Society 5.0 represents a transformative era where the convergence of technology and society reshapes decision-making processes. This paper explores the challenges and opportunities in decision-making within Society 5.0 and focuses on the Sustainable Development Goals (SDGs) as a critical framework for sustainable development. Assessing the progress of Indian states toward these goals is crucial for effective policy formulation. To comprehensively analyze the performance of Indian states in achieving the SDGs, this study employs the TOPSIS method and cluster analysis. Integrating these approaches establishes a robust framework for benchmarking and evaluating state performance. The findings provide valuable insights into progress variations, identifying improvement areas and enabling targeted policy interventions and resource allocation. This research enhances our understanding of sustainable development progress at India’s state level. By informing policy decisions and fostering effective strategies, it contributes to the successful implementation of the SDGs. The manuscript presents the methodology, data analysis, and results, providing a structured assessment of Indian states’ SDG performance. Through this study, we aim to support evidence-based decision-making and promote sustainable development in India.

In the continuous evolving digital era, the escalation of online fraud demands a robust and efficient mechanism for its detection and prevention. In the recent years there has been a significant increase in the online bank transactions. The research delves into the integration of different machine learning algorithms and to enhance the model’s adaptability, Synthetic Minority Oversampling Technique (SMOTE) has been utilized. The approach addresses the challenges of data imbalance and also strengthens the overall detection performance. Through an extensive literature review the study highlights the limitations in the existing issues in online financial fraud. The proposed model employs a heterogeneous ensemble model consisting of K-Nearest Neighbors (KNN), Random Forest, and XGBoost. KNN functions as an anomaly detector, identifying irregularities in transactional data. Simultaneously, Random Forest assesses feature significance and detects intricate patterns, contributing to a comprehensive understanding of fraudulent activity. XGBoost, known for its computational efficiency, ensures real-time responsiveness by adapting to emerging fraud tactics. The system also introduces a soft voting mechanism that seamlessly integrates individual algorithm predictions, resulting in a robust and highly accurate ensemble fraud detection system. Validation on an authentic bank fraud dataset underscores the framework's prowess, showcasing superior fraud detection capabilities and a significant reduction in false positives. The purpose of adopting this approach is to enhance the financial security and safeguard the consumer’s assets.

The COVID-19 pandemic has increased the demand for life-saving devices known as 'ventilators,' which help critically ill patients breathe. Owing to the high global demand for ventilators and other medical equipment, many Indian nonmedical equipment companies have risen to meet this demand. This unexpected demand for ventilators during the COVID-19 pandemic, similar to that for other EOL electronic medical devices, has become a severe problem for the nation. Consequently, the healthcare industry must efficiently handle EOL ventilators, which can be outsourced to 3PRLPs. 3PRLPs play a vital role in a company's reverse logistics activities. This study emphasises the 3PRLP selection process as a complex decision-making problem and the optimisation of order allocation to qualified 3PRLPs. As a result, this study proposes a two-phase hybrid decision-making problem. First phase combines the two multi-attribute decision-making methods to select 3PRLPs based on their assessed SPS and Second phase, the evaluated SPS was utilised as one of the objectives of a multi-objective linear programming model to allocate orders to the selected 3PRLPs. To solve the proposed model, both classical and modern approaches were used. The results show that the proposed framework can be successfully implemented in the current scenario of the healthcare industry.

Multiple-criteria group decision-making (MCGDM) problems mainly consist of multiple factors and multiple Decision Makers (DMs) or Users, for which dimension extension is necessary when considering all the entries of DMs together. Tensor, a generalized form of a matrix, displays a multi-way array item, which is the most suitable and practical way to represent high-dimensional data without losing any information. In this paper, we first reduce the dimension through Principal Component Analysis (PCA), which helps consider the most-informative criteria. Then, we reintroduced the tensor as a fuzzy-form tensor for the MCGDM problem because of user information uncertainty. We choose Interval-valued Neutrosophic Fuzzy numbers (IVNFNs) as the basis for the tensor form because of their ability to distinguish between truth, indeterminacy, and falsity in the data. Lastly, a Generalized Interval-valued Neutrosophic Fuzzy Weighted Geometric (GIVNFWG) operator is defined. Moreover, a generalized framework for fuzzy-form tensors for high-dimensional MCGDM problems is proposed. The feasibility and efficiency of this proposed process is illustrated for a real-world MCGDM problem of ranking the most efficient Third Party Reverse Logistics Partners (3PRLPs), i.e., recycled fiber-based paper mills for the packaging industry. The obtained results are according to the experts and are validated using sensitivity analysis. This analysis facilitates in assessing the impact on the overall ranking performance of 3PRLPs by considering various combinations of environment and technological sub-criteria.

