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Business intelligent connection between quality and evaluation methods: a case study

International Journal of Applied Systemic Studies 2022年第4期9卷 330-343页

作者： Mandellos, Georgios Tsipianitis, Dimitris Telecommunications and Information Technology Division Electrical and Computer Engineering Department University of Patras RionGR-26504 Greece Systems and Automatic Control Division Electrical and Computer Engineering Department University of Patras RionGR-26504 Greece

Nowadays, information and knowledge represent the fundamental wealth of an organisation. Enterprises try to utilise this wealth to gain advantage when making decisions such as a project’s acceptance. After a project finishes, a quality management plan defines its quality level. The business intelligence theories help the individual to evaluate the proposed plan in order to target the project’s results. But, do all the evaluation methods have the same reliability? We check the project’s quality by using paper and online questionnaire administration. Although the scope is the same, there are differences, giving different results. So, we intelligently investigated the quality of the university educational process. The results show that the administration method has a significant effect on overall results. Thus, the quality check of a project using questionnaires needs to have an attentive design, avoid points that introduce uncertainties and able to show the real opinion of the respondents. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Surveys

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. Beijing Institute of Technology School of Information and Electronics Beijing100081 China Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Shenzhen518055 China The University of Sydney School of Electrical and Information Engineering SydneyNSW2008 Australia Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines 487372 Singapore Kyung Hee University Department of Electronic Engineering Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2002-2012 IEEE.

关键词： Graph neural networks

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Sensitivity of DC Network Representation for GIC Analysis

Sensitivity of DC Network Representation for GIC Analysis

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Mishra, Aniruddh Barnes, Arthur K. Tabarez, Jose E. Mate, Adam University of Texas at Austin Department of Electrical and Computer Engineering AustinTX United States Information Systems and Modeling Group Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798331541125

Geomagnetic disturbances are a threat to the reliability and security of our national critical energy infrastructures. These events specifically result in geomagnetically induced currents, which can cause damage to transformers due to magnetic saturation. In order to mitigate these effects, blocker devices must be placed in optimal locations. Finding this placement requires a dc representation of the ac transmission lines, which this paper discusses. Different decisions in this process, including the method of representing the blocking devices, result in significant variations to the power loss calculations. To analyze these effects, we conclude the paper by comparing the losses on a sample network with different modeling implementations. © 2025 IEEE.

关键词： Transformer windings

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A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm

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computers, Materials & Continua 2023年第10期77卷 781-797页

作者： Yazeed Yasin Ghadi Mohammed SAlshehri Sultan Almakdi Oumaima Saidani Nazik Alturki Fawad Masood Muhammad Shahbaz Khan Department of Computer Science Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Institute of Space TechnologyIslamabad4400Pakistan School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghEH105DTUK

Securing digital image data is a key concern in today’s information-driven *** encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption *** study introduces an innovative approach to creating S-boxes for encryption *** proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption *** nonlinearity measure of the proposed S-boxes is *** qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data ***,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption *** validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been *** addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are *** results indicate a 99.71%NPCR and 33.51%*** results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.

关键词： S-box chaos artificial bee colony image encryption

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Fast Learning Network Algorithm for Voice Pathology Detection and Classification

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Multimedia Tools and Applications 2025年第17期84卷 18567-18598页

作者： Albadr, Musatafa Abbas Abbood Ayob, Masri Tiun, Sabrina AL-Dhief, Fahad Taha Al-Daweri, Muataz Salam Homod, Raad Z. Abbas, Ali Hashim Department of Petroleum Project Management College of Industrial Management of Oil and Gas Basrah University for Oil and Gas Al-Basrah Iraq Faculty of Information Science and Technology CAIT Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Department of Communication Engineering School of Electrical Engineering Universiti Teknologi Malaysia UTM Johor Bahru Johor Malaysia Department of Information Systems College of Economics Management & Information Systems University of Nizwa Nizwa Oman Department of Oil and Gas Engineering Basrah University for Oil and Gas Al-Basrah Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Iraq

The utilisation of ML (Machine Learning) techniques in the detection of the VP (Voice Pathology) has recently gained a lot of consideration. However, these efforts still have several drawbacks such as: I) the accuracy rates of most previous works are still not promising and need more improvement. II) The majority of the previous works have concentrated on the task of VP detection only and disregarded the task of VP classification. III) Most of the previous works have been assessed based on a single voice data only like the vowel /a/, and the other vowels (/i/, and /u/) and sentences were disregarded. IV) The majority of the previous works performance were assessed utilising a limited evaluation metrics. Recently, one of the utmost effective ML techniques is FLN (Fast Learning Network), it is an efficient technique for data classification. However, the FLN classifier has not been implemented to the problem of VP detection and classification. Therefore, this research proposes the FLN classifier with MFCC (Mel-Frequency Cepstral Coefficient) features in order to enhance the accuracy of the VP detection and classification. The FLN classifier has the ability to a) solve both binary and multiclass classification issues, b) eliminate overfitting, as well as c) operate akin to a NN (Neural Network) structure while employing the principles of a kernel-based SVM (Support Vector Machine). In this research, the SVD (Saarbrucken Voice Database) dataset was utilised to assess the FLN classifier performance in the VP detection and classification. The assessment of the proposed FLN classifier was conducted based on two phases: the first phase includes all the voice samples of the SVD dataset with the sentences and vowels (i.e., /a/, /i/, and /u/) which are pronounced in neutral, high, and low pitches. While the second phase uses the voice samples of the utmost common 3 pathology types (i.e., paralysis, polyp, and cyst) based on the vowel /a/ that is pronounced in neutral pitch. The

