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All Journal Indonesian Journal of Urology Communications in Science and Technology
Ahmad, Shafiq
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Engineering Health Professions Medicine & Pharmacology Public Health

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STUDY ON IN VITRO DISSOLUTION OF CALCIUM OXALATE RENAL STONE BY SHILAJIT Ahmad, Shafiq; Ansari, Tariq Mahmood; Shad, Muhammad Aslam
Indonesian Journal of Urology Vol 27 No 2 (2020)
Publisher : Indonesian Urological Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32421/juri.v27i2.563

Abstract

Objective: This study aims to test the solubility efficiency of Shilajit in vitro for calcium oxalate renal stone. Material & Methods: A small stone was selected for the experiment. The weighed stone was suspended in 25 ml of aqueous extract of Shilajit for 72 hours with the interval of 24 hours. After each 24 hours, the stone was taken out, washed, dried and difference in weight was calculated. The whole procedure was carried out at room temperature. Results: It was found that the weight of the stone was reduced. Conclusion: Shilajit has the ability to dissolve the calcium oxalate renal stone.
Concise convolutional neural network model for fault detection Al Firdausi, Muhammad; Ahmad, Shafiq
Communications in Science and Technology Vol 7 No 1 (2022)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.7.1.2022.746

Abstract

Fault detection is an urgent need for maintenance to obtain the optimal scheduling of production activities, improve system reliability, and reduce operation and maintenance costs. Many studies published in recent years focus on machine learning models to detect any system anomalies in line with the era of big data and the fourth industrial revolution (Industry 4.0). Say, a working condition of bearing can be monitored and then any fault can be detected using the vibration analysis of bearing acceleration data. Most of the published works are presented based upon the knowledge of signal processing in which the result depends heavily on feature extraction. It becomes a challenge then to apply a machine learning algorithm directly to the raw acceleration data as it has been successfully applied to raw data in other science and engineering domains. In this article, a concise Convolutional Neural Networks-based deep learning model is proposed for bearing fault detection. The proposed model was concise with 98% less number of parameters compared to other well-known models. It produced 21.21% and 7.03% better accuracy and fault detection rate, respectively. The model was also tested in different operating parameter environments and still gave an excellent result. Since the proposed concise architecture of the model needed short training time, it is deemed suitable for application on manufacturing floor where the pace of production moves fast and the change of the production machine configuration likely occurs.
Co-Authors Al Firdausi, Muhammad Ansari, Tariq Mahmood Shad, Muhammad Aslam