Innovations in Smart Cities Applications Edition 3

Smart cities gained the support of scientists, urban planners, and governments all over the world because they suggest innovative solutions for all urban development problems using Information and communication technologies (ICT).Citizen participation is the key challenge to develop a smart city project since the main objective of a smart city is to improve the quality of life of citizens. Thus, decision-makers should cooperate with citizens and stakeholders.In this article, we will explain the current state of the art in the process of empowering citizens within smart cities and detail the results of a survey conducted in the frame of the Smart City Expo event held in Casablanca in April 2018.We administered the survey to key stakeholders in smart cities spread all over the world like city council representatives, technology developers and scientists (n = 20 respondents) in order to evaluate citizen participation in a smart city in practice.

Smart cities have millions of sensors and innovative technologies in order to improve the quality of their citizens and to increase the competitiveness of urban infrastructure. Nowadays these citizens like to communicate using social media such as Facebook and Twitter, thus building a smart city is not free from these platforms that have changed citizen’s daily life and becoming a new source of real-time information. These data are named Big Data and are difficult to process with classical methods. To exploit this data, it must be well-processed to cover a wide range of smart city functions, including energy, transportation, environment, security and smart city management. The aim of this paper is to highlight the advantages of social media sentiment analytics to support smart city by detecting various events and concerns of citizens.Towards the end, an illustrative scenario analyses data on citizens’ concerns about traffic in three main cities in Morocco.

Fake profiles play an important role in advanced persisted threats and are also involved in other malicious activities. The present paper focuses on identifying fake profiles in social media. The approaches to identifying fake profiles in social media can be classified into the approaches aimed on analysing profiles data and individual accounts. Social networks fake profile creation is considered to cause more harm than any other form of cyber crime. This crime has to be detected even before the user is notified about the fake profile creation. Many algorithms and methods have been proposed for the detection of fake profiles in the literature. This paper sheds light on the role of fake identities in advanced persistent threats and covers the mentioned approaches of detecting fake social media profiles. In order to make a relevant prediction of fake or genuine profiles, we will assess the impact of three supervised machine learning algorithms: Random Forest (RF), Decision Tree (DT-J48), and Naïve Bayes (NB).

Advances in technology are quickly paving the way for smart cities. According to Economic Development Board Mauritius, the Government of Mauritius has set up the Smart City Scheme to provide an enabling framework and a package of attractive fiscal and non-fiscal incentives to investors for the development of smart cities across the island. However, prior to the design and implementation of such technologies, it is important to predict the behavioural intention to use such technology so that smart city technologies effectively empower citizens and improve the quality of life of citizens. In this research work, it is proposed to use the Technology Acceptance Model (TAM) to effectively assess the perception and readiness and the perceived usefulness of certain smart city technologies such as for transportation as well as identifying key smart city applications for Mauritius. The aim of this research project is to evaluate and assess the different factors and condition that can have an impact on the perceived ease of use (PEOU), perceived usefulness (PU), attitudes towards using (ATT), behavioural intention (BI) to use and actual use (AU) of smart city technologies. This chapter is a complementary to the conference paper in SCA2019 which comprised Load Factor Analysis, here the focus is on Cronbach Alpha, Correlation and ANOVA.

The authors review the gamification practices implemented in Russia to enhance civic and political participation. Gamification is the spread of game elements beyond traditional entertainment, and it causes both critical (I. Bogost) and optimistic estimates (J. McGonigal) among researchers. While scientists are discussing gamification effects, managers and political strategists use game apps. The Russian practice of intensifying citizen and political participation through gamification covers three key areas: (1) solving urban problems through the organization of public discussions, voting, environmental performances; (2) the interaction of political leaders and the electorate, (3) the introduction of information agenda. The systematization of gamification cases shows that game designers primarily use the balance of game features, such as interactivity and cooperation, for citizen and political involvement.

The scope of this research fits in sentiment analysis. This latter is becoming more and more an active field of research where to extract people’s opinion concerning political, economic and social issues. The objective of sentiment classification is to classify opinions of users as positive, negative or neutral from textual information alone. For that, purpose researchers used data mining classification techniques such as naïve Bayes classifier and the Neural Networks. The sentiment analysis area is confronted to several problems that distinguish it from traditional thematic research, since the sentiment is expressed in a very varied and very subtle ways.In the last few years, several researches focused on sentiment analysis in order to study attitudes, opinions, and emotions. In this paper we present an analytical and comparative study of different researches conducted on sentiment analysis in social networks using machine Learning. This study analyzes in more detail the preprocessing steps which are very important in sentiment analysis process success and are the most difficult especially in the case where the comments are written in not structured language.

Nowadays, hand biometric systems are very used to perform personal recognition as well as for airports as for companies. These systems can be used also for payment to avoid paying using cash or bank card. This chapter presents a survey of different multibometric hand systems developed in the literature. Various biometric modalities offered by the hand and that can be used as an alternative to the fingerprint are presented. Concepts and techniques of multibiometric hand systems which are used to improve recognition accuracy are also detailed. Examples of some commercialized systems are finally exposed.

Nowadays social media content is the fuel of almost all kinds of domains, due to its rich and ever-increasing quantity of data. Digging this content can lead to extracting valuable information that can enhance business products and services, politic decisions, socio-economic systems and more. To this end, sentiment analysis and community detection represent two of the main methods used to analyze and comprehend human interactions within social media. Also, to get meaningful results, filtering social content is needed, here where domain ontology can be a great assistant in collecting specific data, as it describes the domain’s features and their existing relationships. This current work depicts our social media analysis Framework, where we apply lexicon-based and machine learning approaches to extract expressed sentiments of social media users toward a subject, and also used a community detection algorithm to highlight the formed groups within the network. Besides, the resulting Framework not only focuses on analyzing textual data (by taking into account the negation and sentence POS tags), but also visual content shared by users, such as images. For the testing purpose of our Framework, we chose to analyze the British exit (“Brexit”) case by collecting ontology-based data from Twitter and Reddit, and it had some promising results.

Databases are becoming more and more inevitable for websites and applications that handle large amounts of data, such as bank accounts (games, social networks, videos, etc.). However, they are unable to respond to human-type questions that are generally imprecise, uncertain and vague, and therefore need to be appropriately addressed. Therefore, fuzzy interrogation systems have become indispensable to represent and manage these data, and particularly facilitate the interrogation to a non-expert user. Fuzzy logic provides a powerful tool to take into account various aspects of fuzzy information. In this paper, we present a short study of fuzzy querying relational databases. We start by the design and operation of a fuzzy system and present the different types of architectures of fuzzy question database systems. Lastly, we introduce a comparison of the most pertinent features in fuzzy query systems of databases.

The orientations are the trends and behaviors that express an individual’s desire to pursue or apply oneself to a specific occupation and, together, these orientations affect the individual’s decision-making process with respect to occupational choice. This study aimed at generating an acceptance model of the e-orientation Moroccan platform “orientation-chabab.com” that can be used during the conceptual design of the future E-orientation platforms. Firstly We established a qualitative questionnaire based in the Technology Acceptance Model (TAM) as a theoretical model. Our experiment was conducted with the WEKA machine learning software by using four algorithms namely: NaïveBayes, J48, NLMT and SimpleLogistic. The results indicated that the highest classification accuracy performance is for the J48 and classifier gives us the best performance outcomes.

Intelligent university systems are based on the profound integration of information technologies into the educational process, which has greatly influenced the techniques and pedagogical tools used to interact with the learner’s educational environments, indeed, the emergence of Smart University (SU) concept enable smart learning process by encompassing a range of smart components, which involve the implementation of an adaptive educational model using informational smart technologies. Through a process of interaction between academics and the organizational structure, it promotes modern methods of collaboration to increase the success and effectiveness of education. The service-oriented paradigm is crucial in ensuring a collaborative learning environment, which boosted by intelligent layers, additional treatment and data-powered. The paper concludes with “smart collaborative learning”, as a relevant concept that adopts smart interactions to promotes modern methods of collaboration between teams of smart learners.

Adaptive learning systems are defined by their ability to adjust the learning process to the needs, characteristics and learning styles of learners. These systems must acquire and process all the information relating to the learning process characterizing the temporal and dynamic evolution of learners’ behavior so that they make appropriate decisions for each change detected during the learning act and ensure deterministic and predictable behaviors, by offering personalized learning paths in real-time.To ensure this customization of the courses, we propose an architecture of a multi-agent adaptive learning system based on the Reasoning from Hybrid Incremental Case which makes it possible to take the right decision in real time in the face of a dynamic learning situation, by performing a classical cycle or a dynamic cycle of reasoning from case. This decision will be made by assigning at each step of the Reasoning from Hybrid Incremental Case at least one agent performing a well-defined task and collaborating with other agents to dynamically support the learner during the learning process by proposing them a personalized learning path in real time.

It is commonly said that Augmented Reality opens new perspectives for training. Today, it is unlikely that the audit objectives will be met. One of the major criticisms of traditional computing is that the digital world of the computer is too disconnected from the real world in which users evolve. Mixed Reality (RM) focuses on interactive systems in which real objects and computer data are coherently merged. However, integrating these new tools into the existing learning processes remains complex, due to the technological aspects and the data continuum to be implemented, through the identification of use cases and the associated gains and by the diversity actors and experts to involve in this process: the expert of the main field, the designer and the IT developer. In this paper we aim to develop an augmented reality application by following a developing process that will led to the planned results. The proposed application will be dictated for learners in higher education context, especially newbies that will practice their first experiment in the laboratory e.g. “biology, chemistry” by giving them an interactive environment to understand the safety procedure followed during experiments in laboratories.

