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.

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.

The design and the evaluation of communication protocols in WSNs is a crucial issue. Generally, researchers use simulation methods to evaluate them. However, formal modelling and analysis techniques are an efficient alternative to simulation methods. Indeed, these techniques allow performance evaluation and model verification. In this paper, a formal approach is proposed to modelling and to evaluating the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) MAC protocol with a star topology. Moreover, the proposed approach deals with some properties that are not stated in most existing works. The approach uses Hierarchical Timed Coloured Petri Nets (HTCPNs) formalism to model the protocol and exploits the CPN-Tools to analyse the generated models. HTCPNs provide timed aspect which facilitates the consideration of time constraints inherent to the CSMA/CA protocol.

The growth of Internet of Thing (IoT) implies the availability of a very large number of services which may be similar or the same, managing the Quality of Service (QoS) helps to differentiate one service from another. The service composition provides the ability to perform complex activities by combining the functionality of several services within a single process. Very few works have presented an adaptive service composition solution managing QoS attributes, moreover in the field of healthcare, which is one of the most difficult and delicate as it concerns the precious human life.In this paper, we will present an adaptive QoS-Aware Service Composition Approach (P-MPGA) based on multi-population genetic algorithm in Fog-IoT healthcare environment. To enhance Cloud-IoT architecture, we introduce a Fog-IoT 5-layared architecture. Secondly, we implement a QoS-Aware Multi-Population Genetic Algorithm (P-MPGA), we considered 12 QoS dimensions, i.e., Availability (A), Cost (C), Documentation (D), Location (L), Memory Resources (M), Precision (P), Reliability (R), Response time (Rt), Reputation (Rp), Security (S), Service Classification (Sc), Success rate (Sr), Throughput (T). Our P-MPGA algorithm implements a smart selection method which allows us to select the right service. Also, P-MPGA implements a monitoring system that monitors services to manage dynamic change of IoT environments. Experimental results show the excellent results of P-MPGA in terms of execution time, average fitness values and execution time / best fitness value ratio despite the increase in population. P-MPGA can quickly achieve a composite service satisfying user’s QoS needs, which makes it suitable for a large scale IoT environment

Designing a good MAC protocol remains a challenge. Such a protocol has to guarantee access to the medium while reducing energy consumption. With the appearance of energy harvesting-wireless sensor networks (EH-WSNs), energy is no longer a problem but the challenge now is that each sensor remains in its energetically sustainable state as much as possible. This paper proposes a formal study of on demand MAC (ODMAC) one of the well-known protocols proposed for EH-WSNs. An analysis through statistical model checking is made where properties that guarantee the protocol's correctness are verified and a performance evaluation of important aspects is achieved.

With the explosive growth of Web of Things (WoT) and social web, it is becoming hard for device owners and users to find suitable web Application Programming Interface (API) that meet their needs among a large amount of web APIs. Socialaware and collaborative filtering-based recommender systems are widely applied to recommend personalized web APIs to users and to face the problem of information overload. However, most of the current solutions suffer from the dilemma of accuracydiversity where the prediction accuracy gains are typically accompanied by losses in the diversity of the recommended APIs due to the influence of popularity factor on the final score of APIs (e.g., high rated or high-invoked APIs). To address this problem, the purpose of this paper is developing an improved recommendation model called (Personalized Web API Recommendation) PWR, which enables to discover APIs and provide personalized suggestions for users without sacrificing the recommendation accuracy. To validate the performance of our model, seven variant algorithms of different approaches (popularity-based, userbased and item-based) are compared using MovieLens 20M dataset. The experiments show that our model improves the recommendation accuracy by 12% increase with the highest score among compared methods. Additionally it outperforms the compared models in diversity over all lengths of recommendation lists. It is envisaged that the proposed model is useful to accurately recommend personalized web API for users.

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.

Several contention-based MAC protocols for WSNs have been proposed. The control channel is accessed with carrier sense multiple access with collision avoidance (CSMA/CA) method. The complexity of this method and its criticality motivate the formal specification and verification of its basic algorithms. Most existing works do not deal with all possible aspects such as topology, number of nodes, node behaviour, and number of possible retransmissions. In this paper, we propose a stochastic generic model for the 802.11 MAC protocol for an arbitrary network topology which is independent of the number of sensors. In addition to the qualitative evaluation that proves the correctness of the model, we will make a quantitative evaluation using the statistical model checking to measure the probabilistic performance of the protocol.

Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.