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Visualizing Functional Network Connectivity Difference between Healthy Control and Major Depressive Disorder Using an Explainable Machine-learning Method

Visualizing Functional Network Connectivity Difference betwe...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ji Ye Chun Mohammad S. E. Sendi Jing Sui Dongmei Zhi Vince D. Calhoun Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Major depressive disorder (MDD) is a complex mental disorder characterized by a persistent sad feeling and depressed mood. Recent studies reported differences between healthy control (HC) and MDD by looking to brain networks including default mode and cognitive control networks. More recently there has been interest in studying the brain using advanced machine learning-based classification approaches. However, interpreting the model used in the classification between MDD and HC has not been explored yet. In the current study, we classified MDD from HC by estimating whole-brain connectivity using several classification methods including support vector machine, random forest, XGBoost, and convolutional neural network. In addition, we leveraged the SHapley Additive exPlanations (SHAP) approach as a feature learning method to model the difference between these two groups. We found a consistent result among all classification method in regard of the classification accuracy and feature learning. Also, we highlighted the role of other brain networks particularly visual and sensory motor network in the classification between MDD and HC subjects.

关键词： Radio frequency Support vector machines Visualization Functional magnetic resonance imaging Machine learning Hospitals Brain modeling

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MODULAR SENSOR SYSTEM (MSS) FOR URBAN AIR POLLUTION MONITORING

MODULAR SENSOR SYSTEM (MSS) FOR URBAN AIR POLLUTION MONITORI...

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IEEE Sensors

作者： Wei-Ying Yi Kwong-Sak Leung Yee Leung Mei-Ling Meng Terrence Mak Institute of Future Cities Department of Computer Science and Engineering Stanley Ho Big Data Decision Analytics Research Center The Chinese University of Hong Kong China Department of Systems Engineering and Engineering Management Department of Electronics and Computer Science University of Southampton UK

Air pollution attracts extensive attention globally due to its critical impacts on human life. Monitoring systems providing real-time micro-level pollution information have been developed to provide authorities with data to mitigate these impacts. However, current systems are usually application-specific with fixed hardware and software configurations. They are inconvenient in maintenance, infeasible in reconfiguration, and unexpandable in sensing capabilities. This paper proposes a novel Modular Sensor System (MSS), which aims at tackling these issues by adopting the proposed Universal Sensor Interface (USI) and modular design in a sensor node. A compact MSS senor node with expandable plug-and-play sensor modules and multiple Wireless Sensor Networks (WSNs) compatibility is implemented and evaluated. Results indicate that MSS sensor node can be deployed in different scenarios while dynamically adapting to reconfigurations and monitoring air pollution at low concentration levels with high energy efficiency. We anticipate that MSS is able to relax the efforts on system maintenance, adaptation, and evolution in real-life large-scale deployment situation.

关键词： plug-and-play air pollution monitoring wireless sensor network modular sensor node

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Cooperative Multi-source data Trading

Cooperative Multi-source Data Trading

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jin Cheng Ningning Ding John C. S. Lui Jianwei Huang School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Data Science and Analytics Thrust Information Hub Hong Kong University of Science and Technology Guangzhou Department of Computer Science and Engineering The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network and CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In the era of big data, data trading significantly enhances data-driven technologies by facilitating data sharing. Despite the clear advantages often experienced by data users when incorporating multiple sources, the topic of multi-source data trading remains largely unexplored. This paper designs a novel data trading framework, which enables multi-source data trading through multi-source cooperation. The proposed framework aims to improve data usage efficiency and increase seller revenue. In particular, we model data sellers’ cooperative decisions through the Nash bargaining framework and systematically outline the interactions between sellers and buyers as a two-stage Stackelberg game. A key contribution of this work is the consideration of coupling among diverse data products, which is essential but often overlooked in prior studies. We properly classify data’s utility into endogenous and relational categories to disentangle the coupling. Despite the inherent non-convex nature of the optimization problem, we methodically derive the closed-form optimal solutions by decomposing the problem into several subproblems. Interestingly, we reveal that, under our proposed framework, sellers’ revenue initially remains steady with the increase of product coupling level, but begins to rise once the level exceeds a certain threshold due to the substitute effect. Finally, experimental results show that our proposed framework can improve the seller’s profit by up to 46.32% compared to traditional data trading methods in the current data market.

关键词： Couplings Games Big data data models Global communication Optimization

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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Computing (ICOCO), IEEE International Conference on

作者： Mahayaudin M. Mansor Nurain Ibrahim Noor Fadhilah Ahmad Radi Nadirah Abdul Rahim Syarul Heiry Yahaya Mohd Aftar Abu Bakar Roslinazairimah Zakaria School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA (Penang Campus) Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability.

关键词： Visualization Technological innovation Ethics Emotion recognition Bibliometrics Decision making Focusing Electronic healthcare Object recognition Artificial intelligence

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Energetic Content based Image Retrieval Scheme using Improved Deep Learning Strategy with Image Analysis Technique

Energetic Content based Image Retrieval Scheme using Improve...

