The future of electric grids is undergoing a remarkable transformation driven by the increasing adoption of emerging technologies, notably Artificial Intelligence (AI) and Blockchain. These innovati

Renewable energy sources have immense potential for enhancing environmental sustainability; however, addressing their intermittency and irregularity is vital for optimizing economic benefits within

Deep reinforcement learning agents for demand response in home energy management systems are provided via training an agent via power availability data, electricity use data for an electrical load i

Effective feature representations play a critical role in enhancing the performance of text generation models that rely on deep neural networks. However, current approaches suffer from several drawb

Human action recognition (HAR)is the method of analyzing human behavior with the aid of computer and machine vision technology. Different application domains have seen substantial recent advancement

Effective prediction of turning movement counts at intersections through efficient and accurate methods is essential and needed for various applications. Commonly predictive methods require extensiv

Learning response generation models constitute the main component of building open-domain dialogue systems. However, training open-domain response generation models requires large amounts of labeled

With the advances in Natural Language Processing (NLP), the industry has been moving towards human-directed artificial intelligence (AI) solutions. Recently, chatbots and automated news generation h

The increased connectivity of cyber‐physical systems (CPS) to enterprise networks raises challenging security concerns. Detecting attacks on CPS is a vital step to improving their security. Most o

In this paper, we discuss a general framework, namely Opportunistic Distributed Learning (ODL), which allows any node in the network to initiate a learning task while opportunistically leveraging lo

In this paper, we present StEduCov, an annotated dataset for the analysis of stances toward online education during the COVID-19 pandemic. StEduCov consists of 16,572 tweets gathered over 15 months,

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progre

With the smart grid and smart homes development, different data are made available, providing a source for training algorithms, such as deep reinforcement learning (DRL), in smart grid applications.

Residential short-term load forecasting has become an essential process to develop successful demand response strategies, and help utilities and customers optimize energy production and consumption.

The high penetrations of distributed energy resources of renewable energy sources, battery energy storage systems, and electric vehicles introduced several challenges to power networks. It can lead

In this paper, we present StEduCov, an annotated dataset for the analysis of stances toward online education during the COVID-19 pandemic. StEduCov consists of 16,572 tweets gathered over 15 months,

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progre

We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the

We propose a novel approach for visual representation learning called Signature-Graph Neural Networks (SGN). SGN learns latent global structures that augment the feature representation of Convolutio

We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the

Existing learning models often utilise CT-scan images to predict lung diseases. These models are posed by high uncertainties that affect lung segmentation and visual feature learning. We introduce M

This paper proposes a Home Energy Management System (HEMS) that optimizes the load demand and distributed energy resources. The optimal demand/generation profile is presented while considering utili

Existing learning models often utilise CT-scan images to predict lung diseases. These models are posed by high uncertainties that affect lung segmentation and visual feature learning. We introduce M

Traditional techniques for Cyber-Physical Systems (CPS) security design either treat the cyber and physical systems independently, or do not address the specific vulnerabilities of real time embedde

Certain embodiments may generally relate to a smart fault detection device for power grids, and a method of fault detection for power grids. A method may include receiving raw data samples of curren

Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an

Sentiment analysis is a highly subjective and challenging task. Its complexity further increases when applied to the Arabic language, mainly because of the large variety of dialects that are unstand

Arabic is a complex language with limited resources which makes it challenging to produce accurate text classification tasks such as sentiment analysis. The utilization of transfer learning (TL) has

Arabic script has a number of characteristics that makes it unique among other scripts. Several feature extraction methods use statistical pixel distribution-based approach to recognize handwritten

Arabic text sentiment analysis suffers from low accuracy due to Arabic-specific challenges (e.g., limited resources, morphological complexity, and dialects) and general linguistic issues (e.g., fuzz

This paper presents state of the art reconfiguration techniques used for service restoration in distribution systems under different practical considerations. The different formulations of the probl

Measuring acoustic emission (AE) of partial discharge (PD) phenomena can be adopted to estimate the condition of power transformers. However, the environmental noise encountered with AE of PD measur

Arabic text sentiment analysis suffers from low accuracy due to Arabic-specific challenges (e.g., limited resources, morphological complexity, and dialects) and general linguistic issues (e.g., fuzz

The security of Cyber-Physical Systems (CPS) has been recently receiving significant attention from the research community. While the majority of such attention originates from the control theory do

While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language. It becomes more challenging when applied to

Traditionally, service-specific network functions (NFs) (e.g., Firewall, intrusion detection system, etc.) are executed by installation-and maintenance-costly hardware middleboxes that are deployed

While research on English opinion mining has already achieved significant progress and success, work on Arabic opinion mining is still lagging. This is mainly due to the relative recency of research

Accurate sentiment analysis models encode the sentiment of words and their combinations to predict the overall sentiment of a sentence. This task becomes challenging when applied to morphologically

