Zaher Al Aghbari

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• Doctor of Philosophy
• Professor (Full) at University of Sharjah

Offloading computational tasks is vital for real-time applications on mobile devices with limited resources. Mobile edge computing (MEC) is deemed a solution that puts computational resources closer to users. Nevertheless, there are many associated concerns during the offloading procedure (i.e., privacy, delay, and high energy consumption). Federat...

Electric Eel Foraging Optimization (EEFO) algorithm is a metaheuristic inspired by the social predation behavior of electric eels. It incorporates interactions, resting, migration, and hunting activities to enhance search efficiency. Although EEFO is effective for optimization tasks, it is characterized by slower convergence rates and a tendency to...

Breast cancer prognosis prediction is pivotal to improving the survival chances of breast cancer patients. Recently, deep learning models integrated with multi-modal data have been explored extensively to improve the reliability and accuracy of breast cancer prognosis prediction. However, multi-modal data poses challenges to feature extraction due...

Accurate node localization is essential in wireless sensor networks (WSNs) for effective data analysis and the successful operation of applications like environmental monitoring and disaster management. Range-free methods like the distance vector-hop (DV-hop) algorithm are often used due to hardware and cost constraints, but they face challenges in...

The global health crisis of COVID-19 has ushered in an era of unprecedented data generation, encompassing the virus’s transmission patterns, societal consequences, and governmental responses. Data mining has emerged as a pivotal tool for extracting invaluable insights from this voluminous dataset, offering critical support for informed decision-mak...

With the spread of GPS-equipped portable devices, Location-Based Services (LBSs) flourished. Some crucial LBSs require real-time processing of moving spatial-keyword queries over moving objects, such as an ambulance seeking for volunteers. The research community proposed solutions for scenarios assuming that either the queries or the queried object...

Data collection using Mobile Sink (MS) is one of the best approaches to address the hot spot issue resulting from multihop data collection and extend the lifetime of Wireless Sensor Networks wherein the MS tours a few specific locations called sojourn locations that serve as data collecting points (DCPs). The best choice of these locations is an NP...

Discovering discriminating features’ values that distinguish different data groups is vital for understanding the unique characteristics that define each group. These distinct features can be utilized in various applications, such as classification and data mining. One effective technique for this task is Contrast Sets Mining, which identifies sets...

The core algorithms of data mining (DM) enable the discovery of new information and insights by analyzing large amounts of data. Association rules mining (ARM), one of the several DM approaches, is extremely important in DM research. By utilizing ARM in medical diagnosis, early disease detection can be enhanced, and treatment recommendations can be...

The number of extracted features from medical data, such as computer-aided diagnosis, has been known to be too large and affects the performance of the used classifiers. Moreover, the large number of input features affects the accuracy of the classifiers, such as the traditional machine learning classifier. Therefore, in this paper, we proposed the...

Emailing is among the cheapest and most easily accessible platforms, and covers every idea of the present century like banking, personal login database, academic information, invitation, marketing, advertisement, social engineering, model creation on cyber-based technologies, etc. The uncontrolled development and easy access to the internet are the...

With the emergence of GPS-equipped portable devices and Online Social Networks, geo-tagged textual data have been highly produced on a continuous basis, which can provide important information for various applications, such as marketing, disaster response, and so on. Therefore, processing continuous spatial-keyword queries over streaming data is a...

Cloud service providers can offer virtual servers, with powerful processors and massive storage capacity for nominal per-use charges. With the underlying resources, the cloud allows data owners to trade capital expense for the variable cost. The auto-scaling features of the cloud can control the consumption of computing resources and hence the asso...

Pedestrian Attribute Recognition (PAR) deals with the problem of identifying features in a pedestrian image. It has found interesting applications in person retrieval, suspect re-identification and soft biometrics. In the past few years, several Deep Neural Networks (DNNs) have been designed to solve the task; however, the developed DNNs predominan...

This paper presents a pixel selection method for compact image representation based on superpixel segmentation and tensor completion. Our method divides the image into several regions that capture important textures or semantics and selects a representative pixel from each region to store. We experiment with different criteria for choosing the repr...

In this paper, we propose a new adaptive cross algorithm for computing a low tubal rank approximation of third-order tensors, with less memory and demands lower computational complexity than the truncated tensor SVD (t-SVD). This makes it applicable for decomposing large-scale tensors. We conduct numerical experiments on synthetic and real-world da...

