Fully Automated Software for Polar-Map Registration and Sampling from PET Images

2006 ◽  
Author(s):  
R. Klein ◽  
M. Lortie ◽  
A. Adler ◽  
R.S. Beanlands ◽  
R. deKemp
Keyword(s):  
Map Registration ◽  
Automated Software ◽  
Polar Map

Assessment of a semi-automated software program for the identification of vertebral fractures in children

2017 ◽  
Author(s):  
Fawaz Alqahtani ◽  
Fabrizio Messina ◽  
Elzene Kruger ◽  
Heerunpal Gill ◽  
Michael Ellis ◽  
...  
Keyword(s):  
Vertebral Fractures ◽  
Fractures In Children ◽  
Software Program ◽  
Automated Software

Similarity Detection Using Latent Semantic Analysis Algorithm

2018 ◽  
Vol 6 (8) ◽  
pp. 102
Author(s):  
Priyanka R. Patil ◽  
Shital A. Patil
Keyword(s):  
Latent Semantic Analysis ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Mining Method ◽  
Research Papers ◽  
Information Measures ◽  
Automated Software ◽  
Day By Day ◽  
Ways Of Life ◽  
Dirichlet Allocation

Similarity View is an application for visually comparing and exploring multiple models of text and collection of document. Friendbook finds ways of life of clients from client driven sensor information, measures the closeness of ways of life amongst clients, and prescribes companions to clients if their ways of life have high likeness. Roused by demonstrate a clients day by day life as life records, from their ways of life are separated by utilizing the Latent Dirichlet Allocation Algorithm. Manual techniques can't be utilized for checking research papers, as the doled out commentator may have lacking learning in the exploration disciplines. For different subjective views, causing possible misinterpretations. An urgent need for an effective and feasible approach to check the submitted research papers with support of automated software. A method like text mining method come to solve the problem of automatically checking the research papers semantically. The proposed method to finding the proper similarity of text from the collection of documents by using Latent Dirichlet Allocation (LDA) algorithm and Latent Semantic Analysis (LSA) with synonym algorithm which is used to find synonyms of text index wise by using the English wordnet dictionary, another algorithm is LSA without synonym used to find the similarity of text based on index. LSA with synonym rate of accuracy is greater when the synonym are consider for matching.

Synthesizing Conjunctive & Disjunctive Linear Invariants by K-means++ and SVM

2020 ◽  
Vol 17 (6) ◽  
pp. 847-856
Author(s):  
Shengbing Ren ◽  
Xiang Zhang
Keyword(s):  
Software Verification ◽  
State Of The Art ◽  
Positive Sample ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Hoare Logic ◽  
Excellent Performance ◽  
Automated Software ◽  
Inductive Invariants ◽  
Linear Invariants

The problem of synthesizing adequate inductive invariants lies at the heart of automated software verification. The state-of-the-art machine learning algorithms for synthesizing invariants have gradually shown its excellent performance. However, synthesizing disjunctive invariants is a difficult task. In this paper, we propose a method k++ Support Vector Machine (SVM) integrating k-means++ and SVM to synthesize conjunctive and disjunctive invariants. At first, given a program, we start with executing the program to collect program states. Next, k++SVM adopts k-means++ to cluster the positive samples and then applies SVM to distinguish each positive sample cluster from all negative samples to synthesize the candidate invariants. Finally, a set of theories founded on Hoare logic are adopted to check whether the candidate invariants are true invariants. If the candidate invariants fail the check, we should sample more states and repeat our algorithm. The experimental results show that k++SVM is compatible with the algorithms for Intersection Of Half-space (IOH) and more efficient than the tool of Interproc. Furthermore, it is shown that our method can synthesize conjunctive and disjunctive invariants automatically

674 Machine Learning and Automation in Burn Care: A Systematic Review

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S193-S193
Author(s):  
Samantha Huang ◽  
Justin Dang ◽  
Clifford C Sheckter ◽  
Haig A Yenikomshian ◽  
Justin Gillenwater
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Total Body Surface Area ◽  
Burn Wound ◽  
Burn Care ◽  
Surgeon Experience ◽  
Automated Software ◽  
Burn Depth ◽  
Automated Methods ◽  
Equal Weighting

Abstract Introduction Current methods of burn evaluation and treatment are subjective and dependent on surgeon experience, with high rates of inter-rater variability leading to inaccurate diagnoses and treatment. Machine learning (ML) and automated methods are being used to develop more objective and accurate methods for burn diagnosis and triage. Defined as a subfield of artificial intelligence that applies algorithms capable of knowledge acquisition, machine learning draws patterns from data, which it can then apply to clinically relevant tasks. This technology has the potential to improve burn management by quantitating diagnoses, improving diagnostic accuracy, and increasing access to burn care. The aim of this systematic review is to summarize the literature regarding machine learning and automated methods for burn wound evaluation and treatment. Methods A systematic review of articles available on PubMed and MEDLINE (OVID) was performed. Keywords used in the search process included burns, machine learning, deep learning, burn classification technology, and mobile applications. Reviews, case reports, and opinion papers were excluded. Data were extracted on study design, study objectives, study models, devices used to capture data, machine learning, or automated software used, expertise level and number of evaluators, and ML accuracy of burn wound evaluation. Results The search identified 592 unique titles. After screening, 35 relevant articles were identified for systematic review. Nine studies used machine learning and automated software to estimate percent total body surface area (%TBSA) burned, 4 calculated fluid requirements, 18 estimated burn depth, 5 estimated need for surgery, 6 predicted mortality, and 2 evaluated scarring in burn patients. Devices used to estimate %TBSA burned showed an accuracy comparable to or better than traditional methods. Burn depth estimation sensitivities resulted in unweighted means >81%, which increased to >83% with equal weighting applied. Mortality prediction sensitivity had an unweighted mean of 96.75%, which increased to 99.35% with equal weighting. Conclusions Machine learning and automated technology are promising tools that provide objective and accurate measures of evaluating burn wounds. Existing methods address the key steps in burn care management; however, existing data reporting on their robustness remain in the early stages. Further resources should be dedicated to leveraging this technology to improve outcomes in burn care.

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