EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems is open access, a peer-reviewed scholarly journal focused on scalable distributed information systems, scalable, data mining, grid information systems, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2024, the journal started to publish on a monthly frequency (12 issues per year). INDEXING: ESCI-WoS (IF: 1.3), Compendex, DOAJ, ProQuest, EBSCO, Crossref, Dimensions
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Risk Early Warning for Police-Related Online Public Opinion Based on Deep Learning
INTRODUCTION: Police-related online public opinion is highly sensitive and can easily have a negative impact on social stability. OBJECTIVES: This paper aims to address the crucial need for early warning systems for the risks associated with police-related online public opinion to ensure social harmony and effectively prevent and resolve major social risks. METHODS: Based on a literature review and deep learning methods, this research constructs an indicator system from four dimensions, analyzing data from representative police-related online public opinion incidents over the past five years. A CNN-BiLSTM sentiment classification model is built for sentiment analysis, and an optimized SSA-CNN-LSTM-Attention model is used for public opinion risk early warning. RESULTS: The experimental results demonstrate that the SSA-CNN-LSTM-Attention model has the minimum error. CONCLUSION: This research provides a theoretical reference for public security organs in responding to and preventing police-related online public opinion.
Scheduling optimization and load balancing in scalable distributed systems
Copyright (c) 2025 Juan Wang, Yuxiang Guan, Nan Wang, Jie Pan, Peng Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Food Derived Biostimulants Technology Revealed and Retrieved by Natural Language Processing
Food-derived biostimulants support sustainable agriculture; however, the scale and heterogeneity of the field hinder their synthesis. We profiled 2005–2025 innovation by mining 2,586 PATENTSCOPE filings and Web of Science articles; texts were analyzed with KH Coder and topic models, with large language models assisting in interpretation. Patent activity surged after 2018, emphasizing plant growth promotion, yield stability, and abiotic stress tolerance (amino acids, seaweed extracts, polyphenols, humic substances, and microbial consortia). In parallel, academic papers have shifted from descriptive trials to mechanism-level work on drought/salinity responses, gene expression, and metabolomics. Together, these signals outline a translation path in which deployable biological inputs converge with mechanistic evidence. Our NLP pipeline distilled heterogeneous texts into actionable indicators, yielding a reproducible map from patent/literature trends to testable hypotheses for formulation, dose, and seed stage delivery.
Copyright (c) 2025 Yoshiyuki Kobayashi, Kakeru Ota, Maya Iwano, Itsuki Kageyama, Kota Kodama, Kazuhiko Tsuda https://creativecommons.org/licenses/by-nc-sa/4.0
Heterogeneous Distributed Computing-Based AI Video Generation: Real-Time Load Balancing and Intelligent Scheduling in New Media Art
INTRODUCTION: The rapid proliferation of Generative AI (AIGC) in new media art has intensified the need for real-time, distributed video generation with stable performance and low latency. Conventional centralized rendering and static scheduling frameworks often encounter load imbalance and communication bottlenecks in heterogeneous environments, resulting in degraded visual coherence and responsiveness. To address these challenges, this study develops a unified and adaptive distributed framework, termed H-RLSCO (Heterogeneity-aware Reinforcement Learning and Scheduling Co-Optimization), designed to enhance both computational efficiency and artistic consistency in large-scale AI video generation. The framework integrates three complementary modules: a Heterogeneity Perception Module (HPM) for node profiling and adaptive task partitioning, a Reinforcement Learning Scheduling Controller (RLSC) for dynamic task migration, and a Generation-Scheduling Co-Optimization (GSCO) mechanism that incorporates content-complexity feedback into scheduling decisions to maintain multimodal synchronization. Experiments on the ArtScene-4K and StageSyn-Real datasets demonstrate that H-RLSCO reduces average latency by 14.4% and decreases Fréchet Video Distance by approximately 12.5% compared with the RL-Scheduler baseline, while limiting performance fluctuation to within 3% under multi-noise conditions (p < 0.01). These gains remain consistent across varying bandwidths and node capabilities on a five-node heterogeneous cluster, confirming robust real-time behavior and balanced utilization. Nevertheless, the scalability of H-RLSCO remains constrained when applied to large-scale node clusters, suggesting future work should explore multi-agent reinforcement learning and lightweight diffusion-Transformer architectures to enhance efficiency and expand applicability.
Copyright (c) 2025 Qian Fu https://creativecommons.org/licenses/by-nc-sa/4.0
Method for Analysing Information on Psychosomatic Issues Based on Service Recipient Dissatisfaction
Recently, the proliferation of social networking services has facilitated free sharing of opinions and complaints by consumers. For companies, such reviews are a valuable source of information for product and service evaluation, and for identifying areas for improvement. In this investigation, the cosmetics industry was used as a case study for this phenomenon. Content related to ‘skin’ was extracted from consumer review information and related physical and mental problems were analysed. Specifically, a polarity dictionary specialized for cosmetics was built, and related reviews were extracted by identifying expressions of dissatisfaction related to the skin, such as ‘itchiness’, ‘redness’, and ‘dryness’. By classifying and analysing problem types, this analysis revealed the trends in consumer dissatisfaction and related issues, discovering insights useful for improving services and products. Therefore, the present study identified the types and causes of problems in providing practical knowledge that contributes to enhanced consumer satisfaction, reduced product risk, and effective marketing strategies.
