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15th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2023

作者： Barhanpurkar, Kanishk Mandlik, Nikita Rajawat, Anand Singh Goyal, S.B. Mihaltan, Traian Candin Verma, Chaman Raboaca, Maria Simona Thomas J. Watson College of Engineering and Applied Science Binghamton University United States School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca40033 Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest1053 Hungary Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department National Ramnicu Valcea Romania

ISBN: (纸本)9798350321388

The research paper presents a novel analysis of textual data based on Natural Language Processing (NLP) techniques to analyse New York Times articles from January 2019 to May 2023. The purpose of this paper is to gain an understanding of the economic impact that follows Covid-19 disease. New York Times (NYT), it was started in 1884 is one of the most prominent newspapers in the world. Additionally, we have used the New York Times Archive API to collect the data for the given timeframe. By analysing sentiment analysis, topic modelling, entity recognition, and keyword extraction, valuable insights can be gathered into market trends, industry shifts, and policy interventions. The authors have created a data science pipeline using Amazon Web Services (AWS), which enables data collection, storage, and visualization. It contributes to a better understanding of the pandemic's short-term and long-term economic effects. The results of this study demonstrate Natural Language Processing techniques' potential as a tool for financial analytics, assisting policymakers, economists, and businesses in formulating recovery strategies. © 2023 ieee.

关键词： Economic and social effects

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Augmented EEG Signal Classification for Energy data visualizations 4

Augmented EEG Signal Classification for Energy Data Visualiz...

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4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023

作者： Kucukler, Omer Faruk Amira, Abbes Malekmohamadi, Hossein Institute of Artificial Intelligence De Montfort University Leicester United Kingdom Department of Computer Science University of Sharjah United Arab Emirates

ISBN: (纸本)9798350369694

Classification of electroencephalogram (EEG) data is dependent on data size, quality, and generalizability to decode invaluable information from brain-computer interface (BCI) systems. EEG data collection typically occurs within controlled laboratory environments using long sections. Participation in these experiments draws less attention due to the challenging nature of the EEG experiments and the intended objective. Hence, an approach that offers the potential for both cost and time efficiency is the use of artificial data generation. data augmentation can improve the classification performance by increasing training data. This study employs a Generative Adversarial Network (GAN) model to enhance the empirically collected EEG dataset, which was introduced for the purpose of analyzing the perceptions of energy users regarding energy data visualizations. The data size is initially expanded through the use of a GAN model. Subsequently, the augmented data is assessed in terms of its classification performance. Three different sample groups—defined as male, female, and mixed—are shown in the results. Each group is used to generate synthetic EEGs, separately. To evaluate the enhanced EEG data, a scenario of transforming EEG data into spectrogram images and then classifying them using a Convolutional Neural Network (CNN) is used. The results show that the female sample group performed the best in terms of valence and arousal, with accuracies of 87.9% and 89.06%, respectively. This study shows promising results in terms of increasing EEG data size and improving classification performance. © 2023 ieee.

关键词： data visualization

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Compact Artificial Neural Network Based on Task Attention for Individual SSVEP Recognition With Less Calibration

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ieee TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 2525-2534页

作者： Wang, Ze Wong, Chi Man Wang, Boyu Feng, Zhao Cong, Fengyu Wan, Feng Macau Univ Sci & Technol Fac Innovat Engn Macao Ctr Math Sci Taipa Macau Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Resp Dis AI Lab Epidem Intelligence & Med Big Data Taipa Macau Peoples R China Univ Macau Fac Sci & Technol Dept Elect & Comp Engn Taipa Macau Peoples R China Univ Macau Inst Collaborat Innovat Ctr Cognit & Brain Sci Taipa Macau Peoples R China Univ Macau Inst Collaborat Innovat Ctr Artificial Intelligence & Robot Taipa Macau Peoples R China Western Univ Brain Mind Inst Dept Comp Sci London ON N6A 3K7 Canada Dalian Univ Technol Fac Med Sch Biomed Engn Dalian 116024 Peoples R China

