Blood samples were collected from 30 subjects. To a portion from each sample was added Fluoride/Ethylene Diamine Tetra Acetate (E.D.T.A) to inhibit glycolysis and clotting. The remaining portions ...were allowed to clot without any inhibitor. On subjecting to glucose concentration testing, the portions without the inhibitor showed a decline in the glucose level of 8 mg/dl (0.44 mmol/l) in the first hour and of 7 mg/dl (0.39 mmol/l) per hour in the next two hours. It is re-emphasised that a glycolysis inhibitor should always be added to blood samples drawn for glucose level testing. Otherwise, the reported results could be misleading.
Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The ...accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency.
In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier.
The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively.
The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption ...cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA), which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS) is also taken into account for more efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost and user waiting.
Demand side management (DSM) strategy implementation plays a vital role in energy management of smart grid (SG) by involving distributed energy resources (DERs) to reduce operational cost, pollution ...emission and provide end users satisfaction. In this study, day ahead scheduling problem in SG is adopted by using DSM strategy in SG considering different types of consumers to reduce operational cost and pollution emission, load curtailment cost by considering curtailable loads (CLs), and coordination between shiftable loads (SLs) and wind turbines (WTs) output power. The consumers participating in the DSM strategy are responsive consumers, which can shift and curtail loads, and non-responsive consumers who cannot shift or curtail loads. The DERs used in the proposed day-ahead scheduling problem consists of wind energy source (WES), energy storage systems (ESSs), and diesel generators (DGs). Before integrating wind energy sources with SG, its forecasting is necessary; thus, the probability distribution function (PDF) is used to forecast wind speed. The day-ahead scheduling problem with tri objective function is solved using the multi-objective wind driven optimization (MOWDO) technique using the decision-making mechanism (DMM) to obtain the best solution in search space. Simulation results show that the day ahead scheduling multi-objective problem is solved using MOWDO algorithm. To check the effectiveness of the proposed model, it is applied to SG considering different constraints to receive balance power at the user end.
Agriculture is a major part of the world economy as it provides food safety. However, in recent years, it has been noted that plants are extensively infected by different diseases. This causes ...enormous economic losses in agriculture industry around the world. The manual inspection of fruit diseases is a difficult process which can be minimized by using automated methods for detection of plant diseases at the earlier stage. In this paper, a new method is implemented for apple diseases identification and recognition. Three pipeline procedures are followed by preprocessing, spot segmentation, and features extraction, and classification. In the first step, the apple leaf spots are enhanced by a hybrid method which is the conjunction of 3D box filtering, de-correlation, 3D-Gaussian filter, and 3D-median filter. After that, the lesion spots are segmented by the strong correlation-based method and optimized their results by fusion of expectation maximization (EM) segmentation. Finally, the color, color histogram, and local binary pattern (LBP) features are fused by comparison-based parallel fusion. The extracted features are optimized by genetic algorithm and classified by One-vs-All M-SVM. The experimental results are performed on plant village dataset. The proposed methodology is tested for four types of apple disease classes including healthy leaves, Blackrot, Rust, and Scab. The classification accuracy shows the improvement of our method on selected apple diseases. Moreover, the good preprocessing step always produced prominent features which later achieved significant classification accuracy.
Nowadays, automated appliances are exponentially increasing. Therefore, there is a need for a scheme to accomplish the electricity demand of automated appliances. Recently, many Demand Side ...Management (DSM) schemes have been explored to alleviate Electricity Cost (EC) and Peak to Average Ratio (PAR). In this paper, energy consumption problem in a residential area is considered. To solve this problem, a heuristic based DSM technique is proposed to minimize EC and PAR with affordable user’s Waiting Time (WT). In heuristic techniques: Bacterial Foraging Optimization Algorithm (BFOA) and Flower Pollination Algorithm (FPA) are implemented. Furthermore, a novel heuristic algorithm has been proposed by merging the best features of the aforementioned existing algorithms. We test the proposed scheme on single homes and on smart community (involving multiple households). Different Operational Time Intervals (OTIs) are also considered for implementation. We have performed simulations for validating the our scheme. Results clearly demonstrate that the proposed Hybrid Bacterial Flower Pollination Algorithm (HBFPA) shows efficacy for EC and for reduction of PAR with reasonable user WT.
