Articles

Optimizing AI Model Inference on Serverless Cloud Platforms: A Scalable Approach

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The increasing prevalence of Artificial Intelligence (AI) and Machine Learning (ML) models across various industries has highlighted the critical need for efficient and scalable deployment strategies. Traditional deployment methods often struggle with adapting to fluctuating demands and maintaining cost-effectiveness. Serverless computing has emerged as a promising solution to address these challenges. This paper investigates the deployment of AI models within serverless architectures on Amazon Web Services (AWS), specifically focusing on AWS Lambda and Knative. The study analyzes the limitations of conventional deployment approaches and proposes innovative strategies leveraging the capabilities of serverless technologies. Furthermore, it presents a rigorous evaluation of the performance characteristics of these serverless deployment strategies, discusses crucial security and privacy considerations, incorporates illustrative real-world case studies, and outlines potential future research directions.

Optimization of Wireless Mesh Networks for Disaster Response Communication

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Wireless Mesh Networks (WMNs) have emerged as a resilient and adaptable solution for disaster response communication, offering self-healing and self-organizing capabilities that ensure uninterrupted connectivity in emergency scenarios. Traditional communication infrastructures often fail due to network congestion, power outages, and physical damage during disasters, necessitating an optimized approach for rapid and reliable data transmission. This study presents an AI-optimized WMN framework aimed at enhancing network performance by improving packet delivery ratio (PDR), reducing end-to-end delay, optimizing energy consumption, increasing network throughput, and strengthening security. Simulations conducted in MATLAB Simulink compare the performance of AI-optimized routing with conventional protocols such as AODV (Ad hoc On-Demand Distance Vector) and OLSR (Optimized Link State Routing). Results demonstrate that AI-optimized routing achieves a 15.5% higher PDR, 43% lower delay, 49% increased throughput, and 30% reduced energy consumption compared to traditional approaches. Furthermore, an AI-driven Intrusion Detection System (IDS) improves network security by increasing attack detection accuracy to 94.6% while reducing false positive rates to 5.2%. The findings highlight the significance of AI-based routing optimization in disaster scenarios, ensuring robust, energy-efficient, and secure communication for first responders and affected communities. Future research will explore hybrid AI-blockchain security mechanisms, 5G and satellite network integration, and real-world experimental validation to further enhance WMN resilience in extreme disaster conditions.

Safeguarding Patient Confidentiality in Telemedicine: A Systematic Review of Privacy and Security Risks, and Best Practices for Data Protection

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The COVID-19 pandemic accelerated telemedicine adoption, showcasing its potential in improving healthcare delivery. However, privacy and security risks pose challenges, impeding widespread acceptance. The aim is to investigate the integration of data analytics, data analysis, and data cleaning in telemedicine, focusing on patient data privacy and security, with the goal of proposing strategies to mitigate risks and uphold confidentiality. Utilizing a qualitative approach, privacy and security challenges in telemedicine were investigated. Multiple databases, including PubMed, Embase, and Cochrane Library, were searched from 2018-2023. Inclusion criteria involved English-language, peer-reviewed empirical studies focusing on telemedicine privacy and security. Out of 770 unique records screened, eight studies were included. Full-text review and risk of bias assessment were conducted using CASP tool.  Privacy and security, technology hurdles for providers, patient trust, professional training, physical assessment challenges, and disparities among special populations were identified. Environmental, technological, and operational factors contribute to privacy and security risks in telehealth. Technology challenges like restricted access to telehealth tools and poor internet hinder adoption. Data analytics in telemedicine facilitates healthcare transformation, addressing privacy and security while optimizing patient outcomes through advanced analytics techniques and structured data analytics lifecycles. The integration of data analytics in telemedicine shows promise for healthcare transformation by providing insights into patient behavior and policy impacts, while ensuring data privacy and security. Addressing barriers, accelerated by the COVID-19 pandemic, requires infrastructure enhancements and global research efforts for inclusive telehealth ecosystems.

Establishment of Foreign Policy of Uzbekistan and its Priorities

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The article analyzes the process of formation of the foreign policy of the Republic of Uzbekistan, the main factors contributing to the formation of the foreign policy of the new state, taking into account the situation in Central Asia. The study also highlights the principles and priorities of Uzbekistan’s foreign policy activities and topical issues of developing international cooperation in order to preserve and strengthen regional stability.