@article{Huraysi2025,

title = {Seeing the unseen: Intrusion attack detection in connected autonomous vehicles},

author = {Talea Huraysi and Rui Sun and Haoran Duan and Kwabena Adu-Duodu and Rajiv Ranjan and Bo Wei and Tejal Shah},

doi = {10.1016/j.hcc.2025.100375},

issn = {2667-2952},

year  = {2025},

date = {2025-11-00},

journal = {High-Confidence Computing},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Duan2025,

title = {Parameter Efficient Fine-Tuning for Multi-modal Generative Vision Models with Möbius-Inspired Transformation},

author = {Haoran Duan and Shuai Shao and Bing Zhai and Tejal Shah and Jungong Han and Rajiv Ranjan},

doi = {10.1007/s11263-025-02398-3},

issn = {1573-1405},

year  = {2025},

date = {2025-07-00},

journal = {Int J Comput Vis},

volume = {133},

number = {7},

pages = {4590--4603},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

          The rapid development of multimodal generative vision models has drawn scientific curiosity. Notable advancements, such as OpenAI’s ChatGPT and Stable Diffusion, demonstrate the potential of combining multimodal data for generative content. Nonetheless, customising these models to specific domains or tasks is challenging due to computational costs and data requirements. Conventional fine-tuning methods take redundant processing resources, motivating the development of parameter-efficient fine-tuning technologies such as adapter module, low-rank factorization and orthogonal fine-tuning. These solutions selectively change a subset of model parameters, reducing learning needs while maintaining high-quality results. Orthogonal fine-tuning, regarded as a reliable technique, preserves semantic linkages in weight space but has limitations in its expressive powers. To better overcome these constraints, we provide a simple but innovative and effective transformation method inspired by Möbius geometry, which replaces conventional orthogonal transformations in parameter-efficient fine-tuning. This strategy improved fine-tuning’s adaptability and expressiveness, allowing it to capture more data patterns. Our strategy, which is supported by theoretical understanding and empirical validation, outperforms existing approaches, demonstrating competitive improvements in generation quality for key generative tasks.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Miao2025,

title = {Laser: Efficient Language-Guided Segmentation in Neural Radiance Fields},

author = {Xingyu Miao and Haoran Duan and Yang Bai and Tejal Shah and Jun Song and Yang Long and Rajiv Ranjan and Ling Shao},

doi = {10.1109/tpami.2025.3535916},

issn = {2160-9292},

year  = {2025},

date = {2025-05-00},

journal = {IEEE Trans. Pattern Anal. Mach. Intell.},

volume = {47},

number = {5},

pages = {3922--3934},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2025,

title = {LAGD: Local Topological-Alignment and Global Semantic-Deconstruction for Incremental 3D Semantic Segmentation},

author = {Yumin Zhang and Haoran Duan and Rui Sun and Yue Cheng and Tejal Shah and Rajiv Ranjan and Bo Wei},

doi = {10.1609/aaai.v39i21.34427},

issn = {2374-3468},

year  = {2025},

date = {2025-04-11},

journal = {AAAI},

volume = {39},

number = {21},

pages = {22677--22685},

publisher = {Association for the Advancement of Artificial Intelligence (AAAI)},

abstract = {Numerous deep learning-based works focusing on 3D semantic segmentation have been proposed and have achieved impressive performance. However, due to the catastrophic forgetting, existing methods will degrade dramatically in a real-world scenario where new 3D semantic categories are arriving continually. Straightforwardly applying typical class-incremental learning methods on 3D data even aggravates forgetting due to the irregular and noisy geometric structure. Aiming to address this realistic challenge, from the perspective of capturing local topological characteristics and mitigating global semantic shift, we propose a unified framework named Local topological Alignment and Global semantic Deconstruction (LAGD) to incrementally learn semantic knowledge of novel 3D categories while maintaining performance on previously learned knowledge. Specifically, we develop a novel Interaction Topological-aware Alignment (ITA) to maintain the learned knowledge efficiently by capturing the local geometric characteristics with interacted adjacent state-specific knowledge. Besides, to mitigate the forgetting caused by the global semantic shift, we deconstruct the logits into positive and negative parts which are distilled separately, achieving an elaborate distillation process in terms of Semantic-knowledge Deconstruction Distillation (SDD). With the cooperation of ITA and SDD, LAGD achieves a sota performance, especially in the long-term incremental learning scenario. Extensive experimental results illustrate the superiority of our proposed LAGD.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jiang2025,

