Deep learning: emerging trends, applications and research challenges

Brain tumor detection from MRI images is a challenging process due to high diversity in the tumor pixels of different peoples. Automatic detection has got wide spread acclaim because the manual detection by experts is time consuming and prone to error in judgment. Due to its high mortality rate, detection of tumor automatically is a new emerging technique in bio medical imaging. Here we present a review of few methods from simple thresholding to advanced deep learning methods for segmentation of tumor from MRI data. The segmentation of tumor methods is classified to image segmentation using gray level processing, machine learning and deep learning. The results of various methods are compared to find the best methods available. As medical imaging methods have improving day by day this review will help to understand emerging trends in brain tumor detection.

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Machine Learning in 5G Multimedia Communications

Research Anthology on Developing and Optimizing 5G Networks and the Impact on Society ◽

10.4018/978-1-7998-7708-0.ch010 ◽

2021 ◽

pp. 204-225

Author(s):

Dragorad A. Milovanovic ◽

Zoran S. Bojkovic ◽

Dragan D. Kukolj

Keyword(s):

Machine Learning ◽

Wireless Networks ◽

Deep Learning ◽

Complex Networks ◽

Future Research ◽

Fifth Generation ◽

Data Formats ◽

Research Challenges ◽

Wireless Environments ◽

Special Case

Machine learning (ML) has evolved to the point that this technique enhances communications and enables fifth-generation (5G) wireless networks. ML is great to get insights about complex networks that use large amounts of data, and for predictive and proactive adaptation to dynamic wireless environments. ML has become a crucial technology for mobile broadband communication. Special case goes to deep learning (DL) in immersive media. Through this chapter, the goal is to present open research challenges and applications of ML. An exploration of the potential of ML-based solution approaches in the context of 5G primary eMBB, mMTC, and uHSLLC services is presented, evaluating at the same time open issues for future research, including standardization activities of algorithms and data formats.

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Research Challenges in Off-Line Ancient Handwriting Recognition – A Deep Learning Approach

Lecture Notes in Electrical Engineering - Advanced Manufacturing and Automation X ◽

10.1007/978-981-33-6318-2_51 ◽

2021 ◽

pp. 408-415

Author(s):

Yi Wang ◽

Chen Wang ◽

Bo Chen

Keyword(s):

Deep Learning ◽

Handwriting Recognition ◽

Learning Approach ◽

Research Challenges

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Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

Frontiers in Neuroscience ◽

10.3389/fnins.2020.00027 ◽

2020 ◽

Vol 14 ◽

Cited By ~ 4

Author(s):

Tahsin Kurc ◽

Spyridon Bakas ◽

Xuhua Ren ◽

Aditya Bagari ◽

Alexandre Momeni ◽

...

Keyword(s):

Deep Learning ◽

Digital Pathology ◽

Learning Approaches ◽

Research Challenges

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A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos

International Journal of Computer and Information Technology(2279-0764) ◽

10.24203/ijcit.v10i5.166 ◽

2021 ◽

Vol 10 (5) ◽

Author(s):

John Gatara Munyua ◽

Geoffrey Mariga Wambugu ◽

Stephen Thiiru Njenga

Keyword(s):

Deep Learning ◽

Anomaly Detection ◽

Model Accuracy ◽

Surveillance Videos ◽

Emerging Trends ◽

Learning Technique ◽

Learning Reinforcement ◽

Security Incidents ◽

Computing Approach ◽

Continual Learning

Deep learning has proven to be a landmark computing approach to the computer vision domain. Hence, it has been widely applied to solve complex cognitive tasks like the detection of anomalies in surveillance videos. Anomaly detection in this case is the identification of abnormal events in the surveillance videos which can be deemed as security incidents or threats. Deep learning solutions for anomaly detection has outperformed other traditional machine learning solutions. This review attempts to provide holistic benchmarking of the published deep learning solutions for videos anomaly detection since 2016. The paper identifies, the learning technique, datasets used and the overall model accuracy. Reviewed papers were organised into five deep learning methods namely; autoencoders, continual learning, transfer learning, reinforcement learning and ensemble learning. Current and emerging trends are discussed as well.

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