درخت حوزه‌های تخصصی

The objective of this study was to explore the strategies and challenges associated with intellectual property (IP) management in early-stage technology ventures. By examining how these ventures navigate the complexities of IP, the study aims to provide insights into the practices that contribute to their innovation, competitive advantage, and long-term sustainability. This qualitative study employed semi-structured interviews with 27 participants who were actively involved in managing IP within early-stage technology ventures. Participants were selected through purposive sampling to ensure relevant experience in IP management. Data were collected until theoretical saturation was achieved. The interviews were transcribed and analyzed using NVivo software, following a thematic analysis approach to identify key themes and subthemes. The analysis revealed three main themes: the importance of IP, IP management strategies, and challenges in IP management. Participants highlighted the strategic role of IP in providing a competitive edge, attracting investment, and facilitating market differentiation. Key strategies identified included systematic identification and protection of IP, collaboration and partnerships, and enforcement mechanisms. Challenges encompassed financial constraints, legal and regulatory complexities, lack of IP knowledge, internal organizational issues, and external threats such as cybersecurity risks and global competition. These findings align with existing literature on IP management and underscore the multifaceted nature of IP in early-stage ventures. Effective IP management is crucial for the success of early-stage technology ventures. The study emphasizes the need for robust IP strategies, education and expertise in IP, strategic collaborations, and vigilant enforcement mechanisms. Addressing financial and regulatory challenges and investing in cybersecurity are essential for mitigating risks and sustaining competitive advantage. The insights gained can inform entrepreneurs, investors, and policymakers in developing practices and policies that support innovation and growth in the technology sector.

Heart and circulatory system diseases are often referred to as cardiovascular disease (CVD). The health and efficiency of the heart are crucial to human survival. CVD has become a primary cause of demise in recent years. According to data provided by the World-Health-Organization (WHO), CVD were conscientious for the deaths of 18.6M people in 2017. Biomedical care, healthcare, and disease prediction are just few of the fields making use of cutting-edge skills like machine learning (ML) and deep learning (DL). Utilizing the CVD dataset from the UCI Machine-Repository, this article aims to improve the accuracy of cardiac disease diagnosis. Improved precision and sensitivity in diagnosing heart disease by the use of an optimization algorithm is possible. Optimization is the process of evaluating a number of potential answers to a problem and selecting the best one. Support-Machine-Vector (SVM), K-Nearest-Neighbor (KNN), Naïve-Bayes (NB), Artificial-Neural-Network (ANN), Random-Forest (RF), and Gradient-Descent-Optimization (GDO) are just some of the ML strategies that have been utilized. Predicting Cardiovascular Disease with Intelligence, the best results may be obtained from the set of considered classification techniques, and this is where the GDO approach comes in. It has been evaluated and found to have an accuracy of 99.62 percent. The sensitivity and specificity were likewise measured at 99.65% and 98.54%, respectively. According to the findings, the proposed unique optimized algorithm has the potential to serve as a useful healthcare examination system for the timely prediction of CVD and for the study of such conditions.