This document is a 36-page bachelor's thesis written by Duc Minh Luong Nguyen titled "Detect COVID-19 from Chest X-Ray images using Deep Learning". The thesis was submitted to Metropolia University of Applied Sciences in May 2020. It aims to build a deep convolutional neural network to detect COVID-19 using only chest X-ray images. The model achieves an accuracy of 93% at detecting COVID-19 patients versus healthy patients, despite being trained on a small dataset of 115 images for each class.
Agenda:
Introduction
Supercomputers for Scientific Research
Covid-19 Tracking and Prediction
Covid-19 Research and Diagnosis
Use Case 1 NLP and BERT to answer scientific questions
Use Case 2 Covid-19 Data Lake and Platform
Multitask Adversarial Learning of Deep Neural Networks for Medical Imaging an...Debdoot Sheet
A comprehensive review and summary of some of the recent works in the area of adversarial learning of deep neural networks carried out at the Kharagpur Learning, Imaging and Visualization (KLIV) Research Group.
GUGC Info Session - Informatics and BioinformaticsWesley De Neve
Wesley De Neve gave a presentation about informatics and bioinformatics courses at Ghent University Global Campus. He has a Master's and PhD from Ghent University and works at several research institutions including IDLab and KAIST. The informatics course teaches students to automate time-consuming tasks through programming. It focuses on Python and UNIX tools. The bioinformatics course introduces the field and teaches algorithms and programming for problems like motif finding and sequence alignment. Both courses involve theory lectures, hands-on sessions, assignments, and exams.
Using Mask R CNN to Isolate PV Panels from Background Object in Imagesijtsrd
The document describes using Mask R-CNN, a deep learning algorithm, to identify and isolate solar photovoltaic panels from background objects in aerial images. The algorithm was able to accurately detect PV panels and generate pixel masks to remove background pixels from the images. The model performed well with over 75% average precision and 77% accuracy when trained on a small dataset of images. The ability to isolate PV panels from images can help analyze large solar installations and detect potential faults or issues.
This document discusses networks and deep learning, with a focus on their application to analyzing the COVID-19 pandemic. It begins with an overview of networks and graph theory concepts. It then discusses how deep learning, specifically graph neural networks, can be used to analyze networks and learn representations of nodes. Applications discussed include traffic prediction and modeling disease spread. It also introduces the SIR model for modeling epidemics and the basic reproduction number metric.
1. Machine learning is becoming pervasive across many tasks, occupations, and industries due to its abilities in classification, labeling, perception, prediction, and diagnosis.
2. Machine learning capabilities continue to improve over time as more data is analyzed, overcoming limitations.
3. Machine learning acts as a general purpose technology that can spawn new complementary innovations by providing building blocks like perception and problem solving.
This document is a 36-page bachelor's thesis written by Duc Minh Luong Nguyen titled "Detect COVID-19 from Chest X-Ray images using Deep Learning". The thesis was submitted to Metropolia University of Applied Sciences in May 2020. It aims to build a deep convolutional neural network to detect COVID-19 using only chest X-ray images. The model achieves an accuracy of 93% at detecting COVID-19 patients versus healthy patients, despite being trained on a small dataset of 115 images for each class.
Agenda:
Introduction
Supercomputers for Scientific Research
Covid-19 Tracking and Prediction
Covid-19 Research and Diagnosis
Use Case 1 NLP and BERT to answer scientific questions
Use Case 2 Covid-19 Data Lake and Platform
Multitask Adversarial Learning of Deep Neural Networks for Medical Imaging an...Debdoot Sheet
A comprehensive review and summary of some of the recent works in the area of adversarial learning of deep neural networks carried out at the Kharagpur Learning, Imaging and Visualization (KLIV) Research Group.
GUGC Info Session - Informatics and BioinformaticsWesley De Neve
Wesley De Neve gave a presentation about informatics and bioinformatics courses at Ghent University Global Campus. He has a Master's and PhD from Ghent University and works at several research institutions including IDLab and KAIST. The informatics course teaches students to automate time-consuming tasks through programming. It focuses on Python and UNIX tools. The bioinformatics course introduces the field and teaches algorithms and programming for problems like motif finding and sequence alignment. Both courses involve theory lectures, hands-on sessions, assignments, and exams.
Using Mask R CNN to Isolate PV Panels from Background Object in Imagesijtsrd
The document describes using Mask R-CNN, a deep learning algorithm, to identify and isolate solar photovoltaic panels from background objects in aerial images. The algorithm was able to accurately detect PV panels and generate pixel masks to remove background pixels from the images. The model performed well with over 75% average precision and 77% accuracy when trained on a small dataset of images. The ability to isolate PV panels from images can help analyze large solar installations and detect potential faults or issues.
