Purpose: Unemployment is the biggest issue for all the developing countries, especially India, where millions of educated people are passed out every year from different educational institutes, but against this, the jobs are not being generated. This situation will only be addressed effectively when the government/authorities make more efforts to identify/create potential entrepreneurs. The present study investigates the relationship of entrepreneurial characteristics on entrepreneurial attitude and intention among engineering undergraduates engaged in various technical institutions in Chhattisgarh state.Design/methodology/approach: Stratified random sampling was used to collect sample of 1,000 engineering undergraduates enrolled in third and fourth year at different technical institutions of Chhattisgarh state.Findings: Structural equation modelling and hierarchal multiple regression analysis were incorporated, and the analysis revealed that the entrepreneurial characteristic was found to be a significant predictor of entrepreneurial attitude and intention of engineering undergraduates. The study also discusses managerial implications, limitations and avenues for future research.Originality/value: Looking at the current scenario, the present study discusses with several factors influencing entrepreneurial attitude and intention of engineering undergraduates, which might be the only solution to a significant issue, i.e. unemployment. In addition, there is a huge lack of research in addressing unemployment issue through entrepreneurship in the state of Chhattisgarh.

Financial fraud detection is a critical application area within the broader domains of cybersecurity and intelligent financial analytics. With the growing volume and complexity of digital transactions, the traditional rule-based and shallow learning models often fall short in detecting sophisticated fraud patterns. This study addresses the challenge of accurately identifying fraudulent financial activities, especially in highly imbalanced datasets where fraud instances are rare and often masked by legitimate behavior. The existing models also lack interpretability, limiting their utility in regulated financial environments. Experiments were conducted on three benchmark datasets: IEEE-CIS Fraud Detection, European Credit Card Transactions, and PaySim Mobile Money Simulation, each representing diverse transaction behaviors and data distributions. The proposed methodology integrates a transformer-based encoder, multi-teacher knowledge distillation, and a symbolic belief–desire–intention (BDI) reasoning layer to combine deep feature extraction with interpretable decision making. The novelty of this work lies in the incorporation of cognitive symbolic reasoning into a high-performance learning architecture for fraud detection. The performance was assessed using key metrics, including the F1-score, AUC, precision, recall, inference time, and model size. Results show that the proposed transformer–BDI model outperformed traditional and state-of-the-art baselines across all datasets, achieving improved fraud detection accuracy and interpretability while remaining computationally efficient for real-time deployment.

Fault detection and remaining useful life (RUL) prediction are critical tasks in self-healing network (SHN) environments and industrial cyber–physical systems. These domains demand intelligent systems capable of handling dynamic, high-dimensional sensor data. However, existing optimization-based approaches often struggle with imbalanced datasets, noisy signals, and delayed convergence, limiting their effectiveness in real-time applications. This study utilizes two benchmark datasets—EFCD and SFDD—which represent electrical and sensor fault scenarios, respectively. These datasets pose challenges due to class imbalance and complex temporal dependencies. To address this, we propose a novel hybrid framework combining Attention-Augmented Convolutional Neural Networks (AACNN) with transformer encoders, enhanced through Enhanced Ensemble-SMOTE for balancing the minority class. The model captures spatial features and long-range temporal patterns and learns effectively from imbalanced data streams. The novelty lies in the integration of attention mechanisms and adaptive oversampling in a unified fault-prediction architecture. Model evaluation is based on multiple performance metrics, including accuracy, F1-score, MCC, RMSE, and score*. The results show that the proposed model outperforms state-of-the-art approaches, achieving up to 97.14% accuracy and a score* of 0.419, with faster convergence and improved generalization across both datasets.

Parents of children with intellectual disability put more care and effort in raising their children. The perceived stress faced by this segment of parents is more than that of the parents with normal child. It is therefore essential for the caregivers to release their burden to perform normal care giving functions. The objective of the study was to determine the effect of socio-economic demographics and social support on the perceived stress of parents of children with intellectual disability. The study was conducted in Chhattisgarh state among 100 parents of children with intellectual disability. Perceived stress scale and Social support scale were the toolsused in the study for data collection. Purposive sampling technique was the method adopted in the study for the selection of samples in the study. Multiple hierarchal regression analysis was used to test the proposed model. Structural equation modeling (SEM) was used to further confirm the mediating effect of social support on income and stress. Result indicated that socio-demographic variables income, education and gender significantly contributed to the variation of stress and showed negative association with stress. However age did not contribute significantly in the variation of stress. Result of mediating effect of social support showed significant association between income and stress.

The pursuit of a meaningful and purposeful existence has consistently been a universal human aspiration, even in the field of business. Individuals seek significance in every endeavour they undertake. In line with this notion, the Shrimad Bhagavad Gita (SBG) directs individuals towards the path of truth and purpose. The present study attempts to examine the effect of SBG-identified variable entitled as ‘Theory of Work’ or ‘Karma’ (consisting of no desire for fruit (NDF), non-attachment (NA), deterministic intellect (DI), righteous duty (RD), success and failure as equal (SAF)) on self-efficacy (SE) among teachers in higher educational institutions (HEI) in India. It also aims to find the association between SE and goal performance and leadership effectiveness among the teachers of HEI. A judgemental sampling procedure was adopted to collect the data from HEI clustered across five different zones in the country. A total of 600 responses were collected through an online-administered questionnaire. The analysis results indicated that theory of work variables (i.e., NDF, DI, NA and RD) have a significant positive effect on SE, and SE positively affects the goal performance and leadership effectiveness. However, treating SAF as equal variables was not found to have a significant effect on SE.

This involves studying one of the most important parts of natural language processing (NLP): sentiment, or whether a thing that makes a sentence is neutral, positive, or negative. This paper presents an enhanced long short-term memory (LSTM) network for the sentiment analysis task using an additional deep layer to capture sublevel patterns from the word input. So, the process that we followed in our approach is that we cleaned the data, preprocessed it, built the model, trained the model, and finally tested it. The novelty here lies in the additional layer in the architecture of LSTM model, which improves the model performance. We added a deep layer with the intention of improving accuracy and generalizing the model. The results of the experiment are analyzed using recall, F1-score, and accuracy, which in turn show that the deep-layered LSTM model gives us a better prediction. The LSTM model outperformed the baseline in terms of accuracy, recall, and f1-score. The deep layer's forecast accuracy increased dramatically once it was trained to capture intricate sequences. However, the improved model overfitted, necessitating additional regularization and hyperparameter adjustment. In this paper, we have discussed the advantages and disadvantages of using deep layers in LSTM networks and their application to developing models for deep learning with better-performing sentiment analysis.