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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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An Adaptive Reversible Data Hiding Scheme using Image processing and Deep Learning Techniques 5

An Adaptive Reversible Data Hiding Scheme using Image Proces...

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5th international conference on Sustainable Communication networks and Application, ICSCNA 2024

作者： Allwyn, Philip Hemalatha, J. Sampathkumar, Nithyanantham Ramaraj, Kottaimalai Swain, Gandharba Sekar, M. AAA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Sivakasi India AAA College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Sivakasi India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhrapradesh Vaddeswaram India AAA College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Sivakasi India

ISBN: (纸本)9798331530013

Image steganography is the process of hiding data from digital photos without changing how they look on the outside. It functions as a covert mode of communication, giving access to confidential data without raising the suspicions of opponents or unauthorized parties. It provides an extra degree of secrecy and security for digital communications. Prediction-error expansion (PEE) is an extensively used method in the type of reversible data hiding (RDH) that hiding data with intact image quality in the prediction errors. High-accuracy prediction algorithms are typically essential for a PEE-based RDH implementation to be successful. Current PEE-based Reversible Data Hiding (RDH) systems frequently use a single forecasting approach, typically with worldwide optimization, but pay little attention to the local neighborhoods' pixel distribution features. this study aims to analyze the most recent RDH techniques and investigate image processing and deep learning (DL) techniques for reversible data hiding (DH) schemes. RDH is a difficult topic in general which has potential uses in today's digital world. Reversible data hiding techniques properly restore the cover medium while safely transferring secret data (SD). the goal of RDH techniques is to achieve both large capacity and tunable quality. Even though reversible data hiding has been the subject of indepth research recently, there hasn't yet been a thorough literature analysis to identify research gaps and potential future approaches. © 2024 IEEE.

关键词： Data Hiding Data Security Image processing Pixel Sorting Reversible Data Hiding Steganography

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Investigating the Advantages and Limitations of Math Modeling for Wireless sensor networks 5th

Investigating the Advantages and Limitations of Math Modelin...

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

作者： Zaidi, Taskeen Ansari, Tabish Sindhuri, Bonda Prema Alam, Intekhab Karnataka Bangalore India Department of Civil 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 Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

this paper investigates the blessings and obstacles of math modeling for wireless sensor networks (WSNs). WSNs may be immediately evaluated on a performance basis or analyzed to address protection and reliability troubles. However, mathematical models ignore the sensors’ heterogeneity and the networks’ distributed nature. Similarly, the mission is to validate the results to ensure actual performance. Other WSNs that need attention and mathematical modeling include their management algorithms, optimizations, MAC protocols, power management, and *** comparison, sure advantages exist in math modeling, understanding complex problems, coming across new solutions and situations, and the capacity to expect effects. this paper aims to review the advantages and obstacles of math modeling for WSNs and concludes by providing hints on how WSNs can gain from mathematical modeling. Math modeling holds the splendid capacity for wireless sensor networks (WSNs) because it comprehensively clarifies complex actual-global troubles. Math modeling offers an established framework to conceptualize, examine, and endorse answers for diverse WSN deployments. Its relative predictability and simulation accuracy can assist in perceiving hidden insights and generate reliable outcomes. However, it has some critical boundaries. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： modeling sensor simulation

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Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with Data processing Logic 5

Empirical Evaluation of Stock Market Prediction System using...

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5th international conference on Intelligent Communication Technologies and Virtual Mobile networks, ICICV 2024

作者： Prabakar, S. Kumar, Ankit Jayakarthik, R. Venkatesh, D. Pratheeba, R.S. Khan, Bhola Isbr Business School Finance and Accounting Bangalore India Faculty of Science Swami Vivekanand Subharti University Department of Computer Application Meerut India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Commerce School of Commerce and Management Mohan Babu University Tirupati India Velammal Institute of Technology Department of Computer Science and Engineering Panchetti601204 India Mjp Rohilkhand University Department of Regional Economics Bareilly India

ISBN: (纸本)9798350385649

the Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. the system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. this multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. the study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets. © 2024 IEEE.

关键词： Long short-term memory

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A Generic Real Time Autoencoder-Based Lossy Image Compression 5

A Generic Real Time Autoencoder-Based Lossy Image Compressio...

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5th international conference on Communications, Signal processing, and their Applications (ICCSPA)

作者： Tawfik, Abdelrahman Hosny, Shehab Hisham, Sara Farouk, Ali Amr Mustafa, Doha Moaty, Samaa Abdel Gamal, Ahmed Salah, Khaled Ain Shams Univ Dept Elect & Commun Fac Engn Cairo Egypt Siemens Digital Ind Software Fremont CA 94538 USA

ISBN: (纸本)9781665482370

multimedia compression is a fundamental and significant research topic in the industrial field in the past several decades attempting to improve compression techniques. It is always a trade-off between size and quality where the growth rate of image, audio and video data is far beyond the improvement of the compression ratios achieved so far. Here, we are aiming to explore the potential of neural networks to achieve data compression, making use of multilayer neural networks providing a more efficient solution. In this paper, we present a lossy compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to replace the conventional transforms. Experimental results demonstrate that our method outperforms traditional coding algorithms, by achieving better compression ratios over the related work.

