Uzair Aslam Bhatti

Verified

Uzair verified their affiliation via an institutional email.

Learn more

Verified

Uzair verified their affiliation via an institutional email.

Learn more

• Doctor of Philosophy
• PhD Student at Hainan University

Air pollution, one of the most serious environmental issues that people face, affects the standard of living in urban areas. Strategies for assessing and alerting the public to anticipated hazardous levels of air pollution can be developed using particulate matter (PM) forecasting models. Accurate assessments of pollutant concentrations and forecas...

The accurate and early detection of brain tumor types, such as gliomas, meningiomas, and pituitary tumors, is crucial for effective treatment planning and improving patient outcomes. However, advanced Computer-Aided Diagnosis (CAD) systems often face significant limitations in resource-constrained healthcare settings due to their high computational...

Hyperspectral image classification is crucial in remote sensing but faces significant challenges, including long-range dependence in spatial-spectral information and the difficulty of effectively fusing spectral and spatial features. To address these issues, we propose a novel classification framework, Multiscale Attention Augmented Transformer Net...

The precise and timely diagnosis of brain tumors is essential for accelerating patient recovery and preserving lives. Brain tumors exhibit a variety of sizes, shapes, and visual characteristics, requiring individualized treatment strategies for each patient. Radiologists require considerable proficiency to manually detect brain malignancies. Howeve...

This paper investigates the stabilization and synchronization of positive complex networks with dynamic links. A class of positive complex networks is constructed by introducing dynamic link between nodes. A controller and the corresponding coupling term with respect to links are designed to achieve the positivity and stability of complex networks...

Objective Early and accurate detection of COVID-19 and pneumonia through medical imaging is critical for effective patient management. This study aims to develop a robust framework that integrates synthetic image augmentation with advanced deep learning (DL) models to address dataset imbalance, improve diagnostic accuracy, and enhance trust in arti...

Temporal knowledge graph (TKG) representation learning is a pivotal task aimed at transforming entities and relations within TKG from a high-dimensional vector space to a lower-dimensional vector space, while preserving the relational features inherent in TKG. TKG comprises a sequence of knowledge graphs (KGs) at various timestamps. Presently, exis...

Deep learning is revolutionizing the analysis of medical signals and images, offering unprecedented advancements in diagnostic accuracy and efficiency. Techniques such as convolutional and recurrent neural networks are transforming the processing of radiological scans, ultrasound images, and ECG readings. By enabling more detailed and precise inter...

This paper studies the consensus problem of T–S fuzzy positive multi-agent systems with cyber attacks and denial of service attacks. A proportional-integral-derivative protocol is designed based on a matrix decomposition technique. By introducing new variables, the consensus problem is transformed into the stability problem of a new system. In term...

This paper proposes a kind of switched-gain observer-based control approach for unknown nonlinear network systems using distributed process data. By utilizing Willems behavioral system theory, a system explaining the data is constructed by virtue of finite data in collected distributed process data without any system identification step. A data-bas...

Reasoning over Temporal Knowledge Graphs (TKGs) presents challenges in modeling the dynamic relationships and evolving behaviors of entities and relations over time. Traditional approaches often treat entities and relations separately, which limits their ability to capture their joint temporal evolution and interactions. To overcome these limitatio...

Since the onset of the Industrial Revolution, the financial sector has emerged as a substantial contributor to the advancement of human society. Nevertheless, providing financial support for environmentally detrimental projects has raised numerous questions regarding establishing a direct connection between financial market instruments and their im...

Underwater vision is essential in numerous applications, such as marine resource surveying, autonomous navigation, objective detection, and target monitoring. However, raw underwater images often suffer from significant color deviations due to light attenuation, presenting challenges for practical use. This systematic literature review examines the...

Climate change, caused by the burning of fossil fuels, has emerged as an acute world problem, especially in the Group of Twenty (G20), where CO 2 emissions and other greenhouse gases are accumulating. This research examines how energy consumption, digitization, internationalization, and green finance affect the environmental sustainability of G20 c...

In prostate 3D MRI image segmentation methods, it is usually necessary to annotate each slice, and these annotations are generally time-consuming and specialized. In this study, we generate pseudo-labels using an annotation method with one foreground seed point and six edge relaxation points. We design a weakly supervised semantic learning segmenta...

