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17th International Conference on Brain Informatics, BI 2024

作者： Doborjeh, Zohreh lavin, Daniel Hunter, Kirsty Heym, Nadja Heasman, Bryony Doborjeh, Maryam Kasabov, Nikola Gibson, Glen Sumich, Alexander School of Engineering Computer and Mathematical Sciences Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand School of Population Health Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand Department of Sport Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom NTU Psychology School of Social Sciences Nottingham Trent University Nottingham United Kingdom School of Software Engineering Dalian University Dalian China Institute for Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Department of Food and Nutritional Sciences The University of Reading Whiteknights Reading United Kingdom Department of Psychology and Neuroscience Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819632930

The immune and enteric systems have been implicated in psychopathology, including depressive disorders. However, the precise neurocognitive mechanisms remain unclear. The present study uses Brain-Inspired Spiking Neural Network (SNN) technique to model electroencephalographic (EEG) data as a function of gut-microbiota, inflammation, and depressive symptoms. Forty-two participants (nonclinical cohort) were assessed for Bacteroides-Prevotella (BAC) colonization in faecal samples, Interleukin 6 (IL-6) in peripheral blood, depressive symptoms (self-report of cognitive and non-cognitive symptoms) and brain function (resting state EEG). SNN models were applied to EEG data to generate connection weight values across scalp regions. Connection weights were visualized as a function of 1) depression;2) inflammation;3) gut microbiome. Lower depression and higher BAC were associated with higher frontal connection weights (cognitive depression = bilateral, non-cognitive and BAC = left hemisphere only). Lower cognitive depression and lower IL-6 were associated with higher posterior connection weights. The SNN models achieved higher accuracy for theta and alpha EEG sub-bands, highlighting their relevance in differentiating between groups. These findings emphasize the potential of SNNs in predicting parameters from brain functional connectivity data and developing targeted diagnostic and intervention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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Linear Distribution System State Estimation with Synchrophasor Measurements and Voltage-Dependent Load Model

Linear Distribution System State Estimation with Synchrophas...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Behzad Zargar Mohsen Ferdowsi Jinshun Su Yifu Li Payman Dehghanian Richard Hibberts-Caswell Electrical System Studies Department Fichtner GmbH & Co. KG Stuttgart Germany Siemens Grid Software Nurnberg Germany Department of Electrical & Computer Engineering The George Washington University Washington DC USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Distribution system state estimation (DSSE) is a critical enabler for distribution system operators (DSOs) to manage active distribution grids efficiently. Timely detection of state changes requires fast-executing state estimation techniques, yet the prevalent nonlinear weighted least squares approach, though effective, presents computational challenges. Linearizing inherent nonlinearities is a way to expedite DSSE solutions. This paper proposes a linear DSSE method that leverages synchrophasor measurements and a linear voltage-dependent load model for pseudo-measurements. This approach provides a more accurate representation of load response to voltage variations, yielding more realistic estimation results than methods using conventional constant active and reactive load models.

关键词： Voltage measurement Accuracy Power measurement Sensitivity Power distribution Phasor measurement units Real-time systems Loss measurement State estimation Load modeling

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Multi-Classification of Depression Levels Based on Blood Biomarkers

Multi-Classification of Depression Levels Based on Blood Bio...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Rahmi Kavak Selma Ayşe Özel Sema Polat Sinan Özler Department of Software Engineering Adana Alparslan Türkeş Science and Technology University Adana Turkey Department of Computer Engineering Çukurova University Adana Turkey Department of Anatomy Faculty of Medicine Çukurova University Adana Turkey Department of Mental and Nervous Diseases The Seyhan State Hospital Adana Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Depression is a psychiatric condition characterized by a persistent feeling of sadness and diminished interest in significant activities. Diagnosing this health problem is challenging because it relies on several social and physiological factors. The timely identification of depression aids in the prevention of severe outcomes, such as suicide. Early detection of depression levels is crucial to prevent adverse effects and enhance the quality of everyday life. The purpose of this work is to use data preprocessing techniques to create a clean dataset from noisy and incomplete medical data that includes blood biomarker values of patients and then apply data mining techniques to this dataset to predict the degree of depression. Adana Dr. Ekrem Tok Mental Health Hospital supplied the raw data for the study, with consent from the ethics committee. Missing data are completed by filling with the constant value and the most frequent value methods (i.e., 0 and mode values) in the relevant column. The classification of the dataset is performed using AdaBoost, Decision Tree (DT), and Logistic Regression (LR) classifiers, which have been previously used in medical datasets and demonstrated to be effective. The Logistic Regression classifier achieved the highest success rate (Accuracy: 0.541 and weighted F-score: 0.481).

