Publications

Ahmadi, S, Cheraghian, A, Saberi, M, Abir, MT, Dastmalchi, H, Hussain, F & Rahman, S 2025, 'Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-Free Adaptor' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 178-195.
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Alharbi, A, Sharma, N & Hussain, F 2025, 'Arabic Sentiment Analysis Leveraging Hybrid Word Embeddings with Deep Learning Techniques' in Lecture Notes on Data Engineering and Communications Technologies, Springer Nature Switzerland, pp. 129-140.
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Aljaafreh, R, Al-Doghman, F & Hussain, F 2025, 'Toward a Hybrid Cybersecurity Framework of Machine Learning and Business Intelligence into Supply Chain Risk Management' in Studies in Systems, Decision and Control, Springer Nature Switzerland, pp. 1021-1029.
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Alsuhaibani, A, Razzak, I, Jameel, S, Wang, X & Xu, G 2025, 'CLIMB: Imbalanced Data Modelling Using Contrastive Learning with Limited Labels' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 60-75.
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Khuat, TT, Bassett, R, Otte, E & Gabrys, B 2025, 'Online Machine Learning for Real-Time Cell Culture Process Monitoring' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 363-375.
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Kim, J, Xuan, J, Liang, C & Hussain, F 2025, 'A Non-monolithic Policy Approach of Offline-to-Online Reinforcement Learning' in Communications in Computer and Information Science, Springer Nature Singapore, pp. 45-59.
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Loesch, N, Catchpoole, DR & Kennedy, PJ 2025, 'Three-Dimensional Latent Diffusion Model for Weakly-Supervised Brain Tumour Segmentation' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 242-251.
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Xu, W, Wu, Y, Jiang, H, Wang, Y, Wu, Q & Zhang, J 2025, 'Task Consistent Prototype Learning for Incremental Few-Shot Semantic Segmentation' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 147-162.
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Alghanmi, NA, Alghanmi, NA, Alghanmi, SA, Zhao, M & Hussain, FK 2025, 'Data-driven approach for selection of on-chain vs off-chain carbon credits data storage methods', Knowledge-Based Systems, vol. 310, pp. 112871-112871.
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Alharbi, RS & Hussain, FK 2025, 'Smart-contracts-driven personal carbon credit management in smart cities: A review and future research directions', Internet of Things, vol. 32, pp. 101636-101636.
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Alsenwi, M, Abolhasan, M & Lipman, J 2025, 'RIS-UAV Integration for Enhanced Coverage and Energy-Efficient 6G Wireless Networks', IEEE Transactions on Green Communications and Networking, pp. 1-1.
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Amirbagheri, K, Merigó, JM & Corrons, A 2025, '30 years of Energy for Sustainable Development: A bibliometric overview', Energy for Sustainable Development, vol. 87, pp. 101741-101741.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2025, 'Development of a polarization-neutral metamaterial absorber for efficient low-power EM energy harvesting', Sensors and Actuators A: Physical, vol. 381, pp. 116055-116055.
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Bibi, H, Abolhasan, M, Lipman, J, Abdollahi, M & Ni, W 2025, 'A comprehensive survey on privacy-preserving technologies for Smart Grids', Computers and Electrical Engineering, vol. 124, pp. 110371-110371.
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Cai, Q, Cao, J, Xu, G & Zhu, N 2025, 'Distributed Recommendation Systems: Survey and Research Directions', ACM Transactions on Information Systems, vol. 43, no. 1, pp. 1-38.
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With the explosive growth of online information, recommendation systems have become essential tools for alleviating information overload. In recent years, researchers have increasingly focused on centralized recommendation systems, capitalizing on the powerful computing capabilities of cloud servers and the rich historical data they store. However, the rapid development of edge computing and mobile devices in recent years has provided new alternatives for building recommendation systems. These alternatives offer advantages such as privacy protection and low-latency recommendations. To leverage the advantages of different computing nodes, including cloud servers, edge servers, and terminal devices, researchers have proposed recommendation systems that involve the collaboration of these nodes, known as distributed recommendation systems. This survey provides a systematic review of distributed recommendation systems. Specifically, we design a taxonomy for these systems from four perspectives and comprehensively summarize each study by category. In particular, we conduct a detailed analysis of the collaboration mechanisms of distributed recommendation systems. Finally, we discuss potential future research directions in this field.

