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6th IEEE International Conference on Advances in science and technology, ICAST 2023

作者： Bharadi, Vinayak Hamdule, Zohaib Kambli, Sujan Salvi, Raj Chavan, Raj Nimbalkar, Vrishali Finolex Academy of Management and Technology Department of Information Technology Ratnagiri India Finolex Academy of Management and Technology Dept. of Computer Science and Engineering Ratnagiri India

ISBN: (纸本)9798350359817

Online signature verification remains a pivotal component in digital security, ensuring the authenticity of digital transactions and documents. This paper offers a comprehensive overview of prevailing online signature verification techniques, with a particular emphasis on methods employing Deep Learning. RNNs, with their inherent capability to process sequential data, have been instrumental in improving the accuracy and efficiency of signature verification systems. However, as the digital forgery landscape becomes increasingly sophisticated, there is a pressing need to explore advanced neural architectures. In this context, the paper introduces a transformer-based approach for signature verification. Unlike traditional models, transformers, with their self-attention mechanisms, have the potential to capture intricate signature patterns and nuances, offering a promising avenue for future research and application. © 2023 IEEE.

关键词： Recurrent neural networks

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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Optimizing VNF Service Provisioning in Mobile Edge Computing Networks Through PSO-RL 3

Optimizing VNF Service Provisioning in Mobile Edge Computing...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Abinaya, K. Dhanasekaran, S. Vasudevan, V. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Srivilliputtur India Kalasalingam Academy of Research and Education Department of Information Technology Tamilnadu Srivilliputtur India

ISBN: (纸本)9798350361186

This study deals with setting up Virtual Network Function (VNF) services in Mobile Edge Computing (MEC) networks. It aims to balance different user needs in the everchanging MEC environment. Leveraging Particle Swarm Optimization-Reinforcement Learning algorithms (PSO-RL), our proposed system offers a dynamic provisioning approach adept at accommodating user mobility patterns and service delay prerequisites. The system's sequential flow comprises distinct stages designed for optimal functionality. The Firefly Algorithm efficiently maximizes utility by simplifying intricate issues and approximating optimal solutions. Additionally, our system implements Stochastic Gradient Descent and Simulated Annealing techniques for real-time throughput optimization, ensuring adaptability to ever-changing network conditions. Experimental simulations form a critical component of our study, providing a robust assessment of our proposed methodologies. Results obtained demonstrate substantial enhancements across critical metrics such as Throughput, Resource Utilization Efficiency, and End-to-End Delay. Varied scenarios encompassing different quantities of IoT users and mobile edge servers were simulated, reaffirming the efficacy of the PSO-RL method. These simulations underscore the potential of our proposed approach in significantly bolstering the performance and responsiveness of MEC networks. Ultimately, this study showcases promising prospects in addressing the intricate challenges associated with provisioning VNF services to mobile users in dynamic MEC environments. © 2024 IEEE.

关键词： Simulated annealing

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Ia-Mstm: Improved Adaptive Moment Estimation Enabled Mixed Attention-Based Long Short-Term Memory For Crop Yield Prediction 3

Ia-Mstm: Improved Adaptive Moment Estimation Enabled Mixed A...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Vani, B.V. Guruprakash, C.D. Sri Siddhartha Academy of Higher Education Sri Siddhartha Institute of Technology Saraswathipuram Information Science and Engineering Karnataka Tumakuru572105 India Sri Siddhartha Institute of Technology Department Computer Science and Engineering Karnataka Tumakuru572105 India

ISBN: (纸本)9798350364569

Crop yield prediction has sought tremendous attention in recent years several approaches were developed for crop yield prediction. However, these techniques created certain limitations in real-time applications including a reduction in relative errors, and low prediction accuracy. Therefore, this research proposed a deep learning technique known as improved Adaptive Moment estimation enabled mixed attention-based long short-term memory (IA-MSTM) to predict crop yields. The mixed attention techniques combine the advantages of channel attention with the hard attention mechanism (HAM), which can capture long-term dependencies that are crucial for understanding crop growth dynamics over time. Additionally, the improved repeated Loss-based Adam optimizer adapts the learning rate during training, leading to quicker convergence compared to traditional methods. The performance of the IA-MSTM achieves a minimal error value in terms of Mean Absolute Error (MAE) of 2.00 for TP 90. © 2024 IEEE.

关键词： Prediction models

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KIASOntoRec: A Knowledge Infused Approach for Socially Aware Ontology Recommendation 13th

KIASOntoRec: A Knowledge Infused Approach for Socially Aware...

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13th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Valecha, Aastha Deepak, Gerard Surya, Deep ak Department of Mechanical Engineering Indian Institute of Technology Delhi India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031274985

The World Wide Web is progressively adopting web 3.0 standards, and there is a demand for semantically compatible recommendation techniques in today’s world. This paper presents a framework to facilitate Knowledge infused automatic Ontology recommendation. User queries and user clicks data are preprocessed and Structural Topic Modelling is employed to identify the latent sematic structure found in the corpus and generate topics. RDF triplets are generated based on the topics extracted using OntoCollab. RDF data along with the Google’s Knowledge Base API used to obtain the subgraphs and relevant topics. Bagging algorithm based on Random Forest Classification Technique with Decision Trees Algorithm is used to classify the terms. A knowledge base is constructed by merging the subgraphs. Semantic similarity among entities of the knowledge graph and the top 25% of the classified results is computed using SemantoSim and ANOVA. The topic set is extracted and subjected to axiomatization. Finally, the most relevant Ontology to the user query is suggested to the user. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ontology

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Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

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computer Systems science & engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

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Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect Sentiment Triplet Extraction

Prompt Based Tri-Channel Graph Convolution Neural Network fo...

