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- research-articleJuly 2024
Virtual tomography as a novel method for segmenting machining process phases with the use of machine learning-supported measurement
Expert Systems with Applications: An International Journal (EXWA), Volume 250, Issue Chttps://doi.org/10.1016/j.eswa.2024.123945AbstractA new idea of machine learning-based technological process segmentation with the use of multi-sensor measurement is proposed in this article. The proposed segmentation of the machining process through appropriate measurement data modelling ...
Graphical abstractDisplay Omitted
Highlights- Novel unsupervised technique identifies milling phases, enhancing tool SOH prediction.
- Phase-specific feature analysis boosts predictive model accuracy in tool monitoring.
- Feature analysis reveals key features impacting milling ...
- research-articleJuly 2024
LUMIOS – Label using machine in organic samples – A software for dereplication, molecular docking, and combined machine and deep learning
Expert Systems with Applications: An International Journal (EXWA), Volume 248, Issue Chttps://doi.org/10.1016/j.eswa.2024.123447AbstractLUMIOS, short for Label Using Machine In Organic Samples, is a versatile Python-based software designed to assist professionals and students in organic chemistry with computational exploration of natural products (NPs). Offering user-friendly and ...
- ArticleJuly 2024
Combining Convolution and Involution for the Early Prediction of Chronic Kidney Disease
AbstractChronic Kidney Disease (CKD) is a common disease with high incidence and high risk for the patients’ health when it degrades to its most advanced stages. When detected early, it is possible to slow down the progression of the disease, leading to ...
- research-articleJuly 2024
A collaborative filtering recommendation method based on emotional evaluation relations
Soft Computing - A Fusion of Foundations, Methodologies and Applications (SOFC), Volume 28, Issue 13-14Pages 8167–8181https://doi.org/10.1007/s00500-024-09736-6AbstractImproving the accuracy of recommendation systems is a hot research topic, and existing methods have not considered the differences in various types of data. To address this issue, we propose a collaborative filtering recommendation method that ...
- extended-abstractJune 2024
“Beehive” Interactive Installation for Playgrounds: Reflecting on Children's Rights in the Context of Big Data Industry
IDC '24: Proceedings of the 23rd Annual ACM Interaction Design and Children ConferencePages 969–972https://doi.org/10.1145/3628516.3661163This paper introduces the Beehive interactive installation's prototype and explores how the big data sector, particularly YouTube, threatens children's rights during their free time and play. By integrating sensors into playground elements, Beehive turns ...
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- research-articleJuly 2024
A cloud-based data processing and visualization pipeline for the fibre roll-out in Germany
Journal of Systems and Software (JSSO), Volume 211, Issue Chttps://doi.org/10.1016/j.jss.2024.112008AbstractTo support the roll-out of fibre broadband Internet in Germany, Deutsche Telekom has set itself the goal of connecting more than 2.5 million households per year to FTTH (Fibre to the Home). However, planning and approval processes have been very ...
Highlights- Software platform to speed up the fibre roll-out in Germany.
- Cloud-based, Big Data processing pipeline for 360°panorama imagery and 3D point clouds.
- Web-based 3D visualization used for interactive planning.
- More than 8 million ...
- research-articleJune 2024
A wind speed forecasting system for the construction of a smart grid with two-stage data processing based on improved ELM and deep learning strategies
Expert Systems with Applications: An International Journal (EXWA), Volume 241, Issue Chttps://doi.org/10.1016/j.eswa.2023.122487AbstractThe operation and scheduling management of smart grids are important aspects, and wind speed forecasting modules are indispensable in wind power system management. Researchers have contributed significantly to the development of accurate ...
- research-articleJuly 2024
CoreNLP dependency parsing and pattern identification for enhanced opinion mining in aspect-based sentiment analysis
Journal of King Saud University - Computer and Information Sciences (JKSUCIS), Volume 36, Issue 4https://doi.org/10.1016/j.jksuci.2024.102035AbstractAspect-Based Sentiment Analysis (ABSA) aims to identify the sentiment expressed towards a specific feature or aspect of a given text. Although certain ABSA techniques employ syntactic information to capture the connection between the opinion ...
Highlights- The ABSA method leverages syntax, semantics, and CoreNLP’s parsing to gauge aspect sentiment.
- Captures the aspect polarity by identifying patterns and extracting critical relations.
- The method outperforms ABSA approaches using ...
- research-articleJuly 2024
Supervised learning study on ground classification and state recognition of agricultural robots based on multi-source vibration data fusion
Computers and Electronics in Agriculture (COEA), Volume 219, Issue Chttps://doi.org/10.1016/j.compag.2024.108791Graphical abstractDisplay Omitted
Highlights:- A novel vibration-based robotic ground and state classifier is proposed.
- Multi-source vibration signal imaging for feature fusion.
- Attention combines residual structures to characterize complex features.
In agricultural environments, recognizing the walking ground and state of tracked mobile robots is a complex and challenging task, influenced by clay conditions and other external environmental disturbances. Therefore, this paper proposes a novel ...
- research-articleApril 2024
CFI-LFENet: Infusing cross-domain fusion image and lightweight feature enhanced network for fault diagnosis
AbstractData-driven fault diagnosis has become a hot topic of research in recent years, due to its wide applicability, high accuracy, and ease of modeling. In data-driven fault diagnosis, feature extraction is usually performed through a combination of ...
Highlights- Propose an innovative framework for precise fault diagnosis.
- CFI images can translate fault information into texture and color features.
- Differences between different faults can be better highlighted through CFI images.
