Optimizing the DNA fragment assembly using metaheuristic-based overlap layout consensus approach

Uzma,; Halim, Zahid

doi:10.1016/j.asoc.2020.106256

Cited by 18 publications

(16 citation statements)

References 42 publications

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“…In more detail, the population can be encoded in different ways in the GA, one such way is through segmented permutation [6], identity permutation [7]. Random generation, as in [8], the greedy approach, and the 2-opt heuristics, as proposed by Minetti et al [8] and [9], are common strategies for generating the initial populations.…”

Section: A Genetic Algorithm For Dnafa Problemmentioning

confidence: 99%

Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Bennaceur¹,

Almutairy²,

Alqhtani³

2023

IJACSA

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This paper aims to highlight the motivations for investigating genetic algorithms (GAs) to solve the DNA Fragment Assembly (DNAFA) problem. DNAFA problem is an optimization problem that attempts to reconstruct an original DNA sequence by finding the shortest DNA sequence from a given set of fragments. This paper is a continuation of our previous research paper in which the existence of a polynomialtime reduction of DNAFA into the Traveling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP) was discussed. Taking advantage of this reduction, this work conceptually designed a genetic algorithm (GA) platform to solve the DNAFA problem. This platform offers several ingredients enabling us to create several variants of GA solvers for the DNAFA optimization problems. The main contribution of this paper is the designing of an efficient GA variant by carefully integrating different GAs operators of the platform. For that, this work individually studied the effects of different GAs operators on the performance of solving the DNAFA problem. This study has the advantage of benefiting from prior knowledge of the performance of these operators in the contexts of the TSP and QAP problems. The best designed GA variant shows a significant improvement in accuracy (overlap score) reaching more than 172% of what is reported in the literature.

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Section: A Genetic Algorithm For Dnafa Problemmentioning

confidence: 99%

Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Bennaceur¹,

Almutairy²,

Alqhtani³

2023

IJACSA

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“…There is a requirement of finding the correct DNA sequence to get the desired information about the genetic makeup of an organism. A hybrid approach presented utilized Restarting and Recentering Genetic Algorithm (RRGA) with integrated PALS (Halim, 2020). For working on the different datasets, there is a need to get a set of visualization metrics.…”

Section: Anemia and Sickle Cell Detectionmentioning

confidence: 99%

A review of microscopic analysis of blood cells for disease detection with AI perspective

Deshpande

Gite

Aluvalu³

2021

PeerJ Computer Science

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Background Any contamination in the human body can prompt changes in blood cell morphology and various parameters of cells. The minuscule images of blood cells are examined for recognizing the contamination inside the body with an expectation of maladies and variations from the norm. Appropriate segmentation of these cells makes the detection of a disease progressively exact and vigorous. Microscopic blood cell analysis is a critical activity in the pathological analysis. It highlights the investigation of appropriate malady after exact location followed by an order of abnormalities, which assumes an essential job in the analysis of various disorders, treatment arranging, and assessment of results of treatment. Methodology A survey of different areas where microscopic imaging of blood cells is used for disease detection is done in this paper. Research papers from this area are obtained from a popular search engine, Google Scholar. The articles are searched considering the basics of blood such as its composition followed by staining of blood, that is most important and mandatory before microscopic analysis. Different methods for classification, segmentation of blood cells are reviewed. Microscopic analysis using image processing, computer vision and machine learning are the main focus of the analysis and the review here. Methodologies employed by different researchers for blood cells analysis in terms of these mentioned algorithms is the key point of review considered in the study. Results Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to different performance measures. From the extensive review the conclusion is made. Conclusion There are different machine learning and deep learning algorithms employed by researchers for segmentation of blood cell components and disease detection considering microscopic analysis. There is a scope of improvement in terms of different performance evaluation parameters. Different bio-inspired optimization algorithms can be used for improvement. Explainable AI can analyze the features of AI implemented system and will make the system more trusted and commercially suitable.

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“…Therefore, a statistical analysis is performed to check whether the model's high performance is only due to some randomness or the difference is statistically significant. The two-tailed paired sample t test is opted for this task [44]. The formulated null hypothesis (H 0 ) and corresponding alternative hypothesis (H 1 ) are listed as follows:…”

Section: Statistical Significancementioning

confidence: 99%

Imagined character recognition through EEG signals using deep convolutional neural network

Ullah

Halim

2021

Med Biol Eng Comput

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Electroencephalography (EEG)-based brain computer interface (BCI) enables people to interact directly with computing devices through their brain signals. A BCI typically interprets EEG signals to reflect the user's intent or other mental activity. Motor imagery (MI) is a commonly used technique in BCIs where a user is asked to imagine moving certain part of the body such as a hand or a foot. By correctly interpreting the signal, one can perform a multitude of tasks such as controlling wheel chair, playing computer games, or even typing text. However, the use of motor-imagery-based BCIs outside the laboratory environment is limited due to the lack of their reliability. This work focuses on another kind of mental imagery, namely, the visual imagery (VI). VI is the manipulation of visual information that comes from memory. This work presents a deep convolutional neural network (DCNN)-based system for the recognition of visual/mental imagination of English alphabets so as to enable typing directly via brain signals. The DCNN learns to extract the spatial features hidden in the EEG signal. As opposed to many deep neural networks that use raw EEG signals for classification, this work transforms the raw signals into band powers using Morlet wavelet transformation. The proposed approach is evaluated on two publicly available benchmark MI-EEG datasets and a visual imagery dataset specifically collected for this work. The obtained results demonstrate that the proposed model performs better than the existing state-of-the-art methods for MI-EEG classification and yields an average accuracy of 99.45% on the two public MI-EEG datasets. The model also achieves an average recognition rate of 95.2% for the 26 English-language alphabets.

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Optimizing the DNA fragment assembly using metaheuristic-based overlap layout consensus approach

Cited by 18 publications

References 42 publications

Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

A review of microscopic analysis of blood cells for disease detection with AI perspective

Imagined character recognition through EEG signals using deep convolutional neural network

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