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Customer segmentation has been a hot topic for decades, and the competition among businesses makes it more challenging. The recently introduced Recency, Frequency, Monetary, and Time (RFMT) model used an agglomerative algorithm for segmentation and a dendrogram for clustering, which solved the problem. However, there is still room for a single algorithm to analyze the data’s characteristics. The proposed novel approach model RFMT analyzed Pakistan’s largest e-commerce dataset by introducing k-means, Gaussian, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) beside agglomerative algorithms for segmentation. The cluster is determined through different cluster factor analysis methods, i.e., elbow, dendrogram, silhouette, Calinsky–Harabasz, Davies–Bouldin, and Dunn index. They finally elected a stable and distinctive cluster using the state-of-the-art majority voting (mode version) technique, which resulted in three different clusters. Besides all the segmentation, i.e., product categories, year-wise, fiscal year-wise, and month-wise, the approach also includes the transaction status and seasons-wise segmentation. This segmentation will help the retailer improve customer relationships, implement good strategies, and improve targeted marketing.

The use of artificial intelligence to automate PV module fault detection, diagnosis, and classification processes has gained interest for PV solar plants maintenance planning and reduction in expensive inspection and shutdown periods. The present article reports on the development of an adaptive neuro-fuzzy inference system (ANFIS) for PV fault classification based on statistical and mathematical features extracted from outdoor infrared thermography (IRT) and I-V measurements of thin-film PV modules. The selection of the membership function is shown to be essential to obtain a high classifier performance. Principal components analysis (PCA) is used to reduce the dimensions to speed up the classification process. For each type of fault, effective features that are highly correlated to the PV module’s operating power ratio are identified. Evaluation of the proposed methodology, based on datasets gathered from a typical PV plant, reveals that features extraction methods based on mathematical parameters and I-V measurements provide a 100% classification accuracy. On the other hand, features extraction based on statistical factors provides 83.33% accuracy. A novel technique is proposed for developing a correlation matrix between the PV operating power ratio and the effective features extracted online from infrared thermal images. This eliminates the need for offline I-V measurements to estimate the operating power ratio of PV modules.

Parameter identification in a Proton Exchange Membrane Fuel Cell (PEMFC) entails the application of optimization algorithms to ascertain the optimal unknown variables essential for crafting an accurate model that predicts fuel-cell performance. These parameters are typically not included in the manufacturer’s datasheet and must be identified to ensure precise modeling and forecasting of fuel cell behavior. This paper introduces a recently developed hybrid algorithm (Aquila Optimizer Arithmetic Algorithm Optimization (AOAAO)) that enhances the AO and AAO algorithm’s efficiency through a novel mutation strategy, aimed at determining seven unknown parameters of a PEMFC during the optimization process. These parameters function as decision variables, and the objective function aimed for minimization is the sum square error (SSE) between the predicted and actual measured cell voltages. AOAAO demonstrated superior performance across various metrics, achieving an SSE minimum in comparison to other compared algorithm. AOAAO’s robustness was validated through extensive testing with six commercially available PEMFCs, including BCS 500 W-PEM, 500 W SR-12PEM, Nedstack PS6 PEM, H-12 PEM, HORIZON 500 W PEM, and a 250 W-stack, across twelve case studies derived from various operational conditions detailed in manufacturers’ datasheets. For each datasheet, both Current–Voltage (I/V) and Power–Voltage (P/V) characteristics of the PEMFCs scenarios closely aligned with those observed in experimental data, affirming AOAAO’s superior accuracy, robustness, and time efficiency for real-time fuel cell modeling. In terms of computational efficiency, AOAAO runtime is significantly faster than all compared algorithms, demonstrating an efficiency improvement of approximately 98%.

