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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Design and implementation of vehicle type recognition system based on language large model

Design and implementation of vehicle type recognition system...

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Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Xiang Liu Yikuan Zhang Computer Science Department Software Engineering Institute of GuangZhou Guangzhou China Department of Electronics Software Engineering Institute of GuangZhou Guangzhou China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

Using large language model to generate vehicle type recognition algorithm can reduce the burden of developers and realize the rapid development of projects. In this paper, LangChain large model interface provided by Baidu Qianfan large-scale platform is used to realize the auxiliary development of vehicle type recognition. With the support of large model, the training and testing model of vehicle type recognition is constructed by using deep learning method, and the fine-grained vehicle image classification and recognition system is realized. At present, the ResNet50 Enhance MEAL2 model is used for classification recognition, supporting 1748 types of vehicle classification and recognition. In the Car-Dataset of vehicle data, the Accuracy of the training set is about 95.9%, and the Accuracy of the test set is about 84.3%. The backbone network can support Googlenet. ResNet [18, 34, 50], Inception_v3, Mobilenet_v2 and other common models. The use of language large model in this project improves the efficiency of development.

关键词： Training Image recognition Accuracy Large language models Web and internet services Neural networks Internet of Things Testing Residual neural networks Image classification

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Enhancing Environmental Monitoring through Object Detection in Quantum Networks 5

Enhancing Environmental Monitoring through Object Detection ...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Nirmala, P. Kumaresan, S.Joshua Senthilkumar, C. Kongkham, Dinokumar Beenarani, B.B. Saveetha University Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences -SIMATS Tamil Nadu Chennai India Saveetha University Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences-SIMATS Chennai India

ISBN: (纸本)9798350383522

Environmental monitoring is essential for comprehending and addressing the consequences of climate change and pollution on our world. Conventional approaches to environmental monitoring and item detection have demonstrated efficacy, but frequently encounter drawbacks in terms of precision, immediate data collection, and resource utilization. This study investigates the potential of quantum networks to transform environmental monitoring by improving item detection skills. We performed a series of experiments to assess the effectiveness of our Quantum Network methodology in comparison to two established methodologies, Existing Method A and Existing Method B. The quantum network exhibited a notable enhancement in the precision of object detection, with an astounding accuracy of 95.6% in real-time data acquisition for air quality, temperature, and humidity measurements. The performance of this method exceeded the accuracies produced by the existing methods, which were 87.4% and 92.1% respectively. Although current approaches have been extensively used and provide established solutions for environmental monitoring, our quantum network demonstrated distinct benefits. It enhanced the precision of object detection, allowing for more exact gathering of data on crucial environmental characteristics. Furthermore, our methodology facilitated data collection in real time, rendering it exceptionally well-suited for applications that necessitate prompt adaptation to fluctuating environmental circumstances. Nevertheless, it is crucial to recognize that adopting quantum technology brings about intricacy and resource demands. The promise of the quantum network is contingent upon additional refinement and optimization to properly tackle these difficulties. © 2024 IEEE.

关键词： deep learning environmental monitoring object detection quantum networks

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Prediction and detection of nutrition deficiency using machine learning

Prediction and detection of nutrition deficiency using machi...

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2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Kumar Mishra, Amit Tripathi, Neha Gupta, Ashish Upadhyay, Deepak Kumar Pandey, Neeraj Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Dept. of Computer Science and Engineering Dehradun India Tula's Institute Dept. of Computer Science and Engineering Dehradun India Dit University School of Computing Dehradun India

ISBN: (纸本)9781665474924

The plant producers have a hard time identifying nutritional inadequacies in their crops. The capacity to recognize these comprehensive nutritional deficiencies could help regulate crops properly. Using image processing, Convolutional Neural Network (CNN), the researchers were able to categorize and identify complete nutritional deficiencies in various cultivars. The prototypes would provide prescribed plant fertilizers once nutrient insufficiency was recognized. Iron (Fe), magnesium (Mg), potassium (K), nitrogen (N), calcium (Ca) and complete nutrition were examined. For classifying the image processing techniques were used to turn the images into grayscale & binary data. Using identification and prediction, CNN predicts complete nutritional deficiencies in the plant. CNN high accuracy of detection and diagnosis of nutrient deficits in different cultivars, according to the results compared with Artificial Neural Network (ANN) and DenseNet-121. The design has been tested and the results demonstrate a better way to classify and diagnose complete nutritional deficits in different cultivars. © 2023 IEEE.

