The optimization is a discipline which is part of mathematics and which aims to model, analyse and solve analytically or numerically problems of minimization or maximization of a function on a specific dataset. Several optimization algorithms are used in systems based on deep learning (DL) such as gradient descent (GD) algorithm. Considering the importance and the efficiency of the GD algorithm, several research works made it possible to improve it and to produce several other variants which also knew great success in DL. This paper presents a comparative study of stochastic, momentum, Nesterov, AdaGrad, RMSProp, AdaDelta, Adam, AdaMax and Nadam gradient descent algorithms based on the speed of convergence of these different algorithms, as well as the mean absolute error of each algorithm in the generation of an optimization solution. The obtained results show that AdaGrad algorithm represents the best performances than the other algorithms with a mean absolute error (MAE) of 0.3858 in 53 iterations and AdaDelta one represents the lowest performances with a MAE of 0.6035 in 6000 iterations. The case study treated in this work is based on an extract of data from the keratoconus dataset of Harvard Dataverse and the results are obtained using Python.

Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does not feel any pain. Therefore, the development of a method for detecting this disease based on machine and deep learning methods is necessary for early detection in order to provide the appropriate treatment as early as possible to patients. Thus, the objective of this work is to determine the most relevant parameters with respect to the different classifiers used for keratoconus classification based on the keratoconus dataset of Harvard Dataverse. A total of 446 parameters are analyzed out of 3162 observations by 11 different feature selection algorithms. Obtained results showed that sequential forward selection (SFS) method provided a subset of 10 most relevant variables, thus, generating the highest classification performance by the application of random forest (RF) classifier, with an accuracy of 98% and 95% considering 2 and 4 keratoconus classes, respectively. Found classification accuracy applying RF classifier on the selected variables using SFS method achieves the accuracy obtained using all features of the original dataset.

With the rapid growth of Internet of Things (IoT) systems, ensuring robust security measures has become paramount. Microservices Architecture (MSA) has emerged as a promising approach for enhancing IoT systems security, yet its adoption in this context lacks comprehensive analysis. This systematic review addresses this research gap by examining the incorporation of MSA in IoT systems from 2010 to 2024. From an initial pool of 4388 studies, selected articles underwent thorough quality assessment with weighted critical appraisal questions and a defined inclusion threshold. This study represents the first comprehensive systematic review to investigate the potential of microservices in IoT, with a particular focus on security aspects. The review explores the merits of MSA, highlighting twelve benefits, eight key challenges, and eight security risks. Additionally, the eight best practices for implementing MSA in IoT systems are extracted. The findings underscore MSA’s utility in fortifying IoT security while also acknowledging complexities and potential vulnerabilities. Moreover, the study calls attention to the importance of incorporating complementary technologies including blockchain and machine learning to address identified gaps effectively. Finally, we propose a taxonomic classification for Microservice-based IoT security patterns, facilitating the categorization and organization of security measures in this context. Such a review can help researchers and practitioners identify existing gaps, highlight potential research directions, and provide guidelines for designing secure and efficient microservice-based IoT systems.

Early detection of diseases in growing olive trees is essential for reducing costs and increasing productivity in this crucial economic activity. The quality and quantity of olive oil depend on the health of the fruit, making accurate and timely information on olive tree diseases critical to monitor growth and anticipate fruit output. The use of unmanned aerial vehicles (UAVs) and deep learning (DL) has made it possible to quickly monitor olive diseases over a large area indeed of limited sampling methods. Moreover, the limited number of research studies on olive disease detection has motivated us to enrich the literature with this work by introducing new disease classes and classification methods for this tree. In this study, we present a UAV system using convolutional neuronal network (CNN) and transfer learning (TL). We constructed an olive disease dataset of 14K images, processed and trained it with various CNN in addition to the proposed MobileNet-TL for improved classification and generalization. The simulation results confirm that this model allows for efficient diseases classification, with a precision accuracy achieving 99% in validation. In summary, TL has a positive impact on MobileNet architecture by improving its performance and reducing the training time for new tasks.

A Cloud-based Framework to Secure Medical Image Processing