IoT devices controlled using mobile apps

Internet of Things (IoT) shows no sign of slowing down, particularly in the field of mobile applications because many IoT devices can be controlled through an application on a smartphone. There is a clear intersection between the Internet of Things (IoT) and artificial intelligence (AI). IoT allows you to connect machines and use the data generated from these machines. Artificial intelligence is the simulation of intelligent behavior in different kinds of machines. Leading manufacturers like Samsung and Apple obviously participate in the rise of artificial intelligence. The implementation of artificial intelligence (AI) and Internet of Things within terminals with touch screen is spreading at lightning speed in smartphones. With the advantage of detecting objects in front of the camera of lowering energy consumption and better guaranteeing data security than the traditional approach in the cloud. In this paper, authors proposed and present a home automation system to connect artificial intelligence (AI) and internet of things (IoT) controlled with a smartphone. The IoT system proposed allow any user to manage his house on site or remotely to fight against any intrusion or other natural disasters (Wind, Erosion, etc ...) that can cause considerable damage. This solution based Raspberry Pi technology, consist to manage and monitor a home remotely without human intervention by automating the entire house

Optimizing olive disease classification through transfer learning with unmanned aerial vehicle imagery

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

Machine learning for real-time prediction of complications induced by flexible uretero-renoscopy with laser lithotripsy

It is not always easy to predict the outcome of a surgery. Peculiarly, when talking about the risks associated to a given intervention or the possible complications that it may bring about. Thus, predicting those potential complications that may arise during or after a surgery will help minimize risks and prevent failures to the greatest extent possible. Therefore, the objectif of this article is to propose an intelligent system based on machine learning, allowing predicting the complications related to a flexible uretero-renoscopy with laser lithotripsy for the treatment of kidney stones. The proposed method achieved accuracy with 100% for training and, 94.33% for testing in hard voting, 100% for testing and 95.38% for training in soft voting, with only ten optimal features. Additionally, we were able to evaluted the machine learning model by examining the most significant features using the shpley additive explanations (SHAP) feature importance plot, dependency plot, summary plot, and partial dependency plots

Silicon Sheets By Redox Assisted Chemical Exfoliation

In this paper, we report the direct chemical synthesis of silicon sheets in gram-scale quantities by chemical exfoliation of pre-processed calcium di-silicide (CaSi2). We have used a combination of X-ray photoelectron spectroscopy, transmission electron microscopy and Energy-dispersive X-ray spectroscopy to characterize the obtained silicon sheets. We found that the clean and crystalline silicon sheets show a 2-dimensional hexagonal graphitic structure.Comment: Accepted in J. Phys.: Condens. Matte

APPROCHE MDA POUR AUTOMATISER LA GENERATION DE CODE NATIF POUR LES APPLICATIONS MOBILES MULTIPLATEFORMES

The industry of mobile application development nowadays lives a non-stop growth, due to the intensive use of this latter in mobile devices, most of these mobile applications works under Android, iOS and Windows Phone as operating systems.Nonetheless, the development of applications designed for mobile platforms requires more worries such as code efficiency, interaction with peripherals, as well as the speed of market invasion. Since Model-Driven Engineering (MDE) combined with UML, as already adopted in software engineering, could provide abstraction and automation for mobile software developers. To support this, appropriate tools and approaches are neededThis thesis project presents an MDE approach for the development of mobile applications, which includes modeling with a dedicated language, UML modeling and code generation to facilitate and accelerate the development of mobile applications.This thesis is divided into four main parts. In the first part we studied the different mobile development approaches and we proposed a decision-making framework to evaluate the different approaches to the technical needs of the application to be developed.In the second part, we propose an approach based on a dedicated language called DSL GUI, in order to model the graphical interfaces of the web based and mobile applications based on the components, then we generated the source code for Android and the JSF Framework with the Primefaces components. In the third part, we have extended our meta-model based on a dedicated language, presented in the previous section, in order to model the mobile applications taking into account the native functionalities and the different characteristics of a mobile application. Afterwards, we suggested a set of transformations to target platforms (Android, Windows phone and iOS), and we generated the code targeting these three platforms.In the last part we proposed a pragmatic approach by combining the dedicated language and the UML in order to generate a mobile application that respects the layered architecture.L'industrie du développement des applications mobiles ne cesse de croître en raison de l'utilisation intensive de ces dernières dans les appareils mobiles, la plupart d'entre elles fonctionnent sous les systèmes d'exploitation Android, iOS et Windows Phone. Cependant, le développement des applications conçues pour les plateformes mobiles exige plus de soucis tel que l'efficacité du code, l'interaction avec les périphériques, ainsi que la rapidité d'envahissement du marché. L’Ingénierie Des Modèles (IDM) ou Model-Driven Engineering (MDE) combiné avec UML, comme cela a été déjà adopté dans le génie logiciel, pourrait fournir une abstraction et une automatisation pour les développeurs de logiciels mobiles. Pour appuyer cela, des outils et des approches adéquates sont nécessaires. Ce projet de thèse présente une approche MDE pour le développement des applications mobiles, qui inclut la modélisation avec un langage dédié, la modélisation UML et la génération de code afin de faciliter et d'accélérer le développement des applications mobiles. Cette thèse est découpée en quatre parties principales. Dans la première partie nous avons étudié les différentes approches de développement mobiles, et nous avons proposé un cadre décisionnel permettant d’évaluer les différentes approches vis-à-vis des besoins techniques de l’application à développer. Dans la deuxième partie nous avons proposé une approche basée sur un langage dédié nommée GUI DSL, afin de modéliser les interfaces graphiques des applications web et mobiles basées sur les composants, ensuite, nous avons généré le code source pour Android et le Framework JSF avec les composants Primefaces. Dans la troisième partie, nous avons étendu notre méta-modèle basé sur le langage dédié, présenté dans la partie précédente et nous l’avons nommé Mobile DSL, afin de modéliser les applications mobiles en tenant compte des fonctionnalités natives et les différentes caractéristiques d’une application mobile, ensuite, nous avons proposé un ensemble de transformations vers les plateformes cibles (Android, Windows phone et iOS), et nous avons généré le code ciblant ces trois plateformes. Dans la dernière partie nous avons proposé une approche pragmatique en combinant le langage dédié et le langage UML afin de générer une application mobile qui respecte l’architecture en couche

