arXiv subjects

Ashiq Anjum

Publications and source records attributed to Ashiq Anjum.

At least 19 recordsLinked to original sources

arXiv2026

Network Digital Twin for Congestion-Aware Predictive Traffic Routing using Graph MPNNs

Telecom networks scale with growing users and data-intensive applications, generating heavy traffic that causes congestion, reducing throughput, increasing delay, and raising computational costs. Traditional routing protocols act only after performance degradation, making them unsuitable for dynamic traffic and topological changes. Addressing these challenges requires a routing approach that adapts in real time, scales with network growth, operates without disrupting active services, and provides continuous feedback for congestion-aware traffic optimisation. The Network Digital Twin (NDT) addresses these needs by mirroring global network behaviour using Message Passing Neural Networks (MPNNs) through bidirectional communication with the physical network. To align the NDT with physical network behaviour, synthetic traffic is generated with increasing load across topological structures that incrementally scale as routers are added. These topologies are created by graph-generating models such as Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz, customised with vertex degree limitations. The NDT collects performance metrics from routers and links, and MPNNs classify edges based on local vertex and global network behaviours. Based on these classifications, feedback is sent as Policy-Based Routing (PBR) protocol commands to each router, enabling optimal traffic distribution across links of the physical network.

cs.NI↗

arXiv2026

Enhancing Energy Efficiency in Scientific Workflows through CFD based PIVAEs

The growing complexity and scale of scientific workflows in high performance computing (HPC) environments have led to significant challenges in managing energy consumption without compromising computational performance. Traditional scheduling strategies often fail to account for the complex interplay between thermal dynamics, workload diversity, and system scalability, leading to inefficient and unsustainable energy usage. This paper introduces a novel, scalable, and AI-assisted scheduling framework for optimizing energy consumption in HPC environments without compromising performance. Central to our approach is the integration of Computational Fluid Dynamics (CFD) with a Physics-Informed Variational Autoencoder (PIVAE), enabling the generation of physically realistic synthetic workload data that bridges the gap between thermodynamic behavior and scheduler decision-making in complex, multi-scale HPC environments. By categorizing workflows based on resource utilization profiles, we evaluate multiple scheduling strategies such as Locality Aware and Speculative Aware Scheduling. These workflows, ranging from event reconstruction to anomaly detection, represent diverse computational intensities. Our results show that modest reductions in CPU performance (e.g., to 15%) can yield substantial energy savings (up to 10%) with only minor turnaround time increases (approximately 5-6%), identifying an optimal operational sweet spot. This work demonstrates how physics-informed generative modeling can enable adaptive, sustainable, and data-efficient scheduling for next-generation HPC infrastructures.

cs.DC↗

arXiv2025

Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Models

Physics Informed Machine Learning has emerged as a popular approach for modeling and simulation in digital twins, enabling the generation of accurate models of processes and behaviors in real-world systems. However, existing methods either rely on simple loss regularizations that offer limited physics integration or employ highly specialized architectures that are difficult to generalize across diverse physical systems. This paper presents a generic approach based on a novel physics-encoded residual neural network (PERNN) architecture that seamlessly combines data-driven and physics-based analytical models to overcome these limitations. Our method integrates differentiable physics blocks-implementing mathematical operators from physics-based models with feed-forward learning blocks, while intermediate residual blocks ensure stable gradient flow during training. Consequently, the model naturally adheres to the underlying physical principles even when prior physics knowledge is incomplete, thereby improving generalizability with low data requirements and reduced model complexity. We investigate our approach in two application domains. The first is a steering model for autonomous vehicles in a simulation environment, and the second is a digital twin for climate modeling using an ordinary differential equation (ODE)-based model of Net Ecosystem Exchange (NEE) to enable gap-filling in flux tower data. In both cases, our method outperforms conventional neural network approaches as well as state-of-the-art Physics Informed Machine Learning methods.

