Prof. Dr. Eng. Lamiaa Fattouh Ibrahim

Nowadays, the use of e-learning techniques and methods is a very important challenge due to the
importance of digital transformation to all countries. Firstly, the spread of the COVID-19 virus all over the world.
Secondly, all students need to study their courses remotely from home to reduce the communication with others to
save their life. All teachers need to engage their students effectively to study an online course, get more knowledge
and high results at the end of these courses. Data mining is the best tool used to find a hidden pattern. We used an
educational data mining tool to help teachers find the pros and cons of using an e-learning course with their
students. We need to classify students on these online courses according to their ability to understand materials and
quizzes, or assessment methods of the course, by making adaptive e-learning courses. In this paper, we will show
the importance of using adaptive e-learning courses and the challenges faced by authors to build these systems, and
we will list the different methods used with adaptive learning like gamification, brain-hex models, facial emotions,
and we will also list a survey about other authors' techniques and methods used to find the student's learner style.
We build a new proposed model of ILOs(Intended Learning Outcomes) adaptive learning with the emotion-based
system to let the system find the student's learning style and build the material according to their skills and
knowledge outcomes from the course and engage the use of facial emotion while taking the quiz to predict the
student's results and the topics he/she needs to study more via our system to achieve high grades and knowledge.
Our system finds that the visual students have the highest grades with 75%, followed by kinesthetic with 70% and
the lowest grades in auditory with 50%.

Abstract Artificial intelligence (AI) provides opportunities to improve the effectiveness of e-learning by increasing students' engagement. Adaptive e-learning uses AI to support individual learners by responding to their different learning needs which can be determined by analyzing their navigation history of e-learning systems using data mining methods. Educational data mining (EDM) discovers new patterns of learning and teaching to facilitate the process of decision-making to serve education improvement. Gamification is another way of increasing students' engagement by using game elements in a nongame context. In this paper, the gamification technique and EDM methods were used in combination with adaptive learning to increase the students' engagement and learning performance. An adaptive gamified learning system (AGLS) was developed which combines gamification, classification, and adaptation techniques to increase the effectiveness of e-learning. This paper studies the impact of gamification and adaptive gamification on the effectiveness of e-learning through increasing students' engagement and learning performance. AGLS was applied to the data structure course. Results showed that adaptive gamification has a positive effect on students' engagement and learning performance compared to just gamification.

In dense indoor areas, high numbers of people use their smartphones and tablets to share or download pictures, videos, or data. The heterogeneous network (HetNet) solves the problems caused by the explosion of data generated by smartphones and tablets. Heterogeneous networks use a mix of Relay, Femtocell, Pico, and Macro base stations to improve spectral efficiency per unit area. Operators wish to know how to upgrade existing networks and how to design new ones. This subject has become hot in the industry. In this paper, we presented the architecture of heterogeneous networks. The parameters affecting the heterogeneous networks topology plan are discussed. Moreover, a comparison of existing solutions that consider the problems of base station layout planning is presented. Finally, a road map is given to point out to the main future directions of researches on the topological design of dense area heterogeneous mobile networks.

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This article deals with social infrastructure planning problems in urban city. Each facility must serve minimum pre-specified level of demand. The objective is to minimize the distance traveled by users to reach the facilities this means also to maximize the accessibility to facilities. A location model that captures the above features is formulated and different solution methods are tested. Clustering in spatial data mining is to group similar objects based on their connectivity, distance, or their relative density in space. In real word, there exist many physical obstacles such as rivers, lakes, highways and mountains, and their presence may affect the result of clustering significantly. In this paper, we study the problem of clustering in the presence of obstacles to solve location of public service facility problem. In this paper, CSPOD-DBSCAN algorithm (Clustering with short path Obstructed Distance - Density- Based Spatial Clustering of Applications with Noise) is developed in the spirit of DBSCAN clustering algorithms. This algorithm is Density-based clustering algorithm using Dijkstra algorithm to calculate obstructed short path distance. The application of this algorithm is illustrated through a case study involving the location of schools in the districts of Mecca in Saudi Arabia.

Clustering spatial data is a well-known problem that has been extensively studied. In the real world, there are many physical obstacles such as rivers, lakes, highways, and mountains, whose presence may substantially affect the clustering result. Although many methods have been proposed in previous works, very few have considered physical obstacles and interlinking bridges. Taking these constraints into account during the clustering process is costly, yet modeling the constraints is paramount for good performance. Owing to saturation in existing telephone networks and the ever increasing demand for wire and wireless services, telecommunication engineers are looking at technologies that can deliver sites and satisfy the demand and level of service constraints in an area with and without obstacles. In this paper, we study the problem of clustering in the presence of obstacles to solve the network planning problem. As such, we modified the NetPlan algorithm and developed the COD-NETPLAN (Clustering with Obstructed Distance -- Network Planning) algorithm to solve the problem of 2D and 3D obstacles. We studied the problem of determining the location of the multi-service access node in an area with many mountains and rivers. We used a reachability matrix to detect 2D obstacles, and line segment intersection together with geographical information system techniques for 3D obstacles. Experimental results and the subsequent analysis indicate that the COD-NETPLAN algorithm is both efficient and effective.