Multi-focus image fusion is a technique that combines multiple out-of-focus images to enhance the overall image quality. It has gained significant attention in recent years, thanks to the advancements in deep learning...
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In recent years, numerous CNN-based light field (LF) image super-resolution (SR) methods have been developed. However, due to the downsampling inconsistency between low-resolution (LR) testing LF images and LR trainin...
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Learning the accurate dynamics of robotic systems directly from the trajectory data is currently a prominent research *** physics-enforced networks,exemplified by Hamiltonian neural networks and Lagrangian neural netw...
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Learning the accurate dynamics of robotic systems directly from the trajectory data is currently a prominent research *** physics-enforced networks,exemplified by Hamiltonian neural networks and Lagrangian neural networks,demonstrate proficiency in modeling ideal physical systems,but face limitations when applied to systems with uncertain non-conservative dynamics due to the inherent constraints of the conservation laws *** this paper,we present a novel augmented deep Lagrangian network,which seamlessly integrates a deep Lagrangian network with a standard deep *** fusion aims to effectively model uncertainties that surpass the limitations of conventional Lagrangian *** proposed network is applied to learn inverse dynamics model of two multi-degree manipulators including a 6-dof UR-5 robot and a 7-dof SARCOS manipulator under *** experimental results clearly demonstrate that our approach exhibits superior modeling precision and enhanced physical credibility.
The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the exis...
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The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.
A significant portion of research in the hybrid classification area aims to reduce the number of deep features. However, many approaches insufficiently address the relationships between deep features and specific clas...
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Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth...
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This study focuses on enhancing Natural Language Processing (NLP) in generative AI chatbots through the utilization of advanced pre-trained models. We assessed five distinct Large Language Models (LLMs): TRANSFORMER M...
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Deep Learning (DL) techniques have significantly improved the diagnostic accuracy in healthcare, particularly for detecting and classifying skin cancer. Such technological advancements will assist healthcare professio...
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Schizophrenia (SZ) is a complex neuropsychiatric disorder affecting approximately 1% of the global population. The early diagnosis of SZ, with electroencephalograph (EEG) signals using deep learning (DL), can help in ...
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Schizophrenia (SZ) is a complex neuropsychiatric disorder affecting approximately 1% of the global population. The early diagnosis of SZ, with electroencephalograph (EEG) signals using deep learning (DL), can help in timely interventions which may mitigate the risk of progression to clinical psychosis. This study introduces five novel machine learning (ML)/DL-based frameworks for identifying SZ using EEG signals. The first framework involves extracting complexity features using discrete wavelet transform (DWT) of the EEG signal. In the second framework, to capture the interrelatedness among the EEG channels the complexity features are computed using multivariate empirical mode decomposition (MEMD). In both of these frameworks, the complexity features extracted are transformed into their 2D representation which uses convolutional neural network (CNN) based model for classification. Various CNN models, including conventional CNN and pretrained models were used for this purpose. In the third framework, to obtain the benefit of multiple view of the EEG signal, the complexity features extracted from DWT and MEMD features in vector representation were fused using concatenation. The combined feature was integrated with a feedforward neural network (FFNN). To obtain the optimized multiview feature set principal component analysis (PCA) was used on the concatenated feature set in the fourth framework. Finally, in the fifth framework, to further optimize the fusion of DWT and MEMD feature set canonical correlation analysis (CCA) based approach was proposed. This study is one of the first to apply a 2D representation of entropy features extracted from DWT and MEMD transformed signals for diagnosis of SZ. Furthermore, this is the first study to propose an optimized multiview feature derived from the fusion of 1D-DWT and 1D-MEMD transformed complexity features using PCA and CCA for identifying SZ from healthy control (HC). The classification performance of various CNN models in
Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...
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Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
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