This letter investigates an approach based on feature selection and classification techniques to reduce assessment complexities of power transformers. This approach decreases the number of features

Sentiment analysis in Arabic is challenging due to the complex morphology of the language. The task becomes more challenging when considering Twitter data that contain significant amounts of noise s

Many unsupervised learning techniques have been proposed to obtain meaningful representations of words from text. In this study, we evaluate these various techniques when used to generate Arabic wor

Data generated on Twitter has become a rich source for various data mining tasks. Those data analysis tasks that are dependent on the tweet semantics, such as sentiment analysis, emotion mining, and

Three Hilbert fractal antenna designs are proposed in this work to capture and classify common types of partial discharge (PD) in an oil insulated system. Each antenna design shows unique characteri

Partial discharge (PD) can be used as an indicator of impending failure in electrical plant insulation making the accurate classification of particular occurrence patterns useful for anticipating fo

Real-time interactive application workloads (e.g., Web search, social networking, and so on) appear in the form of a large number of mini requests and responses flowing over the datacenters' network

Smart grid communication networks adopt a variety of communication technologies interconnecting numerous and diverse equipment. The requirement of supporting a large traffic volume over such network

Cloud infrastructure services allow organizations to outsource their computing, storage, and networking needs to external providers. These offerings use network virtualization to provision customize

Understanding what people think about an idea or how they evaluate a product, a service or a policy is important for individuals, companies and governments. Sentiment analysis is the process of auto

A system for monitoring and forecasting urban air pollution is presented in this paper. The system uses low-cost air-quality monitoring motes that are equipped with an array of gaseous and meteorolo

To accelerate the implementation of network functions/middle boxes and reduce the deployment cost, recently, the concept of network function virtualization (NFV) has emerged and become a topic of mu

Cloud computing is being widely accepted and utilized in the business world. From the perspective of businesses utilizing the cloud, it is critical to meet their customers' requirements by achieving

Recent studies have shown that wavelet transform can effectively be used for noise reduction in the context of partial discharge (PD) signal detection and classification. Several thresholding approa

In this letter, the ranges of furan content in oil in power transformers are predicted using measurements of oil tests, such as breakdown voltage, acidity, water content, and dissolved gas analysis.

Power transformers are one of the most important and expensive electrical equipment that require online condition monitoring. Partial discharge (PD) measurement is considered the most effective and

This paper proposes and experimentally validates the functionality of a smart IEC 61850 merging unit (MU) that supports self-healing and asset management functions of future power grids. The propose

This article introduces a sentiment analysis approach that adopts the way humans read, interpret, and extract sentiment from text. Our motivation builds on the assumption that human interpretation s

The failure-prone nature of data center networks has evoked countless contributions to develop proactive and reactive countermeasures. Yet, most of these techniques were developed with unicast servi

Every year, road accidents kill more than a million people and injure more than 20 million worldwide. This paper aims to offer guidance on road safety and create awareness by pinpointing the major c

Advance reservation services are being used by a range of applications to schedule connection bandwidth resources at future time intervals. To date many different algorithms have been developed to s

The exponential increase of renewable energy sources (RESs) penetration in the power grid introduces the of voltage instability when RESs are integrated into medium and low voltage networks. This pa

This paper addresses classifying different common partial discharge (PD) types under different acoustic emission (AE) measurement conditions. Four types of PDs are considered for the multi-class cla

Objective The objective of this paper is to formulate an extended segment representation (SR) technique to enhance named entity recognition (NER) in medical applications. Methods An extension to

Network virtualization is regarded as the pillar of cloud computing, enabling the multi-tenancy concept where multiple Virtual Networks (VNs) can cohabit the same substrate network. With network vir

Advance reservation services allows users to pre-reserve network resources at future instants in time. These offerings are already being used by a wide range of applications in scientific/grid compu

This paper puts forth a novel methodology for facilities layout planning and optimization, where the fitness evaluation of layout alternatives is automatically performed by employing an artificial n

In this paper, we explore the effectiveness of deep learning models for text sentiment classification in Arabic. We propose the evaluation of Deep Belief Networks and deep Auto Encoders models. Thre

Cloud Computing is becoming a mainstream paradigm, as organizations, large and small, begin to harness its benefits. This novel technology brings new challenges, mostly in the protocols that govern

Network virtualization is a key provision for improving the scalability and reliability of cloud computing services. In recent years, various mapping schemes have been developed to reserve VN resour

Different brain states and conditions can be captured by electroencephalogram (EEG) signals. EEG-based epileptic seizure detection techniques often reduce these signals into sets of discriminant fea

Characteristic movements of human body parts ranging from eye twitches to limbs jerky movements have been used for decades by physicians as clinical indicators of certain neurological disorders. Thr

