Ranking Approach Research Articles

Implementation of a Combination of Rank Reciprocal and Additive Ratio Assessment Approaches for 3D Printer Selection

With the wide selection of 3D printers available on the market, the challenge arises for consumers and businesses to choose the device that best suits their specific needs. To determine the choice, the decision-maker must know one by one the specifications of the 3D printer to be purchased, which results in making difficult decisions and requiring a long time. This research aims to implement a combination of the Rank Reciprocal and additive ratio assessment (ARAS) approaches to make it easier to determine decisions for selecting a 3D printer. The Reciprocal Rank approach provides weight values by utilizing the reciprocal or inverse principle. Meanwhile, the ARAS approach is used to obtain the best alternative by evaluating alternative rankings based on their utility function. From the case studies that have been carried out, the highest to lowest utility values are Anycubic 4Max Pro (A2) getting a score of 0.8289, Creality Ender-3 Pro (A1) getting a score of 0.6174, Anet 3D Printer ET4 Pro (A3) getting a score of 0.5510, and Mingda Magician X2 (A4) getting a score of 0.5116. The output produced by the system in the case study carried out produces the same value as the manual calculation, meaning that the ARAS method calculation in the system is declared valid. Based on usability testing, it got a score of 90%, which shows the system is suitable for use

Development of a robust hybrid framework for evaluating and ranking smartification measures for sustainable mobility: A case study of Sicilian roadways, Southern Italy

Smart roads are a key component of intelligent transportation. Strict implementation of road smartification measures can help solve many of the traffic problems societies face today. These measures address new challenges of urbanization, reduce traffic and pollution, improve road safety, and bolster the economy through the enhancement of road networks. The objective of this study is to develop a robust hybrid framework to evaluate and rank potential smartification measures, keeping sustainable mobility goals in mind. For this purpose, the roads network in Sicily, Southern Italy, was considered as the case study, and 27 potential smartification measures were selected. The study proposed a robust hybrid framework that integrates the Stepwise Weight Assessment Ratio Analysis (SWARA) with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach, based on a Spherical Fuzzy (SF) set. The performance of the proposed technique was then compared with two other decision-making techniques for evaluating and ranking smartening measures, and the correlation coefficient of each approach was calculated. The findings demonstrated the superiority of the proposed approach in ranking and assessing potential smartification measures. According to the results, the traffic conditions reporting emerged as the most effective smartification method for the case study roads, while the green islands for charging electric vehicles was found to be the least effective.

Risk analysis for urban excavation: a FAHP-VIKOR hybrid method

The construction of urban excavation involves several potential risks, and the application of scientific methodologies is necessary for a reliable risk assessment. This paper proposes a novel risk assessment framework for urban excavation that combines the fuzzy analytic hierarchy process (FAHP) with the multi criteria compromise solution ranking (VIKOR) approach. FAHP is utilised to determine the weights of risk factors and identify high risk factors, and VIKOR is employed to evaluate the risk level of urban excavation. The proposed method comprises five key components: information source collection, risk-factor rating, weight calculation, establishment of project classification standards, and project feedback. By comprehensively considering the weights of various evaluation indicators and the superiority or inferiority of different factors, this method provides comprehensive risk assessment results. The effectiveness and feasibility of the proposed method are validated through a real case conducted in Nanning, China. The FAHP-VIKOR risk assessment method offers scientific decision support for urban excavation construction and serves as a valuable reference for risk reduction and ensuring construction safety.

Models and algorithms for the formation of an event tree used in analysing the quality of system software for knowledge-based intelligent decision support systems

A formal statement of the problem of improving the quality of software for intelligent systems under unfavourable circumstances is made. Its belonging to the class of problems of the calculus of variations on a conditional extremum is established. A ranked approach based on heuristic algorithms to solve this scientific problem is proposed and substantiated.

Drug combinations screening using a Bayesian ranking approach based on dose-response models.

Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloidleukemia.

