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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Aravind Krishnan, A. Deepak, Gerard Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India

ISBN: (纸本)9789819757022

There is a requirement for document recommendation frameworks focusing on certain domains linked to medical sciences and biosciences like biomedical document recommendation in the era of the Web 3.0. This paper proposes a biomedical document recommendation strategy that realizes informative term generation using the encompassment of extraction of terms and categories and the TF-IDF. The framework also generates an ontology from the informative terms harvested from which features are selected to classify the dataset using a random forest classifier. The model also uses very strong knowledge bases that are highly specific to the domain like QIAGEN. It also uses WikiData which is generic but has a full cover of world knowledge to bridge the knowledge present in the datasets and the existing structure web. Metadata generation also increases the quantity of additional knowledge that is encompassed in the model. BioBERT is a strategic large language model that focuses on biosciences and biomedicine as its domain is used to classify the metadata to make it more permeable into the model. Semantic similarity is computed using NPMI, web overlap, SimRank, and Ricklefs and Lau index at different stages in the pipeline to facilitate quantitative reasoning through semantic relatedness computation. The salient semantic analysis also accelerates the overall semantic association into the proposed framework. This framework can demonstrate a precision of 96.56%, recall of 96.93%, and an FDR of 0.04. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ontology

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A Hybrid Natural Language Processing (HNLP) for Model Transformation 5

A Hybrid Natural Language Processing (HNLP) for Model Transf...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Madhavi, Banka Chaithanya, T. Rama Babu, S. Giri Pace Institute of Technology Sciences Department of Computer Science & Engineering AP Ongole India Pace Institute of Technology Sciences Department of Computer Science & Engineering AP Ongole India

ISBN: (纸本)9798350379945

Model-to-model transformations involve converting one type of the model into another. This can be used in many ways for various reasons, considering model performance with particular deployment platforms. This paper introduces a Hybrid Natural Language Processing (HNLP) model to visualize model-to-model (M2M) transformation. The proposed HNLP combines Sub-word Embedding (FastText), Encoder-Decoder Architecture, and Attention-based Seq2Seq. The proposed approach focused on processing standard text into meaningful information. Subword Embeddings with FastText retain grammatical aspects by demonstrating words as n-grams, which enhances the interpretation of out-of-vocabulary phrases and unusual word structures. The Encoder-Decoder Architecture translates input sequences into symbolic representations that can be used for various NLP tasks. The Attention-based Seq2Seq method extends this approach by constantly focusing on vital regions of the input sequence during decoding, improving the model's capacity to provide accurate and contextually appropriate outputs. By combining these techniques, the HNLP model enhances sequence transformation quality and gives a more interpretable and visual path for understanding how input transformations occur between models. This visualization capacity is crucial for debugging, model improvement, and understanding the inner workings of complicated NLP systems. Experimental results show that the HNLP model performs well in various NLP tasks, demonstrating its promise as a reliable tool for practical applications and theoretical investigations into model-to-model transformations. © 2024 IEEE.

关键词： Decoding

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Best Linear Controllers for Decentralized Linear Quadratic Systems

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Journal of Systems Science & Complexity 2025年第1期38卷 210-237页

作者： AFSHARI Mohammad MAHAJAN Aditya Institute for Robotics and Inteligent Machines Georgia Institute of TechnologyAtlanta 30332-0530USA Department of Electrical and Computer Engineering McGill UniversityMontreal H3A 0G4Canada

In this paper,the authors revisit decentralized control of linear quadratic(LQ)*** of imposing an assumption that the process and observation noises are Gaussian,the authors assume that the controllers are restricted to be *** authors show that the multiple decentralized control models,the form of the best linear controllers is identical to the optimal controllers obtained under the Gaussian noise *** main contribution of the paper is the solution ***,optimal controllers for decentralized LQ systems are identified using dynamic programming,maximum principle,or spectral *** authors present an alternative approach which is based by combining elementary building blocks from linear systems,namely,completion of squares,state splitting,static reduction,orthogonal projection,(conditional)independence of state processes,and decentralized estimation.

