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A Lightweight Security Algorithm for Authenticating Mobile Transactions using Wearable Devices and ECC Based Signcryption

A Lightweight Security Algorithm for Authenticating Mobile T...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： A Harrish Shibin David Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Internet of Things (IoT) devices are intelligent and act as the core component for capturing transactional data, initiating communication with wearable devices. Wearable devices such as smartwatches or smart wristbands serve as the user's payment interface. It aligns with near field communication (NFC) technology which gets connected with payment terminals at a closer range. As these terminals have secure payment gateways like Unified Payment Interface (UPI) and other intermediaries, transactions between the user's bank account and the merchant will be easier. To prove authorization in the transaction, an additional layer of security called vein pattern authentication is invoked. Alongside, elliptic curve cryptography (ECC) based signcryption ensures secure transactions by encrypting and digitally signing the payment data which guarantees confidentiality and non-repudiation. Therefore, a system that adds additional security to the mobile transactions by appending vein pattern and ECC-based signcryption technique. As a result, confidentiality, authorization, and robustness in the transaction is achieved at a higher rate by inculcating forward secrecy, verifiability and non-repudiation.

关键词： Authorization Near field communication Non-repudiation Logic gates Elliptic curve cryptography Robustness pattern recognition Internet of Things Cryptography Wearable devices

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Anomaly Handwritten Text Detection for Automatic Descriptive Answer Evaluation 11

Anomaly Handwritten Text Detection for Automatic Descriptive...

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11th international conference on computing and pattern recognition, ICCPR 2022

作者： Chatterjee, Nilanjana Shivakumara, Palaiahnaakote Pal, Umapada Lu, Tong Lu, Yue Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China

ISBN: (纸本)9781450397056

Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and answers to the question. This paper presents a novel method for detecting anomaly handwritten text in the responses written by the students to the questions. The method is proposed based on the fact that when the students are confident in answering questions, the students usually write answers legibly and neatly while they are not confident, they write sloppy writing which may not be easy for the reader to understand. To detect such anomaly handwritten text, we explore a new combination of Fourier transform and deep learning model for detecting edges. This result preserves the structure of handwritten text. For extracting features for classification of anomaly text and normal text, the proposed method studies the behavior of writing style, especially the variation at ascenders and descenders. Therefore, the proposed work draws principal axis which is invariant to rotation, scaling and some extent to distortion for the edge images. With respect to principal axis, the proposed method draws medial axis using uppermost and lowermost points. The distance between the medial axis and principal axis points are considered as feature vector. Further, the feature vector is passed to Artificial Neural Network for classification of anomaly text. The proposed method is evaluated by testing on our own dataset, standard dataset of gender identification (IAM) and handwritten forgery detection dataset (ACPR 2019). The results on different datasets show that the proposed work outperforms the existing methods. © 2022 ACM.

关键词： Students

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30th international Symposium on String Processing and Information Retrieval, SPIRE 2023

30th International Symposium on String Processing and Inform...

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30th international Symposium on String Processing and Information Retrieval, SPIRE 2023

ISBN: (纸本)9783031439797

The proceedings contain 31 papers. The special focus in this conference is on international Symposium on String Processing and Information Retrieval. The topics include: CAGE: Cache-Aware Graphlet Enumeration;Space-Time Trade-Offs for the LCP Array of Wheeler DFAs;computing All-vs-All MEMs in Grammar-Compressed Text;Sublinear Time Lempel-Ziv (LZ77) Factorization;new Advances in Rightmost Lempel-Ziv;engineering a Textbook Approach to Index Massive String Dictionaries;count-Min Sketch with Variable Number of Hash Functions: An Experimental Study;dynamic Compact Planar Embeddings;a Simple Grammar-Based Index for Finding Approximately Longest Common Substrings;on Suffix Tree Detection;On the Number of Factors in the LZ-End Factorization;Non-overlapping Indexing in BWT-Runs Bounded Space;efficient Parameterized pattern Matching in Sublinear Space;largest Repetition Factorization of Fibonacci Words;string Covers of a Tree Revisited;compacting Massive Public Transport Data;constant Time and Space Updates for the Sigma-Tau Problem;linear-Time Computation of Generalized Minimal Absent Words for Multiple Strings;frequency-Constrained Substring Complexity;chaining of Maximal Exact Matches in Graphs;optimally computing Compressed Indexing Arrays Based on the Compact Directed Acyclic Word Graph;algorithms and Hardness for the Longest Common Subsequence of Three Strings and Related Problems;binary Mixed-Digit Data Compression Codes;evaluating Regular Path Queries on Compressed Adjacency Matrices;approximate Cartesian Tree Matching: An Approach Using Swaps;optimal Wheeler Language recognition;approximation and Fixed Parameter Algorithms for the Approximate Cover Problem;Data Structures for SMEM-Finding in the PBWT;compressibility Measures for Two-Dimensional Data.