cs.LG↗

arXiv2024

Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks

Despite basic differences between Spiking Neural Networks (SNN) and Artificial Neural Networks (ANN), most research on SNNs involve adapting ANN-based methods for SNNs. Pruning (dropping connections) and quantization (reducing precision) are often used to improve energy efficiency of SNNs. These methods are very effective for ANNs whose energy needs are determined by signals transmitted on synapses. However, the event-driven paradigm in SNNs implies that energy is consumed by spikes. In this paper, we propose a new synapse model whose weights are modulated by Interspike Intervals (ISI) i.e. time difference between two spikes. SNNs composed of this synapse model, termed ISI Modulated SNNs (IMSNN), can use gradient descent to estimate how the ISI of a neuron changes after updating its synaptic parameters. A higher ISI implies fewer spikes and vice-versa. The learning algorithm for IMSNNs exploits this information to selectively propagate gradients such that learning is achieved by increasing the ISIs resulting in a network that generates fewer spikes. The performance of IMSNNs with dense and convolutional layers have been evaluated in terms of classification accuracy and the number of spikes using the MNIST and FashionMNIST datasets. The performance comparison with conventional SNNs shows that IMSNNs exhibit upto 90% reduction in the number of spikes while maintaining similar classification accuracy.

cs.NE↗

arXiv2022

The least-used key selection method for information retrieval in large-scale Cloud-based service repositories

As the number of devices connected to the Internet of Things (IoT) increases significantly, it leads to an exponential growth in the number of services that need to be processed and stored in the large-scale Cloud-based service repositories. An efficient service indexing model is critical for service retrieval and management of large-scale Cloud-based service repositories. The multilevel index model is the state-of-art service indexing model in recent years to improve service discovery and combination. This paper aims to optimize the model to consider the impact of unequal appearing probability of service retrieval request parameters and service input parameters on service retrieval and service addition operations. The least-used key selection method has been proposed to narrow the search scope of service retrieval and reduce its time. The experimental results show that the proposed least-used key selection method improves the service retrieval efficiency significantly compared with the designated key selection method in the case of the unequal appearing probability of parameters in service retrieval requests under three indexing models.

cs.IR↗

arXiv2022

Digital Twinning Remote Laboratories for Online Practical Learning

The COVID19 pandemic has demonstrated a need for remote learning and virtual learning applications such as virtual reality (VR) and tablet-based solutions. Creating complex learning scenarios by developers is highly time-consuming and can take over a year. It is also costly to employ teams of system analysts, developers and 3D artists. There is a requirement to provide a simple method to enable lecturers to create their own content for their laboratory tutorials. Research has been undertaken into developing generic models to enable the semi-automatic creation of a virtual learning tools for subjects that require practical interactions with the lab resources. In addition to the system for creating digital twins, a case study describing the creation of a virtual learning application for an electrical laboratory tutorial has been presented.

cs.HC↗

arXiv2021

Optimization of Service Addition in Multilevel Index Model for Edge Computing

With the development of Edge Computing and Artificial Intelligence (AI) technologies, edge devices are witnessed to generate data at unprecedented volume. The Edge Intelligence (EI) has led to the emergence of edge devices in various application domains. The EI can provide efficient services to delay-sensitive applications, where the edge devices are deployed as edge nodes to host the majority of execution, which can effectively manage services and improve service discovery efficiency. The multilevel index model is a well-known model used for indexing service, such a model is being introduced and optimized in the edge environments to efficiently services discovery whilst managing large volumes of data. However, effectively updating the multilevel index model by adding new services timely and precisely in the dynamic Edge Computing environments is still a challenge. Addressing this issue, this paper proposes a designated key selection method to improve the efficiency of adding services in the multilevel index models. Our experimental results show that in the partial index and the full index of multilevel index model, our method reduces the service addition time by around 84% and 76%, respectively when compared with the original key selection method and by around 78% and 66%, respectively when compared with the random selection method. Our proposed method significantly improves the service addition efficiency in the multilevel index model, when compared with existing state-of-the-art key selection methods, without compromising the service retrieval stability to any notable level.

