Dr Sara Sharifzadeh

Uwch-ddarlithydd, Computer Science

+44 (0) 1792 987192

sara.sharifzadeh@abertawe.ac.uk

318
Trydydd Llawr
Y Ffowndri Gyfrifiadol
Campws y Bae

Ar gael ar gyfer Goruchwyliaeth Ôl-raddedig

Dolenni Ymchwil

https://orcid.org/0000-0003-4621-2917

Google Scholar

Trosolwg

Cwblhaodd Dr Sara Sharifzadeh ei hastudiaeth PhD mewn Cyfrifiadureg ym Mhrifysgol Technegol Denmarc yn 2015, gyda chyfnod ymchwil estynedig ym Mhrifysgol Waterloo, Canada. Wedi iddi gwblhau ei PhD, dechreuodd yrfa newydd fel Ymchwilydd Cysylltiol mewn Gwyddor Data ym Mhrifysgol Loughborough. Yn 2018, ymunodd â Phrifysgol Coventry fel Darlithydd. Ymunodd â Phrifysgol Abertawe fel uwch-ddarlithydd yn 2021.

Yn ystod ei hastudiaethau a'i gyrfa ymchwil, cymerodd Dr Sharifzadeh ran mewn nifer o brosiectau ymchwil diwydiannol a ariannwyd gan EPSRC a Chyngor Denmarc ar gyfer Ymchwil Strategol a'u partneriaid diwydiannol. Mae ei phif feysydd ymchwil yn cynnwys dysgu peirianyddol, deallusrwydd artiffisial, dadansoddi data amlamryweb a’i ddefnyddio wrth ddadansoddi signalau/delweddau, iechyd digidol e.e. adnabod gweithgarwch dynol, dadansoddi delweddau lloeren a dadansoddi data cwmwl pwynt 3D a geir o synwyryddion sganiwr laser ar robotiaid. Mae hi wedi goruchwylio nifer o brosiectau PhD ac MSc ar y pynciau ymchwil hyn. Mae hi’n adolygydd nifer o gyfnodolion rhyngwladol ag effaith fawr, ac yn aelod o IEEE.

Meysydd Arbenigedd

Dysgu Peirianyddol ac ANN
Modelu rhagfynegi ar gyfer signalau/delweddau sbectrol
Dadansoddi delweddau lloeren
Adnabod gweithgarwch dynol
Dadansoddi data synwyryddion ar robotiaid

Uchafbwyntiau Gyrfa

Diddordebau Addysgu

Artificial Neural Networks

Machine Learning

Cydweithrediadau

Cyhoeddiadau

Uchafbwyntiau Ymchwil

Sharifzadeh, S., Amiri, S., & Abdi, S. (2021). A New Method for Semi-Supervised Segmentation of Satellite Images. In 2021 22nd IEEE International Conference on Industrial Technology (ICIT)IEEE.
https://doi.org/10.1109/icit46573.2021.9453700, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65600
Minto, M., Tan, B., Sharifzadeh, S., Riihonen, T., & Valkama, M. (2020). Shallow Neural Networks for mmWave Radar Based Recognition of Vulnerable Road Users. In 2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)IEEE.
https://doi.org/10.1109/csndsp49049.2020.9249537, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65601
Sharifzadeh, S., Biro, I., & Kinnell, P. (2020). Robust hand-eye calibration of 2D laser sensors using a single-plane calibration artefact. Robotics and Computer-Integrated Manufacturing, 61
https://doi.org/10.1016/j.rcim.2019.101823, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65602
Sharifzadeh, S., Tata, J., Sharifzadeh, H., & Tan, B. (2020). Farm Area Segmentation in Satellite Images Using DeepLabv3+ Neural Networks. In Communications in Computer and Information Science (pp. 115-135). Springer International Publishing.
https://doi.org/10.1007/978-3-030-54595-6_7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65603
Karayaneva, Y., Sharifzadeh, S., Jing, Y., Chetty, K., & Tan, B. (2020). Sparse Feature Extraction for Activity Detection Using Low-Resolution IR Streams. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)IEEE.
https://doi.org/10.1109/icmla.2019.00296, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65604
Sharifzadeh, S., Tata, J., & Tan, B. (2019). Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image. In Proceedings of the 8th International Conference on Data Science, Technology and ApplicationsSCITEPRESS - Science and Technology Publications.
https://doi.org/10.5220/0007954901000108, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65605
Sharifzadeh, S., Biro, I., Lohse, N., & Kinnell, P. (2018). Abnormality detection strategies for surface inspection using robot mounted laser scanners. Mechatronics, 51, 59-74.
https://doi.org/10.1016/j.mechatronics.2018.03.001, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65606
Sharifzadeh, S., Ghodsi, A., Clemmensen, L., & Ersbøll, B. (2017). Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection. Engineering Applications of Artificial Intelligence, 65, 168-177.
https://doi.org/10.1016/j.engappai.2017.07.004, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65607
Al-Yacoub, A., Sharifzadeh, S., Lohse, N., Usman, Z., Goh, Y., & Jackson, M. (2016). Learning Industrial Robot Force/torque Compensation: A Comparison of Support Vector and Random Forests Regression. In Telehealth and Assistive Technology / 847: Intelligent Systems and RoboticsACTAPRESS.
https://doi.org/10.2316/p.2016.847-002, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65608
Sharifzadeh, S., Biro, I., Lohse, N., & Kinnell, P. (2016). Robust Surface Abnormality Detection for a Robotic Inspection System. IFAC-PapersOnLine, 49(21), 301-308.
https://doi.org/10.1016/j.ifacol.2016.10.572, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65609

