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international conference on Secure Cyber computing and Communication (ICSCCC)

作者： Arti Bahuguna Shalom Ben Tenzing Namchyo Deepak Kumar Chaudhary Gopa Bhaumik Mahesh Chandra Govil Computer Science and Engineering National Institute of Technology Sikkim Sikkim India Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India

This paper proposes a handcrafted feature-based descriptor namely Local neighborhood average pattern (LNAP) for static hand gesture recognition. The fact, that the local descriptors are important in numerous computer vision applications and show great performance cannot be overstated. This is accentuated further in difficult environments. This is the major driving force behind continued research in this field. We developed a feature descriptor LNAP in this paper, with the aim of extracting complete microstructural features achieved by evaluating excitation in different ways and directional relevant data based on connections between pixels taken at various spatial configurations inside each 3 × 3 neighborhood. An LNAP descriptor represents the structure of hand textures in a simple and compact coding manner, resulting in its unique code in less time and memory than existing approaches. The proposed LNAP descriptor extracts the dominant features from the hand region which are further classified using SVM. The proposed LNAP's performance is assessed using three benchmark datasets: Massey University Dataset (MUGD), ASL Digit Datasets, and Ouhands Dataset in respect of accuracy and F1-score and found to achieve an accuracy of 93% (MUGD Set1), 96% (MUGD Set2), 92 % (MUGD Set 3), 89% (MUGD Set4), 71% (MUGD Set5), 99%, and 47% respectively. The experimental findings show that the suggested LNAP produces better outcomes than existing techniques.

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Comparative Analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer Models in Predicting Stock Prices

Comparative Analysis of Long Short-Term Memory (LSTM), Gated...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Chinecherem Umezuruike Deborah Olaniyan Julius Olaniyan Abidemi Emmanuel Adeniyi Adedoyin Oyebade David Abaneme Department of Software Engineering Bowen University Iwo Department of Computer Science Bowen University Iwo Department of Computer Science University of Ilorin Ilorin Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

This research explores the application of LSTM, GRU and Transformer models for predicting stock prices, aiming to enhance accuracy in financial forecasting. Stock price prediction is crucial for investment decision-making, yet challenging due to market volatility and complex patterns. The objectives are to evaluate the performance of LSTM, GRU and Transformer models using key metrics such as test loss, MAE, and MSE, and to compare their predictive capabilities. The LSTM model demonstrates robust performance with low test loss and MAE, indicating precise predictions and effective pattern recognition in financial data. In contrast, the Transformer model also shows promising results with relatively low test loss and MAE, albeit with larger errors in MSE and MAE metrics. Both models highlight the potential for accurate stock price prediction, suggesting avenues for future research to optimize model performance and reliability in financial forecasting applications. The experimental results show that GRU Outperformed LSTM and Transformer with an MSE of 0.0008, MAE of 0.0023, and high test accuracy of 0.9833.

关键词： Measurement Analytical models Accuracy Refining Predictive models Transformers pattern recognition Reliability Forecasting Long short term memory

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20th international conference on Artificial Intelligence and soft computing, ICAISC 2021

20th International Conference on Artificial Intelligence and...

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20th international conference on Artificial Intelligence and soft computing, ICAISC 2021

ISBN: (纸本)9783030878962

The proceedings contain 90 papers. The special focus in this conference is on Artificial Intelligence and soft computing. The topics include: Efficient Recurrent Neural Network Architecture for Musical Style Transfer;Impact of ELM Parameters and Investment Horizon for Currency Exchange Prediction;spectroscopy-Based Prediction of In Vitro Dissolution Profile Using Artificial Neural Networks;possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks;convolutional Autoencoder Based Textile Defect Detection Under Unconstrained Setting;clustering-Based Adaptive Self-Organizing Map;applying Machine Learning Techniques to Identify Damaged Potatoes;Quantifying the Severity of Common Rust in Maize Using Mask R-CNN;federated Learning Model with Augmentation and Samples Exchange Mechanism;factor Augmented Artificial Neural Network vs Deep Learning for Forecasting Global Liquidity Dynamics;flexible Data Augmentation in Off-Policy Reinforcement Learning;polynomial Neural Forms Using Feedforward Neural Networks for Solving Differential Equations;quantum-Hybrid Neural Vector Quantization – A Mathematical Approach;A Graphic CNN-LSTM Model for Stock Price Predication;applying Convolutional Neural Networks for Stock Market Trends Identification;the Extreme Value Evolving Predictor in Multiple Time Series Learning;towards Synthetic Multivariate Time Series Generation for Flare Forecasting;the Streaming Approach to Training Restricted Boltzmann Machines;abrupt Change Detection by the Nonparametric Approach Based on Orthogonal Series Estimates;Recommendation System for Signal Processing in SHM;integrate-and-Fire Neurons for Low-Powered pattern recognition;monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines;a Proposal for Hybrid Memories Management Exploring Fuzzy-Based Page Migration Policy;a Learning Automata-Based Approach to Lifetime Optimization in Wireless Sensor Networks.