Multi-stakeholder perspective to generate evidence and strategies for sustainable management of ropes from the fishing sector of Norway

Marine plastic pollution is a growing stressor affecting both marine and terrestrial life. Plastic polymers are widespread in oceans, including sparsely populated Nordic countries. Norway, a fishing-dominant region, faces substantial plastic pollution from fishing ropes, which often end up incinerated, landfilled, or lost in the ocean, contributing to the ghost fishing problem.This research employs a static material flow analysis (MFA) to assess plastic mass flows and the recyclability of 15 rope types used in Norway's commercial fishing sector. Findings reveal that approximately 383 tons of ropes are lost annually in Norwegian waters, endangering fish species. Furthermore, only one-third of the rope types can be efficiently recycled using available recycling technologies, highlighting the need for circularity.The MFA and inventory-based ranking approach shows significant potential as a holistic decision support tool for industry and policymakers in exercising sustainable and circular management for ropes.

Assessing Interactive Web-Based Systems Using Behavioral Measurement Techniques

Nowadays, e-commerce websites have become part of people’s daily lives; therefore, it has become necessary to seek help in assessing and improving the usability of the services of e-commerce websites. Essentially, usability studies offer significant information about users’ assessment and perceptions of satisfaction, effectiveness, and efficiency of online services. This research investigated the usability of two e-commerce web-sites in Saudi Arabia and compared the effectiveness of different behavioral measurement techniques, such as heuristic evaluation, usability testing, and eye-tracking. In particular, this research selected the Extra and Jarir e-commerce websites in Saudi Arabia based on a combined approach of criteria and ranking. This research followed an experimental approach in which both qualitative and quantitative approaches were employed to collect and analyze the data. Each of the behavioral measurement techniques identified usability issues ranging from cosmetic to catastrophic issues. It is worth mentioning that the heuristic evaluation by experts provided both the majority of the issues and identified the most severe usability issues compared to the number of issues identified by both usability testing and eye-tracking combined. Usability testing provided fewer problems, most of which had already been identified by the experts. Eye-tracking provided critical information regarding the page design and element placements and revealed certain user behavior patterns that indicated certain usability problems. Overall, the research findings appeared useful to user experience (UX) and user interface (UI) designers to consider the provided recommendations to enhance the usability of e-commerce websites.

Ranking intuitionistic fuzzy sets with hypervolume-based approach: An application for multi-criteria assessment of energy alternatives

Ranking Intuitionistic Fuzzy Sets (IFS) using distance-based methods involves calculating the distance between an IFS and a reference point which represents either maximum (positive ideal solution) or minimum (negative ideal solution) value. These methods assume that similarity of an IFSs to the reference point increases as its distance from it decreases. While it is a common practice to use nonlinear distance functions for ranking IFSs, this paper proves that no nonlinear IF distance function can be robust. In this study, the shortcomings of the conventional procedure are demonstrated by providing a mathematical proof and an alternative ranking method based on the hypervolume metric is proposed. In addition, the suggested ranking approach is extended as a new multi-criteria decision making method called Hypervolume-based Evaluation and Ranking Technique (HEART). HEART is applied for multi-criteria assessment of Turkey’s energy alternatives. Results are compared with three distance based multi-criteria decision making methods: TOPSIS, VIKOR, and CODAS.

A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems

E-health Industry 4.0 systems ranking based on Blockchain is a multi-criteria decision-making (MCDM) problem, considering the multiple evaluation properties, their significance, and data variety. The final closeness between the evaluation sample and ideal solutions also constitutes an optimisation problem. To the authors’ knowledge, no study has provided a multi-privacy and security ranking approach solution for E-health Industry 4.0 systems based on Blockchain. Consequently, this study proposes and discusses a multi-privacy and security ranking approach solution for E-health Industry 4.0 systems, utilising the SFS-FWZIC method to address the significance of properties issue and the Grey-TOPSIS method to tackle data variation, multiple evaluation properties, and the ideal solutions optimisation problem. The methodology of the proposed approach consists of three sections. First, three decision matrices are constructed based on the intersection of E-health systems with the mentioned properties, intersecting Electronic Health Records (EHRs), Electronic Medical Records (EMRs), and Personal Health Records (PHRs) with seven properties. Second, the weights of each privacy and security property are calculated using the SFS-FWZIC method. Finally, the weights determined by the SFS-FWZIC method and the three decision matrices are input into the Grey-TOPSIS method to rank E-health systems across the three categories. The findings reveal the following: (1) The SFS-FWZIC method effectively assigned weights to privacy and security properties, with access control achieving the highest significance weight (0.1934) and Secure-search receiving the lowest weight (0.0603). (2) The Grey-TOPSIS method efficiently ranked E-health systems across the three categories based on various parameters, including α=0.1,α=0.3,α=0.5,α=0.7, and α=0.9. Sensitivity and correlation analysis were conducted to evaluate the results, revealing high correlation results based on each α value across all discussed scenarios of changing property weights. The implications of this study can lead to better decision-making, improved security and privacy, increased competition, and widespread adoption, ultimately contributing to more efficient and effective healthcare delivery.