Ellis, JT & Kennedy, PJ 2025, 'Multi-criteria decision making and its application to in silico discovery of vaccine candidates for Toxoplasma gondii', Vaccine, vol. 58, pp. 127242-127242.
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Flores-Sosa, M, Merigó, JM & Sanchez-Valenzuela, K 2025, '30 years of the Journal of Heuristics: a bibliometric analysis', Journal of Heuristics, vol. 31, no. 1.
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Gao, S, Wang, C, Gao, C, Luo, W, Han, P, Liao, Q & Xu, G 2025, 'Improving long‐tail classification via decoupling and regularisation', CAAI Transactions on Intelligence Technology, vol. 10, no. 1, pp. 62-71.
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AbstractReal‐world data always exhibit an imbalanced and long‐tailed distribution, which leads to poor performance for neural network‐based classification. Existing methods mainly tackle this problem by reweighting the loss function or rebalancing the classifier. However, one crucial aspect overlooked by previous research studies is the imbalanced feature space problem caused by the imbalanced angle distribution. In this paper, the authors shed light on the significance of the angle distribution in achieving a balanced feature space, which is essential for improving model performance under long‐tailed distributions. Nevertheless, it is challenging to effectively balance both the classifier norms and angle distribution due to problems such as the low feature norm. To tackle these challenges, the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components: classifier norm (i.e. the magnitude of the classifier vector), feature norm (i.e. the magnitude of the feature vector), and cosine similarity between the classifier vector and feature vector. In this way, the authors analyse the change of each component in the training process and reveal three critical problems that should be solved, that is, the imbalanced angle distribution, the lack of feature discrimination, and the low feature norm. Drawing from this analysis, the authors propose a novel loss function that incorporates hyperspherical uniformity, additive angular margin, and feature norm regularisation. Each component of the loss function addresses a specific problem and synergistically contributes to achieving a balanced classifier and feature space. The authors conduct extensive experiments on three popular benchmark datasets including CIFAR‐10/100‐LT, ImageNet‐LT, and iNaturalist 2018. The experimental results demonstrate that the authors’ loss function outperforms several previous state‐of‐th...

Grigorev, A, Saleh, K, Ou, Y & Mihăiţă, A-S 2025, 'Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification', International Journal of Intelligent Transportation Systems Research, vol. 23, no. 1, pp. 259-280.
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Gu, Z, Jia, W, Piccardi, M & Yu, P 2025, 'Empowering large language models for automated clinical assessment with generation-augmented retrieval and hierarchical chain-of-thought', Artificial Intelligence in Medicine, vol. 162, pp. 103078-103078.
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Guan, L, Abbasi, A, Ryan, MJ & Merigó, JM 2025, 'A dynamic risk interdependency network-based model for project risk assessment and treatment throughout a project life cycle', Computers & Industrial Engineering, vol. 201, pp. 110921-110921.
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Guan, L, Laporte, G, Merigó, JM, Nickel, S, Rahimi, I & Saldanha-da-Gama, F 2025, '50 years of Computers & Operations Research: A bibliometric analysis', Computers & Operations Research, vol. 175, pp. 106910-106910.
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Guan, L, Merigó, JM & Beydoun, G 2025, '40 years of Decision Support Systems: A bibliometric analysis', Decision Support Systems, vol. 194, pp. 114469-114469.
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Guillen-Pujadas, M, Alaminos, D, Vizuete-Luciano, E & Merigo, JM 2025, 'Half a Century of The Journal of Portfolio Management: A Bibliometric Overview', JOURNAL OF PORTFOLIO MANAGEMENT, vol. 51, no. 6, pp. 175-215.