Over the past few years, the on-demand transport services (e.g., Uber) have witnessed an unprecedented popularity due to its easy-to-adopt model (i.e., a passenger orders a taxi, a request is sent to all drivers in the vicinity, drivers accept/reject the order). Nonetheless, such a service is affected directly by multiple urban factors such as traf...

Hate speech has become a phenomenon on social media platforms, such as Twitter. These websites and apps that were initially designed to facilitate our expression of free speech, are sometimes being used to spread hate towards each other. In the Arab region, Twitter is a very popular social media platform and thus the number of tweets that contain h...

The number of extracted features from medical data, such as computer-aided diagnosis, has been known to be too large and affects the performance of the used classifiers. Moreover, the large number of input features affect the accuracy of the classifiers, such as the traditional machine learning classifier. Therefore, in this paper, we proposed the...

Maintaining prolonged service lifetime and adequate quality of sensing coverage are the key challenges in constructing Wireless Sensor Network (WSN) based applications. As such networks usually operate in inhospitable and hostile environment, failures are ineludible and providing resilience is a necessity. However, it is challenging to satisfy the...

Data collection is an important task in many mobile wireless sensor network (MWSN) applications. The energy of sensor nodes around the sink depletes rapidly due to transmitting large amounts of data from neighboring nodes. This problem can be mitigated through the use of intelligent mobile vehicles to collect the data. While traditional data collec...

In wireless sensor networks, a Mobile Collector (MC) is used to gather data by periodically traversing the network to avoid hotspot or energy-hole issues. Although the MC’s data collection process and network performance can be enhanced by determining suitable set of Stop Points (SPs), it is challenging to find the best set of SPs and schedule an e...

Although researchers have investigated multiple facets of fault tolerance, majority of them have overlooked fault tolerance in face structured WSNs. Motivated by this, we propose a Fault-Tolerant Coverage Preserving Strategy for Face Topology-based WSNs (FtCFt). Unlike existing methods of recovering failures by merging the adjacent faces, we propos...

Emotion recognition is the process to detect, evaluate, interpret, and respond to people's emotional states and emotions, ranging from happiness to fear to humiliation. The COVID- 19 epidemic has provided new and essential impetus for emotion recognition research. The numerous feelings and thoughts shared and posted on social networking sites throu...

Past studies reveal the benefits of using Mobile Sink in Wireless Sensor Networks to bring about increased data collection efficiency and overall network performance in numerous applications. While several MS data gathering methods have been proposed, most of them are less adaptive to changes in network topology and fails to modify the MS path suit...

Feature vectors are extracted to represent objects in many classification tasks, such as text classification. Due to the high dimensionality of these raw feature vectors, the classification efficiency and accuracy are reduced. Therefore, reducing the size of feature vectors by selecting the relevant features that better represent the objects is an...

Since the recent outbreak of COVID-19, many scientists have started working on distinct challenges related to mining the available large datasets from social media as an effective asset to understand people’s responses to the pandemic. This study presents a comprehensive social data mining approach to provide in-depth insights related to the COVID-...

One of the most important requirements for effective UAV–WSN operations is to perform data collection in timely and safe manner. Identifying an effective path in an environment with various obstacles and ensuring that the path may efficiently cover the selected stop points for effective data collection are both necessary and difficult. We propose a...

While several Mobile Sink (MS)-based data gathering methods have been proposed for Wireless Sensor Networks, most of them are less adaptive to changes in network topology, and the planned MS trajectory cannot be refined to accommodate node failures. Hence, a KD-Tree-based scheme (KDT) is proposed, which is an adaptive and robust algorithm that redu...

An emergent solution to overcome the limitations of traditional multi-hop routing in wireless sensor networks (WSNs) is to use mobile collectors (MCs) for data gathering, thereby reducing energy consumed in internode communications. Most of the existing data collection approaches emphasize data gathering or network lifetime extension, without takin...

Event detection from social media aims at extracting specific or generic unusual happenings, such as, family reunions, earthquakes, and disease outbreaks, among others. This paper introduces a new perspective for the hybrid extraction and clustering of social events from big social data streams. We rely on a hybrid learning model, where supervised...

