Call for Papers:

The International Conference on Advancements in Computational Intelligence and Machine Learning (ICACIML 2026) invites high-quality research contributions from academicians, researchers, industry practitioners, and scholars across the globe. The conference aims to provide a premier interdisciplinary platform for presenting cutting-edge innovations, discussing emerging trends, and addressing real-world challenges in the fields of Artificial Intelligence, Machine Learning, Data Science, and Computational Intelligence.

We welcome original, unpublished research papers, case studies, survey articles, and industry practice reports in (but not limited to) the following thematic areas:

Track 1: Machine Learning Algorithms

• Supervised, Unsupervised, Semi-supervised learning methods
• Reinforcement Learning, Multi-agent RL, Bandits
• Probabilistic and Bayesian models
• Feature engineering, feature selection, dimensionality reduction
• Optimization methods (convex / non-convex / gradient-free / meta-optimization)
• AutoML, model selection, hyper-parameter tuning
• Explainable & interpretable ML (XAI), model auditability
• Time-series prediction, forecasting, temporal-data ML
• Scalable and efficient ML systems & large-scale deployment

Track 2: Deep Learning & Neural Networks

• Transformer architectures, attention models, foundation models
• Convolutional Neural Networks (CNNs) and computer-vision deep learning
• Recurrent Neural Networks (RNNs), LSTM/GRU, sequence modelling
• Graph Neural Networks (GNNs) and deep learning on graphs
• Generative Models: GANs, VAEs, Diffusion Models, Generative AI
• Neural Architecture Search (NAS), Auto-DL
• Edge-AI / TinyML / on-device deep learning
• Multimodal learning (image-text, audio-text, video-text, etc.)
• Optimization techniques in deep learning: regularization, efficient training
• Responsible & trustworthy deep learning: fairness, robustness, ethics

Track 3: Natural Language Processing (NLP)

• Large Language Models (LLMs), fine-tuning, adapter methods, prompt-engineering
• Text classification, summarization, information extraction
• Question Answering, dialog systems, conversational AI, chatbots
• Machine Translation (neural MT), multilingual and low-resource languages
• Sentiment analysis, emotion detection, opinion mining
• Speech processing, speech-to-text, spoken language understanding
• Document understanding, retrieval, search, IR + NLP
• Code-mixed language processing, especially for regional languages (e.g. Indian languages)
• Ethics, bias, fairness and interpretability in NLP
• Applications of NLP in domains such as healthcare, education, law, social media

Track 4: Swarm & Evolutionary Computation

• Genetic Algorithms (GA), Genetic Programming (GP), Evolutionary Strategies
• Particle Swarm Optimization (PSO), Ant Colony Optimization, Bee/Firefly/Other Swarm methods
• Hybrid algorithms: combining evolutionary / swarm methods with ML / DL techniques
• Multi-objective optimization, Pareto-optimal solutions, trade-offs
• Evolutionary Game Theory, multi-agent optimization, co-evolution
• Bio-inspired and nature-inspired computing / optimization
• Swarm robotics, distributed intelligence, self-organizing systems
• Heuristics and metaheuristics for real-world problems (optimization, scheduling, resource allocation, IoT, network optimization)
• Benchmarking, performance evaluation, comparative studies of evolutionary methods

Track 5: ML for Cybersecurity, Healthcare & Emerging Applications

A . ML for Cybersecurity

• Intrusion detection, anomaly detection, network security using ML
• Malware, ransomware, phishing detection and prevention using ML/DL
• Adversarial ML, robustness, secure ML models
• Privacy-preserving ML: federated learning, secure ML, privacy-enhancing tech (e.g. differential privacy, ZKPs)
• Blockchain + ML / Hybrid security-ML frameworks

B. ML for Healthcare & Biomedical Applications

• Medical image analysis (X-Ray, MRI, CT, ultrasound) using ML/DL
• Predictive healthcare analytics, risk scoring, prognosis models
• Remote monitoring via wearables, IoT-based health monitoring, time-series health data
• Clinical decision support systems, diagnostic assistance, prognosis, disease detection
• Drug discovery, genomics / bioinformatics, computational biology using ML
• Explainable & interpretable ML in healthcare decisions (transparency, ethics)

C. ML in Other Emerging Domains & Cross-Cutting Applications

• AI / ML for Smart Cities, IoT-enabled systems, transportation, logistics
• ML for environment, climate modelling, sustainability, resource optimization
• ML in FinTech, fraud detection, financial forecasting
• ML for education: learning analytics, adaptive learning, assessment & evaluation
• AI governance, ethics, policy, fairness, societal impact of ML

