Call for Paper
ICSIAIML 2025 invites full-length original research contributions from science and engineering professionals from industries, R&D organisations, academic institutions, government departments, and research scholars worldwide.
Instructions for Preparing Manuscripts
Prospective authors are invited to submit full-length original research papers. While submitting a manuscript to ICSIAIML 2025, the authors acknowledge that no paper substantially similar in content has been or will be submitted to another journal, conference or workshop during the review period. In such a case, the paper will be rejected without review. Papers must be electronically submitted before the deadline expires without exception through the submission link given.
The paper must not exceed 10 pages in length
(including all text, figures, and references).
The manuscript must be submitted in Word/PDF format only
the file size of the manuscript should not exceed 10 MB.
Use a proper tool to convert the resulting source into a PDF document that only has scalable fonts and all fonts embedded.
The images embedded in the paper must not contain transparent pixels
(i.e., an alpha-channel of a transparent colour).
The PDF manuscript must not have Adobe Document Protection or Document Security enabled
Peer Review Process
ICSIAIML 2025 will maintain a rigorous peer review process to ensure the quality, originality, and relevance of the accepted submissions. The process is as follows:
Initial Screening:
Turnitin checks all papers | 15% similarity is the limit | High similarity leads to rejection | Plagiarism retracts the paper
Assignment to Reviewers:
Papers go to three reviewers | Experts ensure fair evaluation | Program Committee and others help
Double - Blind Peer Review:
ICSIAIML 2025 uses Double - blind | Focus: originality, technical quality | Relevance, clarity, and contribution matter
Reviewer Feedback:
Feedback: accept, revise, or reject | Authors get comments and suggestions | Improvement is based on reviewers' input
Decision Making:
Chairs decide on acceptance or revision | Conflicts need a third reviewer's input | Final decisions follow balanced feedback
Revision and Resubmission:
Authors revise within set timelines | Revised work must address concerns | Further review ensures adequacy
Final Acceptance:
Accepted papers get final requests | Formatting and copyright compliance needed | Proceedings include final versions
This rigorous peer review process ensures that ICSIAIML 2025 maintains high standards of academic integrity and contributes significantly to the advancement of research in these critical fields.
Research manuscripts are invited on the following topics:
- Sustainable innovation practices in the fashion industry.
- Role of green innovation in reducing carbon footprints.
- Circular economy as a framework for sustainable innovation.
- Innovations in renewable energy technologies.
- Sustainable product design in consumer electronics.
- The impact of green innovation on brand reputation.
- Government policies and incentives for green innovation.
- Sustainable supply chain innovation in manufacturing.
- The role of eco-friendly packaging in retail.
- Green innovation in food and beverage industries.
- Challenges of implementing sustainable practices in SMEs.
- Water conservation innovations in agriculture.
- Sustainable construction practices in urban development.
- Integrating environmental impact assessments into innovation.
- Green product innovations in the automotive industry.
- Role of consumers in driving sustainable innovation.
- Waste reduction innovations in hospitality management.
- Exploring zero-waste concepts in product design.
- Circular design principles for sustainable manufacturing.
- NLP for environmental monitoring through text data (social media, news, scientific papers)
- Future directions in sustainability and green innovation.
- AI-driven climate models and forecasting systems
- AI applications in carbon capture, storage, and sequestration
- Predictive analytics for climate change adaptation and resilience
- AI for early warning systems in climate-related disasters (floods, wildfires, storms)
- Energy-efficient algorithms and models in AI systems
- AI for optimising energy consumption in AI training and execution
- Green AI: Minimising the environmental footprint of AI technologies
- Hardware and software innovations for reducing energy consumption in AI systems
- AI for optimising energy distribution in smart grids and decentralised energy systems
- AI applications in reducing waste and promoting circular economy practices
- AI-driven systems for real-time environmental monitoring (air, water, soil quality)
- AI in biodiversity conservation and ecosystem health monitoring
- Autonomous drones and sensors for environmental data collection
- AI for forest management and anti-poaching efforts
- AI-powered autonomous vehicles for sustainable transportation
- Autonomous drones for environmental monitoring and resource management
- Smart robots for waste collection, recycling, and energy conservation
- Autonomous systems in precision agriculture and sustainable farming practices
- AI for optimising delivery routes in logistics to reduce carbon footprints
- AI applications in smart city infrastructure and resource management
- AI for optimising traffic flow and reducing transportation emissions
- AI-based waste management and recycling solutions in cities
- Sustainable urban planning and smart grids powered by AI
- AI-powered health diagnostics for underserved communities
- Machine learning for improving the efficiency of healthcare delivery systems
- AI in healthcare data analytics for resource optimisation
- AI for personalised medicine and reducing waste in pharmaceuticals
- Predictive analytics for managing public health crises and pandemics
- AI for analysing sustainability-related reports and policy documents
- AI-driven systems for translating sustainability-related content across languages
- AI-based optimisation of sustainable supply chains and resource allocation
- AI in sustainable procurement and inventory management
- AI tools for minimising waste and maximising resource efficiency in manufacturing
- Blockchain and AI integration for transparent and sustainable supply chain management