No one can argue that children of today are not the same as children of 10 or 15 years ago. Children are now growing in an ever-changing world, in a context that is way different from what existed before which makes change in education a necessity and an inevitable aim that needs to be taken into consideration because children cannot be taught with traditional teaching methods and approaches anymore. For this purpose, this paper addresses not only teachers of English but also teachers of other subjects to make them aware of the existence of such tools and encourage them to implement them to transform their teaching and Moroccan students’ perception of learning. This paper is twofold; first, an overview of some interactive learning sites. Second, a case study about the extent to which English teachers in Morocco are familiar with, mainly 5 interactive learning sites, namely Kahoot!, Quizlet, Quizalize, Wooclap, and Jeopardylabs, their use of them, their satisfaction of their services, and their recommendations. Data shows that most teachers are familiar with all the sites except Wooclap. Regardless of their familiarity of them, only few teachers use these sites in their classroom. The ones they reported using the most were Kahoot! and Quizlet. Yet, in contrast to their use and the satisfaction they expressed with the two sites, they recommend their colleagues and other teachers to use the other sites as well. The results found cannot be used to generalize the findings on English teachers in Morocco.

Social networkings are relatively recent innovations. Literature on other social technologies in education strongly suggests that such technologies can be educationally beneficial to teachers and learners in supporting the sharing of resources, enhancing motivation, and facilitating reflection, social interaction and knowledge building. However, thus far there have been few empirical studies detailing application of social networking technology in educational contexts based on learners’ interests.This study proposes an automatic learning environment based on the analysis of the social interactions that takes place between users-users and users-resources, the analysis is based on the history of interactions made by learners within the environment to deduce their interests in relation to a module, learners with similar interests will then be assigned to the same learning group in order to propose recommendations regarding their preferences, interests and needs. This system ensures that these recommendations will certainly improve the learning process by providing students with the best learning practices, the desirable collaborators, and the relevant resources that fit better their needs. This study showed that the recommended resources are interesting, approachable, adaptable, and divers for users with several interests.

Profile modeling is an important process that aims to give as complete representation as possible of all the aspects related to the user features. I n the educational field, student profile modeling can give important solutions to variant problems. It can mainly offer the most exact description of student s in order to: be able to act in case of problems such as failure, drop out…; offer students the most appropriate orientation and recommendation; define the most adaptive learning resources depending on their profiles… In this paper, we present an analytical and statistical study on student profile modeling to propose a generic student profile model based on different. Our purpose is to build a student profile model based on many important features that can be used alone or combined for decision making in different fields of the educational domain.

In this digital era, incorporating changes at the business model level becomes a must. Exploiting the digital capabilities by firms is the only way to avoid disruption, to outdo the competition and to answer the changing customer needs. In this respect, digital business models come as a new frame for firms to create and capture a new kind of value, adapted to this era. This paper is an attempt to broaden the literature review about the concept of digital business models. We started by defining the concept of business models, its origins and the peculiarities that characterize the digital ones. We conducted after a systematic literature review, through which, 108 articles have been analyzed in order to extract the patterns that shape the digital business models. Our finding shows that customer experience, data, strategy, innovation, platforms, competition and content are the most important components of digital business models. The article concludes with the proposition of a new and suitable canvas to use in order to craft a digital business model.

Social media has changed everything around us, with just one click of a button, any of us can share their opinions and reviews with a lot of people who can share them in their turn with plenty of others. Hence, the click of the button has become more of a political, economical and social power that can raise millions for charity, tear down companies and brands and even make presidents. The purpose of this paper is to research empirically, the impact of social media on the purchasing behavior of consumers, and especially on how the feedbacks and reviews influence each of the stages of the consumer decision making process, a sample of 828 randomly selected, Moroccan social media users, was investigated through a survey. The results show that consumers’ purchasing decision is highly influenced by social media networks. According to the results, the influence of social media on information and evaluation of alternatives stages is higher than the rest of the stages of the purchase decision making process. Finally, almost most of the survey respondents (85,4%) believe that social media has an impact on our purchasing behavior.

Personalization and relevance have become more and more critical factors for all kind of media used in digital marketing. Email marketing which is the most used media in the field can be more effective if emails can reach the right customers at the right time. However, the use of data analytics technics and machine learning algorithms can help marketers to make a good decision about how to plan a successful campaign strategy based on legitimate predictive intelligence. In this paper, our research team will present several experiences in how to make a learning model, to predict the clicks of the targeted emails. The proposed model will be based on several features extracted from different campaigns and email recipients profiles. Then it will be established by using five Machine learning algorithms for classification including Decision tree, Bagging classifier, adaptive Boosting, Neural Network, Random Forest to choose which one of them is suitable to predict clicks rates according to several criteria like subject-lines, from-lines, device, offers and vertical.

There is much enthusiasm currently about the opportunities created by the data deluge and its new and more extensive sources in the domain of smart sustainable urbanism. This mainly involves the ability to respond to the challenges of sustainability and urbanization thanks to thinking in a data-analytic and data-intensive scientific fashion and using powerful computational processes to generate useful knowledge for enhanced decision-making and deep insights. Indeed, more innovative solutions and sophisticated approaches are needed to tackle the complexity of smart sustainable cities in terms of wicked problems. This paper explores the value of big data analytics and its uses in dealing with such complexity. Further, it analyzes how urban sustainability science can be transformed by urban science and data-intensive science thanks to big data science and analytics and the underpinning technologies. We argue that this area of science and technology embodies an unprecedentedly transformative power—manifested in the form of advancing smart sustainable urbanism and revolutionizing urban sustainability science.

This paper sheds light on the transition from traditional governance to ecological corporate governance in a Moroccan context. In fact, we propose a new desionel model that illustrates the relationship between an eco-responsible decision and the corporate governance system. In this paper we discuss the variables of an eco-responsible decision and their impact on the corporate governance system. The latter is composed, in one hand, by three powers: sovereign, executive and supervisory, and it’s characterized by six regimes, by referring to a review of rich and varied literature in the other hand. The innovative added value of our perception is to demonstrate the importance of the ecological aspect in a corporate governance system by focusing on a company as an active economic agent responsible for climate change. Indeed, to confirm the existence of such a relationship, we choose the mathematical method of neural networks as a method of classification and analysis. This choice highlights our work and makes it different from other previous and current works discussing the same subject.

Credit cards have become a necessity in the virtual world for digitized and paperless transactions. Every day, millions of credit card transactions occur, but these transactions are subject to various types of fraud. Numerous works have proposed techniques developed to analyze, detect and prevent credit card fraud. In recent years, several studies have used machine-learning techniques to find solutions to this problem. In this article, we conducting a comparative study of techniques based on neural networks, applied to the same data set. Our goal is to offer a complete analysis to choose the best credit card fraud detection techniques.

Unsupervised learning refers to training an artificial intelligence system based on unlabeled data. One of its main applications is clustering analysis, which is the study of techniques and algorithms used to create clusters from a set of data. Clustering methods can be applied in various fields, particularly in health insurance. Health insurance is one of the ways that individuals finance their medical needs and maintain a stable financial situation in case of illness, which shows the importance of subscribing to a medical insurance coverage. Therefore, studying the behavior of the population benefiting from it is essential. In this paper, we apply K-means algorithm, which is one of the most used and efficient unsupervised learning methods for data clustering, in order to cluster the subscribers covered by health insurance in homogeneous groups or clusters. The clusters created will serve as a decision support tool for policy makers in the insurance field.

In this work, we will talk about how can us ensuring the secure communication through health system monitoring based on the internet of things, and also the requirements of security that should be included. Then we study the possibility of implementing a secure structure protocol stack for medical tools inside a hospital, taking advantage of the security mechanisms offered by the communication technology, networks, and protocols exist, according to the construction, and energetic properties of this object connected. This chapter can answer at least on this question, how apply a secure interface for communication between e-health care system monitoring devices while ensuring a reliable, efficient and security of transmission. CCS Concepts: Information systems → Secure Communication interfaces. E-health systems monitoring → Secure transmission of data between health devices.

One of the most important domain of application of the Internet of Things is the healthcare field, the new IoT Ehealth Ecosystem can collect the health information of the patient, and transmit it to the Medical Server located in the Internet, to inform the caregiver of the state of health of the patient in real time, warn the emergency for a rapid intervention in place in case of need, and prepare the right specialist in advance in the hospital to receive the patient. To connect all these entities to each other, we were based on a Metropolitan Area Network, the WIMAX mobile protocol, and to collect the health information of the patient we were based on a microcomputer relied to ten health sensors and an alert button. We used the OPNET Network Simulator to study the performance of our IoT Ehealth Ecosystem, in the physical and data link layer using the WIMAX mobile protocol, and in the application layer using the HTTP, FTP, Email protocols, a Voice application and a Remote Login application.

