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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8045 条记录，以下是71-80 订阅

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Dynamic-Feature-Based Object Tracking Using Real-Time Image processing 5th

Dynamic-Feature-Based Object Tracking Using Real-Time Image ...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Rengarajan Bhatt, Rahul Prince, P. Blessed Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

this research explores the utility of today's real-time picture processing for dynamic-characteristic-primarily based object monitoring. Notably, this painting proposes a novel tracking method that combines an actual-time, place-primarily based convolutional neural community (R-CNN) with monitoring with the detection (TbD) technique. the proposed approach is for contemporary R-CNNs to constantly come across transferring objects in video frames and then modern-day the TbD technique to generate accurate item tracking information. the proposed system is evaluated on two benchmark datasets and compared to numerous tracking methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Utilizing Support Vector Machines for Signal processing in Telecommunications 5th

Utilizing Support Vector Machines for Signal Processing in T...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Mehta, Deepak Arya, Rakesh Prasad, P.S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

SVMs had been effectively applied to various signal processing tasks in Telecommunications, along with goal detection and category in cognitive radio networks, channel estimation, exploiting spatial variety for multipath wireless conversation channels, interference cancellation, and era of feature vectors for sign type. In goal detection and category in Wi-Fi conversation networks, SVMs may be used to come across and classify specific types of wireless signals with an excessive diploma of accuracy. the input characteristic vectors are comprised of the signal’s spectrum and temporal features, and they are used for the education of the SVM classifier. In channel estimation, SVMs may be used to estimate the unknown channel parameters in Wi-Fi conversation channels with a low but suitable suggest-squared-blunders charge. that is viable via exploiting the diversity of more than one received signal replicas and the usage of the predicted channel parameters to improve signal reception. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wi-Fi

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TLMMChain: Blockchain-Based Hierarchical Security Management Scheme for three-Level Mobility Model in Next-Generation networks 5th

TLMMChain: Blockchain-Based Hierarchical Security Management...

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5th international Ethical Hacking conference, eHaCON 2024

作者： Chakraborty, Mohuya Palit, Sudip Kumar Cybersecurity Centre of Excellence Institute of Engineering and Management University of Engineering and Management Kolkata India Department of Computer Science and Engineering Institute of Engineering and Management University of Engineering and Management Kolkata India

ISBN: (纸本)9789819784561

this paper focuses on the security aspects of the three-level Mobility Model (TLMM) in the next-generation networks. Prior research has indicated that hierarchical architectures outperform other network topologies in terms of handoff delay, location update, and signaling overhead in mobile networks. the optimality test of the time-bandwidth product proved the supremacy of the three-tiered architecture of mobile networks in literature. However, earlier works of TLMM have not addressed the security issues. Designing a security protocol for TLMM is therefore the goal of this research. Our solution to the current research gap is a lightweight blockchain-based security protocol that can be used for local, regional, and global mobility. the work’s uniqueness resides in its proposal of a protocol to establish a peer-to-peer network where users can communicate with the data stored in a secure distributed ledger, reducing the amount of duplicate information that users must transfer. the implementation of blockchain technology is done using Hyperledger Composer and Hyperledger Fabric. Burrows-Abadi–Needham (BAN) logic has been used in the formal security verification of the suggested system to provide mutual authentication among the participants. Furthermore, the suggested model performs noticeably better than the other current systems by providing thirteen distinct security characteristics, requiring less processing time (about 8 ms) and communication overhead (around 2832 bits). © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile telecommunication systems

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IoT-Based Convolutional Neural networks in a Farm Pest Detection Using Transfer Learning 5th

IoT-Based Convolutional Neural Networks in a Farm Pest Detec...

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5th international conference on Computing Science, Communication and Security

作者： Jani, Keyurbhai A. Chaubey, Nirbhay Kumar Panchal, Esan Tripathi, Pramod Yagnik, Shruti Gujarat Technol Univ Comp IT Engn Ahmadabad 382424 Gujarat India Ganpat Univ Comp Sci Mahesana 384012 Gujarat India Govt Polytech Informat Technol Gandhinagar 382027 Gujarat India Indus Univ Comp Engn Ahmadabad 382115 Gujarat India

ISBN: (纸本)9783031751691;9783031751707

In this study explores agriculture pest detection using transfer learning with IoT devices, evaluating VGG16, VGG19, Inception, and Xception CNN architectures with the agripest dataset. VGG16 and VGG19 show effective learning with consistent accuracy improvements. Inception V3 exhibits strong training but with variability in validation metrics, while Xception demonstrates robust performance and strong generalization to new data. the integrated system utilizes cameras, sensors, and drones for real-time image processing through a gateway and cloud server with a customized agripest dataset. Transfer learning generates a deployable.h5 file for pest identification. the generated custom model deployed on a gateway or server to classifies pests, alerting farmers through SMS, dashboard, or mobile app notifications. this synergy between machine learning and IoT offers rapid and precise pest detection in agriculture.

