Image Classification using Different Machine Learning Techniques

Alaba, Simegnew

doi:10.36227/techrxiv.21691934

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The rationale behind this decision was to sample some of the genes that we had worked on in our previous research endeavors [15], Our objective was to integrate automated learning algorithms and pattern-matching algorithms that are based on specific DNA sequences, in order to create a biological data collection that could be utilized in a classification process. We conducted experiments on a dataset that included DNA sequences, where we compared the effectiveness of searching for a specific pattern with other classification models, such as Random Forest [3,16], KNN [16][17][18][19][20], Naïve Bayes [21][22][23][24], Decision tree [23,[25][26][27][28][29][30], and Support Vector Machine [18,[31][32][33][34][35][36] with Linear [37,38], RBF [37,39], and sigmoid [21,40] classifiers, the results of these classifiers models are calculated by F1 score, recall, precision rate, execution time, and with the accuracy which calculates the most effective patternmatching classifier. The comparison of DNA sequences is a crucial task in various fields of research, including molecular biology and genetics.…”

Section: Methodology For Pm From Dna Sequencesmentioning

confidence: 99%

Optimizing classification efficiency with machine learning techniques for pattern matching

Hamed¹,

Ibrahim²,

El‐Hafeez³

2023

J Big Data

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The study proposes a novel model for DNA sequence classification that combines machine learning methods and a pattern-matching algorithm. This model aims to effectively categorize DNA sequences based on their features and enhance the accuracy and efficiency of DNA sequence classification. The performance of the proposed model is evaluated using various machine learning algorithms, and the results indicate that the SVM linear classifier achieves the highest accuracy and F1 score among the tested algorithms. This finding suggests that the proposed model can provide better overall performance than other algorithms in DNA sequence classification. In addition, the proposed model is compared to two suggested algorithms, namely FLPM and PAPM, and the results show that the proposed model outperforms these algorithms in terms of accuracy and efficiency. The study further explores the impact of pattern length on the accuracy and time complexity of each algorithm. The results show that as the pattern length increases, the execution time of each algorithm varies. For a pattern length of 5, SVM Linear and EFLPM have the lowest execution time of 0.0035 s. However, at a pattern length of 25, SVM Linear has the lowest execution time of 0.0012 s. The experimental results of the proposed model show that SVM Linear has the highest accuracy and F1 score among the tested algorithms. SVM Linear achieved an accuracy of 0.963 and an F1 score of 0.97, indicating that it can provide the best overall performance in DNA sequence classification. Naive Bayes also performs well with an accuracy of 0.838 and an F1 score of 0.94. The proposed model offers a valuable contribution to the field of DNA sequence analysis by providing a novel approach to pre-processing and feature extraction. The model’s potential applications include drug discovery, personalized medicine, and disease diagnosis. The study’s findings highlight the importance of considering the impact of pattern length on the accuracy and time complexity of DNA sequence classification algorithms.

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Section: Methodology For Pm From Dna Sequencesmentioning

confidence: 99%

Optimizing classification efficiency with machine learning techniques for pattern matching

Hamed¹,

Ibrahim²,

El‐Hafeez³

2023

J Big Data

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Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Mohammed,

Tune,

Assefa

et al. 2024

MAKE

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In this review, we compiled convolutional neural network (CNN) methods which have the potential to automate the manual, costly and error-prone processing of medical images. We attempted to provide a thorough survey of improved architectures, popular frameworks, activation functions, ensemble techniques, hyperparameter optimizations, performance metrics, relevant datasets and data preprocessing strategies that can be used to design robust CNN models. We also used machine learning algorithms for the statistical modeling of the current literature to uncover latent topics, method gaps, prevalent themes and potential future advancements. The statistical modeling results indicate a temporal shift in favor of improved CNN designs, such as a shift from the use of a CNN architecture to a CNN-transformer hybrid. The insights from statistical modeling point that the surge of CNN practitioners into the medical imaging field, partly driven by the COVID-19 challenge, catalyzed the use of CNN methods for detecting and diagnosing pathological conditions. This phenomenon likely contributed to the sharp increase in the number of publications on the use of CNNs for medical imaging, both during and after the pandemic. Overall, the existing literature has certain gaps in scope with respect to the design and optimization of CNN architectures and methods specifically for medical imaging. Additionally, there is a lack of post hoc explainability of CNN models and slow progress in adopting CNNs for low-resource medical imaging. This review ends with a list of open research questions that have been identified through statistical modeling and recommendations that can potentially help set up more robust, improved and reproducible CNN experiments for medical imaging.

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Image Classification using Different Machine Learning Techniques

Cited by 2 publications

References 11 publications

Optimizing classification efficiency with machine learning techniques for pattern matching

Optimizing classification efficiency with machine learning techniques for pattern matching

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

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