Background and rationale: Yoga was recommended in both clinical and non-clinical populations as therapy methods. The diversity of yoga practice as a therapy method has rarely been discussed and it is essential to address the effect of yoga in women. Primary study objective: This Study aims to investigate the effect of different types of yoga in women and their health. Method/ Design: A systematic literature review was conducted to identify the articles, The effect of yoga and yoga related interventions on women health. And all Samhita Granthas. Results and conclusion: Yoga Having role in emotional intelligence and empowerment for women.

Integrating Artificial Intelligence (AI) in supply chain management (SCM) signifies a significant advancement with profound implications for modern businesses, including those in Uganda. This research paper critically examines the challenges and opportunities associated with this integration, using Uganda as a case study. A comprehensive analysis of existing literature and specific insights from the Ugandan context identifies critical challenges such as data integration, technology adoption, and organizational readiness within the country. Additionally, it explores AI's diverse opportunities in optimizing supply chain processes for Ugandan businesses, including demand forecasting, inventory management, and logistics optimization within Uganda's unique operational landscape. Furthermore, the paper discusses the potential impact of AI integration on various stakeholders within Uganda's supply chain ecosystem, including suppliers, manufacturers, distributors, and customers. By synthesizing insights from academic research and industry practices in Uganda, this paper provides valuable insights for Ugandan businesses aiming to leverage AI technologies in their SCM strategies. Ultimately, this research contributes to a deeper understanding of the complexities of integrating AI in SCM within the Ugandan context and offers recommendations for addressing challenges while maximizing the opportunities presented by this transformative technology.

The integration of Artificial Intelligence (AI) in Electrocardiography (ECG) and Photoplethysmography (PPG) signifies AI's profound influence on heart rate monitoring and analysis. ECG traditionally offers critical insights into cardiac health, necessitating expert interpretation. This study introduces an AI framework with Fast Fourier Transformation Analysis for swift, human-like interpretation of complex ECG signals. A multilayer AI Network accurately detects intricate features, enhancing ECG analysis precision. Leveraging comprehensive datasets, AI models proficiently identify heart dysfunctions like atrial fibrillation and hypertrophic cardiomyopathy, and can estimate age, sex, and race. The proliferation of mobile ECG technologies has spurred AI-based ECG phenotyping, impacting clinical and population health. This research explores AI's role in enhancing cardiac health assessment and clinical decision-making using MATLAB, acknowledging its transformative potential and inherent limitations.

While several studies have investigated how artificial intelligence (AI) has shaped next-generation user interfaces, most of the literature is Western-based and scant information exists on the technological advancement for people with disabilities. To address this research gap, this study conducts an integrative review to show how AI in China has facilitated the development of next-generation interfaces, allowing more realistic and personalized interactions based on the different needs of people with disabilities. Specifically, it reports various cases adopted and implemented in various Chinese real-scenarios, beyond lab experiments or small tests. This study contributes to the Sustainable Development Goals (SDGs) by depicting different scenarios, techniques and interaction levels that could be adopted in various contexts to facilitate the life of people with disabilities in different fields.

The earth and its environment is currently undergoing changes, which may be natural as a result of the earth’s processes or artificial, as a result of man’s activities that has altered the natural earth processes. Climate change is a result of the alteration of the atmospheric balance which in-turn has diverse effects on the earth’s processes and her inhabitants. The aim of this review is primarily to elaborate the climate change, the causes, economic impact and methods of mitigating climate change. The effect of climate change is massive but this article is targeted towards the economic impact of climate change and methods of eliminating this effect in this area. It is a universal challenge, it therefore requires universal attention. This article is a review work on the previous publications on climate change including journals and other articles that are closely related to this topic, it goes ahead to reveal the current situations and effect of climate change in our society today and scientifically proven means of mitigating such effects. Fossil fuels are by far the largest contributor to global climate change, accounting for over 75% of global greenhouse gas emissions and 90% of all carbon emissions. Human activities have warmed the atmosphere, ocean and land, producing widespread and rapid changes in the atmosphere, ocean, cryosphere and biosphere. The methods of mitigating climate change are primarily by reducing the use of fossil fuels in order to stop adding more CO2, Nitrous acid and methane (which are all greenhouse gases) to the atmosphere, by using more of renewable energy such as solar energy, geothermal energy, and hydroelectricity. Practicing better farming systems to improve crop yield other than using harmful chemicals. Implementing policies against gas flaring, faulty automobiles discharging high amounts of carbon into the atmosphere, bush-burning, deforestation etc, should be practiced more.

This paper addresses the pressing need to evaluate the maturity and performance metrics of generative AI tools dedicated to accessibility in product development. The problem lies in the lack of standardized methods for assessing the maturity of generative AI tools tailored to accessibility needs and the absence of universally accepted performance metrics to measure their efficacy. This deficiency hampers the advancement of inclusive design practices and limits the potential impact of AI-driven accessibility solutions. This paper proposes a comprehensive framework for evaluating the maturity of AI tools specifically designed for accessibility in product development. We elucidate the critical criteria integral to this evaluation, encompassing aspects such as usability, reliability, scalability, and adaptability to diverse user needs and contexts. The proposed solution aims to contribute valuable knowledge to the evolving landscape of generative AI tools dedicated to enhancing accessibility in product development. By establishing a structured approach to assessing maturity and advocating for standardized performance metrics, our research endeavors to empower developers, designers, and stakeholders to make informed decisions regarding the adoption and refinement of AI-driven accessibility solutions.