Objective: Recently, artificial neural networks (ANNs) have been proven effective and promising for the steady-state visual evoked potential (SSVEP) target recognition. Nevertheless, they usually have lots of trainable parameters and thus require a significant amount of calibration data, which becomes a major obstacle due to the costly EEG collection procedures. This paper aims to design a compact network that can avoid the over-fitting of the ANNs in the individual SSVEP ***: This study integrates the prior knowledge of SSVEP recognition tasks into the attention neural network design. First, benefiting from the high model interpretability of the attention mechanism, the attention layer is applied to convert the operations in conventional spatial filtering algorithms to the ANN structure, which reduces network connections between layers. Then, the SSVEP signal models and the common weights shared across stimuli are adopted to design constraints, which further condenses the trainable ***: A simulation study on two widely-used datasets demonstrates the proposed compact ANN structure with proposed constraints effectively eliminates redundant parameters. Compared to existing prominent deep neural network (DNN)-based and correlation analysis (CA)-based recognition algorithms, the proposed method reduces the trainable parameters by more than 90% and 80% respectively, and boosts the individual recognition performance by at least 57% and 7% ***: Incorporating the prior knowledge of task into the ANN can make it more effective and efficient. The proposed ANN has a compact structure with less trainable parameters and thus requires less calibration with the prominent individual SSVEP recognition performance.

关键词： Calibration Task analysis Electroencephalography Brain modeling Correlation visualization Target recognition Artificial neural network attention layer brain-computer interface steady-state visual evoked potential

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A Forest-fire Damage Estimation and visualization Method for Time-series Multi-location 25

A Forest-fire Damage Estimation and Visualization Method for...

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25th International Electronics Symposium, IES 2023

作者： Hamano, Amane Sasaki, Shiori Musashino University Faculty of Data Science Tokyo Japan

ISBN: (纸本)9798350314731

This paper presents a method for estimating and visualizing the extent and severity of forest-fire damage using time-series geographic information and satellite image data, and its application to multi-location analysis for the purpose of evaluating the impact of forest loss on the global environment. First, the Normalized Burn Ratio (NBR) of forests is calculated using multispectral satellite images before and after the forest fire. Second, using the NBRs before and after the fire, the difference (dNBR) is calculated to estimate the severity of the fire and its extent. Third, the size of fire-Affected area is estimated from these results to compare among multiple locations using GIS. The accuracy of this method is verified by comparing the estimated affected area with the actual affected area. With serious forest fires occurring around the world in recent years, there is a growing demand for a method to accurately assess the impact of forest fires and the importance of forests in the urban and global environment. This study demonstrates the feasibility of our method and its potential contribution to 'SDG15 monitoring' as a realization of time-series multi-location analysis and prediction of disaster-Affected areas through case experiments on forest fires occurred in Australia, Indonesia, and Japan. © 2023 ieee.

关键词： Fires

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A Human-Computer Interaction Perspective on Clinical Decision Support Systems: A Systematic Review of Usability, Barriers, and Recommendations for Improvement 11

A Human-Computer Interaction Perspective on Clinical Decisio...

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ieee 11th International Conference on Serious Games and Applications for Health (SeGAH)

作者： Ferreira, Goncalo Oliveira, Eva Stamper, John Coelho, Antonio Paredes, Hugo Rodrigues, Nuno F. Univ Porto Fac Engn Porto Portugal Polytech Inst Cavado & Ave Human Comp Interact Braga Portugal Carnegie Melon Univ Human Comp Interact Inst Pittsburgh PA USA INESC TEC Vila Real Portugal Polytech Inst Cavado & Ave Escola Super Tecnol Braga Portugal

ISBN: (纸本)9798350346077

Clinical decision support systems have been increasingly utilized in the healthcare industry to improve patient outcomes and enhance clinical decision-making, taking advantage of the growing digital medical data. Despite their potential, there are still obstacles in an extensive adoption of these systems, such as low usability and human factors. In this systematic review, several articles describing clinical decision support systems with clinical validation are used to address some of the gaps, as well as to map the current academic landscape for the given context. The selected articles are observed through a Human-Computer Interaction perspective, aiming to identify the state-of-the-art, as well as barriers to the application of these principles. From an initial database search resulting in 121 articles, 16 articles were selected that fulfilled the chosen criteria: (1) article must be available and written in English, (2) article must report experimental work, (3) the reported system must be clinically validated. The research strategy followed the PRISMA framework. We highlight the need for clinical validation, a standardized clinical decision support taxonomy and the evaluation of these tools across multiple variables. Based on the found results, a list of recommendations can be formed to aid the development of future CDSS, or the improvement of current ones.