关键词： Neural networks Compression Image Compression Auto-encoders Lossy

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Malicious Activity Monitoring and Preventing in WSN Using Network Screening 5th

Malicious Activity Monitoring and Preventing in WSN Using Ne...

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5th international conference on Trends in Computational and Cognitive Engineering, TCCE 2023

作者： Dwivedi, Pushpendra Raghuvanshi, C.S. Sharan, Hari Om Department of Computer Science and Engineering Faculty of Engineering and Technology Rama University Uttar Pradesh Kanpur India

ISBN: (纸本)9789819719228

Wireless sensor network is a rapidly growing and challenging field as they are constrained by energy utilization, computation cost, network capacity, and security. Hence conventional network security schemes cannot be employed in it. the lightweight and low-capability sensors raise the risk of unauthorized communication access. In the communication phase, security risks including routing malicious activity (blackhole, grayhole), undesired floods, DoS, packet injection, etc., are considered. Numerous researchers have attempted to address the issue of sensor network security in the past using a variety of strategies, but the subject remains open since new attacks and other irregularities are discovered daily. this paper proposed a scheme to identify and prohibit malicious activity in wireless sensor networks using packet analysis and filtering approaches to resolve the security issue. the proposed approach improves performance with lesser delay, higher throughput, and packet delivery ratio and found successful in protecting wireless sensor networks (WSNs) against malicious incidents like flooding and DoS and improves overall performance. In the event of an attack the network performance goes down, however, the proposed scheme increases performance to almost normal routing and route overhead is less than 1, the PDR improvement is 4% when compared with the previous M-AODV scheme, and the rest of the metrics are also showing performance improvement. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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Boosting EEG-Based Emotion Recognition with Kolmogorov-Arnold networks 5

Boosting EEG-Based Emotion Recognition with Kolmogorov-Arnol...

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5th international conference on Advanced Research in Computing, ICARC 2025

作者： Nimishan, Sivaraj thuseethan, Selvarajah Ragel, Roshan G. Vasanthapriyan, Shanmuganathan University of Peradeniya Faculty of Engineering Sri Lanka Charles Darwin University Faculty of Science and Technology Australia Sabaragamuwa University of Sri Lanka Faculty of Computing Sri Lanka

ISBN: (纸本)9798331530983

As biomedical signal processing continues to advance, the need for reliable and deployable solutions for identifying human emotional states grows. Electroencephalography (EEG) is a promising modality for emotion recognition;however, existing systems often face challenges related to accuracy and efficiency. In this work, the use of the Kolmogorov-Arnold Network (KAN) for EEG-based emotion recognition is studied. KANs are a novel type of neural network that utilize edge-based activation functions, learning complex patterns with fewer parameters than traditional Multi-Layer Perceptrons (MLP). In addition, to enhance the quality of EEG signals, wavelet denoising is utilized. the proposed KAN model is evaluated on two publicly available datasets: GAMEEMO and LUMED. the results demonstrate that KAN significantly outperforms MLP in both accuracy and F1 score. On the GAMEEMO dataset, KAN achieved an average accuracy of 80.79% and an F1 score of 78.63%, while on the LUMED dataset, it reached an accuracy of 90.95% and an F1 score of 89.53%. this is a significant improvement of over 4% in both metrics compared to MLP. these results highlight the potential of KANs as an efficient and effective approach for EEG-based emotion recognition. © 2025 IEEE.

关键词： Signal denoising

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Reliable Routing of Time-Triggered Traffic in Time Sensitive networks 5

Reliable Routing of Time-Triggered Traffic in Time Sensitive...

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5th international conference on Natural Language processing, ICNLP 2023

作者： Hou, Shaojie Yang, Wujun Cheng, Yuanzheng Feng, Liyuan Xi'an University of Posts and Telecommunication Dept. School of Communication and Information Engineering Xi'an China

ISBN: (纸本)9798350302219

With the continuous evolution of industrial network technology, industrial production has higher and higher requirements for reliability and real-time performance. At present Time Sensitive networks (TSN) has become one of the evolution directions of the next generation network bearing technology. In TSN, redundancy is usually adopted to meet the reliability requirements of Time-Triggered (TT) flow. However, a large amount of bandwidth consumption caused by redundancy is also inevitable, which will directly affect the schedulability of TT flows. therefore, this paper proposes a Genetically based Reliable Routing algorithm (GRR) based on Frame Replication and Elimination for Reliability (FRER) standard, which provides reliable and non-overlapping routes for different copies of each TT flow. It not only guarantees the reliability of the TT flow, but also increases its schedulability, so as to meet the needs of industrial applications. through simulation experiments, compared with the existing methods, the reliability of the proposed reliable routing algorithm is improved by 2.08% on average and the schedulability is improved by 6.3% on average. © 2023 IEEE.

