Reviewer Recruitment – Join Our Editorial Mission
2025-05-13
The Global Journal of Intelligent Technologies is expanding its pool of expert reviewers.
Announcements
Announcements
GLOJIT Now Indexed in Google Scholar
2025-05-13
Great news! Global Journal of Intelligent Technologies is now indexed in Google Scholar.
Call for Papers – Upcoming Issue
2025-05-13
We are pleased to invite researchers, scholars, and professionals to submit original research articles, review papers.
Current Issue
Vol. 3 No. 1 (2025): Volume-III, Number-I, 2025
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Personalized Health Interventions Using AI and Wearable Data: A Data Science Pipeline Approach
Abstract 45
The convergence of artificial intelligence (AI), wearable technology, and data science is reshaping the future of personalized healthcare. Wearable devices now generate massive streams of continuous, multimodal physiological data that, when harnessed appropriately, can enable real-time, predictive, and individualized health interventions. This article develops a comprehensive data science pipel ... read more
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Predictive Modeling of Customer Lifetime Value in E-Commerce Using Deep Learning and Causal Inference
Abstract 65
Customer Lifetime Value (CLV) is a central metric in e-commerce for acquisition budgeting, personalization, retention, and strategic planning. Classical statistical and probabilistic models (e.g., Pareto/NBD, BG/NBD) provide principled baselines but struggle with high-dimensional covariates, nonstationary behavior, and counterfactual questions required for causal decisioning. Deep learning has ... read more
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Quantum Machine Learning for Genomic Data Analysis: Unlocking Precision Medicine via Hybrid AI Systems
Abstract 54
Genomic data characterized by extreme dimensionality, complex structure, and subtle signal-to-noise ratios presents formidable computational and statistical challenges for precision medicine. Quantum machine learning (QML) and hybrid quantum–classical AI systems offer novel computational paradigms that may accelerate or improve aspects of genomic analysis: from sequence alignment and assembly ... read more
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Reinforcement Learning Meets Quantum Optimization: A New Paradigm for Dynamic Portfolio Management
Abstract 62
We propose and analyze a hybrid paradigm that integrates Reinforcement Learning (RL) with Quantum Optimization (QO) methods for dynamic portfolio management. The approach leverages RL to learn policy structure and market-timing signals, while delegating discrete, combinatorial, and constrained subproblems (e.g., cardinality-constrained selection, rebalancing under transaction limits) to quantum ... read more