Sökning: "deep machine learning"

Visar resultat 1 - 5 av 180 avhandlingar innehållade orden deep machine learning.

1. 1. Machine learning for building energy system analysis

   Författare :Fan Zhang; Johan Håkansson; Chris Bales; Stefan Byttner; Högskolan Dalarna; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; district heating; machine learning; deep learning; HVAC; neural networks;

   Sammanfattning : Buildings account for approximately 40% of the global energy, and Heating, Ventilation, and Air Conditioning (HVAC) contributes to a large proportion of building energy consumption. Two main negative characteristics that contribute to performance degradation and energy waste in an HVAC system are inappropriate control strategies and faults. LÄS MER

3. 2. Protein Model Quality Assessment : A Machine Learning Approach

   Författare :Karolis Uziela; Arne Elofsson; Liam McGuffin; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Protein Model Quality Assessment; structural bioinformatics; machine learning; deep learning; support vector machine; proq; Artificial Neural Network; protein structure prediction; Biochemistry towards Bioinformatics; biokemi med inriktning mot bioinformatik;

   Sammanfattning : Many protein structure prediction programs exist and they can efficiently generate a number of protein models of a varying quality. One of the problems is that it is difficult to know which model is the best one for a given target sequence. Selecting the best model is one of the major tasks of Model Quality Assessment Programs (MQAPs). LÄS MER

4. 3. Advancing Clinical Decision Support Using Machine Learning & the Internet of Medical Things : Enhancing COVID-19 & Early Sepsis Detection

   Författare :Mahbub Ul Alam; Rahim Rahmani; Jaakko Hollmén; Sadok Ben Yahia; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Internet of Medical Things; Patient-Centric Healthcare; Clinical Decision Support System; Predictive Modeling in Healthcare; Health Informatics; Healthcare analytics; COVID-19; Sepsis; COVID-19 Detection; Early Sepsis Detection; Lung Segmentation Detection; Medical Data Annotation Scarcity; Medical Data Sparsity; Medical Data Heterogeneity; Medical Data Security Privacy; Practical Usability Enhancement; Low-End Device Adaptability; Medical Significance; Interpretability; Visualization; LIME; SHAP; Grad-CAM; LRP; Electronic Health Records; Thermal Image; Tabular Medical Data; Chest X-ray; Machine Learning; Deep Learning; Federated Learning; Semi-Supervised Machine Learning; Multi-Task Learning; Transfer Learning; Multi-Modality; Natural Language Processing; ClinicalBERT; GAN; data- och systemvetenskap; Computer and Systems Sciences;

   Sammanfattning : This thesis presents a critical examination of the positive impact of Machine Learning (ML) and the Internet of Medical Things (IoMT) for advancing the Clinical Decision Support System (CDSS) in the context of COVID-19 and early sepsis detection.It emphasizes the transition towards patient-centric healthcare systems, which necessitate personalized and participatory care—a transition that could be facilitated by these emerging fields. LÄS MER

5. 4. On Deep Machine Learning Based Techniques for Electric Power Systems

   Författare :Ebrahim Balouji; Chalmers tekniska högskola; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Deep Learning; Cable faults; phase locked loop; Flicker; Harmonics and Interharmonics; Reinforcement learning; Voltage Dip; Active Power filter; Machine Learning; Voltage fluctuation; Partial Discharges;

   Sammanfattning : This thesis provides deep machine learning-based solutions to real-time mitigation of power quality disturbances such as flicker, voltage dips, frequency deviations, harmonics, and interharmonics using active power filters (APF). In an APF the processing delays reduce the performance when the disturbance to be mitigated is tima varying. LÄS MER

7. 5. Machine Learning for Wireless Link Adaptation : Supervised and Reinforcement Learning Theory and Algorithms

   Författare :Vidit Saxena; Joakim Jaldén; Mats Bengtsson; Hugo Tullberg; Jakob Hoydis; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Wireless Communications; Reinforcement Learning; Multi-Armed Bandits; Thompson Sampling; Convex Optimization; Deep Learning; Electrical Engineering; Elektro- och systemteknik;

   Sammanfattning : Wireless data communication is a complex phenomenon. Wireless links encounter random, time-varying, channel effects that are challenging to predict and compensate. Hence, to optimally utilize the channel, wireless links adapt the data transmission parameters in real time. LÄS MER