title = {Imaginary-Connected Embedding in Complex Space for Unseen Attribute-Object Discrimination},

author = {Chenyi Jiang and Shidong Wang and Yang Long and Zechao Li and Haofeng Zhang and Ling Shao},

doi = {10.1109/tpami.2024.3487631},

issn = {2160-9292},

year  = {2025},

date = {2025-03-00},

journal = {IEEE Trans. Pattern Anal. Mach. Intell.},

volume = {47},

number = {3},

pages = {1395--1413},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tunheim2025,

title = {Tsetlin Machine-Based Image Classification FPGA Accelerator With On-Device Training},

author = {Svein Anders Tunheim and Lei Jiao and Rishad Shafik and Alex Yakovlev and Ole-Christoffer Granmo},

doi = {10.1109/tcsi.2024.3519191},

issn = {1558-0806},

year  = {2025},

date = {2025-02-00},

journal = {IEEE Trans. Circuits Syst. I},

volume = {72},

number = {2},

pages = {830--843},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10857592,

title = {Laser: Efficient Language-Guided Segmentation in Neural Radiance Fields},

author = {Xingyu Miao and Haoran Duan and Yang Bai and Tejal Shah and Jun Song and Yang Long and Rajiv Ranjan and Ling Shao},

doi = {10.1109/TPAMI.2025.3535916},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},

volume = {47},

number = {5},

pages = {3922-3934},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{QIU2025103087,

title = {D2Fusion: Dual-domain fusion with feature superposition for Deepfake detection},

author = {Xueqi Qiu and Xingyu Miao and Fan Wan and Haoran Duan and Tejal Shah and Varun Ojha and Yang Long and Rajiv Ranjan},

url = {https://www.sciencedirect.com/science/article/pii/S1566253525001605},

doi = {https://doi.org/10.1016/j.inffus.2025.103087},

issn = {1566-2535},

year  = {2025},

date = {2025-01-01},

journal = {Information Fusion},

volume = {120},

pages = {103087},

abstract = {Deepfake detection is crucial for curbing the harm it causes to society. However, current Deepfake detection methods fail to thoroughly explore artifact information across different domains due to insufficient intrinsic interactions. These interactions refer to the fusion and coordination after feature extraction processes across different domains, which are crucial for recognizing complex forgery clues. Focusing on more generalized Deepfake detection, in this work, we introduce a novel bi-directional attention module to capture the local positional information of artifact clues from the spatial domain. This enables accurate artifact localization, thus addressing the coarse processing with artifact features. To further address the limitation that the proposed bi-directional attention module may not well capture global subtle forgery information in the artifact feature (e.g., textures or edges), we employ a fine-grained frequency attention module in the frequency domain. By doing so, we can obtain high-frequency information in the fine-grained features, which contains the global and subtle forgery information. Although these features from the diverse domains can be effectively and independently improved, fusing them directly does not effectively improve the detection performance. Therefore, we propose a feature superposition strategy that complements information from spatial and frequency domains. This strategy turns the feature components into the form of wave-like tokens, which are updated based on their phase, such that the distinctions between authentic and artifact features can be amplified. Our method demonstrates significant improvements over state-of-the-art (SOTA) methods on five public Deepfake datasets in capturing abnormalities across different manipulated operations and real-life. Specifically, in intra-dataset evaluations, D2Fusion surpasses the baseline accuracy by nearly 2.5%. In cross-manipulation evaluations, it exceeds the baseline AUC by up to 6.15%. In multi-source manipulation evaluations, it exceeds the SOTA methods by up to 14.62% in P-value, 10.26% in F1-score and 15.13% in R-value. In cross-dataset experiments, it exceeds the baseline AUC by up to 6.25%. For potential applications, D2Fusion can help improve content moderation on social media and aid forensic investigations by accurately identifying the tampered content.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mishra2024,

