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

作者： Rakesh, Nitin Thirumurugan, R. Mohankumar, N. Kasinathan, Prabakaran Prasad, S. N. Sheela Evangelin Srinivasan, C. Maharashtra Nagpur India Adhi College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kanchipuram India Symbiosis Institute of Technology Nagpur Campus Pune India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Krishna Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

This research introduces an innovative method for the real-time identification of bacterial blight in rice crops via remote sensing and deep learning (DL) technologies. Bacterial blight represents a significant threat to rice production, requiring quick detection for efficient control. To address this challenge, remote sensing data from drones and satellites is analyzed to detect visual signs connected to the diseases. A convolutional neural network (CNN) model is used to classify and identify sick rice plants using high-resolution images. The results demonstrate that the model attained an accurate rate of 98.2%, significantly enhancing the speed and precision of illness identification relative to conventional approaches. The combination of advanced DL algorithms with remote sensing technologies enables quick responses and improves sustainable agriculture practices. It illustrates the capability of using technology for crop monitoring, enhancing food security, and optimizing resource management in agriculture. © 2025 IEEE.

关键词： Convolutional neural networks

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

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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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A Car-Following Model Considering the Next-Nearest-Neighbor Interaction of Electronic Throttle Information 1

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5th International Symposium for Intelligent Transportation and Smart City, ITASC 2022

作者： Kang, Yirong Yang, Shuhong School of Transportation Engineering Guizhou Institute of Technology Guiyang550003 China School of Computer Science and Communication Guangxi University of Science and Technology Liuzhou545006 China

ISBN: (数字)9789819922529

ISBN: (纸本)9789819922512

Aiming at the problem that the nearest preceding car of the considered vehicle may leave the current lane and cause the latter to lose track reference, based on the classical FVD model, this paper design a more effective feedback control signal by considering the ET opening angle difference information from the next-nearest-neighbor interaction at the previous moment, and propose a new CAV cooperative driving following model. The effect of this new consideration upon the stability of CAV traffic flow is examined through linear stability analysis. A modified Korteweg-de Vries (mKdV) equation was derived via nonlinear analysis to describe the propagating behavior of traffic density wave near the critical point. Good agreement between the simulation and the analytical results shows that taking the next-nearest-neighbor interaction of electronic throttle historical information into account leads to the stabilization of traffic systems, and thus can efficiently suppress the emergence of traffic jamming. The results can be expected to provide a theoretical reference for designing more effective traffic congestion control strategies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Linear stability analysis

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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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Towards Understanding Third-party Library Dependency in C/C++ Ecosystem 7

Towards Understanding Third-party Library Dependency in C/C+...

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37th IEEE/ACM International Conference on Automated software engineering, ASE 2022

作者： Tang, Wei Xu, Zhengzi Liu, Chengwei Wu, Jiahui Yang, Shouguo Li, Yi Luo, Ping Liu, Yang School of Software Tsinghua University Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450396240

Third-party libraries (TPLs) are frequently reused in software to reduce development cost and the time to market. However, external library dependencies may introduce vulnerabilities into host applications. The issue of library dependency has received considerable critical attention. Many package managers, such as Maven, Pip, and NPM, are proposed to manage TPLs. Moreover, a significant amount of effort has been put into studying dependencies in language ecosystems like Java, Python, and JavaScript except C/C++. Due to the lack of a unified package manager for C/C++, existing research has only few understanding of TPL dependencies in the C/C++ ecosystem, especially at large scale. Towards understanding TPL dependencies in the C/C++ ecosystem, we collect existing TPL databases, package management tools, and dependency detection tools, summarize the dependency patterns of C/C++ projects, and construct a comprehensive and precise C/C++ dependency detector. Using our detector, we extract dependencies from a large-scale database containing 24K C/C++ repositories from GitHub. Based on the extracted dependencies, we provide the results and findings of an empirical study, which aims at understanding the characteristics of the TPL dependencies. We further discuss the implications to manage dependency for C/C++ and the future research directions for software engineering researchers and developers in fields of library development, software composition analysis, and C/C++ package manager. © 2022 ACM.

关键词： Ecosystems

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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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Visual Static Hand Gesture Recognition for ASL using Multi-Feature Fusion Technique 3

Visual Static Hand Gesture Recognition for ASL using Multi-F...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mishra, Sravani Rani Sahoo, Jaya Prakash Sahoo, Suraj Prakash Vellore Institute of Technology School of Computer Science and Engineering Chennai India Biju Patnaik University of Technology Centre for UG & PG Studies Department of ECE Odisha India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

Hand gestures are the nonverbal communication medium for deaf and dumb people. These gesture symbols give independent information for sign language to understand the messages. Among numerous sign languages, American Sign Language (ASL) is the most widely used for communication and research since it's gesture postures are expressed using a single hand only. Various novel and innovative methods are used for hand gesture identification and recognition. However, accurate recognition of hand gestures in ASL is still challenging due to the similarity among the gesture classes. A systematic approach based on multi-feature fusion is presented in this research for accurate recognition of human hand gestures. The proposed technique involves the following steps in its process pipeline: a) segmenting the hand's region of interest from the image;b) extracting features;and c) utilising an SVM classffier to recognise motions. This work applies several texture-based features, such as histogram of oriented gradient (HOG), local binary pattern (LBP), and Gabor feature (GB) to recognize the hand gestures on the above challenges. The combination of the above features is studied and used to enhance gesture recognition performance. The proposed technique is tested and validated using several publicly available hand gesture datasets. Various parameter and ablation studies prove that the proposed HOG-GB feature fusion technique achieved better recognition performance and outperformed earlier reported gesture recognition methods. © 2024 IEEE.

关键词： Support vector machines

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Semantic-Guided Voting with Segmentation for Visual Localization 6

Semantic-Guided Voting with Segmentation for Visual Localiza...

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6th International Conference on software engineering and computer science, CSECS 2023

作者： Wang, Chao Xie, Dong Cao, Qingjun Guo, Rui Zhu, Feng Nanjing Research Institute of Electronic Engineering Nanjing China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350306378

Scene coordinate regression has significantly enhanced visual localization by segmenting landmark patches and aggregating votes for landmark points within these patches. However, this approach neglects dynamic semantic objects and patch-level semantic inconsistencies, leading to inaccuracies in the voting process. In this paper, we present a novel visual localization framework guided by semantics. Our framework introduces two critical modules: the Pruning Semantically Dynamic Object module, which leverages semantic segmentation to reduce interference from dynamic semantic objects, and the Eliminating Semantic Label Inconsistencies module, aimed at mitigating patch-level semantic inconsistencies. Comprehensive tests on 7-Scenes and Cambridge datasets exhibit the superior performance of our approach. Across both datasets, our method outperforms the scene coordinate regression technique, validating the effectiveness of our proposed framework. © 2023 IEEE.

关键词： Semantics

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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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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