Sang-Mo Kang^1,#, Shifa Shaffique^1,#, Muhammad Imran^2,#, Su-Mi Jeon³, Shabir Hussain Wani⁵, Muhammad Aaqil Khan⁴, Peter Odongkara¹, Eun-Hae Kwon¹, Yosep Kang¹, Joon-Ik Son⁶, Won-Chan Kim^1,*, In-Jung Lee^1,*

Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2699-2711, 2023, DOI:10.32604/phyton.2023.028224 - 28 July 2023

Abstract Food waste is recognized as a valuable source for potential agricultural applications to supply organic matter and nutrients to arable soil. However, the information on the combined application of food waste and the plant growth-promoting bacterial strain, Chlorella, related to plant metabolic features and sodium chloride content in arable soil is limited. The present study was conducted to investigate the exogenous application of food waste along with Chlorella, which improved the physio-morphological features of red pepper. Our results revealed that this combination enhanced the organic matter in the soil, ultimately improving the fertility rate of the More >

Fuad A. M. Al-Yarimi^*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 129-142, 2023, DOI:10.32604/csse.2023.024297 - 01 June 2022

Abstract A critical component of dealing with heart disease is real-time identification, which triggers rapid action. The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias. Recent contributions to cardiac arrhythmia prediction using supervised learning approaches generally involve the use of demographic features (electronic health records), signal features (electrocardiogram features as signals), and temporal features. Since the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats, it is possible to detect some of the irregularities in the early stages of arrhythmia. More >

Shubha Sumesh¹, John Yearwood¹, Shamsul Huda¹ and Shafiq Ahmad^2,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4503-4521, 2022, DOI:10.32604/cmc.2022.015474 - 11 October 2021

Abstract

Clinical Study and automatic diagnosis of electrocardiogram (ECG) data always remain a challenge in diagnosing cardiovascular activities. The analysis of ECG data relies on various factors like morphological features, classification techniques, methods or models used to diagnose and its performance improvement. Another crucial factor in the methodology is how to train the model for each patient. Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy. This paper proposes an adaptive approach to identify performance improvement in building a training model

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Tina Babu¹, Deepa Gupta¹, Tripty Singh^1,*, Shahin Hameed², Mohammed Zakariah³, Yousef Ajami Alotaibi⁴

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 99-128, 2021, DOI:10.32604/cmc.2021.016341 - 04 June 2021

Abstract Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification. This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes: normal, well, moderate, and poor. The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature, Gabor wavelet, wavelet moments, HSV histogram, color auto-correlogram, color moments, and morphological features that can be used to characterize different grades. Besides, the classifier is modeled as a multiclass structure with… More >