title = {Machine learning and deep learning prediction models for time-series: a comparative analytical study for the use case of the UK short-term electricity price prediction},

author = {Bhupesh Kumar Mishra and Vjosa Preniqi and Dhavalkumar Thakker and Erich Feigl},

doi = {10.1007/s43926-024-00075-4},

issn = {2730-7239},

year  = {2024},

date = {2024-12-00},

journal = {Discov Internet Things},

volume = {4},

number = {1},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Miao2024,

title = {CTNeRF: Cross-time Transformer for dynamic neural radiance field from monocular video},

author = {Xingyu Miao and Yang Bai and Haoran Duan and Fan Wan and Yawen Huang and Yang Long and Yefeng Zheng},

doi = {10.1016/j.patcog.2024.110729},

issn = {0031-3203},

year  = {2024},

date = {2024-12-00},

journal = {Pattern Recognition},

volume = {156},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yang2024,

title = {InaudibleKey2.0: Deep Learning-Empowered Mobile Device Pairing Protocol Based on Inaudible Acoustic Signals},

author = {Huanqi Yang and Zhenjiang Li and Chengwen Luo and Bo Wei and Weitao Xu},

doi = {10.1109/tnet.2024.3407783},

issn = {1558-2566},

year  = {2024},

date = {2024-10-00},

journal = {IEEE/ACM Trans. Networking},

volume = {32},

number = {5},

pages = {4160--4174},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2024b,

title = {Long Live the Image: On Enabling Resilient Production Database Containers for Microservice Applications},

author = {Zheng Li and Nicolás Saldías-Vallejos and Diego Seco and María Andrea Rodríguez and Rajiv Ranjan},

doi = {10.1109/tse.2024.3436623},

issn = {1939-3520},

year  = {2024},

date = {2024-09-00},

journal = {IIEEE Trans. Software Eng.},

volume = {50},

number = {9},

pages = {2363--2378},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Paxton2024,

title = {Measuring AI Fairness in a Continuum Maintaining Nuances: A Robustness Case Study},

author = {Kuniko Paxton and Koorosh Aslansefat and Dhavalkumar Thakker and Yiannis Papadopoulos},

doi = {10.1109/mic.2024.3450815},

issn = {1941-0131},

year  = {2024},

date = {2024-09-00},

journal = {IEEE Internet Comput.},

volume = {28},

number = {5},

pages = {11--19},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wan2024,

title = {Sentinel-Guided Zero-Shot Learning: A Collaborative Paradigm Without Real Data Exposure},

author = {Fan Wan and Xingyu Miao and Haoran Duan and Jingjing Deng and Rui Gao and Yang Long},

doi = {10.1109/tcsvt.2024.3384756},

issn = {1558-2205},

year  = {2024},

date = {2024-09-00},

journal = {IEEE Trans. Circuits Syst. Video Technol.},

volume = {34},

number = {9},

pages = {8067--8079},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kayan2024,

title = {CASPER: Context-Aware IoT Anomaly Detection System for Industrial Robotic Arms},

author = {Hakan Kayan and Ryan Heartfield and Omer Rana and Pete Burnap and Charith Perera},