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2024 SIAM International Conference on Data Mining, SDM 2024

作者： Peng, Kun Jiang, Lei Peng, Hao Liu, Rui Yu, Zhengtao Ren, Jiaqian Hao, Zhifeng Philip, S.Yu. Institute of Information Engineering Chinese Academy of Sciences China School of Cyberspace Security University of Chinese Academy of Sciences China School of Cyber Science and Technology Beihang University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China College of Science University of Shantou China University of Illinois at Chicago Department of Computer Science United States

ISBN: (纸本)9781611978032

Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments. Recent studies tend to address this task with a table-filling paradigm, wherein word relations are encoded in a two-dimensional table, and the process involves clarifying all the individual cells to extract triples. However, these studies ignore the deep interaction between neighbor cells, which we find quite helpful for accurate extraction. To this end, we propose a novel model for the ASTE task, called Prompt-based Tri-Channel Graph Convolution Neural Network (PT-GCN), which converts the relation table into a graph to explore more comprehensive relational information. Specifically, we treat the original table cells as nodes and utilize a prompt attention score computation module to determine the edges' weights. This enables us to construct a target-aware grid-like graph to enhance the overall extraction process. After that, a triple-channel convolution module is conducted to extract precise sentiment knowledge. Extensive experiments on the benchmark datasets show that our model achieves state-of-the-art performance. The code is available at https://***/KunPunCN/PT-GCN. Copyright © 2024 by SIAM.

关键词： Extraction

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Hybrid technology for Multilevel Feature Extraction in CBMIR 7

Hybrid Technology for Multilevel Feature Extraction in CBMIR

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7th IEEE International Conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2023

作者： Rao, Manjula Gururaj Priyanka, H. Vatsala, G.A. Reddy, K Hemant Kumar Nmam Institute of Technology Karkala Nitte India Computer Science & Engineering Pesu Bangaluru India Dayanada Sagar Academy of Technology & Management Bangaluru India Vit Ap University Department of Computer Science and Engineering India

ISBN: (纸本)9798350343144

A new technique in the healthcare industry called Content-Based Medical Image Retrieval (CBMIR) gives medical personnel an effective way to search for and access pertinent medical images from huge databases. The successful representation and analysis of medical images is made possible by feature extraction techniques, such as the Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP) which are essential to CBMIR systems. As a result, rather than comparing all of the images directly pixel-wise, the retrieval method concentrates on comparing extracted features that capture the most important image attributes. The proposed system is an hybrid approach. A hybrid feature extraction technique combines LBP and DWT and normalizing the features. Next the reduction of the features is done using principal component analysis (PCA). The representation of the images is done using grey-level cooccurrence matrix (GLCM). The study uses a dataset of wrist images to extract the images, next is used to identify the fractures. © 2023 IEEE.

关键词： Discrete wavelet transforms

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A Comparative Study on Facial Emotion Recognition using Deep Neural Networks 15

A Comparative Study on Facial Emotion Recognition using Deep...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Asha, S. Sundarrajan, R. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Srivilliputtur Tamilnadu India Department of Information Technology Kalasalingam Academy of Research and Education Srivilliputtur Tamilnadu India

ISBN: (纸本)9798331300579

Emotions are strongly associated with individuals' mood and *** the field of Human computer Interaction, human face plays a very vital *** to studies made by researchers' majority of the information conveyed through facial expressions than verbal *** day-to-day life, human expresses different types of feelings such as Happiness, Anger, Sadness, Fear, Disgust and Surprise which is considered as "Universal Emotions".It has always been difficult for computers to recognize human ***, a substantial effort was made by the researchers to build the Facial Emotion Recognition system and which was considered as the best tool for recognizing emotions through facial *** this paper, a detailed study on different methods that can be used in facial emotion recognition is *** this study, the literature is collected from various reputable research *** survey paper is based on the current approaches to face detection and feature extraction techniques for FER and also presented the real-time applications. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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PREDICTION OF OVARIAN SYNDROME USING DEEP FEATURE REPRESENTATION AND LEARNING APPROACHES

PREDICTION OF OVARIAN SYNDROME USING DEEP FEATURE REPRESENTA...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Sherubha, P. Madhumitha, R. Sasi Rekha, S.P. Arivazhagi Department of Information Technology Karpagam College of Engineering Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering University College of Engineering Ariyalur India

ISBN: (纸本)9798350305708

The high incidence of the polycystic ovarian syndrome (PCOS) among fertile women receives more and more attention. Women's physical and mental health may be at risk due to the common disorder PCOS, whose identification is an expanding issue in worldwide public health. In this work, we looked into the potential significance of abnormalities in the identification of PCOS and provided information-based feature representation and learning techniques (IFRL) for PCOS auxiliary identification. Here, a real-time dataset is acquired from the Coimbatore region by examining 74 individuals, and these samples are used in the algorithm. The suggested method performs pre-processing, feature learning and classification using the proposed IFRL. To achieve categorization, a multi-instance model was finally built. Several performance indices were used to evaluate our method's effectiveness, including AUC, precision, recall, accuracy, and F1-score for classification. © 2023 IEEE.

关键词： Classification (of information)

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