26 research outputs found
Solar PV power forecasting at Yarmouk University using machine learning techniques
Renewable energy sources are considered ubiquitous
and drive the energy revolution. Energy producers
suffer from inconsistent electricity generation. They
often struggled with the unpredictability of the weather.
Thus, making it challenging to balance supply and demand.
Technologies like artificial intelligence (AI) and machine
learning are effective ways to forecast, distribute, andmanage
renewable photovoltaic (PV) solar supplies. AI will make the
energy forecasting system more connected, intelligent, reliable,
and sustainable. AI can innovate how energy is used
and help find solutions for decarbonizing energy systems.
There are potential advantages to total energy forecasting.
AI can support the growth and integration of PV solar energy.
The article’s main objective is to use AI to forecast the output
consumed power of the Yarmouk University PV solar system
in Jordan. The total actual yield is 5548.96 MW h, and the
performance ratio (PR) is 95.73%. Many techniques are used
to predict the consumed solar power. The random forest
model obtains the best results of root mean squared error
and mean absolute error are 172.07 and 68.7, respectively.
This accurate prediction allows for the maximum use of
solar power and the minimal use of grid power. This work
guides the operators to learn trends embedded in Yarmouk
University’s historical data. These understood trends can
be used to predict the consumption of solar power output.
Thus, the control system and grid operators have advanced
knowledge of the expected consumption of solar power at
each hour of the day
Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects
Software engineering is one of the most significant areas, which extensively used in
educational and industrial fields. Software engineering education plays an essential
role in keeping students up to date with software technologies, products, and processes
that are commonly applied in the software industry. The software development project
is one of the most important parts of the software engineering course, because it covers
the practical side of the course. This type of project helps strengthening students' skills
to collaborate in a team spirit to work on software projects. Software project involves the
composition of software product and process parts. Software product part represents
software deliverables at each phase of Software Development Life Cycle (SDLC)
while software process part captures team activities and behaviors during SDLC. The
low-expectation teams face challenges during different stages of software project.
Consequently, predicting performance of such teams is one of the most important
tasks for learning process in software engineering education. The early prediction of
performance for low-expectation teams would help instructors to address difficulties
and challenges related to such teams at earliest possible phases of software project
to avoid project failure. Several studies attempted to early predict the performance
for low-expectation teams at different phases of SDLC. This study introduces swarm
intelligence -based model which essentially aims to improve the prediction performance
for low-expectation teams at earliest possible phases of SDLC by implementing Particle
Swarm Optimization-K Nearest Neighbours (PSO-KNN), and it attempts to reduce the
number of selected software product and process features to reach higher accuracy with
identifying less than 40 relevant features. Experiments were conducted on the Software
Engineering Team Assessment and Prediction (SETAP) project dataset. The proposed
model was compared with the related studies and the state-of-the-art Machine Learning
(ML) classifiers: Sequential Minimal Optimization (SMO), Simple Linear Regression
(SLR), Naïve Bayes (NB), Multilayer Perceptron (MLP), standard KNN, and J48. The
proposed model provides superior results compared to the traditional ML classifiers
and state-of-the-art studies in the investigated phases of software product and process
development
Deep neural networks in the cloud: Review, applications, challenges and research directions
Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide
range of important real-world applications. DNNs consist of a huge number of parameters that require
millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A
more effective method is to implement DNNs in a cloud computing system equipped with centralized
servers and data storage sub-systems with high-speed and high-performance computing capabilities.
This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing.