doi = {10.1145/3670414},

issn = {2577-6207},

year  = {2024},

date = {2024-08-31},

journal = {ACM Trans. Internet Things},

volume = {5},

number = {3},

pages = {1--36},

publisher = {Association for Computing Machinery (ACM)},

abstract = {Industrial cyber-physical systems (ICPS) are widely employed in supervising and controlling critical infrastructures, with manufacturing systems that incorporate industrial robotic arms being a prominent example. The increasing adoption of ubiquitous computing technologies in these systems has led to benefits such as real-time monitoring, reduced maintenance costs, and high interconnectivity. This adoption has also brought cybersecurity vulnerabilities exploited by adversaries disrupting manufacturing processes via manipulating actuator behaviors. Previous incidents in the industrial cyber domain prove that adversaries launch sophisticated attacks rendering network-based anomaly detection mechanisms insufficient as the “physics” involved in the process is overlooked. To address this issue, we propose an IoT-based cyber-physical anomaly detection system that can detect motion-based behavioral changes in an industrial robotic arm. We apply both statistical and state-of-the-art machine learning methods to real-time Inertial Measurement Unit data collected from an edge development board attached to an arm doing a pick-and-place operation. To generate anomalies, we modify the joint velocity of the arm. Our goal is to create an air-gapped secondary protection layer to detect “physical” anomalies without depending on the integrity of network data, thus augmenting overall anomaly detection capability. Our empirical results show that the proposed system, which utilizes 1D convolutional neural networks, can successfully detect motion-based anomalies on a real-world industrial robotic arm. The significance of our work lies in its contribution to developing a comprehensive solution for ICPS security, which goes beyond conventional network-based methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Roychowdhury2024,

title = {Integrating Virtual Walkthroughs for Subjective Urban Evaluations: A Case Study of Neighbourhoods in Sheffield, England},

author = {Sneha Roychowdhury and Suvodeep Mazumdar and Dhavalkumar Thakker and Alessandro Checco and Vitaveska Lanfranchi and Barry Goodchild},

doi = {10.3390/land13060831},

issn = {2073-445X},

year  = {2024},

date = {2024-06-00},

journal = {Land},

volume = {13},

number = {6},

publisher = {MDPI AG},

abstract = {This study explores the correlation between residents’ subjective assessments of urban neighbourhoods, obtained through virtual walkthroughs, and objective measures of deprivation. Our study was set within a specific city in the United Kingdom, with neighbourhoods selected based on Indices of Multiple Deprivation (IMD). We invited residents in the UK through Prolific, a crowdsourcing platform. Employing complete case analysis, TF-IDF keyword extraction, the Kruskal–Wallis test, and Spearman’s rank-order correlation, our study examines the alignment between subjective assessments and existing deprivation measures (IMD). The results reveal a nuanced relationship, suggesting potential subjective biases influencing residents’ perceptions. Despite these complexities, the study highlights the value of virtual walkthroughs in offering a holistic overview of neighbourhoods. While acknowledging the limitations posed by subjective biases, we argue that virtual walkthroughs provide insights into residents’ experiences that potentially complement traditional objective measures of deprivation. By capturing the intricacies of residents’ perceptions, virtual walkthroughs contribute to a more comprehensive understanding of neighbourhood deprivation. This research informs future endeavours to integrate subjective assessments with objective measures for robust neighbourhood evaluations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hamal2024,

title = {A comparative analysis of machine learning algorithms for detecting COVID-19 using lung X-ray images},

author = {Susmita Hamal and Bhupesh Kumar Mishra and Robert Baldock and William Sayers and Tek Narayan Adhikari and Ryan M. Gibson},

doi = {10.1016/j.dajour.2024.100460},

issn = {2772-6622},

year  = {2024},

date = {2024-06-00},

journal = {Decision Analytics Journal},

volume = {11},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vandaele2024b,

title = {Deep learning for automated trash screen blockage detection using cameras: Actionable information for flood risk management},

author = {Remy Vandaele and Sarah L. Dance and Varun Ojha},

doi = {10.2166/hydro.2024.013},

issn = {1465-1734},

year  = {2024},

date = {2024-04-01},

volume = {26},

number = {4},

pages = {889--903},

publisher = {IWA Publishing},

abstract = {ABSTRACT

               

