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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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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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A secure double spectrum auction scheme

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Digital Communications and Networks 2024年第5期10卷 1415-1427页

作者： Jiaqi Wang Ning Lu Ziyang Gong Wenbo Shi Chang Choi School of Computer Science and Engineering Northeastern UniversityShenyang110004China School of Computer Science and Technology Xidian UniversityXi©anChina Dept.of Computer Engineering Gachon University1342Seongnam-daeroSujeong-guSeongnam-si13120G School of Computer and Communication Engineering Northeastern UniversityQinhuangdao066004China

With the arrival of the 5G era,wireless communication technologies and services are rapidly exhausting the limited spectrum *** auctions came into being,which can effectively utilize spectrum *** of the complexity of the electronic spectrum auction network environment,the security of spectrum auction can not be *** scholars focus on researching the security of the single-sided auctions,while ignoring the practical scenario of a secure double spectrum auction where participants are composed of multiple sellers and *** begin to design the secure double spectrum auction mechanisms,in which two semi-honest agents are introduced to finish the spectrum auction *** these two agents may collude with each other or be bribed by buyers and sellers,which may create security risks,therefore,a secure double spectrum auction is proposed in this *** traditional secure double spectrum auctions,the spectrum auction server with Software Guard Extensions(SGX)component is used in this paper,which is an Ethereum blockchain platform that performs spectrum auctions.A secure double spectrum protocol is also designed,using SGX technology and cryptographic tools such as Paillier cryptosystem,stealth address technology and one-time ring signatures to well protect the private information of spectrum *** addition,the smart contracts provided by the Ethereum blockchain platform are executed to assist offline verification,and to verify important spectrum auction information to ensure the fairness and impartiality of spectrum ***,security analysis and performance evaluation of our protocol are discussed.

关键词： Secure double spectrum auction SGX technology Privacy information Ethereum platform Verification

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YTBlend: YouTube Video Recommendations System

YTBlend: YouTube Video Recommendations System

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Singh, Kulvinder Sharma, Tanu Yadav, Anup Lal Rani, Jaya Chandigarh University Dept Of Computer Science & Engineering India Panipat Institute Of Engineering & Technology Dept Of Computer Science & Engineering India

ISBN: (纸本)9798350306446

In the dynamic era of online education, the pursuit of a personalized and effective learning experience is paramount. In this research, a sophisticated smart system for learning video recommendations is proposed at the intersection of multimodal data mining and data synthesis in online education. The approach aims to enhance personalized and effective learning experiences by transcending the limitations of traditional recommendation systems. Innovative data synthesis techniques are employed to expand the dataset and improve the adaptability of the system, integrating diverse data sources including text, images, and videos. The methodology involves a comprehensive analysis of learning materials using cutting-edge technologies like natural language processing, computer vision, and video content analysis to deepen the understanding of learner-content interactions. Furthermore, the challenge of hashtag recommendation in short videos is addressed by leveraging their multimodal nature, combining sentiment and content features from image, audio, and text modalities to enhance the accuracy and rationality of hashtag recommendations. This research contributes to redefining the landscape of intelligent learning video recommendations, offering a dynamic platform designed to continuously adapt to the evolving needs and preferences of learners. © 2024 IEEE.

关键词： Data mining

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Moth flame optimization algorithm for efficient feature selection and hybrid classification technique for intrusion detection system

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Multimedia Tools and Applications 2025年 1-21页

作者： Keerthanarani, U. Selvakumar, V. Singaravelan, S. Raja, S. Edwin Thenmozhi, K. Shunmugam, D. Arun Dept. of Computer Science and Engineering P.S.R Engineering College Sivakasi India Dept.Of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai Avadi India Dept. Of Computer Science and Engineering Coimbatore Institute of Engineering and Technology Coimbatore India Dept. Of Electronics and Communication Engineering P.S.R Engineering College Sivakasi India