关键词： Logistic regression Ethics Hospitals Prevention and mitigation Mental health Depression Noise measurement Decision trees Medical diagnostic imaging Blood

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Mathematical models of quality estimating of change point detection algorithm for random processes

Mathematical models of quality estimating of change point de...

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2021 IEEE International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2021

作者： Benderskaya, Elena N. Peter the Great St. Petersburg Polytechnic University Computer Systems and Software Engineering Department St. Petersburg Russia

ISBN: (纸本)9781665412629

The paper is devoted to the change point detection method based on decision-making statistics. The decision-making statistics are constructed on the principle of discrepancy. Under consideration is the problem dealing with quality measures and quality estimating while a priori information about the distribution density of the change moments of the random process properties is absent. Mathematical models have been developed to calculate the average number of delay steps of the change point detection algorithm. The proposed models allow choosing the parameters of the algorithm with the worst-case orientation, it is important for ensuring that the change point detection in real-time systems will be done with a delay not exceeding a given value. © 2021 IEEE.

关键词： Decision making

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Enhancing Medical Image-Based Diagnostics Through the Application of Convolutional Neural Networks Techniques

Enhancing Medical Image-Based Diagnostics Through the Applic...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Haewon Byeon Rajit Nair V. Mahalakshmi Mohammed I. Khalaf Bharat Kaushik Mohammad Shabaz Department of AI and Software Inje University Gimhae Republic of Korea School of Computing Science & Engineering VIT Bhopal University Bhopal India Department of Computer Science College of Engineering and computer science Jazan University Jazan Saudi Arabia Department of computer science Al Maarif University College Al Anbar Iraq Masters University of Northern Iowa Lowa USA Model Institute of Engineering and Technology Jammu J&K India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Perioperative nutrition evaluation is crucial for patient care and resource efficiency in healthcare. This work proposes a deep learning-based perioperative nutrition evaluation tool that leverages sophisticated machine learning to improve predictions and assist clinicians in making better choices. As surgical techniques become more complex and medical technology progresses, a thorough postoperative nutrition study becomes more important than ever. An extensive review demonstrates that the recommended strategy performs better than other machine learning techniques. Our model's accuracy, precision, memory, and F1-score let it predict surgical outcomes more accurately. The higher AVC-ROC and AVC-PR values of the suggested approach show that it performs particularly well. Its sensitivity-specificity balance is demonstrated by this. Our approach generates fewer errors, according to the confusion matrix analysis, which makes it reliable for healthcare decisions. Our work enhances critical healthcare by assessing nutrition with state-of-the-art techniques.

关键词： Fluctuations Reviews Hospitals Measurement uncertainty Surgery Medical services Predictive models

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Fine-Grained Alignment in Vision-and-Language Navigation through Bayesian Optimization

arXiv

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arXiv 2024年

作者： Song, Yuhang Gianni, Mario Yang, Chenguang Lin, Kunyang Chiu, Te-Chuan Nguyen, Anh Lee, Chun-Yi Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan Department of Computer Science National Tsing Hua University Hsinchu300 Taiwan Department of Computer Science National Tsing Hua University Taiwan School of Software Engineering South China University of Technology Guangzhou510006 China

This paper addresses the challenge of fine-grained alignment in Vision-and-Language Navigation (VLN) tasks, where robots navigate realistic 3D environments based on natural language instructions. Current approaches use contrastive learning to align language with visual trajectory sequences. Nevertheless, they encounter difficulties with fine-grained vision negatives. To enhance cross-modal embeddings, we introduce a novel Bayesian Optimization-based adversarial optimization framework for creating fine-grained contrastive vision samples. To validate the proposed methodology, we conduct a series of experiments to assess the effectiveness of the enriched embeddings on fine-grained vision negatives. We conduct experiments on two common VLN benchmarks R2R and REVERIE, experiments on the them demonstrate that these embeddings benefit navigation, and can lead to a promising performance enhancement. Our source code and trained models are available at: https://***/r/FGVLN. © 2024, CC BY.

关键词： Contrastive Learning

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Approaches for Non-Functional Requirement Modeling: A Literature Survey 4

Approaches for Non-Functional Requirement Modeling: A Litera...

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4th IEEE International Conference on Computing and Information Sciences, ICCIS 2021

作者： Tariq, Saman Cheema, Sehrish Munawar University of Management and Technology UMT Department of Software Engineering Sialkot Pakistan Department of Computer Science University of Management and Technology UMT Sialkot Pakistan

ISBN: (纸本)9781665494410

The term 'non-functional requirement' plays an important role for the success and failure of any software project. It have been studied since over 4 decades, however there are many consensus in software engineering community due to that many of research questions are significantly important to researchers. Non-Functional Requirements (NFRs) such as security, accuracy, safety and performance are crucial for the system in order to deliver good and reliable product and to achieve customer satisfaction. These aspects should consider developing and maintaining a system successfully. This paper surveys the existing techniques and tools in terms of their working, applicability while considering the context of the project. The main focus of this study is to identify and analyze the approaches available in literature to elicit, model and integrate non-functional requirements (NFRs). We performed a detailed analysis of existing approaches on the basis of our significant research questions and derive the suitability of these approaches in specific contexts and projects. © 2021 IEEE.

