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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yenho Chen Noga Mudrik Kyle A. Johnsen Sankaraleengam Alagapan Adam S. Charles Christopher J. Rozell Machine Learning Center Georgia Institute of Technology and Coulter Dept. of Biomedical Engineering Emory University and Georgia Institute of Technology Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University Coulter Dept. of Biomedical Engineering Emory University and Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology Machine Learning Center Georgia Institute of Technology and School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (纸本)9798331314385

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. Code is available at: https://***/siplab-gt/probabilistic-decomposed-linear-dynamical-systems

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End-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies

arXiv

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arXiv 2023年

作者： Ai, Xuguang Kavuluru, Ramakanth Department of Computer Science University of Kentucky Lexington United States Division of Biomedical Informatics Dept. of Internal Medicine Univ. of Kentucky Lexington United States

End-to-end relation extraction (E2ERE) is an important task in information extraction, more so for biomedicine as scientific literature continues to grow exponentially. E2ERE typically involves identifying entities (or named entity recognition (NER)) and associated relations, while most RE tasks simply assume that the entities are provided upfront and end up performing relation classification. E2ERE is inherently more difficult than RE alone given the potential snowball effect of errors from NER leading to more errors in RE. A complex dataset in biomedical E2ERE is the ChemProt dataset (BioCreative VI, 2017) that identifies relations between chemical compounds and genes/proteins in scientific literature. ChemProt is included in all recent biomedical natural language processing benchmarks including BLUE, BLURB, and BigBio. However, its treatment in these benchmarks and in other separate efforts is typically not end-to-end, with few exceptions. In this effort, we employ a span-based pipeline approach to produce a new state-of-the-art E2ERE performance on the ChemProt dataset, resulting in > 4% improvement in F1-score over the prior best effort. Our results indicate that a straightforward fine-grained tokenization scheme helps span-based approaches excel in E2ERE, especially with regards to handling complex named entities. Our error analysis also identifies a few key failure modes in E2ERE for ChemProt. © 2023, CC BY.

关键词： Pipelines

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Early Prediction of Depressive Episodes in Mood Disorders Using Circadian Rhythm Indicators and Deep Learning

Early Prediction of Depressive Episodes in Mood Disorders Us...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Byeongsu Kim Minsu Chae Yihyun Kim Seokjin Kong Yeongmin Kim Taewon Jung Jaegwon Jeong Soohyun Park Chul-Hyun Cho Ji Won Yeom Taek Lee Heon-Jeong Lee Hwamin Lee Dept. of Biomedical Informatics College of Medicine Korea University Seoul Republic of Korea Dept. of Psychiatry College of Medicine Korea University Seoul Republic of Korea Chronobiology Institute Korea University Seoul Republic of Korea Dept. of Computer Science and Engineering SunMoon University Asan Republic of Korea

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The early prediction of depressive mood episodes is crucial for effective intervention in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD). This study explores a predictive framework leveraging digital phenotypic data collected from smartphones and smartwatches, with a focus on circadian rhythm indicators such as Dim Light Melatonin Onset (DLMO). Using data from 164 participants within the Mood Disorder Cohort Research Consortium in Korea, time-series features related to sleep, heart rate, activity levels, and light exposure were processed to predict mood episodes seven days in advance. Deep learning models, including LSTM, GRU, and an LSTM-GRU hybrid, were applied to analyze this data, with the GRU model achieving the highest recall (0.767) and the LSTM model displaying superior robustness across metrics. SHAP value analysis of DLMO-related variables further underscored the association between circadian rhythm disruptions and depressive episodes, with delayed wake-up times relative to ideal schedules linked to increased depressive symptoms. Our findings demonstrate the feasibility of using digital phenotypes for early detection of mood episodes. These results highlight the potential of automated monitoring systems in clinical practice, which enable proactive intervention strategies through continuous, objective monitoring of patient conditions.

