LASMCER-2024

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Artificial Intelligence
& Machine Learning

Abstract Submission

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Artificial Intelligence & Machine Learning
is contribution to The Sustainable Development Goals

Goal 4: Quality Education:

Implementing reinforcement and deep learning fosters energy-efficient practices, promoting equal learning opportunities by creating sustainable practices beneficial to all.

Ensuring transparency with explainable AI and interpretable learning methods promotes equal opportunities for vulnerable populations by fostering trust and inclusivity within institutions.

Goal 8 : Decent Work and Economic Growth:

Explore how Artificial Intelligence & Machine Learning revolutionise employment opportunities across sectors in Data Analysis, Robotics, Biomedical Computing, etc.

Investigate how advancements in Artificial Intelligence & Machine Learning drive sustainable technology evolution and economic growth fostering full employment, and decent work opportunities for all.

Goal 9: Industry, Innovation, and Infrastructure:

Illustrate the impact of AI and ML on building resilient infrastructure, fostering inclusive and sustainable industrialisation, and nurturing a culture of innovation.

Investigate how Reinforcement Learning,Image and Speech Processing, Explainable Artificial Intelligence, Deep Learning Algorithms, Sentiment Analysis, Interpretable Learning, contribute to sustainability initiatives.

Goal 12 : Responsible Consumption and Production:

To discuss utilising reinforcement learning and data analysis in robotics can optimise resource allocation and streamline processes, reducing waste and energy consumption in production, thus promoting sustainable consumption and production.

Analyse applying image processing and sentiment analysis in social media mining enables efficient monitoring and control of public sentiments towards sustainable practices, facilitating targeted interventions and awareness campaigns, ultimately minimising energy consumption and promoting sustainable consumption and production.

Goal 16 : Peace, Justice and Strong Institutions:

Analysing environmental data via image and speech processing aids in crafting inclusive policies, ensuring fair resource access and justice.

Harnessing reinforcement and deep learning enhances energy efficiency, fostering sustainable practices benefiting society.

Goal 17: Partnerships for the Goals:

Explore the transformative power of AI-driven computational intelligence trends, fostering impactful collaborations among public, private, and civil society sectors. These partnerships leverage AI's capabilities to optimise resource strategies and enhance collaborative experiences.

Investigate how Artificial Intelligence, Deep Learning Algorithms, Sentiment Analysis, Interpretable Learning, etc., drive the development and implementation of intelligent solutions, accelerating sustainability efforts in harmony with global partnerships for sustainable development.

Who Can Join

Research Scholars

Technology Experts

Students & Alumini’s

Editorial Board Members of Journals

Government Officials

Industry Professionals

Artificial Intelligence & Machine Learning

Tracks

1 Reinforcement Learning

2 Image and Speech Processing

3 Explainable Artificial Intelligence

4 Deep Learning Algorithms

5 Sentiment Analysis

6 Interpretable Learning

7 Opinion Mining

8 Expert Systems

9 Social Media Mining

10 Soft Computing

11 Data Analysis

12 Robotics

13 Biomedical Computing

14 Computer Vision

15 Information Retrieval

16 Natural Language Processing

17 Secure Information Retrieval

18 Image Processing Applications

19 Advanced Software Engineering

20 Ambient Computing

21 Pattern Recognition

22 Data Mining and Knowledge Management

23 Human Computer Interaction

Author Guidelines

Guidelines for Abstract Submission:

Language: Abstracts must be written in English.
Length: Limited to one paragraph with 200-250 words.
Format: Submit in MS Word (.doc or .docx) document format.
Content: Abstracts should provide an informative summary of the original work. Include a brief biography with your abstract, following the example provided in the template.
Formatting: Center-align the Title, Author's Names, and Affiliations. Underline the presenting author's name.
Submission: Please submit your abstract through the designated submission portal.
Acknowledgment: Upon abstract submission, you will receive an acknowledgment email within three working days.

Evaluation Process

Guidelines for Full Paper Submission

If your abstract has been accepted and the registration fee for LASMCER-2024 has been paid, you are invited to submit the full paper. Please adhere to the following guidelines for the submission:

Total number of pages: 6-8 in double-column format
Language: English (checked for grammar and language errors)
Tables, figures, and images should be properly named and of high quality.
Keywords should be written in lowercase letters (except for names/scientific names) and separated by commas.
Affiliation names, including the country, must be provided.
Each paper should be structured into the following sections:
Background, Motivation, and Objective
Statement of Contribution/Methods
Results, Discussions, and Conclusions

Once your full paper is prepared according to the above instructions, please proceed to submit it through the provided link. Submit your Full paper Here.