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• Study analyzes 1374 papers on sensitivity analysis in Scopus. • RStudio, CiteSpace, and VOSviewer are used. • China has most articles published; India leads in international cooperation. • Top three cited journals: J. Clean. Prod., Expert Syst. Appl., and Comput. Ind. Eng. • Most cited authors are Pamučar D, Kahraman C, and Zavadskas EK. In the present era, the implementation of scientifically grounded Multi-Criteria Decision Making (MCDM) has emerged as a pivotal solution to diverse decision-making challenges across various domains. Although a substantial body of exploratory, conceptual, and experimental studies exists, only 9.457% studies have incorporated sensitivity analyses to assess the robustness of MCDM methods. An exhaustive scientific exploration of sensitivity analysis within the scope of MCDM is thus lacking. This research aims to address this gap through Bibliometric Analysis while examining 1374 articles published between January 2000 and March 2023 from the Scopus database. Using RStudio (Biblioshiny), CiteSpace, and VOSviewer software, the study constructs a visual representation of the most prolific countries, institutions, and authors. Impressively, China takes the lead in article publications, while India excels in international collaboration. "An extended TODIM multi-criteria group decision-making method for green supplier selection in an interval type-2 fuzzy environment," featured in the Journal of Environmental Management, emerges as the most cited paper with a total citation of 455. The study also identifies the top three most cited journals, namely "Journal of Cleaner Production," "Expert Systems With Applications," and "Computers and Industrial Engineering." "North China Electric Power University" is the leading institute with the highest research outputs. "Pamučar D" is the most cited author, with 2594 citations and 39 articles, followed by "Kahraman C" and "Zavadskas EK. This study sheds light on trends in scientific developments and collaborations, providing a model for the application of sensitivity analysis in MCDM research and highlighting global trends. An understanding of the current state of sensitivity analysis research can assist researchers working in the entire domain of MCDM. Additionally, the visualization provides prescriptive data for future work and applications related to sensitivity analysis. [ABSTRACT FROM AUTHOR]

• An optimize ensemble machine learning model has been proposed for breast cancer classification on the basis of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Crow Search algorithm. • The proposed model improves the overall performance metrics consists of accuracy, recall, precision, AUC, F1 score, Kappa and MCC. • Even with the limited number of samples, the proposed model performs better. In this paper, we propose a novel framework for breast cancer classification using optical coherence tomography (OCT) images. As the incidence of breast cancer in India continues to rise, early detection is essential for increasing survival rates. For precise classification, an efficient classifier model is required. Due to the variety of datasets and performance metrics, a single classifier may not be sufficient. Thus, we propose an efficient ensemble classifier based on Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) integrated with the Crow Search Algorithm (CSA). TOPSIS, a technique for Multi-Criteria Decision Making (MCDM), ranks and selects the ensemble's base classifiers. The CSA fine-tunes the weights of the base classifiers. In addition, we employ SHAP (SHapley Additive exPlanations) values to visualize feature attributions at the observation level, thereby facilitating model interpretation and comprehension. The proposed classifier performs admirably on the experimental testing dataset, with values of 0.921, 0.921, 0.923, 0.921, 0.846 and 0.846 for precision, recall, F1-score, Kappa, and MCC, respectively. This model's ultimate goal is to reduce reliance on skilled pathologists, reduce interobserver variability, and speed up the processing of breast tissue assessments. [ABSTRACT FROM AUTHOR]

Highlights • This paper proposes a framework for the ongoing urban renewal in India. • The framework comprises of seven criteria. • The framework is validated by means of a case study based in Southern India. • The evaluation of framework is done by using DANP. Abstract India is witnessing rapid urbanization due to increase in population in cities. This poses a major challenge to the urban renewal process. This paper aims to provide an urban renewal framework for the development of cities in India under the ambit of smart cities mission, an initiative by the Government of India. To guide practices related to management of urban areas and advance policy-making decisions and scientific inquiry in this domain, we identify 7 criteria and 27 sub-criteria mainly from the literature related to socio-technical perspectives. To handle the inter-relations among the identified criteria and sub-criteria, we propose a multi-criteria decision-making (MCDM) approach based on Decision Making Trial and Evaluation Laboratory based Analytic Network Process (DANP). Moreover, the effectiveness of the proposed methodology for urban renewal in South India is demonstrated with a real-life case study. Finally, we establish how the obtained results will help the policy makers to initiate urban renewal in southern India. [ABSTRACT FROM AUTHOR]