In this paper artificial neural networks have been constructed to predict different transformers oil parameters. The prediction is performed through modeling the relationship between the insulation

Conventional document mining systems mainly use the presence or absence of keywords to mine texts. However, simple word counting and frequency distributions of term appearances do not capture the me

Evolutionary algorithms have been a popular approach to finding schedules for flexible manufacturing systems. These algorithms, while effective, are dependent on the quality of initial populations a

To discover knowledge form the available volumes of learning objects, the tasks to manage, analyze, search, filter, and summarize them should be automated. This requires understanding of the objects

Smart grids are suscceptible to security vulnerabilities of cyber-physical systems due to the heterogeneity of their interconnected components. There are high risks associated with potential attacks

Membrane fouling is one of the major problems in desalination processes as it can cause a severe drop in the quality and quantity of the permeate water. This paper presents a data-driven approach fo

Effective representation learning is an essential building block for achieving many natural language processing tasks such as stance detection as performed implicitly by humans. Stance detection can

There is a high demand for chatbots across a wide range of sectors. Human-like chatbots engage meaningfully in dialogues while interpreting and expressing emotions and being consistent through under

Effective representation learning is an essential building block for achieving many natural language processing tasks such as stance detection as performed implicitly by humans. Stance detection can

We propose flexgrid2vec, a novel approach for image representation learning. Existing visual representation methods suffer from several issues, including the need for highly intensive computation, t

We propose C-SAR, a Class-specific and Adaptive Recognition algorithm for Arabic handwritten Cheques. Existing methods suffer from low accuracy due to the complex structure of Arabic script and high

Expensive and widely used power and distribution transformers need to be monitored to ensure the reliability of the power grid. Evaluating the transformer oil different parameters is vital to determ

Major Depressive Disorder (MDD) is a serious ailment in mental health and is a medical illness that has a debilitating impact on a person's ability to think effectively. According to the World Healt

Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an

Arabic is a complex language with limited resources which makes it challenging to produce accurate text classification tasks such as sentiment analysis. The utilization of transfer learning (TL) has

The insecurity of the Internet-of-Things (IoT) paradigm continues to wreak havoc in consumer and critical infrastructure realms. Several challenges impede addressing IoT security at large, including

In this paper, we investigate the process of detection of False Data Injection (FDI) in a Linear Parameter Varying (LPV) cyber-physical system (CPS). We design a model based FDI detector capable of

Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of n

While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language. It becomes more challenging when applied to

Partial discharge (PD) can be used to predict insulation failures in power transformers. Accurate detection of particular PD types has a significant role in anticipating forthcoming outages. However

The increase in the amount of data acquired from the monitoring of power system components has motivated utilities to employ effective strategies for processing the information collected. Hence, sal

This paper surveys the conceptual aspects, as well as recent developments in fault detection, isolation, and service restoration (FDIR) following an outage in an electric distribution system. This p

Network Function Visualization (NFV) enables the complete decoupling of Network Functions (NFs) (e.g., firewall, intrusion detection, routing, etc.) from physical middleboxes used to implement servi

To accelerate the implementation of network functions/middle boxes and reduce the deployment cost, recently the concept of Network Function Virtualization (NFV) has emerged and became a topic of muc

Network virtualization allows users to build customized interconnected storage/computing configurations for their business needs. Today this capability is being widely used to improve the scalabilit

A significant portion of data generated on blogging and microblogging websites is non-credible as shown in many recent studies. To filter out such non-credible information, machine learning can be d

Supervisory control and data acquisition (SCADA) systems are used extensively to monitor/control utility power distribution networks. However, the current SCADA systems cannot accommodate the demand

This paper studies progressive recovery in optical cloud substrates supporting virtualized infrastructure services. Several resource placement/scheduling schemes are presented to improve post-fault

Real-time interactive application workloads (e.g. web search, social networking, etc.) are composed of a remarkably large number of mini request partitions that require stringent delay-minimal aggre

Failure in the physical network can cause a temporal or permanent unavailability of some resources, which can lead to a quality of service (QoS) degradation and loss of revenue. While much work has

Wireless electroencephalogram (EEG) sensors have been successfully applied in many medical and computer brain interface classifications. A common characteristic of wireless EEG sensors is that they

In this paper, deep learning framework is proposed for text sentiment classification in Arabic. Four different architectures are explored. Three are based on Deep Belief Networks and Deep Auto Encod

Most advanced mobile applications require server-based and communication. This often causes additional energy consumption on the already energy-limited mobile devices. In this work, we provide to ad

The Health Index represents a practical tool that combines the results of operating observations, field inspections, and site and laboratory testing to manage the asset and prioritize investments in

This paper presents a robust power restoration mechanism that can operate in typical distribution systems without the need of supervision from a central point or intervention from the operator. The

