Mu Mu

University of Northampton

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Nicholas Race

Lancaster University

Andreas Mauthe

Universität Koblenz

Hank F. Kung

University of Pennsylvania

Mei‐Ping Kung

University of Pennsylvania

Steven Simpson

Lancaster University

Jacco Taal

Delft University of Technology

Omar Niamut

Netherlands Organisation for Applied Scientific Research

Michael Opoku Agyeman

University of Northampton

Suraj Ajit

University of Northampton

Murtada Dohan

University of Northampton

Cooperative Institutions

University of Pennsylvania

Lancaster University

Chinese Academy of Medical Sciences & Peking Union Medical College

University of Northampton

Fu Wai Hospital

Mahidol Oxford Tropical Medicine Research Unit

Shoklo Malaria Research Unit

Mahidol University

Technical University of Darmstadt

State Key Laboratory of Cardiovascular Disease

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Quality of Experience Experimentation Prediction Framework Through Programmable Network Management

Ahmed Osama Basil Al-Mashhadani Mu Mu Ali Al-Sharbaz

Quality of Experience (QoE) metrics can be used to assess user perception and satisfaction in data services applications delivered over the Internet. End-to-end metrics are formed because QoE is dependent on both the users’ perception and the service used. Traditionally, network optimization has focused on improving network properties such as the QoS. In this paper we examine the Adaptive streaming over a software defined network environment. We aimed to evaluate and study the media streams, aspects affecting the stream, and network. This was done to eventually reach a stage of analysing the network’s features and their direct relationship with the perceived QoE. We then use machine learning to build a prediction model based on subjective user experiments. This will help to eliminate future physical experiments and automate the process of predicting QoE.

10.20944/preprints202207.0035.v1

Cite

Citations (0)

Developing a Multidimensional Fuzzy Deep Learning for Cancer Classification using Gene Expression Data

Mahmood Khalsan Mu Mu Eman S. Al-Shamery Suraj Ajit Lee R. Machado

In the realm of cancer research, the identification of biomarker genes plays a pivotal role in accurate classification and diagnosis. This study delves into the intersection of machine learning and gene selection to enhance the precision of biomarker identification for cancer classification. Leveraging advanced computational techniques. In the quest for improved cancer classification, studies face challenges due to high-dimensional gene expression data and limited gene relevance. To address these challenges, we developed a novel multidimensional fuzzy deep learning (MFDL) to select subset of significant genes and using those genes to train the model for better accuracy. MFDL is exploring the integration of fuzzy concepts within filter and wrapper methods to select significant genes and applying a fuzzy classifier to improve cancer classification accuracy. Through rigorous experimentation and validation, six gene expression data used, the findings demonstrated the efficacy of our methodology on diverse cancer datasets. The results underscore the importance of integrative computational methods in deciphering the intricate genomic landscape of cancer and spotlight the potential for improved diagnostic accuracy. The developed model showcased outstanding performance across the six employed datasets, demonstrating an average accuracy of 98%, precision of 98.3%, recall of 97.6%, and an f1-score of 97.8%.

Identification

Biomarker Discovery

10.5121/csit.2023.132304

Cite

Citations (1)

Understanding Your Needs: An Adaptive VoD System

Mu Mu William Knowles Nicholas Race

Video-on-demand (VoD) is becoming a popular service for commercial content distribution by offering end users the freedom to access recorded programmes. The management of on-demand assets is essential to maximise the efficiency of storage and network utilisation as well as advertisement. This paper introduces our recent efforts in design and implementation of an adaptive VoD archive system in an IPTV infrastructure. The system exploits live statistics on the user behaviours as well as the dynamic popularity of VoD programmes. Using the modelled programme popularity function, the VoD archive is capable of managing the VoD repository by adapting to the most recent user requests. The design has greatly improved the activity of VoD repository and user experience in on-demand services.

Video on demand

IPTV

Popularity

On demand

10.1109/ism.2012.55

Cite

Citations (4)

Vision social TV:towards personalised media experience and community atmosphere

Mu Mu Jamie Trench-Jellicoe Nicholas Race

The traditional TV viewing experience is being greatly influenced by the emerging social medias and online content distribution technologies. Social TV is becoming a hot topic in the media industry. The demo introduces a social TV platform, which enables the research and experimentation of personalised media experience and community atmosphere.