Various DNN complexities associated with different architectures are presented and discussed alongside
the necessities of using cloud computing. We also present an extensive overview of different cloud
computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications
already deployed in cloud computing systems are reviewed to demonstrate the advantages of using
cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing
systems and provides guidance on enhancing current and new deployments.The EGIA project (KK-2022/00119The
Consolidated Research Group MATHMODE (IT1456-22
Sentiment Analysis of Customers' Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution
Online media has an increasing presence on the restaurants' activities through social media
websites, coinciding with an increase in customers' reviews of these restaurants. These reviews become
the main source of information for both customers and decision-makers in this field. Any customer who
is seeking such places will check their reviews first, which usually affect their final choice. In addition,
customers' experiences can be enhanced by utilizing other customers' suggestions. Consequently, customers'
reviews can influence the success of restaurant business since it is considered the final judgment of the overall
quality of any restaurant. Thus, decision-makers need to analyze their customers' underlying sentiments in
order to meet their expectations and improve the restaurants' services, in terms of food quality, ambiance,
price range, and customer service. The number of reviews available for various products and services
has dramatically increased these days and so has the need for automated methods to collect and analyze
these reviews. Sentiment Analysis (SA) is a field of machine learning that helps analyze and predict the
sentiments underlying these reviews. Usually, SA for customers' reviews face imbalanced datasets challenge,
as the majority of these sentiments fall into supporters or resistors of the product or service. This work
proposes a hybrid approach by combining the SupportVector Machine (SVM) algorithm with Particle Swarm
Optimization (PSO) and different oversampling techniques to handle the imbalanced data problem. SVM is
applied as a machine learning classi cation technique to predict the sentiments of reviews by optimizing the
dataset, which contains different reviews of several restaurants in Jordan. Data were collected from Jeeran,
a well-known social network for Arabic reviews. A PSO technique is used to optimize the weights of the
features, as well as four different oversampling techniques, namely, the Synthetic Minority Oversampling
Technique (SMOTE), SVM-SMOTE, Adaptive Synthetic Sampling (ADASYN) and borderline-SMOTE
were examined to produce an optimized dataset and solve the imbalanced problem of the dataset. This study
shows that the proposed PSO-SVM approach produces the best results compared to different classiffication
techniques in terms of accuracy, F-measure, G-mean and Area Under the Curve (AUC), for different versions
of the datasets
A Multilingual Spam Reviews Detection Based on Pre-Trained Word Embedding and Weighted Swarm Support Vector Machines
Online reviews are important information that customers seek when deciding to buy products or
services. Also, organizations benefit from these reviews as essential feedback for their products or services.
Such information required reliability, especially during the Covid-19 pandemic which showed a massive
increase in online reviews due to quarantine and sitting at home. Not only the number of reviews was boosted
but also the context and preferences during the pandemic. Therefore, spam reviewers reflect on these changes
and improve their deception technique. Spam reviews usually consist of misleading, fake, or fraudulent
reviews that tend to deceive customers for the purpose of making money or causing harm to other competitors.
Hence, this work presents a Weighted Support Vector Machine (WSVM) and Harris Hawks Optimization
(HHO) for spam review detection. The HHO works as an algorithm for optimizing hyperparameters and
feature weighting. Three different language corpora have been used as datasets, namely English, Spanish, and
Arabic in order to solve the multilingual problem in spam reviews. Moreover, pre-trained word embedding
(BERT) has been applied alongside three-word representation methods (NGram-3, TFIDF, and One-hot
encoding). Four experiments have been conducted, each focused on solving and demonstrating different
aspects. In all experiments, the proposed approach showed excellent results compared with other state-ofthe-
art algorithms. In other words, the WSVM-HHO achieved an accuracy of 88.163%, 71.913%, 89.565%,
and 84.270%, for English, Spanish, Arabic, and Multilingual datasets, respectively. Further, a deep analysis
has been conducted to investigate the context of reviews before and after the COVID-19 situation. In addition,
it has been generated to create a new dataset with statistical features and merge its previous textual features
for improving detection performance.Projects TED2021-129938B-I0,PID2020-113462RB-I00, PDC2022-133900-I00PID2020-115570GB-C22, granted by Ministerio Español de Ciencia e InnovaciónMCIN/AEI/10.13039/501100011033MCIN/AEI/10.13039/501100011033MCIN/AEINext GenerationEU/PRT
