As a common bioelectric phenomenon, brainwave is produced by electric field change in human brain. The use of electroencephalogram (EEG) in identity recognition greatly enhances recognition security and accuracy. In this work, we propose a secure identity recognition method by using EEG with multi-factor feature. Machine learning is utilized to extract, classify and select features of EEG data. Structural sparse norm in EEG data has consistency. The feature is helpful in optimization. The experiments show that SIRSE has superiority on security, recognition success rate and reliability in comparison. © 2017 Elsevier Ltd

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Liang W

Tang M

Jing L

Huang Y.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Computers & Electrical Engineering

The significant role of plants can be observed through the dependency of animals and humans on them. Oxygen, materials, food and the beauty of the world are contributed by plants. Climate change, the decrease in pollinators, and plant diseases are causing a significant decline in both quality and coverage ratio of the plants and crops on a global scale. In developed countries, above 80 percent of rural production is produced by sharecropping. However, due to widespread diseases in plants, yields are reported to have declined by more than a half. These diseases are identified and diagnosed by the agricultural and forestry department. Manual inspection on a large area of fields requires a huge amount of time and effort, thereby reduces the effectiveness significantly. To counter this problem, we propose an automatic disease detection and classification method in radish fields by using a camera attached to an unmanned aerial vehicle (UAV) to capture high quality images from the fields and analyze them by extracting both color and texture features, then we used K-means clustering to filter radish regions and feeds them into a fine-tuned GoogleNet to detect Fusarium wilt of radish efficiently at early stage and allow the authorities to take timely action which ensures the food safety for current and future generations. © 2018 Elsevier Inc.

Fulltext

Sustainable Computing: Informatics and Systems

UAV based wilt detection system via convolutional neural networks

SIRSE: A secure identity recognition scheme based on electroencephalogram data with multi-factor feature

Introduction to the special section on Big data and IoT in e-healthcare

Security and Communication Networks

Efficient identity authentication and encryption technique for high throughput RFID system

Pattern Recognition Letters

A new multi-level approach to EEG based human authentication using eye blinking

Wireless Personal Communications

A New Dynamic ID-Based User Authentication Scheme Using Mobile Device: Cryptanalysis, the Principles and Design

Nonlinear Dynamics

Design and analysis of a chaotic maps-based three-party authenticated key agreement protocol

Journal	Data powered by TypesetComputers & Electrical Engineering
Publisher	Data powered by TypesetElsevier BV
ISSN	0045-7906
Open Access	0