Editor-in-Chief: Dr. Ali Khan
Scientific Editor: Dr. Muhammad Sajid
Publication Frequency: Bi-Annual
Abbreviated Title: MLHI
Title: Machine Learning for Human Intelligence
Area of Publication: Artificial Intelligence, Machine Learning, Data Science, Cybersecurity, Blockchain, Explainable AI, Biomedical Informatics, Cross-Disciplinary Computational Sciences
Aim and Scope
Machine Learning for Human Intelligence (MLHI) is a double-blind peer-reviewed, open-access quarterly journal dedicated to advancing research at the intersection of artificial intelligence, machine learning, and human cognition. Recognized by leading academic institutions, MLHI publishes high-quality theoretical, empirical, and applied studies that explore how intelligent systems can augment and interpret human intelligence, behavior, and decision-making.
Core Objectives
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To foster interdisciplinary dialogue between AI/ML researchers and cognitive scientists, neuroscientists, psychologists, and domain experts.
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To promote innovative methodologies that bridge data-driven learning with human-centric design.
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To address ethical and societal challenges in deploying AI systems for human augmentation.
Scope
MLHI covers (but is not limited to) the following areas:
1. Foundational AI/ML Research
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Neural networks, deep learning, and generative models
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Neurosymbolic AI and hybrid reasoning systems
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Reinforcement learning for adaptive human-AI collaboration
2. Human-Centric Intelligence
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Cognitive and behavioral modeling using ML
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Explainable AI (XAI) for interpretable decision-making
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Affective computing and emotion-aware systems
3. Applied AI for Societal Impact
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Healthcare: Predictive diagnostics, personalized therapy, and medical imaging
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Education: Adaptive learning platforms and intelligent tutoring systems
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Social Good: AI for climate modeling, smart cities, and equitable policymaking
4. Emerging Paradigms
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Quantum machine learning
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Neuromorphic computing and brain-inspired algorithms
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Federated learning and privacy-preserving AI
Article Types
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Original Research: Substantive technical/theoretical contributions (6,000–10,000 words).
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Reviews: Critical surveys of emerging trends (8,000–12,000 words).
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Case Studies: Real-world implementations with measurable impact (4,000–7,000 words).
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Short Communications: Brief reports on novel methodologies (3,000–5,000 words).
Submission Criteria
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Novelty: Unpublished work with significant scientific contribution.
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Rigor: Robust methodology (theoretical/experimental) and reproducibility.
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Interdisciplinary Relevance: Priority for studies bridging AI/ML with psychology, neuroscience, or social sciences.