Ultra-Reliable Low-Latency Communication (URLLC) in 5G Networks: Enabling Mission-Critical Applications

Ying, Deng; Shajahan, Mohamed Ali; Khair, Md Abul; Sandu, Arun Kumar

doi:10.18034/ei.v11i1.707

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…With the help of AIoptimized segmentation, marketers can make datadriven decisions by gaining insightful knowledge about user behavior and industry trends. Marketers can adjust their tactics and improve segmentation criteria in response to shifting market conditions by examining the output of segmentation models and tracking key performance indicators (KPIs) over time (Ying et al, 2023). The marketing team should develop a data-literate culture and build a robust data analytics infrastructure to facilitate well-informed decision-making and ongoing development.…”

Section: Data-driven Decision Makingmentioning

confidence: 99%

AI-Optimized Customer Segmentation for Targeted Cryptocurrency Marketing

Khair

2024

Asian bus. rev.

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This study examines how AI-optimized consumer segmentation may enhance well-focused Bitcoin marketing campaigns. The primary goals are to investigate the theoretical underpinnings, conduct empirical evaluations, and offer valuable recommendations for cryptocurrency marketers. Methodologically, a thorough literature research is carried out, and then actual data from the real world is used for empirical analysis. Important discoveries show that AI-driven segmentation promotes user engagement, increases marketing efficacy, and supports long-term growth in the Bitcoin ecosystem. However, restrictions like algorithmic bias and data privacy issues demand policy changes. To solve these issues, it is advised to implement ethical standards, industry collaboration, educational initiatives, and regulatory guidelines. This study demonstrates how AI-optimized segmentation may revolutionize targeted cryptocurrency marketing by promoting user loyalty, meaningful interaction, and sustainable growth.

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Section: Data-driven Decision Makingmentioning

confidence: 99%

AI-Optimized Customer Segmentation for Targeted Cryptocurrency Marketing

Khair

2024

Asian bus. rev.

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“…Distributing processing jobs among several computing nodes can enhance scalability and resource efficiency in Internet of Things analytics systems. Processing capabilities can be flexibly scaled based on workload demands by employing distributed computing frameworks like TensorFlow or Apache Spark and parallelizing computation (Ying et al, 2023).…”

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confidence: 99%

Real-time Multimedia Analytics for IoT Applications: Leveraging Machine Learning for Insights

Shajahan,

Roberts,

Sandu

et al. 2024

Eng. int. (Dhaka)

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The combination of real-time multimedia analytics and Internet of Things (IoT) applications, along with machine learning techniques, has shown great potential in improving the capabilities of IoT systems. This study investigates the potential of machine learning to gain insights into IoT applications. By thoroughly examining existing literature and analyzing current trends, this study explores essential goals such as improving IoT systems' data processing, decision-making, and security. This study extensively examines the literature on real-time multimedia analytics, machine learning algorithms, and IoT applications using a systematic approach. Doing so aims to provide a comprehensive overview of the field's current state and highlight the main challenges and opportunities. The significant discoveries highlight the impressive capabilities of machine learning algorithms, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), in efficiently handling intricate multimedia data. These algorithms empower organizations to gain real-time insights and make informed decisions. Addressing challenges such as computational constraints, data privacy, and multimodal data integration is crucial for policy implications. This can be achieved through investments in edge computing infrastructure, developing low-power machine learning algorithms, and implementing robust privacy and security measures.

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Resilient Supply Chains: Strategies for Managing Disruptions in a Globalized Economy

Karanam,

Sachani,

Natakam

et al. 2024

Am. j. trade policy

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This paper investigates methods for creating robust supply chains in a worldwide economy in light of growing disruptions. The primary goals were to evaluate supply chain resilience through integrating sustainable practices, investigate the impact of new technology, and explore solutions for diversification. A thorough analysis of case studies and literature regarding methodology was carried out, emphasizing secondary data sources from government publications, business reports, and scholarly journals. Significant research indicates that diversifying suppliers, production sites, and logistical routes can reduce the risks associated with localized interruptions. Proactive risk management requires enhanced visibility, predictive analytics, and operational efficiency, which may be achieved by integrating technologies like AI, IoT, blockchain, and cloud computing. Furthermore, integrating sustainability through ethical, sustainable sourcing, and resource-efficient methods reduces environmental and societal dangers and improves brand reputation and economic viability. The policy implications emphasize the need for frameworks that foster technology innovation, sustainability, and stakeholder collaboration. By implementing these tactics and resolving related issues, companies can construct flexible supply networks to sustain operations and competitiveness amidst fluctuations in the worldwide market.

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Ultra-Reliable Low-Latency Communication (URLLC) in 5G Networks: Enabling Mission-Critical Applications

Cited by 3 publications

References 22 publications

AI-Optimized Customer Segmentation for Targeted Cryptocurrency Marketing

AI-Optimized Customer Segmentation for Targeted Cryptocurrency Marketing

Real-time Multimedia Analytics for IoT Applications: Leveraging Machine Learning for Insights

Resilient Supply Chains: Strategies for Managing Disruptions in a Globalized Economy

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