Khursheed KhursheedKing Saud University | KKUH · Department of Computer Engineering
Khursheed Khursheed
PhD - Electronic Engineering
About
180
Publications
37,747
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3,215
Citations
Introduction
Additional affiliations
August 2016 - present
August 2016 - present
January 2016 - present
Education
September 2009 - June 2013
September 2007 - September 2009
January 2002 - March 2006
Publications
Publications (180)
In the context of Cloud and Fog computing settings, recent developments in deep learning techniques show great potential for changing several fields, including healthcare. In this study, we make a contribution to this changing field by proposing an enhanced deep learning‐based strategy for classifying chest X‐ray images, using pre‐trained models su...
The Hybrid Parallel Fuzzy CNN (HP-FCNN) is a ground-breaking method for medical image analysis that combines the interpretive capacity of fuzzy logic with the capabilities of a convolutional neural network (CNN). This novel combination tackles problems related to brain image processing, reducing problems such as noise and hazy borders that are comm...
Wireless Body Area Networks (WBANs) have the potential to revolutionize the field of biomedical monitoring. However, the design of Body Area Networks (BANs) faces numerous challenges, such as limited energy resources, efficient sensor node deployment, sensor node miniaturization, collaborative communication, customized application development, and...
Generating poetry using machine and deep learning techniques has been a challenging and exciting topic of research in recent years. It has significance in natural language processing and computational linguistics. This study introduces an innovative approach to generate high-quality Pashto poetry by leveraging two pre-trained transformer models, La...
Introduction
Alzheimer's disease (AD) is a neurodegenerative disorder and the most prevailing cause of dementia. AD critically disturbs the daily routine, which usually needs to be detected at its early stage. Unfortunately, AD detection using magnetic resonance imaging is challenging because of the subtle physiological variations between normal an...
Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-BiLSTM, which combines attention-based Improved Convolutional Neural Networks (ICNN) and Bidirectional Long Sho...
In recent years, due to the rapid development of social media, there have been many propaganda texts and propaganda activities on the internet. While previous studies have primarily concentrated on linguistic characteristics for detecting propaganda, there has been a lack of systematic investigation into the role of semantic features in the dissemi...
Layout analysis is the main component of a typical Document Image Analysis (DIA) system and plays an important role in pre-processing. However, regarding the Pashto language, the document images have not been explored so far. This research, for the first time, examines Pashto text along with graphics and proposes a deep learning-based classifier th...
Sarcasm detection is challenging in natural language processing (NLP) due to its implicit nature, particularly in low‐resource languages. Despite limited linguistic resources, researchers have focused on detecting sarcasm on social media platforms, leading to the development of specialized algorithms and models tailored for Urdu text. Researchers h...
In this work, we aim to introduce some modifications to the Anam-Net deep neural network (DNN) model for segmenting optic cup (OC) and optic disc (OD) in retinal fundus images to estimate the cup-to-disc ratio (CDR). The CDR is a reliable measure for the early diagnosis of Glaucoma. In this study, we developed a lightweight DNN model for OC and OD...
In delay tolerant networks (DTNs) the messages are often not delivered to the destination due to a lack of end-to-end connectivity. In such cases, the messages are stored in the buffer for a long time and are transmitted when the nodes come into the range of each other. The buffer size of each node has a limited capacity, and it cannot accommodate...
Blinding eye diseases are often related to changes in retinal structure, which can be detected by analysing retinal blood vessels in fundus images. However, existing techniques struggle to accurately segment these delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on specific operations can limit...
Mobile edge computing (MEC) reduces the latency for end users to access applications deployed at the edge by offloading tasks to the edge. With the popularity of e-commerce and the expansion of business scale, server load continues to increase, and energy efficiency issues gradually become more prominent. Computation offloading has received widespr...
The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the image is essential. This study proposes a unique method for feature extraction...
Introduction
Cardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction methodologies within the ambit of healthcare data analysis. The vast volume of medical data available necessitates effective data mining techniques to extract valuable insight...
The Internet of Medical Things (IoMT) has revolutionized healthcare, particularly in ambient assisted living (AAL). Deep learning has emerged as a powerful tool for identifying disorders and making health‐related decisions. Pneumonia, a dangerous and contagious disease, has a significant global impact. Prompt and accurate diagnosis is crucial, but...