关键词： Customer satisfaction

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Playing the video games during COVID-19 pandemic and its effects on player's well-being 2

Playing the video games during COVID-19 pandemic and its eff...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Noaman, Samar Billi Ibrahim, Amer Ali, Liaqat Iqbal, M.W. Ashraf, Asma Haseeb, Usama Muneer, Salman Almajed, Rasah Hamid, K. Canadian University Dubai Faculty of Management Dubai United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Superior University Department of Software Engineering Lahore54000 Pakistan Superior University Department of Computer Science Lahore54000 Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan American University in the Dubai College of Computer and Information Technology United Arab Emirates

ISBN: (纸本)9798350335644

COVID-19 affected our lives intensly. That state of affairs made humans helpless. They had been depressed and experienced loneliness. At that time many human beings were determined to play video games just to loosen up their minds. Many games changed into an additional source of revenue wherein during COVID-19 they were playing while earning money. With these advantages, there were also some poor effects was accrued. A number of players remained playing video games post COVID-19. The carried out survey is based on the social video games results on players' well-being and additionally on the effects of gamers' health and their sensible lifestyles. We are going to investigate the behavior of gamers engaged with video games during the COVID-19 lockdown and the video games affects on their well-being, the time they served in playing video games, and the consequential effect on their behavior and social and mental well-being. The results provide a start line for empirically grounded discussions on video games at some stage in the pandemic, their use, and potential outcomes. Different agegroups of players have been investigated. Most players are between 18 and 30 years. A number of the gamers during lock down played a few hours but most of players were males who spent most of their day playing video games. However, now the ratio of playing video games is reduced as examined with past circumstances. Roughly we can say that the condition as a whole is better, the reason why players enforced video games in their post COVID-19 practical life1. © 2023 IEEE.

关键词： Coronavirus

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A New Identity-Based Encryption Scheme with Accountable Authority Based on SM9 11

A New Identity-Based Encryption Scheme with Accountable Auth...

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11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： Wang, Ke Zhao, Yuan Luo, Song Guan, Zhi MoE Beijing China Department of Computer Science and Technology EECS Peking University Beijing China School of Computer Science and Engineering Chongqing University of Technology Chongqing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9781643682402

Accountable authority identity-based encryption (A-IBE) is an extension of identity-based encryption (IBE) in which private key's source can be traced, i.e., whether the key comes from a private key generator or a user. SM9 is an official cryptography standard of China which defines a practical IBE scheme. In this paper, we construct a practical A-IBE scheme from the SM9-IBE scheme. Our A-IBE scheme has public traceability and is proven secure if the based SM9-IBE scheme is secure. Compared with other A-IBE schemes, our A-IBE scheme has better efficiency in encryption and decryption. © 2022 The authors and IOS Press.

关键词： Cryptography

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PV-Cap: 3D Dynamic Scene Understanding Through Open Physics-based Vocabulary

PV-Cap: 3D Dynamic Scene Understanding Through Open Physics-...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Hidetomo Sakaino Thao Nguyen Phuong Vinh Nguyen Duy Data Solution Dept. AI-Image Group (FCJ.ABC) FPT Consulting Japan FPT Software Department of Computer Science and Communications Engineering Waseda University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, large Vision Language (VL) models, i.e., CLIP, have demonstrated impressive capabilities in training solely on internet-scale image-language pairs. Moreover, almost all VL models have tackled indoor objects under controlled illumination and camera views. However, outdoor 3D environments are time-varying uncontrolled scenes under natural phenomena. Therefore, captions from such unseen scenes and objects are hard to obtain in a state-of-the-art (SOTA) one-shot algorithm, resulting in insufficient captions. This paper proposes PV-Cap (Physics-based Vocabulary for Caption) for enhancing 3D scene understanding through enriched captions. Since many tasks in understanding 3D dynamic scenes are hard to deal with, PVCap aims to disentangle such complexities through multiple grouped Deep Learning and Vision Language models step-wisely. Proposed i-VQA (iterative VQA) and 3D-CPP (3D Contrastive Physical-Scale Pretraining) extended from SOTA 2D-CLIP also contribute to generating physical and 3D-based captions. Using many images with 3D dynamic events, i.e., road scenes with traffic flow and accidents, experiments have demonstrated the usability and effectiveness of proposed PV-Cap over SOTA models in terms of segmentation and captions.

关键词： Deep learning Training Solid modeling Vocabulary Three-dimensional displays Roads Cameras

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