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International Conference of Sustainable Development and Smart Built Environments, SDSBE 2024

作者： Okonta, Ebere Donatus Rahimian, Farzad Agu, Nkiru Lilian School of Computing Engineering and Digital Technologies Teesside University Middlesbrough United Kingdom

ISBN: (纸本)9789819640508

Integrating artificial intelligence (AI) into smart city initiatives holds immense potential to enhance urban sustainability, efficiency, and livability. However, the ethical and effective implementation of AI technologies in smart cities requires developing and implementing robust policies and governance frameworks. This paper explores the landscape of emerging policies to guide the ethical and effective deployment of AI in smart cities. The study utilised the Strength, Weaknesses, Opportunities and Threats (SWOT) Analysis to examine the AI policies of three Northern European countries: the United Kingdom, Norway and Denmark. Key findings reveal that the UK emphasises clear financial commitments and strategic planning, positioning itself as a leader in rapid AI advancements. Norway prioritises interoperability and privacy standards, which are crucial for integrating AI across sectors. At the same time, Denmark leverages its digitisation prowess and ethical AI frameworks to foster innovation and attract global talent. Despite these strengths, all three nations face economic instability, skill shortages, and international competition for AI resources. The study underscores the importance of collaborative efforts to establish unified AI standards, invest in education and training, and align AI strategies with global environmental and ethical goals. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ethical technology

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Context-Aware Selection of Machine Learning as a Service (MLaaS) in IoT Environments

Context-Aware Selection of Machine Learning as a Service ...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st International Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th International Conference on Web Information Systems engineering, WISE 2024

作者： Patel, Keya Mistry, Sajib Kanneganti, Deepak Krishna, Aneesh School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia

ISBN: (纸本)9789819614820

The integration of Machine Learning as a Service (MLaaS) into the Internet of Things (IoT) environments presents considerable opportunities for enhancing decision-making and automation. We propose a novel framework for context-aware selection of MLaaS in IoT settings, aimed at optimising the interaction between IoT users’ activities and machine learning services. Our framework considers various contextual dimensions, such as user preferences, locations, IoT device capabilities, and application requirements, to develop a dynamic selection process. By employing context-aware algorithms, our approach seeks to enhance the efficiency, accuracy, and responsiveness of IoT systems. We propose a context change analysis algorithm based on support vector machines (SVM). We develop a contextual bandits algorithm along with skyline queries to achieve optimal mapping between abstract MLaaS services and concrete MLaaS services for quality of service (QoS) attributes. Experiments conducted with real-world and simulated datasets demonstrate the effectiveness of our proposed methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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De-Supply: Deep Reinforcement Learning for Multivariate Supply Chain Optimization

De-Supply: Deep Reinforcement Learning for Multivariate Supp...

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International Conference of Sustainable Development and Smart Built Environments, SDSBE 2024

作者： Mtope, Franck Romuald Fotso Pandit, Diptangshu Joneidy, Sina Rahimian, Farzad School of Computing Engineering and Digital Technologies Teesside University Middlesbrough United Kingdom

ISBN: (纸本)9789819640508

This paper presents De-Supply, a novel approach utilizing deep reinforcement learning for multi-objective supply-chain optimization. It formulates the problem as a Markov Decision Process with complex action and observation spaces, addressing real-world supply chain challenges. De-Supply’s custom policy network enables agents to make informed procurement decisions, resulting in efficient stock management and cost control. Extensive experiments demonstrate its effectiveness, using historical data to outperform baselines and human-level performance. De-Supply offers robust solutions for warehouse businesses and holds promise for broader applications in the industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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RAHBA-Net: Implementation of Rand Augment and Hybrid Bat Algorithm to Optimize Convolutional Neural Network Performance in Classifying Coffee Plant Diseases

RAHBA-Net: Implementation of Rand Augment and Hybrid Bat Alg...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Putri, Yusnita Nuzeren, Prins Naval Kurniawan, Isman School of Electrical Engineering Telkom University Bandung Indonesia School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331513320

Coffee is one of the most popular beverages, with approximately 1.6 billion cups consumed daily worldwide. To meet global demand, global coffee production must increase by at least 50% by 2050. However, coffee plants in every country are susceptible to pests and diseases, posing a significant challenge for nations to improve both the quality and quantity of coffee production. Leaves are the primary source for detecting diseases in coffee plants. Rust, miner, and phoma are the most common diseases that affect coffee plants. Experts often use conventional methods to identify diseases through visual inspection, but these methods are time-consuming, costly, prone to errors, and impractical. Machine learning and image processing have also been widely used in classifying coffee plant diseases, yet there is still limited implementation of image processing, with many still relying on manual tuning. Therefore, this research proposes the implementation of RandAugment and HBA to optimize the performance of CNN architectures in classifying coffee plant diseases. The proposed model achieved the best accuracy of 98.81% in the first model and the best parameters of 0.376 million in the second model, ensuring both efficiency and effectiveness in classifying coffee plant diseases. © 2025 IEEE.

