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Статті в журналах з теми "DBN-CO"
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A. Geetha, Geetha, and Gomathi N. Gomathi. "A Robust Grey Wolf-based Deep Learning for Brain Tumour Detection in MR Images." International Journal of Engineering Education 1, no. 1 (June 15, 2019): 9–23. http://dx.doi.org/10.14710/ijee.1.1.9-23.
Повний текст джерелаАнотація:
In recent times, the detection of brain tumour is a common fatality in the field of the health community. Generally, the brain tumor is an abnormal mass of tissue where the cells grow up and increase uncontrollably, apparently unregulated by mechanisms that control cells. A number of techniques have been developed so far; however, the time consumption in detecting brain tumor is still a challenge in the field of image processing. This paper intends to propose a new detection model even accurately. The model includes certain processes like Preprocessing, Segmentation, Feature Extraction and Classification. Particularly, two extreme processes like contrast enhancement and skull stripping are processed under initial phase, in the segmentation process, this paper uses Fuzzy Means Clustering (FCM) algorithm. Both Gray Level Co-occurrence Matrix (GLCM) as well as Gray-Level Run-Length Matrix (GRLM) features are extracted in feature extraction phase. Moreover, this paper uses Deep Belief Network (DBN) for classification. The DBN is integrated with the optimization approach, and hence this paper introduces the optimized DBN, for which Grey Wolf Optimization (GWO) is used here. The proposed model is termed as GW-DBN model. The proposed model compares its performance over other conventional methods in terms of Accuracy, Specificity, Sensitivity, Precision, Negative Predictive Value (NPV), F1Score and Matthews Correlation Coefficient (MCC), False negative rate (FNR), False positive rate (FPR) and False Discovery Rate (FDR), and proven the superiority of proposed work.
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Geetha, A., and N. Gomathi. "A robust grey wolf-based deep learning for brain tumour detection in MR images." Biomedical Engineering / Biomedizinische Technik 65, no. 2 (April 28, 2020): 191–207. http://dx.doi.org/10.1515/bmt-2018-0244.
Повний текст джерелаАнотація:
AbstractIn recent times, the detection of brain tumours has become more common. Generally, a brain tumour is an abnormal mass of tissue where the cells grow uncontrollably and are apparently unregulated by the mechanisms that control cells. A number of techniques have been developed thus far; however, the time needed in a detecting brain tumour is still a challenge in the field of image processing. This article proposes a new accurate detection model. The model includes certain processes such as preprocessing, segmentation, feature extraction and classification. Particularly, two extreme processes such as contrast enhancement and skull stripping are processed under the initial phase. In the segmentation process, we used the fuzzy means clustering (FCM) algorithm. Both the grey co-occurrence matrix (GLCM) as well as the grey-level run-length matrix (GRLM) features were extracted in the feature extraction phase. Moreover, this paper uses a deep belief network (DBN) for classification. The optimized DBN concept is used here, for which grey wolf optimisation (GWO) is used. The proposed model is termed the GW-DBN model. The proposed model compares its performance over other conventional methods in terms of accuracy, specificity, sensitivity, precision, negative predictive value (NPV), the F1Score and Matthews correlation coefficient (MCC), false negative rate (FNR), false positive rate (FPR) and false discovery rate (FDR), and proves the superiority of the proposed work.
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Maylawati, Dian Sa'adillah, Yogan Jaya Kumar, Fauziah Kasmin, and Muhammad Ali Ramdhani. "Deep sequential pattern mining for readability enhancement of Indonesian summarization." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (February 1, 2024): 782. http://dx.doi.org/10.11591/ijece.v14i1.pp782-795.
Повний текст джерелаАнотація:
In text summarization research, readability is a great issue that must be addressed. Our hypothesis is readability can be accomplished by using text representations that keep the meaning of text documents intact. Therefore, this study aims to combine sequential pattern mining (SPM) in producing a sequence of a word as text representation with unsupervised deep learning to produce an Indonesian text summary called DeepSPM. This research uses PrefixSpan as an SPM algorithm and deep belief network (DBN) as an unsupervised deep learning method. This research uses 18,774 Indonesian news text from IndoSum. The readability aspect is evaluated by recall-oriented understudy for gisting evaluation (ROUGE) as a co-selection-based analysis; Dwiyanto Djoko Pranowo metrics, Gunning fog index (GFI), and Flesch-Kincaid grade level (FKGL) as content-based analysis; and human readability evaluation with two experts. The experiment result shows that DeepSPM yields better than DBN, with the F-measure value of ROUGE-1 enhanced to 0.462, ROUGE-2 is 0.37, and ROUGE-L is 0.41. The significance of ROUGE results also be tested using T-Test. The content-based analysis and human readability evaluation findings are conformable with the findings of co-selection-based analysis that generated summaries are only partially readable or have a medium level of readability aspect.
