Efficient Analysis Model Research Articles

Persistent and transient inefficiencies in the U.S. airline industry

The competitive advantage of airlines depends on their technical efficiency. Efficiency analysis models now differentiate between persistent and transient inefficiencies. Persistent inefficiencies stem from fundamental flaws in production processes or ongoing managerial shortcomings, requiring long-term strategic interventions. Transient inefficiencies, however, result from irregular setbacks that can be swiftly addressed, causing temporary efficiency dips. Focusing on the decade from 2010 to 2019, we investigated how business models, mergers, and upgauging influence these inefficiencies. Our results reveal that 21.2% of the overall technical inefficiency can be accounted for by persistent inefficiency. Despite the U.S. airline industry improving transient inefficiency during the sample period, prevailing persistent inefficiency hindered any significant rise in overall efficiency. The research shows that full-service airlines (FSA) and low-cost carriers (LCC) often exhibit higher persistent and transient efficiency than ultra-low-cost carriers (ULCC). However, there seem to be no significant differences in transient and persistent inefficiency between FSA and LCC. Additionally, the results suggest that the upgauging and higher ramp-to-ramp hours may lead to higher transient efficiency. Finally, we could not find any influence of mergers and stage length on transient inefficiency.

On value efficiency analysis and cone-ratio data envelopment analysis models

AbstractIn this paper we relate the Value Efficiency Analysis (VEA) efficiency scores with those of three different Cone-Ratio Data Envelopment Analysis (CR-DEA) models, when the chosen model Decision Making Units (DMUs) in CR-DEA jointly comprise the Most Preferred Solution (MPS) in VEA. In particular, we consider the cases where the set of feasible weight vectors is given respectively by (i) the weight vectors that are jointly optimal in DEA for all the model DMUs, (ii) every weight vector that is optimal in DEA for at least one of the model DMUs, and (iii) only those weight vectors with strictly positive input and output weights, each of which is jointly optimal in DEA for all the model DMUs. In each of these cases we show that the CR-DEA efficiency scores can be obtained or approximated by estimating either a VEA model or a series of VEA models, without the need of identifying a priori the set of feasible weight vectors. We illustrate the usefulness of our results by means of an empirical application using data on Japanese regional banks.

Protocols for MPS choices in VEA models with variable returns to scale

AbstractIn this paper, we examine the implications of using different strongly efficient Decision Making Units (DMUs) as the Most Preferred Solution (MPS) in Value Efficiency Analysis (VEA) models with variable returns to scale. We provide protocols for consistent MPS choices, where a MPS choice is consistent if and only if the resulting VEA technology does not allow for free and/or unlimited production. In particular, we show that either a DMU that belongs to the set of strongly CCR-efficient DMUs or a combination of jointly strongly CCR-efficient DMUs constitute the only consistent MPS choices for both constant-returns-to-scale and variable-returns-to-scale VEA models. In addition, we show that the scale properties of the efficient VEA frontier are affected by the choice of the MPS and in essence depend on the range of the interval between the MPS’s right- and left-side scale elasticities.

Cascaded Adaptive Dilated Temporal Convolution Network-Based Efficient Sentiment Analysis Model from Social Media Posts

Sentimental analysis is one of the most complicated tasks in text mining. It is the process of converting unstructured text to structural text to identify the meaningful pattern of data. Different kinds of sentiments in social media posts are classified into positive and negative. Natural Language Processing (NLP) plays an imperative task in examining sentiments on social media. Sentimental analysis techniques frequently use text data to help to monitor customer feedback about products and understand the needs of customers in the field of business. The text data are gathered from social media comments that lead to several drawbacks such as spelling errors, noise, and unstructured data, and some of the data are in abbreviation form in which abbreviations are hard to understand from the text data. Therefore, the innovative sentiment analysis method is implemented to identify the kinds of sentiments from the collected social media posts. At first, the text data are assembled from the social media posts. The collected text data are subjected to text preprocessing techniques to improve the data quality. NLP techniques such as a bag of n-grams, glove embedding, and Bidirectional Encoder Representations from Transformers (BERT) are applied to separate the features from the text data. The removed features from these techniques are given to the fusion of the optimal feature stage, where the weight optimization takes place via the Mutated Random Parameter-based Serval Optimization Algorithm (MRP-SOA). The attained optimal weighted fused features contain the most relevant information, and it is fed to the classifier for analyzing the sentiments. For analyzing the sentiments, the Cascaded Adaptive Dilated Temporal Convolutional Network (CADTCN) is utilized, and the parameters from ADTCN are optimized with the same to get higher classification outcomes. The analysis outcomes are compared with the traditional sentiment analysis models in terms of various metrics to show the success of the advanced design.