关键词： clinical decision support system humancomputer interaction taxonomy evaluation data visualization

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k-Hopped Link Prediction With Graph Embedding

k-Hopped Link Prediction With Graph Embedding

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2023 Congress in Computer science, Computer Engineering, and Applied Computing, CSCE 2023

作者： Jui, Tonni Das Baker, Erich Benton, Mary Lauren Baylor University Computer Science WacoTX United States

ISBN: (纸本)9798350327595

Graph embedding approaches aim to provide a low-dimensional latent representation of the graph with minimal reconstruction error. In addition, these approaches attempt to capture local and global topological neighborhood information and data distribution in the latent representation. The primary purpose of the graph's latent representation is simply implementing them into various straightforward machine learning models for graph prediction tasks such as link prediction, clustering, and visualization. Among these tasks, link prediction is a critical task in which researchers mainly analyze the performance of the embeddings on the information of adjacent nodes. Although many embedding techniques claim to capture the hopped neigh-borhood into the embedding, researchers need to pay more attention to analyzing the performance of the graph embeddings on hopped link prediction. Hopped link prediction demonstrates the performance of capturing a global view of the neighborhood. Our proposed framework develops k-hopped graph topological and feature information to analyze six widely recognized graph embeddings: ARGE, ARVGE, Node2vec, Attri2Vec, GraphSage, and GCN with k-hopped link prediction. We experiment with three graph datasets and show that k-hopped link prediction performance significantly increases for 1-hopped graph information and continuously depletes after 1-hop, demonstrating the importance of embedding performance analysis with k-hopped link prediction. © 2023 ieee.

关键词： embedding graph hop link-prediction performance Regular Research Paper Type of the submission

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Kinect Sensor-Based Indoor 3D Reconstruction - A Pilot Study 7

Kinect Sensor-Based Indoor 3D Reconstruction - A Pilot Study

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7th ieee International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Amruth Mandappa, T.S. Pavan Kumar, B.N. Chakravarthi, Bharatesh Manjunath, V. Girish, N. Prabhu Prasad, M. Iiit Dharwad Dept. of Cse India Iiit Sri City Dept. of Cse India Scai Arizona State University Active Perception Group United States Iiit Dharwad Dept. of Data Science and Intelligent Systems India

ISBN: (纸本)9798350343144

This study investigates the application of a low-cost Kinect sensor for 3D reconstruction inside. The project uses structured light technologies and the Kinect depth sensor to collect and rebuild interior surroundings in three dimensions. The primary goal is to provide realistic 3D representations of interior locations for a variety of purposes. Using the Kinect sensor and specialized software, interior scenes are scanned and then recreated. The outcomes indicate how well the Kinect-based method works for producing accurate indoor 3D reconstructions, and also suggest possible uses for it in a number of industries, including robotics, virtual reality, and architectural visualization. This work advances the use of Kinect sensors for indoor 3D reconstruction, providing new opportunities for improved spatial comprehension and virtual environment engagement. © 2023 ieee.

关键词： Three dimensional computer graphics

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Coronary Artery Disease Prediction Using Enhanced Multi Layer DCNN 10

Coronary Artery Disease Prediction Using Enhanced Multi Laye...

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10th ieee Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Malik, Varun Sharma, Smita Mittal, Ruchi Rao, A. Kakoli Martin, R. John Khan, Akhilesh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amity School of Engineering and Technology Amity University Department of Electronics and Communication Engineering Greater Noida India Lloyd Institute of Engineering & Technology Greater Noida India Jazan University Faculty of Computer Science and Information Technology Saudi Arabia Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