The U-net Architecture is a U-shaped neural network developed from Fully Convolutional Networks FCNs in medical image segmentation. However, the traditional Convolutional Neural Networks (CNNs), like the one in U-Net, poses challenges in addressing long-distance relationships relating to the subject matter hence making their applicability to severa...

With the widespread use of digital medical images in recent years, the efficient and secure protection of sensitive data has become an urgent issue. Existing research has mostly focused on optimizing individual techniques, lacking comprehensive solutions that integrate the strengths of different methods. This article proposes a hybrid digital water...

This paper investigates regional proportional-integral-derivative consensus of switched positive multi-agent systems with multiple equilibria. A distributed proportional-integral-derivative control protocol is developed by integrating the communication protocol, agent state, and consensus error. A novel switched positive consensus error system is e...

Brain tumor segmentation and the accurate detection of tumor types remain significant challenges due to image variability, tumor heterogeneity, and boundary demarcation difficulties. These challenges are exacerbated by limited annotated datasets, class imbalances, and the high computational cost of existing methods. Our study introduces the enhance...

Hyperspectral imaging (HSI) can capture a large amount of spectral information at various wavelengths, enabling detailed material classification and identification, making it a key tool in remote sensing, particularly for coastal area monitoring. In recent years, the CNN framework and transformer models have demonstrated strong performance in HSI c...

To address the real-time transmission challenges of multiple types of data frames in Advanced Driver Assistance Systems, this paper proposes an in-vehicle Ethernet transmission technology based on Time-Sensitive Networking. In the ADAS environment, existing shortest-path-based routing algorithms often lead to load imbalance, and time-slot schedulin...

Factory aquaculture has emerged as a pivotal approach for farmers and researchers dedicated to sustainable, low-carbon agricultural practices. With the advent of agricultural consumer electronics, such as automated feeders, sensors, and controllers, integrating sustainable computing into these devices is crucial for optimizing resource usage. This...

Remote sensing image captioning is crucial as it enables the automatic interpretation and description of complex images captured from satellite or aerial sensors, facilitating the efficient analysis and understanding of vast amounts of geospatial data. This capability is essential for various applications, including environmental monitoring, disast...

Medical image segmentation is important for medical diagnosis, and deep convolutional neural networks (CNNs) have made significant progress in this area. However, CNNs are deficient as they mainly focus on localized features. In contrast, the Transformer architecture is able to consider the entire input sequence and is therefore more effective in c...

The task of predicting entities and relations in Temporal Knowledge Graph (TKG) extrapolation is crucial and has been studied extensively. Mainstream algorithms, such as Gated Recurrent Unit (GRU) models, primarily focus on encoding historical factual features within TKGs, often neglecting the importance of incorporating entities and relational fea...

The water quality in indoor mariculture systems directly impacts the health of aquaculture species. Dissolved oxygen (DO) is a crucial indicator in these systems, and accurate, rapid prediction of DO levels is essential for improving the yield and quality of aquaculture products. This study addresses the challenge of complex DO variations and low p...

This review paper delves into the extensive research on urban forms, focusing on urban sprawl through both traditional and contemporary approaches within urban geography. The complexity of defining urban sprawl has hindered the development of reliable models, with debates among researchers complicating a unified understanding. This selective review...

The long-term development goals of most countries face significant challenges in reducing emissions, improving environmental sustainability, and mitigating the negative effects of climate change. This study looks at how the ecological sustainability of BRICS countries is affected by economic growth, financial development, new technologies, and rene...

The long-term development goals of most countries face significant challenges in reducing emissions, improving environmental sustainability, and mitigating the negative effects of climate change. This study looks at how the ecological sustainability of BRICS countries is affected by economic growth, financial development, new technologies, and rene...

The quality of life in cities is impacted by air pollution, which is one of the most dangerous environmental problems that humans confront. Strategies for evaluating and alerting the public to expected dangerous levels of air pollution can be developed using particulate matter (PM) forecasting models. Precise assessments of pollutant concentrations...

Analysis of hyperspectral imagery (HSI) is a critical aspect of remote sensing in precision agriculture, for which effective dimensionality reduction (DR) strategies for the inherent complexity and uncertainty of the data are highly necessary. The fusion of fuzzy logic with DR techniques offers potential promises to refine enough feature informatio...

The prediction task of entities and relationships in Temporal Knowledge Graph (TKG) extrapolation is crucial and extensively studied. Mainstream algorithms like Gated Recurrent Unit (GRU) models primarily focus on encoding historical factual features within TKGs, often neglecting the importance of incorporating entity and relationship features duri...

