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RIS Assisted Dual-Function Radar and Secure Communications Based on Frequency-Shifted Chirp Spread Spectrum Index Modulation

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China Communications 2023年第10期20卷 85-99页

作者： Xiaoping Jin Peng Zhang Chuan Wan Dingyou Ma Yudong Yao Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province College of Information EngineeringChina Jiliang UniversityHangzhou 310018China Key Laboratory of Universal Wireless Communications Ministry of EducationSchool of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenNJ 07030USA

Reconfigurable intelligent surface(RIS)assisted dual-function radar communications(DFRC)system is a promising integrated sensing and communication(ISAC)technology for future *** this paper,we propose a scheme of RIS-assisted DFRC system based on frequency shifted chirp spread spectrum index modulation(RDFI)for secure *** proposed RDFI achieves the sensing and transmission of target location information in its radar and communication modes,*** both modes,the frequency-shifted chirp spread spectrum index modulation(FSCSS-IM)signal is used as the baseband signal for radar and communications,so that the signal sent by the radar also carries *** scheme implements the RIS-assisted beamforming in the communication mode through the azimuth information of the target acquired in the radar mode,so that the signal received from the eavesdropper is distorted in amplitude and *** addition,this paper analyzes the radar measurement accuracy and communication security of the FSCSS-IM signal using ambiguity function and secrecy rate(SR)analysis,*** results show that RDFI achieves both excellent bit error rate(BER)performance and physical layer security of communications.

关键词： index modulation secure communications ISAC DFRC reconfigurable intelligent surface

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Predictive Modeling Techniques of Social Dynamics in Multilayer Social Networks: A Survey 4th

Predictive Modeling Techniques of Social Dynamics in Multila...

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Jaya Krishna, Raguru Vamshi Krishna, B. Gopalakrishnan, T. Anagha, P. Kumar Sharma, Vijay Prasad Sharma, Devi Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer and Communication Engineering and Department of Information Technology Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819736898

Various techniques involved in analyzing social media data have become an important aspect of research. There is a need for efficient techniques to explore huge amounts of data for information prediction like product rating, political election prediction, viral marketing, and information cascading, etc. Nowadays multilayer social networks offer huge platforms for getting opinion reviews and recommendations about any user-interested topic. In multilayer social networks, multiple users are connected and the relationship between them is represented in different layers indicating different connections and interactions. Network structure is more complex and the social interactions between the individuals are dynamic in nature. This study reviews a thorough overview of multilayer social network predictive modeling methodologies and examines contemporary multilayer social network algorithms that have been published in the literature. In addition, we also discussed various key issues and algorithms to address the issues. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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Video-based Hand Gesture Recognition using Random Forest for Sign Language Interpretation

Video-based Hand Gesture Recognition using Random Forest for...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Himasree, J. Jeevitha, P.L. Deekshitha, K. Kolisetty, Aashrita Naveen, Soumyalatha REVA University School of Computer Science & Information Technology Bangalore India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science & Engineering Manipal India

ISBN: (纸本)9798350361537

Effective communication is essential for all individuals, yet it can be particularly challenging for the deaf community, as sign language is their major means of communication. We are researching how to create a reliable and effective system that can understand sign language motions correctly without the help of a human, realizing the importance of this *** this research work we have chosen to construct our own dataset rather than depending on pre-existing ones that may be accessed online because we understand how important authenticity and relevance are. This method gives us more control over the quality of the data and guarantees that the dataset closely matches our study goals. In addition, our system can be readily expanded and recognizes a wide range of hand motions and signs, including the alphabet. We can support a broad variety of sign language expressions thanks to this versatility, which improves inclusivity and usability. Random Forest is used to encode temporal variations among motions in sign language. The system's overall accuracy and reliability are increased thanks to this methodology, which enables it to detect minute variations in hand gestures and movements. Using the Keras and OpenCV libraries in conjunction with the Python programming language, we build a strong foundation for sign language recognition. In addition to streamlining the development process, these technological tools open the system to a larger developer and research community. In alignment with the research, the proposed work achieved an accuracy of 97% with overall precision, recall and F1 score of 99%. © 2024 IEEE.

关键词： Gesture recognition

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High Dynamic Collaborative Team Query via Multi-Fuzzy-Constrained Graph Pattern Matching 9

High Dynamic Collaborative Team Query via Multi-Fuzzy-Constr...

