In the digital age, online businesses often rely on advertising as a primary source of revenue. Many services and platforms depend on advertisement income to remain operational, allowing free services to be sustained through ad funding. Advertisers typically pay per click, with fees determined by click-through rates. However, a major challenge in this system is click fraud, a deceptive practice in which advertisements are repeatedly clicked without genuine interest or intent to make a purchase. This behavior leads to financial losses for advertisers, damages platform reputations, and distorts the effectiveness of online advertising. Detecting fraudulent clicks remains a complex challenge due to the constantly evolving tactics employed by fraudsters. This study explores the potential of artificial intelligence (AI) classifiers for identifying instances of click fraud. Because the performance of AI-based classifiers strongly depends on proper hyperparameter selection, a modified optimization metaheuristic is introduced to improve classification accuracy. A comparative analysis is conducted using real-world data, demonstrating promising results from the best-performing models with accuracy as high as 0.786496. Finally, the legal and policy implications of such a system are examined within the context of an evolving digital landscape.
This paper propose a secure and robust color-image watermarking framework for open-network environments in the Discrete Quaternionic Quadratic Phase Fourier Transform (DQQPFT) domain. Unlike RGB-separable schemes with fixed kernels, the method exploits a controllable non-rigid DQQPFT kernel to preserve inter-channel correlation and provide flexible quaternion frequency representations. The kernel parameters are incorporated as cryptographic keys to enhance key sensitivity and resist brute-force guessing. To reduce computational cost, DQQPFT is implemented via a real-matrix equivalent representation of quaternions while remaining consistent with the original transform. For adaptive watermark embedding in Quaternion Singular Value Decomposition (QSVD), we formulate a bi-objective optimization that jointly considers imperceptibility and robustness. An Enhanced Multi-Objective Ant Colony Optimization algorithm (E-MOACO) is developed by introducing dynamic weight scheduling and adaptive Gaussian perturbation, and a knee-point selection strategy is used to obtain well-balanced solutions. Extensive experiments demonstrate high visual quality and reliable detection: average PSNR exceeds 39 dB without attacks; average normalized correlation (NC) remains above 0.99 under common single attacks and above 0.90 under mixed attacks.
In cross-enterprise supply chain collaboration scenarios, transaction data such as transaction amounts and cross-enterprise exchange rates all possess rational number properties. However, existing approaches to processing rational numbers in secure computing involve scaling by the same multiple, which may result in loss of rational number precision. Any loss of precision or simplistic conversion of rational numbers to integers may pose risks of privacy leaks and erroneous judgments. To address the aforementioned issues, this paper studies the application of secure computation based on the intersection and union of rational number sets in cross-enterprise supply chain collaboration. this paper transforms rational numbers into matrices and designs secure computation protocols for set intersection and union under semi-honest and malicious models using the Elgamal homomorphic encryption algorithm. The security of the protocol under the malicious model is proven using an ideal-practical example. Finally, this paper analyzes the efficiency of existing protocols versus the proposed protocol, and proves its practicality.
Collaborative editing, as observed in projects such as Google Docs, often relies on costly central services. CRDTs are proposed as decentralized data structures for consistent collaborative editing. Nonetheless, to date no CRDT proposal supports a closed group of peers while both preventing interleavings and rejecting operations from distrusted nodes outside that group. We present the Syncordian CRDT, which guarantees strong eventual consistency and intention preservation across nodes. Syncordian avoids interleaving anomalies by detecting overlapping edit blocks and re-issuing the lower priority one, and it authenticates every operation with a keyed message authentication code rooted in a secret shared by the peers admitted when the document is created, so it needs no public key infrastructure. To evaluate Syncordian we use two collaborative documents taken from GitHub, with over 1000 edit operations contributed by over 100 network peers, together with a suite of controlled workloads that build the concurrent authoring a replayed history cannot. We validate that Syncordian keeps every peer’s document consistent, even in the presence of distrusted nodes, and we report what the guarantees cost in memory, in messages, and in bytes, and how each cost grows with the number of peers. Syncordian authenticates group membership rather than individual peer identity, so its guarantees are against nodes outside the admitted group, and a compromised member is out of scope.
