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The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia

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Water Supply 2024年第11期24卷 3848-3861页

作者： Mohammadi, Maryam Gato-Trinidad, Shirley Kuok, Kuok King Department of Civil and Construction Engineering Swinburne University of Technology HawthornVIC3122 Australia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Kuching Sarawak93350 Malaysia

Many cities around the world have faced water scarcity due to climate change, population growth, and urbanization. Accurate water supply and demand forecasting is critical for sustainable urban water management. Machine learning (ML) models provide new possibilities for forecasting compared with traditional models in handling non-linearity. This study aims to address the efficacy of ML models, long short-term memory (LSTM), stacked LSTM, bidirectional LSTM (Bi-LSTM), and multilayer perceptron (MLP), for forecasting water supply and demand in Greater Melbourne, Australia. The ML modelling utilized daily water supply and demand, and climatic variables (rainfall and maximum temperature) recorded by Melbourne Water and the Bureau of Meteorology from 1990 to 2019. The stacked LSTM performs better than other models in forecasting with R2, RMSE, and MAPE values of 0.908, 335.74 ML, and 23.5% for water supply and 0.791, 94.88 ML, and 5.3% for water demand, respectively. The inclusion of climatic variables enhanced the accuracy of forecasting by improving R2 and reducing RMSE and MAPE. The results indicate effectiveness of ML models, particularly LSTM-based architectures, in forecasting water supply and demand. However, these models have limitations, particularly in forecasting extreme values, emphasizing the need to improve ML models for more reliable and accurate water management. © 2024 The Authors.

关键词： Demand response

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A proposed strut efficiency factor for strut-and-tie design of steel fibre-reinforced concrete

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Materials Today: Proceedings 2022年 64卷 1054-1060页

作者： Ng, L.T. Tang, A.C.S. Looi, D.T.W. Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak 93350 Malaysia

Strut-and-tie model (STM) is a lower-bound plastic theory to achieve force equilibrium. It uses a simplified truss analogy method to define the load transfer mechanism in the disturbed region (D-region) in concrete structures. STM was mostly applied in the design of conventional reinforced concrete (RC), and there were few discussions on the use of STM for newer concrete technology, e.g., steel fibre-reinforced concrete (SFRC). SFRC presents opportunities in reducing web reinforcements in serviceability checks and in improving the ductility of D-regions. In this paper, a strut efficiency factor for STM is proposed for the design of SFRC by collecting a database of SFRC deep beams. It was discovered that the strut efficiency factor best fits with a function of the product of the fibre factor and concrete cylinder compressive strength. Baseline deep beam numerical models were developed and calibrated with the experimental load-displacement graphs collected through literature. A parametric study with variables such as concrete cylinder compressive strengths, fibre volumes and strut angles were investigated. The proposed strut efficiency factor was found to have a peak capacity lower than the ultimate load capacity computed in the calibrated numerical models, which justified the safe use of STM as a lower-bound plastic method in SFRC. © 2022

关键词： Compressive strength

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Telugu Language Analysis with XLM-RoBERTa: Enhancing Parts of Speech Tagging for Effective Natural Language Processing

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SN Computer science 2025年第2期6卷 1-13页

作者： Bharathi Mohan, G. Prasanna Kumar, R. Krishna Jayanth, K. Doss, Srinath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Faculty of Engineering and Technology Botho University Gaborone Botswana

