Prediction of groundwater table for Chennai Region using soft computing techniques

Ramasamy, Viswanathan; Alotaibi, Youseef; Khalaf, Osamah Ibrahim; Samui, Pijush; Jayabalan, Jagan

doi:10.1007/s12517-022-09851-4

Cited by 10 publications

(10 citation statements)

References 78 publications

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“…One of the most common methods for obtaining texture information is the co-occurrence of Grey Levels Matrices [39][40][41][42]. An image is represented by a GLCM matrix, which contains the frequency value of the brightness difference between one pixel and its surroundings that occurs in the image.…”

Section: Related Workmentioning

confidence: 99%

Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Rajagopal¹,

Thanarajan²,

Alotaibi³

et al. 2023

Computer Systems Science and Engineering

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Automated segmentation of brain tumors using Magnetic Resonance Imaging (MRI) data is critical in the analysis and monitoring of disease development. As a result, gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods. It is intended to extract characteristics from an image using the Gray Level Co-occurrence (GLC) matrix feature extraction method described in the proposed work. Using Convolutional Neural Networks (CNNs), which are commonly used in biomedical image segmentation, CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain tumor. Using two segmentation networks, a U-Net and a 3D CNN, we present a major yet easy combinative technique that results in improved and more precise estimates. The U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going on. Using the dataset, two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour sub-region. Then, the estimates was made by two separate models that were put together to produce the final prediction. In comparison to current state-of-the-art designs, the precision (percentage) was 98.35, 98.5, and 99.4 on the validation set for tumor core, enhanced tumor, and whole tumor, respectively.

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Section: Related Workmentioning

confidence: 99%

Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Rajagopal¹,

Thanarajan²,

Alotaibi³

et al. 2023

Computer Systems Science and Engineering

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“…Every buffalo forms a path by finding different test cases to reveal faults until each of the faults is uncovered or the termination criteria are reached [77][78][79][80][81]. The buffalo leading to a path having minimum execution time eventually leads to the global best path, if it is found better [82][83][84][85][86][87][88][89][90].…”

Section: Explanation With Examplementioning

confidence: 99%

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

Singhal¹,

Jatana²,

Subahi³

et al. 2023

Computers, Materials &Amp; Continua

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Software needs modifications and requires revisions regularly. Owing to these revisions, retesting software becomes essential to ensure that the enhancements made, have not affected its bug-free functioning. The time and cost incurred in this process, need to be reduced by the method of test case selection and prioritization. It is observed that many nature-inspired techniques are applied in this area. African Buffalo Optimization is one such approach, applied to regression test selection and prioritization. In this paper, the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization. The proposed algorithm converges in polynomial time (O(n 2 )). In this paper, the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations. An astounding 62.5% drop in size and a 48.57% drop in the runtime of the original test suite were recorded. The obtained results are compared with Ant Colony Optimization. The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities (80%), and a reduction in the overall execution time and size of the resultant test suite. The results and analysis, hence, advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.

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“…All strategies used to lower energy-specific hardware components/levels are covered in extreme detail. There is much emphasis on techniques deployed at the hardware-level (network-or server-level) that can lead to energy-efficient or ecologically friendly data centers [122][123][124][125][126][127][128][129][130][131][132].…”

Section: Related Surveysmentioning

confidence: 99%

“…A hypervisor is the system software that works as an operating system (abstraction layer) for virtual machines and coordinates with the underlying hardware components according to the virtual machine's predefined instructions [124][125][126][127]. Virtualization is not a new concept in the IT sector as it has already been implemented with our grand old Main Frames, which belong to second-generation computing devices.…”

Section: Rq5: Describe Various Energy Efficiency Techniques Employed ...mentioning

confidence: 99%

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

et al. 2022

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Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

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Prediction of groundwater table for Chennai Region using soft computing techniques

Cited by 10 publications

References 78 publications

Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

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