Copyright (c) 2025 Maya Iwano, Yoshiyuki Kobayashi, Kakeru Ota, Kazuhiko Tsuda https://creativecommons.org/licenses/by-nc-sa/4.0
A Method for Extracting Dissatisfaction Entities in the Pharmaceutical Sector Using Large Language Model
In today’s VUCA environment, pharmaceutical companies face mounting pressure to enhance both innovation and patient satisfaction amid shrinking domestic markets and intensifying global competition. Despite the growing emphasis on Patient Centricity—the integration of patients' voices into the drug development process—Japan still lags in the systematic incorporation of patient feedback. This study proposes a novel, patient-centered approach that leverages large language models (LLMs) and prompt engineering to extract dissatisfaction entities from patient-generated content, automatically generate a Dissatisfaction Dictionary, and visualize the interrelationships between these entities. Using the “Medical & Welfare_Pharmaceuticals” category from the Dissatisfaction Survey Dataset provided by the National Institute of Informatics, we analysed 300 patient comments with a Conversation Chain built on the GPT-4o API and LangChain. As a result, dissatisfaction entities were extracted and vectorized using TF-IDF and cosine similarity to form thematic clusters. This analysis revealed interconnected concerns related to pricing, efficacy, usability, and medical service processes. For example, discrepancies in pricing and perceived ineffectiveness of generics frequently co-occurred with complaints about pharmacist communication. Our findings offer pharmaceutical firms a systematic, scalable framework to reflect patient dissatisfaction in drug development, thereby enhancing patient engagement, satisfaction, and strategic alignment with Patient Centricity principles.
Copyright (c) 2025 Kakeru Ota, Takumi Uchida, Maya Iwano, Tsuda Kazuhiko https://creativecommons.org/licenses/by-nc-sa/4.0
An Artifact-Centric Process Mining Approach for Learning Style Analytics
As an integrated discipline encompassing data mining, machine learning, process modeling and analytics, process mining is increasingly being applied in the field of education and has emerged as a prominent research topic. Traditional business process modeling approaches, which are primarily based on control flow rather than data flow, exhibit a limited capacity to capture a holistic view of critical business data within complex business procedures. This study focuses on the impact of data-driven process modeling techniques on the performance of analytical models and proposes an artifact-centric process mining approach for learning style analysis. Based on the artifact life-cycle model, we extracted sequences of data attribute operations that encapsulate learning style features. The similarity among different data attribute operation sequences was quantified. The proposed method was evaluated using the OULAD, a benchmark dataset in the learning analytics domain. Experimental results demonstrate that the method effectively enhances the performance of learning style prediction models, with SVM and GBoost algorithms outperforming other modeling approaches.
Copyright (c) 2025 Jiehua Lu, Jun Li https://creativecommons.org/licenses/by-nc-sa/4.0
System Design and Development for Industry-Education Integration in Art Universities Based on Generative Adversarial Networks and Attention Mechanism
This study focuses on the algorithm design and system development of Generative Adversarial Networks and attention mechanisms in the context of industry-education integration at art universities. To address issues such as training instability, missing details, and insufficient personalization in the intelligent generation of art resources, a hybrid algorithm architecture integrating multi-head self-attention mechanisms and Wasserstein distance optimization is proposed. The generator incorporates multi-scale feature extraction and local-global joint attention mechanisms, significantly enhancing the style consistency and detail expression in image generation. The discriminator combines gradient penalty strategies to enhance the model's training stability. The study trains and evaluates using the COCO and ArtBench datasets, achieving excellent results in terms of generation quality, computational efficiency, and diversity. The highest image quality score is 94.8, and the diversity score is 92.1. The experimental results demonstrate the effectiveness of the designed algorithm in meeting the customized and high-quality resource generation needs of art universities, providing reliable technical support and application foundation for intelligent content generation in industry-education integration.
Copyright (c) 2025 Xingye Leng, Zhao Yifeng https://creativecommons.org/licenses/by-nc-sa/4.0
An Integrated Cybersecurity Defense Framework for Attack Intelligence Analysis, Counteraction, and Traceability in Complex Network Architectures
INTRODUCTION: In response to the increasingly severe and complex cybersecurity threats posed by advanced persistent threats (APTs) and other sophisticated attacks, this paper proposes an integrated security defense framework for attack intelligence analysis, counteraction, and traceability in complex network architectures. Consolidating threat intelligence from multiple sources that provide different types of intelligence is a major obstacle for the cyber security defense community, particularly when serious challenges arise from complex advanced persistent threats (APTs) involving multiple entities. Inconsistent data formats and methods of structuring data further complicate analyzing the intelligence and creating improved strategies for defense from an all-source intelligence model. OBJECTIVES: The proposed approach leverages both internal and external threat intelligence sources, standardizes and integrates them into a heterogeneous threat intelligence knowledge graph, and transforms it into a homogeneous representation to facilitate analysis. Here introduces a framework that allows integration of a heterogeneous threat intelligence knowledge graph, Bayesian game theoretic modeling, and a Graph Attention Network (GAT). It is a method that converts multi-source intelligence into a single, homogeneous graph that allows for more informative analysis and a greater adaptive decision-making capacity. METHODS: To model the strategic interaction between attackers and defenders under incomplete information and resource constraints, we construct a Network Attack-Defense Game Model (NADGM) based on Bayesian game theory and derive the equilibrium strategies using linear programming and Harsanyi transformation. RESULTS: Furthermore, a graph attention network (GAT) is applied to perform node classification on the threat intelligence reports, exploiting the semantic relations between entities to enhance the accuracy of organization-level attribution. The framework is validated through experiments using real-world APT reports and a case study on ransomware attack-defense scenarios. Experimental results demonstrate that the proposed method achieves superior classification performance, effective strategy optimization, and reasonable attack-defense situation evolution compared to baseline models such as GCN and GraphSAGE. Integrating heterogeneous threat intelligence from diverse sources is a significant challenge in cybersecurity, especially when dealing with complex, advanced persistent threats (APTs). The variation in data formats and structures complicates analysis and defense strategy optimization. Key Contributions This paper introduces a framework that integrates a heterogeneous threat intelligence knowledge graph with Bayesian game-theoretic modeling and a Graph Attention Network (GAT). It standardizes multi-source intelligence into a homogeneous graph for improved analysis and adaptive decision-making. Results Experimental results show that the proposed framework outperforms traditional models, achieving a classification accuracy of 0.81 for threat intelligence reports. This leads to enhanced detection performance and optimized strategic defense decisions. CONCLUSION: The findings suggest that integrating threat intelligence, game-theoretic modeling, and graph-based learning can significantly improve the efficiency of threat detection, response, and decision-making in large-scale, complex network environments. The novelty is to integrate a mixed threat intelligence knowledge graph with the application of Bayesian game-theoretic modeling and classifier based on Graph Attention Network (GAT). The classifier leads to a single structured representation of different threat data, recommends defenses that are best and at the same time, enhances the overall detection accuracy across multiple datasets.