关键词： Classification (of information)

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Joint Deployment and Resource Allocation for Multi-AeBS Networks: A Two-Timescale Optimization Framework Using MADRL

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IEEE Transactions on Communications 2024年第6期73卷 4272-4289页

作者： Zhao, Yikun Zhou, Fanqin Feng, Lei Sun, Yao Li, Wenjing Lim, Wei Yang Bryan Xiong, Zehui Mao, Shiwen Han, Zhu State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom Nanyang Technological University Alibaba Group and Alibaba-NTU Joint Research Institute Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheless, the scarce spectrum resources and difficulty in accessing global information bring necessity and challenges to the deployment and resource allocation of AeBSs. In this paper, we propose a practical two-timescale framework to solve the resource allocation and deployment optimization problem in multi-AeBS networks. Specifically, the subcarrier allocation problem is first transformed into a many-to-one matching game coupled with power allocation and solved in a small timescale. Then, in a large timescale, the AeBS deployment subproblem is transformed into a distributed partially observable Markov decision process (Dec-POMDP), and then a novel multi-agent hypergraph convolutional deep reinforcement learning (MAHGCDRL) is proposed to solve this problem. The proposed MAHGCDRL extracts features of neighboring AeBSs through hypergraph convolutional networks, enabling AeBS agents to achieve better coordination in a distributed manner. Simulation results show that our proposed approach can attain a higher sum rate, and the proposed MAHGCDRL algorithm achieves better learning performance compared to the existing benchmarks in the literature. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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Predictive Analytics for Business Decision-Making: Unleashing the Power of Data-driven Insights 15

Predictive Analytics for Business Decision-Making: Unleashin...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nishat, Nourin Mehedi, Mithila Rashid, Saidur Raasetti, Muniroopesh Lamar University Department of Management Information Systems TX United States Rajshahi University of Engineering and Technology Department of Electrical and Computer Engineering Rajshahi6204 Bangladesh Lamar University Department of Electrical and Electronics Engineering TX United States Lamar University Department of Electrical Engineering TX United States

ISBN: (纸本)9798350370249

This study examines how Predictive Analytics may alter company decision-making by employing sophisticated data-driven insights to improve strategic decisions. The report examines predictive analytics' methods, algorithms, and applications, showing how it has transformed industry decision-making. Predictive Analytics' ability to anticipate future trends, recognize patterns, and generate accurate forecasts based on previous data is examined in corporate operations. The paper uses case studies and practical examples to show how predictive models enhance risk management, resource efficiency, and strategic planning. Business decision-making is explored in relation to predictive analytics components including machine learning algorithms, statistical modeling, and data mining. The study highlights predictive analytics' benefits in sales forecasting, consumer behavior research, and market trend projections. The study also examines the social and economic effects of corporate predictive analytics. It examines how data-driven decision-making boosts competitiveness, innovation, and economic development, serving national interests. For a balanced and transparent deployment, ethics, data privacy, and predictive analytics are considered. This study shows how predictive analytics can help firms make smart decisions in an age of unparalleled data availability. The study shares successful applications and addresses problems to help business professionals, analysts, and researchers navigate datadriven business strategies. © 2024 IEEE.

关键词： Business Decision Making Linear Regression Polynomial Regression Predictive analytics

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Smart Water-Powered Renewable Energy System with IoT Integration

Smart Water-Powered Renewable Energy System with IoT Integra...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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Improving VANET Localization Performance with Data Verification: Challenges and Solutions

Improving VANET Localization Performance with Data Verificat...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials & Continua 2025年第6期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoNSW 2007Australia School of Data Science and Engineering Indian Institute of Science Education and ResearchBhopal462066India Research Institute of Marine Systems Engineering Seoul National UniversitySeoul08826Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyDelhi NCR CampusGhaziabad201204India Battery System Modelling Department CLARIOS VARTA Hannover GmbHHanover30419Germany Science and Innovation Group Bureau of MeteorologyBrisbane4000Australia

Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric *** study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 *** indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s *** computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction *** GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational *** contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery *** of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep *** findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained *** work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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