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Hybrid CNN-ViT architecture to exploit spatio-temporal feature for fire recognition trained through transfer learning

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Multimedia Tools and Applications 2025年第8期84卷 4703-4732页

作者： Shahid, Mohammad Wang, Hong-Cyuan Chen, Yung-Yao Hua, Kai-Lung Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Fires are becoming one of the major natural hazards that threaten the ecology, economy, human life and even more worldwide. Therefore, early fire detection systems are crucial to prevent fires from spreading out of control and causing destruction. Based on vision sensors, many fire detection techniques have evolved with the recent surge of curiosity in deep learning, which exploits the spatial features of individual images. However, fire can take different forms, scales, and combustion materials can produce different colors, making accurate fire detection from an image challenging. Small fires captured from long-distance cameras lack salient features, further complicating detection. This paper proposes a hybrid structure that uses attention-enhanced convolutional neural networks and vision transformers (CNN-ViT) to detect fire. The proposed CNN-ViT first pays spatial attention to every frame and then aggregates temporal contextual information from neighboring frames to improve detection performance. Due to the limited availability of training fire datasets, the study employs deep transfer learning for feature extraction using pre-trained CNN. We used various metrics to examine the efficacy of the proposed approach. The results showed that the CNN-ViT method outperformed previous models based on spatial-temporal characteristics by achieving a relative improvement in accuracy and F1 score. The satisfactory results on images contaminated with different intensities of noise confirm the robustness of the approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fires

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J-NECORA: A Framework for Optimal Resource Allocation in Cloud-Edge-Things Continuum for Industrial Applications With Mobile Nodes

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IEEE Internet of Things Journal 2025年第11期12卷 16525-16542页

作者： Pettorali, Marco Righetti, Francesca Vallati, Carlo Das, Sajal K. Anastasi, Giuseppe Univ. of Pisa Dept. of Information Engineering Italy Missouri Univ. of Science and Technology Dept. of Computer Science RollaMO United States

In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of MNs. If the optimal allocation does not exist, J-NECORA provides a best-effort allocation that minimizes the delay. The proposed framework is highly modular and flexible, and can be easily customized to different scenarios. We validated its effectiveness through simulation experiments, focusing on a specific use case. For the considered scenarios, the performance predicted by our framework matches quite closely the simulation results. © 2014 IEEE.

关键词： Cloud platforms

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Improvising Early Detection of Diabetes through Deep Learning on Retinal Fundus images 3

Improvising Early Detection of Diabetes through Deep Learnin...

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

作者： Balaji, Krishna Ganesh, Maniprabha Shankar Murali, Daneesh Pachiappan, Kalaivani Iyyanar, P. Jayakumar, Nithya Kongu Engineering College Dept. of Computer Science Erode India Sona College of Technology Dept. of Information Technology Salem India K.S Rangasamy College of Technology Dept. of Information Technology Tiruchengode India

ISBN: (纸本)9798331509675

Diabetes is a chronic disease whose timely and accurate diagnosis will prevent serious complications from health. This paper explores using iridology principles in a deep learning method to detect diabetes from retinal images in order to streamline the process and automate the procedure. The objective here is to create a non-invasive procedure for early and reliable detection of the disease. Deep learning in medical imaging is the new way to interfere with conventional modes of diagnostics. The retinal imaging is non-invasive and has, over the years, been put to use in medical examinations, and its potential is now absolutely being utilized in the early diagnosis of diabetes. A retinal analysis that is based on principles, integrating them into iridology, concentrating on the patterns and signs portrayed in the iris for possible reflections of a general health condition, is aimed at achieving earlier indications of diabetes in a more effortless and rapid way. Models such as ResNet50, ResNet101, and EfficientNetB7 were precious but actually, it was InceptionV3 that delivered a great accuracy of 97.14%. This result proves the feasibility application of InceptionV3 for diagnosing diabetes in the early stages, thus eventually providing an easy and non-invasive way of moving forward to better health. © 2025 IEEE.

