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2024 International Conference on Smart Devices, ICSD 2024

作者： Aditya Pai, H. Agarwal, Jyoti Chandrappa, S. Mittal, Tanusha Joshi, Kapil School of Computer Science and Engineering Bengaluru India Computer Science and Engineering Dehradun India Uttaranchal University Uttaranchal Institute of Technology Department of CSE Dehradun India

ISBN: (纸本)9798350394412

The purpose of the paper is to find the clinical parameters of an individual using normal webcam. The paper aims to help the healthcare domain. In recent times there has been emergence of smartphone devices which monitor vital information's. These devices have special sensors like Electrocardiogram (ECG) and Photoplethysmography (PPG) to check heart and respiratory rate. The smartphone camera reads the heart and respiratory data from the user's face. The method used in the paper mentions about the variation in the Hue that reads both heart and respiration rate from the face of a user. The result in the paper explains how the proposed approach gives more accurate results than the other detection algorithms. © 2024 IEEE.

关键词： Photoplethysmography

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Fusing Multimodal Streams for Improved Group Emotion Recognition in Videos 27th

Fusing Multimodal Streams for Improved Group Emotion Recogni...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Kumar, Deepak Dhamdhere, Piyush Raman, Balasubramanian Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India

ISBN: (纸本)9783031783043

Recognizing social cues and emotions is vital for navigating daily interactions, understanding emotions in conversations, interpreting body language in meetings, and supporting friends in difficult situations. This work focuses on analyzing group-level emotions in videos captured in natural settings, marking an attempt at multimodal group-level emotion analysis. Automatic group emotion recognition is pivotal for understanding complex human-human interactions. Group emotion recognition in videos presents several challenges because existing work predominantly focuses either on individual emotion recognition or group emotion analysis in static images. To address this challenge, we introduce a deep-learning-based multimodal fusion model that integrates diverse modalities, including audio, video, and scene. Feature extraction employs advanced models like TimeSformer for video description and wav2vec2.0 for audio analysis. All the experiments are conducted on the VGAF dataset. Our key findings include: (1) Multimodal approaches outperform their unimodal counterparts, (2) Experimental results confirm the superior performance of proposed approach compared to benchmark methods on the given dataset, and (3) There is a strong correlation between modalities and respective emotions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Emotion Recognition

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Face Mask Detection and Alert System 1

Face Mask Detection and Alert System

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Singh, Sukhvir Rawat, Daksh Narang, Harshit Das, Purushottam Dhanalakshmi, R. Yadav, Satya Prakash Graphic Era Hill University Computer Science and Engineering Dehradun India Indian Institute of Information Technology Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350371802

The COVID-19 pandemic has underscored the importance of preventive measures, with the use of face masks being a crucial aspect of controlling the virus's spread. Thus, in the response to the global imperative to curb the spread of infectious diseases, particularly in the context of the COVID-19 pandemic, this research paper delves into the realm of face mask detection and alert systems. With the ubiquity of technology, the development of automated solutions for ensuring adherence to mask-wearing protocols has become most necessary. This paper provides a thorough examination of existing methodologies for face mask detection, ranging from image& video-based approaches to sophisticated deep learning models. Additionally, explores the integration of alert systems into these detection frameworks, emphasizing real- time processing considerations and user interface design. The review incorporates case studies, ethical considerations, and privacy implications, offering a comprehensive overview of the current state of the art. By elucidating the challenges, successes, and future directions, this research paper contributes to the discourse on leveraging technology for public health measures and sets the stage for continued advancements in face mask detection systems and also provides a comprehensive review of the state-of-the-art in face mask detection technologies and explores the design. © 2024 IEEE.