This document discusses networks and deep learning, with a focus on their application to analyzing the COVID-19 pandemic. It begins with an overview of networks and graph theory concepts. It then discusses how deep learning, specifically graph neural networks, can be used to analyze networks and learn representations of nodes. Applications discussed include traffic prediction and modeling disease spread. It also introduces the SIR model for modeling epidemics and the basic reproduction number metric.
1. Machine learning is becoming pervasive across many tasks, occupations, and industries due to its abilities in classification, labeling, perception, prediction, and diagnosis.
2. Machine learning capabilities continue to improve over time as more data is analyzed, overcoming limitations.
3. Machine learning acts as a general purpose technology that can spawn new complementary innovations by providing building blocks like perception and problem solving.
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
This document provides an overview of recent advances in applying artificial intelligence and machine learning techniques to matters and materials. It discusses several key ideas and approaches, including:
- Using graph neural networks and message passing algorithms to model molecules as graphs and predict molecular properties.
- Generative models like variational autoencoders and generative adversarial networks to represent molecules in a continuous latent space and generate new molecular structures.
- Reinforcement learning approaches for predicting chemical reactions and planning chemical syntheses.
- Directed generation of molecular graphs using graph variational autoencoders to overcome limitations of string-based representations.
The document outlines many promising directions for using deep learning to tackle important problems in chemistry, materials science
MOST READ ARTICLES IN ARTIFICIAL INTELLIGENCE - International Journal of Arti...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
This document lists publications by Olli Virmajoki, including:
1) 12 refereed scientific journal articles on topics like clustering algorithms.
2) 11 articles in international scientific conferences on clustering and machine learning.
3) 4 theses including a PhD thesis on the pairwise nearest neighbor clustering method.
4) 4 technical reports from the University of Joensuu on clustering algorithms.
5) 2 other publications on virtual education and a book about pairwise nearest neighbor clustering.
6) 2 manuscripts under review on random swap clustering and legal issues during cutbacks.
This document summarizes a research project on applying nanotechnology to cognitive radio and electronic warfare. It outlines the project members and faculty leads, problem statement, approach, and schedule. The problem aims to identify how nanotechnology can address spectrum sensing requirements for cognitive radio. The approach includes a literature review on integrating nanotechnology into wireless networks and studying the role of GPU technology in nanocomputing. Students conducted this research and a workshop on robotics and cybersecurity for high school students.
This is the talk given at the Faculty of Information Technology, Monash University on 19/08/2020. It covers our recent research on the topics of learning to reason, including dual-process theory, visual reasoning and neural memories.
Machine Learning for Chemistry: Representing and InterveningIchigaku Takigawa
Joint Symposium of Engineering & Information Science & WPI-ICReDD in Hokkaido University
Apr. 26 (Mon), 2021
https://www.icredd.hokudai.ac.jp/event/5430
Challenges and opportunities for machine learning in biomedical researchFranciscoJAzuajeG
1. Machine learning faces challenges in biomedical research due to data heterogeneity, lack of labeled data, and complexity in biological patterns and networks.
2. Combining machine learning and biological network models can help address these challenges by encoding data in biologically meaningful networks and extracting network-based features for prediction.
3. Examples applying this approach to cancer datasets showed that models based on network centrality features outperformed other methods, and deep learning using these features achieved the best prediction performance across multiple neuroblastoma datasets.
This document discusses the need for geoinformatics and cyberinfrastructure in the geosciences. It argues that answering complex scientific questions requires integrating all available data, but that currently it is too difficult to find, work with, and access relevant data and tools. The document advocates for strong partnerships between geoscientists and computer scientists to build user-friendly tools that facilitate data sharing, integration and analysis in order to accelerate scientific progress. It emphasizes that data needs to be organized into databases and data systems with standards and formats to make it easily discovered and used by the broader community.
Visual Analytics in Omics - why, what, how?Jan Aerts
This document discusses visual analytics in omics data. It begins by noting the shift from hypothesis-driven to data-driven research due to large datasets. Visual analytics can help explore these data by opening the "black box" of algorithms and enabling researchers to develop hypotheses. Effective visualization leverages human perception through techniques like preattentive vision and Gestalt laws. Challenges to visual analytics include scalability issues for large datasets and identifying interesting patterns for further analysis. Examples demonstrate data exploration, filtering, and user-guided analysis in genomic applications.
This document provides an introduction to machine learning and its applications in genomics and biology. It discusses how biology and genomics data have become "big data" due to technological advances in sequencing and data generation. Machine learning is well-suited for analyzing these large, multidimensional datasets and addressing complex biological questions. The document outlines different machine learning approaches like supervised and unsupervised learning, and provides examples of real-world applications. R and Python are introduced as popular programming languages for machine learning.
