International Journal of Statistical Computation and Simulation

Sustainable Transportation: Innovations for Green Mobility

Prof. Anu Sharma — Thu, 09 Jan 2025 00:00:00 +0000

Transportation is a significant contributor to carbon emissions and environmental degradation. This paper explores sustainable transportation solutions, focusing on electric vehicles (EVs), hydrogen fuel cells, public transportation systems, and bike-sharing initiatives. It evaluates the potential of green mobility technologies to reduce carbon footprints, improve air quality, and promote urban sustainability. The paper also discusses the role of government policies, infrastructure investments, and behavioral change in fostering widespread adoption of sustainable transportation systems.

Biodiversity Conservation: Protecting Ecosystems for Future Generations

Prof. Ramesh Kumar — Fri, 10 Jan 2025 00:00:00 +0000

Biodiversity is essential for maintaining ecosystem health, providing vital services such as clean air, water, and food production. This paper explores the importance of biodiversity conservation in the context of global sustainability, focusing on the threats posed by habitat loss, climate change, and overexploitation of natural resources. It discusses strategies for protecting biodiversity, including ecosystem restoration, conservation policies, and community-based approaches. The paper also examines the role of corporations, governments, and individuals in safeguarding ecosystems for the benefit of future generations.

AI-Driven Approaches to Database Security and Disaster Recovery: Enhancing Resilience and Threat Mitigation

Sanjay Bauskar — Fri, 14 Feb 2025 00:00:00 +0000

In today's rapidly evolving digital landscape, ensuring the security and resilience of databases in cloud environments is paramount. Traditional methods of database security and disaster recovery are often insufficient to address the growing sophistication of cyber threats and system failures. This paper explores AI-driven approaches to enhancing database security and disaster recovery processes. By leveraging machine learning, predictive analytics, and automation, organizations can proactively detect security vulnerabilities, mitigate threats, and recover from disasters more efficiently. AI algorithms can identify anomalous patterns in database activity, predict potential failures, and automate recovery actions, reducing downtime and minimizing the impact of security breaches. This paper discusses the role of AI in strengthening database resilience, the integration of AI with existing security frameworks, and the application of intelligent disaster recovery strategies to ensure business continuity in the face of unforeseen events.