Nowadays, Mobile Crowdsensing (MCS) is a crucial Internet of Things (IoT) paradigm leveraging mobile sensors for large-scale data collection. Dynamic task allocation remains a key challenge in real-world deployments. This study introduces Multi-Objective Fractal-based Artificial Rabbit Optimization (MOFARO), a multi-objective model within the Artificial Rabbit Optimization Algorithm for task allocation in edge-fog-cloud MCS environments. Tasks are modeled as Directed Acyclic Graphs (DAGs), incorporating Markov chain mobility predictions and experimental constraints to minimize execution time, energy consumption, cost, and Quality of Service (QoS) violations. Simulations on established workflows yield a Pareto front hypervolume of 0.85. MOFARO reduces execution time by 12–25
Underwater acoustic sensor networks (UASNs) are increasingly employed in both military and civilian applications. Owing to the challenging nature of underwater communication—characterized by unstable channels and dynamic environmental factors— UASNs are vulnerable to various security threats. Therefore, designing robust and adaptive trust management frameworks is vital to ensure their secure and stable operation. This paper introduces a novel, smart, and fast, fuzzy based-trust management (SFFTM) scheme in UASNs. In SFFTM, trust values are derived from three types of trust evidence: communication-based, energy-based and, data-based. To refine communication-based trust evidence, SFFTM distinguishes between communication failures caused by malicious nodes and those resulting from channel instability. To achieve this, a new control factor— combining link delay and link usage— is introduced. When failures are caused by malicious nodes, this factor exponentially reduces the trust evidence. Conversely, if the failures result from instability, the control factor enhances the trust evidence. Furthermore, the energy-based trust evidence is derived by analyzing the residual energy of each nod and evaluating the correlation between its actual and expected energy consumption rates using the Pearson correlation coefficient (PCC). SFFTM includes four trust elements: direct trust, recommendation trust, integrated trust, and indirect trust. Direct trust is obtained from a fuzzy inference system that processes the three trust evidences. Recommendation trust is determined by evaluating the honesty degree of recommenders and assigning higher weights to those with higher honesty level. Indirect trust is inferred through a multi-hop trust chain that links the evaluator to the target node, where intermediate recommenders are chosen according to both their spatial proximity and their assessed trustworthiness. The performance of SFFTM is evaluated through extensive simulations and compared with three existing approaches— CFFTM, LTrust, and TMC— under three attack scenarios: bad/good-mouthing, collision, and hybrid attacks. Simulation findings show that SFFTM outperforms the existing techniques by enhancing detection accuracy and significantly reducing the false alarm rate. Specifically, in the first scenario, SFFTM improves the indirect trust of legitimate nodes by 2.58
The use of data mining and artificial intelligence (AI) in educational contexts has attracted increasing attention from researchers over the past decade. However, extracting meaningful insights and actionable patterns from the large volume of student data and activities available in learning management systems (LMSs) remains challenging due to the diversity of course designs and engagement behaviors. Furthermore, differences in how students interact with learning management systems can significantly affect the predictive value of digital engagement metrics. This study investigates the relationship between Moodle engagement and students’ academic performance across three undergraduate courses with differing pedagogical orientations: Research Methods, Arabic Language, and Palestinian Studies. Using a multi-level quantitative approach that combined correlation analysis and predictive modeling, the study assessed how temporal patterns, activity types, and departmental contexts (STEM-oriented vs. humanities-based) shaped the explanatory power of digital engagement data. The results revealed that Moodle activity is a strong predictor of success in assessment-driven courses, such as Research Methods, where quiz- and assignment-related interactions were highly correlated with final grades, and machine learning models achieved predictive accuracy close to 97
Blockchain systems use distributed consensus to establish decentralized trust, yet the proliferation of protocol designs and adversarial capabilities makes it difficult to compare security guarantees and engineering trade-offs in a unified way. This survey reviews blockchain consensus from a security-oriented lens and links distributed-systems fundamentals to concrete attack vectors and defense mechanisms. We summarize the key assumptions that delimit what consensus can guarantee in permissionless settings, including network synchrony and partition models, crash and Byzantine fault models, honest-majority and quorum thresholds, and the safety–liveness tension under partial synchrony. We then introduce a threat model that classifies attacks across the networking layer, the consensus and finality layer, and the incentive layer, and apply consistent finality semantics to analyze representative protocols spanning crash-tolerant and Byzantine-fault-tolerant families, Nakamoto-style longest-chain consensus, proof-of-stake variants, DAG-based BFT, Proof-of-X constructions, and hybrid designs. We distill recurring adversarial patterns, mitigation strategies, and the resulting security–performance trade-offs. Finally, we outline open problems including the post-quantum cryptographic transition, cross-shard security and atomicity in sharded consensus, and adaptive defenses resilient to evolving network and economic adversaries.