Coronary Artery Disease(CAD) is a prevalent and life-threatening cardiovascular condition affecting millions of individuals globally. Early detection and accurate prediction of CAD are vital for timely intervention and prevention of severe cardiac events. Convolutional Neural Networks (CNNs) and other deep learning approaches have recently demonstrated amazing effectiveness in a number of medical applications, including illness prediction. This research proposes an innovative approach, Enhanced Multi-Layer Deep Convolutional Neural Network (EML-DCNN), for the prediction of Coronary Artery Disease. The proposed EML-DCNN architecture integrates advanced deep learning concepts, feature engineering, and data augmentation techniques to enhance the model's predictive performance. The multi-layer structure of EML-DCNN enables the automatic extraction of intricate patterns and features from medical imaging data, specifically focusing on coronary artery images. Additionally, the model incorporates attention mechanisms and residual connections, enhancing its ability to capture subtle details and relationships within the data. To validate the efficacy of the proposed EML-DCNN model, extensive experiments were conducted on a large and diverse dataset comprising coronary artery images obtained from various medical sources. The results demonstrate superior predictive accuracy, sensitivity, and specificity compared to existing CAD prediction models. Moreover, the model's interpretability is enhanced through visualization techniques, providing valuable insights into the features influencing the prediction process. © 2023 ieee.

关键词： Coronary Artery Disease deep learning disease prediction Enhanced Multi-Layer Deep Convolutional Neural Network

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Enhancing Clinical Outcome Predictions through Auxiliary Loss and Sentence-Level Self-Attention

Enhancing Clinical Outcome Predictions through Auxiliary Los...

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2023 ieee International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lee, Sanghoon Jang, Gwanghoon Kim, Chanhwi Park, Sejeong Yoo, Kiwoong Kim, Jihye Kim, Sunkyu Kang, Jaewoo Korea University Department of Computer Science and Engineering Seoul02841 Korea Republic of AIGENSciences Inc Seoul04778 Korea Republic of

ISBN: (纸本)9798350337488

Predicting clinical outcomes such as mortality and length of hospital stay during the initial admission to the intensive care unit (ICU) is vital for enhancing patient survival rates and optimizing hospital resource allocation. Accurate diagnostic information is deeply correlated with mortality and hospitalization duration;however, initial admission data often lack sufficient diagnostic details and may even have discrepancies with discharge diagnoses. Previous studies have leveraged Electronic Health Record (EHR) data, encompassing clinical notes and vital sign monitoring data, to boost the performance of clinical outcomes prediction. Nevertheless, they have often overlooked the relevance between sentence information, resorting to simple averages, and have missed the importance of diagnostic information. To overcome this limitation, we propose the Diagnosis-Enhanced Sentence Attention Model for Clinical Prediction (DESAM-cp), a multimodal neural network. We employed an auxiliary loss to ensure that embeddings of clinical note were informative of the patient's discharge diagnosis information and used sentence-level self-attention to account for the relevance between sentences. Ultimately, clinical note embeddings containing diagnostic information were fused with monitoring data embeddings to predict clinical outcomes. Our experiments demonstrate that the methodology of DESAM-cp outperforms previous research methods. Additionally, through ablation tests, visualization analysis, and attention analysis, we have substantiated that our individual modules function effectively. Our analysis also suggests that our model focuses on medically relevant sentences in relation to diagnosis and clinical outcome predictions and can provide supporting evidence to assist clinicians in making informed decisions regarding diagnoses and treatments. The code for DESAM-cp used in this work is available at https://***/a11525/DESAM-cp © 2023 ieee.

关键词： auxiliary loss clinical outcome prediction diagnosis prediction electronic health records length of stay prediction mortality prediction Multi-modal neural network self-attention mechanisms

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Design of 3D Reconstruction and FDM Printing Testbed for Agricultural Products

Design of 3D Reconstruction and FDM Printing Testbed for Agr...

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2023 International Conference on Applied Physics and Computing, ICAPC 2023

作者： Gao, Zihang Zhao, Nannan Wenzhou Vocational College of Science and Technology Wenzhou China

ISBN: (纸本)9798350359244

Computer vision detection is widely used in the field of agricultural product variety identification and quality inspection. However, the traditional agricultural product phenotypic detection method based on two-dimensional (2D) images lacks spatial dimension information and is difficult to extract comprehensive agricultural product phenotypic parameters. Therefore, it is of great significance to establish a three-dimensional (3D) model of agricultural products and extract comprehensive phenotypic parameters of agricultural products. At present, the construction method of 3D models of agricultural products is single, the molding efficiency is low and the degree of visualization is limited, making it difficult to achieve accurate evaluation of system performance. We designed a testbed for agricultural product that combines 3D reconstruction and FDM printing technology. Experimental results show that the testbed can achieve 3D reconstruction of agricultural product data information and achieve accurate evaluation of FDM 3D printing performance. © 2023 ieee.

关键词： Agricultural products

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