Background: Physicians use diver radiology technologies on a daily basis to diagnose stage and treat cancers with the goal of saving thousands of lives. The use of CT scans in medical diagnosis exposes patients to radiation doses higher than other radiological procedures. It is estimated that CT scans represent about 20% of radiological procedures and contribute about 80% to medical radiological exposure doses. Purpose: The purpose of this study is to estimate the doses delivered during abdominopelvic CT procedures in order to evaluate the biological effects in patients at the Mohamed VI University Health Centre in Oujda, Morocco.Materials and methods: A GENERAL ELECTRIC (GE) Optima CT 540 scanner is used for all CT examinations. A study of 30 patients was carried out in the emergency radiology department. Data were collected from abdominopelvic examinations. For each scan, CT acquisition parameters, including the number of series, contrast medium usage, kV tube, tube current and rotation time, slice thickness, CT dose index (CTDIvol) displayed and Dose Length (DLP) product. The effective dose and the biological effects were estimated using the conversion factors of the International Commission on Radiological Protection (ICRP).Results: The tube voltage (kV), tube current and rotation time were 120 (kV), 275 (mA) and 1.75 (s) respectively. The average CT dose index (CTDIvol) displayed was (8.37 ± 2.52) mGy. The average dose length (DLP) product was (352.41 ± 153.53) mGy.cm, with a third quartile of 416.5 mGy.cm. The average effective dose received by the patient during an abdominopelvic CT was (5.36 ± 2.33) mSv, with a third quartile of 6.33 mSv. The risk of cancer per abdominopelvic CT procedure is in the order of 348 per one million procedures while the hereditary risk per procedure is about 13 per one million procedures. The effective dose and risk factors for abdominopelvic CT are well below several values recorded on an international scale.Conclusion: Cancer and hereditary risks are increased when performing multiple scan acquisitions. The study found that the doses received during abdominopelvic CT procedures at Mohammed VI University Hospital in Oujda were within an acceptable range. However, staff must optimize the radiation dose during abdominopelvic procedures, and a CT dose optimization protocol must be implemented in the Moroccan hospitals.

Blood glucose monitoring is considered the gold standard for diabetes diagnostics and self-monitoring. However, the underlying process is invasive and highly uncomfortable for patients. Furthermore, the process must be completed several times a day to successfully manage the disease, which greatly contributes to the massive need for non-invasive monitoring options. Human serums, such as saliva, sweat and tears contain traces of glucose and are easily accessible. It is very important for human health to rapidly and accurately detect glucose levels in biological environments, especially for diabetes mellitus. We proposed a simple, highly sensitive, accurate, convenient, low-cost, and disposable glucose biosensor on a single chip. Thus, this disposable biosensor will be an alternative for real time tracking of glucose levels from body fluids, e.g. saliva, in a noninvasive, pain-free, accurate, and continuous way.

Healthcare delivery is one of the crucial services in every country. While the education sector train health professionals, the available health facilities are unable to employ majority of the qualified health professionals to offer the required essential services. This leads to the unemployment of qualified health professionals in some countries. In this paper, we focus on the case of Ghana, West Africa to develop a conceptual framework on how to utilize mobile technology to deliver basic healthcare services and to create employment opportunities. This framework ensures registration of graduate nurses/health professionals to assist in delivering basic healthcare services to mobility challenged including aged persons. Identified gaps in the delivery of basic healthcare services in Ghana were taken into consideration in formulating the proposed conceptual framework to guide the implementation of a mHealth application. Data was collected from five (5) healthcare professionals and fifteen (15) patients from a hospital in the Volta region of Ghana. The data collected was used to evaluate the proposed framework and the finding suggests that health professionals who are the respondents do not utilize mobile phone platforms in healthcare delivery to aged persons in Ghana. Similarly, respondents who are patients would like to use mobile phone platform to request for the service of healthcare professional.

For more than a decade, in the context of Microcalcifications detection, radiologists previously use traditional techniques to analyze mammographic images, which leads to a lower precision in the detection of lesions. The effective method of treating breast cancer is to detect it in early stages to increase the chances of cure and reduce the mortality rate, and to do this we propose in this paper to develop a computer aided diagnostic system (CAD) named Earlier Breast Cancer Computer Aided Detection (EBCCAD) which aim is to detect and classify breast cancer images and to replace the previous techniques already used to enhance radiologists performance in determining the pathologic-disease stage of Mccs and to discriminate between normal and abnormal tissues. The results obtained are promising given the rate of good classification obtained by the approaches proposed in the classification phase which lead us to evaluate the proposed system for real cases.

Despite the availability of real, diverse and knowledge-rich data in all domains, they are generally likely to be uncertain, inaccurate, and incomplete. Attracted by the performances and the strong mathematical underpinnings of the Rough Set Theory (RST), many researchers have suggested new efficient methods and algorithms implying RST and able to deal with these aspects characterizing real data. In this paper, we present a survey of rough set based supervised machine learning approaches allowing to induce deterministic or probabilistic decision rules and their different involved methods. The various models of these approaches have been experimentally evaluated and compared in term of prediction accuracy, quality and compactness of the generated decision rules sets when applied to the community-acquired meningitis diagnosis.

In the current smart cities, assistive IoT technologies are employed in several services to improve the physical and social functioning of persons with disabilities (PwD) and elderly people. This book chapter highlights the exploitation of these technologies to build a home-based smart rehabilitation system for a knee injury. This system aims to reduce patient mobility to attend the rehabilitation session, in order to achieve good accessibility and proper management of transport, improving thus smart cities’ characteristics in terms of smart living, smart mobility, and smart healthcare. The architectural model of the proposed system is designed in a manner to ensure the telemonitoring function of the rehabilitation process, and the remote access to feedback information for both the practitioners and patients. It consists of three main components: measurement instrumentation, processing and transmission unit, and cloud data storage and visualization. Furthermore, the results obtained in the experimental test showed that measurements error percentages were lower than 2%, and the data displayed in the cloud are significant, indicating that the accuracy and reliability of the developed system are satisfactory, as well as telerehabilitation engineering has prospects in Inclusive smart city applications.

Hospital complexity required the use of an information system (IS) to ensure the proper management of the various processes of the health facility. The choice of the IS presented a problematic considering the different types and qualities of ISs. To choose one, we completed a comparative study of nine open source ISs (OSHIS). We used the DeLone & McLean IS quality evaluation model and the SQALE method to evaluate the source code. The implementation using the SonarQube platform allowed us to choose MediBoard which scored a minimal technical debt by 42.16%. The installation of MediBoard was not immediate, we conducted a maintenance intervention to complete the usability of this HIS. We used ISO 12207: software life cycle standard, and ISO 14764: software maintenance standard. The implementation of these standards allowed us to have a complete version with 63 modules.

A healthcare system using modern computing techniques is the highest explored area in healthcare research. Researchers in the field of computing and healthcare are persistently working together to make such systems more technology ready. Diabetes is considered as one of the deadliest and chronic diseases it leads to complications such as blindness, amputation and cardiovascular diseases in several countries and all of them are working to prevent this disease at early stage by diagnosing and predicting the symptoms of diabetes using several methods. The motive of this study is to compare the performance of some Machine Learning algorithms, used to predict type 2 diabetes diseases. In this paper, we apply and evaluate four Machine Learning algorithms (Decision Tree, K-Nearest Neighbors, Artificial Neural Network and Deep Neural Network) to predict patients with or without type 2 diabetes mellitus. These techniques have been trained and tested on two diabetes databases: The first obtained from Frankfurt hospital (Germany), and the second is the well-known Pima Indian dataset. These datasets contain the same features composed of mixed data; risk factors and some clinical data. The performances of the experimented algorithms have been evaluated in both the cases i.e. dataset with noisy data (before pre-processing/some data with missing values) and dataset set without noisy data (after pre-processing). The results compared using different similarity metrics like Accuracy, Sensitivity, and Specificity and ROC (Receiver Operating Curve) gives best performance with respect to state of the art.

Breast cancer is one of the most dangerous types of cancer in women sector; it infects one woman from eight during her life and one woman from thirty die and the rate keeps increasing. The early prediction of breast cancer can make a difference and reduce the rate of mortalities, but the process of diagnosis is difficult due to the varying types of breast cancer and due to its different symptoms. So, the proposition of decision-making solution to reduce the danger of this phenomenon has become a primordial need. Machine learning techniques have proved their performance in this domain. In previous work we tested the performance of several machine learning algorithms in the classification of breast cancer such as Bayesian Networks (BN), Support Vector Machine (SVM) and k Nearest Neighbor (KNN). In this work, we will combine those classifiers using the voting technique to produce better solution using Wisconsin breast cancer dataset and WEKA tool.

In this paper, we propose a new Visual Question Answering (VQA) System which is able to give an answer in natural language about an image and a natural language question. In our System, we have employed a Convolutional Neural Network (CNN) for visual input processing and Recurrent Neural Network (RNN) in Encoder-Decoder model which consists of an LSTM to encode sequential input which is an assembling between image and question vectors, and another LSTM to decode the states for predicting target answers in output, this one will be generated in natural language using greedy search algorithm.