The metaverse has recently gained traction as a persistent, realistic, and interconnected virtual-physical ecosystem with high levels of realism and immersivity. According to such a definition, it fundamentally relies on the generation of high-fidelity 3D scenes. Manual construction of these scenes remains a time-consuming process that requires advanced technical skills, posing a significant barrier to widespread adoption. Recent advancements in AI-assisted photogrammetry tools offer an alternative approach by enabling semi-automatic 3D reconstruction from real-world environments. Nevertheless, the usability, quality, and real-time applicability of the resulting assets remain underexplored. This study aims to assess the effectiveness of photogrammetry-based tools for creating functional and visually appealing 3D scenes for metaverse applications. To this end, a comparative experiment was conducted by reconstructing three environments using manual modeling and two photogrammetry tools based on distinct technologies: Polycam (MVS-based) and LumaAI (NeRF-based). The resulting models were evaluated using quantitative metrics, including modeling time, polygon count, visual similarity (SSIM), real-time performance (GPU usage and FPS), and Hausdorff distance. The findings revealed that photogrammetry significantly accelerates the modeling process but does not consistently outperform manual modeling in terms of polygonal optimization and real-time rendering efficiency. Furthermore, the choice between photogrammetry tools depends on object characteristics and application constraints. This study provides practical insights and empirical guidelines for developers and researchers, highlighting the trade-offs between automation, performance, and visual fidelity in 3D scene generation for the metaverse.
The package osESD in Python and R implements online sequential extreme studentized deviate tests, or osESD in short, a statistical testing method for anomaly detection in online streaming datasets with varying patterns. Its foundation lies in the general extreme studentized deviate test, or GESD test. The GESD test, a simple and effective anomaly detection test, lacks online learning and shows poor performance for pattern-varying data. Our proposed osESD algorithm uses sequential ESD, a variation of GESD, which implements online learning with change-rate trend vectors and residual trend vectors to deal with the problem of pattern-varying streaming data. We also automated parameter tuning in the package with multiple options the user can select for immediate deployment and use. We show that the model learning of our implementation is fast and the performance is excellent compared with numerous models with both various synthetic datasets and real datasets. Codes can be found in the PyPI [11].
As the scale of big data continues to expand, traditional single-machine processing methods have been replaced by distributed computing frameworks. With the limited potential for further CPU performance improvements, GPU-accelerated computing has emerged as a trend, particularly for single-instruction multiple-data (SIMD) and computationally intensive tasks. However, with the clustering of GPUs, traditional scheduling algorithms face limitations such as resource waste and inefficient allocation. To meet the diverse needs of big data applications, it is necessary to optimize the task scheduling and architecture of computing frameworks to enhance processing efficiency and adaptability. Addressing these challenges, this paper proposes a novel distributed computing framework, GDSF, based on a thorough examination of existing computing frameworks and task scheduling algorithms, combined with the computational characteristics of GPUs and the mechanism for multi-task GPU sharing. This framework incorporates various task scheduling strategies to ensure effective scheduling for both dependent and independent task models, achieving an optimal match between tasks and GPU resources. Experiments conducted on a real heterogeneous CPU-GPU cluster using diverse GPU workloads demonstrate that the performance of the GDSF computing framework and its scheduling algorithms meets the expected research objectives, thus validating the feasibility and effectiveness of the proposed design.
