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.
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
Massive data are generated by social media users including posts, tweets, images, and videos. Getting valuable information from this big data is a significant, challenging, and interesting issue in the text mining domain. Twitter data are analyzed with text mining techniques to discover society agenda, trends, user behaviors, and feelings. Text analysis method to determine sentiments from tweets is proposed in the present research. Apache Flume is used to collect data stream from Twitter and store into Apache Hadoop. Natural language processing techniques are carried out to put the data into meaningful context followed by a classification model training with data mining methods. It carries out the classification label as people’s opinion, such as positive, negative, and neutral sentiments, using Twitters streaming data. 10 different automobile brands are selected and collected tweets with hashtags about these brands by using Apache Flume are used as case study. Collected data have been pre-processed using TF-IDF, Bi-gram, and SVD metrics and a classification tree model has been generated and the results are compared. The results that were experimented indicated that the classification tree based on SVD has the best accuracy. According to the different brands model, based on bigram is the most stable and performs with the best accuracy. The results from the experiments indicate that the model that uses Bi gram could be used to address data with complex behavior in the sentiment detection.
Research on video description and human activity recognition has been dramatically improving study finding on visual monitoring. Monitoring activities in the examination is a yet unsolved problem in which the students can perform various activities in an Exam room. Such activities can be monitored automatically through an automated surveillance system. We use squeeze net and VGG16 as deep learning constructs for deep feature extraction. These features are then fused serially to form a single feature set. The entropy and ant colony optimization (ACO) based feature selection approaches are applied separately on the acquire feature subsets having qualities of both filter and wrapper-based approaches. The separately selected features are then ensemble to obtain a powerful features subset. SVM based classifiers are finally applied for prediction. From the Exam activities detection dataset, the classification algorithm precisely labels the student activities into abnormal and normal classes. The results depict that the suggested framework for activity recognition in Exam is very effective with acceptable accuracy. The framework will help to analyze student activity in exams, to improve the examination system.
Innovation is seen as one of the effective levers for businesses in the face of competition and rapidly changing markets. However, if technological innovation is identified in its practices and objectives, organizational or managerial innovation is still the subject of controversy and divergence as to its scope of action and research. Today, none, if not the majority of Moroccan industrial companies, especially those operating in the textile sector, have not been able to deploy a managerial transformation despite the emergence of different departments. This document presents advice to the leadership of organizations and other managerial practices inspired by Lean Management, Lewin's theory, and the Kotter model allowing the managerial and agile transformation of industrial companies.
The article presents the results of studying the effect of humic and bacterial preparations, complex liquid micronutrients when treating spring barley seeds on the microbial community of gray forest soils of Ryazan region of the Russian Federation. Studies of microorganisms of various ecological and trophic groups of gray forest medium loamy soil of the studied crop rotation using conventional methods showed that preparations and their complexes used in pre-sowing seed treatment influenced the number of soil microorganisms and their biological activity. It was found that the pre-sowing treatment of seeds with bacterial preparation Rhizoagrin made it possible to sharply increase the content of Firmicutes bacteria in the soil, participating in the decomposition of organic residues. The main part of the microbiome in the variants with humic preparation Ekorost consisted of Actinobacteria bacteria, the major component of which was bacteria of the Micrococcaceae family, which caused the transfer of hard-to-reach nutrients, such as urea, into an ammonia form more accessible to plants. A large proportion of diazotrophic Proteobacteria bacteria was also noted.
Urban traffic flow management requires continuous improvement in order to facilitate traffic in a smart way, solve traffic congestion problems and ensure good quality of service. The system representing urban traffic is a hybrid dynamic system, since it is made up of continuous processes interacting with discrete processes, where they can be represented by continuous dynamic type models (length of queues and time d evacuation of this length…) and has discrete events (control of traffic lights and the quantification of vehicles in the lanes). The objective is to organize the flow of these objects so that they are better distributed in the structure, according to certain criteria and constraints to be defined. We are interested in the structure of Petri nets and the application of the hybrid Petri net formalism as a graphical tool used in the modeling, the study of the dynamics and the performance evaluation of the hybrid system allows us to analyze these properties. By acting on the structure of the study model and on its parameters in order to evaluate its behavior and build a structured and optimal control system. Emphasis on the modeling of the urban traffic flow by the hybrid Petri Nets tool. Exploiting the timing of the Petri networks in the construction of the basic model of an intersection, with the quantification of vehicles we can estimate the Queue lengths so as to intelligently manage evacuation speed in intersections with appropriate time varying in order to control the crossing lights of adjacent intersections, applying this model to three related intersections between them and so on in order to facilitate the flow of traffic between the upstream and downstream intersections, the modeling and analysis of the results are checked and validated by a software simulator 'Visual Objet Net ++' under Windows.