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Large scale data mining using genetics-based machine learning

Jaume Bacardit, Xavier Llorà
2012 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12  
within NVIDIA s Graphic Processor Units • CUDA runs thousands of threads at the same time à Single Program, Multiple Data paradigm • In the last few years GPUs have been extensively used in the  ...  If we have the parallel hardware, this is a trivial way of gaining efficiency -32 million pairs of amino-acids (instances in the training set) with less than 2% of real contacts -Each instance is  ... 
doi:10.1145/2330784.2330936 dblp:conf/gecco/BacarditL12 fatcat:7qpgc572efg5zk3zkz4px3pky4

Large scale data mining using genetics-based machine learning

Jaume Bacardit, Xavier Llorà
2013 Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion - GECCO '13 Companion  
within NVIDIA s Graphic Processor Units • CUDA runs thousands of threads at the same time à Single Program, Multiple Data paradigm • In the last few years GPUs have been extensively used in the  ...  If we have the parallel hardware, this is a trivial way of gaining efficiency -32 million pairs of amino-acids (instances in the training set) with less than 2% of real contacts -Each instance is  ... 
doi:10.1145/2464576.2480807 dblp:conf/gecco/BacarditL13 fatcat:g7weaaxqsrdytcnalrucleoz4u

Large scale data mining using genetics-based machine learning

Jaume Bacardit, Xavier Llorà
2009 Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference - GECCO '09  
within NVIDIA s Graphic Processor Units • CUDA runs thousands of threads at the same time à Single Program, Multiple Data paradigm • In the last few years GPUs have been extensively used in the  ...  If we have the parallel hardware, this is a trivial way of gaining efficiency -32 million pairs of amino-acids (instances in the training set) with less than 2% of real contacts -Each instance is  ... 
doi:10.1145/1570256.1570424 dblp:conf/gecco/BacarditL09 fatcat:gnpp6egslzf7fjnyxkt2naqwam

Large scale data mining using genetics-based machine learning

Jaume Bacardit, Xavier Llorà
2011 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11  
within NVIDIA s Graphic Processor Units • CUDA runs thousands of threads at the same time à Single Program, Multiple Data paradigm • In the last few years GPUs have been extensively used in the  ...  If we have the parallel hardware, this is a trivial way of gaining efficiency -32 million pairs of amino-acids (instances in the training set) with less than 2% of real contacts -Each instance is  ... 
doi:10.1145/2001858.2002137 dblp:conf/gecco/BacarditL11 fatcat:bgbkp6youfauxnc7jetrpjwj4q

Application and Exploration of Big Data Mining in Clinical Medicine

Li-Na Han, Yue Zhang, Shu-Li Guo, Tie-Ling Li
2016 Chinese Medical Journal  
Zhang et al. proposed a new system that analyzed the medical data stream and made predictions in real-time.  ...  important insights for the early diagnosis of breast cancer.  ... 
doi:10.4103/0366-6999.178019 pmid:26960378 pmcid:PMC4804421 fatcat:5esbnukxvzdo3fajojwz4s2ry4

Application of AI and IoT in Clinical Medicine: Summary and Challenges

Zhao-xia Lu, Peng Qian, Dan Bi, Zhe-wei Ye, Xuan He, Yu-hong Zhao, Lei Su, Si-liang Li, Zheng-long Zhu
2021 Current Medical Science  
recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive  ...  In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective  ...  An ANN is a parallel nonlinear dynamic system model, which is a mathematical model for information processing similar to the structure of synaptic connections in the brain.  ... 
doi:10.1007/s11596-021-2486-z pmid:34939144 pmcid:PMC8693843 fatcat:3g3qpksktjhv5koqs3i7ylco7y

Deep learning for healthcare applications based on physiological signals: A review

Oliver Faust, Yuki Hagiwara, Tan Jen Hong, Oh Shu Lih, U Rajendra Acharya
2018 Computer Methods and Programs in Biomedicine  
Conclusions: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis  ...  and Objective: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals.  ...  Acknowledgements Funding: No external funding sources supported this review.  ... 
doi:10.1016/j.cmpb.2018.04.005 pmid:29852952 fatcat:3tn4ookjyjgafgnbb2tyzznhz4

Current Applications and Future Impact of Machine Learning in Radiology

Garry Choy, Omid Khalilzadeh, Mark Michalski, Synho Do, Anthony E. Samir, Oleg S. Pianykh, J. Raymond Geis, Pari V. Pandharipande, James A. Brink, Keith J. Dreyer
2018 Radiology  
Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings  ...  In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology.  ...  Convergence of Computing Power and Data Major advances in processing units based on massive concurrent parallel processing chip architectures for graphics radiology.rsna.org n Radiology: Volume 288: Number  ... 
doi:10.1148/radiol.2018171820 pmid:29944078 pmcid:PMC6542626 fatcat:vrf37sksivfkph47qdicwzeqge

An accelerated framework for the classification of biological targets from solid-state micropore data

