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2nd international conference on Algorithms, Network and Computer Technology, ICANCT 2023

The proceedings contain 16 papers. The topics discussed include: a design pattern of IAPVS platform based on distributed edge computing;side-channel attack security risk assessment model based on mutual information game;research and application of power grid data anomaly tracing method based on time series correlation;military unmanned equipment image target recognition method based on improved deep learning;analysis of the influence of adjacent new pile foundation construction on the structural deformation of the in-service subway tunnels;a numerical study on smoke propagation in metro tunnel fires with different door opening scenarios;effect of door openings on train fire scenarios within a subway depot;numerical simulation study on the embedding depth of anti-slip piles in fully weathered granite landslides;analysis and optimization of brace system in scaffold;and an intelligent fault diagnosis method that combines operating condition attribute encoding and multi-scale cascading concept.

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Advanced Forecasting of Wind Energy Generation Through Integration of AE-CLSTM

Advanced Forecasting of Wind Energy Generation Through Integ...

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2024 international conference on Green Energy, computing and Sustainable Technology, GECOST 2024

作者： Mahin, Md Saidur Rashid Kabir, Shaharier Islam, Md Ashikul Shufian, Abu Lamar University Dept. of Electrical Engineering TX United States American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350357905

The advent of sustainable energy solutions marks a pivotal shift in the global energy landscape, with wind energy playing a crucial role due to its minimal environmental impact. However, the inherent unpredictability of wind necessitates advanced forecasting methods for effective grid management. This research introduces the Autoencoder Convolutional Long Short-Term Memory (AE-CLSTM) model, a novel approach that synergizes the dimensionality reduction prowess of Autoencoders with the spatial and temporal pattern recognition capabilities of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. This integrated model aims to enhance the accuracy and reliability of wind energy forecasting, addressing the challenges posed by the stochastic nature of wind behavior. Employing an extensive dataset from a SCADA system of the wind farm, the AE-CLSTM model outperforms conventional CNN Model, LSTM Model, and CNN-LSTM model in terms of accuracy. The key findings of this research are encouraging: the model achieved a Correlation Coefficient of 95.43%, indicating a strong positive relationship between predicted and actual energy outputs, signifying the model's effectiveness in providing reliable forecasts than other models. These outcomes highlight the potential of AE-CLSTM in transforming wind power prediction, contributing significantly to the effective management of smart grids and the expansion of sustainable energy integration. © 2024 IEEE.

关键词： Long short-term memory

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Musical Brainwaves: Decoding Electroencephalography Signals Elicited by Musical Stimuli 1

Musical Brainwaves: Decoding Electroencephalography Signals ...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Ankitha C Juneja, Hita Bharadwaj, Ishita Varghese, Jeffrey S Honnavalli, Prasad B. Srinivasa, Gowri PES University Dept. of Computer Science Engg. Bengaluru India PES University PES Center for Information Security Forensics and Cyber Resilience Dept. of Computer Science Engg. Bengaluru India PES University PES Center of Pattern Recognition Dept. of Computer Science Engg. Bengaluru India

ISBN: (纸本)9798350353648

The influence of music on the human brain has been recognized for centuries, but recent advancements in brain-computer interfaces (BCIs) offer unparalleled opportunities to probe its complexities. This study investigates the effectiveness of music as a stimulus for driving BCIs using electroencephalography (EEG) signals. We analyze the neural correlates of music perception and imagination in the OpenMIIR dataset, employing advanced machine learning algorithms for EEG decoding. Exploiting various feature engineering techniques in time domain and frequency domain, we leverage convolutional neural networks and speech-inspired architectures like VLAAI to reconstruct the original audio stimuli from EEG data. Our results demonstrate a significant improvement in reconstructing music stimuli from EEG signals in the time domain, achieving a Pearson correlation of 0.35 compared to the current state-of-the-art performance of 0.11. Similarly, an increase in accuracy in classifying lyrical and non-lyrical musical excerpts based on participants' EEG responses was observed, rising from 49.6% to 57%. © 2024 IEEE.

关键词： Frequency domain analysis

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QPDet: Queuing People Detector for Aerial Images Based on Adaptive soft Label Assignment Strategy 27th

QPDet: Queuing People Detector for Aerial Images Based on ...

