In the digital technology environment, business enterprises are focusing in enhancing the precision on marketing efforts so as to remain more competitive and enhance profit margins. The application of Machine Learning, Deep Learning, Data analytics in supply chain management (SCM) is getting more popular due to the growing consumer demand and organisation are identifying various ways in order to lower the cost of transportation of goods from one location to another. Through the enhancement in theology across SCM process, the data is highly critical for analysing the location and movement of the networks so as to reduce the overall cost involvement in the goods and services. The supply chain process is highly interconnected through physical flow of goods from raw materials to finished goods, hence there are more volume of data and financial flow across the supply chain. Therefore, it is highly important in analysing the increasing complexity in supply chain and also to understand the implementation of ML in enhancing the SCM process for sustainable development and growth among the various companies. The study is an empirical investigation on the key factors influencing the design and implementation of ML in the SCM process by major companies located in India for achieving sustainable development. A total of 132 respondents were chosen and closed ended questionnaire were distributed to them, based on the data collected the researchers performed detailed statistical analysis like Correlation analysis, Multiple regression analysis using SPSS package.
The present work proposes to evaluate, compare, and determine software alternatives that present good detection performance and low computational cost for the plant segmentation operation in computer vision systems. In practical aspects, it aims to enable low-cost and accessible hardware to be used efficiently in real-time embedded systems for detecting seedlings in the agricultural environment. The analyses carried out in the study show that the process of separating and classifying plant seedlings is complex and depends on the capture scene, which becomes a real challenge when exposed to unstable conditions of the external environment without the use of light control or more specific hardware. These restrictions are driven by functionality and market perspective, aimed at low-cost and access to technology, resulting in limitations in processing, hardware, operating practices, and consequently possible solutions. Despite the difficulties and precautions, the experiments showed the most promising solutions for separation, even in situations such as noise and lack of visibility.
In general, it is necessary to evaluate the required bandwidth in each segment of the 5G ultradense network. After doing so, it is necessary to decide on the choice of OSI network and connection layer technologies. The most suitable models of network equipment are determined according to the technologies so selected. This question is not easy because performance depends directly on the performance of the hardware and also on the performance, software, and hardware configuration. These channel capabilities are the criteria for evaluating the performance of channels and equipment on 5G networks. In this paper, a model is proposed to increase the channel capacity of the 5G ultradense network. It is designed to increase the bandwidth usage of the channel and increase its functionality. Its main special feature is that its energy and power consumption is very low compared to other methods. This method is also ideal for sending more data with less power.
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