A5069326900- Theoretical and Computational Physics
- Natural Language Processing Techniques
- Semantic Web and Ontologies
- Advanced Malware Detection Techniques
- Geographic Information Systems Studies
- Digital Media Forensic Detection
- Advanced Graph Neural Networks
- Surface and Thin Film Phenomena
- Stochastic processes and statistical mechanics
- Recommender Systems and Techniques
- Digital and Cyber Forensics
- Mobile Ad Hoc Networks
- Opportunistic and Delay-Tolerant Networks
- Complex Network Analysis Techniques
- IoT and Edge/Fog Computing
- Evolution and Genetic Dynamics
- Force Microscopy Techniques and Applications
- Caching and Content Delivery
- Data Mining Algorithms and Applications
- Mathematical Dynamics and Fractals
- Tea Polyphenols and Effects
- Wireless Body Area Networks
- Spam and Phishing Detection
- User Authentication and Security Systems
- Artificial Intelligence in Healthcare
University of Science and Technology of China
2021-2024
Universiti Sains Malaysia
2023
Al Ain University
2021-2022
Nottingham Trent University
2022
Universitat Politècnica de València
2022
Brunel University of London
2022
National University of Sciences and Technology
2021
University of Sialkot
2019
University of Gujrat
2019
Information Technology University
2019
The prevalence of smart devices in our day-to-day activities increases the potential threat to secret information. To counter these threats like unauthorized access and misuse phones, only authorized users should be able device. Authentication mechanism provide a secure way safeguard physical resources as well information that is processed. Text-based passwords are most common technique used for authentication devices, however, they vulnerable certain type attacks such brute force, smudge...
Faster internet, IoT, and social media have reformed the conventional web into a collaborative resulting in enormous user-generated content. Several studies are focused on such content; however, they mainly focus textual data, thus undermining importance of metadata. Considering this gap, we provide temporal pattern mining framework to model utilize content's First, scrap 2.1 million tweets from Twitter between Nov-2020 Sep-2021 about 100 hashtag keywords present these User-Tweet-Hashtag...
We investigate the nature of genetic drift acting at leading edge range expansions, building on recent results in [Hallatschek et al., Proc.\ Natl.\ Acad.\ Sci., \textbf{104}(50): 19926 - 19930 (2007)]. A well mixed population two fluorescently labeled microbial species is grown a circular geometry. As expands, coarsening process driven by gives rise to sectoring patterns with fractal boundaries, which show non-trivial asymptotic distribution. Using simplified lattice based Monte Carlo...
Pattern formation in microbial colonies of competing strains under purely space-limited population growth has recently attracted considerable research interest. We show that the reproduction time statistics individuals a significant impact on sectoring patterns. Generalizing standard Eden model, we introduce simple one-parameter family distributions indexed by variation coefficient $\ensuremath{\delta}\ensuremath{\in}[0,1]$, which includes deterministic ($\ensuremath{\delta}=0$) and...
Many growth processes lead to intriguing stochastic patterns and complex fractal structures which exhibit local scale invariance properties. Such can often be described effectively by space-time trajectories of interacting particles, their large behavior depends on the overall geometry. We establish an exact relation between statistical properties in uniformly expanding fixed geometries, preserves is independent other such as dimensionality. This generalizes standard conformal...
Spammer detection is to identify and block malicious activities performing users.Such users should be identified terminated from social media keep the process organic maintain integrity of online spaces.Previous research aimed find spammers based on hybrid approaches graph mining, posted content, metadata, using small manually labeled datasets.However, such are unscalable, not robust, particular dataset dependent, require numerous parameters, complex graphs, natural language processing (NLP)...
Vehicular delay tolerant network (VDTN) is a widely used communication standard for the scenarios where no end to path available between nodes. Data sent from one node another using routing protocols of VDTN. These use different decision metrics. Based on these metrics, it chosen whether send data connected or find suitable candidate. metrices are Time live (TTL), geographical information, destination utility, relay meeting prediction, total and remaining buffer size many other. Different...
Graph contrastive learning (GCL) aims to contrast positive-negative counterparts learn the node embeddings, whereas graph data augmentation methods are employed generate these samples. The variation, quantity, and quality of negative samples compared positive play crucial roles in meaningful embeddings for classification downstream tasks. Less excessive low-quality cause model be overfitted particular nodes, resulting less robust models. To solve overfitting problem GCL paradigm, this study...
With the increasing proliferation of Internet Things (IoT) devices, digital forensics professionals face numerous challenges whilst investigating cybercrimes. The vast number IoT heterogeneity their formats, and diversity data they generate make identification collection relevant evidence a daunting task. In this research paper, we explore complex landscape forensics, highlighting major emerging solutions. We start by listing available models frameworks. then delve into management during...
VDTN was proposed as a disrupting network which is established on the paradigm of delay-tolerant network. uses vehicular nodes to convey messages as, it permits sparse opportunistic connectivity, considered by low node density where traffic sporadic, and no end-to-end paths exist between nodes. The message bundle directed from sender receiver based routing protocol decision. While Routing protocols take decisions different metrics like Time live, Location, Remaining Buffer Size, meeting...
Digital forensics is essential when performing in-depth crime investigations and evidence extraction, especially in the field of Internet Things, where there a ton information every second boosted with latest smartest technological devices. However, enormous growth data nature its complexity could constrain examination process since traditional acquisition techniques are not applicable nowadays. Therefore, if knowledge gap between digital Things bridged, investigators will jeopardize loss...
Nowadays, even after many advancements in the field of healthcare facilities, one leading causes death is considered to be heart disease. The numbers are soaring with every passing year due complexity involved treating and diagnosing diseases. Most forms disease can prevented, but constantly increasing inadequate number preventive techniques. Various scholars have used machine learning algorithm related data mining develop predictive systems for In this paper, a simple yet effective hybrid...
Humankind has witnessed magnificent outcomes since technology become part of education at all levels. This paper discusses the application a newly hyped in called Virtual Reality (VR). reality is blend technologies to provide an interface with virtual environment. becoming vital educational applications because educators always tend lean towards anything which can be beneficial bringing ease explanation certain topics. It will also not wrong say that usage such kind now imperative...
With the increasing proliferation of Internet Things (IoT) devices, digital forensics professionals face numerous challenges whilst investigating cybercrimes. The vast number IoT heterogeneity their formats, and diversity data they generate make identification collection relevant evidence a daunting task. In this research paper, we explore complex landscape forensics, highlighting major emerging solutions. We start by listing available models frameworks. then delve into management during...
The paper discusses the development of an IoT-based smart traffic system that utilizes real-time data and machine learning algorithms to enhance safety, reduce congestion, improve emergency response times. exponential growth vehicles on roads has increased accidents, environmental pollution, necessitating a smarter management system. reviews several studies demonstrating potential solutions in improving flow, enhancing reducing impact. Furthermore, creating sustainable efficient urban...