Representation and compression of Residual Neural Networks through a multilayer network based approach

Amelio, Alessia; Bonifazi, Gianluca; Cauteruccio, Francesco; Corradini, Enrico; Marchetti, Michele; Ursino, Domenico; Virgili, Luca

doi:10.1016/j.eswa.2022.119391

Cited by 22 publications

(6 citation statements)

References 31 publications

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“…For example, the logic expressed by the method could be encoded as a deep neural network with tuning parameters by the training procedure. Accordingly, different deep neural network approaches could be considered within the proposed architecture [1,2]. Future work will further address consistency between Multi-Context based BDI components.…”

Section: Discussionmentioning

confidence: 99%

Multi-Mind Dynamics in Intentional Agents

Brännström,

Nieves

2024

Communications in Computer and Information Science

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This paper introduces an agent framework that integrates the Belief, Desire, Intention (BDI) model with Multi-Context Systems (MCS), particularly for dealing with diverse knowledge sources in belief revision, deliberation and means-end reasoning. By specifying a separate MCS in each BDI-component, the framework manages the interaction between, possibly conflicting, sets of beliefs, desires, intentions and plans generated by specialized sub-systems. A MCS-based BDI-component generates an equilibrium. An approach is introduced for transferring equilibrium between MCSs according to the BDI control loop. This involves the translation of knowledge bases to Answer Set Programming (ASP) to build a shared logic. The proposed framework contributes to the advancement of hybrid intentional agents, where multiple goals and plans must be interwoven in order to deal with a complex multi-modal domain. The potential of the framework is illustrated in a running example, where a driving assistant agent is designed to manage diverse mental states of a human driver, such as emotions, motivations and norms, within each stage of the BDI control loop, producing a plan that is in balance with the diverse contexts.

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Section: Discussionmentioning

confidence: 99%

Multi-Mind Dynamics in Intentional Agents

Brännström,

Nieves

2024

Communications in Computer and Information Science

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“…4 shows a convolutional layer, which is a fundamental building block of CNNs. The layer applies convolution to the input data using kernels to extract important features for classification and regression tasks [ 47 ]. The convolution operation calculates the dot product between the kernel and the corresponding input values at each position by sliding the kernel over the input data.…”

Section: Theoretical Analysismentioning

confidence: 99%

Optical frequency multiplication using residual network with random forest regression

Zhang,

Han,

Fang

et al. 2024

Heliyon

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“…The dataset used here contains two datasets: 138 X-ray images, including 58 TB infected cases, and 662 X-ray images from Shenzhen Hospital with 336 TB cases. Several studies reported the application of artificial intelligence and DL for the case of healthcare, including COVID-19 [27][28][29][30][31][32][33][34][35][36][37][38]. One interesting study compared a multilayer network technique with a single network for the case of COVID-19 vaccinations; however, they did not consider disease diagnosis [35].…”

Section: Related Workmentioning

confidence: 99%

Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

et al. 2023

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Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep learning model using transfer learning with optimized DenseNet-169 and DenseNet-201 models for three-class classification, utilizing the Nadam optimizer. We modified the traditional DenseNet architecture and tuned the hyperparameters to improve the model’s performance. The model was evaluated on a novel dataset of 3312 X-ray images from publicly available datasets, using metrics such as accuracy, recall, precision, F1-score, and the area under the receiver operating characteristics curve. Our results showed impressive detection rate accuracy and recall for COVID-19 patients, with 95.98% and 96% achieved using DenseNet-169 and 96.18% and 99% using DenseNet-201. Unique layer configurations and the Nadam optimization algorithm enabled our deep learning model to achieve high rates of accuracy not only for detecting COVID-19 patients but also for identifying normal and pneumonia-affected patients. The model’s ability to detect lung problems early on, as well as its low false-positive and false-negative rates, suggest that it has the potential to serve as a reliable diagnostic tool for a variety of lung diseases.

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Representation and compression of Residual Neural Networks through a multilayer network based approach

Cited by 22 publications

References 31 publications

Multi-Mind Dynamics in Intentional Agents

Multi-Mind Dynamics in Intentional Agents

Optical frequency multiplication using residual network with random forest regression

Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

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