With the steady expansion of online commerce, e-commerce sites have become increasingly attractive targets for hackers. These sites serve millions of customers and often hold valuable, confidential, and sometimes financial information in their databases. One particularly dangerous type of attack is SQL injection, which exploits vulnerabilities in web applications to influence backend databases, posing significant threats to such platforms. Traditional defenses like desktop firewalls, input validation, and parameterized queries provide some level of protection but are often insufficient against newer injection variations and sophisticated attackers. The utilization of machine learning to enhance cybersecurity against more advanced threats has been demonstrated as a promising approach. This systematic review examines how various machine learning algorithms are applied to detect SQL injection attacks that could potentially harm e-commerce systems. By identifying and analyzing the relevant literature, this review highlights the effectiveness of different algorithms and their practical applications in enhancing the security of online commerce platforms. More specifically, five techniques were assessed on both real and synthetic datasets: Logistic Regression, Naive Bayes, Random Forest, Artificial Neural Network, and two combined models (Logistic Regression & Naive Bayes, and Artificial Neural Network & Random Forest). The findings indicate that Random Forest performed better than other algorithms in the decision tree family, attributed to its ability to balance precision and recall effectively. However, limitations such as using a single dataset and the computational complexity of some models were noted. This review provides insights for practitioners on selecting appropriate detection models and outlines approaches to address current limitations through future work. Addressing these limitations could involve using more diverse datasets, optimizing computational efficiency, and exploring advanced ensemble methods and neural network architectures.

In the current world, business intelligence systems play a crucial role in guiding organizations on the best strategies to adopt depending on the analytical information and results obtained (Ahmad et al., 2020). Nonetheless, they are being used frequently and, hence, are vulnerable to ransom attacks, requiring appropriate security measures. This study examines the opportunities for BI solution providers to implement cybersecurity, mitigate risks, and gain competitive advantage. The study adopts a mixed-methods approach to the research involving a literature review study, self-complete questionnaires, interviews, and case studies among a variable of industry professionals. The observation in this research process shows that incorporating enhanced encryption methods, two-factor verification, monitoring, and security culture advancements leads to increased data security levels. Furthermore, apart from defending an organization from cyber-threats, properly implementing cyber security in BI systems ensures that an organization acquires better customer trust, compliance with regulations, and a competitive edge in the market. This paper outlines vital tactics and best practices businesses can apply to enhance, design, and implement robust cybersecurity strategies for organizational benefit. This research aims at the following question; How can cybersecurity be integrated in Business Intelligence (BI) systems considering the facts that businesses are adopting data-driven strategies in the current century? Since more organizations opt for BI systems to collect pertinent information and generate strategic decisions, the systems named remain vulnerable to grabs and threats. Similarly, the research on the role of cyber security is a prime example to show how it need not to be a risk factor but in fact could be a key strength.

Financial inclusion is crucial for the growth and sustainability of small and medium-sized enterprises (SMEs), which are significant contributors to economic development and job creation. However, traditional banking models often fall short in serving the unique needs of SMEs due to perceived high risks and insufficient credit history. Leveraging artificial intelligence (AI) and data analytics, the banking sector can transform its approach to financial inclusion, offering tailored financial products and services that cater specifically to SMEs. AI and data analytics enable banks to analyze vast amounts of data from diverse sources, including transactional histories, social media activity, and market trends. This holistic view of an SME's financial health and business potential allows for more accurate risk assessment and credit scoring. Machine learning algorithms can identify patterns and predict creditworthiness, enabling banks to extend credit to SMEs that may have been overlooked by traditional models. Additionally, AI-driven insights facilitate the development of customized financial products, such as flexible loan terms and dynamic interest rates, that align with the cash flow cycles and operational realities of SMEs. Automated processes and AI-powered chatbots enhance customer service, providing SMEs with timely support and financial advice, thereby improving their banking experience. Data analytics also play a critical role in fraud detection and prevention, ensuring the security of transactions and building trust among SME clients. By continuously monitoring and analyzing transaction data, banks can quickly identify and mitigate fraudulent activities, protecting SMEs from financial losses. Moreover, AI and data analytics support the creation of financial literacy programs tailored to the specific needs of SMEs, empowering business owners with the knowledge and tools to make informed financial decisions. This educational aspect is vital in fostering a sustainable financial ecosystem for SMEs. In conclusion, the integration of AI and data analytics in the banking sector holds significant promise for promoting financial inclusion among SMEs. By providing more accessible, customized, and secure financial services, banks can support the growth and success of SMEs, ultimately contributing to broader economic development and financial stability.