关键词： Convolution

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Correction to: Optimized auxiliary physics neural network based multi‑target detection for edge‑cloud in smart internet of things

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Peer-to-Peer Networking and Applications 2025年第3期18卷 1-2页

作者： Prabha, B. Yaramadhi, Mallikarjun Sharma, Abhishek Padmanaban, K. School of Computer science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India Department of Computer Science Engineering SVIIT ShriVaishnavVidyapeethVishwavidyalaya Indore India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram India

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computer Vision And Deep Learning For Fish Classification In Underwater Habitats

Computer Vision And Deep Learning For Fish Classification In...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Mandal, Amitabha Prakash, M. Brindha, T.V. Boyapati, Prasanthi Dr. B. C. Roy Engineering College Computer Science and Engineering West Bengal Durgapur India Vellore Institute of Technology Computing Science and Engineering Vellore India Sri Venkateshwara College of Engineering and Technology Computer Science and Engineering Tiruvallur India Srm University Computer Science and Engineering School of Engineering and Sciences Andhra Pradesh India

ISBN: (纸本)9798350313413

Remote underwater picture and video capture is used by marine biologists to monitor different fish species in their natural environments. This aids in their comprehension and forecasting of the responses of fish to fishing pressure, habitat degradation, and climate change. Having this knowledge is crucial for creating environmentally friendly, sustainable fisheries for human use. Humans, on the other hand, find it difficult and time-consuming to extract useful information from massive amounts of collected videos. Deep learning (DL) appears to have an issue with this. With the help of DL, marine biologists can rapidly and effectively parse massive amounts of film, uncovering specialized information that is not accessible via manual monitoring techniques. We present a two-step deep learning technique in this study that can recognize and classify temperate fishes without the use of pre-filtering. Every fish in a picture must first be identified, regardless of species or gender. For this, we employ the You Only Look Once (YOLO) object detection technique. The classification of each fish in the image is done in the second stage using a squeeze-and-excitation (SE)-designed convolutional neural network (CNN). Despite the short training sample size of temperate fishes, we use transfer learning to improve classification accuracy. For this, the fish classifier was trained using a public dataset, and the object detection model was trained using ImageNet. Both models were then updated with pertinent temperate fishes. Weights are always added both before and after a workout. The CNN-SE model performed admirably, with a 96.22% accuracy. Extensive comparative research revealed that the CNN-SE model outperformed more recent approaches. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Deep learning Monitoring Squeeze-and-Excitation (SE) You Only Look Once (YOLO)

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Unified hybrid quantum classical neural network framework for detecting distributed denial of service and Android mobile malware attacks

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EPJ Quantum Technology 2025年第1期12卷 1-29页

作者： S, Sridevi B, Indira S, Geetha S, Balachandran Kar, Gorkem Kharbanda, Shangirne School of Computer Science and Engineering Vellore Institute of Technology Chennai India Central Library Hindustan Institute of Technology and Science Chennai India Computer Science & Cybersecurity University of Central Missouri Warrensburg USA Master of Engg Gina Cody School of Engineering and Computer Science Concordia Institute for Information Systems Engineering Montreal Canada

The rise of advanced networking and mobile technologies has improved flexibility in software Defined Networking (SDN) management and mobile ecosystems but it has also introduced vulnerabilities like Distributed Denial of Service (DDoS) attacks and Android malware. In this research, we propose a Hybrid Quantum Classical Neural Network (HQCNN) framework that operates with a Dressed Quantum Circuit (DQC) to achieve efficient detection and classification of threats. The input pipeline of the HQCNN integrates Wavelet Transforms based feature pre-processing, Convolutional Neural Network based feature extraction, Linear Discriminant Analysis (LDA) for dimensionality reduction, and quantum layers for enhanced classification with less computational complexity. Experiments were conducted on the SDN DDoS Attack Dataset and the CCCS-CIC-AndMal2020 Static Dataset. Two different model variants were devised for binary and multiclass classification problems addressing various cybersecurity issues. The binary HQCNN model for SDN-based DDoS detection was implemented on AWS Braket’s real Quantum Processing Unit (QPU), achieving 99.86% accuracy, 99.85% precision, 100% recall, and a 99.88% F1-score, thereby outperforming the classical Convolutional Neural Network (CNN). The multiclass HQCNN, on the other hand, attains accuracy of 93.56%, 94.38%, and 95.13% on the 15-class, 14-class, and 12-class versions of CCCS-CIC-AndMal2020 Static, respectively, hence outperforms all existing methods. These results show that HQCNN is efficient, scalable, and very much applicable in cybersecurity, validating its real-world use effectiveness applicability in threat detection.

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Real-World Applications for PIFuHD: In 3D Digitization of Humans 4

Real-World Applications for PIFuHD: In 3D Digitization of Hu...