Sentiment Analysis for e-Payment Service Providers Using Evolutionary eXtreme Gradient Boosting
Online services depend primarily on customer feedback and communications. When this
kind of input is lacking, the overall approach of the service provider can shift in unintended ways. These
services rely on feedback to maintain consumer satisfaction. Online social networks are a rich source
of consumer data related to services and products. Well developed methods like sentiment analysis can
offer insightful analyses and aid service providers in predicting outcomes based on their reviews—which,
in turn, enables decision-makers to develop effective strategic plans. However, gathering this data is
more challenging on Arabic online social networks, due to the complexity of the Arabic language and
its dialects. In this study, we propose an approach to sentiment analysis that combines a neutrality
detector model with eXtreme Gradient Boosting and a genetic algorithm to effectively predict and analyze
customers’ opinions of an e-Payment service through an Arabic social network. The proposed approach
yields excellent results compared to other approaches. Feature analysis is also conducted on consumer
reviews to identify influencing keywords.Deanship of Scientific Research, The University of JordanMinisterio espanol de Economia y Competitividad
TIN2017-85727-C4-2-
A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling
This research received no external funding. The APC is funded by Prince Sultan UniversityThe authors would like to acknowledge the support of Prince Sultan University
for paying the Article Processing Charges (APC) of this publication.Intrusion detection of IoT-based data is a hot topic and has received a lot of interests from researchers and practitioners since the security of IoT networks is crucial. Both supervised and unsupervised learning methods are used for intrusion detection of IoT networks. This paper proposes an approach of three stages considering a clustering with reduction stage, an oversampling stage, and a classification by a Single Hidden Layer Feed-Forward Neural Network (SLFN) stage. The novelty of the paper resides in the technique of data reduction and data oversampling for generating useful and balanced training data and the hybrid consideration of the unsupervised and supervised methods for detecting the intrusion activities. The experiments were evaluated in terms of accuracy, precision, recall, and G-mean and divided into four steps: measuring the effect of the data reduction with clustering, the evaluation of the framework with basic classifiers, the effect of the oversampling technique, and a comparison with basic classifiers. The results show that SLFN classification technique and the choice of Support Vector Machine and Synthetic Minority Oversampling Technique (SVM-SMOTE) with a ratio of 0.9 and the k value of 3 for k-means++ clustering technique give better results than other values and other classification techniques.Prince Sultan Universit
Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis
This work has been partially funded by projects PID2020-113462RB-I00 (ANIMALICOS), granted by Ministerio Espanol de Economia y Competitividad; projects P18-RT-4830 and A-TIC-608-UGR20 granted by Junta de Andalucia, and project B-TIC-402-UGR18 (FEDER and Junta de Andalucia).During the recent COVID-19 pandemic, people were forced to stay at home to protect
their own and others’ lives. As a result, remote technology is being considered more in all aspects
of life. One important example of this is online reviews, where the number of reviews increased
promptly in the last two years according to Statista and Rize reports. People started to depend more
on these reviews as a result of the mandatory physical distance employed in all countries. With no
one speaking to about products and services feedback. Reading and posting online reviews becomes
an important part of discussion and decision-making, especially for individuals and organizations.
However, the growth of online reviews usage also provoked an increase in spam reviews. Spam
reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit
or publicity. A number of spam detection methods have been proposed to solve this problem. As
part of this study, we outline the concepts and detection methods of spam reviews, along with
their implications in the environment of online reviews. The study addresses all the spam reviews
detection studies for the years 2020 and 2021. In other words, we analyze and examine all works
presented during the COVID-19 situation. Then, highlight the differences between the works before
and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine
different detection approaches have been classified in order to investigate their specific advantages,
limitations, and ways to improve their performance. Additionally, a literature analysis, discussion,
and future directions were also presented.Spanish Government PID2020-113462RB-I00Junta de Andalucia P18-RT-4830
A-TIC-608-UGR20
B-TIC-402-UGR18European Commission B-TIC-402-UGR1
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