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Detection of Venous Thromboembolism Using Recurrent Neural Networks with Time-Series Data 24

Detection of Venous Thromboembolism Using Recurrent Neural N...

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3rd Asia conference on Algorithms, computing and Machine Learning, CACML 2024

作者： Xu, Can Huang, Yaqin Xiang, Xinni Lei, Haike Yang, Jie Shanghai Jiao Tong University Shanghai China Chongqing University Cancer Hospital Chongqing China West China Hospital Sichuan University Chengdu China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition China Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment China West China School of Medicine China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center China

ISBN: (纸本)9798400716416

Machine Learning (ML) has been widely applied to medical science for decades. As common knowledge, the progress of many diseases is often chronic and dynamic. Longitudinal data, or time-series data, has better descriptions of certain diseases, as it naturally captures the dynamic over time. Venous thromboembolism (VTE), as a regular complication of cancer tumour treatments, has been emphasized in many studies. Recent research has shown that ML algorithms incredibly contribute to VTE risk estimation and prediction. However, existing methods rarely consider the dynamic changes during the development of VTE, and often make decisions based on time-point data, such as the latest blood test results of patients. In this paper, we propose a VTE detection model that takes advantage of capturing dynamic information using time-series data. The model consists of a Recurrent Neural Network (RNN) based framework with time embedding and Attention mechanism to encode longitudinal features of patients. We test our proposed model on a real-world dataset that contains multiple times of blood test results of patients together with other clinical features. The network is compared with several commonly used ML models in previous VTE studies. The results show that our model outperforms all baseline models, and achieved a state-of-art performance with 80.0% accuracy and 0.881 AUC. © 2024 ACM.

关键词： Risk assessment

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Language-agnostic speech anger identification 44

Language-agnostic speech anger identification

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44th international conference on Telecommunications and Signal Processing (TSP)

作者： Saitta, Alessandra Ntalampiras, Stavros Univ Milan Dept Comp Sci Milan Italy

ISBN: (纸本)9781665429337

Following the constantly increasing adoption of affective computing based solutions, this paper investigates the feasibility of multilingual anger identification. To this end, we formed such a corpus by suitably combining seven different datasets representing five different languages, i.e. English, German, Italian, Urdu, and Persian. After analyzing the diverse characteristics of the datasets, we designed four classification algorithms, namely Support Vector Machine, Decision Tree-based Bagging scheme, Convolutional Neural Network, and Convolutional Recurrent Neural Network. Such classification mechanisms are trained on appropriate features extracted from time and/or frequency domains, while speech data have been balanced considering every diverse characteristic incorporated in the datasets (language, sex, acted, etc.). Our findings render multilingual anger identification feasible since the proposed audio pattern recognition methodology based on Mel-spectrograms and CRNN achieved quite satisfactory identification rates.

关键词： speech emotion recognition multilingual emotion recognition audio pattern recognition deep learning

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Parallelized Partial Discharge Feature Fusion and pattern recognition Based on MaxCompute

Parallelized Partial Discharge Feature Fusion and Pattern Re...

