Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate.

Capital structure is an integral part of the corporate finance that sources the funds to finance growth and operations. Managers always have to maintain value of the firm to be higher than the cost of capital in order to maximize the shareholders wealth. Empirical studies have used sources of finance like debt and equity as variables of capital structure. A choice between debt and equity finance analyzes the firm’s ability to perform under the financially constrained environment to attain the sustainable growth. Therefore, it gives rise to a dire need to estimate the cost of capital precisely. We examined the capital structure of top ten market capitalization of the stock markets included in MSCI Emerging index with the use of artificial neural networks, support vector regression, and linear regression in forecasting methods. The capital structure is measured as the proportion of total debt over total equity (Tang et al., 1991). Other financial ratios such as profitability, liquidity, solvent, and turnover ratios were considered as drivers of the capital structure. Applying logistic and hyperbolic tangent activation functions, it was concluded that ANN has a great potential of replacing other traditional forecasting models with the nonstationary data. This research contributes with a new dimension for estimation through different activation functions. There is a possibility of ANN dominance as compared to the other models applied for predictability in financial markets.

Artificial Intelligence (AI) is transforming organizational behavior by reshaping employee engagement, performance, and decision-making processes. This study examines AI’s impact on workplace dynamics, focusing on its role in optimizing workload distribution, enhancing productivity, and supporting leadership adaptation. AI-driven tools, such as sentiment analysis and predictive modeling, facilitate engagement and efficiency but also introduce challenges related to job security, ethical concerns, and transparency. Using a qualitative approach, this research synthesizes findings from systematic literature reviews and meta-analyses to explore the implications of AI in modern organizations. The results indicate that AI-supported leadership and human-centered design principles contribute to motivation and long-term productivity. However, excessive AI reliance may disrupt trust and psychological contracts, potentially affecting organizational culture. This study emphasizes the need for ethical AI governance and strategic leadership to mitigate risks while leveraging AI’s benefits. By adopting a balanced approach to AI integration, organizations can foster employee well-being, maintain equitable decision-making, and align AI adoption with sustainable growth. These findings contribute to the discourse on AI in management, offering insights into how organizations can harness AI’s potential while safeguarding human-centered values.

This paper examines the impact of AI-driven concept generation on development cycles and market responsiveness. As organisations face pressure to innovate rapidly, AI is increasingly leveraged to accelerate product ideation, streamline prototyping, and enhance decision-making. This study explores whether AI-driven concept generation reduces time-to-market while maintaining product-market alignment. This research synthesises existing literature and industry case studies on AI applications in product management, supply chain optimisation, and business model innovation. Drawing on interdisciplinary perspectives, it critically evaluates AI’s role in automated design exploration, data-driven decision-making, and market validation. A theoretical lens grounded in organisational behaviour and technology adoption frameworks underpins the analysis. AI-driven concept generation substantially reduces development cycles by enabling rapid prototyping, data-informed ideation, and real-time customer feedback loops. AI enhances firms’ adaptability to market fluctuations by automating design exploration and improving strategic decision-making. However, the effectiveness of AI-generated concepts is contingent on data quality, human oversight, and organisational integration. While AI fosters efficiency, its benefits predominantly accrue to larger firms with robust AI infrastructure, potentially reinforcing industry concentration. This paper contributes to the discourse on AI in organisational behaviour by synthesising insights across multiple domains. It provides a nuanced understanding of AI’s role in product innovation and strategic agility, offering implications for managers, policymakers, and researchers.

The Hypertext Transfer Protocol (HTTP) is a common target of distributed denial-of-service (DDoS) attacks in today’s cloud computing environment (CCE). However, most existing datasets for Intrusion Detection System (IDS) evaluations are not suitable for CCEs. They are either self-generated or are not representative of CCEs, leading to high false alarm rates when used in real CCEs. Moreover, many datasets are inaccessible due to privacy and copyright issues. Therefore, we propose a publicly available benchmark dataset of HTTP-GET flood DDoS attacks on CCEs based on an actual private CCE. The proposed dataset has two advantages: (1) it uses CCE-based features, and (2) it meets the criteria for trustworthy and valid datasets. These advantages enable reliable IDS evaluations, tuning, and comparisons. Furthermore, the dataset includes both internal and external HTTP-GET flood DDoS attacks on CCEs. This dataset can facilitate research in the field and enhance CCE security against DDoS attacks.