Essays about: "Machine age"

Showing result 26 - 30 of 114 essays containing the words Machine age.

  1. 26. Implementation of a Pharmacokinetic Model to Estimate the Contribution of Infusion Systems to the Delayed Dosing of Morphine in Children

    University essay from KTH/Medicinteknik och hälsosystem

    Author : Karin Schaedel; [2022]
    Keywords : Infusion Pump; Pharmacokinetic Model; Morphine; Pediatric; MATLAB; Infusionspump; Farmakokinetisk modell; Morfin; Pediatrik; MATLAB;

    Abstract : Infusion pumps administer medications like morphine to pediatric patients in order to manage pain. Drug delivery delays can be the result of flow rate variabilities in the infusion pump system. Due to the risk of over-or underdosing, this could have a high impact on the pediatric population. READ MORE

  3. 27. Predicting Birth Outcome Using Cardiotocography and Machine Learning

    University essay from Lunds universitet/Matematisk statistik

    Author : Josefine Öder; [2022]
    Keywords : Mathematics and Statistics;

    Abstract : Cardiotography, CTG, is a monitoring method that is commonly used during childbirth. The method measures the fetal heart rate, FHR, alongside the uterine contractions, TOCO. Clinicians use this tool to evaluate the health of the infant, and to observe changes which might imply hypoxia, lack of oxygen supply, for the fetus. READ MORE

  4. 28. Key Socioeconomic Factors for Domestic Solar Energy : An interdisciplinary analysis of the characteristics of photovoltaic and solar thermal installations in three Swedish municipalities

    University essay from Uppsala universitet/Byggteknik och byggd miljö

    Author : Sofia Ekbring; [2022]
    Keywords : solar PV; photovoltaic; solar thermal; socioeconomic; demographic; influence; solceller; solvärme; solfångare; socioekonomisk; demografisk;

    Abstract : As a response to the increasing demand for renewable power, the solar photovoltaic (PV) market is growing fast. In addition to PV systems, the energy from solar radiation can be converted intoheat energy in solar thermal (ST) systems. READ MORE

  5. 29. Emotion AI in Mental Healthcare : How can affective computing enhance mental healthcare for young adults?

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Nassim Sheykholeslami; [2022]
    Keywords : Artificial Intelligence; Emotion AI; Affective Computing; Machine Learning; Mental Health; Digital Mental Health Applications; mHealth; Anxiety; Depression; Psychological Therapy; COVID-19; Artificiell intelligens; känslobaserad AI; affektiv databehandling; maskininlärning; psykisk hälsa; digitala tillämpningar för psykisk hälsa; mHälsa; ångest; depression; psykologisk terapi; COVID-19;

    Abstract : There has been a stigma attached to mental health for years, with many people afraid to seek help from mental health professionals due to negative stereotypes about those seeking help. Young adults in particular are still reluctant in regards to seeing a therapist since they are ashamed, do not want to show weakness or they perceive their need for therapy as low. READ MORE

  6. 30. Automatic classification of cardiovascular age of healthy people by dynamical patterns of the heart rhythm

    University essay from Linköpings universitet/Statistik och maskininlärning

    Author : priya kurian pullolickal; [2022]
    Keywords : Electrocardiogram ECG measures the electrical impulses of the heart. The time inter- val between two successive R peaks measured in millisecond using an ECG is called as an RR-interval. The distribution of the RR-intervals as well as classification of cardiovascu- lar age of healthy people from RR-interval was done in this thesis. For that; the data was preprocessed and time series plots were analyzed from sample dataset. The RR-intervals were then aligned to have the same start time using functions written and then an aver- age RR-interval series for each decade was created. The coefficient of variation was very less for this averaged dataset which concluded that averaging the RR-interval was a good approach. The averaged dataset per age decade as well agreed to the conclusion of the sample data set that the heart rate variability decreases with increasing age. Three clusters of age decade were also visible in the averaged dataset. The kurtosis; skew; mean; me- dian; histograms and Q-Q Plot were calculated for the sample as well as averaged dataset to find the distribution. The values all concluded that the RR-intervals follow Gaussian distribution or mixture of Gaussian distribution. The Poincaré plots showed that the dis- tribution of RR-interval is comet shaped for healthy individuals. The features were ex- tracted from the distribution as well as from the distribution of Discrete Fourier Transform DFT for classifying the age group from RR-intervals. Svitzky-Golay filtering was done to smooth the signal before taking the features from DFT. Random Forest and Support Vector Machine was the machine learning algorithms used to classify the age decade. Later the results were compared using a dataset from physionet that had RR-intervals of individuals suffering from myocardial infraction. The age classification using Random Forest and Sup- port Vector Machine concluded that the Gdańsk dataset using Random Forest Algorithm and three classes gave the highest test accuracy of 59% for the dataset.;

    Abstract : .... READ MORE