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4th International Conference on Communication, Computing and Industry 6.0, C216 2023

作者： Agrawal, Arya Gunavathi, C. Balabhadruni, Saket Singh, Harsh School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu Vellore India School of Electronics Engineering Vellore Institute of Technology Vellore Tamil Nadu Vellore India

ISBN: (纸本)9798350327328

This paper presents applications of Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization (PIFuHD), a novel approach for high-resolution 3D human digitization from single images. The objective is to reconstruct detailed object images. The approach employed in PIFuHD utilizes a multi-level pixel-aligned implicit function, enabling comprehensive analysis of both global context and intricate local details. This methodology aims to achieve detailed and high-resolution 3D reconstructions of clothed individuals from single images, eliminating the need for supplementary post-processing or extra contextual information. The key findings are that the multi-level approach is needed for accurate and precise reconstructions. This paper aims to explore future work based on the recommendation of the authors for PIFuHD, to explore PIFu's versatility extends to computer animation, virtual try-on, medical imaging, and numerous applications in forensic science, Augmented Reality (AR), Virtual Reality (VR), Human-computer Interaction (HCI), offering precision and customization potential across various industries. © 2023 IEEE.

关键词： Image reconstruction

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'User AR' - Leveraging AI Based Augmented Reality for User Manuals 15

'User AR' - Leveraging AI Based Augmented Reality for User M...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Bhati, Vipin Subburaj, Maheswari Sivaraman, Arun Kumar Sultana, Ajmery University of Technology and Applied Sciences College of Computing and Information Sciences Suhar Oman Vellore Institute of Technology School of Computer Science and Engineering Chennai India Photon Interactive Inc Digital Engineering Solution Center TorontoON Canada Algoma University School of Computer Science and Technology BramptonON Canada

ISBN: (纸本)9798350370249

The objective of this research is to propose an AI based novel augmented reality (AR) software to assist users in operating a wide range of technical and non-technical devices. The project's aim is to make this AR application, which provides detailed explanations of all the device's functions, more widely used than traditional user manuals. Augmented reality, which overlays digital information onto the real world, can be employed to develop an AR-based user manual for products or devices. Mobile applications that use a camera to recognize a product or device and display dynamic graphics, such as 3 D models, animations, or text, are one example of AR user manuals. These augmented reality user manuals enable consumers to quickly and easily learn how to operate, assemble, repair, or troubleshoot their purchased products. Experiments show that the proposed algorithm was able to recognize the object accurately with a percentage of 95.2%. © 2024 IEEE.

关键词： Artificial Intelligence Augmented reality (AR) Object Recognition

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Integrative Remote Sensing Approaches Using Generative Adversarial Networks for Urban Heat Island Analysis and Mitigation

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 681-698页

作者： Sundar, Ganapathy Patchaiammal, Pitchaimuthu Venkateshwarulu, Balaji Vijayan Kumar, Thangavel Pradeesh Rajamannar, Kesavamoorthy Tripathi, Rajesh Kumar Department of Computer Applications Sindhi College Chennai India Department of Information Science and Engineering HKBK College of Engineering VTU Belagum Bengaluru Nagawara India VIT School of Agricultural Innovations and Advanced Learning (VAIAL) VIT Vellore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India

The phenomenon of urban heat islands (UHI) presents a critical challenge for urban sustainability, exacerbating local temperatures, increasing energy demands, and impairing public health. Traditional methods for addressing UHI are often resource-intensive and slow. This study introduces a novel approach, utilizing a Hybrid CycleGAN-SVM (HCGS) model that leverages the synergy of Generative Adversarial Networks (GANs) and Support Vector Machines (SVMs) to efficiently analyze and mitigate UHI effects through high-resolution satellite imagery and temperature data. The model incorporates Enhanced Vision Transformers (EViTs) for superior feature extraction, adept at capturing intricate spatial and spectral urban patterns. The CycleGAN component of the model generates high-quality synthetic imagery, enhancing the dataset and addressing class imbalances, thereby bolstering the SVM classifier’s ability to precisely pinpoint heat-prone urban areas. Implemented in Google Colab, the HCGS model demonstrated exceptional performance, achieving a classification accuracy of 0.98. This indicates its potential as an effective tool for urban heat mitigation, offering actionable insights for urban planning and policy-making. By integrating advanced machine learning techniques with remote sensing data, the HCGS model paves the way for innovative climate adaptation strategies, fostering more sustainable and resilient urban environments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Enhanced Vision Transformers EViTs GANs Generative Adversarial Networks HCGS model Hybrid CycleGAN-SVM Satellite imagery Support Vector Machines SVM UHI effect Urban heat island

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