关键词： Convolutional neural networks

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Agri-Ontology: A Novel Ontology Framework for Agricultural Domain 3

Agri-Ontology: A Novel Ontology Framework for Agricultural D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Deepa, R. Jayaraman, Venkatesh Srinivasulu, Senduru Umamageswari, B. Surendran, R. Srm Institute of Science & Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600119 India Srm Institute of Science & Technology School of Computing Department of Computing Technologies Chennai600119 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The agricultural information system deals with massive amounts of data from heterogeneous sources. It helps the farmers gain accurate information by providing better insights. A significant issue in agricultural data is its unstructured form, which can be resolved through ontology - a novel framework known as AgriOntology is proposed to address this issue for the Agrovoc dataset. The data in the unstructured form is initially pre-processed through the Natural Language Processing approach to generate features for ontology construction. The BERT and Jaccard similarity are applied to establish a relationship between the features used to construct the Agri-Ontology. Post ontology construction, the BiGAN framework is used to analyze the performance. The performance of the constructed ontology is observed to have an accuracy of 94.64%, which is higher than the existing models. © 2025 IEEE.

关键词： Data assimilation

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Deep Learning Approach for Identifying Red Lesions in Retinal Fundus Imagery 6th

Deep Learning Approach for Identifying Red Lesions in Retina...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Divyashrree, R. Dhawan, Samarth Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9789819774258

One dangerous side effect of diabetes that affects the eyes is called diabetic retinopathy. It happens as a result of alterations in the retina’s blood vessels, which can cause harm and even blindness. The development of red lesions in the retina is one typical sign of diabetic retinopathy. These red lesions may be a sign of microaneurysms or hemorrhages, which are important characteristics to identify and track as diabetic retinopathy progresses. This process becomes even more demanding in the early stages of the disease where symptoms are less pronounced in the images. Leveraging machine-based learning for medical image analysis, especially deep learning algorithms, has demonstrated its capability to enhance the early diagnosis of diabetic retinopathy, providing a more efficient and effective alternative to traditional manual assessments. The proposed work aims to improve the efficiency of early detection of diabetic retinopathy by utilizing Deep Learning (16-layer CNN) to determine a person’s likelihood of having diabetic retinopathy using a 02-Class model that compiles a variety of datasets, specifically IDRiD dataset. The entire dataset contains 18,590 fundus photographs, in which 3662 are allocated for training, 1928 for validation, and 13,000 for testing. All datasets had a similar distribution of output classes, which is a fundamental property of this type of data. Using the IDRiD dataset, the suggested Red Lesion detection system’s specificity and sensitivity were found to be 98% and 89%, respectively, with an accuracy of 95.32%. Using the MESSIDOR dataset, the Diabetic Retinopathy Severity Classification System’s specificity, sensitivity, and accuracy were found to be 93.8, 92.3, and 94%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ophthalmology

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Dual Averaging for Distributed Unbalanced Optimization with Delayed Information

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IEEE Transactions on Signal and Information Processing over Networks 2025年 11卷 366-377页

作者： Huang, Qing Fan, Yuan Cheng, Songsong Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui Hefei230601 China

In this paper, we study a category of distributed constrained optimization problems where each agent has access to local information, communicates with its neighbors, and cooperatively minimizes the aggregated cost functions over time-varying unbalanced graphs. To address the considered problems, we propose a distributed dual averaging algorithm based on a row-stochastic weighted matrix (DDAR), which improves the robustness of network topology compared to conventional push-sum algorithms. Moreover, we develop a modified version of DDAR with delayed information (DDARD), which considers the delays of both network communication and gradient calculation, enhancing the algorithm's flexibility in communication and iteration. Our analysis demonstrates that the DDAR and DDARD achieve the optimal value at rates of O(N/(1-λ)√T) and O(τ2N/(1-λ}})√T}), respectively. Finally, the theoretical results are confirmed by simulation on a logistic regression problem. © 2015 IEEE.