Copernicus is one of the largest Earth Observation data providers. Copernicus’ archives are growing data repositories containing a wealth of data and information that is of utmost importance for policy support and many economic and industrial domains.
AI needs vast amounts of data to be developed. AI works by identifying patterns in available data and then applying this knowledge to new data. The larger the data set is, the better AI can learn and discover. Bringing AI technologies that scale at the Petabyte level to Copernicus operations is an opportunity that Europe shall seize to maximise return on investment and to develop a new generation of products and services based on Copernicus data assets.
This workshop will present EU programmes, challenges and opportunities to connect Copernicus, its data assets and stakeholders to the digital world.
Early identification of Alzheimer's disease (AD) from the Ageing Movement Control (AC) is very important. However, the computer aided diagnosis (CAD) was not widely used, and the classification performance did not reach into practical use. Existing System has a novel CAD system for MRI brain images based on eigenbrains and machine learning with focus on two things: accurate detection of both AD subjects and AD related brain regions. The eigenbrain method was effective in AD subject prediction and discriminated brain region detection in MRI scanning. But, the results showed that existing method achieved 92.36% accuracy, which was competitive with state-of-the-art methods. We, Propose a system to improve the accuracy and easy computation of identification through MRI images based on K-Means Clustering.
The document discusses using buildings and their structural vibrations as sensors for machine learning applications with small datasets. It describes challenges with deploying many sensors that require extensive data collection and maintenance. The presented approach aims to enable "small data" learning by optimizing sensing, integrating physical models to reduce data needs, and adapting data models using physical understanding to transfer learning across applications. Examples are given on using building vibrations to detect footsteps versus non-footsteps with high accuracy, and to identify people by their unique walking patterns. The approach is shown to significantly reduce labeling requirements by transferring models between structures informed by an understanding of physical effects.
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
2011 Big Data - Bigger Problems for Drug Discovery and DevelopmentAyasdi
This document summarizes a presentation about using topological data analysis to solve problems in drug discovery and development. It discusses the challenges of big data in this field, such as scalability issues and a lack of integrated tools. It then describes how the Ayasdi Iris platform uses principles of topology to identify subtle patterns in large, complex datasets. Examples are given of how Ayasdi has used topological data analysis for patient stratification and to analyze next-generation sequencing data.
Upavan Gupta received his PhD in Computer Science and Engineering from the University of South Florida in 2008. His dissertation and research focuses on multi-metric optimization techniques for VLSI circuit design and spatial clustering problems. He has over 15 publications in peer-reviewed journals and conferences. Currently he works as a specialist in the Office of the Provost at USF, where he conducts research and develops systems to support decision making.
The Center for Applied Optimization at the University of Florida conducts interdisciplinary research in optimization involving faculty from various departments. Over the past 5 years, their research has included global optimization, optimization in biomedicine like predicting epileptic seizures, analyzing massive datasets like social networks, developing approximation algorithms, and algorithms for problems like multicast networks. Current projects also involve computational neuroscience, probabilistic classifiers in medicine, research on energy problems, and using Raman spectroscopy for cancer research. The Center collaborates with researchers from other institutions and hosts many visiting scholars each year.
Determination of Various Diseases in Two Most Consumed Fruits using Artificia...ijtsrd
Fruit diseases are manifested by deformations during or after harvesting the components in the fruit, when the infestation is caused by spores, fungi, insects or other contaminants. In early agricultural practices, it is thought that non destructive examination is possible with the analysis of pre harvest fruit leaves and early diagnosis of the disease, while post harvest detection and classification of fruit disease is possible by evaluating simple image processing techniques. Diseases of rotten or stained fruits without destruction. In this way, the disease will be identified and classified and the awareness of the producer for the next harvest will be provided. For this purpose, studies were carried out with apple and quince fruit, images were determined using still fruit pictures and machine learning, and disease classification was provided with labels. Image processing techniques are a system that detects disease made to a real time camera and prints it on the screen. Within the scope of this study, the data set was created and images of 22 apples and 18 quinces were taken. The image was classified by similarities in the literature review. The success of the proposed Convolutional Neural Network architecture in recognizing the disease was evaluated. By comparing the trained network, AlexNet architecture, with the proposed architecture, it has been determined that the success of image recognition has increased with the proposed method. Aysun Yilmaz Kizilboga | Atilla Ergüzen | Erdal Erdal "Determination of Various Diseases in Two Most Consumed Fruits using Artificial Neural Networks and Deep Learning Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38128.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38128/determination-of-various-diseases-in-two-most-consumed-fruits-using-artificial-neural-networks-and-deep-learning-techniques/aysun-yilmaz-kizilboga
Accelerating materials property predictions using machine learningGhanshyam Pilania
The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning
methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific materials.
