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Role of Digital Watermarking in Wireless Sensor Network

Recent Advances in computer Science and Communications 2022年第2期15卷 215-228页

作者： Kumar, Sanjay Singh, Binod K. Akshita Pundir, Sonika Joshi, Rashi Batra, Simran Department of Computer Science and Engineering National Institute of Technology Jamshedpur India Department of Computer Science and Engineering Women Institute of Technology Deharadun India

WSN has been exhilarated in many application areas such as military, medical, envi-ronment, etc. Due to the rapid increase in applications, it causes proportionality to security threats because of its wireless communication. Since nodes used are supposed to be independent of human reach and dependent on their limited resources, the major challenges can be framed as energy consumption and resource reliability. Ensuring security, integrity, and confidentiality of the transmitted data is a major concern for WSN. Due to the limitation of resources in the sensor nodes, the traditionally intensive security mechanism is not feasible for WSNs. This limitation brought the concept of digital watermarking in existence. Watermarking is an effective way to provide security, integrity, data aggregation and robustness in WSN. In this paper, several issues and challenges, as well as the various threats of WMSN, is briefly discussed. Also, we have discussed the digital watermarking techniques and its role in WMSN. © 2022 Bentham Science Publishers.

关键词： Energy utilization

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Unraveling Dyscalculia: Identifying Mathematical Learning Difficulties in Early Education

Unraveling Dyscalculia: Identifying Mathematical Learning Di...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Mukherjee, Kingshuk Kumar, Reitika Vasishat, Sam Bhargava, Naman Upadhyay, Siddhi Muhuri, Samya Thapar Institute of Engineering and Technology Department of Computer Science & Engineering Patiala India

ISBN: (纸本)9798350319019

This study presents a comprehensive intervention approach designed to address dyscalculia in early education. The strategy employs a comprehensive approach that incorporates colour identification, numerical enumeration, mathematical computation, and verbal communication. The initiative seeks to employ a technology for creating interactive learning modules that successfully engage students in various numerical activities. Student progress is evaluated by the application of fuzzy logic, which allows for a thorough understanding of each student's unique learning trajectory. The adaptive approach tailors the difficulty level of exercises based on the student's performance, ensuring a tailored learning experience. This intervention specifically focuses on primary school students, aiming to provide early support and intervention to those who are either at risk of or have already been diagnosed with dyscalculia. The objective of the study is to provide a robust foundation for forthcoming mathematical abilities. © 2024 IEEE.

关键词： Fuzzy logic

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Frame aggregation aware cluster based association management for heterogeneous users

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2811-2826页

作者： Tewari, Babul P. Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur813 210 India

In this paper, a cluster based association management for heterogeneous users has been addressed. The aim is to explore frame aggregation for the high throughput (HT) users such as 802.11n type, avoiding the drawback of mixed contention from the presence of the coexisting legacy users such as 802.11 g. It has been demonstrated that restricting an access point (AP) to serve only similar type of users may lead suboptimal performance both in terms of throughput and service coverage as well. Additionally, frame aggregation can not perform well under this common contending scenario. Therefore, a cluster based association management has been proposed which uses frame aggregation and suitably designs a user-AP association where both HT users and legacy users coexist and access services from a common AP. A mathematical formulation of the proposed approach has been developed followed by a greedy solution. Extensive simulations have been performed that establish the fact that the proposed approach judiciously associates the heterogeneous users to the selected target AP and outperforms the other existing approaches including some benchmark algorithms. The results also confirm the effective use of frame aggregation and novelty of the proposed approach in terms of throughput, fairness and delay overhead. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Wireless local area networks (WLAN)

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IoT-Enabled Smart Parking System using Machine Learning for Real-Time Parking Prediction 4

IoT-Enabled Smart Parking System using Machine Learning for ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Suresh, A. Chandu, P.M.S.S. Bose, S. Subhash Chandra Dhamini, V. Narayana, P. Adi Balaji, K. Dilli Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Puttur India

