26 papers · ranked by Valyu relevance
Poorya Amirajlo, Hossein Hassani, Amin Beiranvand Pour, Narges Habibkhah
In mineral prospectivity mapping (MPM), the scarcity of labeled data and severe class imbalance often undermine the stability and reliability of machine learning models. This study advances a reliability-centered framework that prioritizes calibration and reproducibility over marginal accuracy gains when training data…
Sertaç Oruç, Mehmet Ali Hınıs, Türker Tuğrul
Machine-learning techniques are widely used across many disciplines, including electricity generation forecasting. In this study, the Support Vector Machine (SVM) based models, one of the machine learning techniques, were developed for daily PV power forecasting. To improve model performance, models were tuned with…
Yoosoon Chang, Joon Park, Guo Yan
The support vector machine (SVM) has an asymptotic behavior that parallels that of the quasi-maximum likelihood estimator (QMLE) for binary outcomes generated by a binary choice model (BCM), although it is not a QMLE. We show that, under the linear conditional mean condition for covariates given the systematic…
Mohammad Jafari Jozani, Bahram Moeinianfar
Support vector machines (SVMs) are a standard tool for binary classification, but their classical formulations are purely data-driven and offer no direct way to encode trusted benchmark models or structured preferences on selected subsets of the data. We propose Elite-Driven Support Vector Machines (EDSVM), a general…
Türker Tuğrul, Sertaç Oruç, Jessica Louise Hall, Ali Ulvi Galip Şenocak + 1 more
Drought is a natural disaster that often remains unnoticed until ecosystem impacts become severe. Therefore, monitoring and detecting droughts are important research topics. Consequently, drought indices with different focuses, such as precipitation or soil moisture, have been developed. Yet, the utility of the indices…
Muralikrishna Narra, Anamika Ray, Brittany Polley, Hui Yang + 1 more
The advent of artificial intelligence (AI) holds great promise for revolutionizing the fields of plant tissue culture and genome editing. Plant tissue culture is recognized as a powerful tool for rapid multiplication and crop improvement. However, the complex interactions between genetic and environmental factors…
Yasin Atilkan, Berk Kirik, Eren Tuna Acikbas, Fatih Ekinci + 5 more
Crayfish play an important role in freshwater ecosystems, and sex classification is crucial for analyzing their demographic structures. This study performed binary classification using traditional machine learning and deep learning models on tabular and image datasets with an imbalanced class distribution. For tabular…
Artur Miroszewski
Kernel methods are typically formulated under the assumption of exact, noise-free access to the Gram matrix. However, in emerging settings such as quantum machine learning, each kernel entry must be inferred from noisy observations, and its accuracy depends on how a limited measurement budget is allocated. Despite…
Yuliang Yang, Chen Chen, Yuxiang Liu, Huiru Wang
The support vector machine (SVM) is a widely used classifier, but choosing an appropriate loss function remains difficult. Convex losses such as the hinge loss and least-squares loss are sensitive to outliers, while bounded non-convex losses often lead to high computational cost. To address this, we propose a hybrid…
Haiyan Du, Hu Yang
Existing support vector machines(SVM) models are sensitive to noise and lack sparsity, which limits their performance. To address these issues, we combine the elastic net loss with a robust loss framework to construct a sparse $\varepsilon$-insensitive bounded asymmetric elastic net loss, and integrate it with SVM to…
Varun Srivastava, Khushi Garg, Samarth Soni, Arun Balodi + 2 more
This study introduces an enhanced texture-based algorithm for the classification of oral cancer images. The images are first extensively preprocessed to enhance the affected area with techniques like gamma correction, adaptive histogram equalization, and sharpening of images using a Laplacian filter. Then a feature…
Rainer Fährrolfes, Jochen Sieg, Florian Flachsenberg, Matthias Rarey
Machine Learning by Human Observation for Efficient Clustering and Analysis of Structure–Activity Data Authors: Rainer Fährrolfes, Jochen Sieg, Florian Flachsenberg, Matthias Rarey In the early stages of drug discovery, the identification of molecular series with lead-like properties is essential for structure-activity…
Dipro Sinha, Sneha Murmu, Abhik Sarkar, Md Yeasin + 8 more
Histone modifications are central to gene regulation, yet their systematic identification in plants remains limited due to the complexity of epigenomic landscapes. We present OpEnHiMR, an optimization-based ensemble learning framework for multiclass prediction of three key histone modifications, H3K4me3, H3K27me3, and…
Álvaro Sánchez-Paniagua Ríos, Juan P. Llerena, Alberto Lastra, Nuria Torrado + 1 more
The performance of Support Vector Machines (SVMs) critically depends on the kernel function choice, which enables implicit mapping of data into high-dimensional feature spaces. While classical kernels like Radial Basis Function (RBF) remain popular, orthogonal polynomial kernels offer mathematically interpretable…
Neda R. Morakabati, Alison S. Thiha, Eitan Schechtman
Machine learning methods employing neuroimaging data are useful for monitoring the activation of neural representations. Specifically, they can be used to discern the brain networks engaged in processing specific categories of items. This approach has been employed on neuroimaging data, including functional magnetic…
Authors not listed
Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…
Muhammad Ishaq, Naila Yaqub, Muhammad Fayaz, Arshad Khan + 3 more
Many banking and corporate sector organization problems are resolved by clever, creative solutions based on artificial intelligence (AI). Any financial institution has to use AI-enabled churn detection solutions to improve customer relationship management (CRM). In order to effectively predict churn in a publicly…
Priyanshu Sarma-Sarkar, Rajkumar Saini, Partha Pratim Roy
Approximately 50% of the population in India is estimated to experience sleep-related disorders. Sleep deprivation is a prevalent condition that adversely impacts cognitive performance, neural functioning, and overall health. Electroencephalography (EEG) offers an objective means of capturing neural alterations…
Aman Yadav, Arlin Birkby, Noah Armstrong, Assame Arnob + 9 more
Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been investigated. This study explores experimental factors affecting classification performance. Among…
Love Odunlami, Dan MacLean
Effectors are pathogen proteins that facilitate infection by manipulating plant immunity. Computational programs have been developed that identify effectors from sequence data. Many of these programs use internal models that have unavoidable biases due to their training processes and the diverse nature of effector…
Authors not listed
Metal hydrides play a pivotal role in a wide range of applications, including hydrogen storage, compression, heat management, and catalysis, making them a central focus of interdisciplinary research spanning chemistry, materials science, and engineering. The performance of the metal hydride based systems is strongly…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Felipe Roberto Francisco, Geovani Luciano de Oliveira, Guilherme Francio Niederauer, Roberto Fritsche-Neto + 2 more
Although grapevine (Vitis spp.) is among the oldest and most economically significant fruit species globally, its genetic improvement faces major bottlenecks due to long juvenile periods and extended cycles for phenotypic evaluation. In this context, genomic selection (GS) has emerged as an effective alternative to…
S. Patel, V. Patel
Analytical technologies that can provide quick, precise, and continuous information regarding process performance are necessary for the development of biopharmaceutical manufacturing. Conventional bioprocess monitoring is largely dependent on laboratory-based data and offline sampling, which can restrict process…
Authors not listed
Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…
Authors not listed
The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…