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An Optimal Registration on Shearlet domain with Novel Weighted Energy fusion for Multi-Modal Medical Images

OPTIK 2021年 225卷 165742-165742页

作者： Nair, Rekha R. Singh, Tripty Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Bengaluru India

Medical images usually display various attributes of data on human viscera and abnormal tissue in different modalities. The fusion of images, ensures effective deployment of all relevant information from several modalities into a single image. Finally, the paper leads to a novel multi-modal medical image fusion method based on Non-Subsampled Shearlet Transform (NSST) called Denoised Optimum B-Spline Shearlet Image Fusion(DOBSIF) that is based on real-time and standard radiological datasets. The proposed, novel registration based fusion technique DOBSIF, is compared with existing state-of-art techniques like PCA, DWT, SWT etc., and in comparison the proposed DOBSIF works effectively for both the color and grayscale images. To improve the fusion function, a novel pre-fusion method is carried out with the help of the whale optimization algorithm(WOA), using the ideal B-spline-based registration method and in addition a novel Weighted Energy fusion rule is applied to derive relevant information from the source images. The proposed work initially follows normal pre-processing measures such as Gaussian filtering, edge sharpening, and resizing followed by optimal registration, fusion with novel fusion rule and segmentation of tumor part from the fused image. The visual quality of the fused image was evaluated by expert radiologists and the fused resultant image is robust, subject to the modified Active Contour(ACM) model for lesion identification.

关键词： Multi-modal medical image fusion whale optimization algorithm Optimal B-spline registration Weighted Energy fusion rule Active Contour Model

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Efficient Ranking-Based whale Optimizer for Parameter Extraction of Three-Diode Photovoltaic Model: Analysis and Validations

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ENERGIES 2021年第13期14卷 3729-3729页

作者： Abdel-Basset, Mohamed Mohamed, Reda El-Fergany, Attia Askar, Sameh S. Abouhawwash, Mohamed Zagazig Univ Fac Comp & Informat Dept Comp Sci Zagazig 44519 Egypt Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig 44519 Egypt King Saud Univ Dept Stat & Operat Res Coll Sci Riyadh 11451 Saudi Arabia Mansoura Univ Dept Math Fac Sci Mansoura 35516 Egypt Michigan State Univ Coll Engn Dept Computat Math Sci & Engn CMSE E Lansing MI 48824 USA

Efficient and accurate estimations of unidentified parameters of photovoltaic (PV) models are essential to their simulation. This study suggests two new variants of the whale optimization algorithm (WOA) for identifying the nine parameters of the three-diode PV model. The first variant abbreviated as RWOA is based on integrating the WOA with ranking methods under a novel updating scheme to utilize each whale within the population as much as possible during the optimization process. The second variant, namely HWOA, has been based on employing a novel cyclic exploration-exploitation operator with the RWOA to promote its local and global search for averting stagnation into local minima and accelerating the convergence speed in the right direction of the near-optimal solution. Experimentally, RWOA and HWOA are validated on a solar cell (RTC France) and two PV modules (Photowatt-PWP201 and Kyocera KC200GT). Further, these proposed variants are compared with five well-known parameter extraction models in order to demonstrate their notable advantages over the other existing competing algorithms for minimizing the root mean squared error (RMSE) between experimentally measured data and estimated one. The experimental findings show that RWOA is superior in some observed cases and superior in the other cases in terms of final accuracy and convergence speed;yet, HWOA is superior in all cases.

