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Modified Flower Pollination Algorithm for Energy Forecasting and Demand Management Coupled with Improved Battery Life for Smart Building Micro-Grid

Modified Flower Pollination Algorithm for Energy Forecasting...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Ali Hamza Zunaib Ali Sandra Dudley Muhammad Uneeb Sultan M Alghamdi Nicholas Christofides School of Engineering London South Bank University (LSBU) London UK National University of Sciences and Technology(NUST) Islamabad Pakistan Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electrical Engineering Computer Engineering and Informatics Frederick University Cyprus

This paper presents the Modified Flower Pollination Algorithm-based Multi-Layer Perceptron Neural Network (MFPA-MLPNN) as an optimization technique for efficient power flow management in a Smart Building Microgrid (SBMG) in-tegrated with solar and wind generation, and Electric Vehicle Batteries (EVBs) within grid connected structure while concurrently reducing optimization processing time. To achieve both technical and economic superiority, two optimization objectives are addressed. Firstly, a Demand Response (DR) framework is harnessed to accommodate the stochastic behavior and forecasting errors associated with intermittent sources. Secondly, the degradation of EVBs is considered, ensuring an economically viable power flow proposed strategy for both EV owners and mi-crogrid (MG) authorities. Power generation of Variable Renew-able Energy Sources (VRES) has been forecasted using MLPNN. Battery degradation and system stability under the action of the proposed topology have been evaluated using a simulation-based environment. Results show a significant decrease in battery degradation and processing time using the proposed MFPA-MLPNN optimization architecture.

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An Intelligent E-Pharmacopoeia Retrieval System Using Responsive Web Design

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International Journal of engineering and technology Innovation 2024年第2期14卷 177-188页

作者： Huang, Tian-Hsiang Chao, Wan-Ling Hwang, Kao-Shing Chen, Yao-Mei Ho, Wen-Hsien Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Information Management Office Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Healthcare Administration and Medical Informatics Taiwan School of Nursing Kaohsiung Medical University Kaohsiung Taiwan Superintendent Office Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung Taiwan

To develop an e-pharmacopeia retrieval mobile system aimed at assisting medical personnel in efficiently assessing precise drug information and ensuring patient safety, this study utilized responsive web design. The research framework incorporated the convergence of the technology acceptance model (TAM) and the information systems success model (ISSM) to investigate user satisfaction and continuance intention. A survey was administered to 151 pharmacy students who had utilized the system for over 30 minutes. The findings revealed that the TAM-ISSM model significantly influenced user satisfaction, surpassing the individual impacts of TAM and ISSM. Furthermore, user satisfaction exhibited a positive and significant impact on continuance intention, which, in turn, demonstrated a positive and significant influence on both individual and organizational performance. This suggests that the proposed system is proficient in accurately identifying and processing drugs, thereby mitigating the pain induced by diseases in patients. Copyright © by the authors. Licensee TAETI, Taiwan. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC) license (https://***/licenses/by-nc/4.0/).

关键词： Search engines

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An Ensemble Deep Learning Approach for Accurate Urinary Sediment Detection Using YOLOv9e and KD-YOLOX-ViT

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IEEE Access 2025年 13卷 101934-101954页

作者： Mansura Naznine Abdus Salam Muhammad Mohsin Khan Sadia Farhana Nobi Muhammad E. H. Chowdhury Department of Electrical Engineering Qatar University Doha Qatar Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Neurology Hamad Medical Corporation Doha Qatar Department of Public Health and Informatics BSMMU Dhaka Bangladesh

Urinary Tract Infections (UTIs) are common medical diseases that occur from bacterial infections which can impact any area of the urinary system. Detecting urine particles is crucial for diagnosing UTIs and other renal abnormalities, as the presence of red blood cells (RBCs), white blood cells (WBCs), casts, fungi, and bacteria in urine can indicate disorders such as hematuria, kidney stones, and urinary tract malignancies. This study introduces an advanced deep learning method that utilizes ensemble of YOLOv9e and KD-YOLOX-ViT models. YOLOv9e model is built upon the Generalized Efficient Layer Aggregation Network (GELAN) and Programmable Gradient Information (PGI). The KD-YOLOX-ViT model integrates knowledge distillation and Vision Transformer (ViT) modules within the YOLOX framework to improve performance. This ensemble framework combines the strengths of YOLOv9e and KD-YOLOX-ViT models to automate the detection and classification of seven distinct urine sediment particles, effectively addressing challenges related to class imbalance, image resolution, and domain adaptation. The YOLOv9e model exhibited exceptional performance, obtaining precision, recall, and mean average precision (mAP50) scores of 88.5%, 88.1%, and 92.2%, respectively. The KD-YOLOX-ViT model also performed well, achieving precision, recall, mAP50, and mAP50-95 scores of 86%, 88%, 86.7%, and 53.3%, respectively. By ensembling YOLOv9e and KD-YOLOX-ViT using Weighted Box Fusion (WBF), the model achieved a final mAP50 of 94.18%. The model’s adaptability was further validated through rigorous external validation on a novel dataset, yielding in a mAP50 of 94.64%. Comparative analysis with state-of-the-art methods confirms the model’s real-time analytical capabilities. Moreover, the integration of eXplainable AI (XAI) enhances interpretability, offering valuable insights for confident clinical diagnosis. This comprehensive approach shows significant promise in advancing diagnostic accuracy and enabling

