Training Feed-Forward Artificial Neural Networks with a modified artificial bee colony algorithm

Xu, Feiyi; Pun, Chi‐Man; Li, Haolun; Zhang, Yushu; Song, Yurong; Gao, Hao

doi:10.1016/j.neucom.2019.04.086

Cited by 29 publications

(15 citation statements)

References 38 publications

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“…Certainly, there is a trade-off between exploitation and exploration while looking for solution (Hamed Mozaffari and Lee, 2020 ). Xu et al ( in press ) have proposed the modified ABC (ABC-ISB) optimization algorithm for automatically training the parameters of feed-forward artificial neural networks. This clearly indicates the case of customization and variable behavior of the type of methods used in optimization.…”

Section: Methodsmentioning

confidence: 99%

Tuberculosis Diagnostics and Localization in Chest X-Rays via Deep Learning Models

Guo

Passi

Jain

2020

Front. Artif. Intell.

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For decades, tuberculosis (TB), a potentially serious infectious lung disease, continues to be a leading cause of worldwide death. Proven to be conveniently efficient and cost-effective, chest X-ray (CXR) has become the preliminary medical imaging tool for detecting TB. Arguably, the quality of TB diagnosis will improve vastly with automated CXRs for TB detection and the localization of suspected areas, which may manifest TB. The current line of research aims to develop an efficient computer-aided detection system that will support doctors (and radiologists) to become well-informed when making TB diagnosis from patients' CXRs. Here, an integrated process to improve TB diagnostics via convolutional neural networks (CNNs) and localization in CXRs via deep-learning models is proposed. Three key steps in the TB diagnostics process include (a) modifying CNN model structures, (b) model fine-tuning via artificial bee colony algorithm, and (c) the implementation of linear average-based ensemble method. Comparisons of the overall performance are made across all three steps among the experimented deep CNN models on two publicly available CXR datasets, namely, the Shenzhen Hospital CXR dataset and the National Institutes of Health CXR dataset. Validated performance includes detecting CXR abnormalities and differentiating among seven TB-related manifestations (consolidation, effusion, fibrosis, infiltration, mass, nodule, and pleural thickening). Importantly, class activation mapping is employed to inform a visual interpretation of the diagnostic result by localizing the detected lung abnormality manifestation on CXR. Compared to the state-of-the-art, the resulting approach showcases an outstanding performance both in the lung abnormality detection and the specific TB-related manifestation diagnosis vis-à-vis the localization in CXRs.

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Section: Methodsmentioning

confidence: 99%

Tuberculosis Diagnostics and Localization in Chest X-Rays via Deep Learning Models

Guo

Passi

Jain

2020

Front. Artif. Intell.

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“…Consequently, uncertain search direction then guide the algorithm to slower convergence rate. On that account, we use improve iterative expression according to Reference 51 as:

\begin{align} b_{i l}^{'} = c_{i l} + false(b_{m l} prefix- b_{i l} false) θ_{1} + false(b_{g b e s t l} prefix- b_{m l} false) θ_{2} \end{align}

here,

b_{m}

denotes neighbor of food source

b_{i}

m \neq i

b_{g b e s t}

represents current global best solution in the entire population, whereas

b_{g b e s t l}

shows j th variable of

b_{g b e s t}

vector.…”

Section: Partial Transmit Sequence (Pts) Technique and Algorithm Bee Optimizermentioning

confidence: 99%

Peak to average power ratio alleviation by utilizing swarm intelligence along with machine learning for multiple input multiple output‐orthogonal frequency division multiplexing based underwater acoustic communication system

BanooriFarhad

ShiJinglun

ArshadJehangir

et al. 2021

Trans Emerging Tel Tech

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An underwater acoustic (UWA) network uses multiple input multiple output‐orthogonal frequency division multiplexing (MIMO‐OFDM) to achieve high data rate with high amplitude that results in terms of augmented peak to average power ratio (PAPR). This significant PAPR deemed as a stumbling block to degrade the performance of MIMO‐OFDM in UWA communication by causing nonlinear distortion within the high‐power amplifier (HPA). While PAPR mitigation techniques have been suggested for the single input single output UWA case, there has been limited research work on controlling the PAPR dilemma in the MIMO‐OFDM UWA case. To address this flaw, this article proposes a modified behavior of the artificial bee colony algorithm at the transmission end, which is further cross‐verified using machine learning techniques. The simulation results show that the proposed technique outperforms current state‐of‐the‐art PAPR diminishing methods, which is further enhanced in accordance with varying neuron counts and population size. Following that, we also analyzed and compared energy efficiency and performance gain of our proposed technique along with various advanced techniques. Furthermore, a desirable ratio between new and trained data was obtained to improve network efficiency by keeping PAPR values low.

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“…Different DL practices use ANN (Artificial neural network) to extract the features accordingly. The approach of feed-forward by Xu et al [ 42 ] is used to provide knowledge in which every level refreshed its input value to get results in the summarized form. Enhanced ANN technique deals with evolutionary models that can provide better results comparing with traditional methods.…”

Section: Background and Motivationmentioning

confidence: 99%

Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review

Kaur

2021

Arch Computat Methods Eng

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From the month of December-19, the outbreak of Coronavirus (COVID-19) triggered several deaths and overstated every aspect of individual health. COVID-19 has been designated as a pandemic by World Health Organization. The circumstances placed serious trouble on every country worldwide, particularly with health arrangements and time-consuming responses. The increase in the positive cases of COVID-19 globally spread every day. The quantity of accessible diagnosing kits is restricted because of complications in detecting the existence of the illness. Fast and correct diagnosis of COVID-19 is a timely requirement for the prevention and controlling of the pandemic through suitable isolation and medicinal treatment. The significance of the present work is to discuss the outline of the deep learning techniques with medical imaging such as outburst prediction, virus transmitted indications, detection and treatment aspects, vaccine availability with remedy research. Abundant image resources of medical imaging as X-rays, Computed Tomography Scans, Magnetic Resonance imaging, formulate deep learning high-quality methods to fight against the pandemic COVID-19. The review presents a comprehensive idea of deep learning and its related applications in healthcare received over the past decade. At the last, some issues and confrontations to control the health crisis and outbreaks have been introduced. The progress in technology has contributed to developing individual’s lives. The problems faced by the radiologists during medical imaging techniques and deep learning approaches for diagnosing the COVID-19 infections have been also discussed.

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Training Feed-Forward Artificial Neural Networks with a modified artificial bee colony algorithm

Cited by 29 publications

References 38 publications

Tuberculosis Diagnostics and Localization in Chest X-Rays via Deep Learning Models

Tuberculosis Diagnostics and Localization in Chest X-Rays via Deep Learning Models

Peak to average power ratio alleviation by utilizing swarm intelligence along with machine learning for multiple input multiple output‐orthogonal frequency division multiplexing based underwater acoustic communication system

Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review

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