Conference Information

ICDCIT 2027: International Conference on Distributed Computing and Intelligent Technology

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ICDCIT
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Submission Date:
2026-08-28
Notification Date:
2026-10-12
Conference Date:
2027-01-08
Location:
Bhubaneswar, India
Years:
Viewed: 18181   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

53.9 / 100
Ranked #936 of 5,682 conferences · Top 17%

#123 of 341 in Systems & Architecture

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
86
Community attention (10%)
10
Public record completeness (15%)
55

Inputs used: Editions on record: 23 · Researchers who opened this page in the past 24 months: 3

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-17

Call For Papers

ICDCIT 2027 (International Conference on Distributed Computing and Intelligent Technology) is an academic conference held in Bhubaneswar, India on 2027-01-08. The paper submission deadline is 2026-08-28. Acceptance notifications are sent on 2026-10-12.

The International Conference on Distributed Computing and Intelligent Technology, from its inception (2004), provides a platform where researchers, educators, and practitioners can create, exchange, and synthesize their ideas and discuss the most recent innovations, trends, experiences, and challenges in the field of distributed computing and intelligent technology. The swift advancement and widespread integration of Artificial Intelligence across various fields of science and engineering have led to the inclusion of “intelligent technology” within the conference’s scope. These technologies are essential for individuals and organizations designing, developing, and deploying large-scale systems incorporating AI-based components and subsystems. The forthcoming 23 rd International Conference on Distributed Computing and Intelligent Technology (ICDCIT-2027) is scheduled to convene in Bhubaneswar from 8 January to 11 January 2027. Hosted by the Kalinga Institute of Industrial Technology (KIIT) in Bhubaneswar, India, the event seeks to foster discussions and progress in the field. In keeping with tradition, Springer plans to publish the conference proceedings in the prestigious Lecture Notes in Computer Science series, as done in previous editions. ICDCIT-2027 seeks original research papers that contribute to the foundations and applications of Distributed Computing, Intelligent Technology and AI for Science. The topics include, but are not limited to: Distributed Computing Foundations of Distributed Computing Distributed algorithms, lower bounds, and complexity Fault tolerance, self-stabilization, and resilience Consistency models, replication, and formal guarantees Distributed data structures and concurrency Game-theoretic and economic aspects of distributed systems Systems and Infrastructure Cloud, edge, and serverless computing Distributed storage, databases, and data platforms Distributed operating systems and middleware High-performance and heterogeneous distributed systems Observability, debugging, and testing of distributed systems Real-time and streaming distributed systems Cross-Cutting and Emerging Areas Distributed machine learning and AI systems Federated and privacy-preserving learning Blockchain and decentralized systems Security, privacy, and trust in distributed environments Multi-agent and autonomous distributed systems Geo-distributed and edge-to-cloud systems Sustainable and energy-efficient distributed computing Intelligent Technologies Affective Computing AI and Formal Verification AI planning AI in Supply Chain Optimization Authenticity, Honesty & Deception Automated Vehicles Business Analytics Computational Intelligence Constraint Satisfaction Problems Cyber-Physical Systems Deep Learning Engineering Intelligent Systems Ethic in AI — Bias mitigation, Fairness Explainability in AI Federated Learning Game Theory and Mechanism Design Health Care Systems Intelligent Manufacturing Intelligent Transport Systems Knowledge Management Knowledge Representation Machine Learning Machine Vision Meta Learning Multi-agent systems Natural Language Processing Neuro-symbolic Computing Profiling and Social Networking Recommendation Systems Reinforcement Learning Robotics Security Systems Sentiment Analysis Social Memory & Cognition Society of Apps Speech Technologies Topic Detection Trustworthy computing AI for Science Drug discovery & cheminformatics Retrosynthesis prediction & reaction modeling Virtual screening & ADMET prediction Druggability & binding affinity prediction Drug repurposing & discovery Solubility, permeability & polymorph prediction Thermal stability & toxicity prediction Generative AI for molecular, organic & peptide design Protein, genomics & structural biology Protein & RNA structure prediction and de novo design Protein-ligand binding & stability prediction Biomarker & carcinogenicity/genotoxicity prediction LLMs for genomic & peptide analysis Multi-omics data integration & Digital twins at cellular scale Medical and clinical AI Medical image analysis (fMRI, CT, X-ray, PET) EHR analysis & drug pricing optimization Physics, materials & earth sciences AI in high-energy & nuclear physics Physics-informed ML & neural operators Neural force fields & molecular dynamics AI for materials discovery & design AI for astrophysics & cosmological simulation AI for Earth system & climate/weather modeling AI for environmental & ecological monitoring Agriculture & remote sensing Precision agriculture, crop genomics & food security Satellite imagery analysis & remote sensing Foundation models, autonomous discovery & trustworthiness Scientific foundation models & SciLLMs Autonomous experimentation & lab-in-the-loop AI AI agents for hypothesis generation & verification Active learning & synthetic data for data-scarce science Benchmarking, reproducibility & responsible AI for science
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