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Jia, Meng, and Zhiqiang Zhao. "Change Detection in Synthetic Aperture Radar Images Based on a Generalized Gamma Deep Belief Networks." Sensors 21, no. 24 (December 11, 2021): 8290. http://dx.doi.org/10.3390/s21248290.
Повний текст джерелаАнотація:
Change detection from synthetic aperture radar (SAR) images is of great significance for natural environmental protection and human societal activity, which can be regarded as the process of assigning a class label (changed or unchanged) to each of the image pixels. This paper presents a novel classification technique to address the SAR change-detection task that employs a generalized Gamma deep belief network (gΓ-DBN) to learn features from difference images. We aim to develop a robust change detection method that can adapt to different types of scenarios for bitemporal co-registered Yellow River SAR image data set. This data set characterized by different looks, which means that the two images are affected by different levels of speckle. Widely used probability distributions offer limited accuracy for describing the opposite class pixels of difference images, making change detection entail greater difficulties. To address the issue, first, a gΓ-DBN can be constructed to extract the hierarchical features from raw data and fit the distribution of the difference images by means of a generalized Gamma distribution. Next, we propose learning the stacked spatial and temporal information extracted from various difference images by the gΓ-DBN. Consequently, a joint high-level representation can be effectively learned for the final change map. The visual and quantitative analysis results obtained on the Yellow River SAR image data set demonstrate the effectiveness and robustness of the proposed method.
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Whitney, Ralph A., Anca Penciu, J. Scott Parent, Rui Resendes, and William Hopkins. "Cross-Linking of Brominated Poly(isobutylene-co-isoprene) by N-Alkylation of the Amidine Bases DBU and DBN." Macromolecules 38, no. 11 (May 2005): 4625–29. http://dx.doi.org/10.1021/ma047850+.
Повний текст джерела
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David, Leo Gertrude, Raj Kumar Patra, Przemysław Falkowski-Gilski, Parameshachari Bidare Divakarachari, and Lourdusamy Jegan Antony Marcilin. "Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network." Applied Sciences 12, no. 16 (August 14, 2022): 8130. http://dx.doi.org/10.3390/app12168130.
Повний текст джерелаАнотація:
In recent decades, tool wear monitoring has played a crucial role in the improvement of industrial production quality and efficiency. In the machining process, it is important to predict both tool cost and life, and to reduce the equipment downtime. The conventional methods need enormous quantities of human resources and expert skills to achieve precise tool wear information. To automatically identify the tool wear types, deep learning models are extensively used in the existing studies. In this manuscript, a new model is proposed for the effective classification of both serviceable and worn cutting edges. Initially, a dataset is chosen for experimental analysis that includes 254 images of edge profile cutting heads; then, circular Hough transform, canny edge detector, and standard Hough transform are used to segment 577 cutting edge images, where 276 images are disposable and 301 are functional. Furthermore, feature extraction is carried out on the segmented images utilizing Local Binary Pattern (LBPs) and Speeded up Robust Features (SURF), Harris Corner Detection (HCD), Histogram of Oriented Gradients (HOG), and Grey-Level Co-occurrence Matrix (GLCM) feature descriptors for extracting the texture feature vectors. Next, the dimension of the extracted features is reduced by an Improved Dragonfly Optimization Algorithm (IDOA) that lowers the computational complexity and running time of the Deep Belief Network (DBN), while classifying the serviceable and worn cutting edges. The experimental evaluations showed that the IDOA-DBN model attained 98.83% accuracy on the patch configuration of full edge division, which is superior to the existing deep learning models.
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Bhardwaj, Shikha, Gitanjali Pandove, and Pawan Kumar Dahiya. "A Web Application-Based Secured Image Retrieval System With an IoT-Cloud Network." International Journal of Web Services Research 18, no. 1 (January 2021): 1–20. http://dx.doi.org/10.4018/ijwsr.2021010101.
Повний текст джерелаАнотація:
Many encryption and searching techniques have been used, but they did not prove effective to support smart devices in order to provide input image. Therefore, based on these facts, an effective and novel system has been developed in this paper which is based on content-based search concentrated on encrypted images. Four type of features, namely color moment (CM), Gray level co-occurrence matrix (GLCM), hybrid of CM and GLCM, and lastly, a deep belief network (DBN) has been used here. This deep neural network is based on clustering in combination with indexing and the developed model is called as cluster-based deep belief network (CBDBN) in the present work. A web based application has also been developed using Apache Tomcat server and MATLAB engine. Analysis of many parameters like precision, recall, entropy, correlation coefficient, and time has been done here on benchmark datasets, namely WANG and COIL.
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Yao, Yunhao, Xiaoxing Zhou, and Merle Parmak. "Risk assessment for yachting tourism in China using dynamic Bayesian networks." PLOS ONE 18, no. 8 (August 23, 2023): e0289607. http://dx.doi.org/10.1371/journal.pone.0289607.
