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Top SCIE and SCOPUS Indexed Journals for Machine Learning (ML)

Top SCIE and SCOPUS Indexed Journals for Machine Learning

Top SCIE and SCOPUS Indexed Journals for Machine Learning (ML)

  • Machine learning (ML) is a trending research area in computer science for the next breakthrough. ML is also becoming a dominant problem-solving technique in many interdisciplinary research areas.

  • Building an intelligent system needs strong ML expertise with various machine reasoning and learning techniques at different levels of abstraction.

  • The universe is generating data exponentially, so collecting and understanding the data is crucial for many applications.

  • As data is becoming more meaningful and contextually relevant, ML is applied in multiple research disciplines, from cognitive sciences to biomedical informatics, medical imaging, cloud computing, language processing, business management, engineering, financial analysis, data networks, and physics, to name a few, where data-driven, intelligent solutions are paramount to solve many key problems.

  • Many leading journals are available for ML research, which provides high-quality articles with rigorous and rapid reviewing process from the international forum of ML research experts.

  • This list provides a complete list of impact factor journals in ML that publishes leading articles with cite score, scientific journal rank (SJR), and H-Index. These journals publish articles reporting state-of-the-art results on a wide range of learning methods applied to various learning problems.

General Scope and Topics Coverage of Machine Learning Journals

  • Machine learning journals publish high-quality research articles in a wide range of core and interdisciplinary research fields in machine learning. these journals create a significant impact of these fields in many scientific disciplines.

  • These journals publish peer reviewed articles that deal with the theoretical analysis, design and applications of machine learning and deep neural network models. the machine learning models include supervised, unsupervised, semi-supervised, ensemble and reinforcement.

  • The topics coverage in deep learning models are deep recurrent neural networks, deep belief networks, graph neural networks, deep boltzmann machine, deep autoencoder, generative neural networks,- deep ensemble learning, deep reinforcement learning, convolutional neural networks, transfer learning, extreme learning machines, deep generative models, dynamic neural networks, radial basis function networks, self-organizing maps, meta learning, multimodal deep learning, quantum machine learning, one-shot learning, interpretable machine learning, imitation learning, federated learning, active learning, representation learning, deep cascade learning, graph representation learning, distributed active learning, word embedding models, neural machine translation, attention mechanisms, domain adaptation, data augmentation, neural architecture search, hyperparameter optimization and performance tuning.

  • The fast pace of developments in machine learning enables many countless opportunities where it can augment the capabilities and knowledge of computing in fields such as applications of natural language processing, data stream processing applications, online recommendation systems, sentiment analysis and opinion mining, time series data analysis, medical machine learning applications include disease detection and prediction, medical image processing applications, machine learning applications of multimedia, stock market analysis and prediction, cyber security applications, pattern recognition, speech recognition, computer vision, malware detection system, intrusion detection system, intelligent wireless networks, big data analytics, intelligent transportation systems, autonomous vehicles, and edge intelligence.

List of Machine Learning Journals with High Impact Factor