Multi-Attribute Decision-Making Based on Picture Fuzzy Einstein Operator and The TOPSIS Method

Picture Fuzzy Sets (PFSs) denote the extension of conventional fuzzy sets, which capture a broader spectrum of human opinions, encompassing responses such as acceptance, neutrality, rejection, and hesitation. This wider range of responses cannot be accurately accommodated within fuzzy sets as well as intuitionistic fuzzy sets framework. In the realm of Multiple Attribute Group Decision-Making (MAGDM) methods, attributes frequently exhibit conflicts, uncertainties, imprecisions, as well as a lack of commensurability. To tackle the complexities inherent in MAGDM, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method has demonstrated its effectiveness. This method is employed in a compromise ranking approach founded on aggregation functions that showcase closeness to the reference points. This study's goal is to instigate a fresh approach to aggregation, referred to as the Picture Fuzzy Einstein Weighted Averaging Distance-based TOPSIS (PFEWAD-TOPSIS) method. To validate the effectiveness of this method in addressing MAGDM problems, a detailed example is conducted.

Flood susceptibility modeling using geo-morphometric ranking approach in Jhelum River basin, Pakistan

This study is modeling flood susceptibility in Jhelum River Basin by employing Geo-Morphometric ranking approach. The study area is located in the Monsoon region and exposed to heavy rainfall events. Every year monsoon rainfall and melting of snow and glaciers together generate flash floods in the upper catchment and then riverine flood in the low-lying areas. Watershed modeling approach is used to delineate the Jhelum watershed and its sub-watershed. Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) is utilized as input data in ArcMap software environment. Then Geo-Morphometric ranking model is applied to rank the susceptibility of each sub-watershed to flood. The analysis showed that geo-morphometric characteristics vary from basin to basin. There are 44 sub-basins which cover area less than 1000 ​km2. The area of sub-basins ranges from 47.68 ​km2 to 7667.77 ​km2. Based on our results, area of sub-basin directly impacts flood susceptibility. Geo-morphometric parameters such as gradient, relief and geology of basin such as type of bed rock govern the drainage pattern. The results of the study can assist line agencies and concerned government departments to design location specific flood risk reduction strategy.

SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender System

The recent development of recommender systems has a focus on collaborative ranking, which provides users with a sorted list rather than rating prediction. The sorted item lists can more directly reflect the preferences for users and usually perform better than rating prediction in practice. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” and unobserved data due to the precondition of the entire list permutation. To this end, in this article, we propose a novel setwise Bayesian approach for collaborative ranking, namely, SetRank, to inherently accommodate the characteristics of user feedback in recommender systems. SetRank aims to maximize the posterior probability of novel setwise preference structures and three implementations for SetRank are presented. We also theoretically prove that the bound of excess risk in SetRank can be proportional to \(\sqrt {M/N}\) , where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.

Optimization of a quadratic programming problem over an integer efficient set

Multi-objective programming problem often contains numerous efficient solutions, which confuses the decision-maker. To assist in selecting the most desirable solution, optimizing a function over the efficient set becomes crucial. In this paper, we present a novel method for optimizing a general quadratic function over the efficient set of a multi-objective integer linear programming problem. To solve this problem, a ranking approach and efficiency test is utilized. The proposed methodology obtains a globally optimal solution by systematically scanning ranked solutions of an integer quadratic programming problem until the efficiency condition is satisfied. For generating ranked solutions, we construct a related integer linear programming problem. Then, ranked solutions of the integer linear programming problem are used for enumerating ranked solutions of the integer quadratic programming problem. The convergence of our algorithm is established theoretically, and its steps are illustrated using a numerical example. Aparticular case of the proposed method for optimizing a linear function over the efficient set of a multi-objective integer linear programming problem is also discussed. Further, extensive computational results demonstrate the effectiveness of our method for solving problems with large number of constraints, variables, and objective functions. Moreover, comparative analysis shows that the developed algorithm came out to be computationally more efficient as compared to the existing state-of-the-art algorithms.