This document summarizes Gunnar Rätsch's presentation on machine learning in science and engineering. It introduces several applications of machine learning, including spam classification, drug design, and face detection. It also provides overviews of boosting algorithms like AdaBoost and support vector machines. Key algorithms like boosting, SVMs, and kernels are explained at a high level. Finally, applications of machine learning across various domains are briefly mentioned.
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
This document provides an overview of recent advances in applying artificial intelligence and machine learning techniques to matters and materials. It discusses several key ideas and approaches, including:
- Using graph neural networks and message passing algorithms to model molecules as graphs and predict molecular properties.
- Generative models like variational autoencoders and generative adversarial networks to represent molecules in a continuous latent space and generate new molecular structures.
- Reinforcement learning approaches for predicting chemical reactions and planning chemical syntheses.
- Directed generation of molecular graphs using graph variational autoencoders to overcome limitations of string-based representations.
The document outlines many promising directions for using deep learning to tackle important problems in chemistry, materials science
MOST READ ARTICLES IN ARTIFICIAL INTELLIGENCE - International Journal of Arti...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
This document lists publications by Olli Virmajoki, including:
1) 12 refereed scientific journal articles on topics like clustering algorithms.
2) 11 articles in international scientific conferences on clustering and machine learning.
3) 4 theses including a PhD thesis on the pairwise nearest neighbor clustering method.
4) 4 technical reports from the University of Joensuu on clustering algorithms.
5) 2 other publications on virtual education and a book about pairwise nearest neighbor clustering.
6) 2 manuscripts under review on random swap clustering and legal issues during cutbacks.
This document summarizes a research project on applying nanotechnology to cognitive radio and electronic warfare. It outlines the project members and faculty leads, problem statement, approach, and schedule. The problem aims to identify how nanotechnology can address spectrum sensing requirements for cognitive radio. The approach includes a literature review on integrating nanotechnology into wireless networks and studying the role of GPU technology in nanocomputing. Students conducted this research and a workshop on robotics and cybersecurity for high school students.
This is the talk given at the Faculty of Information Technology, Monash University on 19/08/2020. It covers our recent research on the topics of learning to reason, including dual-process theory, visual reasoning and neural memories.
Machine Learning for Chemistry: Representing and InterveningIchigaku Takigawa
Joint Symposium of Engineering & Information Science & WPI-ICReDD in Hokkaido University
Apr. 26 (Mon), 2021
https://www.icredd.hokudai.ac.jp/event/5430
Challenges and opportunities for machine learning in biomedical researchFranciscoJAzuajeG
1. Machine learning faces challenges in biomedical research due to data heterogeneity, lack of labeled data, and complexity in biological patterns and networks.
2. Combining machine learning and biological network models can help address these challenges by encoding data in biologically meaningful networks and extracting network-based features for prediction.
3. Examples applying this approach to cancer datasets showed that models based on network centrality features outperformed other methods, and deep learning using these features achieved the best prediction performance across multiple neuroblastoma datasets.
This document discusses the need for geoinformatics and cyberinfrastructure in the geosciences. It argues that answering complex scientific questions requires integrating all available data, but that currently it is too difficult to find, work with, and access relevant data and tools. The document advocates for strong partnerships between geoscientists and computer scientists to build user-friendly tools that facilitate data sharing, integration and analysis in order to accelerate scientific progress. It emphasizes that data needs to be organized into databases and data systems with standards and formats to make it easily discovered and used by the broader community.
Visual Analytics in Omics - why, what, how?Jan Aerts
This document discusses visual analytics in omics data. It begins by noting the shift from hypothesis-driven to data-driven research due to large datasets. Visual analytics can help explore these data by opening the "black box" of algorithms and enabling researchers to develop hypotheses. Effective visualization leverages human perception through techniques like preattentive vision and Gestalt laws. Challenges to visual analytics include scalability issues for large datasets and identifying interesting patterns for further analysis. Examples demonstrate data exploration, filtering, and user-guided analysis in genomic applications.
This document provides an introduction to machine learning and its applications in genomics and biology. It discusses how biology and genomics data have become "big data" due to technological advances in sequencing and data generation. Machine learning is well-suited for analyzing these large, multidimensional datasets and addressing complex biological questions. The document outlines different machine learning approaches like supervised and unsupervised learning, and provides examples of real-world applications. R and Python are introduced as popular programming languages for machine learning.
Copernicus is one of the largest Earth Observation data providers. Copernicus’ archives are growing data repositories containing a wealth of data and information that is of utmost importance for policy support and many economic and industrial domains.
