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A combined continuous-time Markov chain and queueing-inventory model for a blood transfusion network considering ABO/Rh substitution priority and unreliable screening laboratory

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Aghsami, Amir Samimi, Yaser Aghaie, Abdollah KN Toosi Univ Technol KNTU Sch Ind Engn Tehran Iran

The uncertain nature of blood supply and demand in a blood supply chain network (BSCN) makes it impossible completely meet the blood demands on time. That's why there is a vital need to develop a comprehensive model for optimally managing blood networks considering all challenges they face with. Although many researches have studied blood inventory management (BIM) in BSCNs, there is still a lack of models simultaneously considering blood inventory levels in hospital blood banks (HBBs) and blood centers (BCs) as a stochastic pro-cess. Also, most models have not evaluated the impact of false positive (FP) and false negative (FN) errors in the screening laboratories (SLs) of BCs. Moreover, no study has modeled the HBB demands as a queueing-inventory model with multiple blood types according to ABO/Rh substitution priority. To fill these gaps, this paper de-velops a two-echelon blood bank network considering ABO/Rh factors and substitution priority in which in-ventory levels of all red blood cell (RBC) types in an HBB and BC are simultaneously modeled as eight dependent continuous-time Markov chains (CTMCs). An unobservable M/M/m queueing-inventory system with infected RBCs and (r, Q) policy in the HBB is proposed. The blood requests arrive at the requests queueing system in the HBB, and after processing and departing from the queue, they leave the HBB with exactly-one RBC unit with the same ABO and Rh (Rhesus) groups or compatible with other types of RBC. Lost sales occur if all possible types of RBC for transfusion are unavailable. HBB continuously checks each RBC type's inventory level and places an order to the BC once the inventory level of each one of them reaches a predetermined reorder point. Due to transfusion-transmissible infections (TTIs), an SL conducts a 100 % quality-assured screening in the BC modeled as an M/M/m queueing system. Due to unreliable inspectors, FP and FN errors are considered in the SL. The stationary distribution of the number of request

关键词： Blood inventory management ABO Rh compatibility and priority CTMC Queueing-inventory models Unreliable screening queueing process grasshopper optimization algorithm

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Secure hyper intelligence in routing protocol with low-power (RPL) Networks in IoT

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ADVANCES IN ENGINEERING SOFTWARE 2022年第0期173卷

作者： Ambika, K. Malliga, S. AVS Engn Coll Dept Comp Sci Salem India Kongu Engn Coll Dept Comp Sci & Engn Perundurai Erode India

The IoT is beginning at now hopping up, particularly in human organizations and shrewd condition applications including limitless low-controlled sensors and actuators to improve the way of life and acclimate new associations with the framework. Not equivalent to the past light-weight guiding shows focusing on the decline of the control messages, the proposed scheme gives the light-weight IPv6 address auto-plan, IPv6 neighbor revelation, and coordinating show in an IoT skilled infra-less remote frameworks with the sprout filer and redesigned rank thoughts. Furthermore, because of the survey, our proposed plot is subject to the showing of various probability transports in the IoT conditions with the lossy remote association. Specifically, the proposed redesigned RPLbased light-weight guiding show improves the quality with the multi-ways secretly settled reliant on the improved position thoughts regardless of the way that lossy remote associations have existed. It perceives to get to the information uplink and downlink with guaranteeing the exhibition which aides of low information rate in LPWAN (Low Power Wide Area Network) partner with the RPMA (Random Phase Multiple Access). These are for the most part the joining prerequisites that approach low information accepting to play out the hyper-network in IoT. It includes the hyper-availability correspondence to the shared by utilizing the LTE 5 G (Long Term Evolution in 5 G) range used to build the productivity and execution contrasted with existing. It assists with expanding the security without obstruction to MUX/DEMUX and has been accepted in the LP (Low Power) in systems administration and the defensive field added to the correspondence of the hyper-network with the assistance of IoT in the RPL framework. The sign was perceived to be changed by Zigbee and GOA (grasshopper optimization algorithm) throughout the whole correspondence and it was required to satisfy the low piece information move rate while conveying the

关键词： LPWAN Low Power Wide Area Network RPMA Term Evolution in 5 G MUX DEMUX - Multiplexing De -multiplexing LP Low Power Zigbee GOA grasshopper optimization algorithm

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Efficient customer behaviour prediction in Indian metropolitan cities for E-commerce applications

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EXPERT SYSTEMS 2024年第9期41卷 e13604-e13604页

作者： Suresh, B. S. Suresh, M. St Peters Inst Higher Educ & Res Dept Management Studies Chennai India St Peters Inst Higher Educ & Res Dept Commerce Chennai India