Multiple-criteria decision-making (MCDM) explicitly assesses several conflicting criteria for our daily lives in selecting products, vehicles, techniques, etc. Weighting on criteria is a critical step in MCDM as the invalid weight of criteria will lead to a wrong decision. The proposed method addresses some drawbacks of the entropy-based weighting method commonly used in MCDM. The proposed new weighting method considers multiple evaluation factors, including the performance of the decision-maker, the edge weight basis of a digraph, and dominance relationships in the data. By incorporating these factors, the proposed method overcomes the limitations of the entropy-based method and reduces the total computation required. We conducted experiments using sustainable transportation data and comprehensively analyzed the results. We also propose a fuzzy MCDM model incorporating the proposed weighting method and Dempster-Shafer theory. Our model aims to handle uncertainty and imprecision in decision-making. Finally, the correctness and effectiveness of the proposed model were tested on real-life applications. The results of these tests demonstrated that the proposed method provides a practical and effective approach to decision-making in various domains. Overall, the work introduces a new weighting method based on rankability in MCDM, addresses the limitations of the entropy-based method, and presents a fuzzy MCDM model for handling uncertainty. The experimental results suggest that the proposed approach is promising and offers valuable insights for decision-makers.

With the advent of fast computers, availability of data, and development of sophisticated algorithms, every sector is undergoing a process of rapid advancement. Industries utilize advanced technologies to digitize various phases of the supply chain (SC) for better production and enhanced customer experience. Supplier selection is a fundamental part of supply chain management (SCM) and has an immense scope of exploiting emerging technologies like IoT (Internet of Things), big data analytics, cloud computing (CC), etc. The present study provides brief research and a comparison of the conventional methods or models accessible in literature and the role of Industry 4.0.

Performance measurement is a complex but important task required in all sectors. The problem however arises when usage of different methods for performance assessment provides different results. Under such circum-stances when there is a difference of opinions, rank aggregation methods can be used to provide the best solution to decision-makers (DMs). Such approaches, also known as data fusion approaches, combine ranked lists from various methods to generate a consensus. In this study, a novel rank aggregation method is proposed for addressing the problem of conflicting MCDM ranking results. The suggested method uses genetic algorithm (GA) to minimize the Euclidean distance between the ideal ranking and the ranking computed by multiple MCDM methods. This model is embedded into a hybrid multi-criteria decision-making (HMCDM) approach, which is divided into three distinct phases. The first phase identifies the most efficient alternatives; the second analyses the rankings obtained through various MCDM methods; and finally, a compromise ranking result is generated. The proposed approach is employed to measure the performance of Indian Pulp and Papermaking Industries (IPPI).

Pulp and Paper Industries (PPI) manufactures a wide range of papers based on three different GSM (Grams/sq. meter). i.e., lower GSM, middle GSM and higher GSM. In order to maximize the profit, the PPI must efficiently utilize its available resources thereby producing optimal units of three different GSMs. Such problems lie under the category of product mix problems and forms an important part of production planning for every process industry like paper mill. In the present study, this problem is represented as a Fuzzy Linear Programming (FLP) model, to include the inherent vagueness and uncertainties. The solutions obtained through FLP are further refined with the help of AHP (Analytical Hierarchy Process) to determine the most profitable solution. Results indicate that ranking results obtained by integrating AHP into FLP may help in providing a better guidance to the Decision Maker (DM) for determining an optimal product mix.

Selection of a suitable fibrous raw material for the pulp and papermaking industries is a tedious process involving a lot of experimental procedures. This process can be reduced to some extent through computer-based simulation tools and treating the problem as a multi-criteria decision making (MCDM) problem. In the present study, selection of fibrous raw material is treated as an MCDM problem where chemical, morphological and physical properties of the fibers are considered as different criteria and study is done in context of Indian Pulp and Paper Mills. TOPSIS, its fuzzy variant (FTOPSIS) and its modified variant (MTOPSIS) are employed to identify the suitable selection of fibers. The results obtained by these methods when discussed with experts indicate that MCDM tools like TOPSIS (and its variants) can be an attractive alternative for simulation-based selection of fibrous raw material for pulp and paper industries.