cs.IR↗

arXiv2021

Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural Networks

Object recognition from live video streams comes with numerous challenges such as the variation in illumination conditions and poses. Convolutional neural networks (CNNs) have been widely used to perform intelligent visual object recognition. Yet, CNNs still suffer from severe accuracy degradation, particularly on illumination-variant datasets. To address this problem, we propose a new CNN method based on orientation fusion for visual object recognition. The proposed cloud-based video analytics system pioneers the use of bi-dimensional empirical mode decomposition to split a video frame into intrinsic mode functions (IMFs). We further propose these IMFs to endure Reisz transform to produce monogenic object components, which are in turn used for the training of CNNs. Past works have demonstrated how the object orientation component may be used to pursue accuracy levels as high as 93\%. Herein we demonstrate how a feature-fusion strategy of the orientation components leads to further improving visual recognition accuracy to 97\%. We also assess the scalability of our method, looking at both the number and the size of the video streams under scrutiny. We carry out extensive experimentation on the publicly available Yale dataset, including also a self generated video datasets, finding significant improvements (both in accuracy and scale), in comparison to AlexNet, LeNet and SE-ResNeXt, which are the three most commonly used deep learning models for visual object recognition and classification.

cs.CV↗

arXiv2021

Virtual Reality based Digital Twin System for remote laboratories and online practical learning

There is a need for remote learning and virtual learning applications such as virtual reality (VR) and tablet-based solutions which the current pandemic has demonstrated. Creating complex learning scenarios by developers is highly time-consuming and can take over a year. There is a need to provide a simple method to enable lecturers to create their own content for their laboratory tutorials. Research is currently being undertaken into developing generic models to enable the semi-automatic creation of a virtual learning application. A case study describing the creation of a virtual learning application for an electrical laboratory tutorial is presented.

cs.AI↗

arXiv2020

Multiclass Disease Predictions Based on Integrated Clinical and Genomics Datasets

Clinical predictions using clinical data by computational methods are common in bioinformatics. However, clinical predictions using information from genomics datasets as well is not a frequently observed phenomenon in research. Precision medicine research requires information from all available datasets to provide intelligent clinical solutions. In this paper, we have attempted to create a prediction model which uses information from both clinical and genomics datasets. We have demonstrated multiclass disease predictions based on combined clinical and genomics datasets using machine learning methods. We have created an integrated dataset, using a clinical (ClinVar) and a genomics (gene expression) dataset, and trained it using instance-based learner to predict clinical diseases. We have used an innovative but simple way for multiclass classification, where the number of output classes is as high as 75. We have used Principal Component Analysis for feature selection. The classifier predicted diseases with 73\% accuracy on the integrated dataset. The results were consistent and competent when compared with other classification models. The results show that genomics information can be reliably included in datasets for clinical predictions and it can prove to be valuable in clinical diagnostics and precision medicine.

q-bio.GN↗

arXiv2018

A Taxonomy and Survey of Grid Resource Planning and Reservation Systems for Grid Enabled Analysis Environment

The concept of coupling geographically distributed resources for solving large scale problems is becoming increasingly popular forming what is popularly called grid computing. Management of resources in the Grid environment becomes complex as the resources are geographically distributed, heterogeneous in nature and owned by different individuals and organizations each having their own resource management policies and different access and cost models. There have been many projects that have designed and implemented the resource management systems with a variety of architectures and services. In this paper we have presented the general requirements that a Resource Management system should satisfy. The taxonomy has also been defined based on which survey of resource management systems in different existing Grid projects has been conducted to identify the key areas where these systems lack the desired functionality.

cs.DC↗

arXiv2017

Towards In-Transit Analytics for Industry 4.0

Industry 4.0, or Digital Manufacturing, is a vision of inter-connected services to facilitate innovation in the manufacturing sector. A fundamental requirement of innovation is the ability to be able to visualise manufacturing data, in order to discover new insight for increased competitive advantage. This article describes the enabling technologies that facilitate In-Transit Analytics, which is a necessary precursor for Industrial Internet of Things (IIoT) visualisation.