Huang, Y, Huang, Y, Zhang, Z, Wu, Q, Zhong, Y & Wang, L 2025, 'CSFRNet: Integrating Clothing Status Awareness for Long-Term Person Re-identification', International Journal of Computer Vision, vol. 133, no. 6, pp. 3180-3202.
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Hussain, W, Merigó, JM, Rahimi, I & Lev, B 2025, 'Half a century of Omega – The International Journal of Management Science: A bibliometric analysis', Omega, vol. 133, pp. 103226-103226.
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Jacob Kedziora, D, Musiał, A, Rudno-Rudziński, W & Gabrys, B 2025, 'Transfer learning and the early estimation of single-photon source quality using machine learning methods', Machine Learning: Science and Technology, vol. 6, no. 2, pp. 025014-025014.
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Abstract The use of single-photon sources (SPSs) is central to numerous systems and devices proposed amidst a modern surge in quantum technology. However, manufacturing schemes remain imperfect, and single-photon emission purity must often be experimentally verified via interferometry. Such a process is typically slow and costly, which has motivated growing research into whether SPS quality can be more rapidly inferred from incomplete emission statistics. Hence, this study is a sequel to previous work that demonstrated significant uncertainty in the standard method of quality estimation, i.e. the least-squares fitting of a physically motivated function, and asks: can machine learning (ML) do better? The study leverages eight datasets obtained from measurements involving an exemplary quantum emitter, i.e. a single InGaAs/GaAs epitaxial quantum dot; these eight contexts predominantly vary in the intensity of the exciting laser. Specifically, via a form of ‘transfer learning’, five ML models, three linear and two ensemble-based, are trained on data from seven of the contexts and tested on the eighth. Validation metrics quickly reveal that even a linear regressor can outperform standard fitting when it is tested on the same contexts it was trained on, but the success of transfer learning is less assured, even though statistical analysis, made possible by data augmentation, suggests its superiority as an early estimator. Accordingly, the study concludes by discussing future strategies for grappling with the problem of SPS context dissimilarity, e.g. feature engineering and model adaptation.

Jafaryahya, J, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2025, 'Integrating Electrical Features for Simultaneous Prediction of Soil Moisture and Potassium Levels Based on Neural Network Prediction Model', IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-12.
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Khorshidi, MS, Merigó, JM & Beydoun, G 2025, 'Half a Century of Information Processing & Management: A bibliometric retrospective', Information Processing & Management, vol. 62, no. 6, pp. 104238-104238.
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Kim, J, Xuan, J, Liang, C & Hussain, FK 2025, 'Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model'.
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Lan, D, Sun, C, Dong, X, Qiu, P, Gong, Y, Liu, X, Fournier-Viger, P & Zhang, C 2025, 'TK-RNSP: Efficient Top-K Repetitive Negative Sequential Pattern mining', Information Processing & Management, vol. 62, no. 3, pp. 104077-104077.
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Lee, S, Chen, T, Sze, NN, Mao, T, Ou, Y, Mihaita, A-S & Chen, F 2025, 'Analysing driver behaviour and crash frequency at railway level crossings using connected vehicle and GIS data', Travel Behaviour and Society, vol. 39, pp. 100957-100957.
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Li, A, Yang, B, Huo, H, Hussain, FK & Xu, G 2025, 'Self-supervised dual graph learning for recommendation', Knowledge-Based Systems, vol. 310, pp. 112967-112967.
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Li, H, Yu, B, Wei, Y, Wang, K, Xu, RYD & Wang, B 2025, 'KERMIT: Knowledge graph completion of enhanced relation modeling with inverse transformation', Knowledge-Based Systems, vol. 324, pp. 113500-113500.
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Li, Q, Wang, Z, Xia, H, Li, G, Cao, Y, Yao, L & Xu, G 2025, 'HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 3, pp. 4371-4384.
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Li, X, Zhang, Y, Long, G, Hu, Y, Lu, W, Chen, M, Zhang, C & Gong, Y 2025, 'Adaptive Traffic Forecasting on Daily Basis: A Spatio-Temporal Context Learning Approach', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14.
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Liu, X, Song, W, Musial, K, Li, Y, Zhao, X & Yang, B 2025, 'Stochastic Block Models for Complex Network Analysis: A Survey', ACM Transactions on Knowledge Discovery from Data, vol. 19, no. 3, pp. 1-35.
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Complex networks enable to represent and characterize the interactions between entities in various complex systems which widely exist in the real world and usually generate vast amounts of data about all the elements, their behaviors and interactions over time. The studies concentrating on new network analysis approaches and methodologies are vital because of the diversity and ubiquity of complex networks. The stochastic block model (SBM), based on Bayesian theory, is a statistical network model. SBMs are essential tools for analyzing complex networks since SBMs have the advantages of interpretability, expressiveness, flexibility and generalization. Thus, designing diverse SBMs and their learning algorithms for various networks has become an intensively researched topic in network analysis and data mining. In this article, we review, in a comprehensive and in-depth manner, SBMs for different types of networks (i.e., model extensions), existing methods (including parameter estimation and model selection) for learning optimal SBMs for given networks and SBMs combined with deep learning. Finally, we provide an outlook on the future research directions of SBMs.