This research investigates the efficacy of XLM-RoBERTa, a potent deep learning architecture rooted in transformer networks, for Part-of-Speech (POS) tagging—a foundational task in Natural Language Processing (NLP). The study narrows its focus to address the formidable UCD Telugu dataset, renowned for its intricate morphological characteristics that pose challenges to accurate tagging. Through meticulous fine-tuning of the pre-trained XLM-RoBERTa model. This study adeptly discerns and exploit intricate syntactic and semantic patterns underpinning the interplay between lexical elements and their respective POS tags. The rigorous evaluation regimen, complemented by a comprehensive statistical comparison with the Multilingual BERT model, conclusively establishes XLM-RoBERTa’s superiority. This advanced model attains a remarkable accuracy score of 91%, decisively surpassing the Multilingual BERT model, which achieves an accuracy of 88%. These findings not only underscore XLM-RoBERTa’s prowess in precise POS tagging, especially in the context of morphologically rich languages like Telugu, but also carry significant implications for the broader NLP community. This research unlocks valuable insights and positions XLM-RoBERTa as an exemplar of reliability and robustness in POS tagging. Furthermore, it extends an invitation for further exploration and innovation in deep learning architectures within the NLP domain. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bert Multilingual Bert NLP Parts-of-speech tagging Telugu XLM-RoBERTa

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Measurement of Parameters of BLDC Motor under Various Conditions for Autonomous Vehicles Applications 16

Measurement of Parameters of BLDC Motor under Various Condit...

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16th International Conference on Sensing Technology, ICST 2023

作者： Hojati, Mohammadreza Mukhopadhyay, Subhas C. Han, Richard Taghizadeh, Foad Macquarie University School of Engineering Faculty of Science and Engineering Sydney Australia Macquarie University School of Computing Faculty of Science and Engineering Sydney Australia

ISBN: (纸本)9798350395341

Brushless DC (BLDC) motors find extensive application across diverse fields due to their versatility. Consequently, the imperative to enhance their performance becomes paramount. To achieve this objective, a thorough measurement of their operational parameters under varying working conditions is indispensable as it lays the foundation for potential improvements. Accordingly, this research endeavors to investigate the performance of multiple BLDC motors and drivers through rigorous experimentation. The measurements include crucial parameters such as motor input current, input voltage, motor phase current, rotational speed, and force output, facilitated by the incorporation of requisite sensors. The findings from these experiments are of substantial significance for subsequent stages in motor optimization, providing valuable insights to enhance their operational efficiency and effectiveness in practical applications. © 2023 IEEE.

关键词： Brushless DC motors

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Signature Based Identification using Locality-Sensitive Hashing for IoT Devices 2

Signature Based Identification using Locality-Sensitive Hash...

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2nd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Singh, Amit Sindhu Madhuri, G. Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

On current days, the growing applications in Internet of Things (IoT) is increasing in various real-time sectors as smart city applications (air pollution, atmospheric noise canceling and traffic flow management). Now, these technologies are combined to provide the comfort configuration for each application. However, malware / virus are highly possible to extract the device software or hardware information in IoT. All sensed information about the environment or any event is stored in these devices which are hacked by the malicious attackers. To overcome these adversities, we introduced the concept of locality sensitive hash (LSH) for IoT device trustworthiness identification. The main objective of our proposed system is to allow secure transfer of data from the resource constrained devices. In this project, the resource constrained devices are the wireless sensor nodes. The experimental analysis confirmed that our proposed approach is suitable for the application or environment with resource constrained devices. Also, the time, energy, latency and packet overhead is considerably reduced than the existing systems considered for this project. © 2023 IEEE.

关键词： Internet of things

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering Systems

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Computer Modeling in engineering & sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

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Computer Systems science & engineering 2023年第8期46卷 2141-2157页

作者： J.Prabakaran P.Selvaraj Department of Networking communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Department of Computing Technologies Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high storage,and computational *** this paper,an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography(CT)images of patients with pneumonia,Covid-19,tuberculosis(TB),and ***,the CT images are captured and transmitted to the fog node through IoT *** the fog node,the image gets modified into a convenient and efficient format for further *** encryption Standard(AES)algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating ***,the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning *** different pre-trained deep learning architectures(Inception-ResNet-v2,VGG-19,ResNet-50)used transfer learning is adopted for feature *** softmax of heterogeneous base classifiers assists to make individual *** a meta-classifier,the ensemble approach is employed to obtain final optimal *** predicted image is consigned to the recurrent neural network with long short-term memory(RNN-LSTM)for severity analysis,and the patient is directed to seek therapy based on the *** proposed method achieved 98.6%accuracy,0.978 precision,0.982 recalls,and 0.974 F1-score on five class *** experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.