Developing a resilient and sustainable non-linear closed-loop supply chain management framework for the automotive sector industry using a gaussian fuzzy optimization based non-linear model predictive control approach

ABSTRACT Efforts to merge sustainability and resilience within the automotive industry’s supply chain models have proven challenging. This paper proposes a novel non-linear closed-loop supply chain management framework tailored to the tire industry supply chain from the automotive sector to address the issue of exploring interrelationships. Framework employs trapezoidal linguistic cubic fuzzy Z-score technique for order of preference by similarity to the ideal solution ranking approach to prioritize resilience strategies to maintain sustainability performance during sudden disturbances. Furthermore, Gaussian fuzzy optimization-based non-linear model predictive control acts as a feedback controller to integrate sustainability and resilience by providing a stable output based on the objective function related to sustainability dimensions. An experimental study assesses the impact of resilience strategies on total supply chain costs, highlighting significant cost savings. Adopting strategies like multiple sourcing, information sharing, and improved design quality of the supply chain keeps total expected costs optimal for various sustainability levels.

Early and fair COVID-19 outcome risk assessment using robust feature selection

Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.

Addressing agricultural challenges: An identification of best feature selection technique for dragon fruit disease recognition

Dragon fruit is a prominent substance in global agriculture. Despite this, it is gaining popularity and is a viable solution in resource-poor, environmentally degraded areas because of its many health benefits. Nevertheless, many dragon fruit plantations have been impacted by the disease, reducing their yield, and the detection system is still conventional. Farmers’ lack of disease identification and management expertise diminished crop quality and products. As a result, little research was carried out to assist those specific farmers requiring adequate agricultural support. This research has proposed an autonomous agro-based system to recognize dragon diseases using in-depth analysis of feature selection techniques. After the collection of real-time images of the dragon, the images are preprocessed using various image-processing techniques. The two important features are retrieved after segmentation. The analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) are used as feature selection techniques to assess the feature rank based on the mutual score. To analyze the effectiveness of the machine learning algorithms that were used, six distinct machine learning classifiers were applied to the top-ranked feature sets, and their performance was measured using seven distinct performance evaluation metrics. AdaBoost and Random Forest classifiers for the LASSO feature ranking approach got the maximum accuracy, which is 96.29%, based on a comparison of classifiers based on the ANOVA and LASSO feature set. Despite this, we have optimized the computational resources of each classifier for the LASSO feature set.

Software defect prediction using learning to rank approach

Software defect prediction (SDP) plays a significant role in detecting the most likely defective software modules and optimizing the allocation of testing resources. In practice, though, project managers must not only identify defective modules, but also rank them in a specific order to optimize the resource allocation and minimize testing costs, especially for projects with limited budgets. This vital task can be accomplished using Learning to Rank (LTR) algorithm. This algorithm is a type of machine learning methodology that pursues two important tasks: prediction and learning. Although this algorithm is commonly used in information retrieval, it also presents high efficiency for other problems, like SDP. The LTR approach is mainly used in defect prediction to predict and rank the most likely buggy modules based on their bug count or bug density. This research paper conducts a comprehensive comparison study on the behavior of eight selected LTR models using two target variables: bug count and bug density. It also studies the effect of using imbalance learning and feature selection on the employed LTR models. The models are empirically evaluated using Fault Percentile Average. Our results show that using bug count as ranking criteria produces higher scores and more stable results across multiple experiment settings. Moreover, using imbalance learning has a positive impact for bug density, but on the other hand it leads to a negative impact for bug count. Lastly, using the feature selection does not show significant improvement for bug density, while there is no impact when bug count is used. Therefore, we conclude that using feature selection and imbalance learning with LTR does not come up with superior or significant results.