The rapid expansion of the Internet of Things (IoT) brings numerous benefits but further presents fresh difficulties, especially in terms of security. The distributed and interconnected nature of IoT devices makes them susceptible to various security threats. An Intrusion Detection System (IDS) plays a crucial role in safeguarding IoT networks by monitoring, detecting, and responding to potential security incidents. The Internet of Things (IoT) connects billions of devices, sensors, and systems, transforming industries and everyday life. However, this extensive interconnectivity also introduces significant security risks, making Intrusion Detection Systems (IDS) a critical component for safeguarding IoT environments. Feature selection is a crucial aspect of building effective Intrusion Detection Systems (IDS). Selecting relevant features not only helps in achieving better classification accuracy but also contributes to reducing false alarm rates. In the proposed system a moth flame optimizer is utilized to select the efficient features from the dataset, overall accuracy of over 97%. Further it is classified employing Random Forest and J48 classifier which attains high classification accuracy and low false alarm rate. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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Optimizing relay node placement and routing in WBANs using free search krill herd and harmony search algorithm

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Multimedia Tools and Applications 2025年第10期84卷 7257-7295页

作者： Patra, Sushree Chinmayee Samal, Tusharkanta Kabat, Manas Ranjan Mishra, Manas Ranjan Barik, Ram Chandra Dept. of Computer Sc. & Engineering Veer Surendra Sai University of Technology Odisha Sambalpur India Dept. of Computer Sc. & Engineering DRIEMS University Odisha Cuttack India Dept. of Computer Sc. & Engineering C.V. Raman Global University Odisha Bhubaneswar India

Wireless Body Area Network (WBAN) plays an important role in healthcare applications like in medical, non-medical and psychological sector for remote monitoring of health conditions of patients. Since the biosensors in WBAN are energy constrained in nature, the architecture of energy efficient and cost sensitive WBAN has attracted the researchers during the last decade. In this paper, a mathematical model for optimization of relay node placement in WBAN using Free Search Krill Herd (FSKH) algorithm because of its diversified phases of searching has been proposed. Furthermore, a routing protocol for energy efficient and reliable for data transmission over WBAN by using Harmony Search (HS) algorithm has been designed. The proposed algorithm has less 11.02%, 6.8% energy consumption in standing position, 5.6% and 11.02% in siting position and 12.5% and 18.75% siting on chair than its counterparts EAWD and Modified EAWD. Throughput has increased significantly by approximately 4–5 times as compare to iM-SIMPLE and *** proposed methodology has also shown greater improvement as its first node is completely exhausted after 28,545 rounds of data transmission while its existing counterpart’s iM-SIMPLE and Co-LAEEBA first node dies at 5100 rounds and 6800 rounds of transmission respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wireless local area networks (WLAN)

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Brain Tumour Detection using MRI Images 1

Brain Tumour Detection using MRI Images

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jayashree Mallick, Ahmed Faraz Das, Ashutosh Singh, Abhiraj Shivaprakash, T. Preethi Salian, K. Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering India Vijaya Vittala Institute of Technology Dept of Computer Science and Engineering Bangalore India Nmamit Dept of Information Science and Engineering Nitte Udupi India

ISBN: (纸本)9798331529246

An essential diagnostic technique for brain tumors, Magnetic Resonance Imaging (MRI) enables early detection and improved patient outcomes. However, manual interpretation of MRI scans can be time-consuming and subject to human error. Deep learning has revolutionized medical image analysis by providing automated and accurate classifications, significantly enhancing diagnostic processes. This study leverages the pre-trained Convolutional Neural Network (CNN) model Xception to classify MRI brain data, demonstrating its effectiveness in identifying brain tumors. The model was trained and validated using a diverse dataset, achieving a high test accuracy of 98.78% and training accuracy of 99.96%. These findings highlight the potential of the Xception model in supporting clinical decision-making and reducing diagnostic workloads. The study contributes to the field by showcasing Xception's implementation, dataset preparation, model design, training, and evaluation. Furthermore, it emphasizes the importance of using deep learning techniques for reliable and efficient brain tumor classification, setting the groundwork for future advancements in automated diagnostic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Predicting Mental illness (Depression) with the help of NLP Transformers 2

Predicting Mental illness (Depression) with the help of NLP ...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Kokane, Vedant Abhyankar, Ajit Shrirao, Nikhil Khadkikar, Prajakta Pune Institute Of Computer Technology Dept. Of Computer Engineering Pune India