关键词： Decentralized estimation decentralized stochastic control linear quadratic systems team theory

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An Adaptive IDS Scheduling Framework for Host Based Mimicry Attack Detection in IoT Networks 18

An Adaptive IDS Scheduling Framework for Host Based Mimicry ...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sanyal, Surja Khatua, Manas Chattopadhyay, Pratik Indian Institute of Technology Guwahati Department of Computer Science and Engineering Guwahati India Varanasi Department of Computer Science and Engineering Varanasi India

ISBN: (纸本)9798350391725

With the expanding applicability of the Internet of Things (IoT), novel IoT network security challenges also appear more frequently. Host-based Mimicry Attacks (HMA) are one of them that is difficult to detect by traditional Intrusion Detection Systems (IDS) since they mimic legitimate network and host behaviors to evade detection. Additionally, IDSs consume host device resources when their implementations, partially or fully, are executed in the device, and thus it is needed to be scheduled properly so as to save device resources when possible. Current IDS solutions capable of detecting HMAs mostly use randombased scheduling or do not use scheduling at all and so are always executed. Random-based scheduling solutions have low HMA detection accuracy, while solutions without scheduling do not save device resources. This work proposes an adaptive IDS scheduling framework that suspends IDS execution based on device trust and resumes IDS execution when the device is under attack. Extensive experimentation using the Cooja simulator and Python script shows that the proposed framework achieves a device behavior classification accuracy of at least 97.15% when under attack while saving at least 81.98% of CPU energy when not under attack for a network of 40 devices, as compared to those when executing the IDS without any scheduling scheme. © 2024 IEEE.

关键词： Network intrusion

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Enhancing intrusion detection in IIoT: optimized CNN model with multi-class SMOTE balancing

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Neural Computing and Applications 2024年第24期36卷 14643-14659页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nassif, Ali Bou Injadat, MohammadNoor Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model’s performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural network models

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On the Maximum Distance Sublattice Problem and Closest Vector Problem 26

On the Maximum Distance Sublattice Problem and Closest Vecto...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Kumar, Rajendra Mehta, Shashank K Rajasree, Mahesh Sreekumar Indian Institute of Technology Delhi Department of Computer Science & Engineering New Delhi India Indian Institute of Technology Kanpur Department of Computer Science & Engineering Uttar Pradesh India

ISBN: (纸本)9798331532833

In this paper, we introduce the Maximum Distance Sublattice Problem (MDSP). We observed that the problem of solving an instance of the Closest Vector Problem (CVP) in a lattice L is the same as solving an instance of MDSP in the dual lattice of L. We give an alternate reduction between the CVP and MDSP. This alternate reduction does not use the concept of dual lattice. © 2024 IEEE.

关键词： CVP geometry GSO Karp reduction lattice

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Feature engineering Techniques for Stegware Analysis: An Extensive Survey 1

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1st International Conference on Intelligent Computing for Sustainable Development, ICICSD 2023

作者： Anitha, M. Azhagiri, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9783031612985

ISBN: (纸本)9783031612978

The art of hiding secret text within an innocuous cover medium is steganography. Steganalysis is the counterpart of steganography which focuses on the detection and extraction of the secret text from the medium. Feature engineering is the crucial field in Stegware Analysis which intends to identify more specific features, focusing on the accuracy and efficiency. Feature engineering is a process in Machine learning where the features of any dataset are selected and extracted for further use. Feature engineering is the process of extracting, transforming and selecting the most relevant features form the data that aids in discriminating between the stego and cover image. This is because, most of the time, the data will be in a raw format. Any ML model needs the data to be pre-processed and kept ready to train the model. Thus, from the pool of raw data, the required data needs to be selected and can be used in training the model. Further, the data at point needs to be extracted to get the precise data. The scope of the work is to identify the various feature engineering techniques available in practice and efficiently use them to achieve high accuracy and precision in the system. The survey focuses on the several feature selection and extraction techniques like filter method, wrapper method and embedded methods. Correlation being one of the feature selection methods is focused;while statistical moments computes the mean, variance and skewness of the feature. The extraction method holds the Computation of Invariants and other such. Comparative study is made on both the methods to understand the concepts with ease. The work starts by taking a sample from the dataset and few feature extraction techniques are applied on the same. Then the original image is compared with the extracted images with the view of histogram. The paper gives valuable insights into the effectiveness of different feature engineering techniques using the dataset and underscores the importance of featu

关键词： Steganography

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VoteChain: Promising a Secure and Transparent Election using Blockchain and Biometrics 4

VoteChain: Promising a Secure and Transparent Election using...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Vivekanandan, G. Prabahar Godwin James, T. Divya, B. Tejas, Madhav V. Naveen Karthick, T.K. Yuvanesh, P. Sri Sai Ram Institute of Technology Department of Computer Science and Engineering India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India