关键词： Analytical models Phenotypes Mood Predictive models Data models Robustness Circadian rhythm Long short term memory Smart phones Monitoring

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A Web and Mobile Application to Identify the Availability of Drinking Water Information During a Water Crisis

A Web and Mobile Application to Identify the Availability of...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Razeeth, Suhail Shihara, Fathima Pavithra, Kasuni Kaliyamoorthy, Maran Nawaz, Samsudeen Sabraz Kariyapper, R.K.A.R. South Eastern University of Sri Lanka Dept. of Information and Communication Technology Sri Lanka Uva Wellassa University of Sri Lanka Dept. of Computer Science and Informatics Sri Lanka Sri Sai Ram Institute of Management Studies Sri Sai Ram Engineering College India South Eastern University of Sri Lanka Department of Management and Information Technology Sri Lanka

ISBN: (纸本)9798350394580

Sri Lanka faces drinking water related issues often due to many reasons like weather changes, floods, droughts, and tsunamis, affecting the drinking water availability. In such instances, people find difficult to gather information regarding consumable drinking water availability. In order to facilitate the meeting of commercial water sellers and customers and to engage the general public and social services in addressing the drinking water problem, this study is focused on building both web and mobile applications. The web application was implemented with a selected set of functionalities such as social login, social sharing, geocoding, reverse geocoding, viewing, and filtering posts using Angular 6, *** web framework, and Leaflet. The mobile application was developed to direct the customers to the water availability location with a shortest path using the Dijkstra's algorithm. Case evaluation was conducted with the Rotaract Club members in Badulla and thinking aloud method was used to pick up the user's ideas while interacting with the system. It can be concluded that the created web and mobile applications would offer the public a clever way to draw attention to an issue with water availability, and the commercial water service providers are given a venue to market their services and locate potential customers. © 2023 IEEE.

关键词： crowdsourcing GIS mobile application water availability web application

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A Machine-Learning Based IoT Smart Home System to Detect and Reduce Urban Insecurity in Uganda: A Case of Kampala Metropolitan Area

A Machine-Learning Based IoT Smart Home System to Detect and...

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International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD)

作者： Edward Miiro Ismael Kato Department of Computer Science & Engineering Faculty of Computing & Informatics Makerere University Business School Kampala Uganda Dept. of Applied Computing & Information Technology Faculty of Computing & Informatics Makerere University Business School Kampala Uganda

ISBN: (数字)9798350387902

ISBN: (纸本)9798350387919

This study sought to develop and test a machine learning based prototype of a smart home security system for Kampala Metropolitan area in Uganda. The researchers used a Design science Research (DSR) method to execute the project. The researchers identified a problem- which was high level of insecurity and crimes around homes in Kampala and its metropolitan areas, defined objectives, collected requirements, designed and developed the artifact, then demonstrated, evaluated, and communicated the artifact to stakeholders. The team created a working prototype of a home-security system that was later tested, demonstrated, validated among potential users. Practically, this project was implemented in a simulated environment using Tinkercad Arduino software. The developed system prototype was simulated on screen and tested on at least one home to ensure its effectiveness in reducing unauthorized entry, robbery, and other forms of crimes that are widely prevalent in Kampala today. One limitation of the project was that Arduino board used was quite limited on the number of pins that were available for use. The initial project idea had featured a temperature sensor to that was intended to regulate the temperature in the home by making use of the fan. However, the researchers did not implement this due to the limited pins and the capacity of the Arduino Uno R3 board that was used. Higher capacity industrial tools could provide a more realistic design of a similar system. The project simulated an environment of a smart home security system that can be used to detect and reduce crime. This study is part of a wider project to test ML and IoT systems in building secure smart homes. The authors envisage the application on more advanced ML algorithms and techniques in improving the system.

关键词： Temperature sensors Urban areas Prototypes Smart homes Machine learning Software Pins

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The Classification of Timor Weaving Using Artificial Neural Network

The Classification of Timor Weaving Using Artificial Neural ...

