Title: Next-Generation Adaptive AI Framework for Smart Healthcare Big Data Analytics
Author:
Naveed Farhana
Department of Computer Engineering, College of Computers and Information Technology, Taif University, Kingdom of Saudi Arabia.
email: d.nfmaqsud@tu.edu.sa, naveedfarhana123@gmail.com
Pages: 1-14
DOI: https://doi.org/10.55306/CJIESN.2025.010101
Abstract:

The explosive growth in the quantity of healthcare data are produced every day from IoT devices, EHRs, and wearable sensors, thus demanding sophisticated frameworks for real-time processing and prediction analytics. In this paper, we propose an Adaptive AI-Driven Big Data Processing Framework for smart healthcare, in which we highlight and tackle concerns in data velocity, scalability and privacy. The engine is powered by distributed computing using Apache Spark and container orchestration with Kubernetes for the scale and resiliency. For prediction, we adopt a HDNN that includes the RNN for the time-series analyses and a CNN for the medical image construction. The explainable AI (XAI) methods, such as SHapley Additive exPlanations (SHAP), are also incorporated for achieving the interpretability in clinical decision-making. Secured trust- Blockchain based patient data management immutability and fine grain access controls, multi cloud infrastructure optimization for storage and retrieval. The platform lends itself to use for key functions including real time monitoring of vitals, chronic conditions early alarm, personalized treatment recommender and respiratory therapy assist and critical care alert. We empirically verify that HATE is effective to process streaming health care data at high velocity, obtain higher prediction accuracy and ensure the data security. This methodology highlights the revolutionary capabilities of AI enhanced big data analytics in the development of intelligent healthcare systems of tomorrow.
Key Words: AI, Big Data Analytics Blockchain Technology, Explainable, IoT in Healthcare, Multi-Cloud Architecture, Personalized Treatment, Real-Time Data Processing, Smart Healthcare
Citation: Farhana. N., “Next-Generation Adaptive AI Framework for Smart Healthcare Big Data Analytics,” Ci-STEM Journal of Intelligent Engineering Systems and NetworksDigital Technologies and Expert Systems, Vol. 1(1), pp. 1-13, 2025

Title: EdgeSmart: Hybrid Evolutionary Optimization for Edge-AI in Smart Cities
Authors:
Sayamuddin Ahmed Jilani
Department of Computer Science & Engineering, Maulana Abul Kalam Azad University, West Bengal, India.
email: 1075sam@gmail.com
Soumitra Kumar Mandal
Department of Electrical Engineering, National Institute of Technical Teachers Training and Research, Kolkata, West Bengal, India.
email: skmandal@nitttrkol.ac.in
Pages: 15-25
DOI: https://doi.org/10.55306/CJIESN.2025.010102
Abstract:

Efficient AI Processors for AI Processing in Smart Cities Using Genetic Algorithm Optimized Edge Networks introduces a novel system which aims to enhance the efficiency of distributed edge computing systems for time critical smart urban services. It employs GA to dynamically allocate and schedule task at edge nodes in a distributed way in order to balance the load and achieve low-latency. The system utilizes genetic searching methods to find configurations better than the optimal and saves the energy with powerful processing of the device. This optimization helps making decisions in a fast, context-guided manner, as required for smart city applications such as traffic control, health control and energy saving. Experimental results validate the superior of Edge Smart performance with respect to traditional edge-aware management, and evident improvements in processing speed, energy efficiency and system scalability are shown. These results show the capability of the framework as a viable solution to facilitate the deployment of edge intelligence in AI-based smart city infrastructures.
Key Words: AI, Computational Load Balancing, Decentralized Edge Networks, Edge Computing, Edge Intelligence. Energy Efficiency, Genetic Algorithms, Internet of Things (IoT), Latency Reduction, Optimization Techniques, Real-time Distributed Systems, Resource Optimization, Smart AI Processing, Scalability, Smart Cities, Task Scheduling.
Citation: S. A. Jilani et al., “EdgeSmart: Hybrid Evolutionary Optimization for Edge-AI in Smart Cities,” Ci-STEM Journal of Intelligent Engineering Systems and Networks, Vol. 1(1), pp. 15-25, 2025, doi: 10.55306/CJIESN.2025.010102