+91 8870915303 [email protected]
Secure Registration 안전한 등록
5th - 6th June 2027 Budapest, Hungary

International Conference on AI in Data Science and Deep Learning (ICIADL-27)

Official Invitation Letter Available 공식 초청장 제공

An official invitation letter will be provided upon successful registration for your participation in the conference.

학술대회 참가 등록이 정상적으로 완료되면 공식 초청장이 발급됩니다.

Benefits of Registering as Listener참관객(청중) 등록 혜택
Access to All Conference Sessions 전체 세션 참관

Plenary, keynote and parallel sessions.

전체회의, 기조연설 및 분과 세션.

Networking Opportunities 네트워킹 기회

Connect with fellow researchers.

동료 연구자들과의 교류.

Certificate of Participation 참가 증명서

Digital certificate of participation.

디지털 참가 증명서 발급.

Invitation Letter Support 초청장 지원

Official letter after successful registration.

등록 완료 후 공식 초청장 발급.

Conference Kit / Digital Materials 학술대회 자료 / 디지털 자료

E-proceedings & resource materials.

전자 논문집 및 참고 자료.

Access to Keynote Sessions 기조연설 참관

Learn from leading experts & scholars.

저명한 전문가 및 학자들의 강연.

Show Bank convenience charges 은행 결제 수수료 보기
Claim Now! / 적용
Refund, Cancellation, Postponement, Travel & Registration Transfer환불, 취소, 연기, 출장 및 등록 양도 안내
1. Refund Policy환불 정책

If a participant is unable to attend and the registration cannot be transferred, the following refund conditions will apply.

참가자가 행사에 참석할 수 없으며 등록 양도가 불가능한 경우 아래 환불 규정이 적용됩니다.

  • Cancellations submitted 60 days before the conference are eligible for a full refund after deduction of a USD 100 administrative fee. 학술대회 시작 60일 이전 취소 시, 행정 수수료 USD 100 공제 후 전액 환불됩니다.
  • Cancellations made between 30 and 60 days before the conference are eligible for a 50% refund. 행사 30일~60일 전 취소 시 등록비의 50%가 환불됩니다.
  • No refunds will be issued for cancellations made within 30 days of the conference. 행사 시작 30일 이내 취소 건은 환불이 불가능합니다.
  • Registration fees for virtual participation are non-refundable. 온라인 참가 등록비는 환불되지 않습니다.
  • Once an official invitation letter has been issued, the registration fee becomes non-refundable. 공식 초청장 발급 이후에는 환불이 불가능합니다.
  • If attendance becomes impossible due to personal, travel, or external circumstances, the paid amount may be converted into conference credit valid for one year. 개인 사정, 여행 제한 또는 외부 사유로 참석이 어려운 경우 등록비는 환불되지 않지만 1년간 사용 가능한 학술대회 크레딧으로 전환될 수 있습니다.
  • As KSAA conferences may operate in hybrid format, the organizer reserves the right to modify the participation mode if necessary. Such changes are not eligible for refund requests. KSAA 학술대회는 하이브리드 방식으로 운영될 수 있으며, 필요 시 운영 방식이 변경될 수 있습니다. 이에 따른 환불은 제공되지 않습니다.
  • If the registrant fails to submit the registration form within 7 days from the date of registration, the registration will be considered invalid, and a refund will not be applicable. 등록일로부터 7일 이내에 등록 양식을 제출하지 않을 경우 해당 등록은 무효로 간주되며, 환불은 적용되지 않습니다.
2. Cancellation Policy행사 취소 정책
  • If the conference is cancelled by the organizer, participants will receive full registration credit equivalent to the paid amount. 주최 측 사정으로 행사가 취소될 경우 참가자는 등록 금액 전액에 해당하는 크레딧을 제공받게 됩니다.
  • This credit may be used for another KSAA conference within one year from the date of registration. 해당 크레딧은 등록일로부터 1년 이내 다른 KSAA 학술대회 등록에 사용할 수 있습니다.
3. Postponement Policy행사 연기 정책
  • If the conference is postponed and participants are unable to attend on the revised dates, full registration credit will be provided. 행사가 연기되고 참가자가 변경된 일정에 참석할 수 없는 경우 등록비 전액 크레딧이 제공됩니다.
  • The credit must be utilized within one year from the original conference date. 제공된 크레딧은 원래 행사일 기준 1년 이내 사용해야 합니다.
4. Travel & Accommodation출장 및 숙박 안내
  • Participants are responsible for arranging their own travel, accommodation, and related expenses. 참가자는 항공, 숙박 및 기타 관련 비용을 직접 준비해야 합니다.
  • KSAA is not responsible for travel or accommodation expenses resulting from schedule changes, postponements, or cancellations. 일정 변경, 연기 또는 취소로 인해 발생하는 교통 및 숙박 비용에 대해 KSAA는 책임지지 않습니다.
5. Registration Transfer등록 양도 정책
  • Registrations may be transferred to another individual from the same institution with prior written approval. 등록은 사전 서면 요청을 통해 동일 기관 소속 다른 참가자에게 양도할 수 있습니다.
  • Registrations may also be transferred to another KSAA conference event. 등록은 다른 KSAA 학술대회로 이전할 수도 있습니다.
  • Transfer requests must be submitted at least 14 days before the conference date. 양도 요청은 행사 최소 14일 전에 제출되어야 합니다.
  • Transferred registrations are not eligible for refund requests. 양도 완료된 등록은 환불 대상이 아닙니다.
6. Visa Information비자 안내
  • Participants are solely responsible for visa applications and travel arrangements. 비자 신청 및 여행 준비는 참가자 본인의 책임입니다.
  • KSAA does not intervene in embassy procedures or visa approval processes. KSAA는 대사관 절차 및 비자 승인 과정에 관여하지 않습니다.
  • Visa-related issues will not qualify for refund consideration. 비자 문제로 인한 불참은 환불 사유에 해당되지 않습니다.
7. Important Notice중요 안내 사항

