Search Results - Applied Machine Learning for Code Assessment

A large empirical assessment of the role of data balancing in machine-learning-based code smell detection by Pecorelli, Fabiano, Di Nucci, Dario, De Roover, Coen, De Lucia, Andrea

“… To overcome these limitations, previous work applied Machine-Learning that can learn from previous datasets without needing any threshold definition…”
Get full text

Can we screen for pancreatic cancer? Identifying a sub-population of patients at high risk of subsequent diagnosis using machine learning techniques applied to primary care data by Malhotra, Ananya, Rachet, Bernard, Bonaventure, Audrey, Pereira, Stephen P., Woods, Laura M.

“…Pancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying…”
Get full text

How Good Is Your Verilog RTL Code? A Quick Answer from Machine Learning by Sengupta, Prianka, Tyagi, Aakash, Chen, Yiran, Hu, Jiang

“…), design iterations become prohibitively expensive. To this end, we propose a machine learning approach to Verilog-based Register-Transfer Level (RTL…”
Get full text

QAcon: single model quality assessment using protein structural and contact information with machine learning techniques by Cao, Renzhi, Adhikari, Badri, Bhattacharya, Debswapna, Sun, Miao, Hou, Jie, Cheng, Jianlin

“…Protein model quality assessment (QA) plays a very important role in protein structure prediction…”
Get full text

Development of a code-free machine learning model for the classification of cataract surgery phases by Touma, Samir, Antaki, Fares, Duval, Renaud

“…This study assessed the performance of automated machine learning (AutoML) in classifying cataract surgery phases from surgical videos…”
Get full text

Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study by Clift, Ash Kieran, Dodwell, David, Lord, Simon, Petrou, Stavros, Brady, Michael, Collins, Gary S, Hippisley-Cox, Julia

“… (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning…”
Get full text

Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth by Abraham, Abin, Le, Brian, Kosti, Idit, Straub, Peter, Velez-Edwards, Digna R., Davis, Lea K., Newton, J. M., Muglia, Louis J., Rokas, Antonis, Bejan, Cosmin A., Sirota, Marina, Capra, John A.

“… Results We find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC = 0.75, PR-AUC…”
Get full text

A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data by Akhavan, Javid, Lyu, Jiaqi, Manoochehri, Souran

“…This work presents an in-situ quality assessment and improvement technique using point cloud and AI for data processing and smart decision making in Additive Manufacturing (AM…”
Get full text

Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea by Yu, Na Hyeon, Shin, Daeun, Ryu, Ik Hee, Yoo, Tae Keun, Koh, Kyungmin

“…—are commonly utilized in clinical settings for disease risk assessment. This study aimed to develop a machine learning model to predict RVO risk in the general population…”
Get full text

Machine learning-assisted distinct element model calibration: ANFIS, SVM, GPR, and MARS approaches by Fathipour-Azar, Hadi

“… This paper explores the use of the adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM…”
Get full text

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art by Walia, Rasna R, Caragea, Cornelia, Lewis, Benjamin A, Towfic, Fadi, Terribilini, Michael, El-Manzalawy, Yasser, Dobbs, Drena, Honavar, Vasant

“… Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition ‘code…”
Get full text

A Survey of Automatic Source Code Summarization by Zhang, Chunyan, Wang, Junchao, Zhou, Qinglei, Xu, Ting, Tang, Ke, Gui, Hairen, Liu, Fudong

“…Source code summarization refers to the natural language description of the source code’s function…”
Get full text

Assessment of punching shear strength of FRP-RC slab-column connections using machine learning algorithms by Truong, Gia Toai, Hwang, Hyeon-Jong, Kim, Chang-Soo

“… Recently, the use of fiber-reinforced polymer (FRP) bars replacing steel reinforcement has been widely applied to overcome the corrosion issue, particularly concrete slab-column connections using FRP bars as flexural reinforcement (FRP-RC slabs…”
Get full text

Seismic Impact on Building Structures: Assessment, Design, and Strengthening

“… This Special Issue focuses on the structural impact of earthquakes on buildings. Original research and reviews covering structural modeling, vulnerability assessment…”
Get full text

An empirical assessment of machine learning approaches for triaging reports of static analysis tools by Yerramreddy, Sai, Mordahl, Austin, Koc, Ugur, Wei, Shiyi, Foster, Jeffrey S., Carpuat, Marine, Porter, Adam A.

“… To improve the usability of these tools, researchers have recently begun to apply machine learning techniques to classify and filter incorrect analysis reports…”
Get full text

Application of Machine Learning for Assessment of HS Code Correctness by Spichakova, Margarita, Haav, Hele-Mai

“… The paper provides an automated solution to this problem by applying machine learning methods to assess the correctness of Harmonized System codes…”
Get full text

Development and validation of ‘Patient Optimizer’ (POP) algorithms for predicting surgical risk with machine learning by Kowadlo, Gideon, Mittelberg, Yoel, Ghomlaghi, Milad, Stiglitz, Daniel K., Kishore, Kartik, Guha, Ranjan, Nazareth, Justin, Weinberg, Laurence

“… (referred to as Patient Optimizer or POP) using Machine Learning (ML) that predict the development of post-operative complications and provide pilot data to inform the design of a larger prospective study…”
Get full text

OP67 “Black Box Bottleneck” Paradigm And Transparency Issues On Artificial-Intelligence-Based Tools In Health Technology Assessment: A Scoping Review by Komoda, Denis Satoshi, de Almeida Cardoso, Marilia Mastrocolla, Lima, Ana Renata, Visacri, Marília Berlofa, Silveira Correa, Carlos Roberto, Fernandes, Brígida Dias

Get full text

Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms by Tassi, Andrea, Vizzari, Marco

“… (OO) Land Use–Land Cover (LULC) classification approaches can be implemented, thanks to the availability of the many state-of-art functions comprising various Machine Learning (ML) algorithms…”
Get full text

Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations by Mascolini, Alessio, Cardamone, Dario, Ponzio, Francesco, Di Cataldo, Santa, Ficarra, Elisa

“… In this paper, we present Generative Adversarial Network Discriminator Learner (GAN-DL), a novel self-supervised learning paradigm based on the StyleGAN2 architecture…”
Get full text