Event detection from social media aims at extracting specific or generic unusual happenings, such as, family reunions, earthquakes, and disease outbreaks, among others. This paper introduces a new perspective for the incremental extraction and clustering of social events from big social data streams. We present ‘E-ware’, a scalable and efficient bi...

Wireless Sensor Networks based on Mobile Sinks (MS) are becoming more popular because of their benefits in enhancing performance and minimizing unequal energy usage of the nodes. While several MS data gathering methods have been proposed, most of them are less adaptive to changes in network topology and fails to modify the MS path suitably in respo...

In recent years, the number of online social networks users is dramatically increased. Cyberbullying is a serious threat in online social networks especially toward children and teenagers. Victims are harassed by perpetrators even with no physical attendance. Cyberbullying causes psychological damage to victims resulting in anxiety, depression, and...

The intelligent collaboration of Unmanned Aerial Vehicles (UAV) and Wireless Sensor Networks (WSN) can bring about increased data collection efficiency and overall network performance in numerous applications. Despite the success of different studies, UAV-WSN collaboration still faces many open challenges including trajectory planning, data collect...

Wireless Sensor Network with Mobile Collectors (MCs) enable increased flexibility and convenience in data gathering for numerous large-scale applications. However, introducing MCs also brings a new set of challenges to overcome. To reduce the data delivery latency of the application, it is required to select the minimum number of Rendezvous Points...

Influenza is a common seasonal disease that affects people worldwide. Quick reporting methods are needed to detect sudden influenza outbreaks so that health authorities can respond swiftly. Using social media posts to detect influenza related tweets may provide early insights about influenza outbreaks. In this paper, we introduce Deepluenza, a deep...

With the increased demand for outsourcing databases, there is a demand to enable secure and efficient communications. The concern regarding outsourcing data is mainly providing confidentiality and integrity to the data. This paper proposes a novel solution to answering kNN queries at the cloud server over encrypted data. Data owners transform their...

MATLAB was used to compute the effective thermal conduction of different samples of hard isotropic low porosity composites. The computational algorithms intended to use the Effective Medium Theory (EMT) Model to estimate the effective thermal conductivity (keff) of homogeneous composites. It estimates keff of a homogeneous mixture of components wit...

Finding influential members in social networks received a lot of interest in recent literature. Several algorithms have been proposed that provide techniques for extracting a set of the most influential people in a certain social network. However, most of these algorithms find influential nodes based solely on the topological structure of the netwo...

The tremendous growth of event dissemination over social networks makes it very challenging to accurately discover and track exciting events, as well as their evolution and scope over space and time. People have migrated to social platforms and messaging apps, which represent an opportunity to create a more accurate prediction of social development...

After the recent outbreak of COVID-19, researchers have risen working on several challenges related to the mining of social data to learn about people’s reactions to the epidemic. Recent studies have largely focused on extracting current themes and inferring broad attitudes, with a particular emphasis on the English language. This study presents va...

The popularity of social media together with the widespread of location-aware devices have let to the explosion of geo-tagged data. As a result, many interesting location-based services were developed. In particular, road traffic jam detection and analyzer service is of great importance to Intelligent Transportation Systems. Current works proposed...

Maintaining prolonged service lifetime and adequate quality of sensing coverage are the key challenges in constructing Wireless Sensor Network (WSN) based applications. As such networks usually operate in inhospitable and hostile environment, failures are ineludible and providing resilience is a necessity. However, it is challenging to satisfy the...

PurposeGlioblastoma is one of the most common and aggressive brain tumors in the world with a poor prognosis. A glioblastoma prognostication model has the potential to improve the cancer’s standard of care. No other paper has looked at using ensemble learning with a population database to predict multiple binary glioblastoma survival outcomes.Metho...

We address the problem of facial expression analysis. The proposed approach predicts both basic emotion and valence/arousal values as a continuous measure for the emotional state. Experimental results including cross-database evaluation on the AffectNet, Aff-Wild, and AFEW dataset shows that our approach predicts emotion categories and valence/arou...

Electronic Health Records (EHRs) are aggregated, combined and analyzed for suitable treatment planning and safe therapeutic procedures of patients. Integrated EHRs facilitate the examination, diagnosis and treatment of diseases. However, the existing EHRs models are centralized. There are several obstacles that limit the proliferation of centralize...

A popular unsupervised learning method, known as clustering, is extensively used in data mining, machine learning and pattern recognition. The procedure involves grouping of single and distinct points in a group in such a way that they are either similar to each other or dissimilar to points of other clusters. Traditional clustering methods are gre...