This article offers a novel boost inverter construction with a Nine-level quadruple voltage boosting waveform. The primary drawback of conventional MLI is the need for a high voltage DC-DC converter to increase the voltage when using renewable energy sources. Consequently, the developed method, complete with a quadruple voltage boost ability, can alleviate that shortcoming by automatically increased the incoming voltage. A single DC source, two switching capacitors, and eleven switches are all that are used in the newly presented architecture. The voltage of the capacitor automatically balances. The switched capacitor MLI is distinguished by the fewer parts that are required and the substitution of a capacitor for a DC source. The switching capacitor has to be charged and discharged properly in order to produce the nine-level output voltage waveform. The SPSC unit makes these levels attainable. To achieve voltage boosting, switched capacitors are coupled in parallel and series in the conduction channel. The quality of this proposed topology has been analyzed through different parameters based on the components count, THD, and cost; the resulting efficiency reaches 97.85%. The switching order of the proposed method has been controlled by the Nearest Level Modulation Method (NLC). MATLAB and PLECS software were used to evaluate the constructed Nine-level converter.

Despite the increasing focus on sustainability within the tourism and hospitality sector, there is still a substantial gap in understanding how digital dynamic capability impacts sustainable performance. Particularly, the mediating effects of green knowledge management and green technology innovation in this context have not been extensively investigated. Thus, this research seeks to examine how Digital Dynamic Capability (DDC) affects Sustainable Performance (SP) in the tourism and hospitality industries, with a focus on the mediation of Green Technology Innovation (GTI) and Green Knowledge Management. The study adopts a quantitative approach, employing the PLS-SEM technique and WarpPLS statistical software version 7.0. The research analyzes 430 survey responses from full-time employees at five-star hotels and top-tier travel companies (Category A) in Egypt. Results showed that DDC has a positive impact on SP, GKM, and GTI. Moreover, GKM and GTI also impact SP. Additionally, DDC has a significant influence on SP through GKM and GTI. The research has a substantial impact on Innovation Diffusion Theory and offers useful suggestions for tourism and hospitality organizations to sustain success in a competitive market.

The never-ending issue of inadequate energy availability is constantly on the outermost layer. Consequently, an ongoing effort has been made to improve electric power plants and power system configurations. Photovoltaic Distributed Generators (PVDG) and compensators such as Distributed Static Var Compensator (DSVC) are the center of these recent advances. Due to its high complexity, these devices’ optimum locating and dimensions are a relatively new issue in the Electrical Distribution Grid (EDG). A modified version of Newton Raphson Based Optimizer (mNRBO) has been carried out to optimally allocate the PVDG and DSVC devices in tested IEEE 33 and 69 bus EDG. The mNRBO algorithm integrates four parameters to enhance NRBO’s performance by addressing its limitations in balancing exploration and exploitation. The article suggested novel Multi-Objective Functions (MOF), which have been considered to optimize concurrently the overall amount of active power loss (APL), voltage deviation (VD), relays operation time (TR ELAY ), as well as improve the coordination time interval (CTI) between primaries and backup relays set up in EDG. The proposed mNRBO algorithm surpasses its basic NRBO version, as long as another alternative algorithm, while providing very good results, such as minimizing the APL from 210.98 kW until 26.482 kW and 224.948 kW until 18.763 kW for the IEEE 33 and 69 bus respectively. Which proves the capability of the mNRBO algorithm of solving such power system challenges.

Investigation of thermal transport in nanofluid flow squeezed inside a channel formed by two sheets with zero slope is common in industrial and engineering applications. The heat transmission could be affected by various physical constraints which reduce the machine efficiency for desired products. Therefore, this attempt clearly focus on the development of new nanofluid thermal transport model using the significance effects of Koo-Kleinstreuer-Li correlation which used for the estimation of nanofluid thermal conductivity, impacts of magnetic field, internal heating species, and thermal radiations. Then, the LSM (Least Square Method) is magnificently implemented and obtained the physical results for multiple ranges of parameters. It is noticed that when the squeezed parameter varied in the ranges of − 0 . 1 to − 2 . 6 and 0 . 1 to 2 . 6 , the fluid loss their velocity and more reduction is occurred about η = 0 . 0 . However, outward movement of the plate lead to quick declines in the velocity. Further, when the Hartmann number increased for 1 . 0 – 6 . 0 then the fluid moves slowly and stronger magnetic field resists its motion. Moreover, the Eckert and Radiation numbers boosted the fluid temperature by keeping the feasible nanoparticles concentration in the range of ϕ = 0 . 02 – ϕ = 0 . 12 .