Comparative study of enterprise efficiency and environmental, social and governance performance before and after COVID‐19 epidemic—A study of China's A‐share listed Fintech companies

AbstractSince the outbreak of COVID‐19, it is worth noting how the enterprise efficiency and ESG performance of Chinese fintech enterprises are affected by pandemic. However, researches using Data Envelopment Analysis (DEA) to explore this issue are still very scarce. Therefore, this paper selects 45 A‐share listed fintech enterprises to construct A two‐stage RDM‐DDF efficiency analysis model, and evaluates the efficiency of different stages and different input–output indexes by using the window efficiency analysis method. At the same time, target enterprises were grouped according to their registered locations, so as to explore the regional differences in efficiencies and changes in the efficiency before and after the outbreak of COVID‐19. The analysis results show that: (1) Regional differences in corporate efficiency are quite pronounced. (2) There is no obvious positive correlation between ESG performance and traditional financial performance of target enterprises. (3) Following the outbreak of the COVID‐19 pandemic, the efficiency changes in target companies have been complex.

Efficient and novel multidomain feature analysis model with incremental optimizations for enhancing pediatric myelodysplastic syndrome detection

Pediatric myelodysplastic syndromes (MDS) are complicated, thus early and accurate diagnosis is essential for treatment planning and patient care. Diagnostic processes often use discrete data domain analysis, which reduces accuracy and delays diagnosis. This work addresses these limitations by introducing an advanced Multi domain Feature Analysis Model (MFAM) enhanced with incremental optimizations to improve pediatric MDS detection. Traditional pediatric MDS diagnosis relies on subjective evaluations and limited data fusion, not modern computational methods. These constraints may reduce diagnosis accuracy and postpone action. The proposed MFAM integrates data from Clinical History, Physical Examination, Blood Cell Counts, Peripheral Blood Smear, Bone Marrow Aspiration and Biopsy, Cytogenetic Analysis, Flow Cytometry, Genetic Testing, Iron Studies, and Bone Marrow Cytology to overcome these challenges. The MFAM increases feature variance by fusing Bidirectional Long Short-Term Memory (BiLSTM) with Bidirectional Gated Recurrent Units (BiGRU). Deep Q Learning with Graph Recurrent Convolutional Neural Networks (DQGRCNN) boosts efficiency. Additionally, the model integrates the Vector Autoregressive Moving Average with Exogenous Inputs (VARMAX) to facilitate early prediction of paediatric MDS. These enhancements have resulted in significant improvements in the precision of paediatric MDS detection by 4.5%, accuracy by 3.5%, recall by 2.3%, Area Under the Curve (AUC) by 1.5%, and specificity by 2.4% while reducing diagnostic delays by 8.5%. Furthermore, the model enhances the precision of predictive analysis by 2.9%, accuracy by 3.5%, recall by 2.5%, AUC by 2.9%, specificity by 5.5%, and reduces delays in predictive analysis by 8.5%. The MFAM presented in this paper revolutionizes the diagnosis and treatment of paediatric MDS by efficiently combining diverse diagnostic data, employing advanced transformation and fusion techniques, and optimizing responses through DQGRCNN. The integration of VARMAX further enables early prediction of the disease. MFAM will enhance diagnostic precision, therapy start, and clinical outcomes for young MDS patients.

Performance of Simplified Damage-Based Concrete Models in Seismic Applications

Significant progress in the finite-element (FE) modeling at the member-level of reinforced-concrete (RC) structures under seismic excitation has been achieved in the past decades; however, reliable and accurate analysis models for full-scale 3D system-level RC structures validated with experimental data are scarce. As the development of a complete nonlinear model is expensive and time consuming, simpler models, typically elastic or lumped-plastic in nature, are employed in practice with additional provisions prescribed to account for nonlinear behavior. Depending on the assumptions made by the analyst, there may be substantial uncertainties related to the response of a complete structure. Capturing global failure modes is challenging, and the assessment of strength and ductility capacities may be inaccurate, resulting in potentially unsafe designs. The objective of this research is to assess the performance of simplified damage-based concrete biaxial models in analyzing and capturing the structural behavior of full-scale RC systems. Damage-based models for concrete require minor input from the analyst, facilitating their use in a design setting, while their explicit, non-conditional convergence formulations allow for non-iterative solutions. This results in damage-based models featuring efficient computational analysis while accounting for complex phenomena such as the capacity to account for stiffness recovery in reversal loading (crack closing) and permanent strains in the concrete. A comparison between analytical and experimental data at both the element- and system-levels is conducted, and a viable damage-based model is proposed for a full-scale structure analysis. The results of the study show that damage-based models are a viable alternative to developing efficient analysis models for elements and whole structures.