关键词： photovoltaic units three-diode model parameter estimation optimization methods whale optimization algorithm ranking method cyclic exploration-exploitation strategy

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Boosted hunting-based fruit fly optimization and advances in real-world problems

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 159卷 113502-113502页

作者： Fan, Yi Wang, Pengjun Heidari, Ali Asghar Wang, Mingjing Zhao, Xuehua Chen, Huiling Li, Chengye Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 117417 Singapore Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Wenzhou Med Univ Dept Pulm & Crit Care Med Affiliated Hosp 1 Wenzhou 325000 Peoples R China

Fruit fly optimization algorithm (FOA) is a well-established meta-heuristic method with a clear core concept, a straightforward computation, and a code framework that is easy to build. In the case of large scale and multifaceted practical problems, the optimization effect of FOA may be unsatisfactory, and it is prone to stagnation. In this paper, to enrich the exploration and exploitation capability of the classic FOA, an effective whale-inspired hunting strategy is introduced to replace the random search plan of the original FOA, which we named it as WFOA. The proposed WFOA is compared with 9 state-of-the-art FOA's variants on a comprehensive set of 23 benchmark set and 30 IEEE CEC 2014 functions and advanced algorithms on a set of 21 test functions to validate its effectiveness. In addition, the effectiveness of WFOA is also verified on 20 IEEE CEC 2011 benchmark problems for tackling real-world problems. The statistical data shows that developed components effectively expand the exploration and exploitation capacity of the original FOA. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Fruit fly optimization algorithm whale optimization algorithm Swarm intelligence Global optimization

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Closed loop fractional order drug delivery control scheme for chemotherapy

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RESULTS IN CONTROL AND optimization 2022年 6卷

作者： Pachauri, Nikhil Yadav, Drishti Sharma, Tarun Kumar Verma, Om Prakash Ahn, Chang Wook Gwangju Inst Sci & Technol AI Grad Sch 123 Cheomdangwagi Ro Gwangju 61005 South Korea Tech Univ Wien Fac Informat Inst Comp Engn A-1040 Vienna Austria Shobhit Univ Gangoh Dept Comp Sci & Engn Saharanpur 247341 India Dr BR Ambedkar Natl Inst Technol Dept Instrumentat & Control Engn Jalandhar 144011 India

Chemotherapy is one of the most extensively utilized cancer treatment strategies worldwide. It is intended to eliminate fast -developing cancer cells in a patient's body. The amount of chemotherapeutic drug that must be administered precisely into a patient's body determines the efficacy of the treatment and governs the patient survival during chemotherapy. Therefore, controlling the chemotherapeutic drug dose delivered to the patient is essential. This research aims to propose a two -degree -of -freedom fractional order proportional-integral-derivative (2FOPID) controller with a set point filter for implementing an automatic drug delivery control scheme during chemotherapy. The whale optimization algorithm (WOA) is used to tune the parameters of the 2FOPID controller, resulting in a WOA-tuned 2FOPID controller (W2FOPID). The performance of the proposed W2FOPID is compared with the Integral-Proportional-Derivative (IPD), Internal Model Control (IMC), and Fractional Order IMC (FOIMC) schemes. The experimental results demonstrate that the proposed W2FOPID controller is effective, accurate, and robust for drug concentration control during chemotherapy. W2FOPID outperforms IPD, IMC, and FOIMC schemes in terms of Integral Absolute Error by 79.9%, 25.3%, and 23.36%, respectively. In addition, W2FOPID exhibits excellent set -point tracking, noise suppression and uncertainty handling capabilities.

关键词： FOPID Chemotherapy Drug concentration FOIMC IPD whale optimization algorithm

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LFC Scheme for A Deregulated Multi-Area Power System using Cascaded Fractional Order Controller 1

LFC Scheme for A Deregulated Multi-Area Power System using C...

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1st International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET)

作者： Murali, Sariki Shankar, Ravi Shrivastav, Shivam Dhawal, Utkarsh Kannauijia, Vishal Natl Inst Technol Patna Dept Elect Engn Patna 800005 Bihar India

ISBN: (纸本)9781728175492

Active power imbalance between electrical power generation and demand causes deviation in frequency and tieline power of an interconnected power system. Load Frequency Control (LFC) is an epoch-making method to maintain nominal frequency and tie-line power of a power system. Also, continuous research is going on to improve the effectiveness of the LFC mechanism. In this scenario, this paper contributes to design an effective controller configuration for LFC mechanism of a nonlinear multi area interconnected system. A cascaded combination of Proportional-Integral controller with Fractional Order Proportional-Derivative controller (PI-FOPDAN) has been proposed in this work. One of the popular whale optimization algorithm (WOA) has been deployed to optimize the proposed controller parameters. The proposed scheme has been compared with several conventional controllers and its robustness has been checked by subjecting it to variation in power system model parameters. Deregulation environment of power system is also incorporated for more realistic approach.

