25 papers · ranked by Valyu relevance
Víctor Blanco, Kothari, Harshit, James Luedtke
In this paper, we propose a new mathematical optimization model for multiclass classification based on arrangements of hyperplanes. Our approach preserves the core support vector machine (SVM) paradigm of maximizing class separation while minimizing misclassification errors, and it is computationally more efficient…
Niall Rodgers
Palaeontology has seen widespread and growing use of machine learning to classify and analyse large datasets of fossils. However, palaeontology is a challenging field in which to apply machine learning. Datasets may be small or unlabelled, images may be complex and different from standard datasets and palaeontologists…
Adi Alhudhaif, Mehmet Cunkas
Background This article aims to determine the coefficients that will reduce the in-class distance and increase the distance between the classes, collecting the data around the cluster centers with meta-heuristic optimization algorithms, thus increasing the classification performance. Methods The proposed mathematical…
Moayad Alnammi, Shengchao Liu, Spencer S Ericksen, Gene E Ananiev + 6 more
Traditional small molecule drug discovery is a time consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine learning methods for virtual chemical screening enables training models on known active and…
Ben Lanza, Deepak Parashar
Biomarkers are known to be the key driver behind targeted cancer therapies by either stratifying the patients into risk categories or identifying patient subgroups most likely to benefit. However, the ability of a biomarker to stratify patients relies heavily on the type of clinical endpoint data being collected. Of…
Yituo Feng, Jungryeol Park, Varun Gupta
Background In today’s digital economy, enterprises are adopting collaboration software to facilitate digital transformation. However, if employees are not satisfied with the collaboration software, it can hinder enterprises from achieving the expected benefits. Although existing literature has contributed to user…
Arockiaraj Simiyon, Chaitanya Sachidanand, Manthana Halmakki Krishnamurthy, Ananya V. Bhatt + 1 more
Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine Authors: ['Arockiaraj Simiyon' 'Chaitanya Sachidanand' 'Manthana Halmakki Krishnamurthy' 'Ananya V. Bhatt' 'Thirunavukkarasu Indiran'] Identifying and diagnosing faults is a critical task in process industries to maintain effective…
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
Mingkai Zheng, Shan You, Lang Huang, Xiu Su + 4 more
'Xiaogang Wang' 'Chang Xu'] Image classification is a longstanding problem in computer vision and machine learning research. Most recent works (e.g. SupCon [1], Triplet [2], and max-margin [3]) mainly focus on grouping the intraclass samples aggressively and compactly, with the assumption that all intra-class samples…
Mohammad H. Zhoolideh Haghighi
Classification is a popular task in the field of Machine Learning (ML) and Artificial Intelligence (AI), and it happens when outputs are categorical variables. There are a wide variety of models that attempts to draw some conclusions from observed values, so classification algorithms predict categorical class labels…
Sarah E. Lindley, Yiyang Lu, Diwakar Shukla
Guide to Machine Learning for Small Molecule Design Authors: ['Sarah\nE. Lindley' 'Yiyang Lu' 'Diwakar Shukla'] Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML…
Nureni Ayofe Azeez, Sanjay Misra, Davidson Onyinye Ogaraku, Ademola Philip Abidoye + 2 more
The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive…
Mélanie Lubrano, Tristan Lazard, Guillaume Balezo, Yaëlle Bellahsen-Harrar + 3 more
In computational pathology, predictive models from Whole Slide Images (WSI) mostly rely on Multiple Instance Learning (MIL), where the WSI are represented as a bag of tiles, each of which is encoded by a Neural Network (NN). Slide-level predictions are then achieved by building models on the agglomeration of these tile…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Snigdha Sarkar, Md. Shahjaman, Sukanta Das
Supervised machine learning (SML) is an approach that learns from training data with known category membership to predict the unlabeled test data. There are many SML approaches in the literature and most of them use a linear score to learn its classifier. However, these approaches fail to elucidate biodiversity from…
Feng Tang, Zhongmin Zhang, Weige Zhou, Guangpeng Li + 1 more
A major challenge in scRNAseq analysis is how to recover the biologically meaningful cell ontology tree and conserved gene modules across datasets. Data integration and batch-effect correction have been the key to effectively analyze multiple datasets, but often fail to disentangle cell states in heterogeneous samples…
Jens F. Tillmann, Alexander I. Hsu, Martin K. Schwarz, Eric A. Yttri
To identify and extract naturalistic behavior, two schools of methods have become popular: supervised and unsupervised. Each approach carries its own strengths and weaknesses, which the user must weigh in on their decision. Here, a new active learning platform, A-SOiD, blends these strengths and, in doing so, overcomes…
Shizhou Ma, Yifeng Zhang, Delong Li, Yixin Sun + 3 more
2.2### SP block for the generation of the echocardiogram superclass pseudo-label To utilize the unlabeled data of out-of-distribution, we proposed a novel superclass pseudo-label from the perspective of superclass probability. Specifically, our approach first considered all in-the-distribution classes as the…
Moayad Alnammi, Shengchao Liu, Spencer S Ericksen, Gene E Ananiev + 6 more
Traditional small molecule drug discovery is a time consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine learning methods for virtual chemical screening enables training models on known active and…
Raheel Hammad, Sownyak Mondal
Auxetics are a rare class of materials that exhibit a negative Poisson's ratio. The existence of these auxetic materials is rare but has a large number of applications in designing exotic materials. We build a complete machine learning framework to detect Auxetic materials as well as Poisson's ratio of non-auxetic…
Linrui Dai, Wenhui Lei, Xiaofan Zhang
As research interests in medical image analysis become increasingly fine-grained, the cost for extensive annotation also rises. One feasible way to reduce the cost is to annotate with coarse-grained superclass labels while using limited fine-grained annotations as a complement. In this way, fine-grained data learning…
Authors not listed
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via…
Hyuna Kwon, Zulfikhar Ali, Bryan Wong
Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this…
Jianmei Zhong, Junyao Yang, Yinghui Song, Zhihua Zhang + 8 more
In this study, we have devised a computational framework SuperFeat that allows for the training of a machine learning model and evaluate the canonical cellular states/features in pathological tissues that underlie the progression of disease. This framework also enables the identification of potential drugs that target…
Feifei Shao, Ya‐Wei Luo, Ping Liu, Jie Chen + 3 more
'Jun Xiao'] The expensive annotation cost is notoriously known as the main constraint for the development of the point cloud semantic segmentation technique. Active learning methods endeavor to reduce such cost by selecting and labeling only a subset of the point clouds, yet previous attempts ignore the…