Brain imaging techniques play an important role in determining the causes of brain cell injury. Therefore, earlier diagnosis of these diseases can be led to give rise to bring huge benefits in improving treatment possibilities and avoiding any potential complications that may occur to the patient. Recently, brain tumor segmentation has become a common task in medical image analysis due to its efficacy in diagnosing the type, size, and location of the tumor in automatic methods. Several researchers have developed new methods in order to obtain the best results in brain tumor segmentation, including using deep learning techniques such as the convolutional neural network (CNN). The goal of this survey is to present a brief overview of magnetic resonance imaging (MRI) modalities and discuss common methods of brain tumor segmentation from MRI images, including brain tumor segmentation using deep learning techniques, as well as the most important contributions in this field, which have shown significant improvements in recent years. Finally, we focused in summary on the building blocks of the CNN algorithms used for image segmentation. In entire survey methodology, it has been observed that hybrid techniques and CNN-based segmentation are more effective for brain tumor segmentation from MRI images.

The 3rd edition of the International Conference on Big Data and Machine Learning (BML’22) is a major gathering of researchers, engineers, and practitioners within the fields of Big Data, Machine Learning, and Artificial Intelligence, from both theoretical and practical perspectives. We strongly encourage contributions that describe real-world problems, interdisciplinary research, and experimental and/or theoretical studies that yield new insights to advance Big Data and Machine Learning. Submitted papers should be original and relevant to any of the areas listed below. All accepted papers will be presented at the conference by one of the authors and published in the proceedings with an ISBN. The acceptance will be based on their quality, relevance, and originality. Presentation options include both oral and poster sessions. We have planned special sessions for case studies and commercial presentations, as well as tutorials focusing on technical or scientific matters. Companies interested in presenting their products or methodologies, as well as researchers wishing to demonstrate a demo or present a tutorial are encouraged to contact the conference secretariat. We cordially invite all speakers and participants to present their research and/or experiences at BML’22concerning the subjects listed below. While each of the main topic areas is

Edge Computing (EC) is an innovative architecture that aims to provide cloud computing services near data sources. In conventional deep learning systems that use edge computing, data producers frequently need to transmit and exchange data with other entities, such as edge or cloud servers, to train their models. Federated Learning (FL) has recently emerged as a potential remedy for the problems of undesired bandwidth use, data confidentiality, and legal compliance. To enhance the status of research in this field and effectively implement the federated learning strategy, it is essential to identify and analyze its security and privacy issues first. Federated learning (FL) is the optimal method for addressing security and privacy concerns in edge computing. A profound grasp of risk factors allows FL implementers to create a secure environment while researchers benefit from a clear understanding of prospective research areas. The primary objective of this study is to evaluate previous contemporary studies in relevant disciplines to understand the issues and subjects covered in recent surveys. Furthermore, FL research is categorized into architectures, applications, and issues. Moreover, we analyze substantial security and privacy risks and propose new mitigation strategies.