The mobility impact is a crucial element that can impact the stability of network performances in Mobile Ad hoc Networks (MANETs). Choosing a path with stability, security with an extended network lifetime is one of the critical points in routing protocols design. In this paper, the author suggests an enhanced algorithm based on a metric of mobility by discovering the modification of MPRs selection and incorporating the metric in the routing decision. The metric based on the spatial mobility of neighbor nodes named average spacial dependancy. The principal objective is to discover more performing MPRs that can improve the network performances even in a critical situation of the dynamic environment. The authors implement the proposed metric in OLSR protocols and get the results of the performance using a network simulator (NS3) under the Manhattan Grid mobility model. The obtained results prove important performance gains for the modified version. Besides, the proposed metric can be used as a new mechanism to increase network performances for protocols in MANETs.

In recent years, 3D model visualization techniques have made enormous progress. This evolution has not only touched the technical side but also the hardware side. It is no longer necessary to have expensive machines to see a world in 3D; a simple computer can do the trick. Historically, research has focused on the development of 3D information and acquisition techniques from scenes and objects. These acquisition methods require expertise and complex calibration procedures whenever the acquisition system was used. All this creates an important demand for flexibility in these methods of acquisition because of these different factors, many techniques have emerged. Many of them only need a camera and a computer to create a 3D world from a scene.

The Nowadays, Networks in biology gained a lot of attention, due to recent advances in biological technologies. The massive data produced with these novel techniques allowed us to perform deep analysis on cells and understand its functional system. In addition, the combination of data integration methods with analytical approaches, which consists of representing biological data as networks and make it possible to perform analytical approaches to formulate hypotheses and get accurate conclusions, is improving the results obtained from biological network analysis. In the following sections, we are going to give a general description of biological networks, especially how to interpret data inside a network and extract valuable information’s, we’ll talk about some general concepts of biological networks taxonomy including pathways, interactions, and similarity between network entities. Furthermore, we will present the core concepts of analytical approaches for biological network analysis; the main features provided by Cytoscape application, with a real example of a sample use case about expression data analysis among the protein-protein interactions and show how the combination of data integration with analytical approaches helped us to extract hidden information’s. Finally, we will discuss about how we are improving the domain of MicroCancer platform.

The evolvement of wireless communication technologies, smart city conception and computing capabilities in the devices are enlarging the connected world and improving human life well-being level. There are many initiatives aimed at analyzing the conception process, deployment methods and the outcomes of smart city which are being developed in multiple fields. Today’s smart city network applications such as smart transportation and home automation have driven wireless usage levels ever higher. The consequences arise in challenging the wireless network spectral efficiency, better quality of service and increasing network capacity with limited availability of radio frequency. To solve this issue, this paper analyses the latest antenna technologies of cooperative multiple input multiple output (coMIMO) which is the bridging technologies to fifth generation (5G) by firstly studying the benefits of cooperative MIMO in smart city applications such as smart transport, home automation and security. Secondly it assess the impact of cooperative MIMO technologies to achieve higher spectral efficiency. The results show that cooperative MIMO has more advantages than SISO, MISO, SIMO and MIMO in the optimization of smart city systems application.

Embedded systems are the principal element in modern electronic devices and in intelligent systems. An Embedded system (ES) is generally composed of hardware blocks (ASIC, FPGA) and software blocks that run on a microprocessor. The hardware (HW) and the software (SW) are executing in collaboration to achieve specific functionalities of the system. The non-functional requirements have a big impact on the design of modern ES. The objective of new design methodologies such as the Co-design is to meet the functional specifications and to achieve the best possible balance between the non-functional requirements. The Hardware Software Partitioning (HSP) is a key step in this process of Co-design. For each block of the system, the HSP decides whether it is more advantageous to be assigned to the hardware part or to the software part. The most important metrics involved in the HSP process, are the cost of the hardware area and the execution time. The majority of previous works study the optimization of one metric with the respect of a given constraint on the other metric. In this paper, we propose a novel approach aimed to simultaneously optimize the hardware area and the execution time of the system. The approach is inspired from the GO game and based on Minimax algorithm. Experimental results show that the proposed approach leads to more optimal solutions compared to the Genetic Algorithm (GA).

VANET network is a communication network between smart vehicles equipped with several equipment such as calculators, network devices and different types of sensors when we talk about a network vanet it can also be referred to Vehicle-to-vehicle communication (V2V) in fact V2V is a technology that makes our transportation systems smart. Among the advantages of this system it can avoid accidents and traffic, V2V communication connects vehicles to each other in order to exchange data that also has the potential to avoid a crash and avoid traffic jams, Various routing protocols for VANETs and V2V communication have been recently proposed, such as Ad hoc On-Demand Distance Vector (AODV) routing protocol which is topology based and Greedy Perimeter Stateless Routing (GPSR) protocol which is geography-based, this paper compares routing protocols based on topology and position-based routing protocols and a comparative study between the various protocols such as AODV, DSR, DSDV, OLSR, ZRP and GPSR, thus, this article propose improvements on various protocols based on their weak points such as AODV, DSR and GPSR.

Nowadays, we are moving towards smart environments and Internet of things. These new intelligent devices offer us an easy and comfortable life support the difficult work or complex tasks that humans have done so far. In such environment, the communication is a decisive way to ensure proper functioning of the intelligent system, especially in health and transportation. The mobile device collect important information and share knowledge between them or with the cloud to manage resources and assets. The routing protocols in such environment must meet the challenges and respect the characteristics of devices like restricted access to power, small computational capacity, and weak wireless signals, etc. The overhead generated by the routing protocol affects the quality requirements of smart applications in terms of reliability and availability. In network without infrastructure, as sensor or mobile ad hoc networks, reducing the overhead constitutes the major reason to build a reliable and efficient network. In this paper, we propose a hybrid zoning algorithm called the Zone Geographic Forwarding Rules (Z-GFR) that combines two zoning strategies in one and same routing protocol to enhance the performances of mobile communication in ad hoc network. We have implemented our solution in the optimized link state routing protocol (OLSR). We will study the enhancement of the Z-GFR technique compared to other zoning techniques and to the OLSR protocol in terms of bandwidth, packets delivery ratio and end-to-end delay. We will show by simulations that Z-GFR scales down the overhead related to the protocol and meets quality of service requirements while it ensures a good dissemination of the topology.

With the immigration to IPv6 and the large range of addresses provided by this protocol, reducing the size of IP routing tables becomes one of the main and the most compelling challenges facing the internet.In this paper, we present an aggregation process, that perfectly makes use of the summarization technique and the hierarchical address allocation of IPv6, to generate a network structure with optimal routing tables. The proposed process optimizes and divides up the routing tables which may help resolve the problem of the massive growth of routing table entries. Furthermore, the hierarchy of the proposed model permits to customize the routing table size of every router according to its features and the speed of the incoming and the outgoing traffic that go through that router.

During the last decade, the Internet of Things (IoT) became an emerging technology, it is exploring in every area of human life. The IoT provide facilities to identify and communicate the Smart devices, this last can transfer data in Mobile Ad hoc Networks (MANETs) across all active devices without the need for a centralized approach. The connection between MANETs and Internet of Things opens new ways or provision of services in smart environments and challenging issues in its networking aspects also. Simultaneously, MANETs are threatened by different attacks, which aim to degrade the network’s performances. In this article, we simulate and study the impact of Link-spoofing, Data-flooding and Replay attacks with Optimized Link State Routing Protocol (OLSR) (RFC 3626) on using ns-3 simulator. In this work, we took into consideration the density of the network by the number of nodes included in the network, the speed of the nodes, the mobility model and even we chose the IEEE 802.11ac (WIFI 5) standard, in order to have a simulation, which deals more general and more real scenarios. To analyze the impact of the attack on the network, we chose the Quality of Service (QoS) metrics for performance evaluation: Packet delivery rate (PDR), Normalized routing load (NRL) and End-to-End delay (EtED).

The concept of the Internet of Things (IoT) is widely recognized in the field of scientific research. In the last year the focus of objects is becoming more and more important in our daily lives and in all sectors of activity. IoT aims to simplify our daily lives, using a multitude of possible applications. One of the key aspects of IoT is the communication between the various components of the overall system. There are many ways to connect and communicate objects. For that, the application and network protocols adapted for IoT applications. So, these proto-cols have been widely investigated. In this paper, we will present the modeling of the functioning application protocols. These protocols are: CoAP, MQTT, XMPP, AMQP, DDS, WebSocket and Rest API. For modeling, we used the activity diagram which is a behavioral diagram of UML.

The 5G technology is characterized by the use of high frequency band corresponding to millimeter waves, these latter are highly sensitive to the propagation environment and the atmospheric conditions. Thus, for proper planning of future 5G radio network it is necessary to have a reliable tool that can predict the behavior of mm-waves in a particular environment and under some specific climate conditions. This article focuses on the study of the Close in Free (CI) model, a model that combines those two aspects of study. Indeed the CI model introduces an atmospheric attenuations term which is rarely taken into account and considered in the existing 5G channel models. This channel model is integrated in 5G radio planning tool called NYUSIM, which is developed by NYU WIRELESS research group at New York University (NYU). Nevertheless, we found some difficulties and limitations on this software that we tried to resolve, by designing a new tool which will give more information about the radio part of 5G network. The new software is designed in Research laboratory in Telecommunication Systems, Networks and Services (STRS) of the National institute of posts and telecommunications (INPT) in Morocco.