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Advances in Electrical Engineering and Computer Applications( AEECA), 2020 IEEE international conference on

作者： Zhaoyang Zhong Xiang Zheng Wenjiao Yang School of Automation and Electrical Engineering Dalian Jiaotong University Dalian China CRRC Changchun Railway Vehicles Co. Ltd Changchun China

ISBN: (纸本)9798350355260

With the rapid growth of the power grid scale and the increasing complexity of grid structures, the scale of partial discharge data in electrical equipment has gradually increased from the TB level to the PB level. The extraction of partial discharge characteristics is a crucial step in pattern recognition to accurately represent the features of partial discharge and improve the accuracy of identification results. In this paper, a parallel partial discharge feature fusion and pattern recognition method based on MaxCompute is proposed. Addressing the problem of information loss and the need to improve identification accuracy caused by single signal-level features or image-level features, this paper uses the ResNet50 residual neural network to extract PRPD spectrum features and fuses them with statistical features through Principal Component Analysis and Weighted Load Evaluation (PCA-WLE). The fused features are input into a Support Vector Machine (SVM) for classification and identification. Furthermore, t-Distributed Stochastic Neighbor Embedding (t-SNE) is used to compare and visualize feature distributions. The algorithm's computational speed is compared in three different computing environments. The results demonstrate that compared to single feature extraction methods, the proposed feature extraction method exhibits better clustering discernibility. When compared with common algorithms, the proposed method achieves over 95% accuracy in the identification of four types of partial discharge. In the MaxCompute environment, the algorithm maintains fast and stable computational performance, confirming the reliability and superiority of the proposed method.

关键词： Partial discharges Support vector machines Accuracy Clustering algorithms Big Data Feature extraction Discharges (electric) pattern recognition Data mining Residual neural networks

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Automated CNN-based Semantic Segmentation for Thermal Image of Solar Photovoltaic (PV) Panel

Automated CNN-based Semantic Segmentation for Thermal Image ...

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IEEE international conference on Control System, computing and Engineering (ICCSCE)

作者： Nurul Huda Binti Ishak Iza Sazanita Binti Isa Muhammad Khusairi Bin Osman Kamarulazhar Bin Daud Mohd Zulhamdy Bin Ab Hamid Mohd Shawal Bin Jadin Centre for Electrical Engineering Studies Universiti Teknologi MARA Cawangan Pulau Pinang Permatang Pauh Pulau Pinang Malaysia Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Driven by the growing worldwide need for sustainable energy sources, the utilization of solar photovoltaic (PV) panels has significantly increased in many applications. However, monitoring solar PV panels using thermal imaging significantly impacts panel efficiency and performance. To address this, thermal imaging offers a promising technique for monitoring and diagnosing these issues using computer vision and intelligence methods. This study proposes an automated semantic segmentation approach based on CNN to segment solar PV panels’ thermal images. The proposed method used U-Net and ResNet as CNN-based models for segmenting two different regions, solar panels and the background of thermal images. This segmentation approach leverages the power of deep learning to analyze thermal images, effectively highlighting precise results that facilitate prompt maintenance and optimization of PV panel performance. The model’s performance and pixel are evaluated using Intersection over Union (IoU) metrics and accuracy. The result, which showcases the potential of the proposed method, reveals that the proposed method using ResNet 50 has achieved superior performance compared to ResNet 18 and augmented UNet, with an IoU score of 0.9383 and an accuracy of 97%. In conclusion, semantic segmentation based on the CNN model significantly impacts thermal image pattern recognition in solar PV pattern recognition.

关键词： Deep learning Renewable energy sources Computer vision Semantic segmentation Computational modeling Imaging Thermal analysis pattern recognition Maintenance Monitoring

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Medicinal Herbs Identification

Medicinal Herbs Identification

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： M. Preethi S. Jansi Rani K. S. Pradhiksha J. Ram Kumar T. Vishal Sri Ramakrishna Engineering College Coimbatore India

Many rural communities have a strong belief in plant diversity. They collect useful plants and herbs and use them with indigenous knowledge and customs. One such oldest system that results in the use of medicinal herbs is Ayurveda. Approximately 10,000 plants are used medicinally in India, but not all plants are included in the official Ayurvedic Pharmacopoeia. Before becoming part of Ayurvedic medicine, all plants need to be thoroughly studied. For this reason, identifying herbs is the most important step. Many of these identifications are fully supported by human perception, leaving room for error and misjudgement. Therefore, it is necessary to develop an efficient system using computer vision, pattern recognition, and image processing algorithms alongside the availability of various combinations of feature detection methods with different classifiers that are often utilized in building an automatic identification system for herbal leaves using leaf images and reveal its associated information to realize knowledge.

