A Computationally Efficient Skull Scraping Approach for Brain MR Image

Srinivasu, Parvathaneni Naga; T., Srinivasa Rao; Srinivas, G.; P.V.G.D., Prasad Reddy; Kumari, Anshu; Gaur, Loveleen; Arun, Solanki; Chengshi, Ding; Haidan, Bi; Deya, Wang; Mei-ling, Kang; Zhongjing, Tian; Zhang, Yingxia; Wang, Hongkai; Tianshun, Zhu; Ma, Ji; Saluja, Shivani; Usha, B A; Rajeshwari, M.; Kavitha, K. S.; Atheeq, C.; M., Munir Ahamed Rabbani; Inayat, B. Pathan; Mahesh, Sakhare; Wahid, Ambekar; Chitral, M. Setty; Enas, M. Hadi; Safa, L. Jasim; T.P., Dash; S., Dey; Das, Sudipta; Jena, J.; E., Mahapatra; C.K., Maiti

doi:10.2174/2213275912666190809111928

Cited by 13 publications

(5 citation statements)

References 22 publications

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“…Averaging of the absolute differences among observed and projected values across the dataset is determined by mean absolute error. The rooted value of the mean absolute error is the RMSE [53], which is used in assessing the standard deviation of the residuals and R-Squared, whose value is always less than one. This shows how much the model explains the dependent variable's variation.…”

Section: Resultsmentioning

confidence: 99%

A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron

Ahmed

2023

Sustainability

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Predicting crop yields is one of agriculture’s most challenging issues. It is crucial in making national, provincial, and regional choices and estimates the government to meet the food demands of its citizens. Crop production is anticipated based on various factors such as soil conditions and meteorological, environmental, and crop variables. This study intends to develop an effective model that can accurately anticipate agricultural production in advance, assisting farmers in better planning. In the current study, the Crop Yield Prediction Dataset is normalized initially, and then feature engineering is performed to determine the significance of the feature in assessing the crop yield. Crop yield forecasting is performed using the Multi-Layer Perceptron model and the Spider Monkey Optimization method. The Multi-Layer Perceptron technique is efficient in dealing with the non-linear relations among the features in the data, and the Spider Monkey Optimization technique would assist in optimizing the corresponding feature weights. The current study uses data from the Food and Agriculture Organization and the World Data Bank to forecast maize yield in the Saudi Arabia region based on factors such as average temperature, average rainfall, and Hg/Ha production in past years. The suggested MLP-SMO model’s prediction effectiveness is being evaluated using several evaluation metrics such as Root-Mean-Square Error, R-Squared, Mean Absolute Error, and Mean Bias Error, where the model has outperformed in the prediction process with a Root-Mean-Square Error value of 0.11, which is lowest among all the techniques that are considered in the statical analysis in the current study.

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Section: Resultsmentioning

confidence: 99%

A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron

Ahmed

2023

Sustainability

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“…The open operation is based on the morphological operations of expansion and corrosion. 27 It is a filter that can remove the isolated dots, burrs and break narrow connections by the geometrical calculation, but do not destroy the integrity of the pattern. The open operation is used mainly to eliminate the noise of the bright color in the image, but the segmented color after binarization is dark, as shown in Figure 3(d).…”

Section: Methodsmentioning

confidence: 99%

Color segmentation of multicolor porous printed fabrics by conjugating SOM and EDSC clustering algorithms

Qian

Wang

Huang

et al. 2022

Textile Research Journal

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In order to achieve accurate color segmentation of printed fabrics, the color segmentation algorithm combining the self-organizing maps neural network and the efficient dense subspace clustering was proposed in this paper. After pre-processing of the fabric image, the primary clustering was implemented by the self-organizing maps algorithm, then the secondary clustering was done by the efficient dense subspace clustering algorithm. The optimal silhouette coefficient is introduced into the clustering process of the efficient dense subspace clustering algorithm to determine the number of clustering centers automatically. Finally, by the post-processing including gray-scale transformation, binarization and open operation, the mis-segmentation of edge color was eliminated, making the algorithm more suitable for industrial application. Experiments were carried out and results show that the algorithm proposed in this paper can recognize the color of small areas accurately and segment the color of complex printed fabric images. The color segmented results of 20 printed fabrics show that the accuracy of the algorithm proposed in this paper reaches 88.3%.

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“…The distance measure is assessed between the CH and the BS using the Euclidean distance measure. The Euclidean distance measure is generally used in spatial image processing techniques [45]. The same distance measure is used in measuring the distance among the coordinates in terms of measuring metrics, i.e., meters [46].…”

Section: E Tdmentioning

confidence: 99%

Energy Aware Software Defined Network Model for Communication of Sensors Deployed in Precision Agriculture

Ahmed

2023

Sensors

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A significant technological transformation has recently occurred in the agriculture sector. Precision agriculture is one among those transformations that largely focus on the acquisition of the sensor data, identifying the insights, and summarizing the information for better decision-making that would enhance the resource usage efficiency, crop yield, and substantial quality of the yield resulting in better profitability, and sustainability of agricultural output. For continuous crop monitoring, the farmlands are connected with various sensors that must be robust in data acquisition and processing. The legibility of such sensors is an exceptionally challenging task, which needs energy-efficient models for handling the lifetime of the sensors. In the current study, the energy-aware software-defined network for precisely selecting the cluster head for communication with the base station and the neighboring low-energy sensors. The cluster head is initially chosen according to energy consumption, data transmission consumption, proximity measures, and latency measures. In the subsequent rounds, the node indexes are updated to select the optimal cluster head. The cluster fitness is assessed in each round to retain the cluster in the subsequent rounds. The network model’s performance is assessed against network lifetime, throughput, and network processing latency. The experimental findings presented here show that the model outperforms the alternatives presented in this study.

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A Computationally Efficient Skull Scraping Approach for Brain MR Image

Cited by 13 publications

References 22 publications

A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron

A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron

Color segmentation of multicolor porous printed fabrics by conjugating SOM and EDSC clustering algorithms

Energy Aware Software Defined Network Model for Communication of Sensors Deployed in Precision Agriculture

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