Multi-objective energy optimization is pivotal for reliable and secure power system operation. However, multi-objective energy optimization is challenging due to interdependent and conflicting ...objectives. Thus, a multi-objective optimization model is needed to cater to conflicting objectives. On this note, a multi-objective optimization model is developed, where a non-dominated genetic sorting algorithm is employed to optimize objectives pollution emission, operation cost, and loss of load expectation (LOLE) considering renewable energy sources (RES). RES, like wind and solar, are intermittent and uncertain, which are modelled using a beta probability density function (PDF). The developed method's effectiveness and applicability are analyzed by implementing it on the 30-bus system, and the results are compared for two cases. Findings reveal that the developed multi-objective optimization model minimizes operation cost, pollution emission, and LOLE by 59%, 7%, and 2.67%, respectively, compared to existing models.
The main purpose of this study is to investigate the relationship between psychological capital and employee performance through the mediating role of job embeddedness. Psychological climate is used ...as moderator to intensify the link between psychological capital and job embeddedness. Using data from 350 nurses belonging to four public hospitals, partial least squares structural equation modeling was utilized to analyze the hypothesized model. Job embeddedness significantly mediates the link between psychological capital and employee performance. Psychological climate heightens the relationship between psychological capital and job embeddedness. The findings offer suggestions for researchers on the conservation of resources and concept of psychological capital, public hospitals, and practitioners on how to improve the performance of employees.
•Propose a genetic algorithm (GA) based multiphase fault tolerance (MFTGA) approach.•MFTGA efficiently maps optimal VMs with users according to the service level agreement (SLA).•Calculate the local ...fitness (fl) and global fitness (fg) of multiuser according to the SLA.•MFTGA improve the reliability, latency, and reduce the failure of the task in the cloud computing environment.
Cloud datacenter (Dc) have become popular in recent years with the rising popularity and high performance of cloud computing. The multi-step of data computation and diverse task dependencies fail in the task, energy consumption, overloading of Virtual Machines (VMs), and violation of the agreement. To overcome these challenges, we propose a genetic algorithm (GA) based multiphase fault tolerance (MFTGA) approach for intelligently schedule the tasks over the VMs for multiuser. This MFTGA approach efficiently maps optimal VMs with users according to the service level agreement (SLA). The presented approach comprises four phases namely individual phase, local phase, global phase, and fault tolerance phase. In the individual phase of the MFTGA algorithm, we calculate the local fitness (fl) of each user. Then calculate the global fitness (fg) of multiuser according to the SLA in the global fitness phase. After mapping the optimal VMs with the multiuser, we check the status of task execution in the fault tolerance phase. MFTGA method is used to improve the reliability, latency, and reduce the failure of the task in the cloud computing environment. The proposed MFTGA scheme is compared against the GA and Adoptive Incremental Genetic Algorithm (AIGA). The simulation results validate that the proposed method exhibits better performance than GA and AIGA in terms of execution time, memory utilization, cost, SLA violation, and energy consumption.
An efficient algorithm for the persistence operation of data routing is crucial due to the uniqueness and challenges of the aqueous medium of the underwater acoustic wireless sensor networks ...(UA-WSNs). The existing multi-hop algorithms have a high energy cost, data loss, and less stability due to many forwarders for a single-packet delivery. In order to tackle these constraints and limitations, two algorithms using sink mobility and cooperative technique for UA-WSNs are devised. The first one is sink mobility for reliable and persistence operation (SiM-RPO) in UA-WSNs, and the second is the enhanced version of the SiM-RPO named CoSiM-RPO, which utilizes the cooperative technique for better exchanging of the information and minimizes data loss probability. To cover all of the network through mobile sinks (MSs), the division of the network into small portions is accomplished. The path pattern is determined for MSs in a manner to receive data even from a single node in the network. The MSs pick the data directly from the nodes and check them for the errors. When erroneous data are received at the MS, then the relay cooperates to receive correct data. The proposed algorithm boosts the network lifespan, throughput, delay, and stability more than the existing counterpart schemes.
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