Cognitive radio (CR) is a form of wireless communication, that provides solution for the spectrum underutilization problems via dynamic spectrum sharing among primary users and secondary users. In cognitive radio network, interference and power consumption must be carefully controlled to maximize the channel usage. Game theory has emerged in the last fifteen years as an effective framework for communications problems, which is used to analyze those problems and help to derive solutions. In this paper, we propose a sufficiently opportunistic utilization for spectrum resources by solving two challenges: interference and power consumption. We analyze the spectrum allocation problem under game theoretical framework and we propose an efficient algorithm to examine the design specification issues regarding the choice of optimal power, optimal speed, and optimal amount of information in a wireless network. Our objectives are to regulate the opportunistic spectrum access, by the secondary users in order to guarantee the protection on licensed users from harmful interference. More especially, to optimize the quality of communication link, transmission levels, and battery life of the wireless devices.

Energy consumption was and is an interesting issue that is still a factor in the development of WSN protocols especially in the MAC and physical layers. This factor directly affects the lifetime of the network. Combining Network Coding (NC) and efficient scheduling in the Medium Access Control (MAC) layer of such networks gives more efficiency and copes with the problem of energy waste and inefficiency management as well as transmission’s lose and collision. In this work, we proposed the network coding technique to improve the performance of WSN based on IR-UWB in term of energy consumption. Especially; we introduce the implementation of NC at the MAC level using SWIMAC protocol.

Over the years, several generations of mobile radio have followed one another every decade. The transition from one generation to another is dictated by a concern about performance in terms of mobility, speed, delay, multiplicity of services etc. Nowadays, unlike previous generations, 5G technology is not only interested in the needs of mobile operators (telephony, video download, mobile applications, etc.) but also, and above all, in different applications and diverse uses such as Smart City, safety, transportation, energy, health, industry and security. All these services are unified by 5G technology. This technology manages an Ultra Dense Network (UDN) and therefore consumes a lot of energy. This chapter deals with the issue of Call Admission Control (CAC) in 5G networks, energy efficiency and state of the art in this area. It suggests a CAC modeling algorithm in the case of a New Radio Access (NR 5G). It also addresses the issue of handover and more generally mobility management, power control and interference. All of this is done by considering and ensuring acceptable QoS and QoE, as well as energy efficiency. In this chapter, we focused on the development of an algorithm for improving call admission control essentially based on minimal energy consumption, knowing that there are other parameters on which we can act for better energy efficiency.

In this paper, we suggest a new watermarking scheme. The purpose of this technique is based on image content description and specifically the corner points. In our proposed scheme, Harris detector is used for get some corner points, which are united with a rectangle decomposition method in order to get a set of insertion blocks and to bind the watermark with the image content. The proposed scheme is divided into two phases: The embedding scheme, where we embed the watermark into the original image (one bit of message in each rectangle insertion block) to get the watermarked image. The extracting scheme is the reverse phase that aims to extract the embedded message from the watermarked image. The experimental results demonstrate that the proposed scheme preserves the image visual quality (PSNR up than 53 dB) and with less algorithm complexity (0.3 s as average) and we arrive to detect 100% of message embedded (messages between 9 to 20 bits) in various images.

Besides the development of the design of the renewable hybrid system and its configuration, there is the topic of power management which responds especially to the renewable intermittency issue. Indeed, we are interested in the energy optimization of a hybrid system by managing instantly the energy produced and consumed by the architecture’s components. Our proposed plays a very important role for smart houses that will be the basis of a smart city in the near future. Indeed, we considered a PV-Wind-Battery hybrid system as a primary source that enables the house to satisfy its own energy needs while considering the grid only as a storage facility with infinite capacity. The connection of various components is done via the proposed hybrid architecture, and the different electrical switches mentioned on it are efficiently conducted by our proposed energy management system. Our proposal is a combination of energy management in an optimal way whether on the load or source side. The results showed that the source-side management system is efficient at all times to distribute the energy produced. Otherwise, the internal control of the different types of load is efficient during its launching during critical cases.

In this paper, we present a new hybrid approach multi-agents system and Case-Based Reasoning (CBR). We have designed a generic and scalable class diagram to develop complex multi-agent systems [3] for Decision Support System based on CBR to predict and anticipate an environmental risk. Our approach inherits from Model Driven Architecture (MDA [11]), which aims to design, develop and implement models of multi-agent systems that we build from AUML. The source code of the models is generated by an open source tool called AndroMDA [13]. The model and source code will be used to design and develop applications to implement and simulate multi-agent models for Management of Common Renewable Resources [4].

The vector control of induction motors provides a high performance independent control of torque and flux in similar way to the separate excitation DC machine. Nevertheless, the main disadvantages of this control type is the sensitivity to the machine parameters variation, like the stator and the rotor resistances. To avoid a such problematic, a high robust sliding mode controllers against uncertainties are replaced the classical PI controllers. This research presents an induction motor robust control facing a sudden rotor resistance variation at very low speed, the rotor flux and speed are observed by an extended Kalman filter. Simulation results show that the proposed control provide good performance dynamic characteristics under a large scale rotor resistance variation. All simulations have been realized in MATLAB/Simulink.

Morocco, through its policy of integrating renewable energy into its electricity consumption, has called to all parts of its territory containing a potential for green energy. Ksar Sghir is a coastal village in the North of Morocco with huge assets at the center of our article: annual wind speed profiles around 3 m/s and for tides, the annual average is capable of reaching 1.9 m/s. The total energy consumption of the village reaches 2.17 GWh/year.The main idea of this article will be to find a compromise between different types of energies: wind energy and tidal energy. If possible, add energy storage electrochemically and/or connect to the national grid.Load profiles, wind speed and marine current speed will be visualized on the HOMER software at first. Then, based on unit nominal production costs for these different types of energy, the optimal installation will be dimensioned in the programming environment offered by GAMS. For the hybrid facility found, an economic simulation will be done to study the scope of costs incurred over a period of 20 years.

At present, generating energy from renewable sources is an important topic and is attracting significant attention because of its many benefits. Recent technological developments have made generating renewable energy from various sources such as the solar, sun, wind, geothermal energy, With the continuous increase of grid-connected photovoltaic (PV), high-precision PV power prediction is increasingly important. Extant deterministic forecasting methods do not facilitate fully effective dispatching decisions or power grid risk analysis. To find the most influencing variables for PV power prediction, this paper proposes a model for predicting the output PV power, different combinations of weather variables were used to develop this model. The determination coefficient of the proposed model is 0.98501 with an RMSE value of 30.663. The proposed model was tested on new data, the results showed that the model works with a good preferment and that the prediction quality depends on the time of year with a determination coefficient of 0.9972, 0.9856, 0.9487 and 0.9942 for summer, autumn, winter and spring respectively.

Internet of Vehicle (IoV) is a keystone in establishing smart cities IoT applications. In such highly connected network, embedded wireless communication technologies are used to facilitate the different Vehicule-to-X communications. To do so, mainly two technologies are used (i) short range communication such as DSRC (Dedicated Short Range Communications), and (ii) cellular long-range communication including LTE (Long Term Evolution). Where DSRC can ensure the delay-sensitive safety application, LTE-based communications are in charge of ensuring the remaining applications. In this paper, we design a new real-time data exchange protocol in IoV, Our protocol called Efficient Real-Time protocol in IoV (ERTP-IoV). Involves both both DSRC and LTE technologies for short range and long-range communications, respectively. Simulations results, in real urban environments, will be presented later to evaluate the performance of our protocol, as compared to other existing protocols.

Random Forests are an ensemble learning method that refers to train individual classifiers and aggregates their predictors to produce one optimal predictive model. In this paper, we compare the accuracy metric of six Random Forest methods implemented in the ‘CARET package’ of the R language. We explore the Time Resolution Universe (HTRU2) dataset collected from the UCI dataset.

Supply Chain Management (SCM) is of increasing interest to companies facing strong competition, market globalization and rapid changes in information and communication technologies. This evolution has led to a rapid integration of new digital practices in this field. In the field of services, little research has addressed the issue of the SCM the company. According to [1] “service logistics is an approach that stabilizes and guarantees the continuity of flows: it is then oriented more towards the service provided than towards reducing traffic costs”.So, what does the digitization of the SCM of service companies look like today and what will be the future trends of this digitization? On the one hand, with the help of the literature review, we seek to identify the concept of SCM in services and its specificities, and then that of digitalization of service supply chains. On the other hand, we are attempting a prospective approach to current practices and the prospects for digitizing the SCM of service companies.

Smart Car is an automobile with an advanced driver assistance system that can provide a more enjoyable driving experience as well as active safety features. The aim of these systems is to ensure road safety and reduce the risk of road accidents. For these purposes, a driver assistance system must be context-sensitive by monitoring the car and its environment in real time besides sensing, analyzing, predicting and reacting according to the following contextual situation: the vehicle state, the driver state, and the physical environment surrounding them. This paper presents a state of the art of the research published in the last three years on context-based driver assistance systems. Simply put, the context-aware plays a critical role and the inclusion of contextual factors influence the intelligence level of the Smart Car. The proposed comparison which falls within the scope of the research addresses the context of the system (driver, vehicle, physical environment), the algorithms used (machine learning algorithm ML, deep-learning algorithm DL, HMM etc.), the technology used (internet of thing sensor, Smartphone sensor, Radar, Lidar…), and the performance provided. The first analysis indicates that machine learning algorithms are widely used in the suggested solutions in general and in the support vector machine (SVM) and the artificial neural network (ANN) in particular. Moreover, the given analysis shows that the monitoring of a complex driving context becomes possible with the emergence of IoT technology.