Specific IgE against Aspergillus fumigatus has been used as a specific marker for the diagnosis of ABPA with cutoff value of >0.35KUA/L. The cut off value of specific IgE could vary with ethnicity, age, and geographic distribution. The present study determines the cutoff values of specific IgE and total IgE in north Indian children with ABPA. The present study was conducted in Advanced Pediatrics Centre, PGIMER, Chandigarh with sample size of 140 children (poorly controlled asthma = 70, ABPA = 70. Children within age group of 5-15 years were included in this study after taking written informed consent/assent from families/patients. ABPA was diagnosed according to Rosenberg and Patterson criteria. Peripheral blood sample was collected to quantify total and specific IgE levels, absolute eosinophil count. There was a significant difference in the total IgE levels between the two groups (1995.03±1884.56 vs 5114.15±4339.38; p<0.0001). Specific IgE levels were significantly higher in the ABPA group compared to the poorly controlled asthma children (17±20vs 0.15±0.19, p< 0.0001). Receiver Operating Curve ROC was constructed to determine cut off values of specific IgE and Total IgE in the diagnosis of Children with ABPA. The ROC analysis of specific IgE levels of children with ABPA determined cut off value of 0.49KAU/L with sensitivity of 88.89% (95%CI 75.94 to 96.29%) and specificity of 94.03% (95%CI 85.41 to 98.35)and Likelihood Ratio of 14.89%. The present study determines the ROC cut off value of specific IgE levels as 0.49KAU/L and total IgE levels of 1204 IU/L for the diagnosis of children with ABPA.

Ageing population is a universal phenomenon. One of the undeniable facts of human life is that the aging process is basically normal. Life is a progression from young to old. Aging is a complicated process that most influences the biological, psychological and sociological functioning of the organism; it is a normative process and not a fixed dimension of the human life cycle. Like all previous life stages, aging consists of a series of status passages. A main concept in any discussion ageing is the meaning of age itself. Elderly as per the United Nations are referred to as the people of 60 year of age and above. In India also they are described as persons of sixty years and above (Prasad, 2017). Another major challenge of population aging in India is income and housing security for Aged. This is due in part to a changing social and economic landscape in which the traditional family support system is diluted and depleted in the households of many older adults (Alam, James et al., 2012).

The Decision-making is undeniable and is an integral part of in almost all the processes either in the complex form or as a simple procedure. It often refers to the prioritizing (ranking) the alternatives based on several conflicting criteria. To ensure that the process of decision making runs smoothly with minimum errors, multiple criteria decision-making abbreviated as "MCDM" is used for obtaining the solution. The present work emphasized on the weighing methods, an important aspect in MCDM methods, that accurately determines the relative importance of each criterion. The relative importance of each criterion is determined by a set of preferences, called weights, represented between 0 and 1. The weights of criteria influence the outcome of any decision-making process, so it is essential to highlight the significance of weighing methods in determining the criteria preference. In literature, researchers have reported various weighing methods for calculating the relative weights of criteria used for ranking the alternatives. The present study, provides an overview of the some popular weighing methods applicable to the MCDM process and also shows the performance of these methods through a case study.

BACKGROUND AND OBJECTIVES:The efficacy of nutrient interventions to prevent/reverse stunting is considered to be restricted to early life. Whether such interventions are equally effective in later childhood is not clear. The present study evaluated the effect of a food-based high-quality protein and micronutrient intervention on the linear growth of Indian primary school children.METHODS AND STUDY DESIGN:A secondary analysis of a one-year milkprotein and micronutrient fortified food product intervention (protein-energy ratio: 12.8%) on the height of 550 children aged 6-10 years, of poor-socioeconomic background, was carried out. Height and weight increments were compared between groups of each year of age using multiple linear regression. Comparisons in prevalence of stunting and underweight between these groups was also made.RESULTS:The overall mean height increment at the end of 1-year was 6.10±1.07 cm, the highest being for 6-year olds (6.38±0.84 cm). The mean height increments in 6, 7 and 8-year-olds were significantly higher (all p<0.05) than the expected median growth. Height-forage score increased across all age-groups (by 0.14±0.18) and was significantly higher in 6-year olds compared to the rest. Stunting reduced by 12% in 6- year olds in comparison to the older age-groups. No significant association was observed between height gain and gender. The increased BMI-for-age scores were significantly lower for the 6-year olds compared to older children.CONCLUSIONS:Food supplements containing high-quality protein (like milk) along with micronutrients, can continue to influence height of children even in primary school, although the most effect is seen in younger children.