Applied Intelligence - Springer - Impact Factor

Applied Intelligence - Springer | 2024 Impact Factor:3.5 | Cite Score:9.1 | Q2

Impact Factor and Journal Rank of Applied Intelligence

About: Applied Intelligence is a peer-reviewed journal published by Springer. It focuses on research in the field of artificial intelligence (AI) and its applications across various domains. The journal provides a platform for scholars, researchers, and practitioners to publish original research articles, reviews, and case studies that advance the understanding and application of AI technologies.
Objective: The primary objective of Applied Intelligence is to promote research and innovation in artificial intelligence and its practical applications. The journal aims to explore theories, methodologies, and applications that enhance the capabilities of intelligent systems and their integration into real-world settings. By publishing high-quality research, the journal contributes to the advancement of AI technologies and their deployment across diverse domains.
Interdisciplinary Focus: Applied Intelligence adopts an interdisciplinary approach, welcoming contributions from various fields related to artificial intelligence and its applications, including but not limited to, Machine Learning, Data Mining, Natural Language Processing, Computer Vision, Expert Systems, Robotics, Intelligent Agents, Autonomous Systems, AI Ethics and Governance. This interdisciplinary perspective fosters collaboration and innovation, leading to the development of advanced AI solutions that address complex challenges in different domains.
Global Reach and Impact: With a broad international readership and authorship, Applied Intelligence has a global reach and impact. Its publications contribute to the dissemination of knowledge and advancements in artificial intelligence worldwide. The journal content influences both academic research and practical applications, driving progress in areas such as healthcare, finance, manufacturing, transportation, and cybersecurity.
High Standards and Rigorous Review: Maintaining high academic standards, Applied Intelligence conducts a rigorous peer-review process. Each submitted manuscript undergoes thorough evaluation by experts in the field to ensure the quality, originality, and scientific rigor of the research. This stringent review process upholds the integrity and reputation of the journal, ensuring that only high-quality and impactful research is published.
Significance: Applied Intelligence plays a significant role in advancing research and practice in the field of artificial intelligence. By providing a platform for the publication of cutting-edge research findings, the journal contributes to the growth of knowledge and innovation in AI technologies and their applications. It serves as an essential resource for researchers, practitioners, and policymakers seeking to leverage AI to address societal challenges, drive economic growth, and improve quality of life in the digital age.

Journal Home: Journal Homepage

Editor-in-Chief: Hamido Fujita

Scope: The Applied Intelligence journal, published by Springer, is a distinguished peer-reviewed publication that focuses on the application of artificial intelligence (AI) techniques and methodologies to real-world problems. It serves as a vital platform for researchers, practitioners, and professionals to share their latest research findings, innovative applications, and practical insights in the field of applied intelligence. Here is an overview of its key focus areas and scope:
1. Artificial Intelligence Applications:
Research on the application of AI techniques, including machine learning, natural language processing, and computer vision, to various domains and industries.
Studies on AI-driven solutions for healthcare, finance, manufacturing, transportation, and other sectors.
2. Intelligent Systems and Algorithms:
Advancements in the design and development of intelligent systems and algorithms for solving complex problems.
Research on evolutionary algorithms, neural networks, fuzzy systems, and hybrid intelligent systems.
3. Data Mining and Knowledge Discovery:
Exploration of data mining techniques, pattern recognition, and knowledge discovery from large-scale datasets.
Research on predictive analytics, clustering, association rule mining, and outlier detection.
4. Decision Support Systems:
Advancements in decision support systems (DSS) that leverage AI technologies to assist decision-makers in complex scenarios.
Studies on expert systems, recommender systems, and AI-driven decision-making frameworks.
5. Intelligent Agents and Multi-Agent Systems:
Research on intelligent agents and multi-agent systems for autonomous decision-making and coordination.
Advancements in agent-based modeling, negotiation strategies, and distributed problem-solving.
6. Cognitive Computing:
Exploration of cognitive computing technologies that mimic human thought processes, including reasoning, learning, and problem-solving.
Research on cognitive architectures, cognitive models, and applications of cognitive computing in real-world scenarios.
7. Robotics and Autonomous Systems:
Advancements in AI-driven robotics and autonomous systems for various applications, including industrial automation, healthcare, and exploration.
Studies on robot perception, motion planning, human-robot interaction, and swarm robotics.
8. Intelligent Internet of Things (IoT):
Research on intelligent IoT systems that integrate AI capabilities for smart sensing, data analysis, and decision-making.
Advancements in AI-driven IoT applications for smart cities, environmental monitoring, and industrial automation.
9. Natural Language Processing (NLP) and Text Analytics:
Exploration of NLP techniques for text understanding, sentiment analysis, and language translation.
Research on text mining, topic modeling, and information retrieval from unstructured text data.
10. Computational Intelligence in Bioinformatics and Biomedicine:
Advancements in computational intelligence techniques for analyzing biological data, medical imaging, and healthcare informatics.
Research on AI-driven solutions for disease diagnosis, drug discovery, and personalized medicine.
11. AI Ethics and Responsible AI:
Exploration of ethical considerations and societal implications of AI technologies, including fairness, transparency, and accountability.
Research on the development of responsible AI frameworks, ethical guidelines, and governance mechanisms.
12. Applications of Deep Learning:
Advancements in deep learning methodologies, architectures, and applications across diverse domains.
Research on convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep reinforcement learning.