ISBN: (纸本)9798350381665

In today's rapidly developing world, mental health is very important because it directly impacts the human thought process, productivity, relationships, etc. Being one of the most important aspects of a human's life, it is often neglected. Poor mental health can lead to anxiety, hopelessness, struggling to manage emotions, a lack of concentration, and loneliness. A mentally ill person can also have suicidal thoughts. In today's world, almost every person uses social media to share their lives and thoughts. With social media serving as a ubiquitous platform for self-expression and interaction, leveraging artificial intelligence (AI) and natural language processing (NLP) techniques becomes feasible for detecting mental health statuses based on users' social media posts. In this research paper, we proposed a methodology using NLP Transformers to study human behaviour as well as symptoms of depression. We analysed the status of mental health by examining the text of posts posted by different people on Twitter and Reddit. In our proposed methodology, we applied 4 different NLP Transformers (BERT, XLNet, RoBERTa, and DistilBERT) Transformers on two different datasets. Our proposed model concluded that DistilBert was the most accurate Transformer among the four with accuracy with 91% accuracy on twitter dataset and 84% accuracy on reddit dataset. Our proposed methodology will be effective in detecting mental illness in a person. © 2024 IEEE.

关键词： Diseases

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Predicting COPD Severity Using Machine Learning and GOLD Criteria 3

Predicting COPD Severity Using Machine Learning and GOLD Cri...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Kinikar, Atharva Chandwani, Maanav Rane, Tushar Pune Institute of Computer Technology Dept. Information Technology Pune India Pune Institute of Computer Technology Dept. Computer Engineering Pune India

ISBN: (纸本)9798350381931

Breathing problems that get worse over time are a symptom of chronic obstructive pulmonary disease, or COPD. It is a catch-all phrase for a number of conditions including asthma, bronchitis, and emphysema. It is defined by a progressive constriction of the airways and an early aging of the lungs. Experts have attested to the fact that early identification helps lessen the symptoms of COPD. Thus, using GOLD criteria, patients' COPD severity can be predicted using contemporary methods like machine learning algorithms. The proposed prediction models predict severity of COPD of the patient on a scale of 1 to 4 as prescribed by the GOLD system. Our model was trained to utilize a derived feature set and machine learning classifiers, such as SVC, Random Forest Classifier, XGBoost classifier, Naive Bayes, and Hard Voting Ensemble, with the best possible parameter choice to achieve a commendable accuracy of 97.6%. The global COPD epidemic will be lessened with the use of this method and medical professionals' experience. © 2024 IEEE.

关键词： Machine learning

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Subtitle Synchronization Using Whisper ASR Model 4

Subtitle Synchronization Using Whisper ASR Model

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Thara, P. Azneed, Mohammed Sanas, Muhammad Jithu Prakash, P.M. Naik, Harsh P. Divya, B. Dept of Computer Science and Engineering India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India

ISBN: (纸本)9798331508845

This paper introduces a novel approach to subtitle synchronization using the Whisper ASR (Automatic Speech Recognition) model from OpenAI. The primary aim of this research is to achieve accurate and robust synchronization of subtitles with audio content, even in the presence of uniform or non-uniform delays. The project leverages the capabilities of the Whisper ASR model, which is trained on diverse audio datasets and offers multitasking functionalities including multilingual speech recognition, speech translation, and language identification. Key features of the project include Whisper ASR integration, SRT modification, and accuracy enhancement. The system utilizes FFmpeg for audio extraction and preprocessing, followed by speech recognition using the Whisper ASR model. Preprocessing techniques are applied to enhance the precision of generated timestamps, ensuring precise synchronization. The methodology also involves timestamp adjustment based on text comparison between input and transcribed SRT files, resulting in accurately synchronized subtitles. The project offers a user-friendly interface for input acquisition and interaction, guiding users through the synchronization process. While promising, the system has limitations such as the lack of action detection, character name prediction, and challenges with repetitive word handling. Nonetheless, the Whisper ASR-based Subtitle Synchronization project presents a reliable solution for enhancing subtitle accuracy and accessibility in various video content scenarios. © 2024 IEEE.

关键词： Speech recognition

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