ISBN: (纸本)9798350352931

Because of the growing number of cars and the inadequate parking infrastructure, urban parking management has become more and more crucial. To predict parking spot availability in IoT-enabled intelligent systems, this study assesses the performance of many machine learning (ML) models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and extra trees classifier (ETC). F-score, accuracy, recall, and precision were used to gauge performance. The DT model's 88.63% accuracy and 87.61% F-score demonstrated its low predictive power. With an F-score of 91.45% and greater accuracy of 92.30%, RF proved appropriate for medium-sized facilities. XGBoost performed better, capturing intricate parking patterns with 93.87% accuracy and 93.50% F-score. With 93.00% accuracy and 92.74% F-score, ETC performed similarly to XGBoost and provided computational advantages for big datasets. ETC and RF are dependable in medium-sized settings, however XGBoost is advised for large-scale settings. © 2024 IEEE.

关键词： Random forests

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RBCA-ETS: enhancing extractive text summarization with contextual embedding and word-level attention

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International Journal of Information technology (Singapore) 2025年第2期17卷 1127-1135页

作者： Gangundi, Ravindra Sridhar, Rajeswari Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli 620015 India

The existing limitations in extractive text summarization encompass challenges related to preserving contextual features, limited feature extraction capabilities, and handling hierarchical and compositional aspects. To address these issues, the RoBERTa-BiLSTM-CNN-Attention Extractive Text Summarization, i.e., the RBCA-ETS model, is proposed in this work. RoBERTa word embedding is used to generate contextual embeddings. Parallelly connected CNN and BiLSTM layers extract textual features. CNN focuses more on local features, and BiLSTM captures long-range dependencies that extend across sentences. These two feature sets are concatenated and forwarded to the attention layer, highlighting the most relevant features. In the output layer, a fully connected layer receives the attention vector and calculates sentence scores for each sentence. This leads to the generation of the final summary. The RBCA-ETS model has demonstrated superior performance on the CNN-Daily Mail (CNN/DM) dataset compared to many state-of-the-art methods, and it has also outperformed existing state-of-the-art techniques when tested on the out-of-domain DUC 2002 dataset. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Attention BiLSTM CNN RoBERTa Text summarization

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Deep Learning Application in PQRS Segment Detection in ECG Data

Deep Learning Application in PQRS Segment Detection in ECG D...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Pandey, Amit Kumar Gupta, C.L.P. Bansal Institute of Engineering and Technology Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350378092

The electrocardiogram is a vital piece of information about a person's health that provides measurements of changes in the cardiovascular system. When using complex software modules to diagnose diseases, the clinical ECG signal is always tainted by power line interference and electromagnetic fields, leading to inaccurate ***, it's critical to reduce these ECG data acquisition recording inaccuracies in order to perform precise clinical analysis. We are working on an algorithm that will split the data array of three successive ECG signals into three equivalents. The Empirical Mode After decomposing the data matrix and running it through the Independent Component Generation algorithm, we will identify and remove any noisy components using deep learning scheme. To obtain an error-free result of ECG data, we will then apply reverse ICA and EMD features for deep learning. © 2024 IEEE.

关键词： Deep learning

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A fully flexible key delegation mechanism with efficient fine-grained access control in CP-ABE

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12837-12856页

作者： Porwal, Shardha Mittal, Sangeeta Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India

Ciphertext Policy-Attribute Based Encryption (CP-ABE) is a secure one-to-many asymmetric encryption schemes where access control of a shared resource is defined in terms of a set of attributes possessed by a user. Key delegation service, where a user is able to temporally transfer its full or partial access rights, is an important requirement to make CP-ABE based systems practically useful. In existing CP-ABE delegation schemes, a delegator can delegate its entire or partial set of attributes such that all content accessible using those attributes becomes available to the delegatee also. Flexibility to delegate some specific content, which is a practical requirement, is not feasible with these schemes. Further, for a delegated resource, flexibility of fine grained delegation like delegating only a specific subset of read, write and update permissions is not yet available. In this work, CP-ABE key delegation has been enhanced to support specific content and specific permission delegation. The proposed scheme is fully flexible as it additionally supports limits on the number of times delegation can be done, delegatee revocation and traceability of forbidden delegation. Theoretical and practical analysis show that the proposed scheme achieves its objectives and is computationally efficient as well as secure against chosen plaintext and key abuse attacks. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Access control

