This study is an early-bird attempt to probe the reasons behind Cash Holding among Indian urban local bodies (ULBs). The risk of uncertainty, volatility in cash flows, and dynamic economic conditions insist on the management of ULBs viewing cash as a strategic tool for situation management and scenario planning. This paper explores the reasons for Cash Holdings by analyzing 38 Indian municipal corporation’s data over 5 years. The study results revealed the Existence of a high degree of inverse relationship between municipal Cash Holdings and aspects such as Growth, Size, State Revenue, Capital Expenditure, and Debt Per Capita. In contrast, Administrative Expenses and Tax Revenue positively correlated with municipal Cash Holdings. The study results empirically identified the variables, namely Size, Administrative Expense, State Revenue, and Debt Per Capita, as the primary reasons for holding cash by Indian municipal corporations and pointed out the Existence of differences among different categories of municipal corporations in India.
The Marine Predators Algorithm, known for its simplicity and efficiency, is a widely used optimization method. However, it is prone to structural bias, which can cause the algorithm to favor specific areas in the search space without considering the objective function. This bias can hinder exploration, leading to the population repeatedly visiting certain locations without gaining new information, resulting in increased computational burden. In this study, we extensively investigated the occurrence and types of structural bias in the Marine Predators Algorithm. We also assessed newly developed algorithm variants, such as the Opposition-based Local Escaping Marine Predator Algorithm, to determine their susceptibility to structural bias. To detect and analyze structural bias and its types, we employed a straightforward yet effective methodology called the signature test. Through a comprehensive analysis, we have identified algorithms that exhibit unbiased behavior. We believe that our analysis will serve as a valuable resource for practitioners interested in analyzing the theoretical aspects of their algorithms.
Multi-modal medical image fusion plays a crucial role in improving clinical decision-making and boosting diagnostic accuracy by combining comprehensive features from more than one modality. This paper explores the medical image fusion of three different modalities—MRI-gad/MRI-T1/MRI-T2, PET, and CT using three different low-frequency fusion rules—random weighted average, sliding window, and maximum selection. Each rule is evaluated based on its ability to preserve the most salient features from the source images. This study also presents an optimized weighted average fusion rule for the high-frequency coefficients. This approach employs the three most potent metaheuristic optimization algorithms—Differential Evolution, Particle Swarm Optimization, and Genetic Algorithm. These optimization methods aim to dynamically adapt the fusion weights to guarantee the most pertinent and instructive features in the final fused image. The outcome of this study is to present a comparative analysis for the advancement of multi-modal medical image fusion, contributing to improved medical diagnostics, treatment planning, and disease management.
This research paper examines the impact of customer segmentation on purchasing intentions in online shoppers. Using the K-means method and the Online Shoppers Purchasing Intention Dataset, which includes 12,330 sessions from a popular e-commerce website, the study analyzes customer behavior. The findings highlight notable sales surges in August, varying preferences across countries, and the significance of customer engagement and transactional activity. Through RFM and K-means segmentations, exceptional customers with high engagement and value are identified, emphasizing the importance of customer retention strategies. Moreover, opportunities to re-engage less active customers are uncovered. The research provides valuable insights for businesses to optimize marketing efforts, enhance customer satisfaction and loyalty, and maximize revenue potential in the online shopping.
The Grasshopper Optimization Algorithm (GOA) is a relatively recent population-based stochastic approach for solving the nonlinear global optimization problems. A number of attempts have been made in the past to improve the efficiency of population-based approaches by combining them with the features of other approaches. In this paper, an attempt has been made to introduce an enhanced version of GOA by combining it with another population-based approach that is the Self-Organizing Migrating Algorithm (SOMA). GOA is combined with SOMA, and a hybrid variant, SOMGOA, is proposed. The effectiveness of this approach is analyzed on the basis of results, and comparative analysis is made against the previously published results by other algorithms on 15 standard benchmark functions. It is concluded that SOMGOA outperforms all and can be used further to solve nonlinear optimization problems.
