Wathiq Mansoor

University of Dubai · College of Engineering and IT

In this study, we present a cutting-edge ensemble of deep learning models- DenseNet-201, EfficientNet, and ConvNeXt tailored for precise classification of White Blood Cells (WBCs), a crucial aspect of hematological analysis. Leveraging the Raabin WBC and Large Diverse WBC datasets, our approach encompasses the identification of five distinct WBC ty...

With the ever-growing complexity of models in the field of remote sensing (RS), there is an increasing demand for solutions that balance model accuracy with computational efficiency. Knowledge distillation (KD) has emerged as a powerful tool to meet this need, enabling the transfer of knowledge from large, complex models to smaller, more efficient...

Augmented Reality (AR) technology is swiftly progressing within the realm of medicine, notably in the domain of biomedical signal processing. It strengthens diagnostic proficiency while fostering interactive 3D virtual environments. The study presents an affordable AR visualisation for Electrocardiography ’ECG’ arrhythmias classification. The propo...

Introduction: In the evolving landscape of healthcare andmedicine, themerging of extensive medical datasets with the powerful capabilities of machine learning (ML) models presents a significant opportunity for transforming diagnostics, treatments, and patient care. Methods: This research paper delves into the realm of data-driven healthcare, placin...

This article explores the influence of lunar regolith and rover structure, such as mast design and material composition, on antenna parameters. It focuses on the distinctive difficulties of communication in the lunar environment, which need specialized antenna solutions. This study specifically examines the performance of antennas on the lunar Rash...

Many incurable diseases prevalent across global societies stem from various influences, including lifestyle choices, economic conditions, social factors, and genetics. Research predominantly focuses on these diseases due to their widespread nature, aiming to decrease mortality, enhance treatment options, and improve healthcare standards. Among thes...

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of deep learning (DL). However, the latter faces various issues, including the lack of data or annotated data, the existence of a significant gap between training data and test data, and the requirement for high computational resources. To that end, deep transfer lea...

Indoor localization systems predominantly depend on one-dimensional signal measurements, such as the Received Signal Strength Indication (RSSI) from Bluetooth or WiFi access points (AP). Such methods, however, grapple with issues like interference from other APs and environmental challenges. To address these, our paper introduces an innovative indo...

Machine learning has revolutionized the field of agricultural science, particularly in the early detection and management of plant diseases, which are crucial for maintaining crop health and productivity. Leveraging advanced algorithms and imaging technologies, researchers are now able to identify and classify plant diseases with unprecedented accu...

Advancements in genomic technologies have paved the way for significant breakthroughs in cancer diagnostics, with DNA microarray technology standing at the forefront of identifying genetic expressions associated with various cancer types. Despite its potential, the vast dimensionality of microarray data presents a formidable challenge, necessitatin...

Solar Photovoltaic (PV) systems are increasingly vital for enhancing energy security worldwide. However, their efficiency and power output can be significantly reduced by hotspots and snail trails, predominantly caused by cracks in PV modules. This article introduces a novel methodology for the automatic segmentation and analysis of such anomalies,...

Intrusion detection systems (IDS) monitor and analyze network traffic and system activity to detect and alert security personnel to potential security breaches or attacks. Although deep learning models have shown great promise in improving the accuracy and efficiency of IDSs, several challenges are associated with their use, including data scarcity...

Device-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power allocation to reduce co-channel interference is crucial...

This study introduces a novel approach to enhance state and unknown input estimation for the synchronous reluctance motor, utilizing the Takagi-Sugeno fuzzy representation. A proportional multi-integral observer structure is introduced, capable of capturing a wider range of unknown input dynamics compared to the previous proportional integral obser...

Indoor localization (IL) is a significant topic of study with several practical applications, particularly in the context of the Internet of Things (IoT) and smart cities. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing inter...

This study presents an innovative control method for a specific type of DC/DC power converter, known as a three-leg interleaved non-isolated converter (IBC), which is particularly useful for proton exchange membrane fuel cell (PEMFC) electric vehicles (EVs). The control strategy introduced is a dual-loop system, utilizing a fractional-order proport...

This work demonstrates the development and effectiveness of a novel diagnostic device that uses virtual reality (VR) and micro-heating touch feedback to measure the size and temperature of lung tumors. This cutting-edge technology helps with the accurate diagnosis and localization of tumors by providing users with a warm sensation when they touch a...

The growing use of deep learning models in end-device applications has led to various inference attacks and associated data privacy threats. Recent research also reveals the susceptibility of CNN models to model inversion-inference attacks, including the reversal of private training data. In response, this paper proposes a novel defense strategy ca...

This paper presents an in-depth exploration of machine learning (ML) and deep learning (DL) for the optimization and design of dual-band antennas in Internet of Things (IoT) applications. Dual-band antennas, which are essential for the functionality of current and forthcoming flexible wireless communication systems, face increasing complexity and d...

