Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

A comprehensive model for the implementation of agricultural land levelling and consolidation plan in the Abu Fazel region of Ahvaz

Abstract It has been shown that land fragmentation can negatively impact the efficiency of farming. Therefore, experts recommend land consolidation process, as a logical and workable solution to solve the problems and complications caused by land fragmentation. Land levelling and consolidation is a process of land reform that changes the construction of agricultural lands which leads to rural development through reforming farm management. However, a single plan cannot be applied to different regions, even though they might be in the same country. Hence, it is vital to investigate multiple factors in a certain region to devise the perfect consolidation plan. The present study, which is a survey-exploratory research, is conducted to provide a comprehensive model to implement the plan for levelling and consolidation of agricultural lands in the Abu Fazel region of Ahvaz, Iran. This research is an applied field research which uses both library and field methods to collect the required data. The study population is in Abu Fazel in the northeast of Ahvaz in Zargan region. The results of the study show that cultural, social, economic, policy-making, educational, agricultural and managerial factors have an effect on the participation of farmers in the levelling and consolidation of agricultural lands in the study area (p≥0.01). Also, there is a strong positive relationship between these factors and the farmers' participation in levelling and consolidation of agricultural lands (p≥0.01). Among these factors, it is observed that policy is main factor. Policymakers can play an effective role in land consolidation and macro development on the one hand and agricultural and rural development. On the other, by accurately assessing the interactive effect of land consolidation and related factors, along with the effects of this process on the evolution of agronomic systems.

Retracted: Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

[This retracts the article DOI: 10.1155/2022/4886586.].

Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression

In view of the large amount of data collected by an edge server, when compression technology is used for data compression, data classification accuracy is reduced and data loss is large. This paper proposes a data compression algorithm based on the chaotic mutation adaptive sparrow search algorithm (CMASSA). Constructing a new fitness function, CMASSA optimizes the hyperparameters of the Convolutional Auto-Encoder Network (CAEN) on the cloud service center, aiming to obtain the optimal CAEN model. The model is sent to the edge server to compress the data at the lower level of edge computing. The effectiveness of CMASSA performance is tested on ten high-dimensional benchmark functions, and the results show that CMASSA outperforms other comparison algorithms. Subsequently, experiments are compared with other literature on the Multi-class Weather Dataset (MWD). Experiments show that under the premise of ensuring a certain compression ratio, the proposed algorithm not only has better accuracy in classification tasks than other algorithms but also maintains a high degree of data reconstruction.