Shaik Khaleelahmed

Scheduling refers to the process of allocating cloud resources to several users according to a schedule that has been established in advance. It is not possible to get acceptable performance in settings that are distributed without proper planning for simultaneous processes. When developing productive schedules in the cloud, it is necessary for work scheduling to take a variety of constraints and goals into consideration.When dealing with activities that have performance optimization limits, resource allocation is a very important aspect to consider. When it comes to cloud computing, the only way to achieve great performance, high profits, high scalability, efficient provisioning, and cost savings is with an exceptional task scheduling system. This article presents a grey wolf optimization (GWO) based framework for efficient task scheduling in cloud computing environment. The proposed algorithm is compared with particle swarm optimization (PSO) and flower pollination algorithm (FPA) and GWO is performing task scheduling in less execution time and cost in comparison with PSO and FPA techniques. Execution time taken by GWO to finish 200 task in 120.2 ms. It is less than the time taken by PSO and FPA algorithm to finish same number of tasks.

Effective crop categorization is important for keeping track of how crops grow and how much they produce in the future. Gathering crop data on categories, regions, and space distribution in a timely and accurate way could give a scientifically sound reason for changes to the way crops are organized. Polarimetric synthetic aperture radar dataset provides sufficient information for accurate crop categorization. It is essential to classify crops in order to successfully. This article presents wavelet transform (WT) based optimizedlong short-term memory (LSTM) deep learning (DL) for effective crop categorization. Image denoising is performed by WT. Denoising algorithms for images attempt to find a middle ground between totally removing all of the image’s noise and preserving essential, signal-free components of the picture in their original state. After denoising of images, crop image classification is achieved by LSTM and support vector machine (SVM) algorithm. LSTM has achieved 99.5% accuracy.

The main idea of this paper is to select the best power allocation throughput model by simulating the different power allocation theoretical modules in MIMO arbitrary multipath environment to address the effects of channel parameters on throughput. In general, power allocation techniques are used for minimizing the overall Bit Error Rate (BER). In this process, the channel estimation is usually done at the receiver by accessing the Channel State Information (CSI). The optimized system can be designed with respect to channel parameters so that it can be suitable for transmitter side during power allocation. The simulation analysis is carried out in NI LabVIEW and it is observed from the studies that the throughput results are as a function of received power. Under static system parameters, the relative throughput of the water filling (WF) power allocation model is found to be high efficient 15.74% when it is compared with the open loop zero-forcing (ZF) and it is 4.45 % with respect to inverse singular value decomposition (SVD) equal power allocation models.