“sort” papers · AI for Research

Sort, tashi, sort

Fons i Duocastella, Clara · Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) · 2010

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Sort i sort

Anders; id_orcid 0000-0002-6268-6197 Engberg-Pedersen · University of Southern Denmark Research Portal (University of Southern Denmark) · 2014

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Quick Sort , Merge Sort , Heap Sort

Al-Hubaishi, Mohammed · 2016

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Sort, tashi, sort

RECERCAT (Consorci de Serveis Universitaris de Catalunya) · 2021

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Sort, tashi, sort

Fons i Duocastella, Clara · Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) · 2010

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Sort i sort

Anders; id_orcid 0000-0002-6268-6197 Engberg-Pedersen · University of Southern Denmark Research Portal (University of Southern Denmark) · 2014

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Sort, tashi, sort

Fons i Duocastella, Clara · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2010

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Selection Sort against Selection Sort and Insertion Sort

2015

Abstract – Optimized Selection Sort Algorithm is a new sorting algorithm that has been developed to address the shortcomings of the current popular sorting algorithms. The goal of this research is to perform an extensive empirical analysis of Optimized

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Perbandingan Kecepatan Gabungan Algoritma Quick Sort dan Merge Sort dengan Insertion Sort, Bubble Sort dan Selection Sort

Muhammad Ezar Al Rivan · SHILAP Revista de lepidopterología · 2017

Ordering is one of the process done before doing data processing. The sorting algorithm has its own strengths and weaknesses. By taking strengths of each algorithm then combined can be a better algorithm. Quick Sort and Merge Sort are algorithms that divide the data into parts and each part divide again into sub-section until one element. Usually one element join with others and then sorted by. In this experiment data divide into parts that have size not more than threshold. This part then sorted by Insertion Sort, Bubble Sort and Selection Sort. This replacement process can be reduce time used to divide data into one element. Data size and data type may affect time so this experiment use 5 data sizes and 3 types of data. The algorithm dominates in experiment are Merge-Insertion Sort and Merge-Selection Sort.

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Inserge-Sort: Rewriting Insertion-Sort

A. Subramani, K. Subramani · International Journal of Computer Mathematics Computer Systems Theory · 2026

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UCSD SORT Test (U-SORT)

Denisse Tiznado, Brent T. Mausbach, Verónica Cárdenas et al. · The Journal of Nervous and Mental Disease · 2010

The present investigation examined the validity of a new cognitive test intended to assess organizational skills. Participants were 180 middle-aged or older participants with a Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition diagnosis of schizophrenia or schizoaffective disorder. Participants' organizational skills were measured using our newly developed University of California, San Diego Sorting Test (U-SORT), a performance-based test of organizational ability in which subjects sort objects (e.g., battery, pens) from a "junk drawer" into "keep" versus "trash" piles. Significant correlations between U-SORT scores and theoretically similar constructs (i.e. functional capacity, cognitive functioning, and clinical symptoms) were acceptable (mean r = 0.34), and weak correlations were found between U-SORT scores and theoretically dissimilar constructs (e.g., health symptoms, social support, gender; mean r = 0.06 ). The correlation between assessment scores provides preliminary support for the U-SORT test as a brief, easily transportable, reliable, and valid measure of functioning for this population.

Cited by 1Open access

Counting Sort (E-Counting Sort)

Keshav Bajpai, Ashish Kots · 2015

Counting sort is a simple, stable and efficient sorting algorithm with linear running time, which is a fundamental building block for many applications. Counting sort algorithm has been widely used in data processing systems, because of its high efficiency, fast speed and stable nature. Therefore, a thorough study of its time complexity is required. This paper presents a modified version of counting sort E-Counting Sort with some efficiency improvements. An analysis of the E-Counting sort algorithm in comparison with original counting sort algorithm clearly shows that E-Counting sort algorithm having a reduced time complexity approximately to half of the original one. The new version can be applied to many real world applications providing the required result as efficient and as effective as original counting sort without affecting its real nature and improve the efficiency of application program.

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Inserge-Sort: Rewriting Insertion-Sort

A. Subramani, K. Subramani · Figshare · 2026

This paper presents a novel transposition-based sorting algorithm, which integrates two distinct sorting paradigms, namely Insertion-Sort() and Merge-Sort(). The resulting algorithm, which we call Inserge-Sort(), exhibits several intriguing properties, which are rigorously demonstrated in the paper. Notably, our approach enables fine-grained control over the quantity of inversion pairs in the input array. A simple modification of our algorithm allows for an exact reduction in the number of inversion pairs. It remains to be determined whether other sorting algorithms can be suitably adapted for this purpose. Our algorithm can be thought of as <i>rewriting</i> Insertion-Sort(). The advantage of a rewrite algorithm is that it facilitates the justification of our algorithm's approach while abstracting away implementation details. Additionally, the properties of our algorithm can be established using straightforward inductive arguments. It is also worth noting that adapting traditional sorting algorithms to handle linked list inputs is a non-trivial task, whereas our algorithm can handle such inputs with a minor modification.

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Inserge-Sort: Rewriting Insertion-Sort

A. Subramani, K. Subramani · Figshare · 2026

This paper presents a novel transposition-based sorting algorithm, which integrates two distinct sorting paradigms, namely Insertion-Sort() and Merge-Sort(). The resulting algorithm, which we call Inserge-Sort(), exhibits several intriguing properties, which are rigorously demonstrated in the paper. Notably, our approach enables fine-grained control over the quantity of inversion pairs in the input array. A simple modification of our algorithm allows for an exact reduction in the number of inversion pairs. It remains to be determined whether other sorting algorithms can be suitably adapted for this purpose. Our algorithm can be thought of as <i>rewriting</i> Insertion-Sort(). The advantage of a rewrite algorithm is that it facilitates the justification of our algorithm's approach while abstracting away implementation details. Additionally, the properties of our algorithm can be established using straightforward inductive arguments. It is also worth noting that adapting traditional sorting algorithms to handle linked list inputs is a non-trivial task, whereas our algorithm can handle such inputs with a minor modification.

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Bubble SortとComb Sort

茂岡本, 功田口, 和子高橋 et al. · Medical Entomology and Zoology · 1994

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SORT

Isabelle R Weir · OSF Preprints (OSF Preprints) · 2018

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SORT

Abhishek Thakur · International Journal of Wireless Networks and Broadband Technologies · 2020

In challenging environments, opportunistic networks can provide limited communication features in a delay tolerant manner. It is extremely difficult to transmit large data like videos in such environments, as delay may be hours and part of the information may be lost. This article proposes a novel system that uses partial information from prior communication to estimate the network congestion and delay. The video is compressed and packetized using scalable video coding (SVC). Extensions to Spray-and-Wait routing protocols are analyzed to ensure better delivery video quality and lower wastage. Through simulation, including real-world traces, performance of the proposed solutions under multiple scenarios is evaluated. Experimental results show that adaptive control reduces overall delay and minimizes wastage while improving the quality of video at the receiver. Adaptive SVC transmissions demonstrate almost three times increase in decoded content, as compared to non-SVC transmission.

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