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Mobile Robot Control Based on Virtual Impedance Force Feedback

Mobile Robot Control Based on Virtual Impedance Force Feedba...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： Masaru Sasaki Taro Shibanoki Hideyuki Tonooka Graduate School of Science and Engineering Ibaraki University Ibaraki Japan Graduate School of Natural Science and Technology Okayama University Okayama Japan

In this study, a tactile feedback method is proposed for virtual repulsive force based on non-contact impedance and applied to collision avoidance for a biological signal-controlled mobile robot. Virtual walls based on mechanical impedance are placed around the robot, it can avoid obstacles using virtual repulsive forces when obstacles meet the virtual walls. The proposed method provides tactile feedback about the force to the operator, which enables the operator to recognize the environment around the robot. In the experiments, a blindfolded participant controlled a mobile robot using myoelectric signals. The results demonstrated that the robot could be operated stably.

关键词： Vibrations Computational modeling Force Force feedback Tactile sensors pattern recognition Impedance

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20th international conference on Artificial Intelligence and soft computing, ICAISC 2021

20th International Conference on Artificial Intelligence and...

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20th international conference on Artificial Intelligence and soft computing, ICAISC 2021

ISBN: (纸本)9783030879853

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Study of robust facial recognition under occlusion using different techniques 5

Study of robust facial recognition under occlusion using dif...

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5th international conference on Intelligent computing and Control Systems, ICICCS 2021

作者： Jayant, Deepanshu Bhat, Aruna Delhi Technological University Department of Computer Science New Delhi India

ISBN: (纸本)9781665412728

This paper represents the different techniques used for the study of robust facial recognition. The need of facial recognition is expanding very rapidly in current technologies as it is possible to identify a humane face features through different digital mediums. Although, facial recognition is not done achieved easily in the real-world situation as most of the time the human face goes through various occlusions making hard for the system to complete the identification process. The sole purpose of this article is to compare the techniques used and identify the most accurate and efficient one from all of them. Robust facial recognition is a task of doing recognition under uncertain environments and features. The main aim of the robust facial recognition is to analyze and identify the face images where the picture proposes difficult viewpoints, angles, poses, illumination, noises and expressions. The techniques studied for this article are Local Binary patterns, HOG features, Occlusion-adaptive Deep Networks (ODN), Robust Principal Component Analysis (RPCA), Progressive Convolutional Neural Network and Region Attention Networks (RAN). In this article, a table is created summarizing the experiments and results of the techniques studied. © 2021 IEEE.

关键词： Local binary pattern

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Systematic Review: Image Processing Based Dorsal Vein pattern Biometric Authentication System

Systematic Review: Image Processing Based Dorsal Vein Patter...

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international conference on computing, Engineering, and Design (ICCED)

作者： Fajar M Syam Rian Maulana Yusup Samsudin Muchtar Ali Setyo Yudono Electrical Engineering Nusa Putra University Sukabumi Indonesia

Biometrics leverages unique biological or behavioral traits for person or object identification, characterized by traits such as uniqueness, indivisibility, immutability, and resilience against loss or theft. Evolving biometric systems include facial recognition, iris scanning, fingerprint recognition, palm line recognition, and dorsal hand vein recognition. Dorsal hand vein identification, concealed beneath the skin, provides a highly secure biometric modality resistant to forgery, tampering, or damage. This systematic literature review offers an overview of dorsal hand vein image processing for biometric authentication systems, covering recent advancements in image processing, including concept clarification, data acquisition, pre-processing, segmentation, feature extraction, and identification phases. The review spans research articles from 2018 to 2023, sourced from reputable outlets such as 208 MDPI journals, Google Scholar, IEEE Xplore, Google Books, and other scholarly repositories. Stringent article selection, guided by predefined keywords, ensures research quality. Findings emphasize the necessity of a camera and infrared LED for dorsal hand vein data acquisition to build an effective identification system. Pre-processing involves Region of Interest (ROI) selection, contrast enhancement, and intensity normalization. Segmentation employs morphological operations, filter banks, and adaptive thresholding. Feature extraction encompasses statistical features and the Gray Level Co-occurrence Matrix (GLCM). Convolutional Neural Networks (CNNs) play a pivotal role in identification, featuring layers such as Conv1_1, Conv1_2, batch normalization, max-pooling, and dropout layers.

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Terrain environment classification and recognition for soft lower-limb exosuit

Terrain environment classification and recognition for soft ...

