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

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Recognition of Cotton Plant Diseases Using Deep Learning Architecture 5

Recognition of Cotton Plant Diseases Using Deep Learning Arc...

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5th international conference on Innovative Trends in Information Technology (ICITIIT)

作者： Haldorai, Anandakumar Lincy, R. Babitha Suriya, M. Balakrishnan, Minu Dhanushkumar, K. S. Sri Eshwar Coll Engn Ctr Future Networks & Digital Twin Coimbatore Tamil Nadu India

ISBN: (纸本)9798350386813;9798350386820

Agriculture is a vital component of any nation's economy, and India is renowned for being an agro-based economy. One of the main objectives in agriculture is to cultivate robust crops that are free from diseases. Cotton has a crucial role in generating money in India. India is the world's leading producer of cotton. Premature leaf abscission or the onset of diseases can have detrimental effects on cotton harvests. However, throughout generations, farmers and agricultural experts have consistently faced numerous problems and chronic issues in the realm of planting, including the prevalence of various cotton diseases. there is a pressing demand in the agricultural information sector for a rapid, efficient, cost-effective, and reliable technique to detect cotton infections. this is crucial since severe cotton diseases can result in a complete failure of grain harvest. Deep learning is utilized to address the challenges of image processing and classification due to its exceptional performance. this technique employs the MobileNet paradigm. Based on the experimental results, the model attains a training accuracy of 0.95 and a validation accuracy of 0.98.

关键词： Xception ResNet ImageNet MobileNet convolutional neural network (CNN) deep CNN

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Research on Network Security Spatial Data Asset Deduplication Technology Based on Neural networks 5

Research on Network Security Spatial Data Asset Deduplicatio...

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5th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Meng, Chunzhi Zeng, Mingfei Meng, Liang Pan, Junbing Information Communication Branch of Guangxi Grid Company Nanning China

ISBN: (纸本)9798350386943

With the rapid development of network technology, network security issues have become increasingly prominent, especially the serious threat posed by data leaks and network attacks to data security. In order to enhance network security and improve the utilization of data assets, this study proposes a network security spatial data asset deduplication technique that integrates Convolutional Neural networks (CNN) and Long Short Term Memory networks (LSTM). this technology significantly improves the accuracy and precision of deduplication through efficient feature extraction and matching comparison using deep learning algorithms. CNN is used to capture local patterns and structural features of data, while LSTM is used to model temporal dependencies of sequential data. the combination of the two significantly improves the efficiency and security of deduplication techniques. Compared with traditional deduplication techniques, this technology not only improves deduplication efficiency, but also enhances the processing ability for complex data types and can resist malicious attacks to a certain extent. the experimental results show that the proposed technology is superior to traditional methods in accuracy and recall, and has practical application value, providing a new direction for future technological development. © 2024 IEEE.

关键词： Spatio-temporal data

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Experimental Evaluation of a Secured Privacy Preservation Scheme using IP Traceback Logic in Wireless sensor networks 5

Experimental Evaluation of a Secured Privacy Preservation Sc...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Shifani, S. Agnes Metilda, M.I. Mary Rajkumar, G. Vinoth Muppudathi Sutha, S. Maheshwari, S. Mary, A. Antony Saveetha University Saveetha School of Engineering Department of Ece Chennai India Sathyabama Institute of Science and Technology Department of Mathematics Chennai India J P College of Engineering Department of Ece Tenkasi India Sardar Raja College of Engineering Department of Ece Alangulam India Srm Institute of Science and Technology Division of Mathematics School of Sciences Trichy India

ISBN: (纸本)9798331504403

this research work proposes a secured privacy preservation scheme for wireless sensor networks (WSNs) that integrates multiple advanced techniques to ensure robust data confidentiality, integrity, and traceability. the methodology encompasses secure network initialization, symmetric key cryptography for data encryption and decryption, probabilistic packet marking for IP traceback, and secure routing protocols. Additionally, the scheme employs privacy-preserving data aggregation methods and an intrusion detection system (IDS) enhanced by machine learning for real-time threat detection and mitigation. the combination of these techniques addresses the critical security challenges in WSNs, making the model particularly suitable for sensitive applications such as military operations, healthcare, and critical infrastructure monitoring. A key feature of the proposed model is its superior performance, with encryption and decryption times of 1 ms and 0.9 ms respectively, which are significantly better than those of existing models. this comprehensive approach ensures that the WSN remains secure and reliable, even in the face of evolving cybersecurity threats. © 2024 IEEE.