End-to-end deep reinforcement learning [1] algorithms used in autonomous car field and trained on lane-keeping task achieve good results in roads that don’t require decision making but cannot deal with situations where getting driving direction is mandatory like choosing to turn left or right in an upcoming crossroads, deciding when to leave a traffic circle or toward which path/destination to go. In this paper we introduce a new Deep Reinforcement Learning model that enable to integrate guidance commands at test time as a complementary input that indicate the right direction, that we call Deep Reinforcement Learning with guidance (DRLG), we apply the DRLG architecture on two algorithms, the asynchronous advantage actor-critic A3C and the Deep Deterministic Policy Gradient algorithm DDPG. For the training and experimentations of the new model, we adopt the CARLA virtual environment, a High-fidelity realistic driving simulator as a testbed since leading driving tests in the real world turns out to be neither safe nor affordable in term of materials and requirements. The results of testing show that DDPG and A3C with Guidance (DDPGG and A3CG) models succeed on their driving task through roads/roundabouts, by being appropriately responsive to the external commands, which allow to the autonomous car to follow the indicated route and take the right turns.

The existing throttle and brake control systems are built as Anti-braking systems with different modes of operation during normal and emergency conditions. Nevertheless, developing an intelligent system in an automated vehicle that supports seamless mobility of the elderly and the physically challenged in dense traffic is an uphill task. Hence, in this paper, a smart vehicle control system with adaptive throttle and brake control is propounded. The self-governing vehicle built using sensors and Raspberry Pi controller uses image processing techniques for taking decisions. The dynamic model controls the throttle valve using servo-motor based Proportional-Derivative (PD) Control. It uses high torque servo motors to pull the brake pedal to the anticipated level using automatic torque control. Correlation based algorithms that integrate brake and speed control are implemented using Firmware. The inherent intelligent functionality in the vehicle based on the sensed traffic offers better accuracy. The steady state error is minimized using Camera. A brake test is performed to examine the performance of the proposed model under loaded conditions of the support vehicle. It is seen that the proposed system outperforms the existing systems in terms of Stopping Time and Braking Torque.

Logistics and transport systems management faces several challenges due to the rapid growth of world trade in the last decades. Several tasks affect directly the quality of maritime logistics and port management systems. Among these tasks, container management plays critical role in the quality of freight service. Our paper aims to make a contribution to Big Data and Internet of Things (IoT) based systems by exploring how these technologies can lead to improvements in maritime container management. We explore several technologies and tools related to Big Data and IoT by comparing their characteristics. A multi-layer management and real-time monitoring system is described in order to meet the different needs of maritime container management. Each layer is detailed according to its functionalities and goals.

Adverse weather conditions are undesirable for drivers and can sometimes cause fatal accidents. In this article, we propose a driver assistance system, offering more secured routes as their weather conditions are more favorable compared to others.Specifically, we propose a system able to forecast meteorological parameters based on the ARIMA models. These parameters play an important role on the classification of the degree of road safety of the three selected routes which connect Tangier and Tetouan cities. The meteorological data for the three routes are extracted from the MERRA-2 Web Service (Modern retrospective analysis for research and applications, version 2).According to the prediction and classification results, the obtained ARIMA models are capable to assimilate the dynamics of meteorological data and produce important forecasts.

Nowadays, traditional data management tools can’t anymore manage the voluminous data which is generated by different domains, that’s why big data was born to remedy this kind of problem and it becomes very essential to many domains and without its support the task is very difficult to manage; one of those domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In the present paper, we first propose a method that detect anomalies in the road, and secondly we calculate the time spent in each road section in real time, and then we construct a base which will contain the estimated time spent in all sections on the road in real time, this base will provide a way to send to vehicles an accurate estimated time of arrival all along their way, not only this but the base will also provide a way to detect an accident or anomaly in a section on the road in real time.

Drowsiness is one of the leading causes of near-miss or real road accidents. Researchers have invested a considerable amount of effort identifying ways to the detect drowsiness state of drivers and alert them in a timely manner to avoid serious consequences. Recent works on drowsiness detection have been focused on proposing wearable solutions that can be portable and used by the driver with ease. Unfortunately, majority of these are difficult to use on a daily basis. In this paper, we propose a usable and wearable solution that tracks the user’s state of activeness using a smartwatch and gives them real-time feedback. Our proposed solution measures the Heart Rate Variability (HRV) coupled with Galvanic Skin Response (GSR) to detect whether the driver is drowsy behind the wheel or not. HRV measures the fluctuations between the heart beats whereas GSR measures the emotional arousal from skin’s sweat gland activity. An auditory feedback is provided to the driver if the HRV and GSR values are found below the expected thresholds. The system demonstrated an accuracy of 80%, precision of 97% and recall of 82%. Furthermore, we also conducted a usability study to assess the acceptance of the proposed application.

Autonomous vehicles will establish their predominance in the near future and humans will experience a hassle-free travel as these vehicles do not demand human intervention. Autonomy diminishes disasters that may happen due to driver’s negligence. Emergency Vehicles (EVs) play a vital role in saving lives. An unobstructed path is to be provided to them to ease the mobility of EVs along busy roads. In this paper, the EVs are identified using Deep Learning (DL) based algorithms. Though they are driven by Neural Networks (NNs), there are some situations in which they have to mimic a human. The ability to perceive and respond to EVs is addressed in this paper. Self-Driving Vehicles (SDVs) are to be incorporated with the knowledge of a fast approaching EV using algorithms like Convolutional Neural Network (CNN), Fast Region-based Convolutional Network (Fast R-CNN) and You Only Look Once (YOLO) algorithm. It is seen that YOLO offers better results.

Smart cities consist an assortment of electronic equipment applied by some applications, such as sensors in a transportation and system cameras in a monitoring system, and so on. Moreover, recent advances and the dynamics of the increase of the use of information and communication technologies (ICT) have contributed to the evolution of the supply chain and logistics sector. In indeed, the analysis of massive data (Big Data) coming from smart-products makes it possible to extract enormous values for the decision-making of strategic choice: commercial or technical. But this also causes research problems because of the speed of data transmission, the massive volume, and the non-homogeneous types of data. In the article, we answer the following problem: what are the possibilities to use Smart Logistics (SL) solutions in Smart Cities (SC)? The basic assumptions of the Smart-Logistics (SL) and Smart-Cities (SC) are characterized. In addition, this article also describes future directions in this promising area.

Nowadays, the adoption of smart city technologies in different contexts has improved individual’s quality of life. Besides, Cities and communities generate huge amount of data through a vast and growing network of connected technologies, cloud infrastructure, big data and internet of things applications. However, the use of these technologies raises several concerns regarding data privacy. Particularly, with the increasing number of actors (service providers, government, citizens….) involved in smart city environment, it becomes harder to protect citizen’s private information. Moreover, citizens become passive actors regarding the use, disclosure and exchange of their sensitive data. Motivated by this, we compare some of privacy preserving solutions suggested in smart cities ecosystems. From this study, we deduct that the major proposed works in the literature do not consider the concerns of the different stakeholders, especially the compliance with relevant privacy regulations. Thus, we make a comparison between the most known privacy laws followed by a case study applied in smart health sector.

Vehicular ad hoc network (VANET) is considered as the new generation of wireless ad-hoc network that change our lives. This technology aims to make our lives and roads safer via alert messages exchanged between local vehicles and the road side units (RSU). Unfortunately, the constraints caused by the high mobility, shared wireless medium and the absence of centralized security services offered by dedicated equipment makes the security of the VANET more problematic than any other type of network. Therefore, to ensure the security of this network, several security measures have been developed. Intrusion Detection Systems (IDS) is the most popular. In traditional networks, IDS has already proved its expertise in the detection of unauthorized entry and malicious activity. In vehicular ad hoc networks, IDS are in charge of analyzing incoming and outgoing packets to identify malicious signatures.This paper presents the different attacks lead against VANET. We give a brief overview of IDS and the different IDS techniques that are used to struggle against attacks. Also, we propose an IDS hybrid that minimizes the weak points of the clustered IDS based RSU.

The concept of Internet of Things is to connect heterogeneous devices; human-to-thing or thing-to-thing, where device can be a human, sensor, or theoretically anything that may send or receive information. Important decisions will be made based on the collected and analyzed information, so securing the exchanged information is a mandatory requirement in this process, also IoT has led to public security concerns, including personal privacy issues, threat of cyber-attacks, and organized crime. Having a secure system will help to ensure trust of system, and to avoid making wrong decisions that can be sometimes dangerous. In this paper, we will present a synthetic study on security concept in network layer for MANET, which is an example of smart environment. Then we will introduce our new protocol called SICAR6 that aims to enhance security requirements in mobile ad-hoc networks, especially availability, integrity and confidentiality without using traditional cryptography and hashing methods.