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International Conference on Smart Structures and Systems (ICSSS)

作者： S. Karkuzhali P. Malathi A Thenmozhi Velu Aiyyasamy Keerthika A G.S. Uthayakumar Department of General Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Department of Electronics and Communication Engineering Kings Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Meenakshi Sundararajan Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of ECE St. Joseph’s Institute of Technology Chennai Tamil Nadu India

One popular approach to retrieving images from huge, unprocessed datasets is content-based image retrieval, or CBIR. The conventional ways of data retrieval, however, are not meeting the needs of consumers. The number of consumer-accessible photographs also keeps growing, and there are an abundance of digital platforms for development and dissemination. Consequently, digital image processing is both ubiquitous and permanent in many fields. Consequently, effective strategies are required for both the speedy access to these massive picture databases and the recovery of comparable photos from this massive number of images based on a specific picture (query). The computation of depiction of features and resemblance is crucial to the efficiency of a CBIR system. We introduce LAIRS, a cutting-edge deep learning method that integrates the extraction of features and aims to do this with classification to facilitate rapid picture retrieval. For a transparent evaluation of the proposed scheme’s efficiency, the suggested logic is cross-validated using Convolutional Neural Networks (CNNs), a classic deep learning paradigm. Several in-depth observational experiments for various CBIR tasks employing an image database have shown encouraging results, teaching us important lessons for making CBIR system more efficient. With the use of CBIR structures, a different collection can find similar pictures to the query image. There is little doubt that Google’s search per photo tool is perhaps the most widely used CBIR approach.

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Benchmarking in Optimization: Best Practice and Open Issues

arXiv

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arXiv 2020年

作者： Bartz-Beielstein, Thomas Doerr, Carola Bossek, Jakob Chandrasekaran, Sowmya Eftimov, Tome Fischbach, Andreas Kerschke, Pascal López-Ibáñez, Manuel Malan, Katherine M. Moore, Jason H. Naujoks, Boris Orzechowski, Patryk Volz, Vanessa Wagner, Markus Weise, Thomas Institute for Data Science Engineering and Analytics TH Köln Germany Sorbonne Université CNRS LIP6 Paris France Optimisation and Logistics School of Computer Science University of Adelaide Adelaide Australia Computer Systems Department Jožef Stefan Institute Ljubljana Slovenia Statistics and Optimization Group University of Münster Münster Germany School of Computer Science and Engineering University of Málaga Málaga Spain Department of Decision Sciences University of South Africa Pretoria South Africa Institute for Biomedical Informatics University of Pennsylvania PhiladelphiaPA United States Department of Automatics AGH University of Science and Technology Krakow Poland modl.ai Copenhagen Denmark Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei China

This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking is its main goal. The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility. The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers. As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic *** Codes 68W50. Copyright © 2020, The Authors. All rights reserved.

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Novel SNP improves differential survivability and mortality in non-small cell lung cancer patients

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BMC Genomics 2014年第9期15卷 1-7页

作者： Mah, Tzia Liang Yap, Xin Ning Adeline Limviphuvadh, Vachiranee Li, Nanpu Sridharan, Srinath Kuralmani, Vellaisemy Feng, Mengling Liem, Natalia Adhikari, Sharmila Yong, Wei Peng Soo, Ross A Maurer-Stroh, Sebastian Eisenhaber, Frank Tong, Joo Chuan Institute for Infocomm Research Data Analytics Department 1 Fusionopolis Way #21-01 Connexis South Tower Singapore 138632 Singapore National University Health System Department of Haematology-Oncology 5 Lower Kent Ridge Road Singapore 119074 Singapore Bioinformatics Institute 30 Biopolis Street #07-01 Matrix Singapore 138671 Singapore Nanyang Technological University (NTU) School of Biological Sciences (SBS) 60 Nanyang Drive 637551 Singapore Nanyang Technological University (NTU) School of Computer Engineering (SCE) 50 Nanyang Drive 637553 Singapore National University of Singapore (NUS) Department of Biological Sciences (DBS) 8 Medical Drive 4 117597 Singapore National University of Singapore Department of Biochemistry Yong Loo Lin School of Medicine Singapore 117597 Singapore Institute of High Performance Computing 1 Fusionopolis Way #16-16 Connexis 138632 Singapore National University of Singapore Cancer Science Institute of Singapore Singapore

Background: Non-small cell lung cancer (NSCLC) is a major cause of cancer-related death worldwide due to poor patient prognosis and clinical outcome. Here, we studied the genetic variations underlying NSCLC pathogenesis based on their association to patient outcome after gemcitabine therapy. Results: Bioinformatics analysis was used to investigate possible effects of POLA2 G583R (POLA2+1747 GG/GA, dbSNP ID: rs487989) in terms of protein function. Using biostatistics, POLA2+1747 GG/GA (rs487989, POLA2 G583R) was identified as strongly associated with mortality rate and survival time among NSCLC patients. It was also shown that POLA2+1747 GG/GA is functionally significant for protein localization via green fluorescent protein (GFP)-tagging and confocal laser scanning microscopy analysis. The single nucleotide polymorphism (SNP) causes DNA polymerase alpha subunit B to localize in the cytoplasm instead of the nucleus. This inhibits DNA replication in cancer cells and confers a protective effect in individuals with this SNP. Conclusions: The results suggest that POLA2+1747 GG/GA may be used as a prognostic biomarker of patient outcome in NSCLC pathogenesis. © 2014 Mah et al.;licensee BioMed Central Ltd.

