There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice

Improving student engagement

Nowadays, in the international scientific community of machine learning, there exists an enormous discussion about the use of black-box models or explainable models; especially in practical problems. On the one hand, a part of the community defends that black-box models are more accurate than explainable models in some contexts, like image preprocessing. On the other hand, there exist another part of the community alleging that explainable models are better than black-box models because they can obtain comparable results and also they can explain these results in a language close to a human expert by using patterns. In this paper, advantages and weaknesses for each approach are shown; taking into account a state-of-the-art review for both approaches, their practical applications, trends, and future challenges. This paper shows that both approaches are suitable for solving practical problems, but experts in machine learning need to understand the input data, the problem to solve, and the best way for showing the output data before applying a machine learning model. Also, we propose some ideas for fusing both, explainable and black-box, approaches to provide better solutions to experts in real-world domains. Additionally, we show one way to measure the effectiveness of the applied machine learning model by using expert opinions jointly with statistical methods. Throughout this paper, we show the impact of using explainable and black-box models on the security and medical applications.

/pdf/black-box-vs-white-box-understanding-their-advantages-and-3rj32l77ou.pdf

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

5G mobile network is expected to serve flexible requirements hence dynamically allocate network resources according to the demands. Network slicing, where network resources are packaged and assigned in an isolated manner to set of users according to their specific requirements, is considered as a key paradigm to fulfil diversity of requirements. There will clearly be conflicting demands in allocation of such slices, and the effective provisioning of network slicing poses several challenges. Indeed, network slicing has a twofold impact in terms of user/traffic prioritization as it dictates for the simultaneous management of the priority among different slices (i.e., interslice) and the priority among the users belonging to the same slice (i.e., intra-slice). In this paper, we propose a novel heuristicbased admission control mechanism able to dynamically allocate network resources to different slices in order to maximize the satisfaction of the users while guaranteeing to meet the requirements of the slices they belong to. Through simulations, we demonstrate how our proposal provides (i) higher user experience in individual slices, (ii) increased utilization of network resources and (iii) higher scalability when the number of users in each slice increases.

Network slicing management & prioritization in 5G mobile systems

The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to different types of cyber-attacks such as denial of service, spoofing, and sniffing attacks. In this paper, an intelligent intrusion detection system (IDS) is proposed based on tree-structure machine learning models. The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks. Furthermore, the proposed ensemble learning and feature selection approaches enable the proposed system to achieve high detection rate and low computational cost simultaneously.

Tree-Based Intelligent Intrusion Detection System in Internet of Vehicles

With the proliferation of technology, the field of e-learning has garnered significant attention in recent times This is because it has allowed users from around the world to learn and access new information This has added to the growing amount of collected data that is already being generated through different devices and sensors employed around the world This has led to the need to analyze collected data and extract useful information from it Machine learning (ML) and data analytics (DA) are proposed techniques that can help extract information and find valuable patterns within the collected data In this paper, the field of e-learning is investigated in terms of definitions and characteristics Moreover, the various challenges facing the different participants within this process are discussed In addition, some of the works proposed in the literature to tackle these challenges are presented Then, a brief survey about some of the most popular ML and DA techniques is given Finally, some of the research opportunities available that employ such techniques are proposed to give insights into the areas that merit further exploration and investigation

E-Learning: Challenges and Research Opportunities Using Machine Learning & Data Analytics

E-learning platforms and processes face several challenges, among which is the idea of personalizing the e-learning experience and to keep students motivated and engaged. This work is part of a lar...

Student Engagement Level in an e-Learning Environment: Clustering Using K-Means.

Modern vehicles, including connected vehicles and autonomous vehicles, nowadays involve many electronic control units connected through intra-vehicle networks to implement various functionalities and perform actions. Modern vehicles are also connected to external networks through vehicle-to-everything technologies, enabling their communications with other vehicles, infrastructures, and smart devices. However, the improving functionality and connectivity of modern vehicles also increase their vulnerabilities to cyber-attacks targeting both intra-vehicle and external networks due to the large attack surfaces. To secure vehicular networks, many researchers have focused on developing intrusion detection systems (IDSs) that capitalize on machine learning methods to detect malicious cyber-attacks. In this paper, the vulnerabilities of intra-vehicle and external networks are discussed, and a multi-tiered hybrid IDS that incorporates a signature-based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks. Experimental results illustrate that the proposed system can detect various types of known attacks with 99.99% accuracy on the CAN-intrusion-dataset representing the intra-vehicle network data and 99.88% accuracy on the CICIDS2017 dataset illustrating the external vehicular network data. For the zero-day attack detection, the proposed system achieves high F1-scores of 0.963 and 0.800 on the above two datasets, respectively. The average processing time of each data packet on a vehicle-level machine is less than 0.6 ms, which shows the feasibility of implementing the proposed system in real-time vehicle systems. This emphasizes the effectiveness and efficiency of the proposed IDS.

MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles

A plethora of research has been done in the past focusing on predicting student’s performance in order to support their development. Many institutions are focused on improving the performance and the education quality; and this can be achieved by utilizing data mining techniques to analyze and predict students’ performance and to determine possible factors that may affect their final marks. To address this issue, this work starts by thoroughly exploring and analyzing two different datasets at two separate stages of course delivery (20% and 50% respectively) using multiple graphical, statistical, and quantitative techniques. The feature analysis provides insights into the nature of the different features considered and helps in the choice of the machine learning algorithms and their parameters. Furthermore, this work proposes a systematic approach based on Gini index and p -value to select a suitable ensemble learner from a combination of six potential machine learning algorithms. Experimental results show that the proposed ensemble models achieve high accuracy and low false positive rate at all stages for both datasets.

Abdallah Moubayed

Papers

Tree-Based Intelligent Intrusion Detection System in Internet of Vehicles

E-Learning: Challenges and Research Opportunities Using Machine Learning & Data Analytics

Student Engagement Level in an e-Learning Environment: Clustering Using K-Means.

MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles

Systematic ensemble model selection approach for educational data mining