Copyright (c) 2025 Xiangyu Le, Hushuang Zeng https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Learning Based Power Load Prediction in Smart Grid Networks
This paper presents a novel deep learning scheme for power load prediction in smart grid networks, combining temporal modeling with adaptive feature integration to tackle the complex dynamics of electricity consumption. The proposed scheme features a hybrid architecture that merges recurrent neural networks with attention mechanisms, enabling simultaneous capture of long-term load patterns and dynamic weighting of external influences like weather conditions and temporal features. Moreover, the model incorporates specialized preprocessing to decompose load data into periodic and volatile components while employing robust normalization techniques to handle non-stationary behavior. Then, a dual-objective loss function is used to enhance both prediction accuracy and resilience to outliers, supported by adaptive optimization with regularization. Simulation results are provided to demonstrate the proposed scheme’s superior performance, achieving 96.1% prediction accuracy with 5 hidden layers. The attention mechanism proves particularly effective, reducing weather-related prediction errors by 22% while maintaining faster convergence rates than conventional methods. This comprehensive solution offers grid operators a reliable tool for demand-side management, renewable integration, and operational planning in modern power systems.
AIGC - Empowered Covert Communications for Scalable Information Systems
Copyright (c) 2025 Yifan Tian, Xiaoyi Chen, Yuanyuan Dai, Jiajia Shao, Jiankai Ma, Xiaoyu Yan, Zan Huang, Qi Wang, Lijun Wu, Zhijing Zhang, Shiyun Huang https://creativecommons.org/licenses/by-nc-sa/4.0
A Cholesky decomposition and fusion clustering based spectrum sensing method
Spectrum sensing is a key technology to detect unused frequency bands, and is widely applied in spectrum sharing and dynamic channel allocation. However, it is a challenge to provide high sensing accuracy under low signal-to-noise-ratio (SNR) environments. To address this issue, this paper proposes a novel method based on feature extraction and fusion clustering. First, the sampling matrix of the received signal is decomposed into two orthogonal components I and Q, and Cholesky decomposition is performed on the covariance matrices of I and Q components to extract their two-dimensional feature vectors. Then, the fusion clustering algorithm is proposed, where the GMM clustering algorithm is performed to classify the feature vectors, and the initial parameters of GMM, such as centroids, weights and covariance matrices, are generated by K-means clustering. Simulation results show that the proposed method accelerates the convergence speed of GMM and improves the classification accuracy. It effectively enhances the performance of spectrum sensing compared to other mainstream methods.
Copyright (c) 2025 Jian Li, Yue Li, Xinpei Zhao, Xiaoxu Chen https://creativecommons.org/licenses/by-nc-sa/4.0
On the Performance of RIS-Enhanced RSMA Networks with On-Off Control
As a pivotal enabling technology for the 6G wireless communication system, Reconfigurable intelligent surface (RIS) is capable of dynamically adjusting its electromagnetic elements, thereby significantly enhancing wireless network coverage, spectral efficiency, and energy sustainability. In this context, this paper investigates the integration of RIS into the rate-splitting multiple access (RSMA) framework, where the base station employs the RIS to transmit superimposed signals to multiple users simultaneously. To reduce computational complexity, a On-Off control scheme is adopted, considering both imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC) scenarios. closed-form expressions for the exact outage probability of the $k$-th user under Rayleigh fading channels are derived. Furthermore, to more comprehensively evaluate system performance, asymptotic outage probability expressions for ipSIC/pSIC in the high signal-to-noise ratio (SNR) regime are also derived. The results demonstrate that, under identical system configurations, the RSMA-based communication scheme outperforms the non-orthogonal multiple access (NOMA) scheme, exhibiting superior spectral resource utilization and interference management capabilities. In the pSIC scenario, the user diversity order is contingent upon the number of RIS reflecting elements and the channel ordering approach. Increasing the number of RIS reflecting elements can effectively compensate for the performance degradation caused by residual interference in the ipSIC scenario, thereby further enhancing system performance
Copyright (c) 2025 Haiyan Huang, Dongjie Jiang, Nina Zhang, Linlin Liang, Hongyan Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Direction-of-Arrival Estimation for Deterministic Networks
Direction-of-arrival (DOA) estimation is a key physical-layer technique for guaranteeing the stringent latency and reliability requirements of emerging deterministic networks. This paper investigates a generalized dual-subarray linear array model in which the inter-subarray displacement vectors can be arbitrarily specified, and devises two subspace-based DOA estimation schemes tailored to this setting. The first scheme is a spectral-search (SS) estimator that exploits the structured relationship between the signal subspaces of the two subarrays via a parametrized phase-rotation matrix, where the DOAs are obtained by searching over angles that induce a rank deficiency in a residual matrix constructed from the estimated signal subspace, yielding a high-resolution spectral function analogous to, but more flexible than the conventional schemes. The second scheme is a search-free (SF) estimator that, under mild geometric assumptions on the linear array, reformulates the same criterion as a polynomial in a complex exponential variable and recovers the DOAs from the roots closest to the unit circle, thereby eliminating grid search and significantly reducing computational complexity. Simulation results are provided for deterministic-network scenarios to show that, for a 2 × 5-element dual-subarray and moderate SNRs, the proposed SF-based estimator achieves an RMSE below 1 while conventional MUSIC exhibits an RMSE around 10, demonstrating roughly an order-of-magnitude accuracy gain and confirming the superior performance and scalability of the proposed SS and SF schemes.