AI needs vast amounts of data to be developed. AI works by identifying patterns in available data and then applying this knowledge to new data. The larger the data set is, the better AI can learn and discover. Bringing AI technologies that scale at the Petabyte level to Copernicus operations is an opportunity that Europe shall seize to maximise return on investment and to develop a new generation of products and services based on Copernicus data assets.
This workshop will present EU programmes, challenges and opportunities to connect Copernicus, its data assets and stakeholders to the digital world.
Early identification of Alzheimer's disease (AD) from the Ageing Movement Control (AC) is very important. However, the computer aided diagnosis (CAD) was not widely used, and the classification performance did not reach into practical use. Existing System has a novel CAD system for MRI brain images based on eigenbrains and machine learning with focus on two things: accurate detection of both AD subjects and AD related brain regions. The eigenbrain method was effective in AD subject prediction and discriminated brain region detection in MRI scanning. But, the results showed that existing method achieved 92.36% accuracy, which was competitive with state-of-the-art methods. We, Propose a system to improve the accuracy and easy computation of identification through MRI images based on K-Means Clustering.
The document discusses using buildings and their structural vibrations as sensors for machine learning applications with small datasets. It describes challenges with deploying many sensors that require extensive data collection and maintenance. The presented approach aims to enable "small data" learning by optimizing sensing, integrating physical models to reduce data needs, and adapting data models using physical understanding to transfer learning across applications. Examples are given on using building vibrations to detect footsteps versus non-footsteps with high accuracy, and to identify people by their unique walking patterns. The approach is shown to significantly reduce labeling requirements by transferring models between structures informed by an understanding of physical effects.
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
2011 Big Data - Bigger Problems for Drug Discovery and DevelopmentAyasdi
This document summarizes a presentation about using topological data analysis to solve problems in drug discovery and development. It discusses the challenges of big data in this field, such as scalability issues and a lack of integrated tools. It then describes how the Ayasdi Iris platform uses principles of topology to identify subtle patterns in large, complex datasets. Examples are given of how Ayasdi has used topological data analysis for patient stratification and to analyze next-generation sequencing data.
Upavan Gupta received his PhD in Computer Science and Engineering from the University of South Florida in 2008. His dissertation and research focuses on multi-metric optimization techniques for VLSI circuit design and spatial clustering problems. He has over 15 publications in peer-reviewed journals and conferences. Currently he works as a specialist in the Office of the Provost at USF, where he conducts research and develops systems to support decision making.
The Center for Applied Optimization at the University of Florida conducts interdisciplinary research in optimization involving faculty from various departments. Over the past 5 years, their research has included global optimization, optimization in biomedicine like predicting epileptic seizures, analyzing massive datasets like social networks, developing approximation algorithms, and algorithms for problems like multicast networks. Current projects also involve computational neuroscience, probabilistic classifiers in medicine, research on energy problems, and using Raman spectroscopy for cancer research. The Center collaborates with researchers from other institutions and hosts many visiting scholars each year.
Determination of Various Diseases in Two Most Consumed Fruits using Artificia...ijtsrd
Fruit diseases are manifested by deformations during or after harvesting the components in the fruit, when the infestation is caused by spores, fungi, insects or other contaminants. In early agricultural practices, it is thought that non destructive examination is possible with the analysis of pre harvest fruit leaves and early diagnosis of the disease, while post harvest detection and classification of fruit disease is possible by evaluating simple image processing techniques. Diseases of rotten or stained fruits without destruction. In this way, the disease will be identified and classified and the awareness of the producer for the next harvest will be provided. For this purpose, studies were carried out with apple and quince fruit, images were determined using still fruit pictures and machine learning, and disease classification was provided with labels. Image processing techniques are a system that detects disease made to a real time camera and prints it on the screen. Within the scope of this study, the data set was created and images of 22 apples and 18 quinces were taken. The image was classified by similarities in the literature review. The success of the proposed Convolutional Neural Network architecture in recognizing the disease was evaluated. By comparing the trained network, AlexNet architecture, with the proposed architecture, it has been determined that the success of image recognition has increased with the proposed method. Aysun Yilmaz Kizilboga | Atilla Ergüzen | Erdal Erdal "Determination of Various Diseases in Two Most Consumed Fruits using Artificial Neural Networks and Deep Learning Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38128.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38128/determination-of-various-diseases-in-two-most-consumed-fruits-using-artificial-neural-networks-and-deep-learning-techniques/aysun-yilmaz-kizilboga
Accelerating materials property predictions using machine learningGhanshyam Pilania
The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning
methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific materials.
This document summarizes Gunnar Rätsch's presentation on machine learning in science and engineering. It introduces several applications of machine learning, including spam classification, drug design, and face detection. It also provides overviews of boosting algorithms like AdaBoost and support vector machines. Key algorithms like boosting, SVMs, and kernels are explained at a high level. Finally, applications of machine learning across various domains are briefly mentioned.
