Kozhobekova Pardaz Zhumabaevna

  Background: The marriage of 5G and Artificial Intelligence (AI) has been brought forward as a key enabler of Industry 4.0 and smart city applications. These technologies solve the problem of latency, scalability, and energy use, providing technology support for real-time decision-making and efficient organization of work. Nevertheless, studies regarding their individual and collective effects in a plethora of industrial and urban contexts are still limited. Objective: The objective of this research is to assess the performance, energy saving, and expansibility of 5G and AI synergies in manufacturing, logistics, healthcare, and smart city applications and highlight their challenges and potential for further exploration. Methods: An experimental data collection, mathematical modeling and comparative analysis approach was employed. Performance indicators including latency, possible and actual throughput, power usage, and predicting achievement were measured in real pilot tests implemented in dense networks and IoT contexts. Available data were compared with other similar studies to gain an understanding of the results. Results: The conjoin with 5G and AI suggested potential optimization of process; the latency has been decreased to more than 90%, its predictive maintenance was sharpened, and its power consumption was decrease to 75%. The feasibility of extending scalability and system reliability of the protocol was confirmed in dense IoT environments, with further potential for emission reduction. Conclusion: The study identifies the use of 5G in Industry 4.0 with AI in addressing dynamic issues but potential drawback includes scalability and security. More studies should be conducted on the novel hybrid architectures and 6G integration concerning more extensive areas.

Background: Neuromorphic computing is a newly developed technology that is based on data-flow architectures similar to the brain, which has the potential to power energy-constrained, latency-sensitive, and large-scale applications. The lack of flexibility in energy consumption and response time of traditional systems is a problem where neuromorphic platforms shine in real-time applications like robotics, IoT and autonomous systems. Objective: The article aims to assess the capabilities of neuromorphic computing platforms with respect to conventional schemes, both quantitatively and qualitatively, in terms of energy consumption, response time, modularity, and application-dependent adaptability, and to determine the drawbacks and application prospects for its further development. Methods: The study uses a comparative analysis approach to compare the identified factors and make statistical comparisons of the performance measures. The performance of the neuromorphic platforms as compared to non-neuromorphic platforms like Intel Loihi, IBM TrueNorth, NVIDIA Tesla V100, and Google TPU is compared based on its applications in robotics, IoT, and especially in healthcare. Data is derived from the experimental assessments of knowledge and theoretical paradigms encountered in prior research studies. Results: Neuromorphic systems showed better energy consumption, system size, and delay characteristics. Nevertheless, that the algorithm so excellently solves particular tasks does not mean that it can successfully be used regardless of its purpose, or can be adapted freely to new, further-reaching trends, such as quantum computing. Regression results demonstrate a high degree of dependency between these measures as well as their potential for real time data processing. Conclusion: Neuromorphic computing can be regarded as a new paradigm of energy-efficient and scalable AI and is especially promising for latency-sensitive deployment. Their shortcomings have been discussed earlier, yet it is worth stating that extension of these approaches by hybrid systems and more sophisticated integration frameworks might open new opportunities and eventually promote them as a foundation for new-generation computation models.