Повний текст джерелаАнотація:
Scientific evaluation of yachting tourism safety risks (YTSRs) is crucial to reducing accidents in this sector. This paper is based on the data of 115 yachting tourism accidents in China’s coastal areas from 2008 to 2021. A fishbone diagram and the analytic hierarchy process (AHP) were used to identify the risk factors of yachting tourism from four aspects human, yachting, environmental, and management risk and to construct an evaluation index system. To perform dynamic evaluation, a dynamic evaluation model of YTSRs was built using dynamic Bayesian networks (DBN). The results indicate that human factors, such as the unsafe behavior of yachtsmen and tourists, are the primary risk factors; the risk is higher in summer than in winter, and the Pearl River Delta region has a greater risk of yachting tourism. It is suggested to improve the normal safety risk prevention and control system of yachting tourism, to advocate for multi-subject coordination and co-governance, and to improve the insurance service system so as to provide a guarantee for the safe and healthy development of yachting tourism in China. The findings provide theoretical and practical guidance for marine and coastal tourism safety management, as well as the prevention and control of YTSRs.
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Sainudeen, Jinu P., Ceronmani Sharmila V, and Parvathi R. "Skin cancer detection: Improved deep belief network with optimal feature selection." Multiagent and Grid Systems 19, no. 2 (October 6, 2023): 187–210. http://dx.doi.org/10.3233/mgs-230040.
Повний текст джерелаАнотація:
During the past few decades, melanoma has grown increasingly prevalent, and timely identification is crucial for lowering the mortality rates linked to this kind of skin cancer. Because of this, having access to an automated, trustworthy system that can identify the existence of melanoma may be very helpful in the field of medical diagnostics. Because of this, we have introduced a revolutionary, five-stage method for detecting skin cancer. The input images are processed utilizing histogram equalization as well as Gaussian filtering techniques during the initial pre-processing stage. An Improved Balanced Iterative Reducing as well as Clustering utilizing Hierarchies (I-BIRCH) is proposed to provide better image segmentation by efficiently allotting the labels to the pixels. From those segmented images, features such as Improved Local Vector Pattern, local ternary pattern, and Grey level co-occurrence matrix as well as the local gradient patterns will be retrieved in the third stage. We proposed an Arithmetic Operated Honey Badger Algorithm (AOHBA) to choose the best features from the retrieved characteristics, which lowered the computational expense as well as training time. In order to demonstrate the effectiveness of our proposed skin cancer detection strategy, the categorization is done using an improved Deep Belief Network (DBN) with respect to those chosen features. The performance assessment findings are then matched with existing methodologies.
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SHANAHAN, TIMOTHY. "DESIGN BY NATURE." BioScience 54, no. 11 (2004): 1044. http://dx.doi.org/10.1641/0006-3568(2004)054[1044:dbn]2.0.co;2.
Повний текст джерелаДисертації з теми "DBN-CO"
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GOYAL, ARCHITA. "ANALYSIS OF EXCAVATION SUPPORT SYSTEM WITH SOIL NAIL OF DIFFERENT PROFILES." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20433.
Повний текст джерелаАнотація:
Stabilizing excavations in various soil types involved the implementation of soil nailing, which
entailed inserting reinforcement elements, such as nails, into the soil. Conventional soil nailing
systems used straight nails, but newer systems utilized helical nails, featuring a twisted shape that
offered enhanced stability and load-bearing capacity. Understanding the effectiveness of soil
nailing systems required a thorough analysis of their behavior under different conditions.
This study's primary aim was to compare and analyze conventional soil nail (CN) and helical soil
nailing (HN) systems. Both finite element analysis (FEA) and limit equilibrium methods (LEM)
were employed to study the behavior of these systems. The goal was to optimize the performance
of helical soil nailing using Response Surface Methodology (RSM) and a Hybrid Deep Belief
Network (DBN)-Coot optimization algorithm. The study included conducting pullout tests and
analytical methods to compare the pullout behavior of CN and HN in cohesive soil. Initially,
stability comparison was achieved by FEA with PLAXIS-2D and theoretical calculations. CN and
HN were assessed for their factor of safety using both FEA and LEM methods. Under comparable
soil and loading conditions, the findings demonstrated that HN exhibited reduced deformation and
a higher safety factor compared to CN. The study was then extended to optimize soil nailing
parameters like inclination angle, surcharge pressure, helical pitch, and shaft diameters for HN.
The optimization study used an RSM-based box behnken design (BBD) with 40 experimental runs
obtained from RSM-BBD. Additionally, a hybrid DBN-COOT machine learning model was
developed and trained to predict the pullout characteristics of HN used in this study. The RSM BBD was performed using Design Expert software, whereas DBN-CO was developed using
MATLAB. While validating both RSM-BBD and DBN-CO models, 3% more optimization
accuracy was achieved from DBN-CO than RSM-BBD due to the use of coot optimization in
DBN's weight optimization process. Overall, the study offered significant insights into the
behavior of soil nailing systems and underscored the potential of utilizing advanced modeling and
optimization techniques to enhance their performance.