Search · four archives
Search · four archives
23 papers · ranked by Valyu relevance
Habeeb Abolaji Babatunde, Owen M. McDougal, Timothy Andersen, Hongbin Pu
'Hongbin Pu'] The preprocessing of infrared spectra can significantly improve predictive accuracy for protein, carbohydrate, lipid, or other nutrition components, yet optimal preprocessing selection is typically empirical, tedious, and dataset specific. This study introduces a Bayesian optimization-based framework…
Nathan W. Churchill, Robyn Spring, Babak Afshin-Pour, Fan Dong + 2 more
'Stephen C. Strother' 'Jerzy Bodurka'] BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although…
Peng Li, Zhiyi Chen, Xu Chu, Kexin Rong
Data preprocessing is a crucial step in the machine learning process that transforms raw data into a more usable format for downstream ML models. However, it can be costly and time-consuming, often requiring the expertise of domain experts. Existing automated machine learning (AutoML) frameworks claim to automate data…
Alexander Isenko, Ruben Mayer, Jeffrey Jedele, Hans‐Arno Jacobsen
Preprocessing pipelines in deep learning aim to provide sufficient data throughput to keep the training processes busy. Maximizing resource utilization is becoming more challenging as the throughput of training processes increases with hardware innovations (e.g., faster GPUs, TPUs, and inter-connects) and advanced…
Sai Prakash Challa, Melvin Alexis Lara de Leon, Jiri Koziorek, Ibrahim A. Hameed + 1 more
Machine vision and AI-based defect detection systems are increasingly deployed in manufacturing to support consistent product quality and high production efficiency. However, these automated inspection systems often suffer from sensitivity to imaging variability, dependence on large labeled datasets, and the need for…
Paulito Palmes, Akihiro Kishimoto, Radu Marinescu, Parikshit Ram + 1 more
'Elizabeth Daly'] The pipeline optimization problem in machine learning requires simultaneous optimization of pipeline structures and parameter adaptation of their elements. Having an elegant way to express these structures can help lessen the complexity in the management and analysis of their performances together…
Alexandre Quemy
In this paper, we present a two-stage optimization process to build data pipelines and configure machine learning algorithms. First, we study the impact of data pipelines compared to algorithm configuration in order to show the importance of data preprocessing over hyperparameter tuning. The second part presents…
Gufran Ahmad Ansari, Salliah Shafi, Lamees Alhazzaa, Dechang Chen + 2 more
Background: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, primarily due to late diagnosis. Although machine learning (ML) techniques have been widely applied for lung cancer classification, many studies lack a fully optimized end-to-end pipeline using routine clinical data. This…
Olesya Melnichenko, Venkat S. Malladi
In the field of genomics, bioinformatics pipelines play a crucial role in processing and analyzing vast biological datasets. These pipelines, consisting of interconnected tasks, can be optimized for efficiency and scalability by leveraging cloud platforms such as Microsoft Azure. The choice of compute resources…
Ben Blamey, Salman Toor, Martin Dahlö, Håkan Wieslander + 6 more
This paper introduces the HASTE Toolkit, a cloud-native software toolkit capable of partitioning data streams in order to prioritize usage of limited resources. This in turn enables more efficient data-intensive experiments. We propose a model that introduces automated, autonomous decision making in data pipelines…
Ramona Leenings, Nils Ralf Winter, Lucas Plagwitz, Vincent Holstein + 14 more
'Jan Ernsting' 'Kelvin Sarink' 'Lukas Fisch' 'Jakob Steenweg' 'Leon Kleine-Vennekate' 'Julian Gebker' 'Daniel Emden' 'Dominik Grotegerd' 'Nils Opel' 'Benjamin Risse' 'Xiaoyi Jiang' 'Udo Dannlowski' 'Tim Hahn' 'Thippa Reddy Gadekallu'] PHOTONAI is a high-level Python API designed to simplify and accelerate machine…
Jochen Sieg, Christian Wolfgang Feldmann, Jennifer Hemmerich, Conrad Stork + 3 more
The open-source package scikit-learn provides various machine learning algorithms and data processing tools, including the Pipeline class, which allows users to prepend custom data transformation steps to the machine learning model. We introduce the MolPipeline package, which extends this concept to chemoinformatics by…
Elliot Xie, Lingxin Cheng, Yujia Cai, Jack Shireman + 1 more
Performance bottlenecks in widely used genomics and bioinformatics software present a substantial and growing burden as biological datasets continue to increase in size and number. Relieving these bottlenecks relies largely on expert manual optimization and therefore remains difficult to scale. Here we present…
Trang T. Le, Weixuan Fu, Jason H. Moore
Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programming to…
Costanza Pascal, Herzeel Charlotte, Verachtert Wilfried
elPrep is an established multi-threaded framework for preparing SAM and BAM files in sequencing pipelines. To achieve good performance, its software architecture makes only a single pass through a SAM/BAM file for multiple preparation steps, and keeps sequencing data as much as possible in main memory. Similar to other…
Authors not listed
The integration of artificial intelligence technologies into pharmaceutical research is crucial for gaining an early understanding of molecular properties, thereby facilitating successful drug design. Constructing a machine learning (ML) model however, requires knowledge spanning from data preprocessing and feature…
Nikolay O. Nikitin, Pavel Vychuzhanin, Mikhail Sarafanov, Iana S. Polonskaia + 5 more
'Iana S. Polonskaia' 'Ilia Revin' 'Irina V. Barabanova' 'Gleb Maximov' 'Anna V. Kalyuzhnaya' 'Alexander V. Boukhanovsky'] The effectiveness of the machine learning methods for real-world tasks depends on the proper structure of the modeling pipeline. The proposed approach is aimed to automate the design of composite…
Isabel Mogollon, Michaela Feodoroff, Pedro Neto, Alba Montedeoca + 2 more
Understanding cellular function within 3D multicellular spheroids is essential for advancing cancer research, particularly in studying cell-stromal interactions as potential targets for novel drug therapies. However, accurate single-cell segmentation in 3D cultures is challenging due to dense cell clustering and the…
Enbin Liu, Changjun Li, Yi Yang
There are many compressor stations along long-distance natural gas pipelines. Natural gas can be transported using different boot programs and import pressures, combined with temperature control parameters. Moreover, different transport methods have correspondingly different energy consumptions. At present, the…
Heeseung Lee, Daeho Kim, Heyin Lee, Namyoung Gwak + 6 more
- 1. Computational Science Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea - 2. Department of Materials Science and Engineering, Korea University, 145 Anam-ro, Seoul 02841, Republic of Korea - 3. Department of Chemical and Biological Engineering, Korea University, Seoul 02841…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Authors not listed
The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However…