Multimodal fusion frameworks, which integrate diverse medical imaging modalities (e.g., MRI, CT), have shown great potential in applications such as skin cancer detection, dementia diagnosis, and brain tumor prediction. However, existing multimodal fusion methods face significant challenges. First, they often rely on computationally expensive models, limiting their applicability in low-resource environments. Second, they often employ cascaded attention modules, which potentially increase risk of information loss during inter-module transitions and hinder their capacity to effectively capture robust shared representations across modalities. This restricts their generalization in multi-disease analysis tasks. To address these limitations, we propose a Hybrid Parallel-Fusion Cascaded Attention Network (HyPCA-Net), composed of two core novel blocks: (a) a computationally efficient residual adap tive learning attention block for capturing refined modality-specific representations, and (b) a dual-view cascaded attention block aimed at learning robust shared representations across diverse modalities. Extensive experiments on ten publicly available datasets exhibit that HyPCA-Netsignificantly outperforms existing leading methods, with improvements of up to 5.2% in performance and reductions of up to 73.1% in computational cost. Code: https://github.com/misti1203/HyPCA-Net.

We propose a novel MLaaS Dataset Generator (MDG) framework that creates configurable and reproducible datasets for evaluating Machine Learning as a Service (MLaaS) selection and composition. MDG simulates realistic MLaaS behaviour by training and evaluating diverse model families across multiple real-world datasets and data distribution settings. It records detailed functional attributes, quality of service metrics, and composition-specific indicators, enabling systematic analysis of service performance and cross-service behaviour. Using MDG, we generate more than ten thousand MLaaS service instances and construct a large-scale benchmark dataset suitable for downstream evaluation. We also implement a built-in composition mechanism that models how services interact under varied Internet of Things conditions. Experiments demonstrate that datasets generated by MDG enhance selection accuracy and composition quality compared to existing baselines. MDG provides a practical and extensible foundation for advancing data-driven research on MLaaS selection and composition.

The dynamic nature of Internet of Things (IoT) environments challenges the long-term effectiveness of Machine Learning as a Service (MLaaS) compositions. The uncertainty and variability of IoT environments lead to fluctuations in data distribution, e.g., concept drift and data heterogeneity, and evolving system requirements, e.g., scalability demands and resource limitations. This paper proposes an adaptive MLaaS composition framework to ensure a seamless, efficient, and scalable MLaaS composition. The framework integrates a service assessment model to identify underperforming MLaaS services and a candidate selection model to filter optimal replacements. An adaptive composition mechanism is developed that incrementally updates MLaaS compositions using a contextual multi-armed bandit optimization strategy. By continuously adapting to evolving IoT constraints, the approach maintains Quality of Service (QoS) while reducing the computational cost associated with recomposition from scratch. Experimental results on a real-world dataset demonstrate the efficiency of our proposed approach.

This study addresses a critical challenge in medical imaging diagnostics by proposing a unified, lightweight deep learning model capable of diagnosing multiple diseases across diverse imaging modalities, including chest X-rays, MRIs, skin images, and endoscopic images, within a single efficient framework. Each modality presents unique feature characteristics, introducing complexities in the diagnostic process. To enhance image quality, we apply Contrast Limited Adaptive Histogram Equalization and utilize the Vision Transformer for improved feature extraction and diagnostic performance. To tackle remaining challenges, we introduce the ChirpMBPru-Net model, designed to analyze multiple image modalities in medical imaging while minimizing computational demands. This model employs the efficient MobileNet architecture as its backbone and systematically applies pruning to remove redundant layers. Moreover, a dense module for multi-scale feature extraction and the Chirplet transformation are employed in the pruned model, capturing both frequency and spatial patterns at varying scales. Additionally, the ChirpMBPru-Net model demonstrates its versatility by adapting to domain shifts in engineering fields, such as defect detection in industrial applications (e.g., scholar defect detection), where it can classify multiple categories of the same object or defect type. The model achieves an impressive accuracy of 97% across 16 disease categories and proves effective in handling real-world domain shifts, demonstrating its potential for both medical and engineering applications.