Copyright (c) 2025 Feifei Hu, Yu Huang, Xubin Lin, Liu Wu https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing Stakeholder Analysis with AI: A Comparative Study of Productivity and Quality in the Educational Context
This paper examines the application of generative artificial intelligence in stakeholder management while studying the business aspects of software development and project management in two different universities. It explores a novel intersection of AI with software development and project management practices, offering valuable insights for both academia and industry. By investigating how students use AI alongside traditional methods under supervision, this study evaluates the effectiveness, quality of results, and creativity of students’ project assignments in identifying stakeholders and defining communication strategies. The findings suggest that AI can enhance work completion speed and contribute to greater project success due to a more complete identification of stakeholders and formulation of innovative stakeholder engagement strategies. There is a consensus, within this context, that while AI can be invaluable for project stakeholder management, human judgment remains essential.
System Analysis and Design Project Management
Copyright (c) 2025 Vijay Kanabar, Kalinka Kaloyanova https://creativecommons.org/licenses/by-nc-sa/4.0
Knowledge Graph Fusion for Cross-Modal Semantic Communication
This paper proposes a knowledge graph-enhanced multi-source fusion (KG-MSF) scheme, a novel cross-modal semantic communication system to robustly fuse visual and textual data for tasks such as visual question answering (VQA) over wireless channels. The proposed KG-MSF scheme integrates knowledge graph reasoning into a multi-stage fusion and encoding pipeline, utilizing bidirectional cross attention between modalities and structured semantic triplets to enhance semantic preservation and resilience to channel impairments. Specifically, image objects and question tokens are first aligned via cross-modal attention, then enriched with shallow and deep semantic triplets extracted through knowledge graphs, which are subsequently fused and transmitted using joint source-channel coding. Extensive simulation results are provided to demonstrate that the proposed KG-MSF scheme significantly outperforms the competing ones under both AWGN and Rayleigh fading channels, indicating KG-MSF’s superior semantic robustness and efficient cross-modal reasoning in wireless environments.
Copyright (c) 2025 Yanrong Yang, Tianxiang Zhong, Mengting Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Leveraging Relation Attention Mechanisms for Enhanced Knowledge Graph Completion with Embedding Translation
In this paper, we propose a novel knowledge graph completion framework to leverage a relation-specific attention mechanism integrated with an embedding translation strategy to improve the accuracy and contextual understanding of link prediction tasks. Unlike traditional models that rely on fixed transformation spaces, the proposed method dynamically captures fine-grained relational semantics by combining hierarchical candidate categorization, relation-guided entity projection, and asymmetric score functions. Specifically, the proposed model employs K-means clustering and principal component analysis (PCA) to identify semantically consistent entity sets, and integrates attention-weighted multi-attribute embeddings to construct robust relational representations. A margin-based ranking loss with normalized embedding constraints ensures effective optimization, further supported by Xavier initialization and stochastic gradient descent. Extensive experiments on two benchmark datasets, WN18 and FB15K, demonstrate the superiority of the proposed method. Specifically, on WN18, the proposed method achieves the lowest mean rank (MR) of 144, with competitive results in mean reciprocal rank (MRR) (0.902), Hits@1 (89.0%), Hits@3 (90.4%), and Hits@10 (96.3%), closely rivaling state- of-the-art models like QuatE and ComplEx. On FB15K, the proposed method again delivers the best Mean Rank of 21, along with strong scores in MRR (0.831), Hits@1 (72.2%), Hits@3 (88.4%), and the highest Hits@10 (92.5%) among all compared methods.
Copyright (c) 2025 Jiahao Shi, Zhengping Lin, Yuzhong Zhou, Yuliang Yang, Jie Lin https://creativecommons.org/licenses/by-nc-sa/4.0
Smart Data Prefetching Using KNN to Improve Hadoop Performance
Hadoop is an open-source framework that enables the parallel processing of large data sets across a cluster of machines. It faces several challenges that can lead to poor performance, such as I/O operations, network data transmission, and high data access time. In recent years, researchers have explored prefetching techniques to reduce the data access time as a potential solution to these problems. Nevertheless, several issues must be considered to optimize the prefetching mechanism. These include launching the prefetch at an appropriate time to avoid conflicts with other operations and minimize waiting time, determining the amount of prefetched data to avoid overload and underload, and placing the prefetched data in locations that can be accessed efficiently when required. In this paper, we propose a smart prefetch mechanism that consists of three phases designed to address these issues. First, we enhance the task progress rate to calculate the optimal time for triggering prefetch operations. Next, we utilize K-Nearest Neighbor clustering to identify which data blocks should be prefetched in each round, employing the data locality feature to determine the placement of prefetched data. Our experimental results demonstrate that our proposed smart prefetch mechanism improves job execution time by an average of 28.33% by increasing the rate of local tasks.
Copyright (c) 2025 Rana Ghazali, Douglas G. Down https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Hybrid Path Planning Algorithms for UAVs in Complex Environments
INTRODUCTION: This paper investigates a UAV path planning algorithm in a UAV-assisted network scenario, integrating both global and local path planning. Firstly, the ASPSO (Adaptive Spherical Vector-Based Particle Swarm Optimization) algorithm is proposed for offline path planning to obtain key global path points, providing a general flight strategy for the UAV. During the flight, the UAV continuously detects surrounding obstacles in real-time. If newly detected obstacles are encountered, the ECAVF (Enhanced Collision Avoidance Vector Field) algorithm is employed for local path planning to dynamically avoid obstacles and ensure the safety of the UAV. OBJECTIVES: The objective of this paper is to enhance the path planning capability of existing algorithms in complex three-dimensional environments, enabling UAVs to operate efficiently and safely. METHODS: The proposed ASPSO algorithm determines parameter ranges for different scenarios during the initialization phase, effectively reducing initialization time. Additionally, a multi-strategy optimization approach is introduced during the search process. Expanding the search space in the early iterations helps escape local optima, while minor perturbations are introduced in the later iterations to continue exploring within the neighbourhood of high-quality solutions. Finally, a method utilizing virtual control points for path refinement is proposed to smooth the trajectory. The ECAVF algorithm incorporates a dynamic adjustment factor based on relative velocity to optimize the vector field in the presence of multiple moving obstacles. By integrating factors such as distance and velocity, a hybrid vector field is constructed, demonstrating superior robustness in complex multi-obstacle scenarios. RESULTS: The proposed method is compared with the PSO (Particle Swarm Optimization), the Spherical Vector-based PSO, and the original CAVF (Collision Avoidance Vector Field) method. The results demonstrate that the proposed method exhibits higher initialization efficiency, superior initial solution quality, and the ability to obtain a more optimal global path. Additionally, it shows stronger dynamic obstacle avoidance capabilities and a higher success rate in avoiding obstacles. CONCLUSION: These results demonstrate that the proposed method effectively enhances the quality of global path planning solutions and improves the success rate of dynamic obstacle avoidance.
Copyright (c) 2025 Xinyue Chang, Liang Ye, Lin Ma, Shuyi Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Wireless Federated Learning Based Building Temperature Estimation With Latency Constraint
The paper proposes a novel approach for temperature estimation in buildings using wireless federated learning (FL) while considering latency constraints. The proposed model utilizes a hierarchical federated learning architecture within a wireless network, incorporating base stations (BS), access points (APs), and user equipment (UEs). Each UE performs local learning and shares model updates with APs, which aggregate them and forward them to the BS for final aggregation. The system aims to minimize both latency and energy consumption while ensuring accurate temperature predictions. Simulation results show the effectiveness of the proposed scheme in comparison to deep reinforcement learning (DRL) and genetic algorithm (GA) approaches. Specifically, at a latency threshold of 10 seconds, the proposed scheme achieves a prediction accuracy of approximately 0.60, while DRL reaches 0.50 and GA stays around 0.48. These results highlight the superior performance of the proposed federated learning-based method, especially in high-latency scenarios, and demonstrate its potential for real-time applications in smart building environments under wireless communication constraints.
Copyright (c) 2025 Kemin Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Optimization of Unmanned Aerial Vehicle Communication Based on Wireless Communication Artificial Intelligence
In the last few years, there has been an increasing use of Unmanned Aerial Vehicles (UAVs), leading to a demand for robust and efficient communication technologies. But by their nature, UAVs operate dynamically and relatively sparsely in the sky, making it inconvenient to perform traditional communication. To overcome communication hurdles, the authors of this research propose an AI approach to optimize wireless communications to enhance UAV (Unmanned Aerial Vehicles) performance. The study will investigate the development of an AI-based algorithm to adjust communication parameters concerning various aspects, including UAV movement, environmental conditions, and network constraints. It will increase the data transmission quality, reduce latency, and service larger areas the network covers for UAVs. Simulations and actual world experiments will be conducted to evaluate the proposed approach, showing that AI can enhance UAV communication. This work could significantly improve the performance of UAVs and optimize their application in many fields ranging from surveillance to delivery services.
Copyright (c) 2025 Yu Geng, Yuqing Tang, Qiang Wang https://creativecommons.org/licenses/by-nc-sa/4.0
The Cutting-Edge Hadoop Distributed File System: Un-leashing Optimal Performance
Despite the widespread adoption of 1000-node Hadoop clusters by the end of 2022, Hadoop implementation still encounters various challenges. As a vital software paradigm for managing big data, Hadoop relies on the Hadoop Distributed File System (HDFS), a distributed file system designed to handle data replication for fault tolerance. This technique involves duplicating data across multiple DataNodes (DN) to ensure data reliability and availability. While data replication is effective, it suffers from inefficiencies due to its reliance on a single-pipelined paradigm, leading to time wastage. To tackle this limitation and optimize HDFS performance, a novel approach is proposed, utilizing multiple pipelines for data block transfers in-stead of a single pipeline. Additionally, the proposed approach incorporates dynamic reliability evaluation, wherein each DN updates its reliability value after each round and sends this information to the NameNode (NN). The NN then sorts the DN based on their reliability values. When a client requests to upload a data block, the NN responds with a list of high-reliability DN, ensuring high-performance data transfer. This proposed approach has been fully implemented and tested through rigorous experiments. The results reveal significant improvements in HDFS write operations, providing a promising solution to overcome the challenges associated with traditional HDFS implementations. By leveraging multiple pipelines and dynamic reliability assessment, this approach enhances the overall performance and responsiveness of Hadoop's distributed file system.
