2 mm: A new technique for sorting data

Mubarak, Abbas; Iqbal, Sajid; Naeem, Tariq; Hussain, Shafiq

doi:10.1016/j.tcs.2022.01.037

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…Insertion sort is best for nearly sorted data. The time complexity of insertion sort in worst and average case scenarios is O(n 2 ) [39]. To reduce the execution time of insertion sort and to make it more efficient this paper presents a novel preprocessing technique.…”

Section: Proposed Preprocessing Technique For Insertion Sortmentioning

confidence: 99%

Preprocessing: A method For Reducing Time Complexity

Mubarak

Iqbal

Rasool

et al. 2022

JCBI

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Data can be processed quickly if it is in some order, whereas unsequenced data can take more time to obtain results. Sorting is used for data arrangement. It is also one of the essential requirement for most applications and this step helps to boost performance. Sorting is also a prerequisite in several computer applications like databases. Over time computer scientists have not only introduced new sorting techniques considering various factors to be improved but they have also presented enhanced variants of existing sorting methods. The main objective has always been to reduce the execution time and space of the sorting algorithms. With every passing day, digital content is growing rapidly, which is a significant cause that encourages researchers to design new time-space efficient sorting algorithms. This paper presents some preprocessing strategies for quicksort and insertion sort to improve their performances. Tha main idea of using these preprocessings is to make input data more suitable for sorting algorithm, as most sorting function performs extraordinary for a specific type of input, such as insertion sort works better on nearyly sorted data. To authenticate the efficiency of existing sorting algorithms, these have been compared with proposed preprocessing strategies. The results with proposed techniqes outperforms the results of original sorting methods. It also helps to convert worst case into average case. By using this approch complexity of many algorithms can be reduced, therfore this is very important.

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Section: Proposed Preprocessing Technique For Insertion Sortmentioning

confidence: 99%

Preprocessing: A method For Reducing Time Complexity

Mubarak

Iqbal

Rasool

et al. 2022

JCBI

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“…A comparison-based sorting algorithm called 2mm was proposed, which is the Modified Min-max Bidirectional Parallel Selection Sort (MMBPSS) algorithm. It has a lower computational cost than other algorithms and reduces more than 50% comparison cost [9]. Moreover, it has better performance in sorting numbers than quick sort and others [10].…”

Section: Introductionmentioning

confidence: 99%

Complex Theory and Batch Processing in Mechanical Systemic Data Extraction

Xue-fang

Hong-xia

et al. 2022

IEEE Access

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This paper designs a new batching program to extract the original data, which helps to traverse the entire sample space quickly and provides a new approach for data extraction based on the motion stroke diagram. The designed program can read thousands of files out of many folders instantly and automatically. Meanwhile, thousands of time nodes are calculated based on the proportional coefficients. Finally, experimental data of many folders are separated into sample space easily and rapidly. The original data, the extracted stroke data, and the name and address of every folder and file are output in the result. The program designed in this paper at the maximum processing speed needs only 0.015 seconds to read, compute, and extract the correlative data information from one file (s/f), and the average time threshold is 0.0866 seconds. The Linear Theory (LT), optimizing Sparrow Search Algorithm (OSSA), and the automatic batch read file method can be employed to obtain the optimal result of data extraction. Through 744 rounds of nine experiments, the Average Processing Speed (APS) is less than 0.110 seconds per second data segment (s/ess). The APS is increased by 79.73%. The accuracy of fault classification and forecast of the eigenvalue extracted from the Automatic Batch Reading File (ABRF) and the Ensemble Empirical Mode Decomposition (EEMD) method is improved by 9% and 13% by Self-Organized Mapping (SOM). It is validated that our proposed data extraction method is faster and more progressive than the existing ones.

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