White noise is a major interference source that affects the partial discharge (PD) signal detection and recognition. Wavelet shrinkage denoising methods can efficiently reject the white noise embedd

Measuring partial discharge (PD) phenomena in power transformers is often conducted by acoustic emission (AE) method. However, many interference sources are usually encountered with the captured PD

Cloud-based services allow users to outsource their application and infrastructure needs over external datacenter/ networking facilities. However, as these offerings gain traction, there is a pressi

Network virtualization provides a promising means of hosting multiple client infrastructures over a physical substrate. Now one of the key concerns here is how to map virtual network requests onto p

Recommender systems face performance challenges when dealing with sparse data. This paper addresses these challenges and proposes the use of Harmonic Analysis. The method provides a novel approach t

Furan content in transformer oil is highly correlated with the transformer insulation paper aging. In this paper, the ranges of furan content in power transformer is predicted using measurements of

With the growth of social media and online blogs, people express their opinion and sentiment freely by providing product reviews, as well as comments about celebrities, and political and global even

Network virtualization enables the multi-tenancy concept and paves the way towards more advancements and innovation in the underlying infrastructure. With network virtualization, allocating resource

Recommender systems provide recommendations on variety of personal activities or relevant items of interest. They can play a significant role for E-commerce and in daily personal decisions. However,

Network virtualization is critical for distributing cloud services over expanded distances and improving scalability and responsiveness. However many providers are very concerned about maintaining h

Microblogs and collaborative content sites such as Twitter and Amazon are popular among millions of users who generate huge numbers of tweets, posts, and reviews every day. Despite their popularity,

Opinion mining is becoming of high importance with the availability of opinionated data on the Internet and the different applications it can be used for. Intensive efforts have been made to develop

Microblogging websites such as Twitter have gained popularity as an effective and quick means of expressing opinions, sharing news and promoting information and updates. As a result, data generated

Accurate partial discharge (PD) classification provides significant information to asses power transformers' insulation condition. The work presented in this paper aims to improve classification fro

Cloud computing services are being adopted to expand applications across dispersed data-center sites. As these new paradigms require active data exchange, they impose further virtual network (VN) ma

Electroencephalogram (EEG) physiological signals are widely used for detecting epileptic seizure. To reduce complexity stemming from the dimensionality problem, EEG signals are often reduced into a

A major challenge in the current research of wireless electroencephalograph (EEG) sensor-based medical or Brain Computer Interface applications is how to classify EEG signals as accurately and energ

Detecting objects, a significant task in computer vision, is accompanied with many challenges. When we focus on medical images, the challenges of detecting an organ or a tumour exhibit their own spe

This paper reports on the experience and lessons learned from introducing a constructivist inquiry-based learning (IBL) in advanced computing courses. The paper describes an iterative problem-centri

This paper presents an ambient air quality monitoring and prediction system. The system consists of several distributed monitoring stations that communicate wirelessly to a backend server using mach

Insulation resistance (IR) or Megger test has been commonly performed in both preventive and corrective maintenance activities to verify power transformers' insulation condition. Other insulation di

This paper proposes a distributed lightpath protection scheme for diverse routing in multi-domain optical networks with correlated and probabilistic failures. This novel solution jointly considers t

Disease diagnosis often involves acquiring medical images using devices such as MRI, CT scan, x-ray, or mammograms of patients’ organs. Though many medical diagnostic applications have been propos

Data mining has been widely applied in various domains, however, there have been limited studies into discovering hidden knowledge from factual data about selected groups of people with special char

This paper investigates the application of data-mining techniques on a user’s browsing history for the purpose of determining the user’s interests. More specifically, a system is outlined that a

The credit derivatives market has experienced unprecedented growth over the past few years. As such, there is a growing interest in tools for pricing the most prominent credit derivative, the credit

A hybridized genetic algorithm is proposed to determine a repair schedule for a network of bridges. The schedule aims for the lowest overall cost while maintaining each bridge at satisfactory qualit

The task of extracting knowledge from text is an important research problem for information processing and document understanding. Approaches to capture the semantics of picture objects in documents

In 311 attempt to address some of the Web inforniation retrieval problems, we propose a conslruction ofa distribuied multi-agent systcoi. Agents in such a SGlliiIg are expected to exhibit intelligen

We present a novel methodology for the representation of sentences by fuzzy semantics, which is applied to the measurement of synonymy. The novelty of this methodology lies in a new way of dealing w

Grammar-based speech recognition systems exhibit performance degradation as their vocabulary sizes increase. Data clustering is deemed to reduce the proportionality of this problem. We introduce an

As an attempt to solve some contemporary web information retrieval problems, a construction of a cooperative multiagent system is proposed. This. paper introduces the system and presents the use of

Benefits of, and needs for, developing multiagent systems could be easily noticed in solving problems that are unfeasibly solved by any individual agent. Such systems are expected to exempt intellig