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27th international conference on pattern recognition, ICPR 2024

作者： Zhang, Yi Su, Yi Li, Siying Yi, Kai Department of Computer Science Sichuan University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Luzhou China

ISBN: (纸本)9783031781681

As a branch of object detection task, the detection of queuing people in the aerial images emerges as a new research direction in recent years. However, it remains challenging due to the complex background, variations in the viewpoints and densely located tiny objects. In this paper, we propose a tiny object detector called QPDet to solve the above issues. Firstly, a large-scale dataset of queuing people with dense and diverse annotations has been constructed to enrich the knowledge of this field, in which a large number of queuing people have been sorted and collected using drones. Secondly, a modified YOLOX has been utilized as our detector, which effectively detects the tiny objects in the aerial images. Finally, an adaptive soft label assignment strategy has been proposed to improve the robustness of our detector. Extensive experiments have been conducted to demonstrate the effectiveness of our approach, the results exhibit the superiority of our detector. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aerial photography

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An Open Framework for Nonverbal Communication in Human-Robot Interaction 15th

An Open Framework for Nonverbal Communication in Human-Robot...

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15th international conference on Ubiquitous computing and Ambient Intelligence

作者： Lozano, Ernesto A. Sanchez-Torres, Carlos E. Lopez-Nava, Irvin H. Favela, Jesus CICESE Comp Sci Carretera Ensenada Tijuana Ensenada 22860 Baja California Mexico UABC Comp Sci Km 106Carretera Tijuana Ensenada Ensenada 22860 Baja California Mexico

ISBN: (纸本)9783031486418;9783031486425

Nonverbal communication plays a vital role in human interaction. In the context of Human-Robot Interaction (HRI), social robots are designed primarily for verbal-based communication with humans, making nonverbal communication an open research area. We present a flexible, open framework designed to facilitate nonverbal interactions in HRI. Among its components is a P2P Browse-rBased Computational Notebook, leveraged to code, run, and share reactive programs. Machine-learning models can be included for real-time recognition of gestures, poses, and moods, employing protocols such as MQTT. Another key component is a broker for distributing data among different physical devices like the robot, wearables, and environmental sensors. We demonstrate this framework's utility through two interaction scenarios: (i) the first one employing proxemics and gaze direction to initiate an impromptu encounter, and (ii) a second one incorporating object recognition and a Large-Language Model to suggest meals to be cooked based on available ingredients. These scenarios illustrate how the framework's components can be seamlessly integrated to address new scenarios, where robots need to infer nonverbal cues from users.

关键词： Human-Robot Interaction Nonverbal communication Broker architectural pattern Langchain ChatGPT Computational Notebook

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Deep Learning Approach for Bone Marrow Cell Detection and Classification on Whole-Slide Images 8

Deep Learning Approach for Bone Marrow Cell Detection and Cl...

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8th IEEE international conference on Engineering Technologies and Applied Sciences, ICETAS 2023

作者： Fadhil Abbas, Najwa Shaizadi Meraj, Syeda Khedher, Akram M.Z. Shah, Asadullah Jazeera University Communication & Information Technology Dubai United Arab Emirates International Islamic University Malaysia Faculty of Information and Communication Technology Malaysia

ISBN: (纸本)9798350327090

The bone marrow cell analysis is taken as the critical standard for diagnosing leukemia. However, owing to the diverse morphology of these bone marrow cells, a lot of patience along with extensive experience is required for the examination. In this research paper, a deep learning method has been proposed for intelligent detection and classification of the bone marrow cells through applying the object detection model and pattern recognition in order to minimize the error probability and work intensity as well as improve the work proficiency on contrary to the human recognition methods for bone marrow cell detection. The proposed method has used Faster R-CNN along with the generalized average precision loss (G-AP loss) method to improve the accuracy of the cell detection. © 2023 IEEE.

关键词： Object detection

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ICCPR 2020 - Proceedings of 2020 9th international conference on computing and pattern recognition

ICCPR 2020 - Proceedings of 2020 9th International Conferenc...