Customers are the most critical component in a business’s success regardless of the industry or product. Companies make significant efforts to acquire and, more importantly, retain their existing customers. Customer churn is a significant challenge for businesses, leading to financial losses. To address this challenge, understanding customer’s cogn...

div class="section abstract"> Light fidelity (LiFi) technology holds immense potential to revolutionize wireless communication networks by utilizing light bulbs for reliable and cost-effective interconnections. Integration of LiFi technology with advanced solutions is proposed to significantly enhance the passenger experience in autonomous buses. T...

Over the previous two decades, a notable array of space exploration missions have been initiated with the primary aim of facilitating the return of both humans and robots from Earth to the moon. The significance of these endeavors cannot be emphasized enough as numerous entities, both public and private, from across the globe have invested substant...

Generative Adversarial Networks (GANs) have gained prominence in medical imaging due to their ability to generate realistic images. Traditional GANs, however, often fail to capture intricate topological features such as holes and connectivity components in real images. This study applies TopoGAN, a recently developed model tailored for medical imag...

This article provides an introduction and overview of the mathematical concept of homotopy continuation and its applications-especially for path tracing-in robotic-assisted surgery. It explains the importance of homotopy continuation in solving path-planning and restriction problems and image reconstruction. The opinion article starts with a short...

Institutional bias can impact patient outcomes, educational attainment, and legal system navigation. Written records often reflect bias, and once bias is identified; it is possible to refer individuals for training to reduce bias. Many machine learning tools exist to explore text data and create predictive models that can search written records to...

Accurately estimating the position and orientation of objects in augmented reality (AR) is a crucial task known as 6 Degrees of Freedom (6 DoF) object pose estimation. This capability enables machines to understand their surroundings in three dimensions, enabling them to perform various tasks like object recognition, tracking, interaction, and mani...

Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years, offering advanced tools and methodologies that promise to revolutionize patient outcomes. This review provides an exhaustive overview of the contemporary frameworks employed in the field, focusing on the objective of AI-driven analysis and dissecting m...

There has been a growing interest in creating intelligent diagnostic systems to assist medical professionals in analyzing and processing big data for the treatment of incurable diseases. One of the key challenges in this field is detecting thyroid cancer, where advancements have been made using machine learning (ML) and big data analytics to evalua...

Objective Scar tissue is an identified cause for the development of malignant ventricular arrhythmias in patients of myocardial infarction, which ultimately leads to cardiac death, a fatal outcome. We aim to evaluate the left ventricular endocardial Scar tissue pattern using Radon descriptor-based machine learning. We performed automated Left ventr...

Computer Vision (CV) is playing a significant role in transforming society by utilizing machine learning (ML) tools for a wide range of tasks. However, the need for large-scale datasets to train ML models creates challenges for centralized ML algorithms. The massive computation loads required for processing and the potential privacy risks associate...

The growing interest in unmanned aerial vehicles (UAVs) from both the scientific and industrial sectors has attracted a wave of new researchers and substantial investments in this expansive field. However, due to the wide range of topics and subdomains within UAV research, newcomers may find themselves overwhelmed by the numerous options available....

p>With the increasing use of technologies and digitally driven healthcare systems worldwide, there will be several opportunities for the use of big data in personalized healthcare. In addition, With the advancements and availability of internet of things (IoT) based point-of-care (POC) technologies, big data analytics and artificial intelligence (A...

The growing interest in unmanned aerial vehicles (UAVs) from both scientific and industrial sectors has attracted a wave of new researchers and substantial investments in this expansive field. However, due to the wide range of topics and subdomains within UAV research, newcomers may find themselves overwhelmed by the numerous options available. It...

Non-intrusive Load Monitoring (NILM) algorithms, commonly referred to as load disaggregation algorithms, are fundamental tools for effective energy management. Despite the success of deep models in load disaggregation, they face various challenges, particularly those pertaining to privacy and security. This paper investigates the sensitivity of pro...

Solar Photovoltaic (PV) is increasingly being used to address the global concern of energy security. However, hot spot and snail trails in PV modules caused mostly by crakes reduce their efficiency and power capacity. This article presents a groundbreaking methodology for automatically identifying and analyzing anomalies like hot spots and snail tr...

Person re-identification (PRe-ID) is a crucial task in security, surveillance, and retail analysis, which involves identifying an individual across multiple cameras and views. However, it is a challenging task due to changes in illumination, background, and viewpoint. Efficient feature extraction and metric learning algorithms are essential for a s...

Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be ad...

Mobile computing requires adopting various techniques to enhance the performance of the hardware used at the end device. Edge and mobile devices usually have limited resources, compared to data centers and dedicated machines that are designed and build to support huge computations. While the performance of these devices is practically doable throug...

The World Health Organization’s (WHO) Global Health Security (GHS) index of 40.2/100 indicates that almost every country has critical healthcare gaps in prevention, detection, reporting, rapid response, the capacity of the healthcare system, compliance with international standards, risk situations, and health financing for sustainable development....

Chat Generative Pre-trained Transformer (ChatGPT) has gained significant interest and attention since its launch in November 2022. It has shown impressive performance in various domains, including passing exams and creative writing. However, challenges and concerns related to biases and trust persist. In this work, we present a comprehensive review...

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to t...