Cloud computing is one of the most commonly used infrastructures for carrying out activities using virtual machines known as processing units. One of the most fundamental issues with cloud computing is task scheduling. The optimal determination of scheduling criteria in cloud computing is a non-deterministic polynomial-time (NP)-complete optimization problem, and several procedures to manage this problem have been suggested by researchers in the past. Among these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm is recognized to produce optimal outcomes in a shorter time period for scheduling tasks in a heterogeneous environment. Literature shows that HEFT gives extraordinary results in terms of quality of schedule and execution time. However, in some cases, the average computation cost and selection of the first idle slot may not produce a good solution. Therefore, here we propose modified versions of the HEFT algorithm that can obtain improved results. In the rank generation phase, we implement different methodologies for calculating ranks, while in the processor selection phase, we modify the way of selecting idle slots for scheduling the tasks. This paper suggests enhanced versions of the HEFT algorithm under user-required financial constraints to minimize the makespan of a specified workflow submission on virtual machines. Our findings also suggest that enhanced versions of the HEFT algorithm perform better than the basic HEFT method in terms of lesser schedule length of the workflow problems running on various virtual machines.

Handwritten bank cheque images (BCI) can be segmented to extract crucial information and improve transactions. However, the segmentation process was disrupted in earlier studies when overlapped Handwritten Texts (HT) and Printed Texts (PT) were not concentrated. As a result, this framework successfully eliminated the overlapped texts for exact HT segmentation using Intelligent Character Recognition (ICR) and Pseudo Letter with Height-based Segmentation (PLHS). Initially, a denoised improved image is obtained by preprocessing the BCI. The overlapped HT with PTs from the improved BCI are identified and segmented independently using PHLS and ICR. After that, Nanonets are used to independently identify the date, signature, name, and amount from HT. The faded texts are restored using contour construction, edge detection, and texture inpainting. Finally, the recognized texts are categorized as forged and genuine cheques using a Sigmoidal Growing Cosine Intermap Pooling-based Convolutional Neural Network (SGCIP-CNN) with a classification accuracy of 98.7899% for effective money transactions. Thus, the proposed work outperformed the existing methodologies.