In this paper, conventional and renewable energy sources for sustainable development of energy sector in India are evaluated from multiple perspectives including economic, technical, social, environmental, political, and flexible criteria. An integrated Shannon's entropy multi-criteria decision making (MCDM) method has been used for the evaluation and assessment of these sources. Thermal, gas, nuclear, solar, wind, biomass, and hydro energy options are used as the alternatives in the decision model. Shannon's entropy method is applied to determine the weights of decision criteria, and fuzzy analytical hierarchy process (AHP) method is applied to prioritize sustainable energy alternatives. The output of the proposed model was compared with six different fuzzy MCDM techniques for the establishment of correlation index. Solar energy was shown to be particularly well suited for India followed by wind and hydro energy sources. Later, the study has developed fourteen scenarios, considering the first five sustainable energy sources (solar, wind, hydro, biomass, and gas power), to evaluate the optimal energy mix scenario for the sustainable development of energy sector in India. An optimal energy mix scenario carries the heroic development of solar, wind, and hydro energy with cross border import-export facility for the time frame of the year 2030. • Reviewed the overview, challenges and development of Indian energy sector. • Developed integrated Shannon entropy fuzzy MCDM approach. • Evaluated the most sustainable energy alternatives in India. • Results validated with six fuzzy MCDM approaches and correlation coefficients. • Evaluated the optimal energy mix scenario in India for the time frame of 2030. [ABSTRACT FROM AUTHOR]

'Water' always has been a key resource for humankind but over the period with increasing population and rapid urbanization, water resource is under immense pressure. Groundwater is the prime source of drinking water and needs proper monitoring in order to have an optimum utilization strategy owing to the current scenario of over-exploitation. Being the part of Deccan trap region, the basin is hot and semi-arid, experiencing moderately dry climate accompanied by episodic drought and erratic showers therefore, the surface water of the basin is ephemeral. In the present study, the probabilistic recharge zones have been delineated by dint of various thematic layers viz. geological setting, geomorphic features, land use, slope gradient, infiltration number, soil physiognomies, curvature and lineament through multi-criteria decision analysis technique in the GIS domain. This has further divided into four categories, viz., very poor, poor, good, and very good zones. Thus, this paper is an attempt to demarcate probabilistic groundwater zones in the Vaniar watershed region using geospatial techniques for better planning and management of groundwater resources. [ABSTRACT FROM AUTHOR]

• The study depicts societal issues associated with the development projects. • A road map developed that focuses on avenues related to establishment of new development projects. • The SEETA model, based on Multi-Criteria Decision Making techniques is proposed. • Model is demonstrated using comparison of fourteen hydropower plants. • The hydropower plants are compared around five main and fifteen sub-criteria. Selecting an appropriate location for a large infrastructure project poses difficult situations. It shall satisfy sustainability indicators and establish harmony among multiple goals of multiple stakeholders. In such a situation, Multi-Criteria Decision Making techniques allow assessing qualitative and quantitative attributes, analysing and removing subjective biases and help in arriving at objective decisions. However, a single Multi-Criteria Decision Making technique may not be an effective tool to generate concrete results as there is no validation of the results. By utilising hybrid Multi-Criteria Decision Making methods, one can incorporate, gauge, and assess the range of social, economic, and environmental impacts precisely and achieve an accurate result by validating results by other methods. Therefore, this paper develops a model called as Social, Economic, Environmental and Technical Assessment model. It combines four fuzzy Multi-Criteria Decision Making techniques viz. Fuzzy Stepwise Weighted Assessment Ratio Analysis, Fuzzy Multi-Objective Optimization by Ratio Analysis, Fuzzy Weighted Aggregated Sum Product Assessment and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution. This paper utilises the case of hydropower plants in India, how the policy and decision-makers can arrive at the selection of the best location for an infrastructure project. According to the assessment value, Teesta Low Dam IV (Darjeeling, West Bengal) is the preferred hydropower plant for all selected methods. The State could bring social justice, ecological stability; achieve economic benefits and sustainability by appropriating this proposed model in other development fields. [ABSTRACT FROM AUTHOR]

