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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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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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Assessing Landslide Susceptibility in Active Tectonic Zones using Convolutional Neural Networks (CNN)

Assessing Landslide Susceptibility in Active Tectonic Zones ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhinakaran, K. Iyyappan, V. Divya, E. Pavithra, S. Lakshmi, R Poornima Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331510022

Assessing landslide susceptibility is vital in mitigating natural disasters, particularly in zones with active tectonics. This paper will discuss a novel approach based on a CNN architecture in a U -Net designed to assess landslide susceptibility in these zones of active tectonism. The model thus takes advantage of spatial data sources from remote sensing and GIS and multi-coupled geospatial factors such as slope, elevation, rainfall, and tectonic activity in a multivariate framework for predicting landslide-prone areas. The U-Net CNN architecture is used as it captures the local and global spatial patterns and results in very precise susceptibility maps. The selected historical data about landslides was then fed into the trained and validated model to predict potential landslide occurrence zones, indicating high accuracy in susceptibility mapping. The CNN-based U-Net approach suggests a higher order of precision and spatial resolution than the conventional machine learning approach. This work portrays the capabilities of deep learning approaches in developing landslide risk assessment with much-improved precision. It thus offers an essential tool for disaster risk management along with urban planning for tectonically active regions. © 2024 IEEE.

关键词： Convolutional neural networks

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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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Effective Machine Learning Model for Disease Recognition and Categorization of Mango Leaves

Effective Machine Learning Model for Disease Recognition and...

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

作者： Kumar, Sandeep Gupta, Bhupesh Grover, Richa Chhabra, Mohit Computer Science & Engineering Dept. Ambala Mullana India Panipat Institute Of Engineering & Technology Computer Science & Engineering Dept. India

ISBN: (纸本)9798350306446

Maintaining the health and production of mango farms depends on the early detection of illnesses in mango trees. Despite the vital role agriculture plays, it remains an industry receiving limited attention from the machine-learning community. A noteworthy challenge is the lack of standardized and openly accessible agricultural datasets, impeding researchers from fully leveraging the potential of advanced computational prediction tools. The study focuses on employing deep learning and image processing techniques for diagnosing prevalent mango leaf diseases in India. The goal is to enhance both the quality and quantity of agricultural production by implementing effective disease control measures upon identifying plant diseases. The automation of plant disease detection proves particularly beneficial, reducing the monitoring efforts required by large farms. Rapid identification of leaf diseases is crucial, given that leaves serve as a plant's primary source of nutrition. Notably, none of the seven different mango leaf illnesses that have been observed in India have ever been the subject of earlier research. In this study, we provide a lightweight Resnet50 model that can reliably classify healthy mango leaves and the seven discovered illnesses on mango leaves. The performance of our proposed lightweight Resnet-50 model is evaluated against many pre-trained models, such as Exception, Resnet101, and VGG16. The findings reveal that the lightweight Resnet-50 model achieves the highest testing accuracy, reaching an impressive 95%. This underscores the model's effectiveness in automating the identification of mango leaf diseases, offering a promising solution for improving agricultural practices in India. © 2024 IEEE.

关键词： Diseases

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Spatial Dynamics for Identification of Individuals through Gait and Other Locomotion Activities

Spatial Dynamics for Identification of Individuals through G...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Anusha, R. Sanshi, Shridhar Bapuji Institute of Engineering and Technology Dept. of Computer Science and Engineering Davanagere India National Institute of Technology Dept. of Computer Science and Engineering Karnataka Surathkal India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379525

Gait, the pattern of walking, has been extensively studied and various methods have been developed to use it as a biometric for individual recognition. Despite this, the potential to identify individuals through running videos has not been thoroughly explored. The paper introduces a novel method that expands the feature-based approach for identifying individuals based on their running style. This work focuses on extracting the mutual information and location specific metric from the key gait poses of subjects in the testing and training datasets. Later on, the assignment of a testing sample to the training sample is accomplished using the proposed classification method. The experiments are conducted on KTH, OU-ISIR A, and Weizmann database. The efficiency of this method is demonstrated by the obtained experimental results. © 2024 IEEE.

关键词： Feature extraction

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An Overview on IoT in Electric Vehicles: Efficiency and Sustainability 2

An Overview on IoT in Electric Vehicles: Efficiency and Sust...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Gupta, Shashank Sounkhla, Indu Dubey, Ranjeet Kumar Yadav, Mithilesh Kumar Singh Yadav, Sandeep NIT Dept. of Computer Science and Engineering Jalandhar India KIPM Dept. of Computer Science and Engineering Gorakhpur India Buddha Institute of Technology Dept. of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Internal combustion engine (ICE) vehicles are very polluting and release high nitrogen oxides, carcinogens, and soot into the environment. ICE vehicles are the best choice in this era, but replacing ICE vehicles with electric vehicles (EVs) and automated vehicle control is a potential transition process. The most important turning point in the history of the automobile occurred with the entry of EVs into the vehicle market. The rise of the Internet of Things (IoT) paradigm in electric vehicles (EVs) has changed the way we look at large-scale transportation by making it intelligent and more sustainable. Recently, with the advent of connectivity through IoT platforms, IoT in EVs has improved performance and reduced environmental impact: from any on-off switch and adoption of IoT through our smartphones placed on the charging base. We can implement smart charging solutions for better energy consumption management, reduction of grid load, and battery life enhancement. IoT allows electric vehicles to communicate directly with charging stations and even the power grid, providing a better way to create balanced transportation networks. But implementing IoT in electric vehicles also raises some issues, including data security and user privacy. With all of these issues, there is huge potential in IoT usage with electric vehicles to help produce cleaner transportation and create a more sustainable future. © 2024 IEEE.

