Diabetic retinopathy is a severe retinal disease that can blur or distort the vision of the patient. It is one of the leading causes of blindness. Early detection of diabetic retinopathy can significantly help in the treatment. The recent development in the field of AI and especially Deep learning provides ambitious solutions that can be exploited to predict, forecast and diagnose several diseases in their early phases. This work aims towards finding an automatic way to classify a given set of retina images in order to detect the diabetic retinopathy. Deep learning concepts have been used with a convolutional neural network (CNN) algorithm to build a multi-classification model that can detect and classify disease levels automatically. In this study, a CNN architecture has been applied with several parameters on a dataset of diabetic retinopathy with different structures. At the current stage of this work, obtained results are highly encouraging.

Reconfigurable manufacturing system (RMS) is a recent manufacturing paradigm, which can easily adjust its capacity and functionality for rapid responsiveness to sudden changes in the market. The core component of RMS is called reconfigurable machine tool (RMT), which has a modular structure. The RMTs can be reconfigured into many configurations. This ability allows RMS to manufacture many types of products with high quantities. In this paper, the scheduling of products in a multi-product line is fulfilled based on three criteria: profit over cost, due date, and reconfiguration responsiveness index. The latter is the combination of reconfiguration time and reconfiguration reliability of machines. An integrated approach of maximum deviation method (MDM) and multi-criteria decision-making (MCDM) approach called technique for order preference by similarity to ideal solution (TOPSIS) is proposed as a solution approach for getting the optimal scheduling of the products to be manufactured in RMS. Weights of criteria have been calculated using MDM and ranking of products is obtained using TOPSIS. A numerical example is presented to illustrate the scheduling of products in RMS.

This paper aims to examine the effects of aging on the efficiency of facial recognition algorithms in terms of age groups and age difference intervals. A comparative analysis of the recognition performance of two approaches is conducted for different age groups and different length time intervals between images. The first approach uses a two-dimensional discrete cosine transform (2D-DCT) and a Kernel Fisher Analysis (KFA) as description tools; classification is made using a k-NN classifier based on Euclidean distance. However, the second one is performed in two ways: first, we considered face as a single entity, then we viewed face as an independent component set. This approach makes use of Convolutional Neural Networks (CNN) for description and Support vector machines (SVM) for classification. Achieved results using the publicly accessible FG-NET face database prove that age groups influence the performance of face recognition algorithms. Also, time length lapses between images can significantly reduce the performance of face recognition.

Multi-objective optimization evolutionary algorithms (MOEAs) are considered as the most suitable heuristic methods for solving multi-objective optimization problems (MOPs). These MOEAs aim to search for a uniformly distributed, near-optimal and near-complete Pareto front for a given MOP. However, MOEAs fail to achieve their aim completely because of their fixed population size. To overcome this limit, an evolutionary approach of multi-objective optimization was proposed; the dynamic multi-objective evolutionary algorithms (DMOEAs). This paper deals with improving the user requirements (i.e., getting a set of optimal solutions in minimum computational time). Although, DMOEA has the distinction of dynamic population size, being an evolutionary algorithm means that it will certainly be characterized by long execution time. One of the main reasons for adapting parallel evolutionary algorithms (PEAs) is to obtain efficient results with an execution time much lower than the one of their sequential counterparts in order to tackle more complex problems. Thus, we propose a parallel version of DMOEA (i.e., PDMOEA). As experimental results, the proposed PDMOEA enhances DMOEA in terms of three criteria: improving the objective space, minimization of computational time and converging to the desired population size.

The fifth-generation (5G) standard is the last telecommunication technology, widely considered to have the most important characteristics in the future network industry. The 5G system infrastructure contains three principle interfaces, each one follows a set of protocols defined by the 3rd Generation Partnership Project group (3GPP). For the next generation network, 3GPP specified two authentication methods systematized in two protocols namely 5G Authentication and Key Agreement (5G-AKA) and Extensible Authentication Protocol (EAP). Such protocols are provided to ensure the authentication between system entities. These two protocols are critical systems, thus their reliability and correctness must be guaranteed. In this paper, we aim to formally re-examine 5G-AKA protocol using micro Common Representation Language 2 (mCRL2) language to verify such a security protocol. The mCRL2 language and its associated toolset are formal tools used for modeling, validation, and verification of concurrent systems and protocols. In this context, the authentication protocol 5G-AKA model is built using Algebra of Communication Processes (ACP), its properties are specified using Modal mu-Calculus and the properties analysis exploits Model-Checker provided with mCRL2. Indeed, we propose a new mCRL2 model of 3GPP specification considering 5G-AKA protocol and we specify some properties that describe necessary requirements to evaluate the correctness of the protocol where the parsed properties of Deadlock Freedom, Reachability, Liveness and Safety are positively assessed.

Formal methods are widely exploited in the performance evaluation of Wireless Sensor Networks (WSNs) protocols and algorithms. These methods help researchers to model and to analyse mathematically such protocols. Numerical results obtained by analysis and performance evaluation can be employed to prove the correctness and consistency of the designed models. However, these methods face a scalability problem when the number of components becomes very high, which is often the case in WSNs. To overcome this challenge, this paper proposes to use a Machine Learning (ML) solution to provide predictions when the number of nodes increases and the formal model becomes enable to make the analysis. Indeed, this work deals with the application of effective Artificial Neural Networks (ANNs) for the prediction of a set of crucial performance metrics of CSMA/CA-MAC protocol in WSNs when the number of nodes increases significantly in the network. This prediction process is based on prior results obtained by the formal model when the number of nodes was manageable by that formal model.