Background
The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error.
Obj...
Over the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. Despite the popularity of ML techniques, only a few research studies have focused on the application of ML especially supervised learning techniques in Requiremen...
Wheat is a critical crop, extensively consumed worldwide, and its production enhancement is essential to meet escalating demand. The presence of diseases like stem rust, leaf rust, yellow rust, and tan spot significantly diminishes wheat yield, making the early and precise identification of these diseases vital for effective disease management. Wit...
Diagnosing various diseases such as glaucoma, age-related macular degeneration, cardiovascular conditions, and diabetic retinopathy involves segmenting retinal blood vessels. The task is particularly challenging when dealing with color fundus images due to issues like non-uniform illumination, low contrast, and variations in vessel appearance, espe...
The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend agai...
The historic evolution of global primary energy consumption (GPEC) mix, comprising of fossil (liquid
petroleum, gaseous and coal fuels) and non‑fossil (nuclear, hydro and other renewables) energy
sources while highlighting the impact of the novel corona virus 2019 pandemic outbreak, has been
examined through this study. GPEC data of 2005–2021 has b...
The evolution of Autonomous Vehicles (AVs) has blurred the distinction between drivers and passengers, resulting in increased demand for in-car entertainment. Simultaneously, social networking platforms are evolving to adopt AR (Augmented Reality)/VR (Virtual Reality)-centric metaverses. Self-driving cars will free up the drivers and passengers to...
Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically e...
Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements. Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. Accurate bug prediction is crucial for software ev...
The research aims to improve the prediction of drug sensitivity on cancer cell lines using gene expression data and molecular fingerprints of drugs. The proposed study uses a deep learning model, BioMarkerX, trained on the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) datasets utilizing Particle Swarm Optimi...
Recent years have witnessed security as a great concern in vehicular networks (VANET). Particularly, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can jeopardize the network by broadcasting a storm of packets. Correspondingly, the network resources are jammed with malicious traffic. In this connection, the existing resear...
In the domain of edge computing for medical image analyses, utilizing advanced deep learning algorithms has shown promise in boosting precision and adaptability. Medical image analysis frequently requires striking a compromise between a more comprehensive contextual understanding and specific local information. Even though U-Net with Transformers i...
The investigation of the behavior of a trihybrid nanofluid flow inner the conoidal gap created among a revolving disc and a stationary cone is examined computationally in this study.The temperature and flow properties of the nanofluid, which is made up ofthree distinct kinds of nanoparticles, $Al_{2}O_{3}$, $TiO_{2}$ and Ag areexamined with the blo...
Hand gestures have been used as a significant mode of communication since the advent of human civilization. By facilitating human-computer interaction (HCI), hand gesture recognition (HGRoc) technology is crucial for seamless and error-free HCI. HGRoc technology is pivotal in healthcare and communication for the deaf community. Despite significant...
Full-duplex HCNs have emerged as a compelling solution to meet the escalating traffic demands of future fifth-generation networks. Theoretical advantages of full-duplex cellular networks include the potential for doubling throughput compared to half-duplex cellular systems, as base stations can both receive and transmit within the same frequency ba...
Ever-increasing dynamic surges in renewable-based electric power systems, notably wind and photovoltaic farms bring adverse impacts and challenges in terms of reliability and stability. The intermittency of renewable sources imposes significant deviations in frequency due to variations in demand. Wind power induces instability in the grid due to it...
Node localization is one of the most essential features of wireless sensor networks (WSNs). Vavarious localization algorithms exist for densely deployed 3-D wireless sensor networks. However, for a sparse 3-D network, range-based localization is still a challenging task because it is difficult to find sufficient anchor nodes and distance informatio...
Cryptocurrencies, recognized by their extreme volatility due to dependency on multiple direct and indirect factors, offer a significant challenge regarding precise price forecasting. This uncertainty has led to investment hesitation within the digital currency market. Previous research attempts have presented methodologies for price forecasting and...
Today Internet of Things (IoT) has become a key part of the modern world as it enables web-based IoT devices to collect, transfer, and analyze the data of individuals, companies, and industries. IoT provides numerous services and applications via a massive number of interconnected devices and has become an innovative attack vector for cyber-attacks...
