Soft computing and intelligent systems: Techniques and applications

Abstract

This special issue is a collection of selected papers submitted to the Third International Symposium on Intelligent Systems Technologies and Applications (ISTA' 17) held in Manipal University, India, during September 13-16, 2017. These papers have been peer reviewed and accepted for presentation at the symposium. A second review has been conducted to improve content and presentation of the manuscripts published in this special issue. The 48 papers cover a wide range of powerful techniques and current applications of soft computing and intelligent systems.The International Symposium on Intelligent Systems Technologies and Applications aims to bring together researchers in related fields and provides a venue to explore and discuss various aspects of intelligent systems technologies and their applications. It provides excellent opportunities for the presentation and discussion of interesting new research results, which contributes to effective transfer of knowledge and dissemination of innovative ideas.Listed is a summary of the contributions of the papers pertaining to various application domains:Cybersecurity is an important concern for every organization. In this special issue, twelve papers * Corresponding author. Sabu M. Thampi, Indian Institute of Information Technology and Management-Kerala, Technopark Campus, Trivandrum-695581, Kerala State, India. E-mail: sabu.thampi@iiitmk.ac.in. deal with intrusion detection, integrity, and confidentiality. In [1], the authors describe an intrusion detection system for wireless mesh networks that identifies intrusion activities using a support vector machine classifier. The proposed method employs a hybrid genetic algorithm and a mutual-information technique for feature selection. In [2], a scheme is proposed to provide a secure outsourcing solution for the matrix-chain multiplication problem in cloud computing. For preserving data confidentiality, the client employs few linear transformation schemes. The proposed verification scheme helps to maintain the integrity of the computed result. In [3], the authors evaluate the efficiency of various deep learning methods to detect and classify domain names as either malicious or benign by automatically extracting the required features thus reducing the burden of manual feature engineering approaches. In [4], the effectiveness of recurrent neural network (RNN) and its variant long short-term memory (LSTM) is analyzed for Android malware detection of time-varying sequences of benign and malicious applications. This paper also provides a comparative analysis with other machine-learning classifiers. In [5], the authors propose a data-driven intrusion detection system for Industrial Internet of Things (IIoT) to hybridize both anomaly and specification-based schemes. In [6], an intelligent monitoring technique for smart grid

Cardiovascular Disease Detection using Ensemble Learning

“…This ML method is often used to predict the likelihood of illness occurrence [ 5 , 6 ]. To forecast the class of fresh samples, a model learned from training data is used [ 9 , 10 ]. Categorizing data into classes is also a supervised learning notion [ 10 ].…”

“…To forecast the class of fresh samples, a model learned from training data is used [ 9 , 10 ]. Categorizing data into classes is also a supervised learning notion [ 10 ].…”

Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications

“…Researchers have presented different ideas to enhance current BC technology. Currently, it requires much computational power due to its mathematical principles, such as the Merkel Hash tree, cryptographic key systems, and PoW [256].…”