cs.CY↗

arXiv2014

Research Traceability using Provenance Services for Biomedical Analysis

We outline the approach being developed in the neuGRID project to use provenance management techniques for the purposes of capturing and preserving the provenance data that emerges in the specification and execution of workflows in biomedical analyses. In the neuGRID project a provenance service has been designed and implemented that is intended to capture, store, retrieve and reconstruct the workflow information needed to facilitate users in conducting user analyses. We describe the architecture of the neuGRID provenance service and discuss how the CRISTAL system from CERN is being adapted to address the requirements of the project and then consider how a generalised approach for provenance management could emerge for more generic application to the (Health)Grid community.

cs.DB↗

arXiv2012

An Architecture for Integrated Intelligence in Urban Management using Cloud Computing

With the emergence of new methodologies and technologies it has now become possible to manage large amounts of environmental sensing data and apply new integrated computing models to acquire information intelligence. This paper advocates the application of cloud capacity to support the information, communication and decision making needs of a wide variety of stakeholders in the complex business of the management of urban and regional development. The complexity lies in the interactions and impacts embodied in the concept of the urban-ecosystem at various governance levels. This highlights the need for more effective integrated environmental management systems. This paper offers a user-orientated approach based on requirements for an effective management of the urban-ecosystem and the potential contributions that can be supported by the cloud computing community. Furthermore, the commonality of the influence of the drivers of change at the urban level offers the opportunity for the cloud computing community to develop generic solutions that can serve the needs of hundreds of cities from Europe and indeed globally.

cs.DC↗

arXiv2012

A Fault Tolerant, Dynamic and Low Latency BDII Architecture for Grids

The current BDII model relies on information gathering from agents that run on each core node of a Grid. This information is then published into a Grid wide information resource known as Top BDII. The Top level BDIIs are updated typically in cycles of a few minutes each. A new BDDI architecture is proposed and described in this paper based on the hypothesis that only a few attribute values change in each BDDI information cycle and consequently it may not be necessary to update each parameter in a cycle. It has been demonstrated that significant performance gains can be achieved by exchanging only the information about records that changed during a cycle. Our investigations have led us to implement a low latency and fault tolerant BDII system that involves only minimal data transfer and facilitates secure transactions in a Grid environment.

cs.DC↗

arXiv2012

Reusable Services from the neuGRID Project for Grid-Based Health Applications

By abstracting Grid middleware specific considerations from clinical research applications, re-usable services should be developed that will provide generic functionality aimed specifically at medical applications. In the scope of the neuGRID project, generic services are being designed and developed which will be applied to satisfy the requirements of neuroscientists. These services will bring together sources of data and computing elements into a single view as far as applications are concerned, making it possible to cope with centralised, distributed or hybrid data and provide native support for common medical file formats. Services will include querying, provenance, portal, anonymization and pipeline services together with a 'glueing' service for connection to Grid services. Thus lower-level services will hide the peculiarities of any specific Grid technology from upper layers, provide application independence and will enable the selection of 'fit-for-purpose' infrastructures. This paper outlines the design strategy being followed in neuGRID using the glueing and pipeline services as examples.

cs.SE↗

arXiv2012

Research Traceability using Provenance Services for Biomedical Analysis

cs.DB↗

arXiv2012

Context-Aware Service Utilisation in the Clouds and Energy Conservation

Ubiquitous computing environments are characterised by smart, interconnected artefacts embedded in our physical world that are projected to provide useful services to human inhabitants unobtrusively. Mobile devices are becoming the primary tools of human interaction with these embedded artefacts and utilisation of services available in smart computing environments such as clouds. Advancements in capabilities of mobile devices allow a number of user and environment related context consumers to be hosted on these devices. Without a coordinating component, these context consumers and providers are a potential burden on device resources; specifically the effect of uncoordinated computation and communication with cloud-enabled services can negatively impact the battery life. Therefore energy conservation is a major concern in realising the collaboration and utilisation of mobile device based context-aware applications and cloud based services. This paper presents the concept of a context-brokering component to aid in coordination and communication of context information between mobile devices and services deployed in a cloud infrastructure. A prototype context broker is experimentally analysed for effects on energy conservation when accessing and coordinating with cloud services on a smart device, with results signifying reduction in energy consumption.

cs.DC↗