Conventional standalone approaches for diagnosing individual diseases often fail to achieve robust generalization because they are severely impacted by overfitting. This results in poor adaptability to diverse image representations and an inability to balance performance with computational efficiency. In this study, we propose KDLight, a lightweight, novel CNN model designed for efficient medical image classification across diverse modalities, including MRI, X-ray, radiography, skin images, and histopathology. We employ Knowledge Distillation (KD), where insights from an efficient teacher model (MobileNet) guide the learning process of the KDLight student model. The KDLight model minimizes the number of parameters while enhancing feature learning across diverse medical image representations. Experimental results show that KDLight achieves 95.55% classification accuracy with only 2.96 seconds and a compact 7.5 MB disk size, significantly reducing parameter size, accelerating inference, and lowering computational costs compared to traditional pre-trained models. Additionally, KDLight ability to efficiently learn diverse image representations can be extended to other domains, such as crack classification (e.g., road, window, and building cracks), enabling high-performance detection across different surface defect categories.

Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it ideal for privacy-sensitive applications. However, FL models often suffer performance degradation due to distribution shifts between training and deployment. Test-Time Adaptation (TTA) offers a promising solution by allowing models to adapt using only test samples. However, existing TTA methods in FL face challenges such as computational overhead, privacy risks from feature sharing, and scalability concerns due to memory constraints. To address these limitations, we propose Federated Continual Test-Time Adaptation (FedCTTA), a privacy-preserving and computationally efficient framework for federated adaptation. Unlike prior methods that rely on sharing local feature statistics, FedCTTA avoids direct feature exchange by leveraging similarity-aware aggregation based on model output distributions over randomly generated noise samples. This approach ensures adaptive knowledge sharing while preserving data privacy. Furthermore, FedCTTA minimizes the entropy at each client for continual adaptation, enhancing the model’s confidence in evolving target distributions. Our method eliminates the need for server-side training during adaptation and maintains a constant memory footprint, making it scalable even as the number of clients or training rounds increases. Extensive experiments show that FedCTTA surpasses existing methods across diverse temporal and spatial heterogeneity scenarios.

Split-fed learning (SFL) is a novel approach within distributed collaborative machine learning that combines federated learning (FL) and split learning (SL). While SFL benefits from FL's speed and SL's efficiency, it also inherits their disadvantages, including trust issues such as model poisoning and training-hijacking attacks, and additional reliability concerns such as a single point of failure and lack of motivation. Existing solutions have integrated blockchain technology to address all reliability issues and poisoning attacks, but are limited to FL. Moreover, there are limited solutions to address training-hijacking, such as the SplitGuard protocol. In this article, we propose two solutions, validated blockchained split-fed learning (VBSFL) and VBSL, focusing on VBSFL, which leverages blockchain technology by building on VBFL and incorporating the SplitGuard protocol to address these challenges. Experimental results with real-world datasets demonstrate the effectiveness, efficiency, and scalability of the proposed approach.

In recent years, the combination of unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) has gained popularity in livestock management (LM) due to energy constraints and network instability. Limited energy storage of sensor nodes (SNs) and the possibility of packet loss contribute to fast energy consumption and unstable networks, respectively. UAVs serve as relay nodes and data sinks, addressing these issues by temporarily storing data to reduce SN workload and establishing mobile nodes for network stability. We propose two innovations based on previous work: 1) We introduce a multi-layer wireless network architecture, categorizing UAVs into two layers based on their functions including data collection and data processing. This enhances task parallelization, bridging performance gaps among multiple UAVs; 2) We overcome the mobility limitation of SNs, considering their real-time movement in the network. Through deep reinforcement learning, UAVs learn to cooperatively locate moving SNs. This accounts for the inevitable mobility of livestock in the industry. Additionally, we simulate the environment and compare our approach to traditional methods, evaluating metrics such as collected data per timestep (DCPS), energy consumed per timestep (ECPS), and network stability (NS). Experimental results demonstrate that our method outperforms traditional approaches, achieving a data collecting gain of 4.84% and 8.20% compared to the methods without considering SN mobility or the multi-layer characteristics of WSNs, respectively. Under energy consumption limits, our method yields energy savings of 3.00% and 1.35% respectively. Furthermore, we extensively study and validate our method against other path planning algorithms, including genetic particle swarm optimization (GPSO), modified central force optimization (MCFO), and rapidly-exploring random trees (RRT). Our approach surpasses these methods in terms of data collecting efficiency and network stability.