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9th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2024

作者： Hu, Tao Zhang, Zan Bu, Chenyang Li, Lei Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350373554

Graph pattern matching is a technique widely used in various fields such as protein structure analysis, social group querying, and expert localization. This technique involves finding matching subgraphs in large social networks that align with the patterns specified in the pattern graph. In this paper, we focus on a specific sub-problem in social group querying, known as the cooperative team query, which arises from practical applications, where the nodes in the pattern graph and the data graph represent team member entities, while the edges represent their social relationships. We note that the requirements of many teams in the real world are dynamic, necessitating iterative computation for graph pattern matching using traditional methods. To address this challenge in highly dynamic systems, we propose a graph pattern matching method based on core pattern graph matching cache. This approach involves extracting the core pattern graph, and comprising core team members based on the characteristics of cooperative teams. The core graph-based matching cache enables the second half of the algorithm to operate on an order-of-magnitude smaller graph, significantly improving efficiency. Additionally, the multi-threaded approach fully leverages hardware resources, synchronizing multiple matching result of the core pattern graph to reduce matching time. Experimental results on three real social network datasets demonstrate that our proposed algorithm, Core Pattern Graph Matching Cache-based Multi-threaded Exploration (CCMTE), significantly outperforms existing methods in terms of efficiency. © 2024 IEEE.

关键词： Efficiency

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Analysis of Neural Machine Translation for English to Hindi using Long Short-Term Memory Model and Transformer Model 4

Analysis of Neural Machine Translation for English to Hindi ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Naveen, Rashmi Kumar, Archana Praveen Dept. of Information Science and Engineering India Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science & Engineering Manipal India

ISBN: (纸本)9798331540364

By providing enhanced accuracy and fluency compared to traditional approaches, Neural Machine Translation (NMT) has transformed the area of machine translation. In this research paper, the specific task of translating English to Hindi using NMT techniques is discussed. Creation and assessment of NMT systems developed for this language pair, considering the linguistic complexities and cultural aspects inherent in English and Hindi is examined. Leveraging large-scale parallel corpora and advanced neural network architectures, this work proposes novel approaches to improve translation quality and efficiency. Experimental results evaluated using standard metrics such as BLEU score, shows the efficacy of the proposed NMT models in accurately capturing the semantics and preserving the naturalness of the translated text. Additionally, this study discusses the implications of findings for cross-language communication and access to information, particularly in the context of the growing importance of Hindi in a globalized digital domain. Overall, this research contributes to develop neural machine translation for English to Hindi to a higher level and highlights the potential of NMT in facilitating multilingual communication and knowledge dissemination. © 2024 IEEE.

关键词： Neural machine translation

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Hybrid RNN Based Text Classification Model for Unstructured Data

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SN computer Science 2024年第6期5卷 726页

作者： Sunagar, Pramod Sowmya, B.J. Pruthviraja, Dayananda Supreeth, S. Mathew, Jimpson Rohith, S. Shruthi, G. Cloudthat Technologies Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education School of Computer Science and Engineering REVA University Department of Computer Science and Engineering Indian Institute of Technology Patna Department of ECE Nagarjuna College of Engineering and Technology

The volume of social media posts is on the rise as the number of social media users expands. It is imperative that these data be analyzed using cutting-edge algorithms. This goal is handled by the many techniques used in text categorization. There are a variety of text categorization techniques available, ranging from machine learning to deep learning. Numerical crunching has become easier with less processing time since the emergence of high-end computer facilities. This has led to the development of sophisticated network architectures that can be trained to achieve higher precision and recall. The performance of neural network models which was evaluated by the F1 score is affected by cumulative performance in precision and recall. The current study intends to analyze and compare the performance of the neural network proposed, A Hybrid RNN model that has two layers of BiLSTM and two layers of GRU to that of previous hybrid models. GloVE dataset is used to train the models and their accuracy, precision, recall, and F1 score are used to assess performance. Except for the RNN + GRU model, the RNN + BILSTM + GRU model has a precision of 0.767, a recall of 0.759, and an F1-score of 0.7585. This hybrid model outperforms the others. © The Author(s) 2024.

关键词： Bidirectional long-short-term memory (BiLSTM) Gated recurrent units (GRU) Recurrent neural networks (RNN) Region-based convolutional neural networks (RCNN) Text classification

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Improved top-down attention phasmatodea population spiking neural network based multi crop recommendation framework

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Multimedia Tools and Applications 2025年第10期84卷 8071-8105页

作者： R, Chetan Ashoka, D.V. B, Ajay Prakash Department of Computer Science and Engineering JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Information Science JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Information Science and Engineering JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Artificial Intelligence and Machine Learning Dr. Ambedkar Institute of Technology Visvesvaraya Technological University Karnataka India

Modern agriculture refers to the contemporary practices, technologies and methods used in food production to meet the growing global demand for agricultural products. With increasing concerns about environmental degradation, soil health and resource depletion, there is an insistent need for agricultural practices that are both productive and sustainable. Multicropping mitigate the negative impacts of monoculture and reduce the reliance on synthetic inputs. This research introduces an innovative approach for multi-crop recommendation through the utilization of an integrated framework that combines advanced optimization techniques and neural networks like Enhanced top-down attention Spiking Neural Network (ETSNN). The proposed framework involves two main phases: the first phase focuses on classifying soils suitable for specific agro-ecological zones using an enhanced top-down attention spiking neural network. The second phase predicts multi-crop yields across different districts based on various environmental parameters using an Improved Phasmatodea population Evolutionary Algorithm (IPEA). Before classifying the crops feature selection takes place for the dataset by Enhanced Sea horse Optimization (ESO) method. The outputs from both phases are harmonized using a stacking-based fusion method. The developed user interface accepts real-time input for location-specific soil properties and provides crop recommendations that take into account both the input data and climate parameters. The proposed method attains 99.7% accuracy, 99.2% precision, 99% recall and 99.9% F1-score respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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Pseudo-label Data Construction Method and Syntax-enhanced Model for Chinese Semantic Error Recognition 31

