Special Issue "Role of Machine Learning and Evolutionary Algorithms for Cancer Detection and Prediction"

Submission Deadline: 24 June 2021 (closed)
Guest Editors
Dr. Ashutosh Kumar Dubey, Chitkara University, India.
Dr. Vicente García-Díaz, University of Oviedo, Spain.
Dr. Abhishek Kumar, Chitkara University, India.


The advancement of machine learning and computational methods has become a driving force for global healthcare development and transformation. Machine learning allows a system to learn from the environment, through re-iterative processes. Along with the computational methods it also improves itself from experience. Recently, machine learning has gained massive attention across numerous fields, and is making it easy to model data extremely well, without the importance of using strong assumptions about the modeled system. The goal of this thematic issue is to explore how machine learning and Evolutionary Algorithms can help in the cancer detection and prediction. Specifically, innovative contributions that either solve or advance the understanding of issues related to new technologies and applications in the real world in the direction of cancer detection and prediction are very welcome.

Potential topics include, but are not limited to the following:
• Computational methods for cancer prediction and detection
• Machine and deep learning approaches for cancer and health data
• Decision support systems for healthcare and wellbeing
• Optimization for cancer detection and prediction
• Medical expert systems
• Biomedical applications
• Applications of artificial intelligence for cancer detection and prediction
• Intelligent computing and platforms in healthcare
• Visualization and interactive interfaces related to healthcare systems.

Published Papers
  • Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms
  • Abstract This study aims to empirically analyze teaching-learning-based optimization (TLBO) and machine learning algorithms using k-means and fuzzy c-means (FCM) algorithms for their individual performance evaluation in terms of clustering and classification. In the first phase, the clustering (k-means and FCM) algorithms were employed independently and the clustering accuracy was evaluated using different computational measures. During the second phase, the non-clustered data obtained from the first phase were preprocessed with TLBO. TLBO was performed using k-means (TLBO-KM) and FCM (TLBO-FCM) (TLBO-KM/FCM) algorithms. The objective function was determined by considering both minimization and maximization criteria. Non-clustered data obtained from the first phase… More
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  • Swarm-Based Extreme Learning Machine Models for Global Optimization
  • Abstract Extreme Learning Machine (ELM) is popular in batch learning, sequential learning, and progressive learning, due to its speed, easy integration, and generalization ability. While, Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence, high time and space complexity. In ELM, the hidden layer typically necessitates a huge number of nodes. Furthermore, there is no certainty that the arrangement of weights and biases within the hidden layer is optimal. To solve this problem, the traditional ELM has been hybridized with swarm intelligence optimization techniques. This paper displays five proposed hybrid Algorithms “Salp Swarm Algorithm (SSA-ELM), Grasshopper… More
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  • Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning
  • Abstract Cancer is the second deadliest human disease worldwide with high mortality rate. Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system. Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response. A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks. Human hepatocellular carcinoma (HepG2) cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab. Prediction models are developed by modifying ResNet101 and… More
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