The popularity of Ethereum as a platform for Stablecoin transactions (for example, AUDN) continues to rise. It is therefore paramount that the integrity and security of transactions within these decentralized systems are guaranteed. The intricate network of interactions occurring during the exchange of cryptocurrencies made the task of identifying specific transactions as fraudulent difficult because fraudulent behaviour can be concealed within legitimate smart contract operations. Leveraging the inherent structure and interconnectedness of Ethereum transactions, this paper proposes a comprehensive framework to address issues such as Frontrunning within the cryptocurrency ecosystem. Constructing a knowledge graph representation of fraudulent Ethereum blockchain transactions, the proposed solution captures the relationships between addresses, transactions, and smart contracts and generates BotVictim recommendations based on Victim Receiver similarity scores exceeding 85%. These results are generated by excluding temporal transactions, a unique approach when examining the Ethereum network. Thus, our approach enables early detection and prevention of fraudulent activities, potentially safeguarding the interests of cryptocurrency users and mitigating potential financial losses. To evaluate the effectiveness of the proposed framework, its performance is compared against traditional fraud detection methods. The proposed solution demonstrates superiority in terms of accuracy and efficiency.

The Crowdsourced IoT Service (CIS) market is inherently different from other service markets, e.g., web services and cloud. The CIS market is dominated by transient services as both consumers and providers are dynamic in space and time. Consumer requests are usually long-term and demand continuity in service provision. We propose a novel egalitarian transient service composition framework from the CIS market perspective. We apply a Dynamic Bayesian Network to model the dynamic service provision behavior of the providers. The proposed framework transforms the composition of transient services into a multi-objective temporal optimization, i.e., providing continuous services to the maximum number of consumers, and minimizing the consumers’ cost of service usages over a long-term period. We incorporate a Pareto-based genetic algorithm to enable the fair distribution of services among the consumers. Experimental results prove the efficiency of the proposed approach in terms of continuous availability of service as well as fair distribution among consumers.

Soccer is one of the most complex and dynamic games. It is challenging to figure out the game’s pattern in real-time. We propose a novel network metric and entropy-based live soccer analytic framework (NMELSA) that identifies the opponent team’s tactics in a live soccer match by observing all the events until the specified minute of the game. We design a live game replacement model which recommends substitute players based on the on-field players’ live game ratings. Experimental results on a real-world dataset demonstrate the efficiency of our proposed approach.

We propose a novel generic reputation bootstrapping framework for composite services. Multiple reputation-related indicators are considered in a layer-based framework to implicitly reflect the reputation of the component services. The importance of an indicator on the future performance of a component service is learned using a modified Random Forest algorithm. We propose a topology-aware Forest Deep Neural Network (fDNN) to find the correlations between the reputation of a composite service and reputation indicators of component services. The trained fDNN model predicts the reputation of a new composite service with the confidence value. Experimental results with real-world dataset prove the efficiency of the proposed approach.

We propose a novel framework to select IaaS providers according to a consumer’s long-term performance requirements. The proposed framework leverages free short-term trials to discover the unknown QoS performance of IaaS providers. We design a temporal skyline-based filtering method to select candidate IaaS providers for the short-term trials. A novel cooperative long-term QoS prediction approach is developed that utilizes past trial experiences of similar consumers using a workload replay technique. We propose a new trial workload generation model that estimates a provider’s long-term performance in the absence of past trial experiences. The confidence of the prediction is measured based on the trial experience of the consumer. A set of experiments are conducted based on real-world datasets to evaluate the proposed framework.