               Trash screens are used to prevent debris from entering critical parts of rivers. However, debris can accumulate on the screen and generate floods. This makes their monitoring critical both for maintenance and flood modeling purposes (e.g., local forecasts may change because the trash screen is blocked). We developed three novel deep learning methods for trash screen maintenance management consisting of automatically detecting trash screen blockage using cameras: a method based on image classification, a method based on image similarity matching, and a method based on anomaly detection. To facilitate their use by end users, these methods are designed so that they can be directly applied to any new trash screen camera installed by the end users. We have built a new dataset of labeled trash screen images to train and evaluate the efficiency of our methods, in terms of both accuracy and implications for end users. This dataset consists of 80,452 trash screen images from 54 cameras installed by the Environment Agency (UK). This work demonstrates that trash screen blockage detection can be automated using trash screen cameras and deep learning, which could have an impact on both trash screen management and flood modeling.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Xie2024,

title = {A survey on vulnerability of federated learning: A learning algorithm perspective},

author = {Xianghua Xie and Chen Hu and Hanchi Ren and Jingjing Deng},

doi = {10.1016/j.neucom.2023.127225},

issn = {0925-2312},

year  = {2024},

date = {2024-03-00},

journal = {Neurocomputing},

volume = {573},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Snášel2024,

title = {Large-scale data classification based on the integrated fusion of fuzzy learning and graph neural network},

author = {Václav Snášel and Martin Štěpnička and Varun Ojha and Ponnuthurai Nagaratnam Suganthan and Ruobin Gao and Lingping Kong},

doi = {10.1016/j.inffus.2023.102067},

issn = {1566-2535},

year  = {2024},

date = {2024-02-00},

journal = {Information Fusion},

volume = {102},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wei2023,

title = {SolarKey: Battery-free Key Generation Using Solar Cells},

author = {Bo Wei and Weitao Xu and Mingcen Gao and Guohao Lan and Kai Li and Chengwen Luo and Jin Zhang},

doi = {10.1145/3605780},

issn = {1550-4867},

year  = {2024},

date = {2024-01-31},

journal = {ACM Trans. Sen. Netw.},

volume = {20},

number = {1},

pages = {1--24},

publisher = {Association for Computing Machinery (ACM)},

abstract = {Solar cells have been widely used for offering energy for Internet of Things (IoT) devices. Recently, solar cells have also been used as sensors for context awareness sensing due to their sensitivity to varying lighting conditions. In this article, we are the first to use solar cells for symmetric key generation. To generate symmetric keys, we take advantage of photovoltage measurements generated from solar cells equipped with a pair of IoT devices. Symmetric keys are essential for pairing IoT devices and further securing wireless communication. Despite the sensitivity to varying lighting conditions, challenges still remain for the use of solar cells for key generation, such as time unsynchronisation and noisy measurements. To solve these challenges, we design a novel key generation framework, SolarKey, which includes the starting point detection and a compressed sensing-based two-tier key reconciliation method. Extensive experiments have been conducted to evaluate the performance of our proposed key generation method in various environments, which shows the proposed method can improve the key matching rate by up to 25%. We also conduct security analysis and the randomness test, which shows that SolarKey is resilient to common attacks such as the eavesdropping attack and the imitating attack and sufficiently random.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mishra2024b,

title = {Evaluating asthma symptoms in relation to indoor air quality: insights from IoT-enabled monitoring},

author = {B. K. Mishra and D. Thakker and R. John and R. R. Kureshi and B. Ahmad and W. Jones and X. Li},

doi = {10.1049/icp.2024.0469},

issn = {2732-4494},

year  = {2024},

date = {2024-01-26},

journal = {IET Conf. Proc.},

volume = {2023},

number = {39},

pages = {90--98},

publisher = {Institution of Engineering and Technology (IET)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kureshi2024,

title = {Digital health and indoor air quality: an IoT-driven human-centred visualisation platform for behavioural change and technology acceptance},

author = {R. R. Kureshi and B. K. Mishra and D. Thakker and S. Mazumdar and X. Li},

doi = {10.1049/icp.2024.0471},

issn = {2732-4494},

year  = {2024},

date = {2024-01-26},

journal = {IET Conf. Proc.},

volume = {2023},

number = {39},

pages = {104--113},

publisher = {Institution of Engineering and Technology (IET)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ravindhran2024b,

title = {Tailored risk assessment and forecasting in intermittent claudication},

author = {Bharadhwaj Ravindhran and Jonathon Prosser and Arthur Lim and Bhupesh Mishra and Ross Lathan and Louise H Hitchman and George E Smith and Daniel Carradice and Ian C Chetter and Dhaval Thakker and Sean Pymer},