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International Conference on informatics and Computing (ICIC)

作者： Mendarissan Aritonang Adi Setiawan Krismiyati Theophilus Wellem Dept of Computer Science Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Dept. Mathematics and Data Science Faculty of Science and Mathematics Satya Wacana Christian University Salatiga Indonesia Dept. Informatics & Computer Engineering Education Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Department of Informatics Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The number of woven fabric on the island of Timor is very large and varied, making it difficult to distinguish between types and origins. Many woven fabric motifs appear similar but represent different types. Therefore, this classification was used to perform pattern recognition of woven fabric in the system. The process allowed the system’s ability to be tested by applying the pattern recognition algorithm to woven images. In this context, the feature extraction method used was gray level co-occurrence matrix (GLCM), while the method used for classification was artificial neural network (ANN). The results showed an accuracy of $87.5 \%$ in the system. Finally, GUI system was developed, enabling tests to be performed on woven images for the identification of Timor Weaving image patterns.

关键词： Training Accuracy Social networking (online) Neurons Artificial neural networks Feature extraction Weaving Fabrics Pattern recognition Web sites

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CRISP - DM Process in Smart Building Management

CRISP - DM Process in Smart Building Management

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International Conf on Electrical and Electronic engineering (ICEEE)

作者： Mahir Dursun Moldir Sabyrzhan Dept. of Electrical and Electronics Engineering Faculty of Technology Gazi University Ankara Türkiye Department of Computer Science Graduate School of Informatics Gazi University Ankara Türkiye

ISBN: (数字)9798350362541

ISBN: (纸本)9798350362558

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM).

关键词： Heating systems Smart buildings Laboratories Energy conservation Decision making Process control Data models Planning Internet of Things Data mining

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

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Advancing Fetal Surveillance with Physiological Sensing: Detecting Hypoxia in Fetal Sheep

Advancing Fetal Surveillance with Physiological Sensing: Det...

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IEEE SENSORS

作者： Weitao Tang Nhi Tran Nasim Katebi Reza Sameni Gari D. Clifford David Walker Vaishnavi Horlali Callum Taylor Robert Galinsky Faezeh Marzbanrad Electrical and Computer System Engineering Monash University Melbourne Australia The Ritchie Centre Hudson Institute of Medical Research Melbourne Australia Dept. of Biomedical Informatics Emory University Atlanta USA Dept. of Biomedical Engineering Georgia Institute of Technology Emory University Atlanta USA Department of Obstetrics and Gynaecology Monash University Melbourne Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

Accurate detection of hypoxia during fetal monitoring is critical for timely intervention and prevention of brain injury. This study investigated hypoxia detection in fetal sheep by developing a classifier to differentiate between baseline, during acute hypoxia (umbilical cord occlusion; UCO), and post-hypoxia recovery (post-UCO), using physiological signals and an XGBoost machine learning model. A multi-modal approach integrating electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) signals was compared to a model based on the ECG signal only. We included recordings from 14 fetal sheep, each of which underwent acute hypoxia via a single UCO procedure. Performance of hypoxia detection was measured by Marco F1 score (F1 m ), Accuracy (Acc), and standard deviation of validation error (SDE). Our multi-modal model achieved F1 m = 0.88, Acc = 0.90, while the ECG-based approach achieved F1 m = 0.82, Acc = 0.85. These results indicate that the multi-modal approach was superior to the ECG-only model for overall classification. While ECG and multimodal models correctly classified over 92% and 97% of acute hypoxia intervals, respectively, the classification of post- UCO intervals proved more challenging. The complexity of interpreting physiological signals underscores the need to consider temporal dynamics and physiological context in fetal monitoring.

关键词： Accuracy Machine learning Electrocardiography Hypoxia Brain modeling Feature extraction Electromyography Electroencephalography Complexity theory biomedical monitoring

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Chaotic American zebra search optimization algorithm for benchmark challenges 15

Chaotic American zebra search optimization algorithm for ben...

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

作者： Kamboj, Vikram Kumar Krishna, Ardhala Bala Verma, Chaman Raboaca, Maria Simona Filote, Constantin Stepanova, Maria Schulich School of Engineering University of Calgary AB Canada Malla Reddy Engineering College for Women Dept. of Electrcical and Electronics Engineering Hyderabad India Eötvös Loránd University Faculty of Informatics Dept. of Media and Edu. Informatics Budapest Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava720229 Romania Bauman Moscow State Technical University Engineering Business and Management Faculty Russia

ISBN: (纸本)9798350321388

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges. © 2023 IEEE.

关键词： Mapping

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