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2021 IEEE international conference on Real-Time computing and Robotics, RCAR 2021

作者： Ni, Jiangpeng Wang, Zhuo Li, Feng Su, Zhilong Cao, Wujing Chen, Chunjie Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China Shenzhen Institute of Advanced Technology Chinese Academy of Sci-ences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

ISBN: (纸本)9781665436786

Reliable environmental context prediction is critical for wearable robots (exoskeletons or prostheses) to assist terrain-adaptive locomotion. Inspired by the mechanism of human perception of the environment, the vision sensor can be introduced into the soft lower limb exosuit robot so that future environmental information can be used to improve the control. In this paper, a stable terrain classification and recognition system (TCRS) is designed for lower-limb soft exosuit robots. When the exosuit robot perceives the environment, it uses the residual network to train the terrain classifier to classify the terrain. During walking, the wearable soft exosuit robot collects the indoor environment, preprocesses, and marks the sampled images separately. The experimental results show that the classification accuracy is more than 90%, and the highest is 97.5%. The results in this study may lead to novel context recognition strategies in reliable decision-making, efficient sensor fusion, and improved intelligent system design in various applications. © 2021 IEEE.

关键词： Landforms

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On the Role of LLM to Forecast the Next Pandemic

On the Role of LLM to Forecast the Next Pandemic

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Tommaso Ruga Eugenio Vocaturo Ester Zumpano CNR-NANOTEC DIMES University of Calabria Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Large Language Models (LLMs) have emerged as powerful tools in medical applications, demonstrating signifi-cant potential for analyzing vast clinical datasets, identifying patterns, and facilitating early detection of health anomalies. These capabilities are particularly relevant for detecting both individual disease onset and potential pandemic scenarios at a population level. The integration of LLMs into medical workflows could revolutionize how data-driven insights are harnessed for pandemic prediction and management, although implementing these technologies requires careful consideration of critical issues, particularly data privacy and security. This paper presents a comprehensive examination of current research at the intersection of LLMs and pandemic response, analyzing the literature from both bibliometric and medical perspectives. Through the selection of 849 publications across major databases, we provide an overview of the current state of research in this domain, identify emerging patterns from a clinical standpoint, and evaluate potential implications for future pandemic prediction. Our findings reveal significant trends in the application of LLMs to pandemic-related challenges, highlighting both opportunities and critical areas requiring further investigation, particularly in mental health impacts and early warning systems. The analysis reveals that while LLMs show promise in early detection and pattern recognition, challenges remain in data privacy, model interpretability, and the integration of diverse data sources. This research serves as a foundation for understanding how LLMs can be effectively deployed in pandemic prediction and management, while acknowledging the complexities and ethical considerations inherent in such applications.

关键词： Data privacy Pandemics Reviews soft sensors Large language models Mental health Medical services Predictive models Market research pattern recognition

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soft computing and Machine Learning in Dam Engineering

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WATER 2023年第5期15卷 917-917页

作者： Hariri-Ardebili, Mohammad Amin Salazar, Fernando Pourkamali-Anaraki, Farhad Mazza, Guido Mata, Juan Univ Colorado Boulder Dept Civil Environm & Architectural Engn Boulder CO 80309 USA Univ Maryland Coll Comp Math & Nat Sci College Pk MD 20742 USA Int Ctr Numer Methods Engn CIMNE P Gral Martinez Campos 41 Madrid 28010 Spain Univ Colorado Denver Dept Math & Stat Sci Denver CO 80204 USA Italian Natl Comm Large Dams I-2300198 Rome Italy Natl Lab Civil Engn LNEC Ave Brasil 101 P-1700066 Lisbon Portugal

Dams have played a vital role in human civilization for thousands of years, providing vital resources such as water and electricity, and performing important functions such as flood control. The scale and complexity of dam projects have increased in recent years, making their safety evaluation even more challenging. Therefore, it is crucial that dam engineers consider all potential risks and take appropriate measures to ensure the safety and stability of these structures [1]. The nature and existence of dams are highly coupled with concepts such as population growth, climate change, global warming, and water security [2]. According to the international Commission on Large Dams’s (ICOLD) [3] most recent update in April 2020, there are about 58,700 registered large dams in the world. Figure 1 illustrates the global distribution of these large *** computing is a collection of techniques in computer science that aim to provide solutions to problems that are difficult or impossible to solve using traditional, “hard” methods of computation. soft computing encompasses various computational techniques that are designed to replicate human-like problem-solving behavior. It includes a variety of techniques such as fuzzy logic, neural networks, genetic algorithms, and probabilistic reasoning, which are used to solve problems that are too complicated for traditional, rule-based approaches [4]. soft computing has a wide range of applications in engineering, including control systems, signal processing, pattern recognition, and optimization. For example, in signal processing, neural networks and genetic algorithms can be used to improve the accuracy of signal classification and feature extraction. In optimization, genetic algorithms and probabilistic reasoning can be used to solve complex optimization *** warning systems for dam failure [15];Real-time monitoring and control of dam systems [16];Predictive maintenance and condition monitoring of dam structures [17,1

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