10.6084/m9.figshare.1032653

Cite

Citations (0)

Real-Walk Modelling: Deep Learning Model for User Mobility in Virtual Reality

Research Square (Research Square) (2022)

Murtada Dohan Mu Mu Suraj Ajit Gary J. Hill

Abstract Understanding human interactions in virtual reality (VR) can help developing intelligent applications that adapt to users' needs and enhance the user experience. The significant development of VR content has expanded the impact on VR complexity, making understanding VR spatial characteristics more difficult. While user mobility is a crucial part of their interactions with the VR environment, the current literature still does not provide a suitable framework to interpret and model VR user mobility data. We conducted a user experiment in the context of an abstract VR painting exhibition where users are prompted to walk naturally in a physical area to explore the VR painting. Deep Learning models are used to model user mobility sequences and predict their future movements while engaging with the art exhibition. Our user mobility model can support the development of new VR applications for the improved user navigation and social experience in VR.

10.21203/rs.3.rs-1855540/v1

Cite

Citations (1)

"Let's Share a Story"': Socially Enhanced Multimedia Storytelling

IEEE Multimedia (2015)

Mu Mu Steven Simpson Nicholas Race Omar Niamut Gijs Koot

User-generated audio-visual content is becoming the most popular medium for information sharing and social storytelling around live events. In this article, the authors introduce an online multimedia storytelling ecosystem comprised of purpose-built user applications, a collaborative story-authoring engine, social context integration, and socially aware media services. As their event-based user experiments illustrate, the system enables online collaborative story coauthoring and provides an ideal platform to study the synergy between social networks and networked media in enhancing the user experience of storytelling. This article is part of a special issue on social multimedia and storytelling.

Interactive Storytelling

10.1109/mmul.2015.32

Cite

Citations (5)

Countering Contextual Bias in TV Watching Behavior

Felix Lorenz Jing Yuan Andreas Lommatzsch Mu Mu Nicholas Race

Context-awareness has become a critical factor in improving the predictions of user interest in modern online TV recommendation systems. In addition to individual user preferences, existing context-aware approaches such as tensor factorization incorporate system-level contextual bias to increase predicting accuracy. We analyzed a user interaction dataset from a WebTV platform, and identified that such contextual bias creates a skewed selection of recommended programs which ultimately locks users in a filter bubble. To address this issue, we introduce the Twitter social stream as a source of external context to extend the choice with items related to social media events. We apply two trend indicators, Trend Momentum and SigniScore, to the Twitter histories of relevant programs. The evaluation reveals that Trend Momentum outperforms SigniScore and signalizes 96% of all peaks ahead of time regarding the selected candidate program titles.

10.1145/3077548.3077552

Cite

Citations (5)

STEER: Exploring the dynamic relationship between social information and networked media through experimentation

Sylvie Dijkstra Omar Niamut Nikolaos Efthymiopoulos Spyros Denazis Nicholas Race

With the growing popularity of social networks, online video services and smart phones, the traditional content consumers are becoming the editors and broadcasters of their own stories. Within the EU FP7 project STEER, project partners have developed a novel system of new algorithms and tool sets that extract andanalysesocial informatics generated by social networks. Combined with advanced networking technologies, the platform creates services that offer more personalized and accurate content discovery and retrieval services. The STEER system has been deployed in multiplegeographical locations during live social events such as the 2014 Winter Olympics. Our use case experiments demonstrate the feasibility and efficiency of the underlying technologies.

Popularity

Source

Cite

Citations (2)

Modeling Quality of Experience for Compressed Point Cloud Sequences based on a Subjective Study

Jannis Weil Yassin Alkhalili Anam Tahir Thomas Gruczyk Tobias Meuser

There is growing interest in point cloud content due to its central role in the creation and provision of interactive and immersive user experiences for extended reality applications. However, it is impractical to stream uncompressed point cloud sequences over communication networks to end systems because of their high throughput and low latency requirements. Several novel compression methods have been developed for efficient storage and adaptive delivery of point cloud content. However, these methods primarily focus on data metrics and neglect the influence on the actual Quality of Experience (QoE). In this paper, we conduct a user study with 102 participants to analyze the QoE of point cloud sequences and develop a QoE model that can enhance the quality of point cloud content distribution under dynamic network conditions. Our analysis is based on user opinions regarding two representative point cloud sequences, three different frame rates, three viewing distances, and two state-of-the-art point cloud compression libraries, Draco and V-PCC. The results indicate that the proposed models can accurately predict the users' quality perception, with frame rate being the most dominant QoE factor.

10.1109/qomex58391.2023.10178579

Cite

Citations (6)

Collective user experience: Community-driven story co-authoring in live events

Steven Simpson Mu Mu Omar Niamut Jacco Taal Nicholas Race

Audio-visual narratives are becoming the most popular medium for information sharing and social storytelling around a live event. This paper explores the collective experience of users of an online creative storytelling ecosystem. The system provides an ideal platform to study community-driven story co-authoring helped by social networks and networked media, as highlighted in an eventbased user experiment.

Interactive Storytelling

Online Community

Source

Cite

Citations (0)