Madiha Hanif, Abdul Hafeez, Yusuf Suleman, M. Mustafa Rafique, Ali R. Butt, Samir M. Iqbal
2016 Computer Methods and Programs in Biomedicine  
The parallel implementation of the algorithm on graphics processing unit (GPU) demonstrates a speedup of three to four folds as compared to a serial implementation on an Intel Core i7 processor.  ...  , such as cancerous/noncancerous cells based on the training data.  ...  Originally aimed for gaming, graphics processing units (GPUs) have evolved as accelerators into a gamut of compute-intensive scientific applications including bioinformatics and biomedical signal processing  ... 
doi:10.1016/j.cmpb.2016.06.001 pmid:27480732 fatcat:3skfpew2ibgffmpjn6xxblunoy

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

Antonio Jesús Banegas-Luna, Jorge Peña-García, Adrian Iftene, Fiorella Guadagni, Patrizia Ferroni, Noemi Scarpato, Fabio Massimo Zanzotto, Andrés Bueno-Crespo, Horacio Pérez-Sánchez
2021 International Journal of Molecular Sciences  
In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when  ...  However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor  ...  In parallel, new frameworks (e.g., TensorFlow, PyTorch) have been developed to exploit all the computing power of graphical processing units (GPUs) and accelerate image analysis.  ... 
doi:10.3390/ijms22094394 pmid:33922356 fatcat:z3mxbx7fajge7pkyrp2odlauz4

EGFR Microdeletion Mutations Analysis System Model Using Parameters Combinations Generator for Design of RADBAS Neural Network Knowledge Based Identifier

Zikrija Avdagic, Vedad Letic, Dusanka Boskovic, Aida Saracevic
2018 International Journal of Computational Intelligence Systems  
20th; and the second mode is intended for application in real time using sample patients' data with microdeletion mutations extracted online from EGFR mutation database.  ...  We propose computing system/model based on two modules: The first module includes training of knowledge based radial basis (RADBAS) neural network using training set generated with combinatorial microdeletion  ...  Acknowledgments We thank BiH Federal Ministry of Education and Science for the financial support in 2012/2013 for the research project "Computer Aided Lung Cancer Classification of Mutated EGFR Exons Using  ... 
doi:10.2991/ijcis.11.1.93 fatcat:xiri3p3myraxng5n7bgzvaa4dm

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Jahanzaib Latif, Chuangbai Xiao, Shanshan Tu, Sadaqat Ur Rehman, Azhar Imran, Anas Bilal
2020 IEEE Access  
A fully automated diagnosis system of prostate cancer can be built in future.  ...  MANB has been outperformed others based on these performance metrics with training time 16s and 0.72 of AUC to predict LOAD patients. 3) Decision Tree Methods A decision tree is one of the commonly used  ... 
doi:10.1109/access.2020.3016782 fatcat:j76bwlyrj5dv5mhhsvs4apynje

Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection

M Luke Marinovich, Elizabeth Wylie, William Lotter, Alison Pearce, Stacy M Carter, Helen Lund, Andrew Waddell, Jiye G Kim, Gavin F Pereira, Christoph I Lee, Sophia Zackrisson, Meagan Brennan (+1 others)
2022 BMJ Open  
We aim to address these limitations by comparing the accuracy of AI and radiologists in a cohort of consecutive of women attending a real-world population breast cancer screening programme.Methods and  ...  AI accuracy will be compared with that of radiologist single-reading based on the difference in the area under the receiver operating characteristic curve.  ...  A laptop with the AI algorithm installed and a graphics processing unit supporting its evaluation will be located at BSWA.  ... 
doi:10.1136/bmjopen-2021-054005 pmid:34980622 pmcid:PMC8724814 fatcat:6cjaxdylxjbsfmvf3drnr5gumy

NOVEL TRENDS OF ARTIFICIAL INTELLIGENCE

Ramling Sugave*, Shraddha Pattewar, Dr. S. S. Thonte, P.H.Bhosale, D.R.Kaudewar,
2018 Zenodo  
Hence, a need for introducing AI techniques arises in order to develop both diagnosis and intervention processes.  ...  (for recognition of special types of disease patterns), patient monitoring, as control units for prosthetic devices, automatic diagnostic systems and the dynamic solution of complex allocation and routine  ...  AI used in biomedical applications diagnosis of various diseases like lung cancer, also used in the diagnosis of acute leukemia and pancreatic cancer.  ... 
doi:10.5281/zenodo.2530516 fatcat:ebk4pq2czfhzbotmabe2x4ruwe

Regional implementation of a national cancer policy: taking forward multiprofessional, collaborative cancer care

FERGUSON, MAKIN, WALKER, DUBLON
1998 European Journal of Cancer Care  
INTRODUCTION services for common and rare cancers.  ...  This paper outlines the process adopted in one region towards implementing the vision of Calman-Hine.  ... 
doi:10.1046/j.1365-2354.1998.00077.x pmid:9793007 fatcat:zsa2emenu5fqne25i6w4r4tugq
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