The primary objective of this manuscript is to find the service quality factors, which influence customer satisfaction, promotional efforts taken by the retailer, and the perception of customers towards the organized retail sector. This investigation uses both offline and online retail sales datasets. Particularly, in the case of the offline retail sale dataset, an energy-efficient analysis is conducted to collect scalable, accurate, and real-time data. The collected data is used in enhancing several aspects of e-commerce operations, including customer experience optimization and inventory management, which results in better decision-making with increased efficiency. After data collection, the outliers are eliminated by implementing the z-score technique. The removal of outliers increases the variability of collected data which reduces statistical power. The statistically sufficient data are given to the Randomized grasshopper optimization algorithm (RGOA) for optimal instance selection. Finally, the selected optimal instances are given to the deep neural network (DNN) model for future customer behaviour prediction. The efficacy of the RGOA-DNN model is analysed by using evaluation measures like mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean percentage error (MPE) and mean absolute percentage error (MAPE). The numerical analysis states that the RGOA-DNN model obtained a minimal error rate with MSE of 0.09 and 0.10, RMSE of 0.13 and 0.14, MAE of 0.12 and 0.16, MPE of 0.10 and 0.11 and MAPE of 0.08 and 0.10 on the offline and online retail sales datasets. The RGOA-DNN model has a minimal error rate in future customer behaviour prediction related to the conventional regression models. Furthermore, the elimination of inactive instances or selection of optimal instances reduces the model complexity to linear and computational time to 22.10 and 23.12 s on the offline and online retail sales datasets.

关键词： deep neural network energy-efficient data collection error rate future customer behaviour prediction grasshopper optimization algorithm Indian metropolitan cities mean absolute error mean absolute percentage error root mean squared error z-score technique

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Hybrid GOA-SVR technique for short term load forecasting during periods with substantial weather changes in North-East India

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Procedia Computer Science 2018年 143卷 124-132页

作者： Mayur Barman Nalin Behari Dev Choudhury Department of Electrical Engineering National Institute of Technology Silchar Assam 788010 India

The power system load demand is extremely prejudiced by thermal inertia caused by weather parameters. This influence even became stronger when there are vast and rapid changes in the weather parameters. Therefore it is necessary to consider the weather changes for a precise short term load forecasting (STLF). This work proposes a hybrid technique for conducting STLF during periods with substantial weather changes. The proposed technique is based on support vector regressing (SVR), grasshopper optimization algorithm (GOA) and a novel Euclidean norm (EN) based data processing unit. Here GOA is utilized for accessing the appropriate SVR parameters. The EN based data processing unit is applied for fixing the training data by assessing some alike days in the recent historicized days. The proposed GOA-SVR technique identifies the substantial changes in weather (temperature) and regulates the training data accordingly. This work is done in North-East India and the proposed technique is tested on the data of load dispatch center of Assam state. Three case studies are conducted and forecasting performances of the proposed technique are compared with the conventional technique of training the system with just recent historicized day’s data. The proposed technique found outperformed the conventional technique in all cases.

关键词： Short term load forecasting Substantial weather changes Euclidean norm based data processing unit Support vector regression grasshopper optimization algorithm

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Data on optimization of the non-linear Muskingum flood routing in Kardeh River using Goa algorithm

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DATA IN BRIEF 2020年 30卷 105398-105398页

作者： Khalifeh, Saeid Esmaili, Kazem Khodashenas, SaeedReza Akbarifard, Saeid Ferdowsi Univ Mashhad Water Sci & Engn Fac Agr Mashhad Razavi Khorasan Iran Ferdowsi Univ Mashhad Dept Water Sci & Engn Fac Agr Mashhad Razavi Khorasan Iran Shahid Chamran Univ Ahvaz Fac Water Sci Engn Dept Hydrol & Water Resources Water Resources Engn Ahvaz Iran

This article describes the time series data for optimizing the Non-linear Muskingum flood routing of the Kardeh River, located in Northeastern of Iran for a period of 2 days (from 27 April 1992 to 28 April 1992). The utilized time-series data included river inflow, Storage volume and river outflow. In this data article, a model based on the grasshopper optimization algorithm (GOA) was developed for the optimization of the Non-linear Muskingum flood routing model. The GOA algorithm was compared with other metaheuristic algorithms such as the Genetic algorithm (GA) and Harmony search (HS). The analysis showed that the best solutions achieved by the GOA, Genetic algorithm (GA), and Harmony search (HS) were 3.53, 5.29, and 5.69, respectively. The analysis of these datasets revealed that the GOA algorithm was superior to GA and HS algorithms for the optimal flood routing river problem. (C) 2020 The Author(s). Published by Elsevier Inc.

