Although cloud computing can help companies accomplish more by breaking the physical bonds between an IT infrastructure and its users, heightened security threats must be overcome in order to benefit fully from this new computing paradigm that offers an innovative business model for organizations to adopt IT without upfront investment. Despite the potential gains achieved from the cloud computing, the model security is still questionable which impacts the cloud model adoption. The security problem becomes more complicated under the cloud model as new dimensions have entered into the problem scope related to the model architecture, multi-tenancy, elasticity, and layers dependency stack. In this paper we introduce a detailed analysis of the cloud security problem. We investigated the problem from the cloud architecture perspective, the cloud offered characteristics perspective, the cloud stakeholders' perspective, and the cloud service delivery models perspective. Based on this analysis we derive a detailed specification of the cloud security problem and key features that should be covered by any proposed security solution.

Within the recent years, mobile technologies have made major progress in its de-velopment. They have become an integral part of our daily life. Therefore, the big interest of using mobile technology in education process is comprehensible. A new technology was born called mobile learning (m-learning). The main benefits of this technology is sharing three important dimensions of learning resources namely learning contents, learning collaborators and learning services. In univer-sity courses, different forms of collaborative and peer learning are progressively used in order to assist students meet an assortment of learning outcomes. There-fore, the use of mobile technologies in education has become a crucial topic for research. Because mobile phone is the first choice of everyone to communicate, our research is focused on the use of smartphones as learning resources. In this paper, we propose an implementation of a new mobile application based peer-to-peer learning system named "EachOther" and a scenario for its usage applied on students of an IT course at the High School of Technology of Fez (ESTF). The aim of this work consists of the conception, the development and the implementa-tion of the mobile application based on Android system to support collaborative learning, by applying an object-oriented modeling language which is Unified Modeling Language (UML).

With the advent of low-power wireless sensor networks, a wealth of new applications at the interface of the real and digital worlds is emerging. A distributed computing platform that can measure properties of the real world, formulate intelligent inferences, and instrument responses, requires strong foundations in distributed computing, artificial intelligence, databases, control theory, and security. Before these intelligent systems can be deployed in critical infrastructures such as emergency rooms and powerplants, the security properties of sensors must be fully understood. Existing wisdom has been to apply the traditional security models and techniques to sensor networks. However, sensor networks are not traditional computing devices, and as a result, existing security models and methods are ill suited. In this position paper, we take the first steps towards producing a comprehensive security model that is tailored for sensor networks. Incorporating work from Internet security, ubiquitous computing, and distributed systems, we outline security properties that must be considered when designing a secure sensor network. We propose challenges for sensor networks - security obstacles that, when overcome, will move us closer to decreasing the divide between computers and the physical world.

Biomedical images, whether acquired by techniques such as magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, X-ray, or other methods, are commonly obtained and permanently stored for diagnostic purposes. Therefore, leveraging this large number of images has become essential for the development of intelligent medical diagnostic systems. In this work, we propose a new biomedical image recognition in two stages: the first stage is to introduce a new image feature extraction technique using quaternion Legendre orthogonal moments (QLOMs) to extract features from biomedical images. The second stage is to use radial basis function (RBF) neural networks for image classification to know the type of disease. To evaluate our computer-aided medical diagnosis system, we present a series of experiments were conducted. Based on the results of a comparative study with recent approaches, we conclude that our method is very promising for the detection and recognition of dangerous diseases.

The recent emergence of conversational Artificial Intelligence (AI) agents has profoundly transformed learning and teaching practices in higher education. These tools offer multiple advantages, ranging from cognitive assistance to enhanced student autonomy and efficiency. However, their actual impact on academic performance remains understudied, and the existing research often presents contradictory findings. To address this gap, the present study is the first to employ a Genetic Algorithm (GA) and Multi-Layer Perceptron Neural Networks (MLPNNs) to evaluate the influence of Generative AI Tools (GAITs) on students’ academic outcomes. A structured questionnaire was administered to 294 students from three Moroccan engineering schools in order to collect data on their use of these tools. An initial attempt to predict their grades using a statistical approach showed that familiarity with GAITs contributed positively to academic performance but had limited accuracy (39%), highlighting the need for more robust methods. Therefore, a hybrid model based on neural networks optimized with a GA was developed to better capture the complex relationships between the explanatory variables and academic performance. The results indicate that the GAIT-related variables considered in this study, taken in isolation, have a limited predictive capacity for students’ academic outcomes. This finding suggests that the available data does not capture the full complexity of the factors shaping academic success in contexts involving GAITs use.