Holl Ymchwil

Erthyglau Cylchgronau

Mehta, R., Sharifzadeh, S., Palade, V., Tan, B., Daneshkhah, A., & Karayaneva, Y. (2023). Deep Learning Techniques for Radar-Based Continuous Human Activity Recognition. Machine Learning and Knowledge Extraction, 5(4), 1493-1518.
https://doi.org/10.3390/make5040075, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64788
http://dx.doi.org/10.3390/make5040075
Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S., Palade, V., & Duncan, M. (2023). Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 158, 106835
https://doi.org/10.1016/j.compbiomed.2023.106835, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63069
http://dx.doi.org/10.1016/j.compbiomed.2023.106835
Karayaneva, Y., Sharifzadeh, S., Jing, Y., & Tan, B. (2023). Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data. Sensors, 23(1), 478
https://doi.org/10.3390/s23010478, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62398
Karayaneva, Y., Sharifzadeh, S., Li, W., Jing, Y., & Tan, B. (2021). Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare. IEEE Access, 9, 62984-63001.
https://doi.org/10.1109/access.2021.3074088, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65599
Sharifzadeh, S., Biro, I., & Kinnell, P. (2020). Robust hand-eye calibration of 2D laser sensors using a single-plane calibration artefact. Robotics and Computer-Integrated Manufacturing, 61
https://doi.org/10.1016/j.rcim.2019.101823, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65602
Sharifzadeh, S., Biro, I., Lohse, N., & Kinnell, P. (2018). Abnormality detection strategies for surface inspection using robot mounted laser scanners. Mechatronics, 51, 59-74.
https://doi.org/10.1016/j.mechatronics.2018.03.001, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65606
Sharifzadeh, S., Ghodsi, A., Clemmensen, L., & Ersbøll, B. (2017). Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection. Engineering Applications of Artificial Intelligence, 65, 168-177.
https://doi.org/10.1016/j.engappai.2017.07.004, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65607
Sharifzadeh, S., Biro, I., Lohse, N., & Kinnell, P. (2016). Robust Surface Abnormality Detection for a Robotic Inspection System. IFAC-PapersOnLine, 49(21), 301-308.
https://doi.org/10.1016/j.ifacol.2016.10.572, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65609
Sharifzadeh, S., Clemmensen, L., Borggaard, C., Støier, S., & Ersbøll, B. (2014). Supervised feature selection for linear and non-linear regression of L⁎a⁎b⁎ color from multispectral images of meat. Engineering Applications of Artificial Intelligence, 27, 211-227.
https://doi.org/10.1016/j.engappai.2013.09.004, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65612
Vega, M., Sharifzadeh, S., Wulfsohn, D., Skov, T., Clemmensen, L., & Toldam‐Andersen, T. (2013). A sampling approach for predicting the eating quality of apples using visible–near infrared spectroscopy. Journal of the Science of Food and Agriculture, 93(15), 3710-3719.
https://doi.org/10.1002/jsfa.6207, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65613
Ardeshir, G., Sharifzadeh, S., & MiarNaimi, H. (2008). Statistical Background Modeling Based on Velocity and Orientation of Moving Objects. International Journal of Engineering, 21(1), 39-54.
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65620
https://www.ije.ir/article_71686.html