关键词： Cost functions

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Three-in-One: Robust Enhanced Universal Transferable Anti-Facial Retrieval in Online Social Networks

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4408-4421页

作者： Lv, Yunna Tang, Long Ye, Dengpan Xie, Caiyun Deng, Jiacheng He, Yiheng Shen, Sipeng Wuhan University School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan430072 China

Deep hash-based retrieval techniques are widely used in facial retrieval systems to improve the efficiency of facial matching. However, it also carries the danger of exposing private information. Deep hash models are easily influenced by adversarial examples, which can be leveraged to protect private images from malicious retrieval. The existing adversarial example methods against deep hash models focus on universality and transferability, lacking the research on its robustness in online social networks (OSNs), which leads to their failure in anti-retrieval after post-processing. Therefore, we provide the first in-depth discussion on robustness in universal transferable anti-facial retrieval and propose Three-in-One Adversarial Perturbation (TOAP). Specifically, we construct a local and global Compression Generator (CG) to simulate complex post-processing scenarios, which can be used to mitigate perturbation. Then, we propose robust optimization objectives based on the discovery of the variation patterns of model’s distribution after post-processing, and generate adversarial examples using these objectives and meta-learning. Finally, we iteratively optimize perturbation by alternately generating adversarial examples and fine-tuning the CG, balancing the performance of perturbation while enhancing CG’s ability to mitigate them. Numerous experiments demonstrate that, in addition to its advantages in universality and transferability, TOAP significantly outperforms current state-of-the-art methods in multiple robustness metrics. It further improves universality and transferability by 5% to 28%, and achieves up to about 33% significant improvement in several simulated post-processing scenarios as well as mainstream OSNs, demonstrating that TOAP can effectively protect private images from malicious retrieval in real-world scenarios. © 2005-2012 IEEE.

关键词： Social networking (online)

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Higher-order Method of Moments Analysis of Waveguide with Irregular Cross-sections

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Applied Computational Electromagnetics Society Journal 2025年第2期40卷 89-95页

作者： Ding, Ning Lin, Zhongchao Yin, Lei Zhao, Xunwang Zhang, Yu School of Electronic Engineering Xidian University Xi’an71000 China Shaanxi Key Laboratory of Large-Scale Electromagnetic Computing Xi’an71000 China

– Antennas fed by waveguides with irregular cross-sections are efficiently simulated through the hybridization of the mode-matching (MM) method and the higher-order method of moments (HOMoM). To obtain the waveguide modes required in the MM method, a 2-D finite element method (FEM) is utilized and combined with the hybrid MM/HOMoM method, which enables the proposed method to model irregular waveports. The accuracy and efficiency of the proposed method are demonstrated by simulating log-periodic antennas (LPAs) and an asymmetric single-ridge slot waveguide antenna. © ACES.

关键词： Eigenvalues and eigenfunctions

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DSAF: A Dual-Stage Attention Based Multimodal Fusion Framework for Medical Visual Question Answering

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SN Computer Science 2025年第4期6卷 1-16页

作者： Mukesh, K. Jayaprakash, S.L. Prasanna Kumar, R. Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamilnadu Chennai 601103 India

In the realm of clinical healthcare, medical visual question answering systems emerge as a pivotal innovation that plays a crucial role in clinical decision-making and patient care. They are designed to interpret medical images in response to free-form natural language questions, thereby facilitating a more nuanced and comprehensive understanding of patient diagnostics. This approach seeks to bridge the gap between complex medical imagery and the clinical questions they prompt, offering a novel avenue for enhancing diagnostic accuracy and efficiency. We introduce the Dual Stage Attention-based Fusion framework, which leverages an advanced feature fusion strategy, along with image encoding techniques and state-of-the-art biomedical language models, to process and analyze medical images and textual queries concurrently. By employing a dual-stage attention mechanism, the proposed model enhances feature extraction and integration, enabling a more accurate and refined analysis of both visual and textual data. The model specifically addresses the challenges commonly associated with medical visual question answering, including the intricate nature of medical imagery and the scarcity of high-quality, annotated medical datasets. The proposed multimodal augmentation technique, involving an autoencoder and generative language model, significantly enriches the data for model training which mitigates the data scarcity issue and enhances the model’s ability to generalize across different medical queries. Empirical validation of our model on the VQA-RAD and SLAKE datasets demonstrates its exceptional performance, achieving state-of-the-art accuracies of 80.8% and 86.2% respectively. These results underscore the model’s potential in AI-enhanced medical diagnostics and decision support systems to offer unprecedented diagnostic insights and patient care solutions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data fusion Dual-stage attention Multimodal learning Vision language model Visual reasoning

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