Pseudo-label Data Construction Method and Syntax-enhanced Mo...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wu, Hongyan Lin, Nankai Jiang, Shengyi Wang, Lianxi Yang, Aimin College of Computer National University of Defense Technology China School of Information Science and Technology Guangdong University of Foreign Studies China School of Information Technology and Engineering Guangzhou College of Commerce China School of Computer Science and Technology Guangdong University of Technology China School of Computer Science and Intelligence Education Lingnan Normal University China

ISBN: (纸本)9798891761964

Chinese Semantic Error Recognition (CSER) has always been a weak link in Chinese language processing due to the complexity and obscureness of Chinese semantics. Existing research has gradually focused on leveraging pre-trained models to perform CSER. Although some researchers have attempted to integrate syntax information into the pre-trained language model, it requires training the models from scratch, which is time-consuming and laborious. Furthermore, despite the existence of datasets for CSER, the constrained size of these datasets impairs the performance of the models. Thus, in order to address the difficulty posed by a limited sample set and the need of annotating samples with semantic-level errors, we propose a Pseudo-label Data Construction method for CSER (PDC-CSER), generating pseudo-labels for augmented samples based on perplexity and model respectively, which overcomes the difficulty of constructing pseudo-label data containing semantic-level errors and ensures the quality of pseudo-labels. Moreover, we propose a CSER method with the Dependency Syntactic Attention mechanism (CSER-DSA) to explicitly infuse dependency syntactic information only in the fine-tuning stage, achieving robust performance, and simultaneously reducing substantial computing power and time cost. Results demonstrate that the pseudo-label technology PDC-CSER and the semantic error recognition method CSER-DSA surpass the existing models. © 2025 Association for Computational Linguistics.

关键词： Syntactics

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Effective Bandwidth based Distributed DoS Attack Detection and Source Identification using Maximum Flow Techniques 3

Effective Bandwidth based Distributed DoS Attack Detection a...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Subburaj, T. Suthendran, K. Darshan, P. Deeraj, C. Jai Shankar, M. Pushpalatha, G. Department of Master of Computer Applications Rajarajeswari College of Engineering Bangalore India Department of Information Technology Kalasalingam Academy of Research and Education TamilNadu India

ISBN: (纸本)9798331527549

The DDoS attack is one of the most threatening factors affecting the internet services. Now days, most of the common attacks are targeting the gaming industry, online shopping websites followed by the software and media sectors. Many of the service providers are available to detect and prevent the DDoS attacks. But their software’s are not updated enough to detect the recent time attacks. Cyber security challenges include rising ransomware attacks, AI-powered threats, supply chain risks, and frequent data breaches. In the current IoT trend, available DDoS defenses aren’t sufficient. This work is aimed to propose a novel BW- DDoS attack detection approach based on maximum data flow identification in networks. The proposed method is faster and taking less number of iterations to determine the maximum data flow and also trace out the source of the attacks with minimum time compared with existing approaches. © 2025 IEEE.

关键词： Attacks BW-DDoS Entropy Maximum flow Trace out

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LAME:Layout-Aware Metadata Extraction Approach for Research Articles

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computers, Materials & Continua 2022年第8期72卷 4019-4037页

作者： Jongyun Choi Hyesoo Kong Hwamook Yoon Heungseon Oh Yuchul Jung Department of Computer Engineering Kumoh National Institute of Technology(KIT)GumiKorea Korea Institute of Science and Technology Information(KISTI) DaejeonKorea School of Computer Science and Engineering Korea University of Technology and Education(KOREATECH)CheonanKorea

The volume of academic literature,such as academic conference papers and journals,has increased rapidly worldwide,and research on metadata extraction is ***,high-performing metadata extraction is still challenging due to diverse layout formats according to journal *** accommodate the diversity of the layouts of academic journals,we propose a novel LAyout-aware Metadata Extraction(LAME)framework equipped with the three characteristics(e.g.,design of automatic layout analysis,construction of a large meta-data training set,and implementation of metadata extractor).In the framework,we designed an automatic layout analysis using PDF *** on the layout analysis,a large volume of metadata-separated training data,including the title,abstract,author name,author affiliated organization,and keywords,were automatically ***,we constructed a pre-trainedmodel,Layout-Meta BERT,to extract the metadata from academic journals with varying layout *** experimental results with our metadata extractor exhibited robust performance(Macro-F1,93.27%)in metadata extraction for unseen journals with different layout formats.

关键词： Automatic layout analysis layout-MetaBERT metadata extraction research article

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