Automated urine sediment analyzers play a crucial role in diagnosing urinary tract infections, offering real-time data analysis and expediting patient diagnosis. This paper introduces a novel hybrid approach combining data-centric and model-centric techniques for automated urine sediment analysis. The proposed methodology addresses challenges such...
In the n-tier framework, data generated by sensors requires immediate execution. The processing elements need powerful resources to entertain incoming requests. Fog computing, unlike cloud computing, provides low latency for real-time applications. However, data generated by real-time Internet of Things (IoT) devices significantly impacts fog devic...
The integration of radiology reports for healthcare treatment using AI presents a transformative opportunity to enhance patient care and optimize healthcare delivery. Generating accurate radiology reports is crucial for guiding patient treatment in the clinical traditional applications or machines. However, the task of writing these reports can be...
Breast cancer has a wide range of possible outcomes due to its complexity and heterogeneity. The process of manually detecting breast cancer is laborious, intricate, and inaccurate. It is essential for individualized treatment planning to have a reliable prognosis of patient survival. Increased focus in recent years has been placed on genomics-base...
The advent of the next generation of wireless communication (NextGen) demands substantial investments and collaborative research for the fundamental need of wireless communications. Therefore, cooperative communication plays a vital role in overcoming challenges such as reliability, throughput, and outage trade-offs. We propose a two-transmission p...
Consumer Internet of Things (CIoT) interconnects multiple devices over internet, like smartphones, wearables, and smart gadgets to simplify tasks and provide convenience. However, it encounters obstacles such as privacy apprehensions arising from data aggregation, security flaws, interoperability discrepancies. Federated learning (FL) mitigates the...
Early detection of brain tumors is vital for improving patient survival rates, yet the manual analysis of the extensive 3D MRI images can be error‐prone and time‐consuming. This study introduces the Deep Explainable Brain Tumor Deep Network (DeepEBTDNet), a novel deep learning model for binary classification of brain MRIs as tumorous or normal. Emp...
Prognostic survival prediction in colorectal cancer (CRC) plays a crucial role in guiding treatment decisions and improving patient outcomes. In this research, we explore the application of deep learning techniques to predict survival outcomes based on histopathological images of human colorectal cancer. We present a retrospective multicenter study...
Demand-side management has garnered significant attention recently as a viable solution for electricity demand management. However, existing control approaches have encountered stability and energy efficiency issues that limit their effectiveness. To address these concerns, this work proposes a multi-agent control technique for optimal Demand-Side...
Cervical cancer is one of the most dangerous and widespread illnesses afflicting women throughout the globe, particularly in East Africa and South Asia. In industrialised nations, the incidence of cervical cancer has consistently decreased over the past few decades. However, in developing countries, the reduction in incidence has been considerably...
Adaptive self-learning is a promising technique in medical image analysis that enables deep learning models to adapt to changes in image distribution over time. As medical image data can vary due to factors like imaging equipment and patient demographics, adaptive self-learning becomes valuable for maintaining the accuracy and robustness of deep le...
Analyzing and understanding the electricity consumption of end users, especially the anomalies (outliers), are vital for the planning, operation, and management of the power grid. It will help separate the group of users with unpredictable consumption behavior and then develop and train specialized deep learning models for power load forecasting or...
In recent years, augmented reality (AR) and virtual reality (VR) have boosted the technology market and become a hot topic of research. AR/VR technology has the potential to revolutionize the traditional education system by providing interactive and immersive learning experiences that are difficult to replicate through traditional classroom or onli...
Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa, computer-aided diagnostic (CAD) methods have eme...
Recent progress in Deep Learning (DL) has shown potential in intelligent healthcare applications, enhancing patients’ quality of life. However, improving DL precision requires a larger and diverse dataset, leading to privacy and confidentiality challenges when consolidating data at a centralized server. To address this, we propose a skin cancer det...
In light of the existing challenges in capturing short-term fluctuations and achieving accurate predictions in stock market time series data, this research presents the “Dual-Attention MDWT-CVT-LSTM,” a revolutionary financial time series forecasting model. This model makes use of a dual-attention mechanism in conjunction with a Variant Transformat...
In the realm of machine vision, the convolutional neural network (CNN) is a frequently used and significant deep learning method. It is challenging to comprehend how predictions are formed since the inner workings of CNNs are sometimes seen as a black box. As a result, there has been an increase in interest among AI experts in creating AI systems t...