Track 6: AI-Driven Business Transformation: Analytics, Digital Innovation, and Sustainable Management Practices

• Predictive Business Analytics & Decision Intelligence
• Digital Transformation, Industry 4.0 & Innovation Management
• FinTech, Risk Analytics & Blockchain Applications
• Marketing Analytics, Consumer Insights & Social Media Mining
• Human Resource Analytics & Future of Work Models
• Healthcare Analytics & Digital Health Systems
• Sustainability, ESG Analytics & Green AI
• AI Governance, Ethics & Policy in Management

Track 7: ECE

• Low-power embedded system design for industrial IoT devices
• Advanced MEMS/NEMS sensors for manufacturing applications
• VLSI and ASIC architectures for high-speed IIoT data processing
• Edge AI hardware accelerators for factory automation
• 5G/6G-enabled real-time communication for manufacturing systems
• Hardware-level cybersecurity for IIoT devices (PUF, secure boot, TPM)
• IoT-enabled robotics control hardware and sensor fusion platforms
• Robust hardware design for extreme temperature and vibration environments

Additional publication avenues may include:

Selected high-quality papers will be invited for extended submission to indexed journals (Scopus / Web of Science), subject to further peer review.

Author Instructions

Paper Template

ICACIML-2026 Review Policy

At the International Conference on Advancements in Computational Intelligence and Machine Learning (ICACIML-2026), we are committed to upholding the highest standards of integrity, quality, and transparency in our review process. Every submission is evaluated with fairness, rigor, and strict adherence to ethical practices.

1. Evaluation Criteria
   Submissions are assessed based on scholarly merit, originality, alignment with conference themes, and compliance with submission guidelines and industry standards.
2. Expert Review
   Papers are reviewed by subject-matter experts possessing relevant domain expertise.
3. Double-Blind Peer Review
   ICACIML-2026 adopts a double-blind review process where the identities of both authors and reviewers remain confidential.
4. Confidentiality
   Reviewers and editors are prohibited from sharing or discussing submissions outside the review process. This safeguards unpublished ideas and results.
5. Conflict of Interest
   Any identified conflict of interest will result in reassignment of the submission to an alternate reviewer to maintain impartiality.
6. Ethical Responsibility
   Reviewers must report suspected cases of plagiarism, conflicts of interest, or undisclosed funding sources. Such cases will be escalated to the conference’s ethics committee.
7. Review Panel
   Each paper will be evaluated by three independent reviewers. Consolidated reviewer feedback will form the basis of the final decision.
8. Timeliness
   Reviewers are expected to meet deadlines, ensuring a timely and efficient review process without compromising feedback quality.
9. Decision Process
   Acceptance is based on the consensus of three reviewers. In cases of conflicting reviews, an additional expert opinion will be sought.
10. Plagiarism & Similarity Check
    The similarity index must not exceed 15% in total, with no single source contributing more than 5%. Submissions failing these criteria will not advance to the review stage.

Committee:

Will be updated soon...

Sponsors

Will be updated soon...

Registration:

To encourage wider participation, the conference registration fee will be charged as given below:

Participation Category	Registration Fee (USD)	Registration Fee (INR)
Academicians	150	10,000
Research Scholars / Students	100	8,000
Participation Fee for Non-presenting Authors / Others	60	5,000
Corporate Delegates / Policy Makers / Government Officials / NGO Professionals	150	10,000

*Additional 18% GST will be included in the registration fee given above.

Accommodation

A limited number of rooms may be available at nominal rates on the campus of IFHE-Hyderabad. The rooms will be allocated on the basis of first-come-first served basis. For any queries, please reach out to us on the email icaciml2026@ifheindia.org.

Keynote Speakers

Conference Venue:

ICFAITECH - Hyderabad Donthanapally,
Shankarapalli Road
Hyderabad - 501203, Telangana, India.

Contact Us:

Dept. of DS & AI, CSE
FST Building
ICFAI University
Dontanpally (V), Shankarpally (M), Rangareddy (D)
Hyderabad, Telangana, 501302.

For Conference queries kindly contact

Steering Committee

International Conference on Advancements in Computational Intelligence and Machine Learning (ICACIML-2026)

Mail: icaciml2026@ifheindia.org