The rapid proliferation of Internet of Things (IoT) devices has resulted in massive, continuous data generation, demanding scalable, low-latency, and energy-efficient processing methodologies. Traditional cloud-based machine learning (ML) inference pipelines often incur high energy consumption due to persistent server provisioning and inefficient resource utilization. This paper proposes an energy-efficient ML inference framework using serverless cloud functions that dynamically scale with IoT workloads. The architecture leverages event-driven execution, model optimization techniques (quantization, pruning, edge pre-filtering), and adaptive model selection based on workload intensity. Experimental evaluations conducted on widely used serverless platforms demonstrate significant reductions in energy consumption, cold-start latency, and operational cost while maintaining high inference accuracy. The study highlights the potential of serverless computing as a sustainable backbone for next-generation IoT–ML systems, offering guidelines for building carbon-aware and cost-efficient inference pipelines for real-world applications.
Silvia Diallo, Grace Ndlovu· International Journal of Art...· 0 citations
The high rate of development of artificially intelligence (AI) has significantly transformed the linguistic profession by introducing the use of AI-based language applications. Machine learning, deep learning, and natural language processing (NLP) are the driving forces of these tools redefining the methods of how language is studied, generated, and maintained. Since automated translation and speech recognition systems, AI systems are now at the center of linguistic studies and applications of language in practice. In the given article, we derive detailed research on how AI-based language tools impact the contemporary linguistics. It explores theoretical and historical developments, methodological and practical changes within the subdomains of linguistics. A systematic review of the literature brings out main milestones, trends in the research, and shortcomings of the available methods. The suggested methodology is going to assess AI-based linguistic tools based on both qualitative and quantitative scales, such as accuracy, linguistic validity, scalability, and interpretability. The findings indicate that AI-enabled applications can significantly improve the efficiency of the analytical process and reveal the linguistic patterns that are not available to ordinary analysis. Yet, another problem like bias, explainability and ethics issues is significant. The research provides a conclusion that although AI has become an inseparable part of linguistics today, there is a need to establish a balanced approach of using computational methods and knowledge of human linguists to be sustainable and ethical.
Silvia Diallo, Chinedu Eze· International Journal of Inn...· 0 citations
This study reviews pre-2018 developments and proposes an improved HAR framework incorporating advanced feature engineering, sensor fusion, and ensemble learning techniques, which shows strong potential in healthcare, fitness, and smart environments.
Silvia Diallo, F. Z. Idrissi· International Journal of Mod...· 0 citations