Indexing stage in information retrieval process has a great importance as an essential tool for the performance of recall and precision. Despite the many studies that have been done on the indexing conducted in the last few decades, to our knowledge, no study has investigated whether indexing real-time based on keywords extraction is efficient to perform of recall and precision. Moreover, relatively fewer Arabic text indexing studies are currently available despite the enormous efforts put together to satisfy the needs of the growing number of Arabic internet users. This paper suggests a method for Arabic text indexing based on keywords extraction. The proposed method consists of two stages. The first stage conducts a real-time indexing. The second stage is a keywords extraction and updating of initial index taking into account the output of keywords extraction process. We illustrate application and the performance of this method of indexing using an Arabic text editor (SIRAT) developed and designed for this aim. We also illustrate the process of building a new form of Arabic corpus appropriate to conduct the necessary experiments. Our findings show that SIRAT successfully identifies the keywords most relevant to the document. Finally, the main contribution of this experiment is to demonstrate the effectiveness of this method compared to other methods. In addition, the paper proposes a solution to issues and deficiencies Arabic language processing suffers from, especially regarding corpora building and keywords extraction evaluation systems.

This paper presents a teaching-learning-based optimization algorithm for discrete large-scale multi-objective problems (DLM-TLBO). Unlike the previous variants, the learning strategy used by each individual and the acquired knowledge are defined based on its level. The proposed approach is used to solve a bi-objective object clustering task (B-OCT) in a swarm robotic system, as a case study. The simple robots have as mission the gathering of a number of objects distributed randomly, while respecting two objectives: maximizing the clustering quality, and minimizing the energy consumed by these robots. The simulation results of the proposed algorithm are compared to those obtained by the well-known algorithm NSGA-II. The results show the superiority of the proposed DLM-TLBO in terms of the quality of the obtained Pareto front approximation and convergence speed.

The core idea of IoT is the connectivity of real-world devices to the Internet, which allows them to expose their functionalities in APIs ways, communicate to other entities, and flow their data over internet. With the massive growth of connected IoT devices, the number of APIs have also increased. Thus, led up to overload information problem, which is making APIs selection more and more difficult for devices owners and users. Therefore, this paper propose web APIs recommendation framework in IoT environment based on social relationships. The main purpose is providing a novel Recommendation method, which enable to discover APIs and provide relevant suggestion for users. The proposed hybrid algorithm is combined content-based filtering and collaborative filtering techniques to improve the accuracy of rating prediction. Finally, experiments are conducted to evaluate the performance of recommendation.

In this paper, we propose a novel, secure, and intelligent IoT approach based on agent, we have implemented in the health care domain, where we developed an intelligent patient monitoring system for monitoring the patients heart rate automatically. Our system is more intelligent that can anticipate the critical condition before it even happens, send a message to the patient family, doctors, nurses, as well as hospital in-charge personal, and launch an alarm to be assisted by the nearest people in place. Also, our architecture ensures the authentication, authorization, and data sensing confidentiality. Hospitals and medical clinics can utilize our system to monitor their outpatients who are in danger of unpredictable health conditions. The approach presented in the paper can also be applied to other IoT domains.

The Internet of things (IoT) is the integration of information space and physical space, becoming more and more popular in several places. In this paper, we will present QoS service composition approach based on multi-population genetic algorithm based on Fog-IoT computing, IoT-cloud architecture problems led us to use the 5-layared architecture implemented on a Fog computing system especially the transport layer. Our work was focus on this transport layer where we divided it into four sub-layers (security, storage, pre-processing & monitoring), it allows us to have promising advantages. Secondly, we implemented a multi-population genetic algorithm (MPGA) based on a QoS model, we considered seven QoS dimensions, i.e. Cost, response time, reliability, reputation, location, security and availability. Experimental results show the excellent results of MPGA in terms of fitness value and execution time to handle our ambulance emergency study case.

In this paper, we have proposed a novel feature-based fragile watermarking scheme for image authentication. The proposed technique extracts Feature Points (FP) by performing the Harris corner detector and used them as germs to decomposes the host image in segments using Voronoi Diagram (VD). The authentication of each segment is guaranteed by using the Cyclic Redundancy Check code (CRC). Then, the CRC encoding procedure is applied to each segment to generate the watermark. Voronoi decomposition is employed because it has a good retrieval performance compared to similar geometrical decomposition algorithms. The security aspect of our proposed method is achieved by using the public key crypto-system RSA (Rivest–Shamir–Adleman) to encrypt the FP. Experimental results demonstrate the efficiency of our approach in terms of imperceptibility, the capability of detection of alterations, the capacity of embedding, and computation time. We have also prove the impact of VD decomposition on the quality of the watermarked image compared to block decomposition. The proposed method can be applicable in the case where the tamper detection is critical and only some regions of interest must be re-transmitted if they are corrupted, like in the case of medical images. An example of the application of our approach to medical image is briefly presented.