- Developing ethical AI frameworks for sustainability applications
- Addressing AI bias and fairness in sustainability decision-making
- Transparency and accountability in AI algorithms for sustainable solutions
- AI governance models for socially responsible innovation
- Ensuring inclusivity and equity in AI systems deployed for sustainable development
- AI in assessing and managing environmental, social, and governance (ESG) risks
- AI for tracking and optimising green bonds and sustainable finance portfolios
- AI-based tools for evaluating the environmental impact of financial portfolios
- Predictive models for financial risk assessment in green and renewable energy markets
- AI for optimising recycling processes and material recovery
- AI-based solutions for reducing electronic waste and supporting circularity
- Smart product design powered by AI to minimise environmental impact
- AI tools for lifecycle assessment of products and materials
- AI for optimising disaster relief and recovery efforts
- Real-time data analytics and decision-making tools in disaster scenarios
- AI for optimising resource allocation during environmental crises
- AI applications in disaster risk reduction and resilience building
- AI solutions for enhancing the efficiency of humanitarian aid delivery
- Machine learning for monitoring and managing refugee and displaced persons’ needs
- AI for improving food security and sustainable resource management in crisis areas
- Data-driven decision-making for sustainable development in conflict zones
- AI-driven systems for disaster response and recovery in low-resource regions
- Precision agriculture and AI-powered farming for sustainable food production
- AI for managing water resources and irrigation systems in agriculture
- Machine learning models for optimising crop yield and reducing pesticide use
- AI-driven solutions for managing sustainable fisheries and aquaculture
- AI in food distribution and waste reduction within the food supply chain
- AI for sustainable fashion design and waste reduction
- Machine learning models for tracking and improving the sustainability of textile supply chains
- AI-driven solutions for recycling textiles and reducing waste in the fashion industry
- Sustainable production and consumption of fashion powered by AI insights
- AI in ethical sourcing and reducing carbon footprints in garment production
- AI tools for promoting sustainability education and awareness
- Machine learning in adaptive learning systems for sustainability-related topics
- AI applications in environmental education and promoting green skills
- AI in designing and delivering sustainable curriculum for schools and universities
- Using AI to empower students and researchers to contribute to sustainable innovation
- Machine learning models for optimising renewable energy production (solar, wind, hydropower)
- Applying reinforcement learning for optimising energy usage in real-time
- Reinforcement learning in autonomous systems for sustainable resource management
- Adaptive AI models for continuous improvement in sustainability processes
- ML in developing sustainable behavioural models for consumers and industries
- Reinforcement learning for sustainable decision-making in supply chains and logistics
- Machine learning for automating environmental impact assessments
- AI models for predicting the environmental impact of infrastructure projects
- ML-driven tools for assessing biodiversity and ecosystem impacts
- AI for long-term monitoring of sustainability goals and environmental health
- Machine learning for assessing and mitigating the impacts of industrial activities
- Machine learning for optimising food production and reducing waste in the supply chain
- Predictive models for climate-resistant crop development
- AI and machine learning in sustainable fisheries and aquaculture management
- Food traceability and sustainability assessment through machine learning
- Machine learning applications in reducing food loss in distribution and retail
- Machine learning for optimising sustainable supply chain operations
- Predictive analytics for reducing waste and maximising resource efficiency
- AI for green logistics: optimising transportation routes to reduce carbon footprints
- Machine learning in managing and minimising the environmental impact of logistics
- ML models for ethical sourcing and tracking sustainability in supply chains
- Machine learning for managing urban energy demand and resource distribution
- AI-powered urban planning using machine learning for sustainability
- Smart city waste management and water conservation through machine learning
- ML for traffic and mobility optimisation in sustainable cities
- AI and machine learning for smart buildings and urban sustainability initiatives
- Machine learning for automated waste sorting and recycling processes
- Predictive models for waste generation and management in urban areas
- ML algorithms for optimising recycling rates and material recovery
- Machine learning applications for reducing food waste across supply chains
- AI-driven solutions for sustainable packaging design and waste reduction
- Machine learning for optimising public transportation routes and schedules
- ML algorithms for reducing traffic congestion and emissions in smart cities
- Autonomous vehicles and machine learning for energy-efficient transportation
- Predictive models for transportation demand forecasting and optimisation
- Predictive analytics for crop yield forecasting and optimisation
- Machine learning models for pest and disease detection in agriculture
- ML in sustainable irrigation management and water conservation
- AI-based solutions for reducing pesticide and fertiliser usage
- Crop monitoring through remote sensing and machine learning algorithms
- Machine learning for optimising energy consumption in smart grids and energy systems
- ML in water resource management: Predicting and optimising usage
- Predictive analytics for sustainable land use and forest management
- Machine learning for optimising natural resource extraction while minimising impact
- AI and ML models for monitoring biodiversity and ecosystem health
Preparing your Paper
Authors must use the manuscript template specified here. The LaTeX and Word templates can be downloaded from below:
