Academic Journal
Q1AI Open
About AI Open
AI Open is a scholarly journal published by KeAi Communications Co.. SCImago 2025 places it in Q1 with an SJR of 2.643 and an H-index of 26.
Its listed coverage is 2020-2025 and its research categories include Artificial Intelligence (Q1); Computer Science Applications (Q1); Computer Vision and Pattern Recognition (Q1); Human-Computer Interaction (Q1); Information Systems (Q1); Software (Q1). The 2025 dataset reports 21 documents and 1169 citations across the latest three-year reporting window.
In the rapidly evolving world of technology, AI Open stands as a beacon of innovation, accessibility, and collaboration in the realm of artificial intelligence. Whether you're a developer, business leader, or tech enthusiast, AI Open offers a dynamic ecosystem where cutting-edge AI research, open-source tools, and real-world applications converge.
What is AI Open?
AI Open is an open platform dedicated to advancing artificial intelligence through transparency, community-driven development, and ethical innovation. Unlike closed, proprietary systems, AI Open promotes the free exchange of ideas, tools, and data to empower developers and organizations worldwide.
At its core, AI Open fosters collaboration between researchers, developers, and industry experts to solve complex problems using AI. It provides access to a rich repository of open-source AI models, datasets, and machine learning frameworks, enabling rapid experimentation and scalable deployment.
Key Features of AI Open
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Open-Source Models: Access a wide range of pre-trained models for natural language processing (NLP), computer vision, robotics, and more. These models are freely available for customization and deployment.
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Collaborative Community: Join a global network of AI practitioners who share best practices, contribute to projects, and provide support through forums, GitHub repositories, and virtual meetups.
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Scalable Infrastructure: With cloud-based integrations and APIs, AI Open helps users build, train, and deploy models at scale, accelerating time-to-market and reducing operational costs.
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Ethical AI Focus: AI Open places strong emphasis on ethical guidelines, fairness, and responsible AI usage. Transparency and accountability are built into every tool and framework offered by the platform.
Why Choose AI Open?
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Transparency: Unlike closed-source systems, AI Open lets users inspect, modify, and improve AI models and algorithms. This fosters trust and enables greater innovation.
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Cost-Effective Solutions: AI Open eliminates expensive licensing fees. Users can access powerful tools and datasets at no cost, making it ideal for startups, educators, and independent developers.
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Rapid Prototyping: With readily available AI components, users can quickly build prototypes, test them in real-time, and iterate based on feedback.
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Continuous Learning: AI Open regularly updates its repository with the latest research, tools, and tutorials. Users stay ahead of the curve with access to state-of-the-art advancements in artificial intelligence.
Future of AI with AI Open
AI Open is more than just a platform—it's a movement toward democratizing artificial intelligence. By removing barriers to entry and promoting a collaborative spirit, it’s shaping a future where AI technology is accessible, responsible, and beneficial to all.
As AI continues to reshape industries from healthcare and finance to education and entertainment, platforms like AI Open will play a pivotal role in ensuring that innovation is inclusive and ethically grounded.
Journal Metrics
Metrics can change by reporting year. Verify time-sensitive values with the publisher or indexing service.
Aims & Scope
In today’s fast-paced digital world, understanding your users is more critical than ever. That’s where Scope AI Open steps in — a groundbreaking open-source platform designed to help product teams and businesses unlock powerful insights from user feedback. By combining the flexibility of open-source development with the intelligence of AI, Scope AI Open transforms how teams listen to, analyze, and act on customer feedback.
What is Scope AI Open?
Scope AI Open is an open-source version of Scope AI’s powerful customer feedback analysis tool. Built with accessibility, transparency, and community collaboration in mind, it empowers teams to take control of their feedback data without being locked into proprietary systems.
Scope AI Open uses natural language processing (NLP) and machine learning to automatically analyze qualitative feedback — from support tickets and surveys to reviews and social media comments. The result? Actionable insights delivered faster, enabling product teams to prioritize features, identify pain points, and enhance user satisfaction.
Why Scope AI Open is a Game-Changer
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Open-Source Flexibility
Unlike traditional customer feedback tools, Scope AI Open is completely open-source. This means your team can customize, self-host, and scale the platform based on your specific needs — no vendor lock-in, no black-box algorithms. -
AI-Powered Insights
Scope AI Open leverages the latest in artificial intelligence to extract themes, trends, and sentiment from large volumes of unstructured data. This enables teams to move from anecdotal feedback to data-driven decision-making in real time. -
Community-Driven Innovation
With its open-source model, Scope AI Open encourages community contributions. Developers, data scientists, and product managers can all participate in shaping the future of the platform, adding integrations, improving algorithms, and creating plugins that suit their workflows. -
Privacy and Data Ownership
One of the standout benefits of using an open-source tool like Scope AI Open is that you own your data. Companies can deploy the platform on their own infrastructure, ensuring full control over sensitive customer feedback and compliance with privacy regulations.
Use Cases for Scope AI Open
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Product Management: Prioritize the most requested features and address user pain points using trend analysis from real-time feedback.