Zach and Aren talk on Materials Informatics at UW WIMSEA 2016-02-12ddm314
Presentation at WIsconsin Materials Science and Engineering Industrial Affiliates (WIMSEA) meeting at University of Wisconsin - Madison on 2016-02-12. Undergraduates Zach and Aren presented on informatics skunkworks activities in materials informatics.
Presentation given on TechnicalAnalyst.com event "Machine learning techniques in finance" on 17th November 2016.
- What is machine learning and how it can help predict finnacial markets
- Technical stock analysis vs. behavioural news and social media analysis
- How machine learning can be applied to technical analysis in the stock market
- How machine learning can be applied to new/social media analysis
This document discusses stakeholder mapping and engagement strategies. It defines stakeholder mapping as identifying relevant individuals, groups, and organizations; categorizing them; mapping relationships; ranking by influence; and prioritizing key audiences. Two specific processes are described: Participatory Impact Pathways Analysis (PIPA) uses network maps of current and desired future relationships to identify strategies; and the Alignment, Interest, and Influence Matrix (AIIM) ranks stakeholders to guide engagement approaches from developing awareness to challenging beliefs. The document serves as an introduction to stakeholder mapping and analysis techniques.
The document discusses machine learning and artificial immune systems for financial security and fraud detection. It provides an introduction to machine learning meetups and outlines various topics to be covered including the biological immune system, danger theory, artificial immune systems, and applications of immune-inspired machine learning techniques for network security, intrusion detection, and movie recommendation systems. Potential solutions discussed include using distributed storage systems like Hadoop, online learning algorithms inspired by immune systems and genetic algorithms, and building a fraud detection system based on constantly updating user behavior profiles.
Data Science and Machine Learning Using Python and Scikit-learnAsim Jalis
Workshop at DataEngConf 2016, on April 7-8 2016, at Galvanize, 44 Tehama Street, San Francisco, CA.
Demo and labs for workshop are at https://github.com/asimjalis/data-science-workshop
This document provides an overview of machine learning techniques that can be applied in finance, including exploratory data analysis, clustering, classification, and regression methods. It discusses statistical learning approaches like data mining and modeling. For clustering, it describes techniques like k-means clustering, hierarchical clustering, Gaussian mixture models, and self-organizing maps. For classification, it mentions discriminant analysis, decision trees, neural networks, and support vector machines. It also provides summaries of regression, ensemble methods, and working with big data and distributed learning.
Machine learning and AI have several useful applications in financial services such as classifying risk in portfolios and for credit assessment, powering robo-advisers to provide automated investment advice, using regression to analyze currency exposure and company earnings for firms like Blackrock, and developing customer service chatbots such as RBS-Luvo.
Data Science, Machine Learning and Neural NetworksBICA Labs
Lecture briefly overviewing state of the art of Data Science, Machine Learning and Neural Networks. Covers main Artificial Intelligence technologies, Data Science algorithms, Neural network architectures and cloud computing facilities enabling the whole stack.
The document discusses stakeholder mapping and engagement for influencing key groups. It defines primary and secondary stakeholders and outlines a framework for stakeholder relationship management. This includes identifying stakeholders, assessing their concerns and level of commitment, developing communication strategies, and obtaining ongoing feedback. An example stakeholder map shows positioning stakeholders on a grid based on their influence and criticality to the project. The document proposes building a game plan to move stakeholders toward more supportive orientations through addressing their key issues and assigning team members responsible for engagement strategies.
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Raai 2019 clinical unmet needs and its solutions of deep learning in medicine3Namkug Kim
Clinical unmet needs such as data imbalance, small datasets, and differences in multi-center trials can be addressed through techniques like data augmentation using Perlin noise or GANs, curriculum learning, and domain adaptation. Efficient labeling solutions like smart labeling using deep learning models can help address the challenge of expensive manual labeling. Interpretability, uncertainty quantification, and developing physics-informed machine learning approaches can help address the "black box" nature of deep learning models and improve deployment in clinical settings.
Fireside chat: Newton Howard, Director of the MIT Synthetic Intelligence Lab ...Codiax
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Frankie Rybicki slide set for Deep Learning in Radiology / MedicineFrank Rybicki
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The proposed approach makes contributions in various stages in the development of a computer-aided diagnosis (CAD) system of brain diseases, namely image preprocessing, intermediate processing, detection, segmentation, feature extraction, and classification. Literature study incorporates many important ideas for abnormalities detection and analysis with their advantages and disadvantages. Literature studies have pointed out the needs of dividing task and appropriate ways for accurate abnormality characterization to provide a proper clinical diagnosis.