关键词： Muskingum Kardeh Non-linear grasshopper optimization algorithm

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Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 186卷

作者： Dong, Jianhua Liu, Xiaogang Huang, Guomin Fan, Junliang Wu, Lifeng Wu, Jie Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China Kunming Univ Sci & Technol Fac Agr & Food Kunming 650500 Yunnan Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Wuhan Polytech Univ Sch Civil Engn & Architecture Wuhan 430023 Peoples R China

Accurate estimation of reference crop evapotranspiration (ET0) is of great significance to crop water use and agricultural water resources management. This study evaluated the performance of four bio-inspired algorithm optimized kernel-based nonlinear extension of Arps decline (KNEA) models, namely KNEA with grasshopper optimization algorithm (GOA-KNEA), KNEA with grey wolf optimizer algorithm (GWO-KNEA), KNEA with particle swarm optimization algorithm (PSO-KNEA), and KNEA with salp swarm algorithm (SSA-KNEA), on estimating monthly ET0 across China. Monthly meteorological data [including maximum air temperature (Tmax), minimum air temperature (Tmin), extra-terrestrial solar radiation (Ra), relative humidity (RH), global solar radiation (Rs), wind speed (U)] during 1966-2015 from 51 weather stations across the seven different climate zones of China were used for model training and testing. Four different combinations of meteorological data were applied as model input, and results from the FAO-56 Penman-Monteith formula were used as a control. Results showed that the GWO-KNEA model overall performed better than the other three coupling models, of which the GWO-KNEA2 model (i.e., the model with input combination 2) was the best (on average R2 = 0.9814, RMSE = 0.2143 mm d-1). The convergence rate and the population size of the GWO-KNEA model were also superior to the other three models. Among input combinations, models with combination 2 had the best overall performance, while models with combination 3 were the worst on average. In terms of the importance of each meteorological parameter contributing to model accuracy, Rs was greater than Ra, RH, or U. Among different climate zones, station specific models in the semi-arid steppe of Inner Mongolia showed the best estimating performance in general, while models in the Qinghai-Tibetan Plateau overall performed relatively poorly. The GOA-KNEA and SSA-KNEA models with the combination 4 showed large increases (29.7% and 28.7

关键词： Reference evapotranspiration Climatic zones grasshopper optimization algorithm Grey wolf optimization algorithm Machine learning

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Double stage controller optimization for load frequency stabilization in hybrid wind-ocean wave energy based maritime microgrid system

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APPLIED ENERGY 2021年第PartA期282卷 116171-116171页

作者： Latif, Abdul Hussain, S. M. Suhail Das, Dulal Chandra Ustun, Taha Selim Natl Inst Technol Dept Elect Engn Silchar 788010 Assam India Natl Inst Adv Ind Sci & Technol AIST FREA Fukushima Renewable Energy Inst Koriyama Fukushima 9630215 Japan

The momentum towards reduction of greenhouse gas emissions by reduced use of conventional source in marine power networks as well as significant development of renewable energy resources (RRs) have been the motivating factors for inclusion RRs in hybrid maritime microgrid system (HM mu GS) and investigation of consequent frequent control mechanism. This article presents an approach of load frequency control in an independent HM mu GS consisting of wind driven generation (WDG), Archimedes wave power generation (AWPG), marine biodiesel generator (MBG), solid-oxide fuel cell (SOFC) energy units, heat pump (HP) and freezer (FZR). The stability of the HM mu GS model have been evaluated through the rigorous tests considering non-availability of renewable resources, concurrent random generation of AWPG, load demand, real recorded data of WDG. Comparative performance of several controllers such as PID, PID with filter (PIDN) and PI-(1 + PD) controller are presented with their parameters optimized using genetic algorithmic technique (GA), particle swarm technique (PSO), firefly algorithmic technique (FA), cultural algorithmic technique (CA) and the recent metaheuristic grasshopper algorithmic technique (GOA). The proposed frequency control strategy of HM mu GS model is benchmarked by comparative statistical assessment and decision indicators. Finally, sensitivity assessment of GOA tuned PI-(1 + PD) controller under uncertain parametric variations such as;variation of WDG gain, droop factor (R), inertia constant (M) and loading without reoptimizing the optimal base condition values is conducted as an evidence of the sturdiness of the proposed frequency control strategy. The analysis of the results shows that the proposed GOA optimized PI-(1 + PD) control strategy perform much better than other control schemes.