Luo, S, Peng, H, Shi, Y, Cai, J, Zhang, S, Shao, N & Li, J 2025, 'Integration of proteomics profiling data to facilitate discovery of cancer neoantigens: a survey', Briefings in Bioinformatics, vol. 26, no. 2.
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Abstract Cancer neoantigens are peptides that originate from alterations in the genome, transcriptome, or proteome. These peptides can elicit cancer-specific T-cell recognition, making them potential candidates for cancer vaccines. The rapid advancement of proteomics technology holds tremendous potential for identifying these neoantigens. Here, we provided an up-to-date survey about database-based search methods and de novo peptide sequencing approaches in proteomics, and we also compared these methods to recommend reliable analytical tools for neoantigen identification. Unlike previous surveys on mass spectrometry-based neoantigen discovery, this survey summarizes the key advancements in de novo peptide sequencing approaches that utilize artificial intelligence. From a comparative study on a dataset of the HepG2 cell line and nine mixed hepatocellular carcinoma proteomics samples, we demonstrated the potential of proteomics for the identification of cancer neoantigens and conducted comparisons of the existing methods to illustrate their limits. Understanding these limits, we suggested a novel workflow for neoantigen discovery as perspectives.

Nguyen, X-B, Phan, X-H & Piccardi, M 2025, 'Fine-tuning text-to-SQL models with reinforcement-learning training objectives', Natural Language Processing Journal, vol. 10, pp. 100135-100135.
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Nik-Khorasani, A, Khuat, TT & Gabrys, B 2025, 'Hyperbox Mixture Regression for process performance prediction in antibody production', Digital Chemical Engineering, vol. 14, pp. 100221-100221.
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Parnell, J, Unanue, IJ, Matthews, S & Piccardi, M 2025, 'APG: Automatic Prompt Generation for Improved Document Summarization', IEEE Transactions on Audio, Speech and Language Processing, pp. 1-15.
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Rathod, S, Sahni, M & Merigo, JM 2025, 'Development and Applications of Penalty‐Based Aggregation Operators in Multicriteria Decision Making', International Journal of Intelligent Systems, vol. 2025, no. 1.
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This article develops a new penalty‐based aggregation operator known as the penalty‐based induced ordered weighted averaging (P‐IOWA) operator which is an extension of penalty‐based ordered weighted averaging (P‐OWA) operator. Our goal is to figure out how the induced variable realigns penalties when gathering information. We extend the P‐OWA and P‐IOWA operators with the different means such as generalized mean and quasi‐arithmetic mean. This article also includes different families of P‐OWA and P‐IOWA operators. The value of these new operators is demonstrated through a case study centered on investment matters. This study evaluates the economic and governance performance of seven South Asian nations utilizing nine indicators from 2021 data. The research examines 5 economic indicators including GDP growth, exports and imports (% of GDP), inflation, and labor force metrics, alongside 4 governance indicators focusing on corruption control, government effectiveness, and political stability. We use min–max normalization to standardize the varied values, which originally ranged from 0.5% to 77.7% across various metrics. Following this, the normalized inverse penalty method is used to derive optimal weights for these indicators, tackling the task of combining multidimensional data. Subsequently, we implement and evaluate various penalty‐based aggregation methodologies on the normalized data, each offering a distinct approach to penalizing outliers and balancing indicator weights. The study compares the results obtained from these operators to assess their impact on country rankings and overall performance evaluation. This approach allows for a comprehensive comparison of countries’ performances, integrating both economic and governance dimensions into a single, quantifiable framework.

