The wealth of gene expression values being generated by high throughput microarray technologies leads to complex high dimensional datasets. Moreover, many cohorts have the problem of imbalanced classes where the number of patients belonging to each class is not the same. With this kind of dataset, biologists need to identify a small number of informative genes that can be used as biomarkers for a disease.

In the evolving landscape of consumer electronics, the Generative Adversarial Learning-based Trust Management (GALTrust) framework emerges as a novel solution, uniquely combining Generative Adversarial Networks (GANs) and type-2 fuzzy logic to tackle trust management challenges within the Internet of Drone Things (IoDT). Addressing the pivotal needs of spatial crowdsourcing scenarios like bushfire management, GALTrust significantly overcomes the limitations posed by traditional machine learning methods in detecting emergent types of malicious nodes and navigating the impact of training data size variations. At its core, GALTrust features a GAN-based codec structure, meticulously trained with trust vectors, enabling precise differentiation between malicious and trustworthy nodes. A key innovation of GALTrust is the introduction of a GAN-based trust redemption model, strategically designed to curtail false positives and safeguard against the unwarranted exclusion of benign drones, thus markedly enhancing network resilience. This framework exhibits dynamic adaptability, continually refining its trust model to align with the latest detection insights within the IoDT ecosystem. Through its application in secure clustering for IoDT, GALTrust has proven its efficacy by achieving an exceptional detection rate of up to 94.1% and maintaining a false positive rate below 9.1%, thereby significantly elevating security and operational efficiency in crucial consumer electronics applications.

The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE) for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimination strategy which is the rationale of RFE algorithm. The rationale behind this is, building ensemble SVM models using randomly drawn bootstrap samples from the training set, will produce different feature rankings which will be subsequently aggregated as one feature ranking. As a result, the decision for elimination of features is based upon the ranking of multiple SVM models instead of choosing one particular model. Moreover, this approach will address the problem of imbalanced datasets by constructing a nearly balanced bootstrap sample. Our experiments show that ESVM-RFE for gene selection substantially increased the classification performance on five microarray datasets compared to state-of-the-art methods. Experiments on the childhood leukaemia dataset show that an average 9% better accuracy is achieved by ESVM-RFE over SVM-RFE, and 5% over random forest based approach. The selected genes by the ESVM-RFE algorithm were further explored with Singular Value Decomposition (SVD) which reveals significant clusters with the selected data.

In this study, we introduce a pioneering framework, DroneSSL, that integrates the concept of spatial crowdsourcing with TinyML to enhance anomaly detection in the Internet of Drone Things (IoDT). This innovative approach leverages drones and unmanned ground vehicles (UGVs) for expansive data collection in environments that are typically inaccessible or hazardous, such as during Australian bushfire incidents. By employing lightweight machine learning models alongside advanced communication technologies, DroneSSL transcends traditional spatial-temporal data analysis methods. It efficiently processes multimodal data from diverse Points-of-Interest (PoIs), significantly improving the quality and speed of data collection and analysis. The framework’s integration of a temporal feature extraction module with a Graph Neural Network (GNN) and its adaptable, scalable GNN architecture tailor DroneSSL for real-time operations in resource-constrained IoDT environments. Achieving an 89.6% F1 score, DroneSSL marks a substantial 4.9% improvement over existing approaches, highlighting its effectiveness in critical applications such as environmental surveillance and emergency response. This advancement not only showcases the potential of combining TinyML with spatial crowdsourcing for IoDT but also sets a new standard for efficient, scalable anomaly detection, paving the way for future innovations in IoT edge devices and environmental monitoring systems.

We introduce the AI-Generated Optimal Decision (AIGOD) algorithm and the Deep Diffusion Soft Actor-Critic (DDSAC) framework, marking a significant advancement in integrating Human Digital Twins (HDTs) with AI-Generated Content (AIGC) within IoMT-based smart homes. Our innovative AI-Generated Content-as-a-Service (AIGCaaS) architecture, optimized for IoMT environments, leverages network edge servers to enhance the selection of AI-Generated Content Service Providers (AISPs) tailored to the unique characteristics of individual HDTs. Extensive experiments demonstrate DDSAC’s HDT-centric approach outperforms traditional Deep Reinforcement Learning algorithms, offering optimal AIGC services for diverse healthcare needs. Specifically, DDSAC achieved a 20% improvement in task completion rates and a 15% increase in overall utility compared to existing methods. These findings highlight the potential of HDTs in personalized healthcare by simulating and predicting patient-specific medical outcomes, leading to proactive and timely interventions. This integration facilitates personalized healthcare, establishing a new standard for patient-centric care in smart home environments. By leveraging cutting-edge AI techniques, our research significantly contributes to the fields of IoMT and AIGC, paving the way for smarter and more responsive healthcare services.