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A k-means algorithm is a method for clustering that has already gained a wide range of acceptability. However, its performance extremely depends on the opening cluster centers. Besides, due to weak exploration capability, it is easily stuck at local optima. Recently, a new metaheuristic called Moth Flame Optimizer (MFO) is proposed to handle complex problems. MFO simulates the moths intelligence, known as transverse orientation, used to navigate in nature. In various research work, the performance of MFO is found quite satisfactory. This paper suggests a novel heuristic approach based on the MFO to solve data clustering problems. To validate the competitiveness of the proposed approach, various experiments have been conducted using Shape and UCI benchmark datasets. The proposed approach is compared with five state-of-art algorithms over twelve datasets. The mean performance of the proposed algorithm is superior on 10 datasets and comparable in remaining two datasets. The analysis of experimental results confirms the efficacy of the suggested approach.
Tribhuvan Singh; Nitin Saxena; Manju Khurana; Dilbag Singh; Mohamed Abdalla; Hammam Alshazly. Data Clustering Using Moth-Flame Optimization Algorithm. Sensors 2021, 21, 4086 .
AMA StyleTribhuvan Singh, Nitin Saxena, Manju Khurana, Dilbag Singh, Mohamed Abdalla, Hammam Alshazly. Data Clustering Using Moth-Flame Optimization Algorithm. Sensors. 2021; 21 (12):4086.
Chicago/Turabian StyleTribhuvan Singh; Nitin Saxena; Manju Khurana; Dilbag Singh; Mohamed Abdalla; Hammam Alshazly. 2021. "Data Clustering Using Moth-Flame Optimization Algorithm." Sensors 21, no. 12: 4086.
Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system is required for the cloud environment, and it should gather information about all phases of task processing and ensuring fair resource provisioning through the levels of Quality of Service (QoS). Virtual machine allocation is a major issue in the cloud environment that contributes to energy consumption and asset utilization in distributed cloud computing. Subsequently, in this paper, a multiobjective Emperor Penguin Optimization (EPO) algorithm is proposed to allocate the virtual machines with power utilization in a heterogeneous cloud environment. The proposed method is analyzed to make it suitable for virtual machines in the data center through Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To compare with other strategies, EPO is energy-efficient and there are significant differences. The results of the proposed system have been evaluated through the JAVA simulation platform. The exploratory outcome presents that the proposed EPO-based system is very effective in limiting energy consumption, SLA violation (SLAV), and enlarging QoS requirements for giving capable cloud service.
Jitendra Kumar Samriya; Subhash Chandra Patel; Manju Khurana; Pradeep Kumar Tiwari; Omar Cheikhrouhou. Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment. Mathematical Problems in Engineering 2021, 2021, 1 -13.
AMA StyleJitendra Kumar Samriya, Subhash Chandra Patel, Manju Khurana, Pradeep Kumar Tiwari, Omar Cheikhrouhou. Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment. Mathematical Problems in Engineering. 2021; 2021 ():1-13.
Chicago/Turabian StyleJitendra Kumar Samriya; Subhash Chandra Patel; Manju Khurana; Pradeep Kumar Tiwari; Omar Cheikhrouhou. 2021. "Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment." Mathematical Problems in Engineering 2021, no. : 1-13.
Artificial intelligence (AI) has made various developments in the image segmentation techniques in the field of medical imaging. This article presents a liver tumor CT image segmentation method based on AI medical imaging-based technology. This study proposed an artificial intelligence-based K-means clustering (KMC) algorithm which is further compared with the region growing (RG) method. In this study, 120 patients with liver tumors in the Post Graduate Institute of Medical Education & Research Hospital, Chandigarh, India, were selected as the research objects, and they were classified according to liver function (Child–Pugh), with 58 cases in grade A and 62 cases in grade B. The experimentation indicates that liver tumor showed low density on plain CT scan, moderate enhancement in the arterial phase of the enhanced scan, and low-density filling defect in the involved blood vessel in the portal venous phase (PVP). It was observed that the CT examination is more sensitive to liver metastasis than hepatocellular carcinoma ( P < 0.05 ). The outcomes obtained depict the good deposition effect of lipiodol chemotherapy emulsion (LCTE) in the contrast group with rich blood type accounted for 53.14% and the patients with the poor blood type accounted for 25.73% showed poor deposition effect. The comparison with the state-of-the-art method reveals that the segmentation effect of the KMC algorithm is better than that of the conventional RG method.
Liping Liu; Lin Wang; Dan Xu; Hongjie Zhang; Ashutosh Sharma; Shailendra Tiwari; Manjit Kaur; Manju Khurana; Mohd Asif Shah. CT Image Segmentation Method of Liver Tumor Based on Artificial Intelligence Enabled Medical Imaging. Mathematical Problems in Engineering 2021, 2021, 1 -8.
AMA StyleLiping Liu, Lin Wang, Dan Xu, Hongjie Zhang, Ashutosh Sharma, Shailendra Tiwari, Manjit Kaur, Manju Khurana, Mohd Asif Shah. CT Image Segmentation Method of Liver Tumor Based on Artificial Intelligence Enabled Medical Imaging. Mathematical Problems in Engineering. 2021; 2021 ():1-8.
Chicago/Turabian StyleLiping Liu; Lin Wang; Dan Xu; Hongjie Zhang; Ashutosh Sharma; Shailendra Tiwari; Manjit Kaur; Manju Khurana; Mohd Asif Shah. 2021. "CT Image Segmentation Method of Liver Tumor Based on Artificial Intelligence Enabled Medical Imaging." Mathematical Problems in Engineering 2021, no. : 1-8.