Design of Multidomain Feature Analysis Model for Estimation of Anemic Conditions via Deep Transfer Learning

The detection of anemia from anemic retinopathy involves the identification and analysis of specific retinal changes that occur as a result of severe anemia which requires analysis of multimodal sources. Existing models for identification of these conditions either showcase high complexity, or have lower efficiency when evaluated on clinical scans. Moreover, most of these models cannot be scaled for multi-disease images, which limits their applicability under real-time use cases. To overcome these issues, this research article proposes the design of an efficient and novel multidomain feature analysis model for the estimation of anemic conditions with the help of deep transfer learning process. The proposed model initially represents retinal image scans into multiple domains via estimation of Frequency, Gabor, Convolution, Wavelet and Entropy features. These features are selected via Bacterial Foraging Optimizer (BFO), which assists in retaining high variance feature sets. Due to this, the model is able to reduce redundancies, that assists in improving the speed and accuracy of classification operations. To perform this task, the model uses a set of customized 1-D Cascaded Binary CNN, that assist in categorizing input images into 4 different classes. These include, “Anemic due to Glaucoma”, “Glaucoma”, “Anemic”, and “Normal”, with 98.3% accuracy which is 4.5% higher than existing methods. The model is also able to improve the precision by 2.9%, recall by 3.5%, while reducing the delay needed for classification by 5.9% when compared with existing techniques. Due to which, the model is highly useful for clinical deployments.

P‐54: XAI Models for Efficient Analysis of Temperature and Power Consumption in High‐Brightness Panels and Modules

Predicting temperature and power consumption in panels and modules under high brightness has long been a daunting and time‐intensive task, often requiring over a week of simulation work. Addressing this challenge, our study introduces a novel machine learning framework, bifurcated into panel and module stages, to streamline the data acquisition process. A key innovation in our approach is the use of symbolic regression to overcome the limitations posed by the reliance on real‐world measurements for power consumption and heat generation. Employing SPICE simulations, we estimated the current density within the panel, with subsequent validation through Explainable AI (XAI) analysis. This revealed a significant correlation between current density and panel heat generation. Our research also revisits the traditionally neglected areas outside the panel's center, uncovering their impact on heat generation. The layered complexity in modules, previously a barrier to physical sample creation and measurement, was navigated using simulation. XAI insights demonstrated the crucial roles of graphite and metal layers in heat dissipation, and the drive IC as a primary heat source. This integrated approach of real and computational data in machine learning significantly reduced the analysis timeframe from a week to mere minutes, marking a breakthrough in predictive accuracy and efficiency for high‐brightness scenarios.

FE-analytical slice model and verification test for load distribution analysis and optimization of planetary gear train in wind turbine

Load distribution behavior of planetary gear train (PGT) is crucial for the contact and bending fatigue failure of wind turbine transmission system. With the increasing power of wind turbines, the load and failure rate of wind turbine become larger, which leads to the demand of accurate and efficient modeling, analysis and optimization for load distribution of PGT. In this paper, a new efficient and accurate FE-analytical slice model for load distribution analysis and optimization of PGT in wind turbine is proposed, which is explicitly formulated by the geometry slice model of external/internal gear contact, original FE-analytical slice model for local contact deformation, and efficient finite element (FE) model for global structural deformation. In the FE-analytical slice model, geometry errors are effectively descripted by discretizing gears into a series of slices, while gear tooth contact elasticity and structural elasticity are addressed by the FE-analytical method accurately and efficiently. Upon the proposed model, the load distribution behaviors of PGT with coupling effects of elasticity and errors are revealed and optimized by tooth modification and in-phase eccentricity arrangement strategy. The load distribution tests of PGT in wind turbine are conducted, verifying the proposed FE-analytical slice model which is significant to improve the load-bearing capacity and avoid the fatigue failure of wind turbine transmission system.