- ...
- research-articleJuly 2024
PyRefra – Refraction seismic data treatment and inversion
AbstractOpen-source software in the geophysical community has been increasingly taking importance since more than a decade. Following this spirit, this study presents an open-source Python software for display, processing, picking of near-surface ...
Highlights- Development of a platform-independent code: PyRefra.
- PyRefra allows visualisation and treatment of refraction seismic data.
- Different picking methods are available.
- Pygimli is used for tomographic inversion.
- An example for ...
- research-articleJune 2024
Deep neural networks accelerators with focus on tensor processors
Microprocessors & Microsystems (MSYS), Volume 105, Issue Chttps://doi.org/10.1016/j.micpro.2023.105005AbstractThe massive amount of data and the problem of processing them is one of the main challenges of the digital age, and the development of artificial intelligence and machine learning can be useful in solving this problem. Using deep neural networks ...
- research-articleApril 2024
Establishment of machine learning-based tool for early detection of pulmonary embolism
- Lijue Liu,
- Yaming Li,
- Na Liu,
- Jingmin Luo,
- Jinhai Deng,
- Weixiong Peng,
- Yongping Bai,
- Guogang Zhang,
- Guihu Zhao,
- Ning Yang,
- Chuanchang Li,
- Xueying Long
Computer Methods and Programs in Biomedicine (CBIO), Volume 244, Issue Chttps://doi.org/10.1016/j.cmpb.2023.107977Highlights- Screening features that passed the hypothesis test were established with reference to the 2019 ESC guidelines for diagnosis and management of acute pulmonary embolism. The data set of the study on pulmonary embolism was established by ...
Pulmonary embolism (PE) is a complex disease with high mortality and morbidity rate, leading to increasing society burden. However, current diagnosis is solely based on symptoms and laboratory data despite its complex ...
- research-articleApril 2024
Novel statistical time series data augmentation and machine learning based classification of unobtrusive respiration data for respiration Digital Twin model
Computers in Biology and Medicine (CBIM), Volume 168, Issue Chttps://doi.org/10.1016/j.compbiomed.2023.107825AbstractDigital Twin (DT), a concept of Healthcare (4.0), represents the subject’s biological properties and characteristics in a digital model. DT can help in monitoring respiratory failures, enabling timely interventions, personalized treatment plans ...
Graphical abstractDisplay Omitted
Highlights- A novel statistical time series data augmentation method is proposed for the generation of a larger respiration synthetic dataset.
- Signal processing methodology implemented with 92.3% accurate estimation of Breaths per Minute (BPM) ...
- research-articleJanuary 2024
Towards an understanding of intra-defect associations: Implications for defect prediction
Journal of Systems and Software (JSSO), Volume 207, Issue Chttps://doi.org/10.1016/j.jss.2023.111858AbstractIn previous studies, when collecting defect data, if the fix of a defect spans multiple modules, each involved module is labeled as defective. In this context, the defect prediction models are built based on the features of each individual module,...
Highlights- The first to leverage intra-defect associations for defect prediction.
- A novel data processing approach for building defect prediction models.
- The majority of defects occur across functions.
- Most cross-module defects have only ...
- short-paperDecember 2023
Towards Building Edge-side Common Data Processing Services on The Computing Continuum
Middleware '23: Proceedings of the 24th International Middleware Conference: Demos, Posters and Doctoral SymposiumPages 27–28https://doi.org/10.1145/3626564.3629089In order to respond data growth from edge to cloud and efficiently use various real data for advanced digital services, we advocate edge-side common data processing services on the computing continuum. This poster presents our background motivation of ...
- ArticleFebruary 2024
Containerized Wearable Edge AI Inference Framework in Mobile Health Systems
AbstractThe proliferation of wearable devices and personal smartphones has promoted smart mobile health (MH) technologies. The MH applications and services are extremely responsive to computation latency. Edge computing is a distinguished form of cloud ...
- ArticleNovember 2023
Fast – Asymptotically Optimal – Methods for Determining the Optimal Number of Features
Integrated Uncertainty in Knowledge Modelling and Decision MakingPages 123–128https://doi.org/10.1007/978-3-031-46775-2_11AbstractIn machine learning – and in data processing in general – it is very important to select the proper number of features. If we select too few, we miss important information and do not get good results, but if we select too many, this will include ...
- research-articleOctober 2023
MAP-FCRNN: Multi-step ahead prediction model using forecasting correction and RNN model with memory functions
Information Sciences: an International Journal (ISCI), Volume 646, Issue Chttps://doi.org/10.1016/j.ins.2023.119382AbstractCurrently, prediction stands as one of the most prominent areas of research. Enhancing the accuracy and generalization capabilities of prediction models remains a crucial and ongoing challenge. Furthermore, the majority of existing ...
Highlights- A data processing method that incorporates prediction objectives is proposed.
- ...
- research-articleOctober 2023
MBGA-Net: A multi-branch graph adaptive network for individualized motor imagery EEG classification
Computer Methods and Programs in Biomedicine (CBIO), Volume 240, Issue Chttps://doi.org/10.1016/j.cmpb.2023.107641Highlights- The proposed model is adaptively adjusted.
- The average accuracy of the proposed model exceeds 85% on several datasets.
- The model ensures better accuracy for each subject.
- The proposed data augmentation method is applicable to ...
Background and objective: The development of deep learning has led to significant improvements in the decoding accuracy of Motor Imagery (MI) EEG signal classification. However, current models are inadequate in ensuring high levels of ...