Raza, MA, Abolhasan, M, Lipman, J & Ni, W 2025, 'Device-Level Learning-Based Distributed Power Control for Uplink NOMA in IoT Networks', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Song, Y, Liu, Z, Li, G, Xie, J, Wu, Q, Zeng, D, Xu, L, Zhang, T & Wang, J 2025, 'EMS: A Large-Scale Eye Movement Dataset, Benchmark, and New Model for Schizophrenia Recognition', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 9451-9462.
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Tang, H, Wu, S, Sun, X, Zeng, J, Xu, G & Li, Q 2025, 'TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic Recommendation', ACM Transactions on Information Systems, vol. 43, no. 1, pp. 1-27.
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Dynamic recommendation systems, where users interact with items continuously over time, have been widely deployed in real-world online streaming applications. The burst of interaction stream causes a rapid evolution of both users and items. To update representations dynamically, existing studies have investigated event-level and history-level dynamics by modeling the newly arrived interactions and aggregating historical interactions, respectively. However, most of them directly learn the representation evolution as new interactions occur, without exploring the collaboration between the newly arrived and historical interactions, thus failing to scrutinize whether those new interactions would benefit the evolution learning process when generating dynamic representations. Moreover, most of them model the two levels of dynamics independently, explicitly ignoring the inherent co-evolving correlation between them. In this work, we propose the Temporal Collaboration-Aware Graph Co-Evolution Learning (TCGC) for the dynamic recommendation scenario. First, we explore the effectiveness of collaborative information and devise the collaboration-aware indicator to guide the evolution learning process. Second, we design a temporal co-evolving graph network, enabling our framework to capture the correlation between event and history dynamics. Third, we leverage the evolution task and recommendation task together for joint training. Extensive experiments on four public datasets demonstrate the superiority and effectiveness of our proposed TCGC.

Tofigh, F & Lipman, J 2025, 'Human Presence Detection Sensor Using Polarization Insensitive Metamaterial Absorbers', IEEE Access, vol. 13, pp. 81725-81735.
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Wang, C, Chaturvedi, K, Nham, B, Reid, N, Bradshaw, A, Rosengren, S, Black, D, Bein, K, Halmagyi, M, Braytee, A, Prasad, M, Bharathy, G & Welgampola, M 2025, 'Separating Stroke and Vestibular Neuritis Using History, Examination and Vestibular Tests: A Machine Learning Approach (S25.009)', Neurology, vol. 104, no. 7_Supplement_1.
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Wang, D, Yao, H, Yu, D, Song, S, Weng, H, Xu, G & Deng, S 2025, 'Graph Intention Embedding Neural Network for tag-aware recommendation', Neural Networks, vol. 184, pp. 107062-107062.
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Wang, J, Chen, L, Zhang, Z, He, J & Zhou, X 2025, 'Short-text topic modeling with dual reinforcement from internal and external semantics', International Journal of Machine Learning and Cybernetics, vol. 16, no. 5-6, pp. 2905-2920.
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Wu, Z, Liu, Y, Cen, J, Zheng, Z & Xu, G 2025, 'A cross-domain knowledge tracing model based on graph optimal transport', World Wide Web, vol. 28, no. 1.
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Yang, M, Li, X, Xu, B, Nie, X, Zhao, M, Zhang, C, Zheng, Y & Gong, Y 2025, 'STDA: Spatio-Temporal Deviation Alignment Learning for Cross-city Fine-grained Urban Flow Inference', IEEE Transactions on Knowledge and Data Engineering, pp. 1-13.
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Yin, Q, Zhong, L, Song, Y, Bai, L, Wang, Z, Li, C, Xu, Y & Yang, X 2025, 'A decision support system in precision medicine: contrastive multimodal learning for patient stratification', Annals of Operations Research, vol. 348, no. 1, pp. 579-607.
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Abstract Precision medicine aims to provide personalized healthcare for patients by stratifying them into subgroups based on their health conditions, enabling the development of tailored medical management. Various decision support systems (DSSs) are increasingly developed in this field, where the performance is limited to their capability of handling big amounts of heterogeneous and high-dimensional electronic health records (EHRs). In this paper, we focus on developing a deep learning model for patient stratification that can identify and explain patient subgroups from multimodal EHRs. The primary challenge is to effectively align and unify heterogeneous information from various modalities, which includes both unstructured and structured data. Here, we develop a Contrastive Multimodal learning model for EHR (ConMEHR) based on topic modelling. In ConMEHR, modality-level and topic-level contrastive learning (CL) mechanisms are adopted to obtain a unified representation space and diversify patient subgroups, respectively. The performance of ConMEHR will be evaluated on two real-world EHR datasets and the results show that our model outperforms other baseline methods.

