Fingerprint recognition based on shark smell optimization and genetic algorithm

Fingerprint recognition is a dominant form of biometric due to its distinctiveness. The study aims to extract and select the best features of fingerprint images, and evaluate the strength of the Shark Smell Optimization (SSO) and Genetic Algorithm (GA) in the search space with a chosen set of metrics. The proposed model consists of seven phases namely, enrollment, image preprocessing by using weighted median filter, feature extraction by using SSO, weight generation by using Chebyshev polynomial first kind (CPFK), feature selection by using GA, creation of a user’s database, and matching features by using Euclidean distance (ED). The effectiveness of the proposed model’s algorithms and performance is evaluated on 150 real fingerprint images that were collected from university students by the ZKTeco scanner at Sulaimani city, Iraq. The system’s performance was measured by three renowned error rate metrics, namely, False Acceptance Rate (FAR), False Rejection Rate (FRR), and Correct Verification Rate (CVR). The experimental outcome showed that the proposed fingerprint recognition model was exceedingly accurate recognition because of a low rate of both FAR and FRR, with a high CVR percentage gained which was 0.00, 0.00666, and 99.334%, respectively. This finding would be useful for improving biometric secure authentication based fingerprint. It is also possibly applied to other research topics such as fraud detection, e-payment, and other real-life applications authentication

Efficiency of coupled invasive weed optimization-adaptive neuro fuzzy inference system method to assess physical habitats in streams

This study presents a coupled invasive weed optimization-adaptive neuro fuzzy inference system method to simulate physical habitat in streams. We implement proposed method in Lar national park in Iran as one of the habitats of Brown trout in southern Caspian Sea basin. Five indices consisting of root mean square error (RMSE), mean absolute error (MAE), reliability index, vulnerability index and Nash–Sutcliffe model efficiency coefficient (NSE) are utilized to compare observed fish habitats and simulated fish habitats. Based on results, measurement indices demonstrate model is robust to assess physical habitats in rivers. RMSE and MAE are 0.09 and 0.08 respectively. Besides, NSE is 0.78 that indicates robustness of model. Moreover, it is necessary to apply developed habitat model in a practical habitat simulation. We utilize two-dimensional hydraulic model in steady state to simulate depth and velocity distribution. Based on qualitative comparison between results of model and observation, coupled invasive weed optimization-adaptive neuro fuzzy inference system method is robust and reliable to simulate physical habitats. We recommend utilizing proposed model for physical habitat simulation in streams for future studies

Metaheuristic nature-inspired algorithms for reservoir optimization operation: A systematic literature review

The purpose of this systematic literature review (SLR) article is to discuss the findings of the state-of-art metaheuristic nature-inspired algorithm (MHNIA) in reservoir optimization operation. The rationale of this approach is to elucidate the optimal way as decision making that implemented MHNIA for several complex problems in reservoir optimization operation. Commonly, the metaheuristic optimization algorithm has always been used in hydrology field, especially in reservoir optimization. Hence, this presented study reviewed a considerable amount from the previous studies of commonly nature-based optimization algorithms applied in reservoir operations. Hence, preferred reporting items for systematic review and meta-analyses (PRISMA) has been used as guidance. The source was utilized from two primary journal databases: Scopus and web of science. According to the proposed search string, the findings managed to express into nine main themes which are optimize in water release, optimize reservoir operation problems, optimize hydropower operation, optimize condensate fluids in reservoir storage, optimize water pumped storage, optimize water quality control, optimize system performance operation, optimize water demand and optimize reservoir control as flood preventing. Overall, 24 articles that passed the minimum quality were retrieved using systematic searching strategies

METHODOLOGIES FOR RESERVOIR SYSTEMS ANALYSIS: APPLICATION OF OPTIMIZATION AND DEEP LEARNING

Reservoir systems operations are challenging given that they must function to meet conflicting goals. Using mathematical programming and deep learning techniques, this dissertation presents innovative methodologies to address some of the challenges. The first chapter focuses on development of a mathematical programming framework for assessing sub-daily hydropower hydropeaking operation and flow regime outcomes of a system of five large sequential hydropower facilities on the mainstem Connecticut River under various operation scenarios. A formulation for the pumped-storage Northfield reservoir is presented that uses binary decision variables to properly model the reservoir operations. The results closely match annual historical power values that indicates the model can replicate the operations. The second chapter presents a novel multiple objective optimization methodology for trade-off analysis of river basins. The novelties include a weighting scheme that normalize different objectives having different range of variabilities and formulations for quantification of ecological and flood control objectives as frequencies of meeting desirable conditions. The methodology is applied to the Connecticut River basin. In this chapter, formulations are developed that use binary decision variables to quantify ecological and flood control objectives along with other operational goals. The key trade-offs of the system objectives are identified. The results indicate hydropower revenue objective highly conflict with any other objective than flood control. Moreover, it is concluded that a balanced operation that equally weight different objectives has the potential to improve all the objectives. The third chapter presents a methodology for designing reservoir operation policy using optimization and deep learning. This chapter addresses the challenge of designing of an operation policy for a reservoir with conflicting objectives under uncertainty of hydrological and energy prices data. A deep neural network is developed to infer near-optimal operation policies under different foresight scenarios using the optimization modeling results. The methodology is applied to the Wilder reservoir on the mainstem Connecticut River. A base method is also developed that uses linear regression and is applied to the problem and the associated results are used as a comparison basis. Results indicate that the designed policies using neural networks perform better than the base method used while having foresight for a longer time improves the performance

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources