ISSN: 2032-9407
Submit Article
Submission Instructions
Open Access Information
Ethics and Malpractice Statement
Most Recent Issue
2025
Issue 3Issue 2Issue 1
2024
Issue 6Issue 5Issue 4Issue 3Issue 2Issue 1
2023
Issue 6Issue 5Issue 4Issue 3Issue 2Issue 1
2022
Issue 6Issue 5Issue 4Issue 36Issue 35Issue 34
2021
Issue 33Issue 32Issue 31Issue 30Issue 29
2020
Issue 28Issue 27Issue 26Issue 25Issue 24
2019
Issue 23Issue 22Issue 21Issue 20
2018
Issue 19Issue 18Issue 17Issue 16
2017
Issue 15Issue 14Issue 13Issue 12
2016
Issue 8
2015
Issue 5Issue 4
2014
Issue 3Issue 2Issue 1

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/sis/index

Editor(s)-in-Chief: Hua Wang and Xiaohua Jia

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 a bi-monthly frequency (six issues per year). 

INDEXING: ESCI-WoS (IF: 1.3), Compendex, DOAJ, ProQuest, EBSCO

The scope of the journal includes:

  • Scalable distributed information systems
  • Scalable grid information systems
  • Parallel information processing and systems
  • Web information searching and retrieval
  • Data mining
  • Content delivery networks (CDN)
  • VLDB
  • P2P systems
  • Scalable mobile and wireless database systems
  • Large scale sensor network systems
  • Index compression methods
  • Architectures for scalability
  • Scalable information system applications
  • Evaluation metrics for scalability
  • Information security

Call for Papers: Special issue on: Real-time image information processing with deep neural networks and data mining technologies (Manuscript submission deadline: 2022-02-28; Notification of acceptance: 2022-04-15; Submission of final revised paper: 2022-05-15; Publication of special issue (tentative): 2022-06-15)

Guest Editor: Dr. Prof. Hang Li (Northeastern University, China) Guest Editor: Dr. Prof. Jochen Schiewe (HafenCity Universität Hamburg, Germany)

  • Editors-in-Chief
  • Hua Wang, Victoria University, Australia
  • Xiaohua Jia, City University of Hong Kong
  • Editorial board
  • Manik Sharma, DAV University, India
  • Ajay Kattepur (Tata Consultancy Services)
  • Chang Choi (Chosun University)
  • Cho-Li Wang (University of Hong Kong)
  • Daniel S. Katz (University of Chicago)
  • Fabrizio Silvestri (ISTI – CNR, Italy)
  • Hamed Taherdoost (University Canada West, Canada)
  • Heng Tao Shen (University of Queensland)
  • Houbing Song (University of Maryland Balitmore, USA)
  • José Manuel Machado (University of Minho, Portugal)
  • Jose Merseguer (Universidad de Zaragoza)
  • Jie Li (University of Tsukuba)
  • Lin Yun (Harbin Engineering University)
  • Phan Cong Vinh (Nguyen Tat Thanh University)
  • Raj Gururajan (University of Southern Queensland)
  • Sherman Chow (Chinese University of Hong Kong)
  • Silva Fábio (University of Minho, Portugal)
  • Steve Beitzel (Telcordia)
  • Tzung-Pei Hong (National University of Kaohsiung, Kaohsing City, Taiwan)
  • Wang-Chien Lee (The Pennsylvania State University)
  • Weili Wu (The University of Texas at Dallas)
  • Xueyan Tang (Nanyang Technological University)
  • Vijayakumar Ponnusamy (SRM University, India)
  • J Amudhavel (KL University, India)
  • Yingshu Li (Georgia State University)
  • Karolj Skala (Ruđer Bošković Institute, Croatia)
  • Xiao-Zhi Gao (University of Eastern Finland, Finland)
  • Thaier Hayajneh (Fordham University, USA)
  • Chin-Ling Chen (Chaoyang University of Technology, Taiwan)
  • Nuno M. Garcia (Faculty of Sciences, University of Lisbon, Portugal)
  • Arianna D'Ulizia (Consiglio Nazionale delle Ricerche (CNR), Italy)
  • Robertas Damaševičius (Kaunas University of Technology (KTU), Lithuania)
  • Hiep Xuan Huynh (Can Tho University, VietNam)
  • Ji Zhang (University of Southern Queensland, Australia)
  • Xiaohui Tao (University of Southern Queensland, Australia)
  • Ye Wang (National University of Defense Technology, China)
  • Nageswara Rao Moparthi (KL University, India)
  • Xiaoming Fu (Georg-August-University of Goettingen, Germany)
  • Zhisheng Huang (Vrije University of Amsterdam)
  • Rose Quan (Northumbria University, UK)
  • Shi Dong (Zhoukou Normal University, China)
  • Limei Peng (Kyungpook National University, South Korea)
  • Hui Ma( Victoria University of Wellington, New Zealand)
  • Venkatesan Subramanian (Indian Institute of Information Technology – Allahabad, India)
  • Pon Harshavardhanan (VIT Bhopal University, India)
  • Manish Kumar (The Indian Institute of Information Technology, Allahabad, India)
  • Muzammil Hussain, University of Management and Technology, Lahore, Pakistan
  • Michael Bewong, Charles Sturt University, Australia
  • Shabir Ahmad, Gachon University, Korea
  • Vu Nguyen, University of Science, Vietnam
  • Xiaodi Huang, Charles Sturt University, Australia
  • Jianming Yong, University of Southern Queensland, Australia
  • Yogeshwar Vijayakumar Navandar; National Institute of Technology, India
  • Zhengyi Chai, Tiangong University in China, China
  • Chuanlong Wang, Taiyuan Normal University, China
  • Chin-Feng Lee, Chaoyang University of Technology, Taiwan
  • Hsing-Chung Chen (Jack Chen), Asia University, Taiwan
  • Wen-Yang Lin, National University of Kaohsiung, Taiwan
  • Chun-Hao Chen, National Kaohsiung University of Science and Technology, Taiwan
  • Mudasir Mohd, University of Kashmir, India
  • Md Rafiqul Islam, Australian Institute of Higher Education, Australia.
  • Jin Wang, Institute of Applied Physics and Computational Mathematics, China
  • Chandu Thota, University of Nicosia, Cyprus
  • Haris M. Khalid, University of Dubai, UAE.
  • G. Reza Nasiri, Alzahra University, Tehran, Iran.
  • Siuly Siuly, Victoria University, Australia
  • Bishnu Prasad Gautam, Kanazawa Gakuin University, Japan
  • Sivaparthipan C B, Bharathiar University, India
  • Ting-Chia Hsu, National Taiwan Normal University, Taiwan
  • Punitha Palanisamy, Tagore IET, India
  • Lakshmana Kumar R, Tagore IET, India
  • Weiwei Jiang, Beijing University of Posts and Telecommunications, Taiwan

  • Research on Optimization of Unmanned Aerial Vehicle Communication Based on Wireless Communication Artificial Intelligence

    Appears in:
    sis 25(3):

    Authors:
    Yuqing Tang , Qiang Wang , Yu Geng

    Published:
    4th Jul 2025

    Abstract:
    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.

  • A Cholesky decomposition and fusion clustering based spectrum sensing method

    Appears in:
    sis 25(3):

    Authors:
    Jian Li, Yue Li, Xiaoxu Chen, Xinpei Zhao

    Published:
    24th Jun 2025

    Abstract:
    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.

  • Mobile Edge Computing Empowered Energy Consumption Optimization for Multiuser Power IoT Networks

    Appears in:
    sis 25(3):

    Authors:
    Mingming Shi, Yongling Lu, Xian Zheng, Yang Xie, Shuangwei Li

    Published:
    18th Jun 2025

    Abstract:
    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.

  • On the Performance of RIS-Enhanced RSMA Networks with On-Off Control

    Appears in:
    sis 25(3):

    Authors:
    Hongyan Zhang, Linlin Liang, Dongjie Jiang, Haiyan Huang, Nina Zhang

    Published:
    11th Jun 2025

    Abstract:
    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

  • Research on Hybrid Path Planning Algorithms for UAVs in Complex Environments

    Appears in:
    sis 25(3):

    Authors:
    Liang Ye, Lin Ma, Shuyi Chen, Xinyue Chang

    Published:
    27th May 2025

    Abstract:
    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.

  • Deep Learning Empowered Enterprise Knowledge Graph with Attention Mechanism

    Appears in:
    sis 25(3):

    Authors:
    Junwei Zhu, Liangbo Zeng, Yadong Shi, Rongyin Tan, Liang Li

    Published:
    27th May 2025

    Abstract:
    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.

  • Fuzzy TOPSIS Method for Sustainable Supplier Assortment in Green Supply Chain Management

    Appears in:
    sis 25(3):

    Authors:
    Huzaifa Saleem, Naseem Ahmad Khan, Shafeeq Ahmad, Umar Badr Shafeeque

    Published:
    26th May 2025

    Abstract:
    INTRODUCTION: Green supply chain management represents one of the crucial ways for organizations to start minimizing their ecological footprint in the era of increasing ecological preoccupation and sustainability objectives. The critical issues of green supply chain management involve the assortment of green suppliers who are well-suited with the environmental objectives of organizations. Traditional methods of supplier selection cannot efficiently depict the complex, uncertain nature of sustainability criteria. In this respect, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution, shortly known as Fuzzy TOPSIS, is proposed for usage in this research for improving the process of supplier assortment in green supply chain management. OBJECTIVES: The aim of this work is, therefore, to present an integrated framework, utilizing the Fuzzy TOPSIS method for selecting sustainable suppliers in green supply chain management. The particular aim of the study will be to incorporate environmental, social, as well as economic criteria in performance evaluation at the supplier level, by considering innate uncertainties and fuzziness related to sustainability metrics. METHODS: The Fuzzy TOPSIS process is applied to assess and rank potential suppliers based on multiple criteria considering both environmental and economic factors. RESULTS: Application of the Fuzzy TOPSIS method in sustainable supplier assortment demonstrates its effectiveness in identifying suppliers that align with green objectives while meeting operational requirements. CONCLUSION: The proposed framework will provide a more fine-tuned and flexible tool for decision-makers by incorporating fuzzy logic into the complexities at hand for sustainability assessment. The findings underline the importance of adopting advanced techniques in decision making in order to attain environmental responsibility and long-term sustainability in supply chain operations.

  • Enhancing Stakeholder Analysis with AI: A Comparative Study of Productivity and Quality in the Educational Context

    Appears in:
    sis 25(3):

    Authors:
    Kalinka Kaloyanova, Vijay Kanabar

    Published:
    23rd May 2025

    Abstract:
    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.

  • The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data

    Appears in:
    sis 25(3):

    Authors:
    Ping Zhang , Bingxin Zhang , Xiaoxue Fan

    Published:
    14th May 2025

    Abstract:
    INTRODUCTION: The advancement of appropriate instructional procedures and data techniques had already facilitated the development of a virtual evolutional innovation and entrepreneurship education process. OBJECTIVES: The adoption of big data has also tended to result in academic revolutionary movements for businesses. To address the limitations of current university instructional setups and to broaden the scope of Big Data's (BDC) application, this study presents an optimization technique for evaluating the quality of innovation and entrepreneurship education. METHODS: This paper presents an optimization technique for measuring the quality of education in innovation and entrepreneurship. This technique should help us move beyond the constraints of our existing approaches to higher education's pedagogical infrastructure into the domain of big data. RESULTS: The proposed optimization algorithm differs from conventional university quality evaluation instructional practices as it integrates big data that dramatically improves the college Entrepreneurship class interaction. This article proposed a Convolution neural network algorithm with BDC for training virtualization representation and employs a standard correlation analysis method in data analysis to retrieve the correlation relationship between the information content enclosed in huge entrepreneurship education and online students. CONCLUSION: Simulation results revealed that the proposed model has provided an accuracy of 98%. The proposed method provides a platform for sharing instructional materials that function more efficiently under heavy load. Up to 98% and 94% security are achieved under heavy and light loads, respectively. The use of cloud computing in this scenario led to improvements of 7% and 8%, respectively, yielding results of 89% and 86%.

  • Smart Data Prefetching Using KNN to Improve Hadoop Performance

    Appears in:
    sis 25(3):

    Authors:
    Douglas G. Down, Rana Ghazali

    Published:
    17th Apr 2025

    Abstract:
    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.

  • Design and Application of Evaluation Method for Civics Classroom Based on CRITIC Fuzzy Algorithm

    Appears in:
    sis 24(1):

    Authors:
    Zhanyu Chang

    Downloads:
    1627

    Abstract:
    INTRODUCTION: Through in-depth study of Civics classroom evaluation, it can provide teachers with scientific evaluation indexes and methods, improve teaching quality and effect, promote the overall development of students, and also promote the professional growth of teachers. OBJECTIVES: Based on critical and fuzzy comprehensive evaluation methods, this study aims to study the effectiveness and accuracy of the evaluation methods in Civics and Politics classrooms. Based on the CRITIC method, the fuzzy comprehensive evaluation method was introduced to solve the problem of subjectivity and uncertainty in the evaluation process. METHODS: Various research methods were used, including observation, interviews, and questionnaires. By comprehensively analyzing the students' performance, feedback, and assessment, and the teachers' pre-course preparation, the advantages and improvement directions of Civics classroom teaching can be accurately evaluated using the CRITIC and fuzzy comprehensive evaluation methods. RESULTS: The study results show that the CRITIC and fuzzy comprehensive evaluation methods can provide a more comprehensive, accurate, and objective evaluation of the Civics classroom. The undefined complete evaluation method plays an essential role in dealing with ambiguity and uncertainty in the evaluation process, making the evaluation results more objective and reliable. CONCLUSION: The Civics classroom evaluation method based on the CRITIC and fuzzy comprehensive evaluation methods is adequate and accurate. These findings strongly support improving the quality of Civics classroom teaching and enhancing students' learning outcomes.

  • Towards Happy Housework: Scenario-Based Experience Design for a Household Cleaning Robotic System

    Appears in:
    sis 23(3): e12

    Authors:
    Yichen Lu, Zheng Liao

    Downloads:
    1579

    Abstract:
    INTRODUCTION: In the interwoven trend of the experience economy and advanced information technology, user experience becomes the substantial value of an interactive system. As one of the early innovations of a smart home, the current design of household cleaning robots is still driven by technology with a focus on pragmatic quality rather than the experiential value of a robotic system. OBJECTIVES: This paper aims to uplift the design vision of a cleaning robot from an automatic household appliance towards a meaningful robotic system engaging users in happy housework. METHODS: Theoretically, experience design and scenario-based design methods were combined into a specific design framework for domestic cleaning robotic systems. Based on the user study and technology trend analysis, we first set three experience goals (immersion, trust, and inspiration) to drive the design process, then chose 3D point cloud and AI recognition as backup technologies and afterwards extracted three main design scenarios (scanning and mapping, intelligent cleaning, and live control). RESULTS: The design features multi-view switching, a combination of animation rendering and real scene, fixed-point cleaning, map management, lens control and flexible remote, and shooting modes are proposed. Seventy-one participants evaluated the concept with online AttrakDiff questionnaires. The results indicate the targeted experience is fulfilled in the design concept. CONCLUSION: By integrating experience design and scenario-based design methods with technology trend analysis, designers can envision experiential scenarios of meaningful life and potentially expand the design opportunity space of interactive systems.

  • Improved Convolutional Neural Network Algorithm for Student Behavior Detection in the Classroom

    Appears in:
    sis 24(5):

    Authors:
    Weirong Wang, Yihua Liu

    Downloads:
    1473

    Abstract:
    The performance of the existing student classroom behavior detection model is affected by various aspects such as dataset, algorithm and height as well as the differences between different classrooms, and there are problems such as a single dataset, low accuracy and low efficiency. In order to improve the accuracy of student classroom behavior detection algorithm, this paper proposes a student classroom behavior detection method based on improved convolutional neural network algorithm. Firstly, the student behavior detection dataset is constructed, and the student classroom behavior detection technology scheme is designed; secondly, in order to improve the detection accuracy, the features are extracted by using the new jumping bi-directional paths, and the attention mechanism module is added at different positions to improve the path aggregation network; weekly, the embedding positions of the attention mechanism strategy are determined by analyzing multiple sets of experiments, and the proposed student classroom behavior detection algorithm's effectiveness and superiority.

  • Data Transmission of Digital Grid Assisted by Intelligent Relaying

    Appears in:
    sis 23(3): e11

    Authors:
    Binyu Xie, Yuda Li, Chun Yang, Shuangbai He, Jiaqi Zhao

    Downloads:
    1357

    Abstract:
    In this paper, we study the relaying and cache aided digital grid data transmission, where the relaying may be equipped by caching or not, depending on specific applications. For both cases, we evaluate the impact of relaying and caching on the system performance of digital grid data transmission through theoretical derivation. To this end, an analytical expression on the outage probability is firstly derived for the data transmission. We then provide an asymptotic expression on the system outage probability. Finally, some simulation results are provided to verify the correctness of the derived analysis on the system performance, and show the impact of relaying and caching on the data transmission of digital grid system. In particular, the usage of caching at the relaying can help strengthen the data transmission performance of the considered system effectively. The results in this paper could provide some reference to the development of wireless transmission and scalable information systems.

  • A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors

    Appears in:
    sis 23(3): e8

    Authors:
    N. Meghana Preethi, Shiva Shankar Reddy , Mahesh Gadiraju, V.V.R.Maheswara Rao

    Downloads:
    1353

    Abstract:
    Gestational diabetes mellitus occurs due to high glucose levels in the blood. Pregnant women are affected by this type of diabetes. A blood test is to be performed to identify diabetes. The Oral Glucose Tolerance Test (OGTT) is a blood test performed between the 24th and 28th week of pregnancy that is necessary to identify and overcome the side effects of GDM. The main objective of this work is to train a model by utilizing the training data, evaluate the trained model using the test data, and compare existing machine learning algorithms with a Gradient boosting machine (GBM) to achieve a better model for the effective prediction of gestational diabetes. In this work, the analysis was done with a few existing algorithms and the Extreme learning machine and Gradient boosting techniques. The k-fold cross-validation technique is applied with values of k as 3, 5, and 10 to obtain better performance. The existing algorithms implemented are the Naive Bayes classifier, Support Vector Machine, K-Nearest Neighbour, ID3, CART and J48. The proposed algorithms are Gradient boosting and ELM. These algorithms are implemented in R programming. The metrics like accuracy, kappa statistic, sensitivity/Recall, specificity, precision, f-measure and AUC are used to compare all the algorithms. GBM has obtained better performance than existing algorithms. Then finally, GBM is compared with the other proposed robust Machine Learning algorithm, namely the Extreme learning machine, and the GBM performed better. So, It is recommended to use a gradient-boosting algorithm to predict gestational diabetes effectively.  

  • A Technique for Cluster Head Selection in Wireless Sensor Networks Using African Vultures Optimization Algorithm

    Appears in:
    sis 23(3): e9

    Authors:
    Gurbinder Singh Brar, Vipan Kusla

    Downloads:
    1339

    Abstract:
    INTRODUCTION: Wireless Sensor Network (WSN) has caught the interest of researchers due to the rising popularity of Internet of things(IOT) based smart products and services. In challenging environmental conditions, WSN employs a large number of nodes with limited battery power to sense and transmit data to the base station(BS). Direct data transmission to the BS uses a lot of energy in these circumstances. Selecting the CH in a clustered WSN is considered to be an NP-hard problem. OBJECTIVES: The objective of this work to provide an effective cluster head selection method that minimize the overall network energy consumption, improved throughput with the main goal of enhanced network lifetime. METHODS: In this work, a meta heuristic based cluster head selection technique is proposed that has shown an edge over the other state of the art techniques. Cluster compactness, intra-cluster distance, and residual energy are taken into account while choosing CH using multi-objective function. Once the CHs have been identified, data transfer from the CHs to the base station begins. The residual energy of the nodes is finally updated during the data transmission begins. RESULTS: An analysis of the results has been performed based on average energy consumption, total energy consumption, network lifetime and throughput using two different WSN scenarios. Also, a comparison of the performance has been made other techniques namely Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Atom Search Optimization (ASO), Gorilla Troop Optimization (GTO), Harmony Search (HS), Wild Horse Optimization (WHO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA) and Biogeography Based Optimization (BBO). The findings show that AVOA's first node dies at round 1391 in Scenario-1 and round 1342 in Scenario-2 which is due to lower energy consumption by the sensor nodes thus increasing lifespan of the WSN network. CONCLUSION: As per the findings, the proposed technique outperforms ABC, ACO, ASO, GTO, HS, WHO, PSO, FA, and BBO in terms of performance evaluation parameters and boosting the reliability of networks over the other state of art techniques.

  • Google Maps Data Analysis of Clothing Brands in South Punjab, Pakistan

    Appears in:
    sis 23(3): e10

    Authors:
    Majdah Alvi, Muhammad Ahmad, Kazim Jawad, Muhammad Bux Alvi

    Downloads:
    1331

    Abstract:
    The Internet is a popular and first-hand source of data about products and services. Before buying a product, people try to gain quick insight by scanning through online reviews about a targeted product. However, searching for a product, collecting all the relevant information, and reaching a decision is a tedious task that needs to be automated. Such composed decision-assisting text data analysis systems are not conveniently available worldwide. Such systems are a dream for major cities of South Punjab, such as Bahawalpur, Multan, and Rahimyar khan. This scenario creates a gap that needs to be filled. In this work, the popularity of clothing brands in three cities of south Punjab has been assessed by analysing the brand's popularity using sentiment analysis by prioritizing brands based on organic feedback from their potential customers. This study uses a combination of quantitative and qualitative research to examine online reviews from Google Maps. The task is accomplished by applying machine learning techniques, Logistic Regression (LR), and Support Vector Machine (SVM), on Google Maps reviews data using the n-gram feature extraction approach. The SVM algorithm proved to be better than others with the uni-bi-trigram features extraction method, achieving an average of 80.93% accuracy.

  • ALGORITHMIC LITERACY: Generative Artificial Intelligence Technologies for Data Librarians

    Appears in:
    sis 24(2):

    Authors:
    Tibor Koltay, Helen Beatriz Frota Rozados, Adilson Pinto, Thiago Dias, Alexandre Semeler, José González, Arthur Oliveira

    Downloads:
    1311

    Abstract:
    INTRODUCTION: Artificial intelligence (AI) is a novel type of library technology. AI technologies and the needs of data librarians are hybrid and symbiotic, because academic libraries must insert AI technologies into their information and data services. Library services need AI to interpret the context of big data. OBJECTIVES: In this context, we explore the use of the the OpenAI Codex, a deep learning model trained on Python code from repositories, to generate code scripts for data librarians. This investigation examines the practices, models, and methodologies for obtaining code script insights from complex code environments linked to AI GPT technologies.  METHODS: The proposed AI-powered method aims to assist data librarians in creating code scripts using Python libraries and plugins such as the integrated development environment PyCharm, with additional support from the Machinet AI and Bito AI plugins. The process involves collaboration between the data librarian and the AI agent, with the librarian providing a natural language description of the programming problem and the OpenAI Codex generating the solution code in Python. RESULTS: Five specific web-scraping problems are presented. The scripts demonstrate how to extract data, calculate metrics, and write the results to files. CONCLUSION: Overall, this study highlights the application of AI in assisting data librarians with code script creation for web scraping tasks. AI may be a valuable resource for data librarians dealing with big data challenges on the Web. The possibility of creating Python code with AI is of great value, as AI technologies can help data librarians work with various types of data sources. The Python code in Data Science web scraping projects uses a machine-learning model that can generate human-like code to help create and improve the library service for extracting data from a web collection. The ability of nonprogramming data librarians to use AI technologies facilitates their interactions with all types and data sources. The Python programming language has artificial intelligence modules, packages, and plugins such as the OpenAI Codex, which serialises automation and navigation in web browsers to simulate human behaviour on pages by entering passwords, selecting captcha options, collecting data, and creating different collections of datasets to be viewed.

  • A Chatbot Intent Classifier for Supporting High School Students

    Appears in:
    sis 22(3): e1

    Authors:
    Manar Alkhatib, Khaled Shaalan, Suha Khalil Assayed

    Downloads:
    1228

    Abstract:
    INTRODUCTION: An intent classification is a challenged task in Natural Language Processing (NLP) as we are asking the machine to understand our language by categorizing the users’ requests. As a result, the intent classification plays an essential role in having a chatbot conversation that understand students’ requests. OBJECTIVES: In this study, we developed a novel chatbot called “HSchatbot” for predicting the intent classifications from high school students’ enquiries. Evidently, students in high schools are the most concerned among all students about their future; thus, in this stage they need an instant support in order to prepare them to take the right decision for their career choice. METHODS: The authors in this study used the Multinomial Naive-Bayes and Random Forest classifiers for predicting the students’ enquiries, which in turn improved the performance of the classifiers by using the feature’s extractions. RESULTS: The results show that the random forest classifier performed better than Multinomial Naive-Bayes since the performance of this model is checked by using different metrics like accuracy, precision, recall and F1 score. Moreover, all showed high accuracy scores exceeding 90% in all metrics. However, the accuracy of Multinomial Naive-Bayes classifier performed much better when using CountVectorizers compared to using the TF-IDF. CONCLUSION: In the future work, the results will be analysed and investigated in order to figure out the main factors that affect the performance of Multinomial Naive-Bayes classifier, as well as evaluating the model with using a large corpus of students’ questions and enquiries.

  • Comments Analysis on Social Media: A Review

    Appears in:
    sis 23(6):

    Authors:
    Brian Alafwan, Utomo Sarjono Putro, Manahan Siallagan

    Downloads:
    1216

    Abstract:
    As the number of people using and participating in social media grows, academics become interest in studying this new media, specifically comment analysis, in order to comprehend public opinion and user behavior. However, there are no studies that map the development of comment analysis domain, which would be valuable for future research. To address the issue, we examine prior publications using PRISMA approach, and offer suggestions for further research. An investigation was conducted to locate pertinent publications published in databases between 2010 and 2022. On the basis of our examination of 115 relevant articles, we found that, within the scope of methodology, prior researches employ two methods (sentiment and content analysis) and three tools (human, software, and mixed coders), and the majority of them concentrate on gathering data from western countries, covering numerous platforms and topics. Based on these findings, we recommend that future research in comment analysis should synthesize methods and instruments. In addition, examine areas that have not been fully explore in terms of platforms (e.g., Instagram and Tiktok), topic (e.g., local government), and regions (e.g., eastern countries) that would be valuable in order to enhance the body of knowledge in this domain.
Publisher
EAI
ISSN
2032-9407
Number of Volumes
12
Last Published
2025-07-04
EAI Logo

About EAI

Community

Publish with EAI