关键词： Differential privacy

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A Logical Appliance of Deep Learning Methodology for an Intelligent Parking System for Smart Cities Using Internet of things Association 5

A Logical Appliance of Deep Learning Methodology for an Inte...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Ramkumar, G. Saveetha University Department of Ece Chennai India

ISBN: (纸本)9798350379945

In this work, a novel Intelligent Parking System (IPS) for Smart Cities to improve large-scale urban parking management using Internet of things (IoT) and Deep Learning techniques is proposed. the system is based on several sensors, cameras, and RFID readers that come together to provide an accurate real-time measurement of the occupation of parking spaces. Using the ImageNet database and utilizing a three-layered deep neural network with Convolutional Neural networks (CNNs) for image processing, and Recurrent Neural networks (RNNs) for predictive analytics, it delivers an accuracy rate of 95%, consuming only 300 milliseconds to the completion. the data is sent to a single cloud server using NodeMCU modules that update and back up the data regularly. A mobile application serves as the interface to the system for users who can check available spaces from anywhere, book space and pay for parking. the integration advanced AI methods not only optimizes the use of parking space and minimizes traffic congestion, but also helps to building smart urban environments sustainably. Future possibilities such as deeper integration with controlled vehicles, greater interoperability with other smart city components as well as dynamic pricing and customized user experiences have the capability to take urban mobility to the next level. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on fusion algorithm based for multimodal sensor 5

Research on fusion algorithm based for multimodal sensor

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5th international conference on Computer Communication and Network Security, CCNS 2024

作者： Zhao, Kaicheng South China Normal University Guangzhou China

ISBN: (纸本)9781510681842

GPS/IMU multi-sensor fusion algorithm is of great significance in the auto drive system. GPS has high precision, but the sampling frequency is low and prone to failure;the IMU sensor has a high sampling frequency and is relatively stable, but it is prone to error accumulation. therefore, the two have good complementarity, and integrating them can obtain a navigation solution with better performance than a single navigation system. In recent years, many algorithms based on Kalman filters (KF) have emerged, and some scholars have proposed using artificial intelligence to fuse GPS/IMU data. this article aims to effectively fuse multimodal sensors and deeply analyzes the advantages and disadvantages of existing algorithms based on Kalman filters, machine learning algorithms, and neural networks. © 2024 SPIE.

关键词： Kalman filters

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Optimization of Outlier Nodes of DBSCAN using PSO in Zone-Based Wireless sensor networks 5

Optimization of Outlier Nodes of DBSCAN using PSO in Zone-Ba...

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5th IEEE international conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Jasmine Beulah, G. Vinothina, V. Kristu Jayanti College Department of Computer Science Bengaluru India

ISBN: (纸本)9781665493604

In real world applications, large amount of raw sensed data is collected by deploying wireless sensor networks. Amidst a lot of challenges faced from WSNs, the key challenge is to extract high level of intelligent knowledge from the sensed data. Outlier nodes play a very important role in finding rare patterns in domains such as web purchase analysis, intrusion detection and fraud analysis. the clusters in zone - based Wireless sensor networks, is challenging with the limitation of the area. the clusters are of different shapes based on the densities of the nodes. DBSCAN (Density Based Spatial Clustering of Applications with Noise) can detect the clusters of arbitrary shapes and identifies the outlier nodes effectively. the shape of Clusters is not limited with the density of the clusters. Reorganizing the spatial structures of the clusters to maintain the shape of clusters for efficiency is still challenging with the nodes of low power and low computation capabilities. In this paper, DBSCAN in ZSEP-E is improved with Particle Swarm Optimization by reorganizing the outlier nodes in the optimized clusters. Simulations conducted with this proposed model, provided better network performance in the zones with more energy utilization and network lifetime. Simulation results reveal that the proposed approach yields increased network lifetime, throughput and energy utilization than the existing protocol. © 2023 IEEE.

关键词： Energy utilization

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Applications and Prospects of Neural networks in Fault Detection: A Visualization Analysis Based on CiteSpace 5

Applications and Prospects of Neural Networks in Fault Detec...

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5th IEEE international conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Wu, Kechen Lu, Xingcheng Chen, Yixuan Nanjing University of Finance and Economics Nanjing China

ISBN: (纸本)9798350355925

With the rapid development of industrial automation and intelligence, fault detection technology has become key to ensuring system stability and safety. Neural networks, as outstanding data processing and pattern recognition tools, have demonstrated extensive application potential in the field of fault detection. this study utilizes CiteSpace visualization software to conduct a thorough analysis of 2,211 papers on neural networks and fault detection in the Web of Science database from 2020 to 2024, aiming to reveal the research dynamics and development trends in this field. the analysis covered indicators such as authors, research institutions and research direction identifying major research hotspots including process monitoring, rolling element bearing monitoring, structural health monitoring, gas turbine performance assessment, and power system stability. the results indicate that advanced deep learning techniques, the integrated application of digital twins, and the development of the industrial Internet of things are driving fault detection towards greater intelligence. Future research is expected to focus more on algorithm optimization, model interpretability, and the enhancement of real-time monitoring technologies, while also emphasizing the importance of security and privacy protection. © 2024 IEEE.