A novel technique for solving intuitionistic fuzzy DEA model: an application in Indian agriculture sector

Fuzzy DEA is a performance measurement tool that is used to assess the performance of DMUs in highly uncertain environments. In this article, the Intuitionistic fuzzy DEA (IFDEA) model is proposed based on the triangular intuitionistic fuzzy numbers (TIFNs). The weighted Possibility mean for TIFN is used to compare and rank the TIFN. The weighted possibility mean approach is proposed to solve the IFDEA model, and the IFDEA model is converted into its equivalent crisp DEA model to assess the relative efficiencies of the DMUs. One advantage of the proposed approach is that the attitude of the decision-maker is considered while measuring the efficiency of the DMUs. The weight or risk factor delta in [0,1] indicates whether the decision-maker is a risk-taker, neutral, or adverse. The crisp DEA model is a LP problem that is solved by using an existing LP method with different risk factors to determine the efficiency score of the DMUs. The DMUs are ranked based on the overall efficiency score of the DMUs, which is the arithmetic mean of the efficiency scores of the DMUs with different risk factors. Two numerical examples are given here to demonstrate the validity and applicability of the proposed technique and to compare the performance of the DMUs in the proposed approach with the exciting ranking approach and the expected value approach. A case study on the agriculture sector has been conducted in order to evaluate the agricultural performance of Indian states using the IFDEA model. According to the results of the IFDEA model, 15 (53.57%) out of the 28 Indian states were found to be efficient.

Test Case Selection through Novel Methodologies for Software Application Developments

Test case selection is to minimize the time and effort spent on software testing in real-time practice. During software testing, software firms need techniques to finish the testing in a stipulated time while uncompromising on quality. The motto is to select a subset of test cases rather than take up all available test cases to uncover most bugs. Our proposed model in the research study effort is termed SCARF-RT, which stands for Similarity coefficient (SC), Creating Acronyms, Regression test (RT), and Fuzzy set (FS) with Dataset (DS). Clustering of test cases using ranking and also based on similarity coefficients is to be implemented. This research considered eleven different features for clustering the test cases. Two techniques have been used. Firstly, each cluster will, to a certain extent, encompass a collection of distinct traits. Depending on the coverage of the feature, a cluster of test cases might be chosen. The ranking approach was used to create these groupings. The second methodology finds similarity among test cases based on eleven features. Then, the maxmin composition is used to find fuzzy equivalences upon which clusters are formed. Most similar test cases are clustered. Test cases of every cluster are selected as a test suite. The outcomes of this research show that the selected test cases based on the proposed approaches are better than existing methodologies in selecting test cases with less duration and at the same time not compromising on quality. Both fuzzy rank-based clustering and similarity coefficient-based clustering test case selection approaches have been developed and implemented. With the help of these methods, testers may quickly choose test cases based on the suggested characteristics and complete regression testing more quickly.

Repeated local ellipsoid protrusion supplements HLA surface characterization.

Allorecognition of donor HLA is a major risk factor for long-term kidney graft survival. Although several molecular matching algorithms have been proposed that compare physiochemical and structural features of the donors' and recipients' HLA proteins in order to predict their compatibility, the exact underlying mechanisms are still not fully understood. We hypothesized that the ElliPro approach of single ellipsoid fitting and protrusion ranking lacks sensitivity for the characteristic shape of HLA molecules and developed a prediction pipeline named Snowball that is fitting smaller ellipsoids iteratively to substructures. Aggregated protrusion ranks of locally fitted ellipsoids were calculated for 712 publicly available HLA structures and 78 predicted structures using AlphaFold 2. Amino-acid sequence and protrusion ranks were used to train deep neural network predictors to infer protrusion ranks for all known HLA sequences. Snowball protrusion ranks appear to be more sensitive than ElliPro scores in fine parts of the HLA such as the helix structures forming the HLA binding groove in particular when the ellipsoids are fitted to substructures considering atoms within a 15 Å radius. A cloud-based web service was implemented based on amino-acid matching considering both protein- and position-specific surface area and protrusion ranks extending the previously presented Snowflake prediction pipeline.