关键词： Cascaded PI-(PDN)-N-2 Deregulation LFC Nonlinear whale optimization algorithm

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Factors affecting injury severity in vehicle-pedestrian crashes: A day-of-week analysis using random parameter ordered response models and Artificial Neural Networks

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International Journal of Transportation Science and Technology 2020年第2期9卷 100-115页

作者： Mokhtarimousavi, Seyedmirsajad Anderson, Jason C. Azizinamini, Atorod Hadi, Mohammed Department of Civil and Environmental Engineering Florida International University Miami 33174 FL United States Department of Civil and Environmental Engineering Portland State University Portland 97201 OR United States Accelerate Bridge Construction University Transportation Center Moss School of Construction Infrastructure and Sustainability Florida International University Miami 33174 FL United States Department of Civil and Environmental Engineering Florida International University Miami 33174 FL United States

The high number of vehicle–pedestrian crashes in the United State has gained increased attention among transportation safety analysts in recent years. Being directly exposed to the collision force makes pedestrians more prone to becoming severely injured when in crash than other road users. Considering the fact that pedestrian-involved crashes is a serious public health problem, the current study's aim is to investigate the contributing factors associated with injury severity of pedestrian crashes by time-of-week. Separate injury severity models for weekday and weekend crashes were developed, and the overall stability of the model estimates was examined through likelihood ratio tests. For this purpose, random parameter ordered-response models were employed to specify the ordinal nature of injury severity levels and capture the potential unobserved heterogeneity. In addition, Artificial Neural Network (ANN) was used to explore the nonlinear relationship between explanatory variables and severity outcomes. Comparison of the prediction performance demonstrated that optimized ANN provides superior results compared to conventional statistical approaches. A variable impact analysis was then conducted on the optimized ANN to investigate the effects of the explanatory variables on injury severity. The results revealed the factors that are significantly associated with pedestrian fatalities. These findings further provide insights for a better understanding of pedestrian injury severity in weekday vs. weekend crashes through the impact analysis of various explanatory variables. © 2020 Tongji University and Tongji University Press

关键词： Artificial Neural Networks Crash injury severity Random Parameter Ordered Logit Model Random Parameter Ordered Probit Model whale optimization algorithm

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Classifying Heart Disease in Medical Data Using Deep Learning Methods

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Journal of Computer and Communications 2021年第1期9卷 66-79页

作者： T. Velmurugan U. Latha PG and Research Department of Computer Science D. G. Vaishnav College Chennai India

Recent days, heart ailments assume a fundamental role in the world. The physician gives different name for heart disease, for example, cardiovascular failure, heart failure and so on. Among the automated techniques to discover the coronary illness, this research work uses Named Entity Recognition (NER) algorithm to discover the equivalent words for the coronary illness content to mine the significance in clinical reports and different applications. The Heart sickness text information given by the physician is taken for the preprocessing and changes the text information to the ideal meaning, at that point the resultant text data taken as input for the prediction of heart disease. This experimental work utilizes the NER to discover the equivalent words of the coronary illness text data and currently uses the two strategies namely Optimal Deep Learning and whale optimization which are consolidated and proposed another strategy Optimal Deep Neural Network (ODNN) for predicting the illness. For the prediction, weights and ranges of the patient affected information by means of chosen attributes are picked for the experiment. The outcome is then characterized with the Deep Neural Network and Artificial Neural Network to discover the accuracy of the algorithms. The performance of the ODNN is assessed by means for classification methods, for example, precision, recall and f-measure values.

