Constrained Linear Model Predictive Control for an Artificial Pancreas

Arbi, Khadidja Fellah; Morakchi, Mohamed Razi; El Aouaber, Zineb Aziza; Saffih, Faycal; Djaddoudi, Mohammed Sobir; Bouregaa, Mohammed; Soulimane, Sofiane

doi:10.1109/ispa59904.2024.10536796

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…In (Mohamed Razi et al, 2022;Mohammed Razi et al, 2022) (Morakchi et al, 2023) integration of IoT for optimizing the data logging through accelerometer. In the field of health management, such as in (Arbi et al, 2024), the Constrained Linear Model Predictive Controller (CLMPC) suggests a method suitable for smartphone applications in diabetes self-management. Furthermore, in (Ghemari et al, 2022;Razi et al, 2022) parameter optimization used to enhance accelerometer performance and it influence on vibratory analysis.…”

Section: Introductionmentioning

confidence: 99%

Predicting project duration using a coupled artificial neural network and Taguchi method approach

Bendada,

Brioua,

Morakchi

et al. 2024

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Accurate project duration prediction is increasingly important for management because it defines the expected timeline for project realization. This study utilizes an integrated approach combining neural networks with the Taguchi method to forecast the time required to complete projects within predetermined deadlines. The methodology involves modelling and simulating the network of project activities to estimate the total average project duration. ration. The neural network model uses input variables such as success probability, effort, and learning factor to predict the total time necessary for project completion. The total average project duration is the output variable that is critical during the design phase. Subsequently, the Taguchi method optimizes the neural network’s outputs, incorporating mean squared error (MSE) values to enhance predictive accuracy. accuracy. This study underscores the efficacy of artificial neural networks (ANNs) as predictive tools, demonstrating their ability to meticulously estimate project duration. The proposed method’s efficiency and applicability are demonstrated by MATLAB simulation analyses, highlighting its effectiveness in precise deadline estimation. In the realm of engineering, ANNs stand tall as formidable predictive tools, their efficacy underscored by this study’s successful application. By harnessing ANNs and simulation data, this research crafts a predictive model adept at estimating the average total duration of projects. Through meticulous consideration of crucial parameters like the probability of success and effort factors, the model emerges as a beacon of accuracy within the design domain. Future research will explore the effects of additional parameters on activity networks and alternative transfer functions, as well as the potential integration of reinforcement algorithms to improve resource allocation, risk management, and project outcomes through online training data.

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Section: Introductionmentioning

confidence: 99%

Predicting project duration using a coupled artificial neural network and Taguchi method approach

Bendada,

Brioua,

Morakchi

et al. 2024

SEES

View full text Add to dashboard Cite

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Deep learning-based diabetic retinopathy diagnosis using transfer learning with VGG19 and InceptionV3

Aiche,

Brik,

Attallah

2024

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Diabetes mellitus is a primary contributor to the development of diabetic retinopathy (DR), a condition characterized by the formation of retinal lesions and progressive vision impairment. Left untreated, DR can culminate in blindness, underscoring the critical importance of early detection and intervention. Despite the absence of a definitive cure for DR, available treatments aim to preserve existing vision. The conventional manual diagnosis of DR through the evaluation of retina fundus images by ophthalmologists is labor-intensive, expensive, and susceptible to human error. As an alternative, computer-aided diagnostic techniques, particularly those based on deep learning, have gained traction for their potential to enhance diagnostic accuracy and reduce errors. Recently, Convolutional Neural Networks (CNNs) have demonstrated exceptional efficacy in medical image analysis due to their capability to extract local image features with high precision. In this study, we propose a multi-level fine-tuned deep learning approach for the classification of diabetic retinopathy using two distinct pre-trained models: VGG19 and InceptionV3. Our findings reveal that the proposed InceptionV3 network outperformed other models as well as the state-of-the-art methods, achieving a classification accuracy of 98.09% in the binary classification of DR images.

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Constrained Linear Model Predictive Control for an Artificial Pancreas

Cited by 2 publications

References 31 publications

Predicting project duration using a coupled artificial neural network and Taguchi method approach

Predicting project duration using a coupled artificial neural network and Taguchi method approach

Deep learning-based diabetic retinopathy diagnosis using transfer learning with VGG19 and InceptionV3

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