A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network

Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. TwoWell-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work’s main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan’s current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

As the COVID-19 pandemic rapidly spreads across the world, regrettably, misinformation and fake news related to COVID-19 have also spread remarkably. Such misinformation has confused people. To be able to detect such COVID-19 misinformation, an effective detection method should be applied to obtain more accurate information. This will help people and researchers easily differentiate between true and fake news. The objective of this research was to introduce an enhanced evolutionary detection approach to obtain better results compared with the previous approaches. The proposed approach aimed to reduce the number of symmetrical features and obtain a high accuracy after implementing three wrapper feature selections for evolutionary classifications using particle swarm optimization (PSO), the genetic algorithm (GA), and the salp swarm algorithm (SSA). The experiments were conducted on one of the popular datasets called the Koirala dataset. Based on the obtained prediction results, the proposed model revealed an optimistic and superior predictability performance with a high accuracy (75.4%) and reduced the number of features to 303. In addition, by comparison with other state-of-the-art classifiers, our results showed that the proposed detection method with the genetic algorithm model outperformed other classifiers in the accurac

Voting-based Classification for E-mail Spam Detection

The problem of spam e-mail has gained a tremendous amount of attention. Although entities tend to use e-mail spam filter applications to filter out received spam e-mails, marketing companies still tend to send unsolicited e-mails in bulk and users still receive a reasonable amount of spam e-mail despite those filtering applications. This work proposes a new method for classifying e-mails into spam and non-spam. First, several e-mail content features are extracted and then those features are used for classifying each e-mail individually. The classification results of three different classifiers (i.e. Decision Trees, Random Forests and k-Nearest Neighbor) are combined in various voting schemes (i.e. majority vote, average probability, product of probabilities, minimum probability and maximum probability) for making the final decision. To validate our method, two different spam e-mail collections were used

An intelligent rule-oriented framework for extracting key factors for grants scholarships in higher education

Education is a fundamental sector in all countries, where in some countries students com-pete to get an educational grant due to its high cost. The incorporation of artificial intelli-gence in education holds great promise for the advancement of educational systems and pro-cesses. Educational data mining involves the analysis of data generated within educational environments to extract valuable insights into student performance and other factors that enhance teaching and learning. This paper aims to analyze the factors influencing students' performance and consequently, assist granting organizations in selecting suitable students in the Arab region (Jordan as a use case). The problem was addressed using a rule-based tech-nique to facilitate the utilization and implementation of a decision support system. To this end, three classical rule induction algorithms, namely PART, JRip, and RIDOR, were em-ployed. The data utilized in this study was collected from undergraduate students at the University of Jordan from 2010 to 2020. The constructed models were evaluated based on metrics such as accuracy, recall, precision, and f1-score. The findings indicate that the JRip algorithm outperformed PART and RIDOR in most of the datasets based on f1-score metric. The interpreted decision rules of the best models reveal that both features; the average study years and high school averages play vital roles in deciding which students should receive scholarships. The paper concludes with several suggested implications to support and en-hance the decision-making process of granting agencies in the realm of higher education

Mapreduce-Enabled Scalable Nature-Inspired Approaches for Clustering

The increasing volume of data to be analyzed imposes new challenges to the data mining methodologies. Traditional data mining such as clustering methods do not scale well with larger data sizes and are computationally expensive in terms of memory and time. Clustering large data sets has received attention in the last few years in several application areas such as document categorization, which is in urgent need of scalable approaches. Swarm intelligence algorithms have self-organizing features, which are used to share knowledge among swarm members to locate the best solution. These algorithms have been successfully applied to clustering, however, they suffer from the scalability issue when large data is involved. In order to satisfy these needs, new parallel scalable clustering methods need to be developed. The MapReduce framework has become a popular model for parallelizing data-intensive applications due to its features such as fault-tolerance, scalability, and usability. However, the challenge is to formulate the tasks with map and reduce functions. This dissertation firstly presents a scalable particle swarm optimization (MR-CPSO) clustering algorithm that is based on the MapReduce framework. Experimental results reveal that the proposed algorithm scales very well with increasing data set sizes while maintaining good clustering quality. Moreover, a parallel intrusion detection system using the MR-CPSO is introduced. This system has been tested on a real large-scale intrusion data set to confirm its scalability and detection quality. In addition, the MapReduce framework is utilized to implement a parallel glowworm swarm optimization (MR-GSO) algorithm to optimize difficult multimodal functions. The experiments demonstrate that MR-GSO can achieve high function peak capture rates. Moreover, this dissertation presents a new clustering algorithm based on GSO (CGSO). CGSO takes into account the multimodal search capability to locate optimal centroids in order to enhance the clustering quality without the need to provide the number of clusters in advance. The experimental results demonstrate that CGSO outperforms other well-known clustering algorithms. In addition, a MapReduce GSO clustering (MRCGSO) algorithm version is introduced to evaluate the algorithm's scalability with large scale data sets. MRCGSO achieves a good speedup and utilization when more computing nodes are used

An Automatic Course Scheduling Approach Using Instructors' Preferences

University Courses Timetabling problem has been extensively researched in the last decade. Therefore, numerous approaches were proposed to solve UCT problem. This paper proposes a new approach to process a sequence of meetings between instructors, rooms, and students in predefined periods of time with satisfying a set of constraints divided in variety of types. In addition, this paper proposes new representation for courses timetabling and conflict-free for each time slot by mining instructor preferences from previous schedules to avoid undesirable times for instructors. Experiments on different real data showed the approach achieved increased satisfaction degree for each instructor and gives feasible schedule with satisfying all hard constraints in construction operation. The generated schedules have high satisfaction degrees comparing with schedules created manually. The research conducts experiments on collected data gathered from the computer science department and other related departments in Jordan University of Science and Technology- Jordan