关键词： Sediments Accuracy Adaptation models Kidney Deep learning Microscopy White blood cells Medical diagnostic imaging Feature extraction Bacteria

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Leveraging LLM in Genetic Programming Hyper-heuristics for Dynamic Microservice Deployment 37th

Leveraging LLM in Genetic Programming Hyper-heuristics for...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Wang, Chen Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany National Institute of Water and Atmospheric Research Wellington New Zealand

ISBN: (纸本)9789819603473

Microservice deployment in cloud computing is a challenging combinatorial optimization problem due to the complex dependencies among microservices and the intricate trade-offs among different QoS requirements, e.g., minimizing Energy Consumption (EC) vs. minimizing Communication Overhead (CO). Recently, some hyper-heuristics methods, particularly Genetic Programming Hyper-Heuristics (GPHH), have been proposed to automatically generate heuristics for solving dynamic microservice deployment problems. Meanwhile, Large Language Models (LLMs) are becoming popular for solving various domain-specific problems thanks to their strong ability to learn problem-related knowledge. However, hybridizing GPHH with LLM by combining their abilities in solving complex optimization problems remains unexplored. In this paper, we propose an LLM-enhanced Genetic Programming Hyper-Heuristic (LLM-GPHH) algorithm to evolve heuristics for the dynamic deployment of applications composed of microservices, to jointly optimize EC and CO. Our experiments on real-world datasets demonstrate the effectiveness of the newly proposed LLM-GPHH. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic programming

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Examining the economic feasibility of hybrid Photovoltaic – Battery Energy Storage System in public building with flexible demand: cyprus case study

Examining the economic feasibility of hybrid Photovoltaic –...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Nikolas G. Chatzigeorgiou George Makrides Spyros Theocharides George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Photovoltaic – Battery Energy Storage Systems (PVBESS) are a promising solution against technical bottlenecks of high PV penetration. Public buildings are suitable for installation, although currently underexploited, while high BESS costs limit technology diffusion. This paper assesses the economic feasibility of PV-BESS in public buildings with flexible demand, through simulations showcasing BESS subsidisation impact and self-sufficiency contribution. The results derived that smaller PV-BESS sizes are more attractive investments, as larger systems are characterised by reduced profitability, while BESS subsidisation enhances system profitability. Shifting a small portion of load demand improves economic feasibility by decreasing BESS size, while combining PV production, energy storage, and energy flexibility is the optimal solution for public buildings against electricity prices. Finally, a trade-off between economic feasibility and energy self-sufficiency is observed. The results can be utilised by policymakers to design strategies for BESS promotion, while professionals can perform accurate system sizing, thus avoiding excess system capacities.

关键词： Photovoltaic systems Costs Accuracy Profitability Battery energy storage system Electricity Buildings Tariffs Production Investment

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A fast parallel classification model for the diagnosis of cancer

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and Communications 2021年第1期14卷 291-301页

作者： Jain, Divya Singh, Vijendra Department of Computer Science and Engineering School of Engineering and Technology The Northcap University Gurugram India Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India

Background: In this era of voluminous data, there is a need to process data speedily in less time. Moreover, it is also essential to diminish the dimensionality of data as well as to apply parallel computations for classification. SVM is a prominent classification tool and is currently one of the most popular state-of-the-art models for solving various classification problems that makes use of parallel computations to speed up its processing. Objective: To develop a fast, promising classification system using optimized SVM classifier with hybridized dimensionality reduction for the diagnosis of cancer disease. Methods: The proposed approach comprises of two stages – the first stage presents a hybrid approach to reduce the dimensionality of cancer datasets, and the second stage presents an efficient classification method to optimize the SVM parameters and improve its accuracy. To lessen the execution time, the proposed approach uses GPUs to concurrently run different processes on machine workers. Results: The proposed method with the combination of dimensionality reduction & parallel classification using optimized SVM classifier is found to give excellent results based on ‘Classification Accuracy’, ‘Selected Features’ and ‘Execution Time’. Conclusion: The experimental findings with benchmark datasets indicate that the proposed diagnostic model yields significant improvement in terms of execution time when compared to the conventional approach. The proposed model can assist doctors and medical professionals in the quick selection of most significant risk factors for the diagnosis and prognosis of cancer diseases. © 2021 Bentham Science Publishers.

