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AIMS' 2026: International Conference on AI and Multimodal Services

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투고 마감일:
2026-05-21
통보일:
2026-06-15
개최일:
2026-08-22
개최지:
Kuala Lumpur, Malaysia
조회: 21461   팔로우: 6   참가: 5

회반 지수 (CP-I)

46.3 / 100
전체 5,652개 중 2,414위 · 상위 43%
근거가 제한적입니다: 이 학회는 CCF / ICORE / QUALIS 어디에도 수록되어 있지 않고 게재율 데이터도 없어, 점수의 대부분이 중립 기준값으로 되돌아갑니다.
학술적 인정 (35%) 데이터 없음 — 중립 기준값 50점으로 계산 —
투고 선별성 (20%) 데이터 없음 — 중립 기준값 50점으로 계산 —
개최 횟수 (20%) 데이터 없음 — 중립 기준값 50점으로 계산 —
커뮤니티 관심도 (10%)
35
공개 자료 충실도 (15%)
35

사용한 입력: 회반에서 팔로우 중인 연구자: 6명 · 지난 24개월 동안 이 페이지를 연 연구자: 8명

공개 자료에서 빠진 항목: 역대 게재율 (+4.5) · 역대 회차 (+3.0) · 최우수 논문 기록 (+2.3)
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신뢰도 25% — 점수 중 중립 기준값이 아니라 실제 관측된 데이터에 근거한 비율. 이 점수는 어떻게 계산되나 · 전체 순위 보기 · 알고리즘 버전 1.1 · 산출일 2026-09-08

논문 모집

AIMS' 2026 (International Conference on AI and Multimodal Services) is an academic conference held in Kuala Lumpur, Malaysia on 2026-08-22. The paper submission deadline is 2026-05-21. Acceptance notifications are sent on 2026-06-15.

Topics AI and Multimodal Services have been driving Internet innovations and transforming traditional businesses. AIMS 2026 covers the following key areas: Key Areas for AI, Especially Generative AI Generative AI Models: Development and application of generative models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and transformer-based models for creating synthetic data, images, text, and more. Creative AI: Leveraging generative AI for creative processes in art, music, and content generation, pushing the boundaries of creativity through machine learning. Automated Content Generation: Utilizing AI to generate text, images, videos, and other media content automatically, enhancing productivity and enabling new forms of content creation. AI-Driven Personalization: Implementing generative AI to create personalized experiences for users in various applications, including marketing, entertainment, and education. Ethical and Responsible AI: Addressing the ethical implications of generative AI, ensuring fairness, transparency, and accountability in AI-generated content and applications. Key Areas for Multimodal Services Multimodal Interaction and User Interfaces: Focuses on the integration of various interaction modes, such as voice, text, video, and gestures, to create seamless user experiences. It also explores advanced interfaces, including augmented and virtual reality. Multimodal Machine Learning: Investigates techniques for combining different data modalities for improved decision-making and developing deep learning models tailored for multimodal applications. Natural Language Processing (NLP) for Multimodal Services: Enhances conversational agents that understand and respond to multiple forms of input, and improves machine translation with integrated text, speech, and visual data. Multimodal Content Creation and Management: Develops platforms for creating and managing content across different media types, and enhances user engagement through personalized and adaptive multimedia content. Human-Computer Interaction (HCI) in Multimodal Systems: Crafts adaptive interfaces that respond to user preferences and behaviors, and evaluates the effectiveness and ease of use of multimodal applications. Multimodal Data Integration and Analysis: Techniques for processing and analyzing multimodal data streams in real time and innovative methods for visualizing complex multimodal datasets. Security and Privacy in Multimodal Services: Develops secure communication methods for multimodal data and ensures user privacy while handling diverse data types. Applications of Multimodal Services: Utilizes multimodal data for monitoring, diagnostics, and treatment in healthcare, creates interactive and immersive learning environments in education, enhances gaming and virtual worlds in entertainment, and implements multimodal interactions in smart homes and cities. Emerging Trends in Multimodal Services: Explores advancements in AI technologies tailored for multimodal applications, new developments in sensors and hardware that support multimodal interactions, and the potential and challenges of multimodal services in various domains. Multimodal Service Platforms and Frameworks: Builds robust architectures for delivering multimodal services, develops middleware and APIs to support multimodal service integration, and leverages cloud and edge computing for scalable multimodal applications. Evaluation and Benchmarking of Multimodal Systems: Establishes metrics for evaluating the performance of multimodal systems, conducts studies to compare different multimodal interaction techniques, and engages in user studies to assess the usability and impact of multimodal applications.
최종 수정: Dou Sun ()

관련 학회

관련 저널

CCF정식 명칭영향력 지수출판사ISSN
AArtificial Intelligence4.6Elsevier0004-3702
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536

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