A5078317952- Energy Efficient Wireless Sensor Networks
- Energy Harvesting in Wireless Networks
- Antenna Design and Analysis
- Neurogenesis and neuroplasticity mechanisms
- Mobile Ad Hoc Networks
- Indoor and Outdoor Localization Technologies
- IoT and Edge/Fog Computing
- Opportunistic and Delay-Tolerant Networks
- Advanced Antenna and Metasurface Technologies
- Microwave Engineering and Waveguides
- IoT-based Smart Home Systems
- AI in cancer detection
- Security in Wireless Sensor Networks
- Nonmelanoma Skin Cancer Studies
- Global Public Health Policies and Epidemiology
- Machine Fault Diagnosis Techniques
- Artificial Intelligence in Healthcare
- Cutaneous Melanoma Detection and Management
- Software Engineering Research
- Water Quality Monitoring Technologies
- Vehicular Ad Hoc Networks (VANETs)
- Software Reliability and Analysis Research
- Privacy-Preserving Technologies in Data
- Brain Tumor Detection and Classification
- IoT Networks and Protocols
COMSATS University Islamabad
2014-2025
Qurtuba University of Science and Information Technology
2025
Association for Social and Environmental Development
2025
Ferghana Polytechnical Institute
2023-2025
Islamia University of Bahawalpur
2022-2024
Universiti Teknologi MARA
2023-2024
Universiti Teknologi MARA System
2024
Abbottabad University of Science and Technology
2019-2023
Jazan University
2022
Qassim University
2022
One of the deadliest diseases, heart disease, claims millions lives every year worldwide. The biomedical data collected by health service providers (HSPs) contain private information about patient and are subject to general privacy concerns, sharing is restricted under global laws. Furthermore, collection have a significant network communication cost lead delayed disease prediction. To address training latency, cost, single point failure, we propose hybrid framework at client end HSP...
The HT-29 cell line, derived from human colon cancer, is valuable for biological and cancer research applications. Early detection crucial improving the chances of survival, researchers are introducing new techniques accurate diagnosis. This study introduces an efficient deep learning-based method detecting counting colorectal cells (HT-29). line was procured a company. Further, were cultured, transwell experiment conducted in lab to collect dataset images via fluorescence microscopy. Of 566...
As higher education faces technological advancement and environmental imperatives, AI becomes a key instrument for revolutionizing instructional methods institutional operations. can improve educational outcomes, resource management, long-term sustainability in education, according to this study. The research uses case studies best practices show how AI-driven innovations minimize impact, enhance energy efficiency, customize learning, creating more sustainable inclusive academic environment....
This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches multiple convolutional neural networks. Breast Ultrasound Images (BUSI) dataset contains various types of lesions. The goal is classify these lesions as benign or malignant, which crucial for the early detection treatment cancer. problem that traditional machine learning deep often fail accurately images due their complex diverse nature. In this research, address problem,...
Resource allocation in smart settings, more specifically Internet of Things (IoT) transportation, is challenging due to the complexity and dynamic nature fog computing. The demands users may alter over time, necessitating trustworthy resource administration. Effective management systems must be designed accommodate changing user needs. Fog devices don’t just run fog-specific software. link failures could brought on by absence centralised administration, device autonomy, wireless...
Software defect prediction plays a crucial role in enhancing software quality while achieving cost savings testing. Its primary objective is to identify and send only defective modules the testing stage. This research introduces an intelligent ensemble-based model that combines diverse classifiers. The proposed employs two-stage process detect modules. In first stage, four supervised machine learning algorithms are employed: Random Forest, Support Vector Machine, Naïve Bayes, Artificial...
In mobile edge computing (MEC), devices limited to computation and memory resources offload compute-intensive tasks nearby servers. User movement causes frequent handovers in 5G urban networks. The resultant delays task execution due unknown user position base station lead increased energy consumption resource wastage. current MEC offloading solutions separate from mobility. For offloading, techniques that predict the user’s future location do not consider direction. We propose a framework...
Electrical machines are prone to faults and failures demand incessant monitoring for their confined reliable operations. A failure in electrical may cause unexpected interruptions require a timely inspection of abnormal conditions rotating electric machines. This article aims summarize an up-to-date overview all types bearing diagnostic techniques by subdividing them into different categories. Different fault detection diagnosis (FDD) discussed briefly prognosis numerous that frequently...
This paper describes a singly-fed circularly polarized rectangular dielectric resonator antenna (RDRA) for MIMO and 5G Sub 6 GHz applications. Circular polarization was achieved both ports using novel-shaped conformal metal strip. To improve the isolation between radiators, "S" shaped defective ground plane structure (DGPS) used. In order to authenticate estimated findings, prototype of suggested radiator built tested experimentally. Over desired band, i.e., 3.57-4.48 GHz, fractional...
In recent years, fire detection technologies have helped safeguard lives and property from hazards. Early warning methods, such as smoke or gas sensors, are ineffectual. Many fires caused deaths damage. IoT is a fast-growing technology. It contains equipment, buildings, electrical systems, vehicles, everyday things with computing sensing capabilities. These objects can be managed monitored remotely they connected to the Internet. Internet of Things concept, low-power devices like sensors...
Effective software defect prediction is a crucial aspect of quality assurance, enabling the identification defective modules before testing phase. This study aims to propose comprehensive five-stage framework for prediction, addressing current challenges in field. The first stage involves selecting cleaned version NASA's datasets, including CM1, JM1, MC2, MW1, PC1, PC3, and PC4, ensuring data's integrity. In second stage, feature selection technique based on genetic algorithm applied...
The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition classification tasks. While previous studies focused on fruits, land sliding, medical there is a need further research the particularly mountains. To address limitations traditional CNNs, vanishing gradients many layers, proposed work introduces novel model called...
Members of the cyclin-dependent kinase (CDK)-inhibitory protein (CIP)/kinase-inhibitory (KIP) family inhibitors regulate proliferation and cell cycle exit mammalian cells. In adult brain, CIP/KIP p27(kip1) has been related to regulation intermediate progenitor cells located in neurogenic niches. Here, we uncover a novel function hippocampus as dual regulator stem quiescence cell-cycle immature neurons. vivo, is detected radial expressing SOX2 newborn neurons dentate gyrus. vitro, Cdkn1b gene...
Heart disease is one of the lethal diseases causing millions fatalities every year. The Internet Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection disease. biomedical data collected using IoMT contains personalized information about patient this has serious privacy concerns. To overcome issues, several protection laws are proposed internationally. These created huge problem for techniques used traditional machine learning....
Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces novel method called the asynchronous federated cardiac prediction (AFLCP), which combines dataset neural networks (DNNs) with technique. The proposed employs asynchronously updating parameters of DNNs incorporates temporally weighted aggregation technique enhance accuracy convergence central model. To...
The accurate and timely diagnosis of skin cancer is crucial as it can be a life-threatening disease. However, the implementation traditional machine learning algorithms in healthcare settings faced with significant challenges due to data privacy concerns. To tackle this issue, we propose privacy-aware approach for detection that utilizes asynchronous federated convolutional neural networks (CNNs). Our method optimizes communication rounds by dividing CNN layers into shallow deep layers,...