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AgriAI: Revolutionizing Agriculture with Data Science 2

AgriAI: Revolutionizing Agriculture with Data Science

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Raut, Nitin B. Ashwin, U. Aswin, S. Bala Selva Vishnu, N. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350365269

In this ambitious agricultural project, cutting-edge technologies such as Machine Learning (ML) and Deep Learning (DL) are harnessed to revolutionize farming practices in India. The primary focus lies in automating vital tasks like crop disease detection, crop recommendation, and fertilizer suggestion, which are traditionally laborious and time-consuming for farmers. Leveraging recent advancements in ML algorithms, a comprehensive solution is envisaged. This innovative approach involves the integration of sophisticated models into a user-friendly website, creating a seamless user experience. Through this platform, farmers gain access to automated tools that significantly enhance their crop management strategies. The platform provides a comprehensive solution by combining deep learning-based crop disease detection mechanisms, intelligent crop recommendation systems, and effective fertilizer recommendations derived from machine learning algorithms. This combination of technologies helps farmers maximize their overall crop yields and minimize losses caused by diseases in addition to streamlining farming procedures. The synergy of ML, and DL on a unified web interface stands as a testament to the transformative power of technology in agriculture, marking a pivotal step toward sustainable and efficient farming practices in India. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Logistics Efficiency With ML-driven ERP Prediction For Distribution Management in Smart Transportation Systems 8

Enhancing Logistics Efficiency With ML-driven ERP Prediction...

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8th IET Smart Cities Symposium, SCS 2024

作者： Gangajaliya, K. Gajjar, J. Darji, K. Gupta, Rajesh Magotra, Bhagyalakshmi Tanwar, Sudeep Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad India Department of Computer Engineering Model Institute of Engineering and Technology J&K Jammu India

ISBN: (纸本)9781837242672

This research paper analyzes the use of machine learning techniques to predict effective radiated power (ERP) and enhance logistics efficiency in distribution management for smart transportation systems (STS). Three regression models, namely Linear regression, Bayesian Ridge regression, and automatic relevance determination (ARD) regression, have been employed to predict the ERP values based on the extracted features. The performance of each of these models has been assessed using established regression metrics, including mean squared error (MSE) and R-squared (R2). To determine the best-performing model, techniques such as marginal-log-likelihood comparison have been utilized. Additionally, we employ cross-validation techniques to ensure the reliability and generalization capability of the models. The comparative analysis of regression metrics and the cross-validation outcomes further validate the suitability of machine-learning approaches for ERP prediction in distribution management scenarios. The obtained results demonstrate the effectiveness of machine learning in accurately predicting ERP values, thereby significantly improving logistics operations by optimized route planning, minimized transportation costs, and enhancing overall operational performance within distribution networks. © The Institution of engineering & technology 2024.

关键词： Linear regression

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Implementation of Enhanced Iris Recognition for Secure Biometric Authentication using Machine Learning 1

Implementation of Enhanced Iris Recognition for Secure Biome...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Duraisamy, Premkumar Rupasri, K.S. Sri K, Rekha Thanishka, T.S. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350307795

Developing effective strategies to address significant variations in segmented iris image quality is crucial for accurate iris recognition from remotely acquired facial or eye images. These variations are closely linked to the consistency of iris feature encoding, suggesting that leveraging such discrepancies can improve matching accuracy. A nonlinear approach is recommended for considering both the local consistency of the iris pattern and the overall quality of the weight map. Principal Component Analysis (PCA) and Support Vector Machines (SVM) are commonly employed in biometric research for iris recognition, identifying individuals through unique iris features. This research aims to enhance the precision and efficiency of iris recognition systems through methods such as feature extraction, classifier algorithms, integration of multiple biometric modalities, and the development of comprehensive and diverse iris datasets. Iris recognition technology offers significant potential to bolster security and privacy in applications including access control, border security, and financial transactions. © 2024 IEEE.

关键词： Principal component analysis

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