Cyfraniadau Cynadleddau

Mehta, R., Palade, V., Sharifzadeh, S., Tan, B., & Karayaneva, Y. (2022). Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)IEEE.
https://doi.org/10.1109/icmla55696.2022.00076, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63203
http://dx.doi.org/10.1109/icmla55696.2022.00076
Sharifzadeh, S. (2022). Spars Kernelized Features for Prediction of Rock’s Carbon Capture using 3D X-Ray Images. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)IEEE.
https://doi.org/10.1109/icmla55696.2022.00081, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63204
http://dx.doi.org/10.1109/icmla55696.2022.00081
Sharifzadeh, S., Amiri, S., & Abdi, S. (2021). A New Method for Semi-Supervised Segmentation of Satellite Images. In 2021 22nd IEEE International Conference on Industrial Technology (ICIT)IEEE.
https://doi.org/10.1109/icit46573.2021.9453700, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65600
Minto, M., Tan, B., Sharifzadeh, S., Riihonen, T., & Valkama, M. (2020). Shallow Neural Networks for mmWave Radar Based Recognition of Vulnerable Road Users. In 2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)IEEE.
https://doi.org/10.1109/csndsp49049.2020.9249537, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65601
Sharifzadeh, S., Tata, J., Sharifzadeh, H., & Tan, B. (2020). Farm Area Segmentation in Satellite Images Using DeepLabv3+ Neural Networks. In Communications in Computer and Information Science (pp. 115-135). Springer International Publishing.
https://doi.org/10.1007/978-3-030-54595-6_7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65603
Karayaneva, Y., Sharifzadeh, S., Jing, Y., Chetty, K., & Tan, B. (2020). Sparse Feature Extraction for Activity Detection Using Low-Resolution IR Streams. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)IEEE.
https://doi.org/10.1109/icmla.2019.00296, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65604
Sharifzadeh, S., Tata, J., & Tan, B. (2019). Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image. In Proceedings of the 8th International Conference on Data Science, Technology and ApplicationsSCITEPRESS - Science and Technology Publications.
https://doi.org/10.5220/0007954901000108, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65605
Al-Yacoub, A., Sharifzadeh, S., Lohse, N., Usman, Z., Goh, Y., & Jackson, M. (2016). Learning Industrial Robot Force/torque Compensation: A Comparison of Support Vector and Random Forests Regression. In Telehealth and Assistive Technology / 847: Intelligent Systems and RoboticsACTAPRESS.
https://doi.org/10.2316/p.2016.847-002, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65608
Sharifzadeh, S. (2014). Evaluation of off-line intestinal in vitro lipolysis of lipid-based drug delivery systems (LbDDS) by diffuse reflectance imaging (DRI). In 9th World Meeting on Pharmaceutics, Biopharmaceutics and Pharmaceutical Technology
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65611
Sharifzadeh, S., Skytte, J., Clemmensen, L., & Ersboll, B. (2013). DCT-based characterization of milk products using diffuse reflectance images. In 2013 18th International Conference on Digital Signal Processing (DSP)IEEE.
https://doi.org/10.1109/icdsp.2013.6622669, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65614
Sharifzadeh, S., Clemmensen, L., Ersboll, B., & Vega, M. (2013). Optimal vision system design for characterization of apples using US/VIS/NIR spectroscopy data. In 2013 20th International Conference on Systems, Signals and Image Processing (IWSSIP)IEEE.
https://doi.org/10.1109/iwssip.2013.6623437, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65615
Sharifzadeh, S., Clemmensen, L., Løje, H., & Ersbøll, B. (2013). Statistical Quality Assessment of Pre-fried Carrots Using Multispectral Imaging. In Image Analysis (pp. 620-629). Springer Berlin Heidelberg.
https://doi.org/10.1007/978-3-642-38886-6_58, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65616
Sharifzadeh, S., Skytte, J., Nielsen, O., Ersbøll, B., & Clemmensen, L. (2012). Regression and sparse regression methods for viscosity estimation of acid milk from it's SLS features. In 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP)IEEE.
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65617
https://ieeexplore.ieee.org/document/6208195
Sharifzadeh, S., Serrano, J., & Carrabina, J. (2012). Spectro-temporal analysis of speech for Spanish phoneme recognition. In 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP)IEEE.
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65618
https://ieeexplore.ieee.org/document/6208200