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9th international conference on computing and pattern recognition, ICCPR 2020

ISBN: (纸本)9781450387835

The proceedings contain 87 papers. The topics discussed include: head posture recognition based on Newton-Raphson method;valid activity acquisition and recognition with WIFI based on blind source separation;an online prediction and trajectory tracking method for human activity recognition;weakly supervised fine-grained image recognition based on multi-channel attention and object localization;a lightweight handwriting recognition system based on an improved convolutional neural network;a model for luggage re-identification based on the network ensemble;AMINN: attention-based multi-information neural network for emotion recognition;a modular lane detection method based on scene understanding;asymmetric convolution-based neural network for SAR ship detection from scratch;and modulation characteristics of coherent motion field for the moving vehicles.

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Visualizing NASDAQ Composite Index Historical Data Using Tableau 6th

Visualizing NASDAQ Composite Index Historical Data Using Tab...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Safuan, Halawati Abd Jalil Nyein, Chit Ko Ko Riskhan, Basheer Ashfaq, Farzeen School of Computing and Informatics Albukhary International University Alor Setar Malaysia School of Computer Science SCS Taylors University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

The study explores the visualization and analysis of historical data for the NASDAQ Composite Index (^IXIC) using Tableau. It examines trends in daily closing prices and volume distribution and compares performance with the NYSE index. Findings reveal significant fluctuations in the NASDAQ index over 22 years, suggesting potential future price changes. Forecasted graphs offer insights into potential economic decline and recovery, highlighting the dynamic nature of the market. Volume distribution analysis identifies periods of heightened trading activity. Comparative performance analysis emphasizes different trends observed in the NASDAQ and NYSE indices. Due to market unpredictability and external influences, additional research is crucial for validation. This study showcases the value of data visualization and analysis in finance through Tableau. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inflation

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Intuitionistic fuzzy statistical correlation algorithm with applications to multicriteria-based decision-making processes

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international JOURNAL OF INTELLIGENT SYSTEMS 2021年第3期36卷 1386-1407页

作者： Ejegwa, Paul Augustine Onyeke, Idoko Charles Univ Agr Dept Math Stat Comp Sci PMB 2373 Makurdi 970213 Benue Nigeria

Intuitionistic fuzzy set is a significance soft computing tool for curbing fuzziness embedded in decision-making processes. To enhance the applicability of intuitionistic fuzzy sets in modelling practical real-life problems, various computing methods have been proposed like distance measures, similarity measures and correlation measures. This paper proposes an intuitionistic fuzzy statistical correlation algorithm with applications to pattern recognition and diagnostic processes. This novel method assesses the magnitude of relationship and indicates whether the intuitionistic fuzzy sets under consideration are correlated in either positive or negative sense. We substantiate the proposed technique with some theoretical results and numerically validate it to be superior in terms of accuracy and reliability in contrast to some hitherto techniques. Finally, we determine decision-making processes involving pattern recognition and diagnostic processes by using JAVA programming language to code the intuitionistic fuzzy statistical correlation measure.

关键词： correlation coefficient measure diagnostic processes fuzzy set intuitionistic fuzzy set pattern recognition

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Robust Color Texture Descriptors for Color Face recognition 5th

Robust Color Texture Descriptors for Color Face Recognition

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5th international conference on Recent Innovations in computing, ICRIC 2022

作者： Majeed, Shahbaz Singh, Chandan Department of Computer Science Punjabi University Patiala147002 India

ISBN: (纸本)9789811998751

Extraction of the local color texture has been one of the most desired and challenging research areas in face image recognition due to the ever-increasing use of color face images in numerous applications including biometric pattern matching, surveillance, and security. In this paper, we propose two novel color local texture descriptors called the local binary pattern of correlation of adjacent pixels (LBPC_A) and the local binary pattern of correlation of neighborhood pixels with the center pixel (LBPC_C). These two operators provide different local characteristics of an image. The operator LBPC_A provides the low-order texture characteristics while the operator LBPC_C provides the high-order texture characteristics. When these two operators are fused at the feature level, they provide high recognition rates with a small feature size for the face images. The resulting approach is referred to as LBPC_A + LBPC_C. The performance of the proposed operators has been compared with the state-of-the-art LBP-like operators on three color face databases-FERET, CMU-PIE, and AR. Experiments conducted on FERET, CMP-PIE, and AR demonstrate that the proposed descriptors outperform the state-of-the-art color texture descriptors. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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