关键词： Simulated annealing

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Machine Learning based Data Traffic Prediction and Classification of sensor Nodes in Wireless sensor Network 5

Machine Learning based Data Traffic Prediction and Classific...

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5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Patidar, Yogesh Jain, Manish Vyas, Ajay Kumar Mandsaur University Department Of Eee Mandsaur India Adani University Department Of Ict Ahmedabad India

ISBN: (纸本)9798350330861

the lifetime and high throughput are two significant concerns of wireless sensor networks. the lifetime of the network can be increased by the efficient allotment of the slots and efficient utilization by sensor nodes. However, the major challenge is that the data generation by the sensor nodes is primarily random in real-time applications. therefore, the allotted channel is not utilized efficiently, and most of the time, the channel's bandwidth is wasted due to a variable rate of data generation. In such cases, few nodes fail to transmit the data, and few nodes waste their slots due to the unavailability of the data. To resolve the issue, a machine learning-based efficient data slot allotment method is proposed for energy-efficient bandwidth utilization in the present work. In the proposed method, the node's data traffic is predicted based on the previous dataset records. In addition, the prediction helps classify the nodes into event monitoring or periodic/continuous monitoring nodes. the dominance of the proposed protocol is presented by the energy consumption of the presented protocol compared to the energy-efficient bit-map-assisted protocol. the results prove that the proposed protocol saves energy by efficiently allotting data slots based on the machine learning approach. © 2023 IEEE.

关键词： sensor nodes

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5th international conference on Data and Information Sciences, ICDIS 2023

5th International Conference on Data and Information Science...

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5th international conference on Data and Information Sciences, ICDIS 2023

ISBN: (纸本)9789819969050

the proceedings contain 45 papers. the special focus in this conference is on Data and Information Sciences. the topics include: bSafe: A Framework for Hazardous Situation Monitoring in Industries;comparative Review of Different Techniques for Predictive Analytics in Crime Data Over Online Social Media;An Approach Towards Modification of Playfair Cipher Using 16 $$\times $$ 16 Matrix;Real and Fake Job Classification Using NLP and Machine Learning Techniques;an Improved Privacy-Preserving Multi-factor Authentication Protocol for Wireless sensor networks;blockchain-Powered Crowdfunding: Assessing the Viability, Benefits, and Risks of a Decentralized Approach;geospatial Project: Landslide Prediction;yoga Pose Identification Using Deep Learning;predicting Size and Fit in Fashion E-Commerce;A Novel Image Captioning Approach Using CNN and MLP;Multimodal Authentication Token through Automatic Part of Speech (POS) Tagged Word Embedding;sensitive Content Classification;online Social networks: An Efficient Framework for Fake Profiles Detection Using Optimizable Bagged Tree;Exploring Risk Factors for Cardiovascular Disease: Insights from NHANES Database Analysis;Review of Local Binary Pattern Histograms for Intelligent CCTV Detection;Enhancing Machine Learning Model Using Explainable AI;ioT-Assisted Mushroom Cultivation in Agile Environment;a Comprehensive Survey of Machine Learning Techniques for Brain Tumor Detection;current Advances in Locality-Based and Feature-Based Transformers: A Review;a Systematic Review on Detection of Gastric Cancer in Endoscopic Imaging System in Artificial Intelligence Applications;route Optimizations Using Genetic Algorithms for Wireless Body Area networks;propchain: Decentralized Property Management System;criminal Prevision with Weapon Identification and Forewarning Software in Military Base;advanced Cyber Security Helpdesk Using Machine Learning Techniques.

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An Efficient YOLOv5n Based Object Detection Heterogeneous SoC Implementation for Remote Sensing Images 5

An Efficient YOLOv5n Based Object Detection Heterogeneous So...

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5th international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Liu, Heming Yao, Bowen Xu, Rong Peng, Yu Liu, Liansheng School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Shanghai Institute of Satellite Engineering Shanghai China School of Electronics and Information Engineering Harbin Institute of Technology Yantai China

ISBN: (纸本)9798331529192

Convolutional neural networks (CNNs) are widely used in the field of remote sensing image object detection due to their high accuracy. However, the large number of parameters and high computational complexity of CNNs make it challenging to deploy them in real-time on embedded devices with limited computational and storage resources. this significantly restricts their practical application. To address this challenge, lightweight YOLOv5n model is chosen to realize object detection. the model is further optimized by activation function modification and parameter quantization to be more hardware-friendly. In addition, deploying a Deep Learning processing Unit (DPU) on the Zynq heterogeneous SoC by hardware-software co-design to significantly accelerate the YOLOv5n based remote sensing object detection. Experimental results show that the optimized YOLOv5n model reaches a lossless accuracy at 61.4% on the DIOR dataset when implemented on an embedded platform based on Zynq. the experimental platform achieves an image throughput of 232.1 FPS with a power consumption of 19.4W. Performance per watt (FPS/W) is 9.0× and 1.8× higher than that of i7-12700H CPU and RTX 3070Ti GPU respectively. © 2024 IEEE.

关键词： Graphics processing unit

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