关键词： Energy Management System System optimization Load frequency control Non sensitive loads Double stage controller grasshopper optimization algorithm

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Incipient fault diagnosis of planetary gearboxes based on an adaptive parameter-induced stochastic resonance method

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APPLIED ACOUSTICS 2022年 188卷

作者： Xu, Baoming Shi, Jiancong Zhong, Min Zhang, Jun Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350116 Peoples R China

The stochastic resonance (SR) method is commonly used in incipient fault diagnosis to extract weak fault features from complex diagnostic signals. However, the extraction effect of a SR system is highly dependent on the choice of system parameters as well as the complexity of input signals. To solve this problem, the present study proposes an adaptive parameter-induced SR method, in which high pass filter and Teager energy operator (TEO) are combined to pre-process the original signal while the grasshopper optimization algorithm is introduced to optimize the SR system parameters. The proposed method is employed to diagnose a set of experimental vibration signals of a planetary gearbox with incipient localized root crack and incipient distributed surface wear as well, leading to satisfactory diagnosis results. Comparing with the existing adaptive stochastic resonance methods, the present method claims the merits of a high signal-to-noise ratio and low computation cost.(c) 2021 Elsevier Ltd. All rights reserved.

关键词： Fault diagnosis Planetary gearbox Stochastic resonance Signal pre-processing grasshopper optimization algorithm

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An adaptive HMM method to simulate and forecast ocean chemistry data in aquaculture

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期207卷

作者： Sun, Yuanwei Li, Dashe Shandong Technol & Business Univ Sch Comp Sci & Technol 191 Binhai Middle Rd Yantai 264005 Shandong Peoples R China Shandong Technol & Business Univ Key Lab Intelligent Informat Proc Yantai 264005 Shandong Peoples R China Shandong Technol & Business Univ Coinnovat Ctr Shandong Coll & Univ Future Intelli Yantai 264005 Shandong Peoples R China

Due to the multifaceted influences of many factors and complex mechanisms, accurate predictions of ocean chemistry data are difficult to achieve. To address this problem, this paper proposes an adaptive hidden Markov model (AHMM) for predicting ocean chemistry data from aquaculture farms in eastern China. The periodic, trend and residual terms of the original sequence are obtained through Seasonal and Trend decomposition using Loess (STL), which effectively reveals the variation trend of the sequence. For the period and trend terms of the sequence, the autonomous search grasshopper optimization algorithm (ASGOA) is employed to train the parameters of the HMM, which avoids the problems of easily falling into local optima and the dependence on the initial value in the parameter training process. To predict ocean chemistry data, a sequence similarity measurement method is proposed to match similar sequences in the historical interval, and a linear regression equation is constructed for sequence similarity and the difference between the predicted value and the value at the previous time based on the HMM. The results have revealed that the MAE value decreases by 25.5%, MSE value by 45.9%, RMSE value by 27.4%, MAPE value by 39.5% and R2 increases by 6%. Compared with other prediction algorithms under the same conditions, the prediction method proposed in this paper is easily understandable and manages to achieve better prediction performance. This work can serve as a reference for predicting other ocean chemistry data and for the sustainable management of aquaculture ecosystems.

关键词： Hidden markov model grasshopper optimization algorithm Ocean chemistry data prediction

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Assessment of Machine Learning Techniques for Monthly Flow Prediction

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WATER 2018年第11期10卷 1676页

作者： Alizadeh, Zahra Yazdi, Jafar Kim, Joong Hoon Al-Shamiri, Abobakr Khalil Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran *** Iran Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea

Monthly flow predictions provide an essential basis for efficient decision-making regarding water resource allocation. In this paper, the performance of different popular data-driven models for monthly flow prediction is assessed to detect the appropriate model. The considered methods include feedforward neural networks (FFNNs), time delay neural networks (TDNNs), radial basis neural networks (RBFNNs), recurrent neural network (RNN), a grasshopper optimization algorithm (GOA)-based support vector machine (SVM) and K-nearest neighbors (KNN) model. For this purpose, the performance of each model is evaluated in terms of several residual metrics using a monthly flow time series for two real case studies with different flow regimes. The results show that the KNN outperforms the different neural network configurations for the first case study, whereas RBFNN model has better performance for the second case study in terms of the correlation coefficient. According to the accuracy of the results, in the first case study with more input features, the KNN model is recommended for short-term predictions and for the second case with a smaller number of input features, but more training observations, the RBFNN model is suitable.

关键词： Gaussian process regression grasshopper optimization algorithm K-nearest neighbor regression neural network support vector machine

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