Spoofed speeches are becoming a big threat to society due to advancements in artificial intelligence techniques. Therefore, there must be an automated spoofing detector that can be integrated into automatic speaker verification (ASV) systems. In this study, we recommend a novel and robust model, named DeepDet , based on deep-layered architecture, to categorize speech into two classes: spoofed and bonafide. DeepDet is an improved model based on Yet Another Mobile Network (YAMNet) employing a customized MobileNet combined with a bottleneck attention module (BAM). First, we convert audio into mel-spectrograms that consist of time–frequency representations on mel-scale. Second, we trained our deep layered model using the extracted mel-spectrograms on a Logical Access (LA) set, including synthesized speeches and voice conversions of the ASVspoof-2019 dataset. In the end, we classified the audios, utilizing our trained binary classifier. More precisely, we utilized the power of layered architecture and guided attention that can discern the spoofed speech from bonafide samples. Our proposed improved model employs depth-wise linearly separate convolutions, which makes our model lighter weight than existing techniques. Furthermore, we implemented extensive experiments to assess the performance of the suggested model using the ASVspoof 2019 corpus. We attained an equal error rate (EER) of 0.042% on Logical Access (LA), whereas 0.43% on Physical Access (PA) attacks. Therefore, the performance of the proposed model is significant on the ASVspoof 2019 dataset and indicates the effectiveness of the DeepDet over existing spoofing detectors. Additionally, our proposed model is robust enough that can identify the unseen spoofed audios and classifies the several attacks accurately.

In this study, we present the EEG-GCN, a novel hybrid model for the prediction of time series data, adept at addressing the inherent challenges posed by the data's complex, non-linear, and periodic nature, as well as the noise that frequently accompanies it. This model synergizes signal decomposition techniques with a graph convolutional neural network (GCN) for enhanced analytical precision. The EEG-GCN approaches time series data as a one-dimensional temporal signal, applying a dual-layered signal decomposition using both Ensemble Empirical Mode Decomposition (EEMD) and GRU. This two-pronged decomposition process effectively eliminates noise interference and distills the complex signal into more tractable sub-signals. These sub-signals facilitate a more straightforward feature analysis and learning process. To capitalize on the decomposed data, a graph convolutional neural network (GCN) is employed to discern the intricate feature interplay within the sub-signals and to map the interdependencies among the data points. The predictive model then synthesizes the weighted outputs of the GCN to yield the final forecast. A key component of our approach is the integration of a Gated Recurrent Unit (GRU) with EEMD within the GCN framework, referred to as EEMD-GRU-GCN. This combination leverages the strengths of GRU in capturing temporal dependencies and the EEMD's capability in handling non-stationary data, thereby enriching the feature set available for the GCN and enhancing the overall predictive accuracy and stability of the model. Empirical evaluations demonstrate that the EEG-GCN model achieves superior performance metrics. Compared to the baseline GCN model, EEG-GCN shows an average R2 improvement of 60% to 90%, outperforming the other methods. These results substantiate the advanced predictive capability of our proposed model, underscoring its potential for robust and accurate time series forecasting.

: Symptoms of dyspepsia are usually encountered by chronic kidney disease patients. Abdominal discomfort is commonly seen in CKD patients with no other causes of organic affection. to determine the prevalence of functional dyspepsia in CKD patients, and which subtype is predominant in them. This observational study included 150 CKD patients. Clinical and laboratory data were recorded for every patient. All the patients were interviewed using the ROME IV questionnaire of functional dyspepsia. Patients fulfilling criteria for functional dyspepsia were exposed to upper GI endoscopy. Overall, 73 (48.7%) of CKD patients were males and 77 (51.3%) were females with mean age of (45.71 ± 9.59) and mean BMI (26.58 ± 5.39). The frequency of functional dyspepsia among CKD patients was determined to be 14.7% (22 out of 150 patients). Among those affected by functional dyspepsia, the most prevalent subtype was found to be Epigastric Pain Syndrome (EPS), accounting for 59% (13 out of 22 cases). The most common predictor of FD in CKD patients was chronic HCV infection, hemodialysis, stage of CKD and eGFR as revealed by Univariate regression analysis. The prevalence of FD amongst CKD patients is 14.7% with EPS the predominant subtype. Male patients, HCV patients, patients with higher CKD stages and highly impaired eGFR (low eGFR) are more probable to have FD.