and 61.54% for the muscle LD veal.  Auf der Basis von Impedanzspektroskopie wurde ein neuartiges in-vitro-Messverfahren zur Überwachung der Frische von biologischem Gewebe entwickelt. Die wichtigsten Herausforderungen stellen dabei die Reproduzierbarkeit der Impedanzmessung und die Klassifizierung der Gewebeart sowie dessen Zustands dar. Für die Reproduzierbarkeit von Impedanzmessungen an biologischen Geweben, wurde ein zylindrischer Multielektrodensensor realisiert, der die 2D-Anisotropie des Gewebes berücksichtigt und einen guten Kontakt zum Gewebe realisiert. Experimentelle Untersuchungen wurden an verschiedenen Geweben über einen längeren Zeitraum durchgeführt und mittels eines modifizierten Fricke-Cole-Cole-Modells analysiert. Die Ergebnisse sind reproduzierbar und entsprechen dem physikalisch-basierten erwarteten Verhalten. Als Merkmale für die Klassifikation wurden die Modellparameter genutzt

Infant Cry Signal Processing, Analysis, and Classification with Artificial Neural Networks

As a special type of speech and environmental sound, infant cry has been a growing research area covering infant cry reason classification, pathological infant cry identification, and infant cry detection in the past two decades. In this dissertation, we build a new dataset, explore new feature extraction methods, and propose novel classification approaches, to improve the infant cry classification accuracy and identify diseases by learning infant cry signals. We propose a method through generating weighted prosodic features combined with acoustic features for a deep learning model to improve the performance of asphyxiated infant cry identification. The combined feature matrix captures the diversity of variations within infant cries and the result outperforms all other related studies on asphyxiated baby crying classification. We propose a non-invasive fast method of using infant cry signals with convolutional neural network (CNN) based age classification to diagnose the abnormality of infant vocal tract development as early as 4-month age. Experiments discover the pattern and tendency of the vocal tract changes and predict the abnormality of infant vocal tract by classifying the cry signals into younger age category. We propose an approach of generating hybrid feature set and using prior knowledge in a multi-stage CNNs model for robust infant sound classification. The dominant and auxiliary features within the set are beneficial to enlarge the coverage as well as keeping a good resolution for modeling the diversity of variations within infant sound and the experimental results give encouraging improvements on two relative databases. We propose an approach of graph convolutional network (GCN) with transfer learning for robust infant cry reason classification. Non-fully connected graphs based on the similarities among the relevant nodes are built to consider the short-term and long-term effects of infant cry signals related to inner-class and inter-class messages. With as limited as 20% of labeled training data, our model outperforms that of the CNN model with 80% labeled training data in both supervised and semi-supervised settings. Lastly, we apply mel-spectrogram decomposition to infant cry classification and propose a fusion method to further improve the infant cry classification performance

Current Perspectives on Viral Disease Outbreaks

The COVID-19 pandemic has reminded the world that infectious diseases are still important. The last 40 years have experienced the emergence of new or resurging viral diseases such as AIDS, ebola, MERS, SARS, Zika, and others. These diseases display diverse epidemiologies ranging from sexual transmission to vector-borne transmission (or both, in the case of Zika). This book provides an overview of recent developments in the detection, monitoring, treatment, and control of several viral diseases that have caused recent epidemics or pandemics

Parasitic Pneumonia and Lung Involvement

Parasitic infestations demonstrated a decline in the past decade as a result of better hygiene practices and improved socioeconomic conditions. Nevertheless, global immigration, increased numbers of the immunocompromised people, international traveling, global warming, and rapid urbanization of the cities have increased the susceptibility of the world population to parasitic diseases. A number of new human parasites, such as Plasmodium knowlesi, in addition to many potential parasites, have urged the interest of scientific community. A broad spectrum of protozoal parasites frequently affects the respiratory system, particularly the lungs. The diagnosis of parasitic diseases of airway is challenging due to their wide varieties of clinical and roentgenographic presentations. So detailed interrogations of travel history to endemic areas are critical for clinicians or pulmonologists to manage this entity. The migrating adult worms can cause mechanical airway obstruction, while the larvae can cause airway inflammation. This paper provides a comprehensive review of both protozoal and helminthic infestations that affect the airway system, particularly the lungs, including clinical and roentgenographic presentations, diagnostic tests, and therapeutic approaches