关键词： IoT deep learning CNN pest detection cloud farm gateway

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Enhancing Multi-crop Disease Prediction Using Convolutional Neural networks: A Global Perspective 5th

Enhancing Multi-crop Disease Prediction Using Convolutional ...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Garigipati, Nagababu Nivrithi, Bathini ShreshtaReddy, Dachireddy Eesha, Mummaneni Nikitha, Patlavath G. Narayanamma Institute of Technology and Science Telangana Hyderabad India

ISBN: (纸本)9789819780426

Given the recent rate of population expansion, it is anticipated that by 2050, global crop productivity will need to double. Getting this production result is significantly hampered by pests and diseases. It is essential to offer efficient methods for automatically diagnosing, classifying, and foretelling diseases and pests in agricultural crops. Machine learning techniques can be applied to the data being worked on to extract information and relationships in order to automate this process. Deep learning techniques are employed in the study, which focuses on the categorization, detection, and pest and disease prediction tasks in the agricultural industry. We seek to advance precision agriculture and smart farming by supporting the development of techniques that will allow farmers to use less pesticides and chemicals while protecting and improving the quality and yield of their crops. We’ll employ image classification method, in which the user uploads a photo of the diseased plant or crop, and the system uses deep learning techniques to identify the disease type. And this disease identification will offer advice on how to treat or avoid that crop. the goal is to create a uniform approach for all features and deliver results for all crops throughout the majority of regions in India that are as accurate as possible. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional Neural networks Deep learning Image processing Plant diseases Prediction

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Performance Analysis of Word Recognition System Using Tensor Flow 5th

Performance Analysis of Word Recognition System Using Tensor...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Kumar, Bittu Sri Ram Rahul, P. Karthikeya, G. Sai Nithin Vishnu, T.V. Srikanth, M. Shivani, Peta Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad500075 India

ISBN: (纸本)9783031770746

Word recognition stands as a pivotal element within natural language processing (NLP) and machine learning, crucial for diverse applications ranging from automatic speech recognition to optical character recognition and from text analysis to machine translation. this paper explores TensorFlow, a robust open-source machine learning framework, to tackle the complexities associated with word recognition. the research introduces innovative methods employing deep learning and neural networks to enhance the precision and efficiency of word recognition tasks. the evolution of word-to-text recognition technologies has witnessed transformative strides in recent years, impacting various industries and applications. this study scrutinizes the performance of a CNN-based word recognition system, evaluating accuracy and time variations with changes in hyperparameters, including the number of hidden layers, epoch size, activation function, and training-testing datasets. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Deep neural networks

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Autonomous and Adaptive Communications Systems in Mobile Ad-Hoc networks 5th

Autonomous and Adaptive Communications Systems in Mobile Ad-...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： J, Bhuvana Yadav, Dhyan Chandra Goyal, Manish Kumar Sharma, Himanshu Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Luckow India Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

Protocols, enabling communication, manipulation, and coordination among cell nodes. those techniques can efficiently use the community topology, connectivity, to-be-had assets, and consumer requirements to alter communication parameters and path selection for record transmissions robotically. Self-sufficient/adaptive systems also manage user requests and sensor measurements in real-time to dynamically regulate verbal exchange protocols and network configurations for higher verbal exchange performance. Furthermore, those structures also provide capabilities, including self-configuration, self-organization, fault-tolerance, and communique reliability. Various mechanisms may permit autonomous and adaptive verbal exchange in MANETs, including geo-vicinity-based total routing, location-established network coding, allotted garages, energy-conscious communication protocols, and allotted coordination techniques. Additionally, simulated annealing and particle swarm optimization can be used to optimize conversation and routing protocols. Self-sufficient and adaptive communications systems in cellular ad hoc networks have become a need in today’s international, as cell gadgets are used extra often and for more complex tasks. these structures are essential for preserving a clean float of verbal exchange without a centralized infrastructure or constant direction topologies. Self-reliant communications systems offer sturdy, self-configuring, self-healing, and self-organizing communication networks. Such structures are designed to constantly alter parameters primarily based on facts amassed from community resources and a person’s desired preference. Adaptive systems, on the other hand, dynamically create a superior topology by way of considering to-be-had assets. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Simulated annealing

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A Machine Learning (ML)-Inspired Method for Intrusion Detection in IoT Devices networks 5th

A Machine Learning (ML)-Inspired Method for Intrusion Detect...

