Feature extraction is essential in Electromyography pattern recognition (EMG-PR) based prostheses control method. Time-domain features have been shown to have good performance in upper limb movement classification. However, the performance of EMG-PR prostheses driven by the existing time-domain features is still unsatisfactory. Hence, this study proposed three new time-domain features to improve the performance of EMG-PR based strategy in arm movement classification. EMG signals were recorded from the residual arms of eight amputees while performing different upper limb movements. Then, the newly proposed features were extracted and used to classify their limb movements. Experimental results showed that the proposed features could achieved an average classification accuracy of 92.00% ± 3.11% which was 6.49% higher than that of the commonly used time-domain features (p < 0.05). With three additional metrics, the proposed features also performed better, which suggest that the new features may be potential for improving the clinical performance of EMG-PR prostheses. © 2017 Elsevier Ltd

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Samuel O.W

Zhou H

Li X

Wang H

Zhang H

Li G.

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

Pattern recognition of electromyography signals based on novel time domain features for amputees' limb motion classification

Heart failure and other chronic diseases have posed huge challenges to the healthcare sectors across different country. As a result of inadequate tools and techniques to address the challenges, numerous patients have died while several others have experienced a decreased quality of life with various degrees of psychological burdens. With the revivification of artificial intelligence in the 1990s, computational intelligence (CI) tools have since been adopted in developing clinical decision support systems for heart disease diagnosis to ensure qualitative healthcare services. In this chapter, we describe some notable CI techniques and their clinical applications as it relate to heart failure risk prediction. Furthermore, the strength and limitation of these CI techniques in terms of accuracy and computational complexity are also discussed. Lastly, the potential role of CI in healthcare delivery systems is equally described. © 2018 Elsevier Inc. All rights reserved.

Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications

Computational Intelligence Enabling the Development of Efficient Clinical Decision Support Systems: Case Study of Heart Failure

Heart failure (HF) has been considered as one of the deadliest human diseases worldwide and the accurate prediction of HF risks would be vital for HF prevention and treatment. To predict HF risks, decision support systems based on artificial neural networks (ANN) have been widely proposed in previous studies. Generally, these existing ANN-based systems usually assumed that HF attributes have equal risk contribution to the HF diagnosis. However, several previous investigations have shown that the risk contributions of the attributes would be different. Thus the equal risk assumption concept associated with existing ANN methods would not properly reflect the diagnosis status of HF patients. In this study, the commonly used 13 HF attributes were considered and their contributions were determined by an experienced cardiac clinician. And Fuzzy analytic hierarchy process (Fuzzy_AHP) technique was used to compute the global weights for the attributes based on their individual contribution. Then the global weights that represent the contributions of the attributes were applied to train an ANN classifier for the prediction of HF risks in patients. The performance of the newly proposed decision support system based on the integration of ANN and Fuzzy_AHP methods was evaluated by using online clinical dataset of 297 HF patients and compared with that of the conventional ANN method. Our result shows that the proposed method could achieve an average prediction accuracy of 91.10%, which is 4.40% higher in comparison to that of the conventional ANN method. In addition, the newly proposed method also had better performance than seven previous methods that reported prediction accuracies in the range of 57.85–89.01%. The improvement of the HF risk prediction in the current study might be due to both the various contributions of the HF attributes and the proposed hybrid method. These findings suggest that the proposed method could be used to accurately predict HF risks in the clinic. © 2016 Elsevier Ltd

Expert Systems with Applications

An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction

Biomedical Signal Processing and Control

Continuous and recurrent pattern dynamic neural networks recognition of electrophysiological signals

The Turkish Journal of Gastroenterology

Clinical characteristics of small bowel tumors diagnosed by double-balloon endoscopy: Experience from a Chinese tertiary hospital

Reducing the effect of wrist variation on pattern recognition of Myoelectric Hand Prostheses Control through Dynamic Time Warping

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