MDA Approach for Designing and Developing Data Warehouses: A Systematic Review & Proposal

A data warehouse (DW) is a vast repository of data that facilitates decision-making for businesses and companies. This concept dates back to the 1980s and it has been widely accepted. One of the key points for the success of the process of data warehousing lies in the definition of the warehouse model depending on data sources and analysis needs. Once the data warehouse is designed, the content and structure of the data sources, as well as the requirements analysis are required to evolve, therefore, an evolution of the model must take place (diagram and data). In this context, several approaches have been developed to design and implement data warehouses. Nevertheless, there is no standard process that deals with designing all of the data warehouse layers, also, there is no software that encompasses this type of problem. In general, the majority of these approaches focus on a particular aspect of data warehouse such as data storage, ETL process, OLAP, reporting, etc, and does not cover its entire lifecycle. A Model-Driven Architecture (MDA) is a standard approach, its aims to support all phases of software manufacturing by promoting the use of models and the transformations between them. Moreover, this approach aims to automate the process of software engineering, thereby decreasing the cost of software development and enhancing its productivity. In this study, we present a systematic review of various works on the data warehouse design methods. We compare and discuss these works according to the criteria that seem relevant for this issue. We present a new design approach for multidimensional schemas construction from relational models using MDA techniques, we also develop the resulting research perspectives

Comparative Study of Multiple CNN Models for Classification of 23 Skin Diseases

Cutaneous disorders are one of the most common burdens world-wide, that affects 30% to 70% of individuals. Despite its prevalence, skin disease diagnosis is highly difficult due to several influencing visual clues, such as the complexities of skin texture, the location of the lesion, and presence of hair. Over 1500 identified skin disorders, ranging from infectious disorders and benign tumors to severe inflammatory diseases and malignant tumors, that often have a major effect on the quality of life. In this paper, several deep CNN architectures are proposed, exploring the potential of Deep Learning trained on “DermNet” dataset for the diagnosis of 23 type of skin diseases. These architectures are compared in order to choose the most performed one. Our approach shows that DenseNet was the most performed one for the skin disease classification using DermNet Dataset with a Top-1 accuracy of 68.97% and Top-5 accuracy of 89.05%

Modèle conceptuel de l’enseignement à distance : une revue de littérature

L\u27apprentissage à distance est une alternative apparente aux méthodes traditionnelles de formation dans l’enseignement supérieur. Différentes méthodologies ont été recommandées pour l\u27apprentissage à distance, allant d\u27une approche didactique à une procédure d\u27apprentissage par problèmes. De nombreuses modèles conceptuel et méthodes d\u27évaluation des cours à distance ont été proposées pour fournir une éducation aux étudiants et aux professionnels non accessibles par les méthodes traditionnelles. Les applications d\u27apprentissage à distance manquent encore de l\u27appui d\u27un cadre théorique solide et ne sont évaluées que dans une mesure limitée. Ce papier présente une revue de littérature sur les différents modèles proposées pour un apprentissage à distance efficace en se basant sur des concepts d’intelligence artificiel

Porting Mobile Apps from iOS to Android: A Practical Experience

The recent rise of smartphones has triggered a revolution in mobile development. As a result of this incremental mobile innovation, new software engineering techniques, software documentation, and tools adapted to the mobile platform remain essential in order to help developers to better understand, analyze, and bootstrap porting mobile applications. In this paper, the authors propose a model-driven reverse-engineering approach based on static analysis, which describes a semantic metamodel of the iOS mobile application and extract design information (such as user interfaces, activity diagram, entities, framework and library dependencies) in order to generate the functional specification documentation and the Android UI skeleton. Thus, aiding the project team, who has in charge porting the app to another mobile platform, to agree upon a consensus on what has to be implemented and safe development cost by auto generating the Android UI skeleton project. To experiment this approach, the authors have implemented a tool called iSpecSnapshot. Moreover, they evaluate the performance of iSpecSnapshot by an experiment involving iOS applications that are ported to Android platform