Copyright (c) 2025 Anish Gupta, P. Santhiya, C. Thiyagarajan, Anurag Gupta, Manish Gupta, Rajendra Kr. Dwivedi https://creativecommons.org/licenses/by-nc-sa/4.0
Improved Authentication in Information Systems through a Mobile Identity Management Scheme (MoIdM-MSS) Utilizing Mobile Signature Service
In today's digital economy, work processes are increasingly digitized using computer information systems. An essential aspect of employees' reliance on these systems is trust in their reliability. Mobile devices and apps play a vital role in this digital landscape, with Mobile Identity at the forefront. Mobile Identity extends the concept of digital identity through mobile networks, acting as a tool for login and transactions and as a crucial element in communication and interaction. This paper introduces a Mobile Identity Management Scheme based on the Mobile Signature Service for information systems. The scheme enables digital signatures on mobile devices for various purposes, enhancing security by leveraging the user's private key and the system's authentication challenge. Through this approach, authentication is ensured by permitting only users with the correct private key to sign the challenge, eliminating the necessity for traditional authentication methods such as usernames and passwords. Furthermore, the scheme leverages mobile device security features like secure computing environments and biometric authentication to bolster authentication. By adding an extra layer of protection and focusing on user convenience, security is heightened without introducing unnecessary complexity. Evaluations conducted in local signing scenarios have demonstrated the scheme's effectiveness, acceptance, and potential, indicating promising results for its application in enhancing work process security.
Copyright (c) 2024 Mohammadjavad Sharifpour, Mehdi Shajari, Seyyed Amir Asghari Tochae https://creativecommons.org/licenses/by-nc-sa/4.0
Bitcoin Price Prediction Using N-BEATs ML Technique
Bitcoin is a decentralised digital currency that has been in existence for some time now. Its value has been volatile, with most of its prices fluctuating significantly, making it difficult to predict its prices when investing. This paper employed a Neural Basis Expansion Analysis Time Serie (N-BEATS) deep learning architecture to predict Bitcoin prices. The model was chosen because of its proven capabilities of modelling intricate patterns in time series data. An hourly Bitcoin price data of 729 days collected from Yahoo Finance extensively assesses the N-BEATS model's performance while comparing it with other machine learning models like the Linear Regression and the long-short-term-memory (LSTM) networks. Mean Absolute Error (MAE) and R-squared (R²) were utilised as performance evaluation metrics. N-BEATS surpassed the others by providing an R² score of 0.00240 and an MAE score of 0.9998. These findings are significant and shed light on how deep learning models can be used for financial forecasting. The result shows that the N-BEATS model is more accurate and reliable for predicting cryptocurrencies' prices, which may be very useful for investors in making decisions and managing risks.
Copyright (c) 2024 G. Asmat, K. M. Maiyama https://creativecommons.org/licenses/by-nc-sa/4.0
NOMA Assisted Energy-Efficient MEC for Environmental Severity Monitoring in Power IoT Networks
This paper proposes an energy-efficient mobile edge computing (MEC) scheme that utilizes non-orthogonal multiple access (NOMA) for environmental severity monitoring in Power Internet of Things (IoT) networks. The primary objective of the proposed approach is to optimize energy consumption while ensuring tasks are completed within their respective deadlines and meet reliability constraints. The scheme integrates NOMA's superposition coding with mobile edge computing to improve task offloading efficiency and reduce computational delays. To achieve this, an iterative water-filling (IWF) algorithm is applied to dynamically adjust the power allocation for each task based on varying channel conditions and latency requirements. The optimization problem is formulated to minimize energy consumption while respecting the given constraints, including outage probability and transmission rate. Simulation results demonstrate that the proposed IWF-based method significantly outperforms traditional schemes. For instance, under a stringent delay threshold of 10 ms, the IWF method reduces energy consumption by approximately 30\% compared to conventional approaches. Furthermore, even as the delay threshold increases, the IWF method consistently maintains a noticeable advantage, achieving up to 20\% lower energy consumption compared to other schemes.
Copyright (c) 2025 Guangmao Li, Gang Du, Hongbin Wang, Hongling Zhou, Jie Yang, Zhikai Pang https://creativecommons.org/licenses/by-nc-sa/4.0
Life Expectancy Prediction using Recursive Partitioning and Bagging Algorithms
Life expectancy is a crucial indicator of the population’s health and well-being. Recent research has highlighted the importance of various socioeconomic and health factors in determining the lifespan of individuals. Those factors include Gross Domestic Product (GDP), healthcare expenditure, mortality rates, and education level. This study employs recursive partitioning (decision trees) and bagging (random forest) techniques on the Life Expectancy dataset from the World Health Organization (WHO) to evaluate the effectiveness of predictive models. The dataset was prepared by encoding categorical features, scaling the features, normalizing them, and handling outliers. Mean imputation was used to handle missing values and produce a quality dataset. Optimized models based on recursive partitioning and bagging algorithms achieved performance efficiencies of 92% and 97%, respectively. The bagging algorithm-based model produced a mean squared error of 1.17, a mean absolute error of 2.0, and an R2-score of 97%. Other key findings included the importance of dataset characteristics—such as HIV/AIDS prevalence, adult mortality, and health resource income—in predicting life expectancy. This research elucidates the impact of feature engineering and data preprocessing strategies on data quality and predictive model precision, offering novel insights for public health policymaking and informing future research directions.
Copyright (c) 2025 Muhammad Bux Alvi, Majdah Alvi, Yasir Hussain, Wajid Rehman, Kavita Tabassum, Shahnawaz Farhan, Fatima Noor https://creativecommons.org/licenses/by-nc-sa/4.0
Cloud Data Integrity Verification Algorithm for Smart Accounting Informatization in Cloud Computing
INTRODUCTION: With the rapid development of cloud computing and intelligent technology, traditional accounting informatization systems are gradually migrating to cloud computing platforms, promoting financial management's automation and intelligence. OBJECTIVES: However, data security in the cloud computing environment, especially the issue of data integrity, has become one of the core challenges for intelligent accounting informatization systems. Accounting informatization systems rely on the storage and processing of a large amount of financial, tax, and transaction data, and the integrity of these data is directly related to the financial transparency, compliance, and decision-making accuracy of an enterprise. METHODS: Therefore, how to design an efficient and scalable cloud data integrity verification algorithm becomes the key to ensuring the reliability and security of intelligent accounting systems. This paper proposes a cloud data integrity verification algorithm for smart accounting informationization based on a cloud computing environment. The algorithm combines the efficient data processing capability of cloud computing with the automation requirements of smart accounting systems to ensure data integrity during storage, transmission, and processing through various data validation techniques such as digital signatures, hash algorithms, and blockchain-based distributed ledgers. RESULTS: The algorithm design considers the dynamics and scalability of the cloud computing environment. It can realize fast, real-time data validation and integrity detection in a large-scale data environment. Through experimental verification, the algorithm proposed in this paper performs well in processing large-scale accounting data with high verification efficiency and accuracy. CONCLUSION: Compared with traditional validation methods, the algorithm improves the speed of data validation. It enhances the system's ability to prevent data tampering and loss, providing a new cloud data integrity assurance scheme for intelligent accounting informatization systems.
Copyright (c) 2025 Jun Zhang, Liang Xian https://creativecommons.org/licenses/by-nc-sa/4.0
Liapunov Exponents and Control Theory-Based Stability Analysis and Parameter Optimization Technique for Dynamical Systems with Periodic Variable Coefficients
INTRODUCTION: This is the introductory text Dynamical systems with periodic variable coefficients find extensive use in physics and engineering domains, including nonlinear circuits, structural dynamics, and vibration control. The intricacy of the parameter changes over time has made the stability and control issues of such systems a popular topic in engineering and academics. The dynamic features of periodic variable coefficient dynamical systems are difficult to adequately characterize using approaches based on standard stability theory, as demonstrated by previous research. This work formulates a methodology that integrates Lyapunov exponents, Floquet theory, and analysis of fractional-order systems to evaluate the stability of variable-coefficient periodic systems. The effectiveness of the approach is illustrated in nonlinear control and bifurcation analysis. OBJECTIVES: This is particularly true when the parameters vary significantly or the system behaves nonlinearly. As crucial instruments for examining dynamical systems, Lyapunov stability theory and Floquet theory are essential for determining global stability and studying periodic systems, respectively. Further research is still required to determine the best way to integrate the two theories in order to provide straightforward and useful conclusions for periodic systems with variable coefficients. METHODS: In the meantime, fractional-order systems have drawn interest recently in the fields of control and chaotic dynamics due to their precise representation of genetic characteristics and memory effects. It has been demonstrated that under parameter changes, fractional-order chaotic systems display a variety of dynamic behaviors, such as multistability, bifurcation phenomena, and complexity shifts. By logically creating control techniques, such systems can be optimized to increase their robustness and stability while also exposing the complex system's dynamical principles. RESULTS: In light of this, this research suggests an integrated framework for a thorough investigation of the stability of dynamical systems with periodic variable coefficients that combines the Lyapunov eigenindex, Lyapunov function, and fractional order complexity analysis. In particular, this work first builds a stability criterion to theoretically support periodic systems using Floquet theory with Lyapunov exponents; The practical issues of modular multilevel dc voltage regulators (MMC-DVR) are then addressed by a nonlinear control method based on Lyapunov functions. Additionally, bifurcation diagrams with complexity index (such as spectral entropy complexity) are used for fractional-order chaotic systems in order to examine the impact of parameter changes on system stability and chaotic behavior. Lastly, numerical examples are used to confirm the efficacy of the suggested approach. CONCLUSION: It is demonstrated that the analytical framework put forth in this research may successfully address challenging issues in the control and stability design of dynamical systems with periodic variable coefficients while also offering fresh concepts for the optimization of intricate system parameters.