关键词： Biomarker Genetic variant Mortality Non-small cell lung cancer Survival Outcome

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Using multiple drug similarity networks to promote adverse drug event detection

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Heliyon 2024年第22期10卷 e39728页

作者： Padhi, Biswajit Liu, Ruoqi Yang, Yuedi Peng, Xueqiao Li, Lang Zhang, Pengyue Zhang, Ping Department of Computer Science and Engineering The Ohio State University 2015 Neil Ave Columbus 43210 OH United States Department of Biostatistics and Health Data Science Indiana University School of Medicine 410 W. 10th Street HITS 3000 Indianapolis 46202 IN United States Department of Biomedical Informatics The Ohio State University 1800 Cannon Drive Columbus 43210 OH United States Translational Data Analytics institute The Ohio State University 1760 Neil Ave Columbus 43210 OH United States

The occurrence of an adverse drug event (ADE) has become a serious social concern of public health. Early detection of ADEs can lower the risk of drug safety as well as the expense of the drug. While post-market spontaneous reports of ADEs remain a cornerstone of pharmacovigilance, most existing signal detection algorithms rely on substantial accumulated data, limiting their applicability to early ADE detection when reports are scarce. To address this issue, we propose a label propagation model for generating enhanced drug safety signals using multiple drug features. We first construct multiple drug similarity networks using a range of drug features. We then calculate initial drug safety signals using conventional signal detection algorithms. These original signals are subsequently propagated across each drug similarity network to obtain enhanced drug safety signals. We evaluate our proposed model using two common signal detection algorithms on data from the FDA Adverse Event Reporting System (FAERS). Results demonstrate that enhanced drug safety signals with pre-clinical information outperform the standard safety signal detection algorithms on early ADE detection. In addition, we systematically evaluate the performance of different drug similarities against different types of ADEs. Furthermore, we have developed a web interface (http://***/) to display our multiple drug similarity scores, facilitating access to this valuable resource for drug safety monitoring. © 2024 The Authors

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Vortex Feature Positioning: Bridging Tabular IIoT data and Image-Based Deep Learning

arXiv

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arXiv 2023年

作者： Park, Jong-Ik Seong, Sihoon Lee, JunKyu Hong, Cheol-Ho Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Intelligent Semiconductor Engineering Chung-Ang University 84 Heukseok-ro Dongjak District Seoul06974 Korea Republic of Institute for Analytics and Data Science University of Essex Wivenhoe Park ColchesterCO4 3SQ United Kingdom

Tabular data from IIoT devices are typically analyzed using decision tree-based machine learning techniques, which struggle with high-dimensional and numeric data. To overcome these limitations, techniques converting tabular data into images have been developed, leveraging the strengths of image-based deep learning approaches such as Convolutional Neural Networks. These methods cluster similar features into distinct image areas with fixed sizes, regardless of the number of features, resembling actual photographs. However, this increases the possibility of overfitting, as similar features, when selected carefully in a tabular format, are often discarded to prevent this issue. Additionally, fixed image sizes can lead to wasted pixels with fewer features, resulting in computational inefficiency. We introduce Vortex Feature Positioning (VFP) to address these issues. VFP arranges features based on their correlation, spacing similar ones in a vortex pattern from the image center, with the image size determined by the attribute count. VFP outperforms traditional machine learning methods and existing conversion techniques in tests across seven datasets with varying real-valued attributes. © 2023, CC BY-NC-ND.

关键词： Vortex flow

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A New Approach for Calculation of Quasi-Normal Modes and Topological Charges of Regular Black Holes

arXiv

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arXiv 2024年

作者： Zhang, M.Y. Hosseinifar, F. Chen, H. Sathiyaraj, T. Hassanabadi, H. College of Computer and Information Engineering Guizhou University of Commerce Guiyang550014 China Department of Physics Faculty of Science University of Hradec Králové Rokitanského 62 Hradec Králové500 03 Czech Republic School of Physics and Electronic Science Zunyi Normal University Zunyi563006 China Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia

This study examines the properties of a special regular black hole. This analysis investigates the Hawking temperature, remnant radius and mass, as well as the effect of parameter ξ on thermodynamic quantities like entropy, heat capacity, and free energy. The emission rate, evaporation process, quasi-normal modes by calculating Rosen-Morse potential, and topological behavior of the black hole are also explored. © 2024, CC0.

关键词： Black holes

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