The post-harvest and marketing system is a chain of interlinked activities starting after the harvest time and continuing until the delivery of the food product to the consumers. An efficient post-harvest system ensures that the harvested product reaches the customer in the shortest possible time interval without compromising the volume, quality, and safety of the product. The objective of this paper is to identify and model the critical causal factors of post-harvest losses (PHL) in the fruit and vegetable (F&V) supply chain in India, with the help of a detailed literature review and expert opinions. Fifteen causal factors were identified using the decision making and trial evaluation laboratory (DEMATEL) method, which is a multi-criteria decision making (MCDM) tool. This was applied for determining the cause-effect relationships among the identified factors. The results highlighted that the most critical factors that should be tackled to ensure progressive PHL reduction are: lack of proper packaging facilities (PHL3), lack of proper storage facilities (PHL1), insufficient infrastructure (PHL4), improved handling of the products at the farm and marketplace (PHL2), lack of processing facilities (PHL5), lack of linkage between the farmers and processing units (PHL6), lack of linkages in the marketing channel (PHL9), and large number of intermediaries (PHL15). The findings of this study are intended to guide various supply chain members and decision-makers for reducing PHL and improving the overall performance of the F&V supply chain. [ABSTRACT FROM AUTHOR]

• Distributed generation using local renewable resources is studied. • Economic assessment is performed. • Investment risk analysis is carried out. • Environmental impact assessment of the combinations is evaluated using LCA. • MCDM approach is used for optimal solution selection. Increasing renewable share in power supply is currently a global mission. Decentralized renewable power with locally available resources is an emerging preferred sustainable energy solution. This is even more recommended for remote locations where reliable grid power is neither available nor feasible. However, insufficient availability and intermittency of local renewable resources, cost of unit electricity and reluctance of investors due to uncertainty in return on investment in new technologies are some of the critical issues to be addressed. A detailed environmental impact assessment is also needed. Considering the research gap, this study aims to find technically efficient, economically optimal with less uncertainty on return on investment and environmentally benign energy combinations for distributed generation. The methodology proposed in this paper integrates techno-economic optimization with uncertainty analysis in return on investment through Monte Carlo analysis and environmental impact assessment using life cycle assessment to explore an even better sustainable solution. A multi criteria decision making approach is then adopted to decide a final optimum solution for three multi criteria of cost, uncertainty in return on investment and environmental impact. Such a comprehensive analysis for an optimum solution is demonstrated for a remote village of India. The analysis result shows that the combination of solar photovoltaic-diesel generator-Battery system is the possible optimum solution with a cost of electricity of $0.21/kW·h, a standard deviation of 0.07 for risk on investment and a moderate environmental impact. [ABSTRACT FROM AUTHOR]

• Feature engineering based framework for location-sensitive disaster event detection. • Augmentation of context-free and context-based features with place of occurrence. • Evaluation with unsupervised machine learning algorithm with 09 performance metrics. • AHP-TOPSIS based selection of an efficient machine learning algorithm and feature set. • Location-augmented context-based features outperformed traditional textual features. Disaster event detection aims to identify events like terrorist attacks, fire incidents, stampede incidents, building collapse, etc., reported in the online news articles or social media. Place of occurrence of disaster event is a significant feature associated with events for location-sensitive disaster event detection. Efficient feature selection and their augmentation with location information can contribute towards the evolution of traditional approaches and their adoption for location-sensitive disaster event detection leading to improvement in the overall process as a whole. Since the evaluation of event detection techniques deliberates various intrinsic and extrinsic performance metrics, the decision-making for the selection of feature sets is treated as a Multiple-Criteria Decision Making (MCDM) problem. This paper proposes a framework, GeoClust , that is based on feature engineering of traditional textual features in order to enhance their capability for improved location-sensitive disaster event detection. The framework augments context-free and context-based textual feature sets with feature sets of place of occurrence of the events and evaluates their performance using unsupervised machine learning algorithms for various performance metrics. Finally, the best feature set is selected using AHP-TOPSIS technique of MCDM in order to tune the system for automatic and efficient location-sensitive disaster event detection in real-time. Extensive set of experiments have been performed in order to evaluate the framework on a dataset of online news articles reporting disaster events about terrorist attacks, fire incidents, stampede incidents, building collapse and maoist attacks happened at different locations in India. The results show that the location-augmented feature sets significantly improve performance of location-sensitive disaster event detection as compared with traditional feature sets. The results also demonstrate that the context-based feature sets with location-augmentation are ranked higher than the context-free feature sets in MCDM analysis. [ABSTRACT FROM AUTHOR]