关键词： Training Image segmentation Machine learning algorithms Image color analysis Neural networks Machine learning pattern recognition

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Hand Sign Language Translator Using Flex Sensors and Gyro Sensors in pattern recognition Method

Hand Sign Language Translator Using Flex Sensors and Gyro Se...

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Biomedical Engineering international conference (BMEiCON)

作者： Nanticha Supmool Pawarit Kositanon Supakorn Chaichalotornkul Udomporn Manupibul Department of Biomedical Engineering Faculty of Engineering Mahidol University Nakhon Phathom Thailand Faculty of Medicine Siriraj Hospital Sirindhorn School of Prosthetics and Orthotics Mahidol University Bangkok Thailand

ISBN: (数字)9798331505431

ISBN: (纸本)9798331505448

This project presents an innovative language translation glove designed specifically for individuals who are deaf or difficult of hearing and rely on sign language for communication. This glove represents a significant advancement in assistive technology because it employs pattern recognition to facilitate real-time, two-way communication. It is medical equipment that has the unique ability to translate both sign language gestures into audible speech and spoken language into text displayed on a screen. The inside wiring of the glove prioritizes efficiency, durability, and user comfort, ensuring practical application in everyday use. This paper presents the development of the software and hardware, with a focus on achieving high accuracy rates ranging from 93.3 to 100 percents for initial word predictions. The successful realization of this glove and system has the potential to impact assistive technologies for individuals with disabilities.

关键词： Sign language Cloud computing Translation Flexible printed circuits Databases Hardware Sensors Gyroscopes Servers Wearable sensors

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Joint IAPR international Workshop on Structural, Syntactic, and Statistical pattern recognition, S+SSPR 2022

Joint IAPR International Workshop on Structural, Syntactic, ...

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Joint IAPR international Workshop on Structural, Syntactic, and Statistical pattern recognition, S+SSPR 2022

ISBN: (纸本)9783031230271

The proceedings contain 32 papers. The special focus in this conference is on Structural, Syntactic, and Statistical pattern recognition. The topics include: Learning Distances Between Graph Nodes and Edges;Self-supervised Out-of-Distribution Detection with Dynamic Latent Scale GAN;a Novel Graph Kernel Based on the Wasserstein Distance and Spectral Signatures;discovering Respects for Visual Similarity;graph Regression Based on Graph Autoencoders;distributed Decision Trees;a Capsule Network for Hierarchical Multi-label Image Classification;Monte Carlo Dropout for Uncertainty Analysis and ECG Trace Image Classification;one-Against-All Halfplane Dichotomies;maximal Independent Vertex Set Applied to Graph Pooling;fast Distance Transforms in Graphs and in Gmaps;retargeted Regression Methods for Multi-label Learning;transformer with Spatio-Temporal Representation for Video Anomaly Detection;efficient Leave-One-Out Evaluation of Kernelized Implicit Mappings;graph Similarity Using Tree Edit Distance;data Augmentation on Graphs for Table Type Classification;improved Training for 3D Point Cloud Classification;on the Importance of Temporal Features in Domain Adaptation Methods for Action recognition;zero-Error Digitisation and Contextualisation of Piping and Instrumentation Diagrams Using Node Classification and Sub-graph Search;Refining AttnGAN Using Attention on Attention Network;annotation-Free Keyword Spotting in Historical Vietnamese Manuscripts Using Graph Matching;an Autoencoding Method for Detecting Counterfeit Coins;tarragona Graph Database for Machine Learning Based on Graphs;Human Description in the Wild: Description of the Scene with Ensembles of AI Models;interactive Generalized Dirichlet Mixture Allocation Model;classifying Me softly: A Novel Graph Neural Network Based on Features soft-Alignment;review of Handwriting Analysis for Predicting Personality Traits;graph Reduction Neural Networks for Structural pattern recognition;Sentiment Analysis from User Reviews U

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