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This document describes a study that used FreeSurfer software to obtain volumetric measurements of subcortical structures and cortical thickness measurements from MRI scans of patients with Alzheimer's disease, mild cognitive impairment, or frontotemporal dementia, as well as healthy control subjects. The study aimed to identify diagnostic markers for these neurodegenerative diseases but was unable to determine correlations between brain structures and specific diseases due to confidentiality constraints on disease diagnoses. Common errors during FreeSurfer processing were corrected before analysis of subcortical and cortical thickness results.
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Machine learning for Science and Society
1. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Faculty of Science
Machine Learning for Science and Society
Some recent results
Christian Igel
Department of Computer Science
igel@diku.dk
Slide 1/30
2. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Why machine learning?
• Sometimes problems cannot be solved in the traditional way
because
• the designer’s knowledge is limited, and/or
• the sheer complexity and variability precludes an
accurate description.
• However, large amounts of data describing the task are often
available or can be automatically obtained – and machine
learning can solve the problem.
Slide 4/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
3. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Why machine learning?
• Sometimes problems cannot be solved in the traditional way
because
• the designer’s knowledge is limited, and/or
• the sheer complexity and variability precludes an
accurate description.
• However, large amounts of data describing the task are often
available or can be automatically obtained – and machine
learning can solve the problem.
Slide 5/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
Machine learning turns data into knowledge
Machine learning automates the process of
inductive inference:
1 Observe a phenomenon
2 Construct a model of that phenomenon
3 Make predictions using this model
4. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Alzheimer’s Disease progression
Disease progression
Cells
healthy neurons formation of A¡ plaques
and NFTs
neuronal death
NFTs
A plaques
MRI
structure normal size/shape normal size/shape abnormal size/shape
(atrophy)
MRI
intensity normal variation abnormal variation abnormal variation
Scale
Slide 7/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
5. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Hippocampal texture from structural MRI
• Image analysis of brain scans
• Segmentation of brain regions
(hippocampi)
• Extraction of image features
• Classification using SVMs
Sørensen, Igel, Hansen, Osler, Lauritzen, Rostrup, Nielsen. Early detection of Alzheimer’s disease using MRI hippocampal
texture. Human Brain Mapping, 2016
Slide 8/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
6. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
CADDementia Challenge
• Computer-Aided Diagnosis of Dementia based on structural
MRI data (CADDementia) competition
• Goal: Diagnosis into Alzheimer’s disease (AD), mild cognitive
impairment (MCI), and normal controls (NC)
• No labelled training data was provided, we trained on freely
available data sets.
• We combined several biomarkers (cortical thickness
measurements, hippocampal shape, hippocampal texture,
and volumetric measurements).
Sørensen, Pai, Anker, Balas, Lillholm, Igel, Nielsen. Dementia Diagnosis using MRI Cortical Thickness, Shape, Texture,
and Volumetry. MICCAI 2014 – Challenge on Computer-Aided Diagnosis of Dementia Based on Structural MRI Data, 2014
Bron et al. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The
CADDementia challenge. NeuroImage, 2015
Slide 9/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
7. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
CADDementia: Results
Slide 10/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
• Our biomarker outperforms the state-of-the-art.
• The texture score remains significant if combined with
other biomarkers.
• The biomarker generalizes across patient populations.
8. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Deep learning
• Deep learning refers to ML architectures
composed of multiple levels of non-linear
transformations.
• Idea: Extracting more and more abstract
features from input data, learning more
abstract representations.
• Representations can be learned in a
supervised and/or unsupervised manner.
• Example: Convolutional neural networks
(CNNs) are popular deep learning
architectures.
LeCun, Bottou, Bengio, Haffner. Gradient-based learning applied to
document recognition. Proceedings of the IEEE, 1998
Bengio. Learning Deep Architectures
for AI. Foundations and Trends in Ma-
chine Learning 2(1): 1–127, 2009
Slide 12/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
9. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Breast cancer
• Breast cancer is a frequent cause of death among women
• Screening programs halve the risk of death1 and reduce
mortality by 28-36 %2
• Still: 33 % of cancers are missed,3 70 % of referrals are false
positives,4 and 25 % of cancers could have been detected
earlier5
• Goal: More accurate image-based biomarkers allowing
personalized breast cancer screening
1Otto et al. Cancer Epidemiol Biomarkers Prev 21, 2012
2Broeders et al. J Med Screen 19, 2012
3Karssemeijer et al. Radiology 227, 2003
4Yankaskas et al. Am J Roentgenol 177, 2001
5Timmers et al. Eur J Public Health, 2012
Slide 13/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
10. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Breast density scoring
Breast density is related to breast cancer risk, the risk of missing
breast cancer, and the risk of false positive referral.
input manual CNN
Current work: Better biomarkers using CNN density scores and
CNN texture scores
Petersen, Nielsen, Diao, Karssemeijer, Lillholm. Breast Tissue Segmentation and Mammographic Risk Scoring Using Deep
Learning. Digital Mammography / IWDM, 2014
Slide 14/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
11. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Knee cartilage segmentation
• Cartilage segmentation in knee MRI is the method of choice
for quantifying cartilage deterioration.