Semi-Additive Integer-Valued Production Technology for Analyzing Public Hospitals in Mashhad

Conventional Data Envelopment Analysis (DEA) models assume real-valued input-output data and ignore the collaboration among decision-making units (DMUs) in the analysis of efficiency. This paper proposes a novel DEA production technology that is capable of dealing with the collaboration concept and resource sharing for both integer and real-valued data in the efficiency measurement and target setting. This is accomplished by way of a mixed integer linear programming (MILP) efficiency analysis model. We also deal with the computational aspect of the proposed model and invent a MILP with the computational complexity of [Formula: see text] rather than [Formula: see text]. We explain the proposed models by numerical examples and graphical illustrations. We apply our models for efficiency analysis of 15 governmental (public) hospitals in Mashhad City in Iran and demonstrate the practical relevance and advanced future of the proposed methods.

Evaluation of Operating Performance of Listed Companies in Taiwan’s Tourism Industry - Application of Data Envelopment Analysis

Data envelopment analysis is an efficiency measurement method proposed by Charnes, Cooper and Rhodes in 1978, including CCR model efficiency analysis and BCC model efficiency analysis, which is used to evaluate the relative efficiency of decision-making units. The measured efficiency value is the most favorable result of the evaluated unit under objective circumstances. This article applies data envelopment analysis method to evaluate the performance of listed companies in Taiwan's tourism industry. After analysis, it was found that the total efficiency of the six tourism industries including Huayuan, No. 1 Store, Gourmet KY, Wangpin, Xiongshi, and Holaday is effective, while the total efficiency of the three tourism industries including Farglory, Phoenix, and Xintiandi is not Effective, but technical efficiency is still effective. Except for the tourism industry whose total efficiency is effective, its returns to scale remain unchanged. Two tourism industries, such as Guobin and Jinghua, have diminishing returns to scale because their return to scale value is >1 and should reduce their business scale. The returns to scale of other tourism industries are all Increasingly, it is recommended to expand the scale of operations.

An efficient latent map multi-fidelity Kriging model and adaptive point-selected strategy for reliability analysis with time-consuming simulations

An efficient latent map multi-fidelity Kriging model and adaptive point-selected strategy for reliability analysis with time-consuming simulations

Feature selection for sentiment analysis using hybrid multiobjective evolutionary algorithm

As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model’s efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it’s crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach.

Strategic assessment of sustainable production in the international automobile sector using efficiency analysis and decision-making models

In the context of escalating global environmental concerns, the automotive industry faces pressing imperatives to align its growth with sustainable practices. This urgency, coupled with the need for a holistic evaluation framework, sets the stage for this study. Motivated by the gaps in quantitatively assessing sustainability, the study aims to discern and quantify the sustainability efforts of leading automotive manufacturers. The objective is to establish a robust, quantifiable link between operational efficiency and sustainability criteria that informs both business strategy and environmental stewardship. To achieve this, an innovative methodology combining the Malmquist Data Envelopment Analysis (Malmquist DEA) framework and the Ordinal Priority Approach (OPA) is employed. The approach analyzes efficiency changes, technological progress, and the industry's adoption of sustainable practices over a five-year period, concluding in the pre-pandemic year of 2020. The findings reveal a notable emphasis on productivity and market exposure, identifying them as pivotal to sustainable manufacturing. The OPA provides a weighted analysis of sustainability criteria based on expert evaluations, emphasizing the integration of environmental initiatives with core business strategies. The study's contribution is twofold: it introduces a novel, quantifiable approach to evaluating sustainability in the automotive industry, and it offers strategic insights for companies seeking to elevate their sustainability profile effectively. However, the reliance on pre-pandemic data and expert opinions marks the limitations of the study, indicating the necessity for further research. Future studies should aim to expand the temporal scope to include the latest industry shifts and to diversify the pool of sustainability criteria, capturing a broader spectrum of environmental and social dimensions. This continued research will enhance the industry's understanding of sustainable manufacturing in the evolving global context.

An efficient model for 3D analysis of steel frames exposed to fire

The paper presents a nonlinear concentrated plasticity frame element for advanced analysis of steel frames exposed to elevated temperatures. The element extends the formulation based on the generalized plasticity material model that was successfully applied to the analysis of steel and composite structures. It considers the material's nonlinear behavior, temperature-induced loading, strength and stiffness degradation typical for structures under fire conditions. The nonlinear geometry is taken into account with the corotational formulation. Because of the generalized plasticity relations adoption, the element can describe the gradual yielding of cross sections. At the same time, the implemented return mapping algorithm ensures the element's high computational efficiency. The governing element equations, selection of parameters and computer implementation are discussed in the paper, followed by element validation through the number of experimental data examples and other numerical models. The comparative analysis demonstrated the element's versatility and capability to accurately predict the structural response of steel frame structures exposed to fire.