关键词： Ophthalmology

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An Integrated Framework with Enhanced Primitives for Post-Quantum Cryptography: HEDT and ECSIDH for Cloud Data Security and Key Exchange

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第11期49卷 165-176页

作者： Ilias, Shaik Mohammad Sharmila, V. Ceronmani Durga, V. Sathya Dept. of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of Computer Science&Engineering Hindustan Institute of Technology and Science Chennai India

If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must be developed. Current PQC schemes primarily focus on data security or key exchange, and further improvement towards enhanced PQC primitives is required. Our proposal in this research is an innovative paradigm for PQC-focused cloud data security. The proposed HEDT approach achieves encryption and decryption with significantly lower latency (20% improvement) and higher reliability than AES, DES, and RSA, as demonstrated through experimental results. Furthermore, ECSIDH, a hybrid key exchange mechanism combining SIDH and ECDH, improves security strength by 50% while maintaining computational costs within 1.13x of SIDH. Compared to individual key exchange schemes like SIDH, ECSIDH offers superior security as a PQC candidate. These results confirm the robustness and efficiency of the proposed framework in ensuring secure data outsourcing and key exchange in cloud environments. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Quantum computers

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture 5

Multispectral Images and IoT Based Tea Plantation Monitoring...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Kariyawasam, K.P.W.D.V. Kahandagamage, P.N. Fernando, M.R.R. Fernandopulle, J.M. Jayasinghearachchi, Vishan Dissanayaka, Kapila Dept. of Software Engineering Sri Lanka Dept. of Information Technology Sri Lanka Dept. of Computer Science Sri Lanka

ISBN: (纸本)9798331530983

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments. © 2025 IEEE.

关键词： Risk perception

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Comparative Analysis of Machine Learning Methods in Climate Data Extraction and Forecasting

Comparative Analysis of Machine Learning Methods in Climate ...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Khajgiwale, Parth Ajit Naveen, Atul Jethani, Nishtha Shitole, Ajitkumar International Institute of Information Technology Dept. of Computer Engineering Hinjewadi Pune India

ISBN: (纸本)9798331507541

Climate forecasting plays a critical role in understanding and mitigating the impacts of climate change. Advances in machine learning (ML) have significantly enhanced the accuracy of climate projections, particularly when dealing with large and complex datasets derived from regional climate models (RCMs) and global climate models (GCMs). This study investigates the application of various ML techniques for predicting precipitation based on temperature data extracted from climate datasets. We evaluate a range of regression models, including linear models and advanced ensemble methods such as Gradient Boosting and XGBoost, along with deep learning approaches like Convolutional Neural Networks (CNNs). Through a rigorous evaluation of these models, we focus on their performance as measured by Mean Squared Error (MSE) to identify the most effective techniques for capturing the intricate relationships within climate data. By analyzing existing studies and performance metrics, we aim to provide insights into the capabilities and limitations of these ML models in improving climate predictions. Furthermore, this research highlights the need for incorporating additional climate variables and optimizing model configurations to enhance predictive accuracy in climate forecasting. © 2025 IEEE.

关键词： Convolutional neural networks

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Exploring the Potential of MANETs: A Study on Raspberry Pi and UAV Integration 3

Exploring the Potential of MANETs: A Study on Raspberry Pi a...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Archana, B. Mahalakshmi, A. Revathi, K. Ashok Kumar, K. Hema, A.B. Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Dept. of Information Technology SNS College of Engineering Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This research investigates the feasibility of utilizing Mobile Ad Hoc Networks (MANETs) in conjunction with Raspberry Pi-equipped Unmanned Aerial Vehicles (UAV) swarms. The primary objective is to overcome the limitations of traditional communication infrastructures in dynamic and decentralized environments. Specifically, we address the challenge of establishing reliable and efficient communication links within a swarm of UAVs engaged in various applications, including disaster response, military reconnaissance, and environmental monitoring. By integrating Raspberry Pi devices with UAVs, we aim to create a self-organizing communication network that enables seamless data transmission, coordinated swarm operations, and enhanced mission effectiveness. The research explores different synchronization strategies, including master-slave and decentralized approaches, to optimize swarm coordination. Furthermore, we investigate the potential of incorporating advanced technologies such as machine learning, computer vision, and swarm intelligence to further enhance the autonomy, adaptability, and overall performance of the UAV swarm. © 2025 IEEE.

