درخت حوزه‌های تخصصی

Learning effective segment depictions and resemblance measures are fundamental to the recuperation execution of a substance based picture recuperation (CBIR) structure. Regardless of wide research tries for a significant long time, it stays one of the most testing open gives that broadly impedes the achievements of real-world CBIR structures. The key test has been credited to the extraordinary "semantic hole" subject that happens between low-level photo pixels got by technologies and raised close semantic thoughts saw by a human. Among various techniques, AI has been successfully analyzed as a possible course to interface the semantic gap in the whole deal. Impelled by late triumphs of significant learning techniques for PC vision and various applications, in this paper, we try to address an open issue: if significant learning is a longing for spreading over the semantic gap in CBIR and how much updates in CBIR endeavors can be cultivated by exploring the front line significant learning methodology for learning feature depictions and likeness measures. Specifically, we explore a structure of significant learning with application to CBIR assignments with a wide game plan of definite examinations by investigating front line significant learning methodologies for CBIR endeavors under moved settings. From our exploratory examinations, we find some encouraging results and compress some huge bits of information for upcoming research.

INTRODUCTION: After the approval of the country's crisis management law, the upstream documents related to this law, including the preparedness and response programs at the national and provincial levels, are among the tasks assigned to increase the efficiency of the crisis management processes. METHODS: This study presents a general framework for developing a provincial crisis response program. The proposed process has been used to develop the crisis response program of Hamadan province. In the phase of understanding the current situation, questionnaires and in-depth interviews were used to collect field data and determine the challenges presented to crisis management in Hamadan province. Moreover, a method for allocating the specialized functions of the response phase to the provincial organizations has been presented using the national crisis documents. FINDINGS: In general, the findings of this research can be assigned to three groups. The first group pertained to the project method, including the stages of understanding the current situation and developing the response program of the provinces. The second group of findings was the summary of the analysis of field data collected regarding the state of preparedness and response in Hamadan province. The third group encompassed determining and allocating specialized crisis response functions in Hamadan province. The functions of the provinces (n=32) and after their compliance with the national functions, the crisis management law, and the capabilities of the existing organization in the province, 56 specialized sub-functions were identified in the provinces, and the responsible, collaborating, and supporting institutions were determined. CONCLUSION: As evidenced by the obtained results, in the process of function allocation, some problems have been identified due to the shortage of information in the upstream documents, which are suggested to the crisis management organization for better planning.

Agriculture is one of the essential sources of occupation and revenue in India. Conferring to existing statistics, most agriculturalists are facing severe losses due to poor farming yield. Farming activities are challenged by various environmental factors that affect agricultural productivity to a greater extent. The present farming situation is above the average of the process involves more biochemical bases for managing the diseases and other destructing facts. The foremost problems they are facing in day-to-day farming tasks are crop or plant diseases affecting productivity. Also, the growth of weeds along with field crops has been another challenge.  The technology has developed to rectify the problems using some machine learning algorithms like Random Forest algorithms, Decision trees, Naïve Bayes, KNN, K-Means clustering, Support vector machines. The result has been evaluated and observed through the performance evaluation metrics using confusion matrix, accuracy, precision, Sensitivity, specificity with the observations, research, and studies. The statistics have expressed the overall accuracy of 98% by achieving the detection of diseases in plants and by removing the weeds that ruin the growth of plants.