We propose a novel framework for composing crowdsourced wireless energy services to satisfy users’ energy requirements in a crowdsourced Internet of Things (IoT) environment. A new energy service model is designed to transform the harvested energy from IoT devices into crowdsourced services. We propose a new energy service composability model that considers the spatio-temporal aspects and the usage patterns of the IoT devices. A multiple local knapsack-based approach is developed to select an optimal set of partial energy services based on the deliverable energy capacity of IoT devices. We propose a heuristic-based composition approach using the temporal and energy capacity distributions of services. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.

We propose a novel robust composition framework for drone delivery services considering changes in the wind patterns in urban areas. The proposed framework incorporates the dynamic arrival of drone services at the recharging stations. We propose a Probabilistic Forward Search (PFS) algorithm to select and compose the best drone delivery services under uncertainty. A set of experiments with a real drone dataset is conducted to illustrate the effectiveness and efficiency of the proposed approach.

We propose a novel Infrastructure-as-a-Service composition framework that selects an optimal set of consumer requests according to the provider’s qualitative preferences on long-term service provisions. Decision variables are included in the temporal conditional preference networks to represent qualitative preferences for both short-term and long-term consumers. The global preference ranking of a set of requests is computed using a k -d tree indexing-based temporal similarity measure approach. We propose an extended three-dimensional Q-learning approach to maximize the global preference ranking. We design the on-policy-based sequential selection learning approach that applies the length of request to accept or reject requests in a composition. The proposed on-policy-based learning method reuses historical experiences or policies of sequential optimization using an agglomerative clustering approach. Experimental results prove the feasibility of the proposed framework.

We propose a novel framework for composing Swarm-based Drone-as-a-Service (SDaaS) for delivery. Two composition approaches, i.e., sequential and parallel are designed considering the different behaviors of drone swarms. The proposed framework considers various constraints, e.g., recharging time and limited battery to meet delivery deadlines. We propose SDaaS composition algorithms using a modified A* algorithm. A cooperative behavior model is incorporated to reduce recharging and waiting time in a delivery. Experimental results prove the efficiency of the proposed approach.

We propose a novel framework to detect conflicts among IoT services in a multi-resident smart home. A novel IoT conflict model is proposed considering the functional and non-functional properties of IoT services. We design a conflict ontology that formally represents different types of conflicts. A hybrid conflict detection algorithm is proposed by combining both knowledge-driven and data-driven approaches. Experimental results on real-world datasets show the efficiency of the proposed approach.

We propose a novel approach to select IaaS cloud services for a long-term period where the service providers offer limited QoS information. The proposed approach leverages free short-term trials to obtain the previously undisclosed QoS information. A new significance-based trial scheme is proposed using frequency distribution analysis to test a consumer's long-term workloads in a short trial. We introduce a novel IaaS signature technique to uniquely identify the variability of a provider's QoS performance. A Signature-based QoS Performance Discovery (SPD) algorithm is proposed which leverages the combination of free trials and IaaS signatures. A set of exhaustive experiments with real-world datasets is conducted to evaluate the proposed approach.

We propose a novel game-theoretic approach for drone service composition considering recharging constraints. We design a non-cooperative game model for drone services. We propose a non-cooperative game algorithm for the selection and composition of optimal drone services. We conduct several experiments on a real drone dataset to demonstrate the efficiency of our proposed approach.

We propose a novel type of service composition, called elastic composition which provides a reliable framework in a highly fluctuating IoT energy provisioning settings. We rely on crowdsourcing IoT energy (e.g., wearables) to provide wireless energy to nearby devices. We introduce the concepts of soft deadline and hard deadline as key criteria to cater for an elastic composition framework. We conduct a set of experiments on real-world datasets to assess the efficiency of the proposed approach.

We propose a novel approach to select privacy-sensitive IaaS providers for a long-term period. The proposed approach leverages a consumer's short-term trial experiences for long-term selection. We design a novel equivalence partitioning based trial strategy to discover the temporal and unknown QoS performance variability of an IaaS provider. The consumer's long-term workloads are partitioned into multiple Virtual Machines in the short-term trial. We propose a performance fingerprint matching approach to ascertain the confidence of the consumer's trial experience. A trial experience transformation method is proposed to estimate the actual long-term performance of the provider. Experimental results with real-world datasets demonstrate the efficiency of the proposed approach.