doi = {10.1093/bjsopen/zrad166},

issn = {2474-9842},

year  = {2024},

date = {2024-01-03},

volume = {8},

number = {1},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               

                  Background

                  Guidelines recommend cardiovascular risk reduction and supervised exercise therapy as the first line of treatment in intermittent claudication, but implementation challenges and poor patient compliance lead to significant variation in management and therefore outcomes. The development of a precise risk stratification tool is proposed through a machine-learning algorithm that aims to provide personalized outcome predictions for different management strategies.

               

               

                  Methods

                  Feature selection was performed using the least absolute shrinkage and selection operator method. The model was developed using a bootstrapped sample based on patients with intermittent claudication from a vascular centre to predict chronic limb-threatening ischaemia, two or more revascularization procedures, major adverse cardiovascular events, and major adverse limb events. Algorithm performance was evaluated using the area under the receiver operating characteristic curve. Calibration curves were generated to assess the consistency between predicted and actual outcomes. Decision curve analysis was employed to evaluate the clinical utility. Validation was performed using a similar dataset.

               

               

                  Results

                  The bootstrapped sample of 10 000 patients was based on 255 patients. The model was validated using a similar sample of 254 patients. The area under the receiver operating characteristic curves for risk of progression to chronic limb-threatening ischaemia at 2 years (0.892), risk of progression to chronic limb-threatening ischaemia at 5 years (0.866), likelihood of major adverse cardiovascular events within 5 years (0.836), likelihood of major adverse limb events within 5 years (0.891), and likelihood of two or more revascularization procedures within 5 years (0.896) demonstrated excellent discrimination. Calibration curves demonstrated good consistency between predicted and actual outcomes and decision curve analysis confirmed clinical utility. Logistic regression yielded slightly lower area under the receiver operating characteristic curves for these outcomes compared with the least absolute shrinkage and selection operator algorithm (0.728, 0.717, 0.746, 0.756, and 0.733 respectively). External calibration curve and decision curve analysis confirmed the reliability and clinical utility of the model, surpassing traditional logistic regression.

               

               

                  Conclusion

                  The machine-learning algorithm successfully predicts outcomes for patients with intermittent claudication across various initial treatment strategies, offering potential for improved risk stratification and patient outcomes.

               },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Duan2024b,

title = {Wearable-based behaviour interpolation for semi-supervised human activity recognition},

author = {H. Duan and S. Wang and V. Ojha and S. Wang and Y. Huang and Y. Long and R. Ranjan and Y. Zheng},

doi = {10.1016/j.ins.2024.120393},

year  = {2024},

date = {2024-01-01},

journal = {Information Sciences},

volume = {665},

pages = {120393},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alqattan2024b,

title = {Modular neural network for Edge-based Detection of early-stage IoT Botnet},

author = {D. Alqattan and V. Ojha and F. Habib and A. Noor and G. Morgan and R. Ranjan},

doi = {10.1016/j.hcc.2024.100230},

year  = {2024},

date = {2024-01-01},

journal = {High-Confidence Computing},

pages = {100230},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Snasel2024,

title = {Large-scale data classification based on the integrated fusion of fuzzy learning and graph neural network},

author = {V. Snášel and M. Štěpnička and V. Ojha and P. N. Suganthan and R. Gao and L. Kong},

doi = {10.1016/j.inffus.2023.102067},

year  = {2024},

date = {2024-01-01},

journal = {Information Fusion},

volume = {102},

pages = {102067},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Suganthan2024,

title = {Euclidean and Poincaré space ensemble Xgboost},

author = {P. N. Suganthan and L. Kong and V. Snášel and V. Ojha and H. A. H. Z. Aly},

doi = {10.1016/j.inffus.2024.102746},

year  = {2024},

date = {2024-01-01},

journal = {Information Fusion},

pages = {102746},

keywords = {},

pubstate = {published},

tppubtype = {article}

}