关键词： Classification (of information)

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Middle-East Ionospheric Signatures Related to the 2023 Turkey Earthquake

Middle-East Ionospheric Signatures Related to the 2023 Turke...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Mefe Moses Haris Haralambous Christina Oikonomou Muhammad Mubasshir Shaikh Department of Geomatics Ahmadu Bello University Zaria Nigeria Frederick Research Center Nicosia Cyprus Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus Cloudwater Ltd Nicosia Cyprus Department of Electrical Engineering University of Sharjah Sharjah UAE Space Weather and Ionosphere Laboratory Sharjah Academy for Astronomy Space Sciences & Technology University of Sharjah Sharjah UAE

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This paper presents co-seismic earthquake-related ionospheric signatures observed by remote sensing instrumentation situated in the eastern Mediterranean and Middle-East and in particular in cyprus, Iraq and UAE by exploiting Total Electron Content measurements from calibrated 1-30 sec RINEX files NICO and ISBA IGS stations and ionograms from Nicosia and Sharjah ionosonde stations. Based on the analysis of measurements from these instruments we observe daytime acoustic ionospheric waves after the second (10:24 UTC) 7.6 (Mw) earthquake shock of the 6 February 2023 Turkey earthquake. Although the GNSS ionospheric signatures are only detectable in the near-field, multiple-cusp ionogram signatures generated by Rayleigh surface waves, generating acoustic waves propagating up to the ionosphere, appear as far as Sharjah.

关键词： Seismic measurements Global navigation satellite system Instruments Electric shock Earthquakes Surface acoustic waves Acoustic measurements

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Green hydrogen storage capacity estimations in depleted gas reservoirs

Green hydrogen storage capacity estimations in depleted gas ...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Charalampos Konstantinou Matheos Giakoumi Panos Papanastasiou Andreas V. Olympios Fanourios Kourougianni Alexandros Arsalis George E. Georghiou Department of Civil and Environmental Engineering University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Fondazione Eni Enrico Mattei Milan Italy Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Green hydrogen is poised to play a crucial role in the transition to renewable energy sources. It acts as a versatile energy storage carrier, helping to balance supply and demand. However, storing hydrogen presents significant challenges, as current technologies can only handle limited volumes. One promising solution involves injecting hydrogen into depleted oil & gas reservoirs, where it can be stored and later extracted when needed. This study explores this approach using the material balance method, which estimates the volumes within a reservoir. It is applied to a depleted natural gas system supported by an aquifer, taking into account pressure and flow rate histories. As the natural gas production rate increases, the hydrogen storage capacity also rises preventing the water to invade in the reservoir. For example, at a reference pressure of 2000 psi, there is no hydrogen storage capacity at a flow rate of 10 MMMCF/day, whereas a storage capacity of 314 BSCF is observed at a flow rate of 1000 MMMCF/day. These insights highlight the potential of utilising depleted reservoirs for hydrogen storage, offering a feasible path to overcoming the current limitations in hydrogen storage technology.

关键词： Water Hydrogen storage Hydrogen Production Reservoirs Green hydrogen Mathematical models History Natural gas Aquifers

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AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach 6

AI Based Prediction for Heart Disease: A Comparative Analysi...

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6th Asian Conference on Artificial Intelligence technology, ACAIT 2022

作者： Raval, Jay Verma, Jai Prakash Islam, Sardar N.M. Jain, Rachna Thakur, Narina Institute of Technology Nirma University Computer Science Engineering Department Ahmedabad India Victoria University Applied Informatics Research Melbourne Australia Bhagwan Parshuram Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665453110

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm. © 2022 IEEE.

关键词： Data mining

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FPGA design and implementation for montgomery multiplication algorithm using MATLAB HDL coder

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Bulletin of the National Research Centre 2024年第1期48卷 1-16页

作者： Elsayed, Ghada Abass, Eman S. Computer Science and Informatics Faculty The British University in Egypt Cairo Egypt Electrical Engineering Department Faculty of Engineering Modern University for Technology and information (MTI University) Cairo Egypt

Modular multiplication for large numbers is especially important in cryptography algorithms such as RSA and elliptic curves. The Montgomery algorithm is the most famous and efficient one for calculating it. Hardware implementation for cryptography co-processors is better than software implementation in terms of speed and security. Many FPGA designs for the Montgomery multiplication algorithm was published based on hardware description languages like VERILOG and VHDL. This paper proposes the FPGA design and implementation using MATLAB HDL Coder. The algorithm is modified such that it can fit any small/large FPGA by introducing scaling factor. The design is configurable in both modulus length and the scaling factor. This paper performs a comparison between the synthesizing results for different scales and for different modulus lengths. The synthesizing is performed up to 8K bit modulus length, and it can be increased easily. In this paper, implementation of different modulus lengths with different frequencies and with different area utilization can be easily achieved. The design utilizes different area resources for each configuration. The target is xc7vx330t-2ffg1157 Virtex-7 Xilinx FPGA. The maximum frequency is 80.81 MHz for 4096-bit modulus length with 8-bit data width and 2 for serialization factor. The minimum area utilization is achieved for minimum configurations, i.e., 1024-bit modulus length with 8-bit data width and for unity serialization factor. This paper proposes a scalable and configurable FPGA design for Montgomery multiplication co-processor-based HDL coder design.

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