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All Journal Journal of Computer Science Advancements Journal of Social Science Utilizing Technology
Le Thi Lan Anh
Hanoi Medical University
Computer Science & IT

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MIMICKING THE HUMAN BRAIN: NEUROMORPHIC ARCHITECTURE SOLUTIONS FOR AI ENERGY EFFICIENCY Nguyen Tuan Anh; Le Thi Lan Anh; Pham Thanh Thao
Journal of Computer Science Advancements Vol. 3 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i5.3330

Abstract

The exponential proliferation of Artificial Intelligence (AI) is currently constrained by the “memory wall” and excessive power consumption inherent in traditional Von Neumann architectures. This study addresses these physical limitations by proposing a bio-inspired neuromorphic architecture that integrates memristive crossbar arrays with event-driven Spiking Neural Networks (SNNs) to mimic biological synaptic efficiency. The research employs a quantitative cross-layer simulation framework to benchmark the proposed design against industry-standard GPUs and TPUs, utilizing standard datasets to evaluate inference latency, power dissipation, and classification accuracy. Results indicate that the neuromorphic architecture achieves a reduction in energy consumption by orders of magnitude (0.12 pJ/operation) compared to baseline accelerators, with power usage scaling linearly with input sparsity. Although a minor trade-off in precision was observed due to device stochasticity, the system maintained a competitive classification accuracy of 92.4%. The study concludes that mimicking the asynchronous nature of the human brain offers a sustainable paradigm for “Green AI,” validating neuromorphic computing as a critical solution for overcoming the energy crisis in next-generation edge intelligence and autonomous systems.
Digital Epidemiology: Using Social Media Data and Machine Learning to Forecast Influenza Outbreaks and Inform Public Health Responses Benny Novico Zani; Ravi Raj Pandey; Le Thi Lan Anh
Journal of Social Science Utilizing Technology Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v3i3.2735

Abstract

Background. Traditional influenza surveillance systems inherently suffer from a critical one-to-two-week reporting lag, severely hindering timely public health interventions and resource allocation. Purpose. This research aims to develop and validate a hybrid digital epidemiology model using unstructured social media data and advanced Machine Learning (ML) to provide accurate, long-range influenza outbreak forecasts. Method. The methodology involved quantitative time-series forecasting, training Long Short-Term Memory (LSTM) and XGBoost models on five years of social media data, and benchmarking against official clinical reports. Results. The optimized LSTM model achieved significantly superior accuracy, recording a Root Mean Square Error (RMSE) of 0.145 for the four-week forecasting horizon, less than half the error of the traditional ARIMA baseline. This high predictive power confirms that social media is a statistically reliable, non-clinical leading indicator. Conclusion. The study establishes a transparent policy translation framework, linking predicted incidence rates (e.g., exceeding 0.20) directly to required operational responses (e.g., hospital surge activation). This model offers a robust, actionable template for transforming public health surveillance from a reactive system into a proactive intelligence platform for epidemic preparedness.
Co-Authors Benny Novico Zani Nguyen Tuan Anh Pham Thanh Thao Ravi Raj Pandey