In solving constrained multi-objective optimization problems (CMOPs), not only is the optimization of the population in the objective space important, but the solutions in the decision space also play a crucial role. However, current constrained multi-objective evolutionary algorithms (CMOEAs) often neglect the impact of the solution generation process on algorithm performance. Therefore, this paper proposes an information-assisted solution generation based dual population constrained multi-objective evolutionary algorithm, called GSCMO. The information includes entropy information reflecting the population distribution and neighborhood information reflecting the proximity relationships between solutions. In GSCMO, we design a global solution generation strategy for the main population based on entropy information. This strategy dynamically optimizes the mutation strength, focusing on enhancing solution diversity and maintaining extensive exploration of the population in the decision space, thereby improving the algorithm’s ability to explore potential feasible solution regions. At the same time, we develop a local solution generation strategy for the auxiliary population based on neighborhood information. This strategy uses Manhattan distance to determine the neighborhood of each individual, selects individuals from the neighborhood as parents, and generates high-quality individuals, thus enhancing the algorithm’s local search capability. Extensive experimental results demonstrate that GSCMO has high competitiveness compared with some state-of-the-art methods.
In the research study, we investigated VoteChain, an energy efficient and secure framework for financial transactions. The proposed model integrates a lightweight CNN–LSTM hybrid anomaly detection model into the consensus layer. The design is an intelligent based validation during transactions, a preemptive identification of fraudulent behavior during block finality. we evaluated system under simulated permission blockchain network through benchmark dataset. The framework reduces energy and achieves 20.0 mWh in a transaction. The anomaly models’ energy metrics profile (CNN_LSTM) distributed as inference at 38
Machine learning has become increasingly important in medical diagnosis, yet its effectiveness depends on access to large, reliable, and high-quality datasets. During epidemics and emerging diseases, such as COVID-19, acquiring sufficient real-world medical images rapidly is challenging. To address these issues, this study presents an Adaptive Conditional Generative Adversarial Network (Adaptive-CGAN) integrated with a cloud-based medical image processing framework. The proposed approach makes three main contributions. First, Adaptive-CGAN generates high-fidelity synthetic medical images that closely resemble real samples while improving the distinction between real and fake images. Second, a scalable TensorFlow Records (TFRecords)-based pipeline is implemented on Google Cloud Platform (GCP) to support efficient storage, loading, and processing of large-scale medical datasets. Third, a real-world COVID-19 medical image dataset comprising four disease classes is compiled and used to enhance diagnostic prediction. Experimental evaluation was conducted against several baseline generative models, including AC-GAN, WGAN, Pix2Pix, BigGAN, and CWGAN, with AC-GAN serving as the primary like-for-like baseline. Adaptive-CGAN improved classification accuracy from 93.75
Graph-based social recommender systems leverage data from user-user relationships and user-item interactions to effectively predict user preferences. Most works are primarily based on the principle of social homogeneity. However, certain users possess limited influence over others, sustaining merely superficial relationships often viewed as redundant. In this paper, we propose two techniques to mitigate this issue. First, we design a social relationship adjustment loss function, which dynamically adjusts the weights of social connections. Second, we introduce the Hilbert-Schmidt independence criterion loss function. It reduces the dependence between pre- and post-adjusted user/item embeddings, thereby amplifying the effect of adjusted social relationships on the updated embeddings. Experiments are conducted on three real-world datasets compared with nine state-of-the-art models. The results demonstrate that our proposed algorithm significantly improves the performance of different base models, with average improvements of 7.977 https://github.com/zhanghrswpu/GRIS .
Wireless Sensor Networks (WSNs) integrated with the Internet of Thing (IoT) face critical challenges. These include limited energy resources leading to rapid node depletion, dynamic topologies due to node failures or mobility disrupting connectivity, and fluctuating traffic demands in applications like healthcare, smart grids, and environmental monitoring. These factors collectively result in reduced reliability, increased latency, and scalability limitations. These issues are exacerbated by unpredictable node behavior, heterogeneous device capabilities, and the need for real-time data delivery in mission-critical scenarios, necessitating robust, adaptive routing solutions. This paper proposes ASGRR (Adaptive Swarm-Guided Graph Policy Routing), a novel hybrid routing framework that integrates Message Passing Neural Networks (MPNN), Policy Gradient Reinforcement Learning (PGRL), and the Artificial Bee Colony (ABC) algorithm in a self-adaptive hybrid form. The MPNN employs a long-term memory module to predict topology changes, capturing complex spatial-temporal node relationships. The Policy Gradient method uses shared experience memory and predictive link stability metric to optimize multi-criteria path selection. The Bees Algorithm leverages entropy-based dynamic clustering to refine paths locally, adjusting scout bee allocation based on network volatility. ASGRR dynamically tunes parameters to adapt to real-time network changes. Simulation analysis demonstrates up to 38.06
A dynamic gain adaptive integral activation function based Zeroing neural network (DGAIZNN) model for distributed consensus control of multi-agent systems is proposed in this work. The proposed DGAIZNN model overcomes the limitations of traditional fixed-gain approaches by designing an adaptive dynamic gain convergence factor. This factor dynamically adjusts the convergence rate based on both the instantaneous and accumulated system errors, achieving a transition from "constant convergence" to "intelligent adaptive convergence". Furthermore, an integral-type activation function is innovatively adopted, which enhances the system’s robustness against external disturbances and parameter uncertainties by leveraging historical error information. This paper provides a detailed theoretical analysis of the DGAIZNN model’s convergence, stability, and robustness in achieving multi-agent consensus. Simulation experiments demonstrate that the proposed DGAIZNN model successfully drives a group of agents to state consensus, exhibiting significant advantages in convergence speed, tracking accuracy, and disturbance rejection. Finally, the practical value of the DGAIZNN-based design scheme is fully demonstrated through its application to coordinated target encirclement in a multi-robot system.