We propose a novel composition framework for drone-based package delivery services termed as Drone-as-a-Service (DaaS). The proposed framework includes a spatio-temporal service model and a quality model for DaaS. A drone service selection algorithm is designed using 3D Rtree. We develop a Dijkstra-based and a heuristic-based drone service composition approach to meet users' delivery requirements, i.e., expected delivery time and cost. Experimental results on a real-world dataset demonstrate the efficiency of our proposed approach.

We present a new spatio-temporal incentive-based approach to achieve a geographically balanced coverage of crowdsourced services. The proposed approach is based on a new spatio-temporal incentive model that considers multiple parameters including location entropy, time of day, and spatio-temporal density to encourage the participation of crowdsourced service providers. We present a greedy network flow algorithm that offers incentives to redistribute crowdsourced service providers to improve the crowdsourced coverage balance within an area. A novel participation probability model is also introduced to estimate the expected number of crowdsourced service providers’ movement based on spatio-temporal features. Experimental results validate the efficiency and effectiveness of the proposed approach.

We propose a novel dynamic metaheuristic optimization approach to compose an optimal set of IaaS service requests to align with an IaaS provider's long-term economic expectation. This approach is designed for the context that the IaaS provisioning subjects to resource and QoS constraints. In addition, the IaaS service requests have the features of dynamic resource and QoS requirements and variable arrival times. A new economic model is proposed to evaluate the similarity between the provider's long-term economic expectation and a composition of service requests. The evaluation incorporates the factors of dynamic pricing and operation cost modeling of the service requests. An innovative hybrid genetic algorithm is proposed that incorporates the economic inter-dependency among the requests as a heuristic operator and performs repair operations in local solutions to meet the resource and QoS constraints. The proposed approach generates dynamic global solutions by updating the heuristic operator at regular intervals with the runtime behavior data of an existing service composition. Experimental results preliminarily prove the feasibility of the proposed approach.

We present a new two-level composition model for crowdsourced Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. The proposed approach is defined based on a formal Sensor-Cloud service model that abstracts the functionality and non-functional aspects of sensor data on the cloud in terms of spatio-temporal features. A spatio-temporal indexing technique based on the 3D R-tree to enable fast identification of appropriate Sensor-Cloud services is proposed. A novel quality model is introduced that considers dynamic features of sensors to select and compose Sensor-Cloud services. The quality model defines Coverage as a Service which is formulated as a composition of crowdsourced Sensor-Cloud services. We present two new QoS-aware spatio-temporal composition algorithms to select the optimal composition plan. Experimental results validate the performance of the proposed algorithms.

We propose a cloud service composition framework that selects the optimal composition based on an end user's long-term Quality of Service (QoS) requirements. In a typical cloud environment, existing solutions are not suitable when service providers fail to provide the long-term QoS provision advertisements. The proposed framework uses a new multivariate QoS analysis to predict the long-term QoS provisions from service providers' historical QoS data and short-term advertisements represented using Time Series. The quality of the QoS prediction is improved by incorporating QoS attributes' intra correlations into the multivariate analysis. To select the optimal service composition, the proposed framework uses QoS time series' inter correlations and performs a novel time series group similarity approach on the predicted QoS values. Experiments are conducted on real QoS dataset and results prove the efficiency of the proposed approach.

We propose a novel composition framework for an Infrastructure-as-a-Service (IaaS) provider that selects the optimal set of long-term service requests to maximize its profit. Existing solutions consider an IaaS provider's economic benefits at the time of service composition and ignore the dynamic nature of the consumer requests in a long-term period. The proposed framework deploys a new multivariate HMM and ARIMA model to predict different patterns of resource utilization and Quality of Service fluctuation tolerance levels of existing service consumers. The dynamic nature of new consumer requests with no history is modelled using a new community based heuristic approach. The predicted long-term service requests are optimized using Integer Linear Programming to find a proper configuration that maximizes the profit of an IaaS provider. Experimental results prove the feasibility of the proposed approach.