关键词： Named Entity Recognition algorithm Neural Network Methods whale optimization algorithm F-Measure Recall Precision

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Parking Spaces Planning for Shared Vehicles Based on Visualized Data Analysis

Parking Spaces Planning for Shared Vehicles Based on Visuali...

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Chinese Automation Congress (CAC)

作者： Xing, Jiaming Chu, Liang Guo, Chong Hou, Zhuoran Qiao, Yiran Jiang, Siru Jilin Univ Coll Automot Engn Jilin Jilin Peoples R China Wuhan Univ Sci & Technol Evergrande Sch Management Wuhan Peoples R China

ISBN: (纸本)9781728176871

With the continuous progress of technology and development of sharing economy, sharing vehicles have rapidly gained wide attention due to their huge market potential. The management on the shared vehicles, however, is not ripe enough to support the widely arrangement. Based on big data, this paper proposes a novel method to manage parking space of the shared vehicles based on the open available data Firstly, visualizing the GPS data of the orders and analyzing the data distribution on the road. Secondly, a density-based DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is employed to cluster the GPS data, determining number of parking spaces of the shared vehicles and obtaining the initial location of parking spaces. Finally, whale optimization algorithm is used to optimize the parking location and improve the utilization rate of parking spaces.

关键词： parking spaces management data visualization DBSCAN clustering whale optimization algorithm

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Blind Signal Extraction algorithm Based on WOA and PCA

Blind Signal Extraction Algorithm Based on WOA and PCA

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IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)

作者： Chu Dingli Chen Hong Luo Luwei Natl Univ Def Technol Coll Elect Countermeasures Hefei Peoples R China

ISBN: (数字)9781728165905

ISBN: (纸本)9781728165905

For blind extraction of independent sources in linear aliasing signals, this paper proposes an orderly blind extraction algorithm based on whale optimization and PCA. The algorithm uses the absolute value of the kurtosis as the target function, uses the whale optimization algorithm to calculate the extraction vector, and uses the PCA to reduce the dimension of the mixed signal, so as to remove the components of the extracted signal in the mixed signal, and make the blind extraction according to the order of the kurtosis in the source signal. Simulation results show that the proposed algorithm is effective for blind signal extraction, and the extraction accuracy is high, and has certain practical application value.

关键词： blind signal extraction whale optimization algorithm PCA

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Numerical simulation and optimization study of the complex fracture network in naturally fractured reservoirs

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2020年 195卷 107726-107726页

作者： Zhang, Hao Sheng, James J. Texas Tech Univ Lubbock TX 79409 USA

Multi-fractured horizontal wells are widely used for unconventional reservoir development since the hydraulic fracture (HF) could stimulate natural fractures (NF) to form a complex fracture network (CFN), which will significantly increase the productivity. So accurately characterizing and optimally designing the fracture network configuration are essential to unconventional reservoir development. Facing this urgent request, a numerical study for the complex fracture network is proposed. The displacement discontinuity method (DDM), together with the fluid flow function, is adopted to simulate the HF dynamic propagation which couples the rock and fluid mechanics. A power-law function is adopted to describe the distribution of natural fractures. Then a fracture crossing criterion is used to determine the interaction of HF and NF to simulate the CFN configuration. Based on the CFN numerical model, the effects of different operational and geological parameters, such as the HF spacing and various NF distribution fields, are investigated. Furthermore, a CFN optimization framework is established to achieve the optimal design of CFN under complex geological conditions, in which a clustering-loop method together with a whale optimization algorithm (WOA) is employed to maintain the accuracy while reducing the calculation time. Multiple CFN placement methods are simulated and optimized and the optimal CFN under a complex naturally fractured reservoir is finally obtained. This numerical simulation and optimization approach makes an effort to the study of CFN configuration and unconventional reservoir development.

关键词： Displacement discontinuity method Complex fracture network propagation Reservoir uncertainty whale optimization algorithm Clustering-loop optimization

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