Improved Spider Monkey Optimization Algorithm to train MLP for data classification

Singh, Prabhat Ranjan; Diallo, Moussa; Xiong, Shengwu; Singh, Bikram Prasad

doi:10.17993/3ctecno.2019.specialissue2.142-165

Cited by 3 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The parameters of these algorithms are shown in Table 2. The comparison is done based on the optimum value, mean absolute error (MAE) denoted by ( 16) [22,23] and convergence speed denoted by the number of iterations to obtain convergence and rank.…”

Section: Testing Of Proposed Improved Driving Training-based Optimiza...mentioning

confidence: 99%

Improved Driving Training-Based Optimization Algorithm Using Levy Flight and Crowding Distance Techniques

Kwegyir,

Terkper,

Effah

et al. 2024

Res. Rep. Comput. Sci.

View full text Add to dashboard Cite

This study presents an improved version of the Driving Training-Based Optimization (DTBO) algorithm, the Improved Driving Training-Based Optimization (IDTBO). The work addresses fundamental issues in selecting drivers and learners for the conventional DTBO, which substantially impact the algorithm's accuracy and convergence speed. Two significant improvements are proposed: including the crowding distance technique for more diverse driver and learner selection and incorporating the Levy Flight distribution for better exploration and local optima avoidance. The IDTBO's performance is evaluated using twelve benchmark functions, including unimodal and high-dimensional multimodal optimization functions. The results indicate that IDTBO performs exceptionally well, with more extraordinary exploitation ability on unimodal functions and consistent achievement of the global optima. The proposed IDTBO demonstrated exceptional exploration capabilities on high-dimensional multimodal functions and performed competitively with other algorithms in the literature. From six functions, the IDTBO obtained zero optimal values. Again, the rate of convergence analysis demonstrates that IDTBO finds optimal solutions in fewer iterations, demonstrating its capacity to balance exploration and exploitation. To assess the strength of the IDTBO in solving real-world problems, the improved DTBO is further tested on two practical benchmark engineering problems. The IDTBO again produced a competitive performance against other algorithms in the literature. The study shows that IDTBO is a valuable metaheuristic algorithm that can tackle various real-world optimization problems.

show abstract

Section: Testing Of Proposed Improved Driving Training-based Optimiza...mentioning

confidence: 99%

Improved Driving Training-Based Optimization Algorithm Using Levy Flight and Crowding Distance Techniques

Kwegyir,

Terkper,

Effah

et al. 2024

Res. Rep. Comput. Sci.

View full text Add to dashboard Cite

show abstract

“…This is particularly a problem when the network has many layers and parameters. These papers [113][114][115][116] discuss the problem of local minima in the context of training MLP networks and propose several Optimization or Hybrid methods to address this issue. • Black Box Nature: MLP networks, like many other neural networks, suffer from being "black boxes" [117,118].…”

Section: B Disadvantagesmentioning

confidence: 99%

Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review

Marwah K. Muttair

2024

jes

View full text Add to dashboard Cite

Breast cancer is a major public health concern, affecting millions of women worldwide. Good patient outcomes and a successful course of therapy depend on a prompt and accurate diagnosis. The application of deep learning algorithms to the breast cancer classification problem has yielded some encouraging results; this might open the way for more rapid and accurate diagnostics. This article examines the many deep learning (DL) approaches taken to date for BC classification tasks, outlining each one's strengths, weaknesses, and current challenges. This study discusses several DL algorithms like convolutional neural networks, multi-layer neural networks, and autoencoders that analyze histopathological pictures, mammograms, and other forms of medical imaging. Furthermore, we investigate the interpretability & explainability features of DL models as they pertain to BC diagnoses, drawing attention to the need for reliable decision-making tools for medical practitioners. Throughout the review, we identify challenges and potential biases in current research. Finally, we provide an outlook on the future directions of DL in BC classification, focusing on promising research areas. By highlighting the achievements and gaps in the existing literature, this review aims to inspire further advancements in DL-based BC diagnosis, eventually resulting in better healthcare and reliable diagnosis.

show abstract

“…SMO algorithm [11] is a population-based algorithm that is mainly inspired by the social actions which are commonly performed by spider monkeys. The premature convergence rate SMO is improved in ESMO [12]. In ESMO, an improvised strategy is applied to update the position of solution towards local and global leader phase.…”

Section: Introductionmentioning

confidence: 99%

“…Importance of local leader limit The ESMO [12] improves on the basic SMO algorithm's performance. The ESMO recommended various changes to the L 2 P of basic SMO.…”

mentioning

confidence: 99%

An Optimized Uncertainty Aware Fine-Tuned Transfer Learning for COVID-19 Diagnosis from Medical Images

2023

IJIES

View full text Add to dashboard Cite

In worldwide, COVID-19 has had a significant influence on patients and healthcare systems. Earlier stage of COVID-19 diagnosis and identification are the primary problems in the current pandemic condition. The identification of COVID-19 in CT and chest-X-ray (CXR) imaging is essential for diagnosis, treatment, and evaluation. However, radiologists face a foreseeable issue when it comes to coping with analytical ambiguity in medical imaging. In that situation, a paradigm based on convolutional neural network (CNN) with transfer learning (TL) and taking uncertainty into account was suggested to identify COVID-19 from CT and CXR scan images. However, this method was less capable to extract more useful and distinct image attributes. By fine-tuning the TL network design, this issue can be resolved. The fine tuning model can only fine tune specific layers associated with various goal objectives. However, one of the primary issues with such a method is the selection of layers. To solve this issue, this research uses an enhanced spider monkey optimization (ESMO) technique to select layers of ResNet architecture. Every population of an initialized spider monkey (SM) selects layer and parameter for fine tuning architecture. The fitness value of each SM is used to find best optimal solutions. Categorical cross-entropy loss (CCEL) is considered as fitness of SM. The fitness value of each SM is employed to determine the highest optimal solutions. Subsequent processes such as the stance update process, the learning and decision phase for the local and global leaders of ESMO algorithms, iteratively search for near optimal solutions until convergence. The proposed method can automatically estimate the various CNN layers, which can then be fine-tuned to extract more significant and discriminative features for efficient COVID-19 identification. Finally, the results reveal that the proposed ESMO-ResNet model on SARS-CoV-2 CT database achieves 91.23% accuracy, which is 21.46%, 20.2%, 12.4%, and 6% higher than the AlexNet, multi-source deep transfer learning (MSDTL), stacked convolutional neural network (S-CNN) and dynamic mutual training (DMT) models, respectively. Similarly, the ESMO-ResNet model on Covi-19 Radiography dataset achieves 90.06% accuracy, which is 21.34%, 20%, 9.7%, and 3.5% higher than AlexNet, MSDTL, S-CNN, and DMT models, respectively.

show abstract

Improved Spider Monkey Optimization Algorithm to train MLP for data classification

Cited by 3 publications

References 22 publications

Improved Driving Training-Based Optimization Algorithm Using Levy Flight and Crowding Distance Techniques

Improved Driving Training-Based Optimization Algorithm Using Levy Flight and Crowding Distance Techniques

Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review

An Optimized Uncertainty Aware Fine-Tuned Transfer Learning for COVID-19 Diagnosis from Medical Images

Contact Info

Product

Resources

About