关键词： Structural health monitoring

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the Trend of Published Research for Predicting Bankruptcy Via Machine Learning: A Bibliometric Analysis 5

The Trend of Published Research for Predicting Bankruptcy Vi...

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5th international conference on Data Analytics for Business and Industry, ICDABI 2024

作者： Khudhur, Ahmed Abdulaziz Al-Shammari, Minwir University of Bahrain College of Business Administration Sakhir Bahrain

ISBN: (纸本)9798350368710

Bibliometric analysis is a valuable technique for quantitative research on academic output to analyze pre-existing materials and address research patterns in a particular field. this paper aims to investigate machine learning approaches in predicting business failure. the findings suggest that author collaboration remains lacking despite a recent rise in publications, particularly internationally. Furthermore, We note that the subject of bankruptcy prediction has not placed enough emphasis on some machine-learning techniques. For example, Convolutional Neural networks (CNNs) excel at processing images rather than numbers. In addition to (CNN), Long-Short Term Memory (LSTM) networks have also faced limitations in their application within this field due to several factors, including the requirement for large datasets and the necessity to investigate the 'black box' of these models. these obstacles present opportunities for researchers to improve existing work in this area. this study has implications for theory and practice. In the first place, it gives academics knowledge about the intellectual framework and theoretical development of this topic, which is helpful for future research. Secondly, the study illuminates underutilized machine learning techniques for bankruptcy prediction, which hold significant value for corporate firm managers and policymakers regarding management and decision-making. © 2024 IEEE.

关键词： Convolutional neural networks

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Handwriting Anomalies and Learning Disabilities through Recurrent Neural networks and Geometric Pattern Analysis 5

Handwriting Anomalies and Learning Disabilities through Recu...

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5th international conference on Electrical, Communication and Computer Engineering, ICECCE 2024

作者： Alevizos, Vasileios Edralin, Sabrina Simasiku, Akebu Malliarou, Dimitra Messinis, Antonis Papakostas, George A. Xu, Clark Yue, Zongliang Karolinska Institutet Solna Sweden University of Illinois Urbana-ChampaignIL United States Zambia University Ndola Zambia IntelliSolutions Athens Greece HEDNO SA Athens Greece Democritus University of Thrace MLV Research Group Department of Informatics Thrace Greece Mayo Clinic Artificial Intelligence & Discovery Minnesota United States Auburn University Harrison College of Pharmacy AL United States

ISBN: (纸本)9798331529437

Dyslexia and dysgraphia are learning disabilities that profoundly impact reading, writing, and language processing capabilities. Dyslexia primarily affects reading, manifesting as difficulties in word recognition and phonological processing, where individuals struggle to connect sounds with their corre-sponding letters. Dysgraphia, on the other hand, affects writing skills, resulting in difficulties with letter formation, spacing, and alignment. the coexistence of dyslexia and dysgraphia complicates diagnosis, requiring a nuanced approach capable of adapting to these complexities while accurately identifying and differentiating between the disorders. this study utilizes advanced geometrical patterns and recurrent neural networks (RNN) to identify handwriting anomalies indicative of dyslexia and dysgraphia. Handwriting is first standardized, followed by feature extraction that focuses on baseline deviations, letter connectivity, stroke thickness, and other anomalies. these features are then fed into an RNN-based autoencoder to identify irregularities. Initial results demonstrate the ability of this RNN model to achieve state-of-art performance on combined dyslexia and dysgraphia detection, while showing the challenges associated with complex pattern adaptation of deep-learning to a diverse corpus of about 33,000 writing samples. © 2024 IEEE.

关键词： Recurrent neural networks

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Automating Potato Leaf Disease Detection with Lightning-Fast CNNs: Precision using PyTorch Lightning 5

Automating Potato Leaf Disease Detection with Lightning-Fast...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kaur, Arpanpreet Gupta, Sheifali Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331504403

Potato diseases, such as late blight, early blight, potato scabs, and blackleg, can significantly reduce crop yield and quality. Early detection of these diseases is crucial for effective farm management and maximizing productivity. Traditional methods of disease identification, relying on visual inspection, are time-consuming and prone to human error. this research explores the application of Convolutional Neural networks (CNNs) for automated potato disease detection. By focusing on early and late blight, as well as healthy potato leaves, the study leverages the 'Potato Disease Pytorch Lightning CNN' dataset from Kaggle. the proposed method utilizes PyTorch Lightning to build the CNN model, incorporating pre-processing techniques and a comprehensive dataset. the CNN is trained and validated using the Adam optimizer, resulting in a significant improvement in disease classification accuracy, reaching approximately 98.6%. Future research aims to further enhance the performance of these models and explore their practical applications in agricultural settings. By enabling early disease detection, these models can contribute to more effective disease control and improved crop health monitoring systems. © 2024 IEEE.

关键词： Convolutional neural networks

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