Blockchain (BCn) revolution across the world leads the global transformation by altering the existing structure and introducing the accessible and decentralized paradigm. This technology facilitates access to resources and redefines conventional frameworks, empowering people and communities globally. Motivated by these attention‐seeking potentialit...

The digital twin of the ocean (DTO) is a groundbreaking concept that uses interactive simulations to improve decision-making and promote sustainability in earth science. The DTO effectively combines ocean observations, artificial intelligence (AI), advanced modeling, and high-performance computing to unite digital replicas, forecasting, and what-if...

Due to rapid expansion in the global economy and industrialization, PM2.5 (particles smaller than 2.5 µm in aerodynamic diameter) pollution has become a key environmental issue. The public health and social development directly affected by high PM2.5 levels. In this paper, ambient PM2.5 concentrations along with meteorological data are forecasted u...

The Chinese government has implemented severe restrictions and lockdown (LD) measures in response to the COVID-19 pandemic. This kind of environment offers a terrific opportunity to work in this area. The current study sought to evaluate the COVID-19 lockdown’s spatiotemporal impact on Jiangsu Province, China’s air quality. We examined the data gat...

The primary objective of interactive medical image segmentation systems is to achieve more precise segmentation outcomes with reduced human intervention. This endeavor holds significant clinical importance for both pre-diagnostic pathological assessments and prognostic recovery. Among the various interaction methods available, click-based interacti...

The recent advancements in automated lung cancer diagnosis through the application of Convolutional Neural Networks (CNN) on Computed Tomography (CT) scans have marked a significant leap in medical imaging and diagnostics. The precision of these CNN-based classifiers in detecting and analyzing lung cancer symptoms has opened new avenues in early de...

Understanding and identifying polyps in their early stages is crucial, and computerized diagnostic systems can significantly boost the effectiveness of screening through a large volume of endoscopy images. To achieve this, it's essential to enhance the quality of these images. In our research, we employ a well-established model called CycleGAN, int...

The integration of multi-source sensors based AIoT (Artificial Intelligence of Things) technologies into air quality measurement and forecasting is becoming increasingly critical in the fields of sustainable and smart environmental design, urban development, and pollution control. This study focuses on enhancing the prediction of emission, with a s...

In the context of the rapidly evolving climate dynamics of the early twenty-first century, the interplay between climate change and biospheric integrity is becoming increasingly critical. The pervasive impact of climate change on ecosystems is manifested not only through alterations in average environmental conditions and their variability but also...

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated. It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction. Early detection is crucial for successful treatment, and cardiac magnetic resonance imaging (CMR) is a valuable tool for identifying this co...

In the intricate network environment, the secure transmission of medical images faces challenges such as information leakage and malicious tampering, significantly impacting the accuracy of disease diagnoses by medical professionals. To address this problem, the authors propose a robust feature watermarking algorithm for encrypted medical images ba...

With the development of artificial intelligence technology and edge computing technology, deep learning-based automatic modulation classification (AI-based AMC) deployed at edge devices using centralised or distributed learning methods for optimisation has emerged in recent years, and has made great progress in the recognition accuracy and recognis...

Abnormal crops image data play crucial role in controlling crop diseases and pest for smart agriculture. However, current agricultural image acquisition methods suffer from low-value data. This article presents a new strategy for collect high-quality image data for abnormal crops. First, a novel agricultural Internet of Things (IoT) image acquisiti...

Image denoising is a critical challenge in digital image processing, particularly when dealing with random-value impulse noise (RVIN). Existing methods for RVIN detection and removal often struggle with poor generalization performance, requiring manual adjustment of detection thresholds or local window information. These approaches have difficulty...

The performance of image-based visual servoing (IBVS) in dynamic, high-speed, and high-precision applications is a major issue in sustainable and smart manufacturing systems. The proposed solution addresses the need for systematic optimization of control laws and constraint treatments in IBVS processes. Limited exploration of this topic is evident...

This paper considers the practical consensus of time-varying fuzzy positive multi-agent systems. A novel error variable is introduced by adding an additional constant term. Under the framework of time-varying fuzzy copositive Lyapunov functions, a fuzzy control protocol with time-varying gain matrices is designed in terms of matrix decomposition te...