Mobile Ad hoc Network (MANET) consists of a set of nodes which stand randomly in the operating environment. Since nodes are then vulnerable to intrusion and attack without any pre-defined infrastructure and flexibility. Securing in this type of network is an important area. To tackle these security problems, current cryptography schemes cannot completely safeguard MANET in terms of new threats and vulnerabilities. By implementing Deep learning techniques in IDS, MANET will be able to adapt complex environments and allow the system to make decisions on intrusion while continuing to learn about their mobile environment. IDS represent the second line of defense against malevolent behavior to MANET since they monitor network activities in order to detect any malicious attempt performed by Intruders. Recently, more and more researchers applied deep neural networks (DNNs) to solve intrusion detection problems. The two major forms of DNN architectures, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), are commonly explored to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of CNN and RNN on the deep learning-based intrusion detection systems, aiming to give basic guidance for DNN selection in MANET.

For many years now, the available data on the internet became more and more complex, and its volume is increasing at a terrifying speed. Nowadays, filtering these data and selecting the ones which are the most suitable for our needs is the real challenge. Many researchers have proposed intelligent filtering systems that proved their efficiency and managed to recommend only a selection of items, able to meet the initial expectations. Most of these systems employ both collaborative and content-based techniques but within different approaches, often combined to provide the most suitable results. As input, they exploit textual and history data from users’ activities, taken either from their profiles or from other valuable resources. However, due to the complexity of data, the recommendation becomes more redundant, and the same elements get proposed repeatedly. In this paper, we present a literature review of traceability and interesting recommendation approaches, and we intend to discuss the benefits of using traceability and machine learning and how they can improve the recommendation and make it more efficient and reliable.

The rapid growth of the Internet has resulted in an exponential increase in the type and frequency of anomalies, and many of the well-known anomaly detection solutions are in place.However, each computer system collects information about the different tasks performed. Each piece of information is stored in specific files called log files. The log files consist of log messages or simply journal. A log message is what a computer system, software. Generate in response to some kind of stimulation. The information that has removed a log message and declares that the log message was generated is called log data. A common log message contains the timestamp, source, and data. The timestamp indicates the time at which the login message was created. The source is the system that created the log message and the data is the core of the log message. Unfortunately, this format is not a standard; A log message can be significantly different from one system to another. Classification is an important data mining technique with broad applications. It classifies data of various kinds.This paper has been carried out to make a performance evaluation of KNN, J48 and – Naive Bayes classification algorithm and sets out to make comparative evaluation of classifiers in the context of dataset.

Malware detection process is based principally on extracting data given to classifier model; those data are information about application’s behavior during its execution, permissions required by it or activities made in response to some commands. Which clearly make the features chosen and build as features vector highly influence the credibility of the model in classifying with high accuracy the unknown applications. For this reason, the research field gave a decent attention to resolve this problematic in malware detection models by improving the quality of features used in classification process, and performing feature selection processes in order to reduce dimensionality of features vectors, selecting most relevant, correlated and informative features and to eliminate redundant information. Many solutions were invented for this purpose using machine-learning algorithm to evaluate performance of classification using a specific set of features or by using filter feature selection algorithms that give a rank to each feature depending on its occurrence frequency, weight or its correlation. In this paper, we proposed an approach using CNN deep learning model for classifying and detecting android malwares as a solution for feature selection and redundancy problematic.

The biggest problem with recovering from cyberattacks is that security professionals rarely get the chance to deal with them immediately. So, using advanced intelligent techniques, we can defense systems against malware the moment it begins to download. For that reason, a new type of feature has been recently introduced for malware classification task, borrowing techniques from computer vision community called malware visualization technique. Malware classification goal is to know how they work, and then we can rapidly defend them especially in the case of zero-days attacks. In this paper, we adopt KNN algorithm to classify malwares based on their image visualization. So, a malware binary is converted to grayscale image. Then to extract similarities and dis-similarities from these images a GIST descriptor is computed. We used a database of 9339 samples of malwares belonging to 25 families. Our malware classifier reached a high score of 97%, which is very close to the results found in literature.

The intelligent filtering systems known as Recommender systems, are important tools meant to assist users in their choices and solve the issue of information overload, as they can predict and provide suggestions, intended to meet users’ interests and expectations. For this purpose, the recommendation process uses different data which is extracted from the users’ preferences and interests (User Profile) and the items’ characteristics. These systems employ the same techniques but different approaches that exploit textual and history data from users’ activities, taken either from their profiles or from other valuable resources and are often combined to provide the most suitable results. However, due to the increasing volume of data, the recommendation becomes more redundant and the same elements get proposed repeatedly. In this paper, we intend to discuss the benefits of using traceability and machine learning and how they can improve the recommendation and make it more efficient and reliable.

Before being smart, a construction should first be sustainable. If it is true that the notion of sustainable building is closely linked to the matter of environment and multidisciplinary approach, the question of comfort and energy saving remains crucial. The bioclimatic approach constitutes precisely a sustainable construction mode that revolves around the concern of energy and comfort. However, the use of this mode often leads to an additional cost which may be a significant inhibitor of using this sustainable approach. The purpose of this paper is to demonstrate that despite the implementation of regulations that encourage this construction method, the additional cost constraint cannot be addressed without the establishment of a financial governance dedicated to the sustainable building sector. By analyzing four experienced projects that were inspired by the bioclimatic approach, namely Al Karama, Jacaranda, Al Ouroud and the new city «Lakhyayta», we will explore what were the main sustainable practices used. Then we will discuss the question of additional coast and measures or arrangements that can be made for a financial governance specific to sustainable building.

Patients or elderly individuals’ surveillance is no more restricted to hospitals thanks to the advanced development of new technologies, where we have the capability to supervise their conditions remotely throughout medical surveillance systems that are based on the Internet of Things (IoT), multiagent systems (MAS) and artificial intelligence (AI). These computing paradigms can be brought together to establish an intelligent system that would focus on patient comfort and ultimately reduce costs, speed up controlling operations and finally enhance the diagnosis accuracy and precision. Hence, a smart home conception following specific architectures and using the appropriate technologies of medical surveillance would form an essential aspect for the smart healthcare paradigm. Accordingly, an optimal design and architecture for an intelligent home and an effective intelligent healthcare would consider a resilient balance of IoT, MAS and an adequate machine-learning model for human behavior analysis. In this paper, a new smart home design for medical surveillance is proposed in order to contribute to the expansion of the number of smart healthcare users and providers on a winner-winner relationship that promotes individuals comfort and prosperity and drives-up the Information Technology contributions and business.

Energy management is one of the most important challenges for humanity, and this requires primarily the evolution of people’s lifestyles. Today, the building sector represents the largest consumer of electrical energy in the world, as well as in Morocco with a rate of 25%. This energy consumption is expected to rise due to the significant increase in household equipment rates in HVAC facilities, especially air conditioners. So, reducing energy consumption in buildings is therefore a major economic and ecological challenge. This work has recently made a positive contribution to this issue by studying the effect of the use of overhangs and wings on the thermal comfort and energy performance of a typical Moroccan R+1 building in three different climates. This was achieved through the use of dynamic thermal simulation, which is performed by TRNSYS software. The results show that the use of overhangs and wings has made a positive contribution, firstly, to thermal comfort and, secondly, to the energy performance of the studied building in Marrakech, Casablanca and Tangier.

In this study, the finite volume method is combined with the discrete ordinate method to study numerically the natural convection coupled to surface radiation in a square cavity with an elliptic inner cylinder maintained at a high temperature $$ 299\;{\text{K}} \le T_{H} \le 341\;{\text{K}} $$. The cavity is filled with air cooled by a single vertical wall maintained at a cold temperature $$ T_{C} = 293\;{\text{K}} $$. The hydrodynamic and thermal behaviors of the fluid and the convective and radiative heat transfer are investigated for a several geometric parameters of the elliptical cylinder. These parameters are the inclination angle $$ \left| \phi \right| $$ ≤ 90° of the elliptic body and the eccentricity $$ 0 \le \xi \le 0.98 $$. Effect of the Rayleigh number and emissivities of both the inner and the outer surfaces are also investigated. The numerical work is carried out using an in-house CFD code written in FORTRAN. It is found that the geometric parameters of the inner elliptic cylinder have remarkable effects on the streamlines, isotherms and the convective and radiative heat transfer in the enclosure. These effects are generally more dependent on the Rayleigh number for the convective heat transfer than for the radiative one.

Care has advanced in a significant manner in the most recent decade by differentiating modes and practices, which has prompted an appearance in savvy wellbeing. Be that as it may, ventures that address wellbeing in brilliant homes are still in the test organize with blended and unpromising outcomes, as they are still gone up against with the truth of offering understanding information to outsiders. The reason for this work is to anticipate the wellbeing status of at least one people living in a savvy home utilizing AI calculation [1] and depending on information gathered through the sensors introduced in the keen home. what’s more, this to avoid local mishaps.