ISBN: (纸本)9798331508845

Democratic voting is one of the significant aspects of governance that has various complications which are as follows;Complication arising from transparency Complication arising from low voter turnout Complication arising from vulnerability to the manipulation of the elections Complication arising from low confidence in the election process Complication arising from forgery of voter id cards Complication arising from delays in the announcement of the results Complication arising from security breach. All these issues erode the credibility of elections and public faith. To them, blockchain has a perfect solution in that it is decentralized, secure, transparent, and cannot be tampered with technology. To realize our e-voting system, we built an evoting application as a smart contract on the Ethereum platform using digital wallets and solidity. A given voter is assigned tokens (gas) which are utilized every time they cast their vote in order to eliminate vote forgeries and other undesirable incidents. This blockchain-based system does not require physical polling stations;also, voting is scalable and secure with flexible consensus algorithms, cryptographic hashing, and measures in response to a 51% attack. It also incorporates fingerprint scanning, to further discourage fraud and make sure that the process is completely secure and transparent. Since these records are stored in a blockchain, the ability of a single unscrupulous agency trying to manipulate the records is very hard given that records are validated by the nodes in the network, making the voting process more secure and credible. © 2024 IEEE.

关键词： Blockchain

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Sentiment and hashtag-aware attentive deep neural network for multimodal post popularity prediction

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Neural Computing and Applications 2024年第4期37卷 2799-2824页

作者： Bansal, Shubhi Kumar, Mohit Raghaw, Chandravardhan Singh Kumar, Nagendra Department of Computer Science and Engineering Indian Institute of Technology Indore Khandwa Road Simrol Madhya Pradesh Indore453552 India

Social media users articulate their opinions on a broad spectrum of subjects and share their experiences through posts comprising multiple modes of expression, leading to a notable surge in such multimodal content on social media platforms. Nonetheless, accurately forecasting the popularity of these posts presents a considerable challenge. Prevailing methodologies primarily center on the content itself, thereby overlooking the wealth of information encapsulated within alternative modalities such as visual demographics, sentiments conveyed through hashtags and adequately modeling the intricate relationships among hashtags, texts, and accompanying images. This oversight limits the ability to capture emotional connection and audience relevance, significantly influencing post popularity. To address these limitations, we propose a seNtiment and hAshtag-aware attentive deep neuRal netwoRk for multimodAl posT pOpularity pRediction, herein referred to as NARRATOR that extracts visual demographics from faces appearing in images and discerns sentiment from hashtag usage, providing a more comprehensive understanding of factors influencing post popularity. Moreover, we introduce a hashtag-guided attention mechanism that leverages hashtags as navigational cues, guiding the model’s focus toward the most pertinent features of textual and visual modalities, thus aligning with target audience interests and broader social media context. Experimental results demonstrate that NARRATOR outperforms existing methods by a significant margin on two real-world datasets. Furthermore, ablation studies underscore the efficacy of integrating visual demographics, sentiment analysis of hashtags, and hashtag-guided attention mechanisms in enhancing the performance of post popularity prediction, thereby facilitating increased audience relevance, emotional engagement, and aesthetic appeal. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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Deep Learning Based Automated Lip Reading for Deaf 3

Deep Learning Based Automated Lip Reading for Deaf

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Prathyakshini Prathwini Pratheeksha Hegde, N. Vaishali Rashmi, N. Kumar, Archana Praveen NMAM Institute of Technology Department of Information Science and Engineering Karkala Nitte India NMAM Institute of Technology Department of Master of Computer Applications Karkala Nitte India NMAM Institute of Technology Department of Computer Science and Engineering Karkala Nitte India Manipal Academy of Education Manipal Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350354171

Speech recognition systems play an integral role in numerous applications, from virtual assistants to accessibility tools. This paper offers a new viewpoint to speech recognition utilizing computer vision and deep learning techniques. The proposed system is trained on a sizable dataset comprising 700 video clips of individuals uttering predefined words, add up to approximately 3 GB of data. Leveraging TensorFlow and Keras, model architecture is designed incorporating convolutional and dense layers. The training process yielded promising results, with a training accuracy of 95.7% and a validation accuracy of 98.5%, indicative of robust classification performance. The integration of computer vision enriches the system's ability to extract meaningful features from audio-visual inputs, enhancing its overall recognition accuracy. Proposed method demonstrates significant potential for real-time speech recognition applications in different areas, like human-computer interaction and assistive technologies. © 2024 IEEE.

关键词： Assistive technology

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