关键词： COVID-19

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DP-BERT: A pre-trained deep language model for depression prediction using microarray data

DP-BERT: A pre-trained deep language model for depression pr...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gao, Junyu Zeng, Min Li, Yiming Wang, Fang Zheng, Ruiqing Liu, Jin Guo, Fei Li, Min Central South University School of Computer Science and Engineering Changsha410083 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China

ISBN: (纸本)9798350386226

In recent years, the increasing number of individuals diagnosed with depression and the growing awareness of its impact on modern society have highlighted the significance of accurate depression diagnosis. Microarray data has played a crucial role in uncovering the genetic mechanisms underlying depression. However, existing methods for depression prediction using microarray data often rely on the selection of differentially expressed genes. This approach disregards important information from other genes and is susceptible to batch effects, thereby limiting generalizability and model stability. To address these limitations, we propose DP-BERT, a depression prediction model based on Bidirectional Encoder Representations from Transformers (BERT). DP-BERT follows a pre-training and fine-tuning paradigm, leveraging a large amount of unlabeled microarray data from diverse sequencing platforms for pretraining to extract comprehensive genetic-level representations of psychiatric disorders. Subsequently, supervised fine-tuning is performed for depression prediction. Experimental results demonstrate that the pre-trained model achieves superior performance in depression prediction. The source code can be obtained from https://***/CSUBioGroup/DP-BERT. © 2024 IEEE.

关键词： Prediction models

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Physics-Based 2D Direction Finding of an Amplitude Modulated Signal Using a Uniform Triangular Array

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Progress in Electromagnetics Research C 2025年 155卷 121-126页

作者： Ren, Kai Department of Electrical Engineering and Computer Science South Dakota School of Mines and Technology Rapid CitySD57701 United States

A uniform triangular array (UTA) is proposed for physics-based 2D direction-of-arrival (DOA) estimations of unknown incoming signals. Three capacitively loaded top-hat antennas are used as array elements. Unlike conventional array-based direction finding (DF) systems, complex antenna radiation patterns are used in array manifold calculations and DOA predictions, where coupling among array elements is naturally resolved. Both continuous wave (CW) signals and single tone signals with amplitude modulation (AM) are considered in DF simulations. Cramer-Rao bound (CRB) values are calculated to provide theoretical lower bounds of DF accuracies. Multiple signal classification (MUSIC) algorithm is used to further demonstrate the DF performance of the triangular antenna array without any angle ambiguities within the field of view from 0◦ to 360◦ in the azimuth direction and from 1.25◦ to 178.75◦ in the elevation direction. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Amplitude modulation

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A Secure Vehicle Registration and Authentication System Using Distributed Blockchain technology 3

A Secure Vehicle Registration and Authentication System Usin...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Rakibul Hasan, M.M. Kashem, Mohammod Abul Tahsin, Akil Mohammad Ronju, Md Raziur Rahman Bangladesh University of Professionals Department of Information and Communication Technology Dhaka Bangladesh International University of Business Agriculture and Technology Department of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering and Technology Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350388282

Vehicle fraud is a significant problem as the vehicle registration data can easily be fabricated and duplicated. The reason for the forgery is that in the usual registration process, car data is maintained in a centralized database, which is readily compromised. Again, the law enforcement agencies waste a large amount of time authorizing the license and registration papers. We used blockchain in our work to solve the vulnerability issues with traditional database. In this blockchain network, the data is encrypted using keccak256 hashing algorithm and the documents are secured in an IPFS network to provide topmost security. Our research also implements a secure authentication process to verify the registration and license of a vehicle owner and keep track of crime history. The use of this system will eliminate the deception of vehicle registration and traffic will be much safer with easier knowledge of vehicle fitness and crime history. © 2024 IEEE.

关键词： Blockchain

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Enhanced Weld Defect Categorization via Nature-Inspired Optimization-Driven Neural Networks

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SN computer science 2024年第8期5卷 1035页

作者： Antony Vigil, M.S. Maheswari, K. Minu, M.S. Kulkarni, Gururaj L. Chandra Sekhar Reddy, L. Satishkumar, P. Haldar, Barun Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Ramapuram Chennai India Department of Computer Science and Engineering CMR Technical Campus Telangana Kandlakoya Hyderabad India Computer Science and Engineering Vardhaman College of Engineering Hyderabad Shamshabad India Department of Computer Science and Engineering (Data Science) CMR College of Engineering & amp Technology Kandlakoya(V) Hyderabad Medchal India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Mechanical and Industrial Engineering Department College of Engineering Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia

In many different sectors, the safety and dependability of welded structures are crucially dependent on the accurate identification of weld defects. To address this, we present a novel method for categorizing weld defects named as Pelican Optimization-based convolutional Bi-Long Short-Term Memory Networks (POCBi-LSTM). With this integrated strategy, weld defect categorization is intended to be more accurate and effective. To train and assess a suggested classification algorithm, a collection of 220 samples with 5various types of weld faults is utilized. The study's technique comprised denoising with a Modified Median Filter (MMF) for noise reduction and binary histogram equalization (BHE) for image enhancement. The Scale Invariant Feature Transform (SIFT) was used to extract features. The outcomes demonstrate that the proposed strategy performs greater than conventional methods and delivers superior recognition accuracy, precision, recall, and F1 scores of 92.9%, 94.5%, 89.5%, and 92.8%, respectively. Experiment findings demonstrate the effectiveness and advantage of the recommended approach in classifying weld faults. Through the use of an integrated strategy, it is hoped to increase the dependability and effectiveness of weld defect categorization by handling local pattern recognition and long-term interdependence efficiently. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Modified median filter Pelican Optimization-Based convolutional Bi-Long Short Term Memory Networks (POCBi-LSTM) Weld defects Weld structures

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An Intensive Review on Deep Learning Approaches in Non-Invasive Fetal Healthcare Monitoring 4

An Intensive Review on Deep Learning Approaches in Non-Invas...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Harismithaa, L.R. Sadasivam, G. Sudha Psg College of Technology Dept. of Computer Science of Engineering Coimbatore India

ISBN: (纸本)9798331540364

Fetal health care is one of the most crucial concerns in today's world. Evaluating fetal well-being has always been challenging. Proper development and monitoring of the fetus are indispensable for its growth and healthy life. Monitoring and diagnosing abnormalities at the right gestational age is very significant. There are remarkable technical advancements in fetal well-being evaluation, bridging the gap between prenatal medicine and Artificial intelligence. This study provides an intensive review of various modes of non-invasive data that can be used for fetal health monitoring and decision support. Also, existing studies on fetal health care that use deep learning techniques for diagnosis and monitoring are explored. Also, a generic framework designed for an Automated Fetal healthcare Monitoring system using AI approaches is presented. © 2024 IEEE.

关键词： Fetal monitoring

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Metaphor Detection with Context Enhancement and Curriculum Learning

Metaphor Detection with Context Enhancement and Curriculum L...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Jia, Kaidi Li, Rongsheng College of Computer Science and Technology Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798891761148

Metaphor detection is a challenging task for natural language processing (NLP) systems. Previous works failed to sufficiently utilize the internal and external semantic relationships between target words and their context. Furthermore, they have faced challenges in tackling the problem of data sparseness due to the very limited available training data. To address these two challenges, we propose a novel model called MiceCL. By leveraging the difference between the literal meaning of the target word and the meaning of the sentence as the sentence external difference, MiceCL can better handle the semantic relationships. Additionally, we propose a curriculum learning framework for automatically assessing difficulty of the sentence with a pre-trained model. By starting from easy examples and gradually progressing to more difficult ones, we can ensure that the model will not deal with complex data when its ability is weak so that to avoid wasting limited data. Experimental results demonstrate that MiceCL achieves competitive performance across multiple datasets, with a significantly improved convergence speed compared to other models. Our model is available at https://***/Evilxya/***. © 2024 Association for Computational Linguistics.

关键词： Curricula

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Microfacet rendering with diffraction compensation

Microfacet rendering with diffraction compensation

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作者： Yang, Xudong Lyu, Aoran Xian, Chuhua Cai, Hongmin School of Computer Science and Engineering South China University of Technology Guangzhou China

The traditional microfacet rendering models usually only consider the straight propagation of light and do not take into account the diffraction effect when calculating the radiance of outgoing light. However, ignoring the energy generated by diffraction can lead to darker rendering results when the object's surface has many small details. To address this issue, we introduce a diffraction energy term in the microfacet model to compensate for the energy loss caused by diffraction. Starting from the Fresnel-Kirchhoff diffraction theorem, we combine it with the Cook-Torrance model. By incorporating the computed diffraction radiance into the outgoing radiance of the microfacet, we obtain a diffraction-compensated BRDF (Bidirectional Reflectance Distribution Function) model. Experimental results demonstrate that our proposed method has a significant effect in compensating for outgoing light and produces more realistic rendering results. © 2024 John Wiley & Sons Ltd.

关键词： Diffraction

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