Improving sanitation in future smart cities required to use a constructing wastewater treatment systems based on a sustainable approach which can minimize environmental problems and facilitates utilization of the resources in wastewater. Constructed Wetlands are considered as one of the natural systems which proved their efficiency to treat wastewater in many areas of the world. Compared to other wastewater treatment technologies, treatment wetlands have low operation and maintenance requirements. The aim of this study is to evaluate the performance of Subsurface-vertical flow constructed wetland system (VFCW) to treat wastewater in Moroccan conditions. To do this, a pilot scale was designed and built to treat domestic wastewater and his performance was investigated. This filter was planted by Chrosopogon zizanioides L. Several water quality parameters including BOD5, COD, TSS, NH4+, NO3− and PO43− were monitored. This filter showed good performance (≥80%) to remove organic pollution (BOD5, COD, TSS). Removal rates of ammonium and orthophosphates were about 50% and 70%, respectively. The effluent quality was evaluated in accordance with the standard of the rejection limit value adopted by Morocco.

The treatment of water for human consumption generates a significant amount of the by-product found in the literature as hydroxide sludge. Their chemical composition, depending on the periods of collection of the raw water to be treated and the type of treatment to be carried out. The objective of our work is the optimization of the sludge treatment unit by centrifugation. Liquid sludge from settler purging and filter wash water is discharged to the thickener to increase its ability to dehydrate, and the sludge is extracted towards the dehydration stage.The analysis was carried out on the dryness of the dehydrated sludge which represents the dry matter, by carrying the sludge sample in the oven at 105 °C until constant masses were obtained, on the one hand, and on the other on the operating parameters. The results of our study highlight the excellent performance achieved with the coagulant FeCl3 better than that Al2(SO4)3 for the elimination of turbidity and oxydability under optimal conditions. On the other hand, the quality of dehydrated sludge (dryness) varies from day to day, due to the variation in the raw water quality to be treated. Thus, well-concentrated sludge reduce polymer consumption.

Currently, gray water reuse after treatment is emerging as an essential instrument for effective management of water because it promotes preservation of freshwater sources, potentially reduces pollutants in the environment. Appropriate technologies and practices for wastewater treatment for reuse are one way to reduce risks to public health where direct wastewater use is prevalent. One new technology which holds interested of researchers today is called Multi-Soil-Layering system (MSL). This new system is recognized to be efficient, economic, ecological and sustainable technic to treat wastewater. In order to prove his performance in Moroccan conditions, a pilot scale of Vertical Multi-Soil-Layering (V-MSL) was designed and built to treat domestic wastewater (instead of gray water alone) and his performance was investigated. The main treatment performance results showed the high removal rates for the organic matters and nutrients. The quality of the treated wastewater was evaluated according to Moroccan Reject Limit Value (RLV).

In order to make efficient decisions in agriculture, it is imperative to analyse the weather data collected from various sources. These data are generated by automated weather stations. Unfortunately, weather observations may be missing or altered since weather stations may be stopped for maintenance or became out of order. Thus, it would affect significantly the process of data analysis. The purpose of this study is to estimate those missing values by using interpolation methods and others. We study the effectiveness of each method and compare them on different weather attributes. The methods were applied on different patterns of missing values and outliers. The experimental results prove that the two methods based on geographical proximity are performing better.

Leishmaniasis is parasitic infestations common to humans and to certain animals (anthropozoonoses). It constitutes a real public health problem despite the progress of research.In Morocco, as in most countries around the Mediterranean, and despite its adherence to the leishmaniasis control program put in place by the WHO, leishmaniases in these different forms are endemic in many areas and continue to pose a significant public health problem.The purpose of this study is to create a database containing geographic parameters (prefectures) and health data (incidence of leishmaniasis) in a geographic information system (GIS). This database was then exploited by spatio-thematic analyzes, which allows the identification of the places at risk. This spatial representation known as the risk map is essential in the careful identification of high-risk prefectures.This study shows that cartography is an important tool that helps to eliminate this disease in endemic foci, to anticipate the emergence of possible epidemics and to identify possible “emerging” risk factors that can create epidemiological news.

In Morocco, leishmaniases are endemic diseases that constitute a major public health threat. During recent years in Sefrou Province, It has been found that the number of leishmaniasis cases is continuously increasing. This study concerns the average incidence of leishmaniasis cases recorded from 2007 to 2010 and presents the role of the mapping tool in the epidemiological study in order to determine the area’s most affected of the Province of Sefrou and to draw up a map of breakdown of the leishmaniasis cases by altitude class. The database has contained the geographic, environmental parameters. The results are analyzed by Qgis 2.18 software which is open source software and by SPSS software (version 20). The commune of Tazouta represents a high average incidence (0.165%). Cartographic analysis of spatial relation among average incidence of leishmaniasis and environmental factor of different communes of Sefrou Province reveled the strong correlation between distribution of cases and altitude parameter. These results well be useful in enlightening health authorities to develop screening, treatment and control strategies to reduce the incidence rate of the disease.

The objective of this work is to monitor the bacteriological quality of urban wastewater from the Bouishak wadi in the city of Meknes (Morocco). In this study, bacteriological parameters has monitored at three selected upstream-downstream sites (B1, B2 and B3). The sampling has carried out monthly between 10 and 11 h during a period from January to December 2017. The results obtained show that the average concentrations of the various parameters sought are. 9.88 106 for the total germs; 8.01 106 for total coliforms; 5.92 106 for fecal coliforms; 4.98 106 for fecal streptococci; 1.54 104 for anaerobic sulphite-reducing bacteria and 2.46 105for Escherichia coli (these concentrations are in colony-forming units per 100 ml) and pathogenic germs such as salmonella and anemic vibrio. The genus Salmonella has detected in July and August at station B1. Their presence at this hot time could be due to human activities. For cholera vibrio, no cases were isolated in the waters of the selected sites. However, this contamination exceeds the standards of Moroccan wastewater discharge. The origin and degree of bacterial contamination are due to the human activities and climatic conditions of the region. In view of the above, the urban waters of Bouishak Wadi are of poor quality.

In the region of Meknes, the water of Boufekrane River is used for agricultural irrigation. This important river receives the discharge of domestic wastewater from the village of Boufekrane and the city of Meknes.The objective of this work is to evaluate the degree of parasitic contamination of wastewater discharging directly into the Boufekrane River and its seasonal fluctuation. For this purpose, a parasitological study is carried out in the collector of “Agdal” district, located in the urban center of the city of Meknes. Samples were taken once a month at the same daytime and processed by Arther Fizerald Fox’s flotation concentration method.In this study, twenty-one species have been identified. The results showed an average parasite load of 98.42 eggs/2 L with a maximum of 312 eggs/2 L and a minimum of 16 eggs/2 L. The results showed the presence of a variation of the parasite load during the year with a maximum observed in March and a minimum in October. The study also showed that nematodes constituted 62,32% of parasite eggs followed by protozoa (21,42%) followed by cestodes (14,65%) and finally trematodes (1,61%).

Despite the recognized state of conservation of RAMSAR sites, the Aguelmam Sidi Ali wetland, especially the pastoral stratum, is frequented by users and over-staffed livestock, thus affecting the quality of its surface waters and therefore the ecological value of the site.In order to evaluate and spatialize the extent of the impact of pastoral over-exploitation of the site, this study aims to monitor the spatiotemporal variation of the water quality of the Aguelmam Sidi Ali pastoral stratum.The assessment of the quality of water resources of the pastoral stratum was monitored monthly at 7 sampling stations, during a hydrobiological cycle from January to December 2017, using physicochemical and bacteriological parameters. A typology of the water resources quality of the pastoral stratum have been elaborated according to the dynamic water quality index (DWQI) and the significant statistics tests.Indeed, the use and the state of pollution of the water resources could identify three spatial subsets. The first is of poor quality with a WQI < 50, the second is of average quality with a WQI of 50 to 70 and the third is of good quality with a WQI > 70. These results confirm that overgrazing and the anarchic use of the wetland by users have a negative impact on the ecological functions for which the site.To overcome these challenges, measures must be taken as a partnership approach with users, incorporating innovative ideas such as mobile dry toilets could protect the site and improve the daily lives of the population.

Water allocation planning in an equitable and sustainable way is intrinsically complex. This study proposes a water resource allocation system using an integrated Soil and Water Assessment Tool and Water Evaluation and Planning tool (SWAT-WEAP) model for hydrological simulation and prognostic scenarios sustainability prediction. The study explores the use of Digital Elevation Model (DEM), soil and land raster image in deriving physiographic information for land degradation impact assessment, quantification of optimal water allocation and generation of minimum ecosystem water requirement. Consequently, the SWAT quantifies the catchment water yield before been allocated optimally based on percentage dependable flow rates of 70% and 85% reliability flow regime at Makurdi, Nigeria discharge station. The WEAP model assesses the water resources utilization following scenarios adaptation by riparian users. Both models performed satisfactorily for streamflow and water yield prediction and resource sharing both in the calibration and validation phases with a correlation coefficient (R2) of 0.57–0.74 and root squared error (RSR) of 0.66–0.82. The results show how drainage network, channel length, drainage boundary, slope, and sub-catchment geometric properties demonstrate Geographic Information Systems (GIS) utility in morphoclimatic impacts assessment as a data management, scenario analysis, and decision support tool in water management for the Lower Benue River Basin, Nigeria. Planners and decision-makers need to consider several integrated plans as alternatives to adapting to climate change impacts and anthropogenic human activities in resolving the unmet demands.