• Cartilage deterioration implies osteoarthritis, one of the major
reasons for work disability in the western world.
input manual CNN
Prasoon, Petersen, Igel, Lauze, Dam, Nielsen. Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar
Convolutional Neural Network. Medical Image Computing and Computer Assisted Intervention (MICCAI), LNCS 8150, 2013
Slide 16/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
12. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Nuclear test ban treaty verification
• CTBTO (Peparatory Commission for the Comprehensive
Nuclear-Test-Ban Treaty Organisation) watches out for
nuclear weapon tests
• Verification of the comprehensive nuclear-test-ban treaty
• 15 GB of data every day from world-wide sensor network
• Data analysis must be partly automatic
Tuma et al. Integrated optimization of long-range underwater signal detection, feature ex-
traction, and classification for nuclear treaty monitoring. IEEE Transactions on Geoscience
and Remote Sensing, to appear
Slide 18/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
13. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Typical tasks in astronomy
Slide 20/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
“Can you group all detected objects into classes?
For instance, into stars, galaxies, and other objects
. . . ’ ?’
“Can you find weird objects (which look different
from other ones)?”
“Can you find weird objects (which look different
from other ones)?”
“I am interested in these distant galaxies. Can you
find more of them? Can you find the most distant
ones?”
14. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Big data
Today’s and future projects
• Sloan Digital Sky Survey (SDSS) = 50TB in total
• Large Synoptic Survey Telescope (LSST) = 30TB per night
• Low Frequency Array (LOFAR) = 250TB per night
• Square Kilometer Array (SKA) = 250TB per second
Huge amounts of photometric data, however, detailed
spectroscopic follow-ups are expensive.
Slide 21/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
15. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Specific star formation rate
• Goal: Predict specific star formation rate based on
photometric data
• Approach: Machine learning (ML) augmenting standard
features with image texture features
Slide 22/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
16. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Texture features
Original image
Scale-space
Curvedness
Shape Index
Shape Index
weighted by
Curvedness
Slide 23/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
17. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Predicting the specific star formation rate
• ML achieves higher accuracy than physical modelling
• New data structures and GPU programming allow to process
huge amounts of data
Stensbo-Smidt, Igel, Zirm, Steenstrup Pedersen. Nearest Neighbour Regression Outperforms Model-based Prediction of
Specific Star Formation Rate. IEEE Big Data 2013, 2013
Steenstrup Pedersen, Stensbo-Smidt, Zirm, Igel. Shape Index Descriptors Applied to Texture-Based Galaxy Analysis.
International Conference on Computer Vision (ICCV), 2013
Gieseke, Heinermann, Oancea, Igel. Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs. International
Conference on Machine Learning (ICML), 2014
Slide 24/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
18. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Outline
1 Machine Learning
2 Medicine
Diagnosis and Prognosis of Alzheimer’s Disease
Analysis of Mammograms
Knee Cartilage Segmentation
3 Nuclear-Test-Ban Treaty Monitoring
4 Astronomy
5 End Titles
Slide 26/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
19. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Shark
image.diku.dk/shark
Igel, Glasmachers, Heidrich-Meisner: Shark, Journal of Machine Learning Research 9:993–996, 2008
Gold Prize at Open Source Software Wold Challenge 2011
Slide 27/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
20. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Buffer k-d trees
http://bufferkdtree.readthedocs.org
Gieseke, Heinermann, Oancea, Igel. Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs. International
Conference on Machine Learning (ICML), 2014
Slide 28/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
21. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Industrial Data Analysis Service (IDAS)
Slide 29/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk
22. U N I V E R S I T Y O F C O P E N H A G E N D E P A R T M E N T O F C O M P U T E R S C I E N C E
Thanks
Machine Learning Lab
http://image.diku.dk/MLLab
Image Section
http://www.diku.dk/english/research/imagesection
Special thanks: Sune Darkner, Fabian Gieseke, Mads Nielsen,
Cosmin Oancea, Akshay Pai, Kim Steenstrup Pedersen, Kai
Lars Polsterer, Lauge Sørensen, Kristoffer Stensbo-Smidt
Slide 30/30 — Christian Igel — Machine Learning for Science and Society — igel@diku.dk