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AMA, Agricultural Mechanization in Asia, Africa and Latin America

AMA, Agricultural Mechanization in Asia, Africa and Latin America (AMA) (issn: 00845841) is a peer reviewed journal first published online after indexing scopus in 1982. AMA is published by Farm Machinery Industrial Research Corp and Shin-Norinsha Co. AMA publishes every subjects of general engineering and agricultural engineering.

Submission Deadline
28 Sep 2022 (Vol - 53 , Issue- 10 )
Upcoming Publication
30 Sep 2022 (Vol - 53 , Issue 09 )

Aim and Scope :

AMA, Agricultural Mechanization in Asia, Africa and Latin America

AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) is a peer-reviewed journal. The journal covers Agricultural and Biological Sciences and all sort of engineering topic. the journal's scopes are in the following fields but not limited to:

Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology
Agricultural and Biological Sciences
Electrical Engineering and Telecommunication
Electronic Engineering
Computer Science & Engineering
Civil and architectural engineering
Mechanical and Materials Engineering
Transportation Engineering
Industrial Engineering
Industrial and Commercial Design
Information Engineering
Chemical Engineering
Food Engineering

Effect of irrigation scheduling on growth, yield and wateruse efficiency of Shatavar (Asparagus racemosus)

Paper ID- AMA-19-09-2021-10726

Asparagus racemosus belonging to family Liliaceae and commonly known as Shatavar, Shatavari, Satmuli, is a perennial climber found in the tropical and subtropical parts of India. Its tubers contain pharmaceutically active ingredients (steroidal saponins) and thus used in both traditional and modern system of medicines. In ayurvedic system of medicines, it is used as a galactagogue, aphrodisiac, anodyne, diuretic, antispasmodic and nervine tonic. The present investigation was conducted to assess the effect of irrigation scheduling on growth and yield of Shatavar. The experiment was conducted under Randomized Block Design with ten treatments and three replications. In the present study, 3 irrigation water depths i.e., 40mm, 50mm and 60mm were used to achieve IW and CPE ratio of 0.75, 1.0 and 1.25 in each depth. Maximum dry tuber yield ha-1 of 3.30 ton was recorded, when 60mm irrigation was given at IW/CPE of 1.00 (T9) but was statistically alike to T10 (3.10 ton) and T8 (3.01 ton). Minimum dry tuber yield ha-1 of 1.63 ton was observed in rainfed (control). Irrigation at 60mm depth given at IW, CPE=0.75 is beneficial to increase tuber yield based upon comparable yield and less water consumption. The crop can be grown as rainfed based on non-significant difference in WUE in different irrigation schedules.

Autonomous Detection of Cardia Ailments diagnosed by Electrocardiogram using various Supervised Machine Learning Algorithms

Paper ID- AMA-18-09-2021-10724

The electrocardiogram (ECG) signal is used to diagnose various Cardiac ailments as it holds the fundamental information to make appropriate decisions about different types of heart diseases. Hence several strategies were proposed to extract critical features from the ECG signal with highest accuracy which helps for the autonomous detection of Cardiac ailments. A methodology has been proposed in this work for state of the art in automatic detection of Cardiac ailments which include pre-processing, Feature extraction and Classification steps. A Butterworth third order band pass filter is used in pre-processing step and a four level Maximal overlap discrete wavelet packet transform (MODWPT) with symlet as mother wavelet is used for feature extraction step. Finally, for classification of considered three Cardiac ailments from MIT-BIH database i.e., Arrhythmia, Congestive Heart Failure and Atrial Fibrillation from Normal Sinus rhythm, five supervised Machine learning algorithms i.e., Support vector machine (SVM), K-nearest neighbour (KNN), Naive Bayes (NB), Decision tree (DT) and Random Forest (RF) were used which gives an overall accuracy of 90.83%, 90.56%, 90.28%, 91.39% and 91.94% for each classifier respectively. Clearly, random forest classifier for the proposed methodology gives better accuracy of the model for multiclass classification of cardiac ailments.

Optimization Study for Maximum Material Removal Speed when Grinding SKD11 tool steel with CBN Wheel on CNC Milling Machine

Paper ID- AMA-18-09-2021-10723

In this paper, the results of an optimization study when grinding SKD11 tool steel cylindrical parts with the use of CBN wheel and CNC milling machine. In the study, the maximum material removal speed (MRS) is the objective function. In addition, the influence of process parameters, including the spindle rotation speed, the depth of cut, the feed rate, and the wheel diameter on MRS was investigated. Moreover, optimal input parameters to achieve the maximum MRS have been proposed.

Comparison Between Analytical and Numerical Analysis on Blasting Pressure Relief of Tunnels

Paper ID- AMA-17-09-2021-10722

This paper uses analytical calculation analysis and numerical simulation research methods to study the pressure relief of tunnels. Blasting pressure relief achieves the purpose of improving surrounding rock support conditions. The high ground stress of the surrounding rock of the tunnel is transferred to the surrounding rock far away from the surface of the tunnel by loosening blasting to relieve the pressure so as to achieve the purpose of reducing the stress of the surrounding rock and protecting the tunnel. Finally, the simulation results of the internal force of the lining structure are found to be consistent with the analytical research and the numerical research results.

Optimization Study for Minimum Surface Roughness when Grinding with CBN Wheel on CNC Milling Machine

Paper ID- AMA-16-09-2021-10718

This paper presents the results of an optimization study when grinding SKD11 steel cylinder parts with CBN grinding wheels on CNC milling machines. In this study, the minimum surface roughness is chosen as the objective function. In addition, the influence of process parameters, including the spindel rotation speed, the depth of dressing cut, the feed rate, and the wheel diameter on the surface roughness was investigated. In addition, optimal input parameters to achieve minimum surface roughness were investigated.