Cynnyrch Ymchwil Arall

Sharifzadeh, S. (2015). Multivariate Analysis Techniques for Optimal Vision System Design
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65610
https://orbit.dtu.dk/en/publications/multivariate-analysis-techniques-for-optimal-vision-system-design
Sharifzadeh, S. (2010). Automatic Speech Recognition: From Study to Practice
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65619
https://repository.lboro.ac.uk/articles/educational_resource/Automatic_speech_recognition_from_study_to_practice/9577682

Goruchwyliaeth

Untitled (cyfredol)

PhD

Goruchwyliwr arall: Dr Joe Macinnes
AI-Driven Anomaly Detection in Human Activity Monitoring Systems for Enhancing Health and Well-being Tracking (cyfredol)

PhD

Goruchwyliwr arall: Dr Joe Macinnes
Predictive modelling for hyperspectral satellite images and crop yield: a generative data-driven strategy for understanding climate change induced challenges in Agriculture (cyfredol)

PhD

Goruchwyliwr arall: Prof Xianghua Xie
Application of Reinforcement Learning (RL) Paradigms to the Control of Water Systems at Dwr Cymru (Welsh Water) (cyfredol)

PhD

Goruchwyliwr arall: Dr Thomas Reitmaier
AI Methods for Crop Detection in Multi-Spectral Satellite Imagery (cyfredol)

PhD

Goruchwyliwr arall: Prof Xianghua Xie

Modiwlau a Addysgir

CS-150 Concepts of Computer Science

This module gives an overview of some of the main principles underlying computers and computing from both a theoretical and an applied point of view. Following a brief history of computers and software an introduction to the representation of data and the basic components of a computer will be given. Students will be introduced to the principles of programming at assembly language level. Further topics include simple algorithm analysis, operating systems, file systems, computer networks, and the world wide web. A brief discussion on the limitations of computing is also given. The module is accessible and relevant to students of all disciplines who wish to learn about, or reinforce their understanding of, computers and computer science
CSC345B Big Data and Machine Learning

This module provides a broad introduction to artificial intelligence, machine learning, pattern recognition, and their applications to big data problems. The students will gain understanding and knowledge of the theoretical foundations of learning, learn effective machine learning techniques, and acquire practical know-how in applying some of those theories and techniques to real world problems. Topics include big data concept, data mining, learning theories, supervised and unsupervised learning, and reinforcement learning.
CSCM45J Big Data and Machine Learning

This module will discuss in-depth some of the most widely used and state-of-the-art artificical intelligence and machine learning techniques and their applications to big data problems. The students will gain both theoretical understanding of learning and practical know-how in applying those theories to real world problems. Topics include big data concept, data mining, learning theories, supervised and unsupervised learning, and reinforcement learning.

Dr Sara Sharifzadeh

Rhif ffôn

Cyfeiriad ebost

LinkedIn Account

Dolenni Ymchwil

Trosolwg

Meysydd Arbenigedd

Uchafbwyntiau Gyrfa

Cyhoeddiadau

Uchafbwyntiau Ymchwil

Holl Ymchwil

Erthyglau Cylchgronau

Cyfraniadau Cynadleddau

Cynnyrch Ymchwil Arall

Goruchwyliaeth

Untitled (cyfredol)

AI-Driven Anomaly Detection in Human Activity Monitoring Systems for Enhancing Health and Well-being Tracking (cyfredol)

Predictive modelling for hyperspectral satellite images and crop yield: a generative data-driven strategy for understanding climate change induced challenges in Agriculture (cyfredol)

Application of Reinforcement Learning (RL) Paradigms to the Control of Water Systems at Dwr Cymru (Welsh Water) (cyfredol)

AI Methods for Crop Detection in Multi-Spectral Satellite Imagery (cyfredol)

Modiwlau a Addysgir

CS-150 Concepts of Computer Science

CSC345B Big Data and Machine Learning

CSCM45J Big Data and Machine Learning