An Efficient Strategy for Collision Resolution in Hash Tables

Nimbe, Peter; Frimpong, Samuel; Opoku, Michael

doi:10.5120/17411-7990

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…The evaluation criteria of all recurrent neural networks were defined as accuracy, precision, recall, f1-score, and AUC scores. The calculation formula of these metrics can be seen in [32,39,40]. Classification of protein families was performed with four different application scenarios.…”

Section: Application Resultsmentioning

confidence: 99%

A novel Fibonacci hash method for protein family identification by using recurrent neural networks

Alakuş¹,

Türkoğlu²

2021

Turk J Elec Eng & Comp Sci

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Identification and classification of protein families are one of the most significant problem in bioinformatics and protein studies. It is essential to specify the family of a protein since, proteins are highly used in smart drug therapies, protein functions and in some case, phylogenetic trees. Some sequencing techniques provide researchers to identify the biological similarities of protein families and functions. Yet, determining these families with sequencing applications requires huge amount of time. Thus, it is needed a computer and artificial intelligence based classification system to save the time, and avoid complexity in protein classification process. In order to designate the protein families with computer-aided systems, protein sequences need to be converted to the numerical representations. In this paper, we provide a novel protein mapping method based on Fibonacci numbers and hashing table (FIBHASH). Each amino acid code is assigned to the Fibonacci numbers based on integer representations respectively. Later, these amino acid codes are inserted a hashing table with the size of 20 to be classified with recurrent neural networks. To determine the performance of the proposed mapping method, we used accuracy, f1-score, recall, precision, and AUC evaluation criteria. In addition, the results of evaluation metrics with other protein mapping techniques including EIIP, hydrophobicity, CPNR, Atchley factors, BLOSUM62, PAM250, binary one-hot encoding, and randomly encoded representations are compared. The proposed method showed a promising result with an accuracy of 92.77%, and 0.98 AUC score.

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Section: Application Resultsmentioning

confidence: 99%

A novel Fibonacci hash method for protein family identification by using recurrent neural networks

Alakuş¹,

Türkoğlu²

2021

Turk J Elec Eng & Comp Sci

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“…Peter et al [12] presented an approach for resolving collision in one-dimensional array. The technique concatenated (dot (.))…”

Section: Related Workmentioning

confidence: 99%

Collision Resolution Techniques in Hash Table: A Review

Yusuf¹,

Sudi²,

Boukar³

et al. 2021

IJACSA

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One of the major challenges of hashing is achieving constant access time O(1) with an efficient memory space at a high load factor environment when various keys generate the same hash value or address. This problem causes a collision in the hash table, to resolve the collision and achieve constant access time O(1) researchers have proposed several methods of handling collision most of which introduce a non-constant access time complexity at a worst-case scenario. In this study, the worst case of several proposed hashing collision resolution techniques are analyzed based on their time complexity at a high load factor environment, it was found that almost all the existing techniques have a non-constant access time complexity. However, they all require an additional computation for rehashing keys in a hash table some of which is as a result of deadlock while iterating to insert a key. It was also found out that there are wasted slots in a hah table in all the reviewed techniques. Therefore, this work, provides an in-depth understanding of collision resolution techniques which can serve as an avenue for further research work in the field.

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“…However, a hash table has the problem of collision, where multiple different keys are hashed into a single bucket. Two different methods are used in resolving hash collisions: chaining and open addressing [41,42]. The chaining stores the collided key in an additional bucket using a linked list pointer.…”

Section: Hash Tablementioning

confidence: 99%

Comparison on Search Failure between Hash Tables and a Functional Bloom Filter

Byun

Lim

2020

Applied Sciences

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Hash-based data structures have been widely used in many applications. An intrinsic problem of hashing is collision, in which two or more elements are hashed to the same value. If a hash table is heavily loaded, more collisions would occur. Elements that could not be stored in a hash table because of the collision cause search failures. Many variant structures have been studied to reduce the number of collisions, but none of the structures completely solves the collision problem. In this paper, we claim that a functional Bloom filter (FBF) provides a lower search failure rate than hash tables, when a hash table is heavily loaded. In other words, a hash table can be replaced with an FBF because the FBF is more effective than hash tables in the search failure rate in storing a large amount of data to a limited size of memory. While hash tables require to store each input key in addition to its return value, a functional Bloom filter stores return values without input keys, because different index combinations according to each input key can be used to identify the input key. In search failure rates, we theoretically compare the FBF with hash-based data structures, such as multi-hash table, cuckoo hash table, and d-left hash table. We also provide simulation results to prove the validity of our theoretical results. The simulation results show that the search failure rates of hash tables are larger than that of the functional Bloom filter when the load factor is larger than 0.6.

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An Efficient Strategy for Collision Resolution in Hash Tables

Cited by 6 publications

References 1 publication

A novel Fibonacci hash method for protein family identification by using recurrent neural networks

A novel Fibonacci hash method for protein family identification by using recurrent neural networks

Collision Resolution Techniques in Hash Table: A Review

Comparison on Search Failure between Hash Tables and a Functional Bloom Filter

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