Periodic variable coefficient dynamical systems
Copyright (c) 2025 Caixia Fu, Dong An https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Learning Empowered Enterprise Knowledge Graph with Attention Mechanism
Enterprise knowledge graphs (EKGs) are pivotal in structuring and analyzing vast amounts of enterprise data, yet conventional construction methods struggle to efficiently capture complex relationships and dynamic enterprise contexts. This paper proposes a Deep Learning (DL)-based enterprise knowledge graph framework that integrates transformer-based architectures, graph attention networks (GATs), and reinforcement learning to enhance the construction, refinement, and querying of EKGs. Specifically, we employ a business-enhanced RoBERTa (BERTO) model for entity and relation extraction from unstructured data, a graph attention network for refining edge weights, and a reinforcement learning agent to adaptively update relationships based on user feedback. Additionally, a query-aware attention mechanism is incorporated for context-sensitive knowledge retrieval. Simulation results demonstrate that the proposed scheme outperforms conventional knowledge graph (GK) and deep learning (DL) models in predictive accuracy, especially under varying signal-to-noise ratio (SNR) conditions. Numerical comparisons reveal that at 10 dB SNR, the proposed scheme achieves a prediction accuracy of 0.74, surpassing the conventional GK (0.49) and conventional DL (0.34) methods. These results underscore the effectiveness of the proposed framework in improving accuracy, adaptability, and scalability in enterprise knowledge management.
Copyright (c) 2025 Yadong Shi, Liangbo Zeng, Liang Li, Junwei Zhu, Rongyin Tan https://creativecommons.org/licenses/by-nc-sa/4.0
Mobile Edge Computing Empowered Energy Consumption Optimization for Multiuser Power IoT Networks
Mobile edge computing (MEC) has emerged as a promising solution to enhance the computational capabilities of resource-constrained IoT devices while optimizing energy consumption. In this paper, we investigate energy-efficient resource allocation strategies for multiuser power IoT networks by jointly optimizing the offloading ratio, transmit power, and wireless bandwidth allocation. We propose an optimization framework that minimizes the total energy consumption of the system while ensuring the latency constraints of computation tasks. To solve this challenging non-convex problem, we employ an alternating optimization approach, where the offloading decision, wireless bandwidth allocation, and transmit power control are iteratively refined using convex optimization techniques and successive convex approximation (SCA). Simulation results are provided to show that the proposed scheme significantly outperforms the competing approaches in terms of energy efficiency. Specifically, for a system with 6 users, the proposed scheme maintains an energy consumption of approximately 0.1 Joules, reducing the energy consumption of the conventional schemes to less that 40 percentages.
Copyright (c) 2025 Shuangwei Li, Yang Xie, Mingming Shi, Xian Zheng, Yongling Lu https://creativecommons.org/licenses/by-nc-sa/4.0
User-Centric Development of Good Delivery Applications Using Design Thinking and Business Model Canvas
INTRODUCTION: The development of internet technology drives companies to innovate and integrate technology into business processes. This study focuses on designing a goods delivery management application using a combination of Design Thinking and the Business Model Canvas (BMC). Design Thinking emphasizes user needs, while BMC ensures business sustainability. Through stages of empathizing, defining, ideating, and prototyping, this research maps user needs, business requirements, and application frameworks to address inefficiencies and modernize goods delivery processes, improving accuracy and operational effectiveness. OBJECTIVES: This research is to design a goods delivery management application by combining Design Thinking and the BMC to align user needs with business goals, addressing inefficiencies, and improving operational processes and data accuracy. METHODS: The research method includes five stages: literature study, data collection (empathize), problem analysis (define), ideation (solution sketch, wireframe, user flow), and prototyping. This approach combines Design Thinking and BMC to address user and business needs. RESULTS: Defining business process inefficiencies, creating user personas and empathy maps, developing user journey maps and information architecture, designing wireframes and user flows, and proposing a Business Model Canvas (To Be). CONCLUSION: The study highlights inefficiencies in goods delivery processes and proposes a new BMC strategy, emphasizing user-centric application development, real-time updates, resource allocation, and modular features, with future recommendations focusing on user experience enhancement and application evaluation.
Copyright (c) 2025 Johanes Fernandes Andry, Francka Sakti Lee, Kevin Christianto, Yunianto Purnomo, Aziza Chakir, Lydia Liliana https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Enabled Tools: Shaping the Future of Technology
INTRODUCTION: AI-enabled tools are revolutionizing various fields, ushering in a transformative era for technology and industry. These advancements impact diverse sectors, from customer service and industrial design to cybersecurity and computer vision, reshaping human-computer interactions. OBJECTIVES: This article explores the applications and advancements of AI tools, focusing on chatbots, image and text generation, literature review research, AI-driven coding tools, and emerging applications in cybersecurity and media analysis. It also provides an overview of publicly available AI tools and conducts a comparative analysis of leading chatbots. METHODS: A comprehensive review and analysis were conducted on tools including ChatGPT, CodeGPT, and AI systems for image and text generation. The study also examined AI applications in spear phishing defense, facial recognition across age variations (FaceNet), and deepfake detection, alongside a comparative analysis of chatbots such as ChatGPT, Google Bard, LLaMA, and MS Bing. RESULTS: The exploration revealed that chatbots like ChatGPT have redefined customer service, while AI tools for image generation impact art, medical imaging, and industrial design. AI-driven text generation and coding tools enhance content creation and software development efficiency. Additionally, AI applications in cybersecurity, facial recognition, and deepfake detection demonstrate the technology’s growing societal relevance. Comparative analysis of chatbots highlighted their distinct capabilities across platforms. CONCLUSION: AI-enabled tools are shaping the future of technology, driving innovation, and expanding possibilities across industries and societal domains. The findings emphasize the need for continued exploration, ethical application, and responsible deployment to maximize their potential while addressing associated challenges.
Copyright (c) 2025 Venkata Rama Padmaja Chinimilli, Priyanka Kumari Bhansali, J Sirisha Devi, Sukanya Ledalla, Mary Swarna Latha Gade https://creativecommons.org/licenses/by-nc-sa/4.0