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Customer Support: Quickly categorize and route support tickets based on themes and sentiment.
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UX Research: Identify common usability issues across channels with automated tagging and filtering.
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Marketing & Growth: Discover what users love (or don’t) about your brand and use those insights for better messaging and positioning.
Recent Research Articles
Latest publications matched automatically by ISSN.
Bio-inspired adaptive neurons for dynamic weighting in Artificial Neural Networks
Ashhadul Islam, Abdesselam Bouzerdoum, Samir Brahim Belhaouari
2026 · DOI: 10.1016/j.aiopen.2026.02.001Not another imputation method: A transformer-based model for missing values in tabular datasets
Camillo Maria Caruso, Paolo Soda, Valerio Guarrasi
2026 · DOI: 10.1016/j.aiopen.2026.02.005TRiSM for Agentic AI: A review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems
Shaina Raza, Ranjan Sapkota, Manoj Karkee, Christos Emmanouilidis et al.
2026 · DOI: 10.1016/j.aiopen.2026.02.006WorldArena: A unified benchmark for evaluating perception and functional utility of embodied world models
Yu Shang, Zhuohang Li, Yiding Ma, Weikang Su et al.
2026 · DOI: 10.1016/j.aiopen.2026.07.002DualFlexKAN: Dual-stage Kolmogorov-Arnold Networks with independent function control
Andrés Ortiz, Nicolás J. Gallego-Molina, Carmen Jiménez-Mesa, Juan M. Górriz et al.
2026 · DOI: 10.1016/j.aiopen.2026.08.003Which type of students can LLMs act? Investigating authentic simulation with graph-based Human–AI collaborative system
Haoxuan Li, Jifan Yu, Xin Cong, Yang Dang et al.
2026 · DOI: 10.1016/j.aiopen.2026.05.001Pioneering multimodal emotion recognition in the era of large models: Towards open vocabularies
Jing Han, Zhiqiang Gao, Shihao Gao, Jialing Liu et al.
2026 · DOI: 10.1016/j.aiopen.2026.06.001A survey on generative recommendation: Data, model, and tasks
Min Hou, Le Wu, Yuxin Liao, Yonghui Yang et al.
2026 · DOI: 10.1016/j.aiopen.2026.05.002Integrating cross-view multi-scale perception and RAG-enabled expert fusion for medical prediction
Cheng Wang, Yongbin Liu, Ying Yu, Chunping Ouyang et al.
2026 · DOI: 10.1016/j.aiopen.2026.02.004GHOST 2.0: Generative high-fidelity one shot transfer of heads
Alexander Groshev, Anastasiia Iashchenko, Pavel Paramonov, Denis Dimitrov et al.
2026 · DOI: 10.1016/j.aiopen.2026.02.003Human professional level driving agent for race car simulation environments
Gergely Bári, László Palkovics
2026 · DOI: 10.1016/j.aiopen.2026.02.007Knowledge intensive agents
Zhenghao Liu, Pengcheng Huang, Zhipeng Xu, Xinze Li et al.
2026 · DOI: 10.1016/j.aiopen.2026.02.002Who is the most suitable one? Compliance review method based on multi-agent routing
Chutian Yu, Jiangqian Huang, Xin Chen, Meijin Gao et al.
2026 · DOI: 10.1016/j.aiopen.2026.03.001KGMedQA: An automated and comprehensive KG-based benchmark for biomedical LLM assessment
Qirui Hao, Kewei Cheng, Xinyu Liu, Hongliang Wang et al.
2026 · DOI: 10.1016/j.aiopen.2026.07.001Privacy-preserving in-the-wild bodily expressed emotion recognition: A dual-teacher distillation framework with psychological priors
Shuang Wu, Daniela M. Romano
2026 · DOI: 10.1016/j.aiopen.2026.08.001ADAP: Adaptive & Dynamic Arc Padding for predicting seam profiles in Multi-Layer-Multi-Pass robotic welding
He Wang, Sen Li, Xiaobo Liu, Chengxiao Dong et al.
2025 · DOI: 10.1016/j.aiopen.2025.10.003LLMKG＋: Systematically improving knowledge quality and coverage in KGs using LLMs – A case study in medical domain
Xincan Feng, Hejie Cui, Kazuki Hayashi, Huy Hien Vu et al.
2025 · DOI: 10.1016/j.aiopen.2025.11.003SafeCast: Risk-responsive motion forecasting for autonomous vehicles
Haicheng Liao, Hanlin Kong, Zhenning Li, Chengzhong Xu et al.
2025 · DOI: 10.1016/j.aiopen.2025.08.001Optimal RoPE extension via Bayesian Optimization for training-free length generalization
Xinrong Zhang, Shengding Hu, Weilin Zhao, Huadong Wang et al.
2025 · DOI: 10.1016/j.aiopen.2025.01.002ChatLLM network: More brains, more intelligence
Rui Hao, Linmei Hu, Weijian Qi, Qingliu Wu et al.
2025 · DOI: 10.1016/j.aiopen.2025.01.001Reviews
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Version History
April 14, 2025 at 10:53 am
April 14, 2025