All requests related to cancellation, modification, or registration transfer must be submitted in written form through the official conference email .

취소, 변경 또는 등록 양도와 관련된 모든 요청은 반드시 공식 이메일 을 통해 서면으로 제출되어야 합니다.

Conference Programme and Participation Policy학술대회 프로그램 및 참가 정책
  • Subject to programme design and accepted submissions, research papers from connected academic fields may be scheduled together in an interdisciplinary or multidisciplinary format to promote cross-field discussion and collaborative learning. 프로그램 구성 및 채택된 논문에 따라, 연관된 학문 분야의 연구 논문이 학제간 또는 다학제 형식으로 함께 편성될 수 있으며, 이를 통해 분야 간 토론과 협력적 학습을 촉진합니다.

I agree and accept the Terms and Conditions of the website https://ksaakorea.com/ 본인은 https://ksaakorea.com/ 웹사이트의 이용약관에 동의합니다.

Conference Session Tracks학술대회 세션 트랙

SDG Wheel

Aligned with UN Sustainable Development Goals 유엔 지속가능발전목표(SDGs)와의 연계

The conference's session tracks effectively support the following SDGs.

본 학술대회의 세션 트랙은 다음의 지속가능발전목표를 효과적으로 지원합니다.

SDG 4 SDG 8 SDG 9 SDG 10

This track focuses on the latest innovations in convolutional neural networks (CNNs) for image and video analysis. Researchers are invited to present their findings on novel architectures, optimization techniques, and applications in various domains.

This session explores the advancements in recurrent neural networks (RNNs) and their applications in sequence prediction tasks. Contributions may include novel methodologies, performance evaluations, and case studies in natural language processing and time series analysis.

This track delves into the theoretical foundations and practical applications of generative adversarial networks (GANs). Participants are encouraged to share their research on GAN architectures, training strategies, and real-world implementations.

This session focuses on the application of reinforcement learning techniques in complex and dynamic environments. Researchers are invited to discuss novel algorithms, case studies, and the integration of reinforcement learning with other AI methodologies.

This track highlights recent advancements in natural language processing (NLP) leveraging deep learning techniques. Topics may include sentiment analysis, machine translation, and conversational agents, with an emphasis on novel architectures and methodologies.

This session aims to explore the intersection of computer vision and data science, focusing on the application of deep learning techniques for visual data analysis. Contributions may include innovative approaches to image classification, object detection, and video analysis.

This track investigates the role of transfer learning in enhancing model performance on large-scale datasets. Researchers are encouraged to present their findings on domain adaptation, knowledge transfer, and practical applications across various fields.

This session focuses on the use of deep learning techniques for predictive modeling across diverse domains. Contributions may include novel model architectures, evaluation metrics, and case studies demonstrating the effectiveness of deep learning in predictive analytics.

This track addresses the critical need for explainability in AI systems, particularly in deep learning models. Researchers are invited to share their work on interpretability techniques, frameworks, and the implications of explainable AI in real-world applications.

This session explores various optimization strategies for training neural networks, focusing on improving convergence rates and model performance. Topics may include novel optimization algorithms, regularization techniques, and their impact on deep learning outcomes.

This track examines the ethical considerations and societal implications of deploying AI technologies in data science and deep learning. Researchers are encouraged to discuss frameworks for responsible AI, bias mitigation, and the impact of AI on society.

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