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5th international conference on Computing Science, Communication and Security

作者： Venkatesh, Veeramuthu Raj, Pethuru Nedium, Roshitha Edara, Jahnavi Reddy, Kalluru Amarnath Anushiadevi, R. SASTRA Deemed Univ Sch Comp Thanjavur 613401 Tamilnadu India Reliance Jio Cloud Serv JCS Bangalore 560025 Karnataka India

ISBN: (纸本)9783031751691;9783031751707

the Internet of things (IoT) and Machine Learning (ML) represent dynamic research fields with significant growth. IoT applications have gained popularity among technology researchers and developers, and the increasing deployment of IoT devices in critical infrastructures improves efficiency and reliability and raises concerns about cyber-attacks. Enterprises have effectively implemented IoT services, enabling automated production through remote and intelligent control. However, this adoption has concurrently introduced novel security vulnerabilities. Addressing the security and privacy challenges in IoT, especially considering energy limitations and scalability issues, remains a critical focus in computer security. this paper aims to prevent multiple attacks targeting sensor nodes' data manipulation. It encompasses a range of threats, such as sensing layer attacks, malfunctions, tamper attacks, false data injection, base node and clone attacks. the proposed approach involves a threat model and a pairing algorithm that utilizes machine learning to associate each sensor node with its corresponding node. To accomplish the goal, the performance of the proposed solution is compared with various machine learning models, including Decision Tree, Linear Regression, k-nearest neighbors (KNN), Random Forest, and AdaBoost. the evaluation utilized two openly accessible real-world datasets, with metrics such as accuracy in attack detection, training time, and testing time being considered. By incorporating machine learning algorithms to improve attack detection in IoT environments, the proposed approach represents a significant advancement in enhancing security and privacy. the results highlight the importance of adopting advanced techniques to safeguard IoT systems from potential threats. the proposed method achieved an impressive 96.75% accuracy rate in detecting attacks, surpassing existing solutions with nearly twice the speed in training and testing times. As the IoT landsc

关键词： sensor nodes Security attacks Intrusion Detection System Critical infrastructure Machine learning Internet of things (IoT)

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A small-sample fault diagnosis approach for RV reducer with RP-SST parallel CNN-SVM model 5

A small-sample fault diagnosis approach for RV reducer with ...

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5th international conference on Mechanical Engineering and Materials, ICMEM 2024

作者： Pan, Jiahao Liu, Chuanmeng Jin, Nan Peng, Yulong Qiu, Guangqi School of Mechanical and Electrical Engineering Jiangxi University of Science and Technology 1958 Hakka Avenue Jiangxi Ganzhou China

ISBN: (数字)9781510688971

ISBN: (纸本)9781510688964

For addressing the problem of low accuracy in the available diagnostic methods due to the insufficient fault-sample, this article proposes a small-sample fault diagnosis method with Recurrence plot (RP), Synchrosqueezed transform (SST) and parallel convolutional neural network (CNN) and support vector machine (SVM). this method first utilizes RP and SST techniques to extract the multimodal features into image processing. then, the parallel CNN is used for multimodal feature fusion, enabling it to utilize more fault information within a limited number of samples. Finally, SVM is used for fault classification. the fault experiments for RV reducer were performed to verify the effectiveness of the proposed fault diagnosis method. the complementarity of RP-SST in multimodal feature extraction was verified for solving the problem of insufficient feature extraction due to sample size limitations effectively. Further, the SOTA fault diagnosis model showed that the proposed method achieved a fault diagnosis accuracy of 96.26% in small sample situations, significantly better than CNN-BIGRU, CNN-LSTM, and Resnet model. © 2025 SPIE.

关键词： Convolutional neural networks

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Plant Leaves Disease Detection Using Integrated Convolutional Neural networks: A Case Study on Potato 5th

Plant Leaves Disease Detection Using Integrated Convolutiona...

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5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Kumar, Anuj Upreti, Abha Pandey, Vandana School of Computing Graphic Era Hill University Bareilly Road Uttarakhand Haldwani263139 India

ISBN: (纸本)9789819773701

Agriculture is very important for India, making up a big part (17–18%) of the country’s economy. Many people rely on farming for their livelihood. India has a lot of farmland, even more than the U.S. and China. But keeping crops safe from pests and diseases is crucial for getting good amount of produce. the research focuses on using technology like image processing and machine learning to help with this. Specifically, we looked at potato plants and their leaves. the model uses convolutional neural network (CNN) and got really good results. Our model could tell with 99.78% accuracy if a potato leaf was healthy or had diseases like early blight or late blight. the cool thing about our research is that it’s not just for the lab—it can be super helpful for farmers with big fields. the technology we used can quickly check large areas of farmland for any issues. this means we can find and deal with problems early, keeping crops healthy. Using technology like image processing, machine learning, and deep learning makes it easier and more accurate to identify diseases. this is a big deal for farmers with large farms, as it helps them keep an eye on everything without spending too much time. Our research is like giving a boost to India’s farming strength. the high accuracy of the model and its ability to measure the affected leaf area are big steps forward in making farming more precise. As India stays on top in global farming, adopting these advanced technologies is crucial for making sure the future of agriculture is strong and sustainable. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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