Yu, P, Zhang, X, Gong, Y, Zhang, J, Sun, H, Zhang, J, Zhang, X & Yin, Y 2025, 'Enhancing origin–destination flow prediction via bi-directional spatio-temporal inference and interconnected feature evolution', Expert Systems with Applications, vol. 264, pp. 125679-125679.
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Zhang, X, Cao, M, Gong, Y, Wu, X, Dong, X, Guo, Y, Zhao, L & Zhang, C 2025, 'Enhancing urban flow prediction via mutual reinforcement with multi-scale regional information', Neural Networks, vol. 182, pp. 106900-106900.
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Zhao, Z, Cao, L & Wang, C-D 2025, 'Gray Learning From Non-IID Data With Out-of-Distribution Samples', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 1396-1409.
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Zhou, K, Jiang, M, Gabrys, B & Xu, Y 2025, 'Learning Causal Representations Based on a GAE Embedded Autoencoder', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 6, pp. 3472-3484.
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Alshatri, N, Ghannam, S & Hussain, FK 1970, 'Multi-factor Predictive Models of Carbon Credit Prices', Springer Nature Switzerland, pp. 200-212.
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Haddas, M & Hussain, FK 1970, 'Human Factors Influencing Australian Manufacturing SMEs’ Adoption of Collaborative Robots: A Qualitative Study', Springer Nature Switzerland, pp. 154-164.
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Jin, K, Zi, X, Thiyagarajan, K, Braytee, A & Prasad, M 1970, 'IP-VQA Dataset: Empowering Precision Agriculture with Autonomous Insect Pest Management through Visual Question Answering', Companion Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 2000-2007.
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Ma, J, Na, H, Wang, Z, Hua, Y, Liu, Y, Wang, W & Chen, L 1970, 'Detecting Conversational Mental Manipulation with Intent-Aware Prompting', Proceedings International Conference on Computational Linguistics Coling, pp. 9176-9183.
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Mental manipulation severely undermines mental wellness by covertly and negatively distorting decision-making. While there is an increasing interest in mental health care within the natural language processing community, progress in tackling manipulation remains limited due to the complexity of detecting subtle, covert tactics in conversations. In this paper, we propose Intent-Aware Prompting (IAP), a novel approach for detecting mental manipulations using large language models (LLMs), providing a deeper understanding of manipulative tactics by capturing the underlying intents of participants. Experimental results on the MentalManip dataset demonstrate superior effectiveness of IAP against other advanced prompting strategies. Notably, our approach substantially reduces false negatives, helping detect more instances of mental manipulation with minimal misjudgment of positive cases. The code of this paper is available at https://github.com/Anton-Jiayuan-MA/Manip-IAP.

Sood, R, Anaissi, A, Huang, W & Braytee, A 1970, 'MemHateCaptioning: Enhancing Hate Speech Detection in Memes with Context-Aware Captioning and Chain-of-Thought', Companion Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 2034-2041.
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Van Huynh, N, Nguycn, DN, Mucchi, L, Caputo, S, Piccardi, M, Hoang, DT & Dutkiewicz, E 1970, 'Defeating Eavesdropping Attacks with Inter-Cell Interference and Deep Reinforcement Learning', 2025 IEEE Wireless Communications and Networking Conference (WCNC), 2025 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6.
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Xu, C, Wang, Y, Guo, J, Liu, D, Dong, M, George, Y, Barthelemy, J, Liu, Y & Chen, L 1970, 'Workshop on Sustainable AI for the Future Web', Companion Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 2052-2055.
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Anaissi, A, Akram, J, Chaturvedi, K & Braytee, A 2025, 'Detecting and Understanding Hateful Contents in Memes Through Captioning and Visual Question-Answering'.

Hussein, A, Prasad, M, Anaissi, A & Braytee, A 2025, 'Vision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging'.

Miao, Y, Yang, X, Fan, H, Li, Y, Hong, Y, Guo, X, Braytee, A, Huang, W & Anaissi, A 2025, 'FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservation'.

Wu, Y, Ji, Y, Lee, S, Akram, J, Braytee, A & Anaissi, A 2025, 'Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology'.

Zhao, T, Li, M, Chen, K, Wang, L, Zhou, X, Chaturvedi, K, Prasad, M, Anaissi, A & Braytee, A 2025, 'Enhancing Sentiment Analysis through Multimodal Fusion: A BERT-DINOv2 Approach'.