As optimization problems grow more complex, traditional Differential Evolution (DE) algorithms face challenges like premature convergence, loss of population diversity, and inadequate local search ability in high-dimensional spaces. To address these issues, this paper introduces TSDE-SSA, a Two-Stage Differential Evolution algorithm with Squirrel Exploration. TSDE-SSA divides the optimization process into two phases: an early exploration phase and a later exploitation phase. In the exploration phase, it combines the strengths of DE/rand/1 and DE/best/2 mutation strategies with a self-adaptive mechanism for historical parameters. This enhances global exploration and swiftly identifies potential optimal solution regions. In the exploitation phase, it mimics the foraging behavior of the Squirrel Search Algorithm (SSA) and integrates DE crossover operations. By dynamically adjusting parameters like the scaling factor and gliding constant, it boosts local search accuracy and prevents premature convergence. CEC2017 and CEC2020 benchmarks show that TSDE-SSA surpasses three fundamental algorithms and five outstanding DE variants in convergence speed, precision, and stability. It also outperforms eight new meta-heuristic algorithms and shows strong applicability and reliability in five typical engineering design problems. In summary, TSDE-SSA balances global and local search effectively, offering an efficient and stable solution for complex optimization problems with broad application potential.
Fault detection and classification in underground transmission cables remain challenging because of transient complexity and real-time protection constraints, particularly in safety-critical infrastructures such as nuclear facilities. This paper proposes a two-stage WT-PSO-RBFNN framework in which adaptive db4 level-3 wavelet thresholding performs fault detection and a PSO-optimized radial basis function neural network performs phase/ground classification using normalized wavelet-domain features. Under a stratified 70/15/15 train-validation-test protocol, the proposed model achieved 98.12
The convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Mobile Cloud Computing (MCC) has revolutionized the interactions between these technologies. As the world becomes increasingly interconnected, seamless integration of AI, IoT and cloud services is crucial for transforming vital sectors such as healthcare, finance, and transportation. However, securing IoT-based MCC environments requires both conventional and innovative approaches to mitigate privacy risks, as exposing sensitive data to the cloud raises significant vulnerabilities. With its adaptive learning capabilities and availability, AI has emerged as a promising tool for enhancing the privacy and security of IoT data within MCC environments. This convergence of advanced technologies can assist research communities in addressing novel challenges that each technology has faced. To explore these potentials, a Systematic Literature Review (SLR) was conducted, covering research published between 2021 and 2025, to analyze and synthesize recent advances in AI-based security solutions for IoT and MCC. Unlike existing surveys, this study addresses AI-based security solutions for both MCC and IoT environments, highlighting key gaps in lightweight deployment, cross-layer integration, and contextual security. This survey explores recent trends and challenges in securing IoT–Mobile Cloud systems using AI. It provides a comprehensive review of various approaches and proposed solutions for improving security in IoT and MCC environments. It also examines emerging mechanisms such as authentication, identity management, data encryption, and intrusion detection. Furthermore, it classifies relevant solutions based on the security requirements they address, evaluates current security measures, identifies emerging threats and outlines future research direction.