关键词： Electric Vehicles Internal Combustion engine Internet of Things

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A review on 3D image reconstruction on specific and generic objects

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Materials Today: Proceedings 2023年 80卷 2400-2405页

作者： Shalma, H. Selvaraj, P. Dept. of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Information Technology SRM Institute of Science and Technology Kattankulathur Chennai India

With the emergence of various techniques involved in deep learning the researchers of computer vision tends to focus on the strategies such as object recognition and segmentation of image. This has inclined to accomplish the deep learning techniques in 3D reconstruction of both specific and generic objects. As the space for reconstruction of 3D images either in single or multi view has envisioned the researchers to concentrate on the available technologies used for reconstruction. With the available built in methods and technologies in deep learning, the performance of the proposed methods were reviewed and analyzed using several parameters. As the remaking of 2D images is still in the beginning stage, it is important to study the 3D shape representations, various network architecture, methodologies and approaches behind 3D reconstruction. In this work a review of deep learning methods for single or multiple RGB images of specific and generic object 3D reconstruction was done. Several methods and their importance were also discussed along with the challenges encountered and with further research directions. This paper critically analyzes the various 3D Shaped Representations, 3D Data Network Architectures, dept. Estimation methods, Multi View Representations and the Data Representation Techniques. © 2021

关键词： Object recognition

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Gauging Deep Learning Archetypal Effectiveness in Haematological Reclamation

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SN computer science 2024年第7期5卷 963页

作者： Pradeep kumar, B.P. Ravikumar, J. Dept. of Computer Science and Design Atria Institute of Technology VTU Karnataka Bangalore India Dept. of Computer Science and Engineering Atria Institute of Technology VTU Karnataka Bangalore India

This paper project an system for reclamation of medical images in an effective way for the extraction and retrieving of data from large archives is critical. This study aims to improve haematological image reclamation by combining visual-based image reclamation (VBIR) approaches with the GLCM algorithm, specifically for characteristics related to the prostate gland. While previous research has focused on computer-aided methods for Gleason grading, our technique focuses on early-stage feature reclamation across 14 paired glands. The experimental findings, including Bicubic Interpolation The resolution Enrichment with a standard deviation of 12.37, kurtosis of 122.83, and skewness of 2.92, demonstrate improved reclamation performance. Our suggested methodology uses a handmade learning approach that includes critical components such as GLCM contrast and correlation. This extensive feature extraction step includes descriptors for morphology, texture, fractals, and contextual information, which are then subjected to in-dept. statistical analysis to identify significant features. Using an RNN, our technique improves performance somewhat but significantly when compared to alternatives, with time required for execution offering insights into the intricate interaction of stages and the effect of sample variability. These findings not only encourage more research, but also help to advance standard reviews in haematological analysis of images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gland reclamation Gray-level co-occurrence matrix (GLCM) Haematological Haematological image Prostate gland

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Utilising Convolutional Neural Networks for the Automated Detection of Diabetic Retinopathy

Utilising Convolutional Neural Networks for the Automated De...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Lakshmi, R Poornima Shopika, S. Yaazhini, A. Hemalatha, M. Saraswathi, V. Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331510022

Diabetic retinopathy has emerged as one of the leading causes of eye diseases among people suffering from long-term diabetes. Indeed, it raises the risk of being blinded without proper detection and treatment. Conventional detection of retinal fundus images by an ophthalmologist is time-consuming and prone to mistakes owing to human intervention. This is particularly a cause for alarm in the wake of the growing incidence of diabetes globally. The need for automated, accurate detection systems for early diagnosis of diabetic retinopathy has never been more relevant. In this work, we develop a deep learning model based on CNN for classifying four stages of diabetic retinopathy, from no DR to proliferative DR. We tested the model in a public dataset to extract features from retinal images and to screen for abnormalities automatically. Our CNN model achieved 98.5% classification and detection accuracy, thus indicating the ability to make a real difference in the early detection and treatment plan, thereby preserving the vision of many diabetic patients. Practical value The present research is very relevant to clinical practice and, therefore, practically useful because of its relevance to healthcare technology. Using accuracy and loss function metrics, the proposed model performs well compared to the latest techniques in DR detection. An approach based on CNNs is expected to ease much of the workload that healthcare professionals bear in diagnosis and improve the precision, potentially resulting in a very high cutback in vision loss in diabetic patients with better patient outcomes. Our study has highlighted the practical utility of the CNN model, which has improved patient outcomes. DR is a serious condition affecting the eyes, which, in case of untimely detection and untreated in diabetic patients, can cause loss of vision. For centuries, the conventional diagnosis for this disease has been through the manual inspection of retinal fundus images taken through a camera

关键词： Clinical research

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