关键词： Intelligent health care cloud computing fog computing ensemble learning RNN-LSTM

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Graph-Theoretic Approaches in Semantic Web Service Orchestration and Discovery: A Comprehensive Literature Survey 5

Graph-Theoretic Approaches in Semantic Web Service Orchestra...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Vignesh, T.V. Vijaya, K. Minu, R.I. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies School of Computing Tamil Nadu Kattankulathur603203 India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Semantic Web Services (SWS) have become a powerful means for automating the discovery, composition, and orchestration of web services by providing them with semantic descriptions. This paper provides a comprehensive survey of graph-theoretic approaches in the orchestration and discovery of SWS, systematically categorizing and analyzing various methodologies. We examine how graph-based models contribute to enhancing the efficiency, accuracy, scalability, performance, reliability, and predictability of SWS processes. Furthermore, the paper discusses research gaps, challenges, and potential future directions, offering insights into emerging solutions and state-of-the-art techniques in the field. The survey also integrates recent advancements such as hybrid frameworks combining knowledge graphs and semantic web services, service clustering using graph embedding, and enhanced recommendation models. © 2024 IEEE.

关键词： Knowledge graph

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The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation 29

The Evolution of Zero Trust Architecture (ZTA) from Concept ...

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29th International Conference on Information Technology, IT 2025

作者： Nasiruzzaman, Md Ali, Maaruf Salam, Iftekhar Miraz, Mahdi H. Xiamen University Malaysia School of Computing and Data Science Malaysia Universiteti Metropolitan Tirana Faculty of Computer Science and IT Albania The Doctoral College United Kingdom Xiamen University Malaysia Mahdi H. Miraz Is with the School of Computing and Data Science Malaysia Wrexham University School of Computing Faculty of Arts Science and Technology United Kingdom University of South Wales Faculty of Computing Engineering and Science United Kingdom

ISBN: (纸本)9798331517649

Zero Trust Architecture (ZTA) is one of the paradigm changes in cybersecurity, from the traditional perimeter-based model to perimeterless. This article studies the core concepts of ZTA, its beginning, a few use cases and future trends. Emphasising the always-verify and least privilege access, some key tenets of ZTA have grown to be integration technologies like Identity Management, Multi-Factor Authentication (MFA) and real-time analytics. ZTA is expected to strengthen cloud environments, education, work environments (including from home) while controlling other risks like lateral movement and insider threats. Despite ZTA's benefits, it comes with challenges in the form of complexity, performance overhead and vulnerabilities in the control plane. These require phased implementation and continuous refinement to keep up with evolving organisational needs and threat landscapes. Emerging technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) will further automate policy enforcement and threat detection in keeping up with dynamic cyber threats. © 2025 IEEE.

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Safe Spaces Analysis for Mental Health 1

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12th International Conference on Recent Trends in computing, ICRTC 2024

作者： Kelawala, Harsh Shah, Zeal Selvaraj, P. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur 603203 India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

Mental health is a paramount concern in contemporary urban environments, necessitating comprehensive approaches to understanding its determinants and formulating effective interventions. This research project adopts a multifaceted methodology to explore the factors influencing mental well-being in urban areas, specifically focusing on cities within Gujarat. By integrating Geographic Information Systems (GIS) technology and predictive modeling techniques, we analyze various socio-environmental factors spanning air quality, noise pollution, green space availability, educational attainment, population density, income levels, and employability. At the same time, the GIS approach encompasses all major cities across India, the Random Forest algorithm is tailored specifically to the cities of Gujarat, reflecting the unique socio-geographical context of the region. Through rigorous data analysis and visualization, we identify spatial patterns and correlations, enabling the identification of areas most vulnerable to mental health challenges and prioritizing targeted interventions. This work presents our research's methodology, findings, and implications, offering valuable insights for policymakers and stakeholders striving to cultivate safe and supportive urban environments conducive to optimal mental health outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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