Applying machine learning to pattern analysis for automated in-design layout optimization

2018 ◽  
Author(s):  
Jason P. Cain ◽  
Moutaz Fakhry ◽  
Piyush Pathak ◽  
Jason Sweis ◽  
Frank Gennari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Analysis ◽  
Layout Optimization

NPABT: Naming Pattern Analysis Of Bengali Text To Detect Various Community Using Machine Learning Approach

2021 ◽  
Author(s):  
Nushrat Jahan Ria ◽  
Jannatul Ferdous Ani ◽  
Mirajul Islam ◽  
Abu Kaisar Mohammad Masum ◽  
Tanupriya Choudhury ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Analysis ◽  
Learning Approach ◽  
Machine Learning Approach

Supervised and Unsupervised Machine Learning Methodologies for Crime Pattern Analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 83-99
Author(s):  
Divya Sardana ◽  
Shruti Marwaha ◽  
Raj Bhatnagar
Keyword(s):  
Machine Learning ◽  
San Francisco ◽  
Pattern Analysis ◽  
Gradient Boosting ◽  
Unsupervised Machine Learning ◽  
Task Forces ◽  
Crime Types ◽  
Grave Problem ◽  
Crime Pattern

Crime is a grave problem that affects all countries in the world. The level of crime in a country has a big impact on its economic growth and quality of life of citizens. In this paper, we provide a survey of trends of supervised and unsupervised machine learning methods used for crime pattern analysis. We use a spatiotemporal dataset of crimes in San Francisco, CA to demonstrate some of these strategies for crime analysis. We use classification models, namely, Logistic Regression, Random Forest, Gradient Boosting and Naive Bayes to predict crime types such as Larceny, Theft, etc. and propose model optimization strategies. Further, we use a graph based unsupervised machine learning technique called core periphery structures to analyze how crime behavior evolves over time. These methods can be generalized to use for different counties and can be greatly helpful in planning police task forces for law enforcement and crime prevention.

MAPPING THE HETEROGENEITY OF NEUROANATOMY AND FUNCTIONAL CONNECTIVITY DEVIATION FROM TYPICAL BRAIN AGING: A PATTERN ANALYSIS AND MACHINE LEARNING STUDY

2017 ◽  
Vol 13 (7) ◽  
pp. P1464
Author(s):  
Harini Eavani ◽  
Mohamad Habes ◽  
Yang An ◽  
Meng-Kang Hsieh ◽  
Nicolas Honnorat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Pattern Analysis ◽  
Brain Aging ◽  
Learning Study

scholarly journals Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma

Neurosurgery ◽  
2016 ◽  
Vol 78 (4) ◽  
pp. 572-580 ◽  
Author(s):  
Hamed Akbari ◽  
Luke Macyszyn ◽  
Xiao Da ◽  
Michel Bilello ◽  
Ronald L. Wolf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Tumor Recurrence ◽  
Pattern Analysis ◽  
Early Recurrence ◽  
Magnetic Resonance Images ◽  
Tumor Infiltration ◽  
Multiparametric Imaging ◽  
Peritumoral Tissue ◽  
Spatial Maps

Abstract BACKGROUND: Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging, which is insufficient for delineating surrounding infiltrating tumor. OBJECTIVE: To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival. METHODS: Preoperative multiparametric magnetic resonance images (T1, T1-gadolinium, T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion tensor imaging, and dynamic susceptibility contrast-enhanced magnetic resonance images) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross-validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation. RESULTS: This technique produced predictions of early recurrence with a mean area under the curve of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% confidence interval: 8.95-9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning. CONCLUSION: Visually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment.

scholarly journals Machine Learning Algorithms for Urban Land Use Planning: A Review

Urban Science ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 68
Author(s):  
Vineet Chaturvedi ◽  
Walter T. de Vries
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Use Change ◽  
Urban Growth ◽  
Land Use Planning ◽  
Pattern Analysis ◽  
Urban Land ◽  
Earth Observation ◽  
Urban Land Use

Urbanization is persistent globally and has increasingly significant spatial and environmental consequences. It is especially challenging in developing countries due to the increasing pressure on the limited resources, and damage to the bio-physical environment. Traditional analytical methods of studying the urban land use dynamics associated with urbanization are static and tend to rely on top-down approaches, such as linear and mathematical modeling. These traditional approaches do not capture the nonlinear properties of land use change. New technologies, such as artificial intelligence (AI) and machine learning (ML) have made it possible to model and predict the nonlinear aspects of urban land dynamics. AI and ML are programmed to recognize patterns and carry out predictions, decision making and perform operations with speed and accuracy. Classification, analysis and modeling using earth observation-based data forms the basis for the geospatial support for land use planning. In the process of achieving higher accuracies in the classification of spatial data, ML algorithms are being developed and being improved to enhance the decision-making process. The purpose of the research is to bring out the various ML algorithms and statistical models that have been applied to study aspects of land use planning using earth observation-based data (EO). It intends to review their performance, functional requirements, interoperability requirements and for which research problems can they be applied best. The literature review revealed that random forest (RF), deep learning like convolutional neural network (CNN) and support vector machine (SVM) algorithms are best suited for classification and pattern analysis of earth observation-based data. GANs (generative adversarial networks) have been used to simulate urban patterns. Algorithms like cellular automata, spatial logistic regression and agent-based modeling have been used for studying urban growth, land use change and settlement pattern analysis. Most of the papers reviewed applied ML algorithms for classification of EO data and to study urban growth and land use change. It is observed that hybrid approaches have better performance in terms of accuracies, efficiency and computational cost.

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