Academic advising of students is a task that requires a lot of time, expertise, and intellectual investments from the academic advisor. In order to help students to find a suitable decision in short time and effort, this project implements an Intelligent Algorithm in order to design an expert application on smart phone. The proposed system was implemented and tested for validation with real data that collected from valid students. The experimental results showed that a system have an average root mean square error of 6.64 % and thus can be used successfully to identify the possibility that registering for the course is a correct decision.

Activity recognition has become of great importance in many fields especially in fitness monitoring, health and elder care by offering the opportunity for large amount of applications which recognize human's daily life activities. Human activity recognition (HAR) was not only limited on health care field or monitoring sports, but it also started to emerge in the religious branch and monitor people behavior while performing their religious activity like praying. The prevalence of smart phones in our society with their ever growing sensing power has opened the door for more sophisticated data mining applications which takes the raw sensor data as input and classify the motion activity performed. The main sensor used in performing activity recognition is the accelerometer. This paper presents a framework for activity recognition using smart phone sensors to recognize simple daily activities and then aggregate these simple activities (walking, standing, sitting,...) to recognize a more complex one which is prayer. Features extracted from raw sensor data are used to train and test supervised machine learning algorithms.

Diabetes mellitus is one of the most common chronic diseases. The number of cases of diabetes in the world is likely to increase more than two fold in the next 30 years; from 115 million in 2000 to 284 million in 2030. In type I diabetes, the disease is caused by the failure of the pancreas to produce a sufficient amount of insulin which leads to an uncontrolled increase in blood glucose unless the patient administers insulin, typically by subcutaneous injection. This work is concerned with managing diabetic patients by trying to predict their glucose levels in the near future (30 minutes) on the basis of the current levels. The goal of this paper is to determine the future blood glucose value of a diabetic patient using an artificial neural network. Unlike other approaches, which involve questioning the patients, feature extraction procedures were implemented (diabetic dynamic model) on diabetic blood glucose time series, in order to extract a knowledge (how patient blood glucose level will change a according to the external food intake and any human day activities). An artificial neural network was then trained using these features in order to predict the future value of blood glucose level with an accepted accuracy.

The application of Artificial Intelligence (AI) in agriculture, particularly within the date fruit industry, is advancing rapidly. Traditional machine learning methods, such as support vector machines (SVM), artificial neural networks (ANN), and logistic regression, have been employed to classify dates based on morphological features like color, texture, and shape. While effective, these approaches often lack the flexibility and comprehensive quality control needed in modern agricultural practices. To address these limitations, the SmartDate system represents a significant technological advancement by integrating deep learning with genetic algorithms and reinforcement learning. This AI-driven system not only excels in date fruit classification but also predicts expiration dates, filling a crucial gap in existing solutions. SmartDate leverages multispectral and hyperspectral imaging, coupled with Visible-Near-Infrared (VisNIR) spectral sensors, to assess key quality indicators such as moisture content, sugar levels, firmness, and internal defects. This allows for a more thorough evaluation of fruit quality compared to conventional methods. Moreover, the inclusion of reinforcement learning enables SmartDate to adapt in real-time to production environment changes, optimizing sorting accuracy and ensuring that only premium quality dates reach the market. Continuous enhancement through genetic algorithms further boosts the system's reliability and efficiency. In summary, SmartDate surpasses previous AI-based classification systems by integrating advanced techniques with adaptive capabilities. This innovative system improves sorting accuracy, reduces waste, and provides critical insights into the shelf life of dates, setting a new standard for agricultural efficiency and effectiveness.

Choosing the right cloud service provider and cloud services according to cloud user requirements is a complex task, due to the increase in the number of cloud providers. Multi-criteria decision making (MCDM) is used where selecting, comparing and ranking of different Criteria of multiple alternatives is a requirement. Cloud providers present all kinds of cloud services, but they vary in quality, costs, customer experience, and service delivery. One of the problems with current cloud computing is the commitment of cloud service providers to the Service Level Agreement (SLA) with cloud providers users, but there are little verification frameworks to verify that cloud service providers provide cloud services according to their commitment. In the current literature, there is a lack of assessment models that provide the true status of cloud providers to cloud users. Therefore, this paper focuses on designing a new technique for selecting cloud service in the fuzzy environment by relying on Fuzzy AHP-TOPSIS by integrating the fuzzy analytical hierarchy process (AHP) concepts and the ideal solution (TOPSIS) concepts. The structure of the cloud service selection problem and the weights of the standards are determined by The Fuzzy AHP technique concepts. Fuzzy TOPSIS concepts used to arrange the final solution. The effectiveness and efficiency of the proposed technique is clarified by increasing accuracy by almost 15% through implementation and case studies.