COVID-19 is a respiratory infection caused by a coronavirus that spreads from person to person. In the present situation, the COVID-19 pandemic is a swiftly rising phase. Now the time is the second wave ending phase of coronavirus and the third wave coming phase of coronavirus in India. The pandemic situation is moving forward all over India. Nowadays, the worldwide COVID-19 pandemic structure is a very hazardous situation. The COVID-19 vaccine can suppress this situation and gain preventive measures against coronavirus. In producing the COVID-19 vaccine, the Indian medical board plays a significant role. The COVID-19 vaccines have exhibited 90%–95% efficacy in preventing symptomatic COVID-19 infections. Against COVID-19, for emergency purposes, the Indian medical board has approved three vaccines: Covishield, Covaxin, and Sputnik V. Generally, the Indian people are embarrassed about the vaccination of COVID-19. All people are thinking about which vaccine is best for them. This labyrinth can be evaluated effectively using the multi-criteria decision-making (MCDM) technique. Therefore, we have proposed a novel MCDM technique for selecting COVID-19 vaccines. The main aim of this paper is to develop an MCDM technique based on a λ -weighted ranking interpreter ( R λ + , R λ − ). The first time, we have defined positive and negative λ -weighted rank interpreter for the ranking of single-valued bipolar neutrosophic (SVbN) number. Additionally, positive and negative λ -weighted values and positive and negative λ -weighted ambiguity of an SVbN-number are formulated here. Some important, valuable theorems and corollary of SVbN-number are formulated. To show the applicability of the proposed MCDM technique, we have considered a real decision-making problem where ratings of the alternatives are with SVbN-numbers. [ABSTRACT FROM AUTHOR]

Abstract: Building construction in India is estimated to grow at a rate of 6.6% per year between 2005 and 2030 resulting in a continuous increase in demand for building materials. Fired clay bricks are the most widely-used walling materials in the country. However, over the past few decades, the development of other materials such as concrete blocks, fly ash bricks, stabilized mud blocks, etc., has created viable alternatives to bricks. There is limited understanding of the broader environmental consequences of these building materials addressing natural resource depletion, energy, environment and socio-economic impacts. The main objective of this paper is to present a comprehensive assessment of materials used for wall construction by comparing one square meter of constructed wall for each of the materials. A composite Environmental Index was developed by weighting and aggregating normalized numerical scores of several parameters making use of a Multi-Criteria Decision Analysis (MCDA) framework. The Environmental Index was then ranked to determine walling systems that are best suited in the context of India. Our analysis shows that wall assemblies that use non-fired products as masonry units are ranked higher compared to fired masonry unit wall assemblies. Clay fired masonry wall assemblies exhibit poorer environmental performance compared to non-fired masonry wall assemblies. When a more efficient form of construction such as the Rat-trap bond wall construction is considered, the environmental performance of clay fired brick walls is significantly improved. [Copyright &y& Elsevier]

Abstract: Given the civic and social commitments of urban local bodies in developing countries, renovation and maintenance of all heritage buildings and sites is a financially non-viable proposition. Urban planners, therefore, need to take decisions on which heritage sites are to be protected (either in their original form, or with suitable modifications that permit commercial use without destroying their architectural integrity) and the sites to be pulled down, ensuring more efficient land use. Such selection can generate considerable dispute between policy makers and various stakeholders unless the selection process is transparent, consistent and incorporates views of stakeholders. The difficulty in creating an analytical framework to be used for grading is the multiple (often qualitative) attributes of heritage sites. How to combine the performances of heritage structures with respect to these attributes into an aggregative index is a major concern for urban planners and conservationists. This paper argues that the Multicriteria Decision Making (MCDM) method may be a relevant method for grading heritage sites. The application of this method is illustrated for several heritage buildings in Calcutta, India. [Copyright &y& Elsevier]

Community Energy systems are predicted to emerge as a clean and sustainable means of providing last mile energy access to off-grid communities in developing nations like India. Interestingly, rate of deployment and sustainability of such systems are low due to lack of conceptual framework determining sustainability of community energy projects with respect to inherent grassroots barriers. There is a dearth of pertinent research studies providing best practices to project planners and decision makers for accelerated deployment and sustainable operation of community microgrids. This paper aims to identify the typical grassroots barriers and their relationship with corresponding enablers under seven broad dimensions with respect to solar, biomass and micro-hydro based community energy projects. Thereby, set of critical barriers and enablers is extracted by applying integrated multi criteria decision making approach to develop decision maps and conceptual framework for realizing sustainable community energy projects. The developed decision maps offer best practices to community energy projects' stakeholders to ensure sustainable project planning. The conceptual framework establishes direct and indirect relationships among significant enablers and critical barriers while providing policy implications to decision makers and utilities for achieving Universal Electrification through clean energy sources. • Community Energy barriers and enablers are identified with respect to solar, biomass, and micro-hydro technologies. • Barriers and enablers are evaluated through multi criteria decision analysis under seven broad dimensions. • Critical factors are determined through integrated Fuzzy TOPSIS-IRP methodology. • Decision map and conceptual framework is developed for sustainable CE systems. [ABSTRACT FROM AUTHOR]