The most fundamental task of the wireless sensor network (WSN) is to monitor a specified region of interest with sufficient sensor coverage. This task is jeopardized when coverage holes appear after the network's deployment. The emergence of coverage holes is unavoidable for many reasons such as sensor node energy depletion, physical damage, or ext...

The increase in GPS-enabled devices and proliferation of location-based applications have resulted in an abundance of geotagged (spatial) data. As a consequence, numerous applications have emerged that utilize the spatial data to provide different types of location-based services. However, the huge amount of available spatial data presents a challe...

Providing effective sensing coverage of an observation area with reduced set of working nodes for maximum duration of time is an important concern for the development of durable and energy efficient WSN applications. A well-organized network structure can greatly promote such requirements. Motivated by the use of computational geometry in network d...

Wireless Sensor Networks (WSNs) consist of sensor nodes that have limited batteries, and are hard to replace due to their deployment in inaccessible areas. Hence, the goal of any WSN protocol is to implement energy efficient mechanisms that prolong the lifetime of WSNs. The existing face topology in WSNs can lead to high energy consumption due to a...

Social media has gained a lot of popularity and become the main source of information. Recently, immediate popular news or stories, known as trending topics have found social networks, such as Twitter, an attractive platform for its spread. Detection of trending topics from social media is a commonly tackled issue in the data mining community as it...

Optimal performance and improved lifetime are the most desirable design benchmarks for IoT based WSNs and the mechanism for data gathering is a major constituent in uencing these standards. Several researchers have provided signi�cant evidence on the advantage of Mobile Sink (MS) in performing e�ective gathering of relevant data. However, determini...

The ubiquity of geo-positioning technologies stimulates continuous growth in dynamic spatial datasets that fuels the development of location-based services. These services require tracking and querying a large population of moving objects. High workloads of users’ requests, both location updates and queries, need to be processed concurrently. Curre...

Social media bots (automated accounts) attacks are organized crimes that pose potential threats to public opinion, democracy, public health, stock market and other disciplines. While researchers are building many models to detect social media bot accounts, attackers, on the other hand, evolve their bots to evade detection. This everlasting cat and...

Over the past few decades, one of the important advancements in wireless communication is low cost and limited power devices known as wireless sensor networks (WSNs). Sensor nodes are used to transmit data but have limited amount of energy. As the transmission takes place, energy gets depleted. So energy consumption and network lifetime are the maj...

Over the past few decades, one of the important advancements in wireless communication is low cost and limited power devices known as wireless sensor networks (WSNs). Sensor nodes are used to transmit data but have limited amount of energy. As the transmission takes place, energy gets depleted. So energy consumption and network lifetime are the maj...

We proposes an energy-aware distributed scheme GPD (General Phenomena Detection) to detect phenomena in data gathered from mobile sensors. In the proposed algorithm, mobile sensors self-organize themselves into groups and elect group heads based on the location of the phenomena. To better share the extra battery power overhead, group heads and subs...

An efficient discovery algorithm of frequently occurring patterns, called motifs, in a time series would be useful as a tool for summarizing and visualizing big time series databases. In this paper, we propose an efficient approximate algorithm, called DiscMotifs, to discover the K most significant (KMS) motifs from time series. First, the proposed...

We address the problem of emotional state detection from facial expressions. Our proposed approach simultaneously detects faces and predicts both discrete emotion categories and continuous valence/arousal values from raw input images. We train and evaluate our approach on 3 different datasets, compare our approach to other state-of-the-art approach...

The increased popularity of micro-blogging applications together with the widespread of location- aware devices have resulted in the creation of large streams of geo-tagged data. Such data provides a great opportunity for researchers to explore event detection and prediction. In particular, road traffic detection and prediction are of great importa...

The work of wireless sensor networks (WSNs) depends on reliable coverage of the area to be monitored. The problem of coverage holes arises when one or more nodes fail due to energy depletion or harsh physical environments. Furthermore, random deployment of nodes could lead to a high degree of overlapping coverage among the sensor nodes. While there...

People nowadays rely heavily on technology and prefer being provided with personalized services. Location based services (LBS) are one of those highly popular services. Users provide their location information in some query to the LBS server, which in turn processes the query and returns personalized results. The major concern when dealing with the...