“Booster” or “Obstacle”: Can digital transformation improve energy efficiency? Firm-level evidence from China

To identify the influence of digital transformation on energy consumption, this paper introduces digitalization and energy elements into the multi-sector energy efficiency analysis model. Furthermore, this paper empirically tests the influence of digital transformation on energy efficiency from the micro level. The results show that the digital transformation indeed improves the enterprises energy efficiency, in which enterprises with higher energy efficiency are more affected. The improvement effect of digital transformation on enterprise energy efficiency continues to increase over time. In comparison, digital transformation shows more effective improvement effect on energy efficiency in enterprises that substantive transformation priority, with same group and maturity stage. Mechanism analysis shows that digital transformation can improve the energy efficiency of enterprises by reducing costs, increasing income, improving production efficiency and promoting technological innovation. In addition, the digital inclusive finance, government subsidies, intellectual property protection and service industry opening show positive regulatory effects. This paper proposes that, with the goal of peak carbon dioxide emissions and carbon neutrality, enterprises should actively increase digital investment and strengthen digital management of energy utilization.

Application of Ensemble Learning and Feature Selection Models in Energy Expenditure Estimation of Fitness Tracking Devices

The use of fitness tracking devices to monitor energy expenditure (EE) is becoming increasingly popular among consumers. However, the accuracy of EE estimation still needs to be improved due to the lack of accurate data filtering and more efficient data analysis models. To address this issue, we designed an energy expenditure estimation (EEE) model based on smart tracking devices. This paper proposes an algorithm for data reconstruction called PC-DF-RFECV: firstly, Pearson correlation analysis (PC) is used for initial data screening, then Feature Derivation (DF) is applied to reconstruct new features, and finally, the Recursive Feature Elimination algorithm based on cross-validation (RFECV) is employed to select the new features. Furthermore, the EEE model adopts a weighted average (Wei-Voting) strategy to enhance the robustness of the model by integrating the predictions of multiple base learners (XGBoost, LightGBM, Random Forest). During algorithm optimization, a Bayesian hyperparameter optimization technique is employed to fine-tune the model's hyperparameters. Empirical findings indicate that the EEE model, when applied to the dataset collected from motion tracking devices, attains an score of 0.9985, of 1.033, and of 0.865. These results demonstrate superior performance compared to current state-of-the-art research and conventional algorithms including random forests, gradient boosting, and bagging.

ENSEMBLE CLASSIFIER BASED FAKE NEWS IDENTIFICATION IN ONLINE SOCIAL NETWORK

In today’s context, one of the main mediums of news consumption is through social media. It has become a common trend now to produce fake news for speedy propagation and popularity. Thus, it creates a sort of illusion and deception for readers. Amidst the umpty number of researches done, we could say that there is no research that could accurately predict fake news over online. In this study, the ensemble classifier is employed to develop a model for fake news identification in online social networks. This proposed method applies the stacked ensemble classification model, the proposed approach that learns the represented text model and classifies the textual data into real news and fake news. Then, the proposed approach takes the collaborative decision from the classified data that is generated from the multiple base learners using the weight-based ensemble method. The accuracy prediction and performance evaluation of time consumption of detection fake news are consecutively 80 Percentage and 11 ms respectively. Thus, it improves the efficiency and classification accuracy over large-scale social media messages through an efficient sentiment analysis model.

DWS-YOLO: A Lightweight Detector for Blood Cell Detection

ABSTRACT Peripheral blood cell detection is an essential component of medical practice and is used to diagnose and treat diseases, as well as to monitor the progress of therapies. Our objective is to construct an efficient deep learning model for peripheral blood cell analysis that achieves an optimized balance between inference speed, computational complexity, and detection accuracy. In this article, we propose the DWS-YOLO blood detector, which is a lightweight blood detector. Our model includes several improved modules, including the lightweight C3 module, the increased combined attention mechanism, the Scylla-IoU loss function, and the improved soft non-maximum suppression. Improved attention, loss function, and suppression enhance detection accuracy, while lightweight C3 module reduces computation time. The experiment results demonstrate that our proposed modules can enhance a detector’s detection performance, and obtain new state-of-the-art (SOTA) results and excellent robustness performance on the BCCD dataset. On the white blood cell detection dataset (Raabin-WBC), the proposed detector’s generalization performance was confirmed to be satisfactory. Our proposed blood detector achieves high detection accuracy while requiring few computational resources and is very suitable for resource-limited but efficient medical device environments, providing a reliable and advanced solution for blood detection that greatly improves the efficiency and effectiveness of peripheral blood cell analysis in clinical practice.