Birla Global University (BGU) is a private university in Bhubaneswar, Odisha, India. It was founded in 2015, by Basant Kumar Birla and Sarla Birla.It is a self-financed private unitary University and has been established by the enactment of "Birla Global University Odisha Act, 2015", with its main campus spread over an area of nearly 30 acres of land situated at IDCO Plot No.2, Gothapatna, Bhubaneswar..
Software reliability prediction and defect detection are essential for improving software quality and reducing development costs. Early and accurate defect prediction supports timely interventions and leads to better project outcomes. This study presents a hybrid approach that combines ensemble learning with the Synthetic Minority Oversampling Technique (SMOTE) and a Modified Jaya Optimization Algorithm (MJOA) for feature selection. The MJOA uses adaptive control parameters that improve convergence and prevent the issue of local optima, making the feature selection process more effective. A brief comparison shows that Greedy Feature Selection (GFS) is faster but often becomes trapped in local optima, whereas the Modified Jaya Optimization method provides stronger global search ability and selects more relevant features for accurate defect prediction. The proposed framework uses eight classifiers, namely Extreme Learning Machine (ELM), Random Forest (RF), Weighted Support Vector Machine (W SVM), Gradient Boosting (GB), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Bernoulli Naive Bayes (BNB), and Multinomial Naive Bayes (MNB), combined through average probability voting. This ensemble method leverages the strengths of individual classifiers, reduces variance and over fitting, and improves generalization performance. Experiments were carried out on six PROMISE datasets, specifically PC2, PC4, MC1, KC3, Camel 1.6, and Ant 1.7, using tenfold cross validation. The Jaya based ensemble performed better than baseline models as well as those using GFS and achieved up to 0.97 percent accuracy, an F measure of 0.95, and a precision of 0.93 on benchmark datasets.These results demonstrate the robustness and practical applicability of the proposed approach, and future work may investigate the integration of deep learning models and alternative optimization techniques to further improve scalability and performance.The study is limited by its use of PROMISE datasets, which may reduce generalization to large industrial settings, and by the higher computational cost of optimization based feature selection. Future work may explore deep learning methods and alternative optimizers to improve scalability and performance.
As countries strive to balance economic growth with climate commitments under the Sustainable Development Goals, understanding whether financial development supports or undermines environmental sustainability across different stages of development has become increasingly important. The study examines the impact of financial development on environmental sustainability by studying the top five economies in the world. The study also offers a comparative analysis of developing and developed countries. The data for this study have been collected from the WDI database for the period from 1999 to 2020. I have applied both Pooled OLS and Fixed Effect Model for the analysis. The non-linear relationship between financial development and environmental sustainability has also been analysed. The results show that for developed countries, financial development increases CO2, but the effect decreases at higher credit levels. In developing countries, the impact is gradual and less pronounced. Urbanisation has a positive impact on environmental sustainability (ES) in developed economies, but it has a negative impact on ES in developing ones. GDP growth generally has a positive impact on emissions for both groups. These findings have important implications for achieving SDG 13 (Climate Action) and SDG 11 (Sustainable Cities and Communities) by highlighting the need for tailored financial and urban policies based on development level.
The objective of the study is to analyse and compare the pre- and post-acquisitions financial performance of 50 Indian acquirers. The selected acquirers are publicly listed companies. The study has used the secondary data obtained from the financial statements. The M&A for acquiring firms become questionable if the financial performance of the acquirers does not improve in the long run. This research study analyses the financial performance of M&A deals in respect of the publicly listed Indian acquirers using accounting ratios at three different levels: all deals, manufacturing industry and service industry. The deals taken for this study constitute 85% of the value of the M&A market for the period chosen between 2010 to 2014. The financial analysis has been compared for three years pre-and-post the deal. The data has been obtained from companies’ annual reports and online databases available in the public domain. The study leads to the conclusion that the acquirers failed to gain financially even after three years of the deal.
PurposeThe purpose of this study is to examine how customers' perceptions of the shopping process across a multi-stage electronic retail delivery system (e-RDS) affect overall satisfaction. This study explores the perceptions of process across four stages of e-RDS: search, agreement, fulfilment and after-sales service. This study also investigates how customers' perception of one stage influences the subsequent stages.Design/methodology/approachThe data are collected from 341 online shoppers and analysed using structural equation modelling.FindingsThis study suggests that customers' perception of a particular stage in multi-stage e-RDS impacts the subsequent stages of service delivery. This study confirms that a smooth process experience at each stage has a positive impact on satisfaction and other factors, including perceived ease of use, perceived usefulness, perceived control and perceived flexibility. Interestingly, perceptions of process at the fulfilment and after-sales service stages are particularly crucial for customer satisfaction.Research limitations/implicationsThis paper finds that it is equally important for e-retailers to enhance customer experience by focusing on process features in all four stages of e-RDS, in addition to product quality and price.Originality/valueThis research distinguishes itself by examining how customers' perceptions of the e-RDS process affect overall experience. This study is one of the few studies that address the significance of process dimensions in evaluating customer experience within the e-retail customer journey. By holistically studying multi-stage e-RDS from a process perspective, this paper offers detailed insights for e-retailers. Understanding process perceptions throughout the customer journey is crucial for shaping customer experience, ultimately leading to positive evaluation of customer satisfaction.
In this study, a multiscale framework combining the Lifting Wavelet Transform (LWT) and multi-path Convolutional Neural Network (CNN) is proposed to enhance the analysis of histopathological breast cancer images. LWT is employed due to its capability to obtain multi-resolution features that effectively retain key textural details. In particular, a multi-path CNN facilitates the concurrent processing of features at multiple levels of wavelet decompositions, thereby preserving diagnostic information that sole-path CNN models may lose. The approach was evaluated on the BreakHis dataset, which contains 7,638 rated images at varying magnifications, achieving 99.34