关键词： Mobile ad hoc networks

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Zero-Trust Architecture for Smart City Healthcare Systems 2

Zero-Trust Architecture for Smart City Healthcare Systems

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Alsofyani, Shahd Ishaque, Mohammad Nofal, Mostafa Jedah International College Dept. of Computer Science and Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

This paper introduces a tailored Zero-Trust Architecture (ZTA) for healthcare systems in smart cities, addressing the security challenges posed by interconnected infrastructures. The model enforces continuous access validation through micro-segmentation, identity and access management (IAM) with multi-factor authentication (MFA), and device trust verification, significantly reducing vulnerabilities. AI-powered real-time monitoring and anomaly detection enhance the system's ability to identify and mitigate advanced persistent threats (APTs) and insider risks. Secure communication protocols, end-to-end encryption, and audit logging ensure data protection, regulatory compliance, and forensic capabilities. The proposed ZTA provides a scalable, future-proof solution that strengthens patient data security and safeguards healthcare infrastructure against evolving cyber threats in smart cities. © 2025 IEEE.

关键词： Patient treatment

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Quantum Cryptography and Post-Quantum Security: Safeguarding Cryptographic Protocols Against Quantum Threats 2

Quantum Cryptography and Post-Quantum Security: Safeguarding...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Ishaq, Muhammad Al-Anesi, Bushra Jeddah International College Computer Science & Information Technology Dept. Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

As quantum computing progresses towards practical realization, the security of existing cryptographic protocols, such as RSA and Elliptic Curve Cryptography (ECC), faces unprecedented risks. Quantum algorithms like Shor's Algorithm threaten to undermine the security of these widely used encryption schemes, necessitating the development of quantum-resistant alternatives. This paper explores the vulnerabilities of current cryptographic protocols in the quantum era and investigates quantum-safe cryptographic techniques such as lattice-based, hash-based, and code-based cryptography. Additionally, we delve into Quantum Key Distribution (QKD), a method that leverages the principles of quantum mechanics to securely distribute cryptographic keys, ensuring secure communication even in the presence of quantum adversaries. The integration of QKD with post-quantum cryptographic algorithms is proposed as a robust approach to future-proofing cybersecurity. The paper concludes with a discussion of practical challenges in implementing quantum-safe systems and highlights future research directions to strengthen cryptographic resilience against quantum threats. © 2025 IEEE.

关键词： Qubits

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LeXNet++: Layer-wise eXplainable ResUNet++ framework for segmentation of colorectal polyp cancer images

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Neural Computing and Applications 2025年第1期37卷 213-229页

作者： Das, Surajit Khan, Soumya Suvra Sengupta, Diganta Debashis, De Dept. of Information Technology Meghnad Saha Institute of Technology Behind Urbana Complex Near Ruby General Hospital Anandapur Rd Uchhepota Kolkata700150 India Dept. of Computer Science & Engineering Meghnad Saha Institute of Technology Behind Urbana Complex Near Ruby General Hospital Anandapur Rd Uchhepota Kolkata700150 India Dept. of Computer Science & Engineering Heritage Institute of Technology Chowbaga Road West Bengal Anandapur Kolkata700107 India Dept. of Computer Science & Engineering Maulana Abul Kalam Azad University of Technology West Bengal NH-12 Simhat Haringhata West Bengal Nadia741249 India

Colorectal polyps are benign lesions that develop in the colon and can progress to cancer if left untreated. Clinical observations from medical images are often preferred over computational results due to the lack of trust in the machine learning models, thereby posing serious challenge for the explainability of the results. In order to computationally diagnose colorectal polyps from cancerous images and explain the results, we propose a Layer-wise eXplainable ResUNet++ (LeXNet++) framework for segmentation of the cancerous images, followed by layer-wise explanation of the results. We utilize a publicly accessible dataset that contains of 612 raw images with a resolution of 256×256×3 and an additional 612 clinically annotated and labeled images with a resolution of 256×256×1, which includes the infected region. The LeXNet++ framework comprises of three components—encoder, decoder and the bridge. The encoder and the decoder components each comprise of four layers. Each of the four layers in the encoder and the decoder comprises of 14 and 11 internal sub-layers, respectively. Among the sub-layers of the encoder and the decoder, there are three 3×3 convolutional layers with an additional 3×3 convolution-transpose layer in the decoder. The output of each of the sub-layers has been explained through heatmap generation after each iteration which have been further explained. The encoder and the decoder are connected by the bridge which comprises of three sub-layers. The results obtained from these three sub-layers have also been explained to inculcate trust in the findings. In this study, we have used three models to segment the images, namely UNet, ResUNet, and proposed LeXNet++. LeXNet++ exhibited the best result among the three models in terms of performance;hence, only LeXNet++ was explained layer-wise. Apart from explanation of the results fetched in this study, the performance of the proposed explainable model has been observed to be 2% greater than the existing poly

关键词： Image segmentation

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