Essays about: "Conclusion -computer networks"

Showing result 21 - 25 of 443 essays containing the words Conclusion -computer networks.

1. 21. Machine Learning Clustering andClassification of Network DeploymentScenarios in a Telecom Networksetting

   University essay from Linköpings universitet/Institutionen för datavetenskap

   Author : Chayan Shrang Raj; [2023]
   Keywords : Telecommunications; Statistics; Machine Learning; Masters; PySpark; Python; Data Visualization; LTE; eNodeB; Data analysis; Data Science; AI; Jupyter; HDFS;

   Abstract : Cellular network deployment scenarios refer to how cellular networks are implementedand deployed by network operators to provide wireless connectivity to end users.These scenarios can vary based on capacity requirements, type of geographical area, populationdensity, and specific use cases. READ MORE

3. 22. Physiology-Guided Machine Learning for Improved Reliability of Non-Invasive Assessment of Pulmonary Hypertension

   University essay from Linköpings universitet/Avdelningen för medicinsk teknik

   Author : Frida Hermansson; [2023]
   Keywords : Pulmonary Hypertension; pulmonary hypertension; improving; physiological-guided; machine learning; neural networks; NN; artificial neural networks; non-invasive; PH; tricuspid regurgitation; peak tricuspid regurgitation velocity; tricuspid regurgitation velocity; right ventricular systolic pressure; VGG16; Unet; TR-CNN; CNN; pulmonell hypertension; förbättra; fysiologisk-guidning; neurala nätverk; trikuspidal regurgitation; maximal trikuspidal regurgitation; icke-invasivt;

   Abstract : Diagnosing pulmonary hypertension (PH) with right heart catheterization (RHC) is associated with a risk for complications and high expenses, leading to late diagnoses [1]. Transthoracic echocardiography can be used to assess non-invasive indicators for PH such as right ventricular systolic pressure (RVSP), which can be estimated by combining the peak tricuspid regurgitation velocity (TRV) with the estimated right arterial pressure (RAP). READ MORE

4. 23. Technology Acceptance for AI implementations : A case study in the Defense Industry about 3D Generative Models

   University essay from KTH/Skolan för industriell teknik och management (ITM)

   Author : Michael Arenander; [2023]
   Keywords : Technology Acceptance; Artificial Intelligence; Machine Learning; 3D Generative Models; Innovation; Teknisk Acceptans; Artificiell Intelligens; Maskininlärning; 3D Generativa Modeller; Innovation;

   Abstract : Advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has emerged into 3D object creation processes through the rise of 3D Generative Adversarial Networks (3D GAN). These networks contain 3D generative models capable of analyzing and constructing 3D objects. READ MORE

5. 24. Third-party risks in industrial control systems: : A case study in the wind power sector

   University essay from KTH/Skolan för industriell teknik och management (ITM)

   Author : Abdullahi Ahmed; [2023]
   Keywords : Supply chain security; Wind power; Risk management; Third-party risk management; SCADA-system; Säkerhetshantering för försörjningskedjan; Vindkraft; Riskhantering; Tredjeparts riskhantering; SCADA-system;

   Abstract : This report investigates third-party risk for SCADA systems, using Swedish wind power as an empirical case study. Supervisory, Control, and Data Acquisition (SCADA) systems are used in the wind power industry to monitor and control the operational process. The paper also proposed potential strategies for reducing third-party risks and exposures. READ MORE

6. 25. Multiclass Brain Tumour Tissue Classification on Histopathology Images Using Vision Transformers

   University essay from Linköpings universitet/Statistik och maskininlärning

   Author : Christoforos Spyretos; [2023]
   Keywords : medical imaging; deep learning; classification; CNN; Vision Transformer; glioblastoma; GBM; IvyGAP; brain tumour; histopathology; digital pathology; histology;

   Abstract : Histopathology refers to inspecting and analysing tissue samples under a microscope to identify and examine signs of diseases. The manual investigation procedure of histology slides by pathologists is time-consuming and susceptible to misconceptions. READ MORE