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International Conference on Information Technology and Electrical engineering (ICITEE)

作者： Fuenglada Manokij Kanoksri Sarinnapakorn Peerapon Vateekul Department of Computer Engineering Faculty of Engineering Chulalongkorn University Big Data Analytics and IoT Center (CUBIC) Chulalongkorn University Bangkok Thailand Hydro-Informatics Institute (Public Organization) Ministry of Higher Education Science Research and Innovation Bangkok Thailand

Precipitation prediction is necessary to use in water management, especially in Thailand, it can be applied for various ways, such as flood warning, agriculture planning, etc. There are many prior attempts to forecast rainfall from the rain-gauge station. Some deployed traditional machine learning approaches: ARIMA, k-NN, etc. Recently, deep learning approach has shown promising results in this task. However, the accuracy is still limited since the raining period throughout the year in Thailand is very scarce, so most rainfall amount is zero. In this paper, we propose to cascade two deep learning networks to tackle this problem: one is a classification model to classify whether it rains or not, and the other is a regression model to predict rainfall amount. Our network is a combination of CNN and GRU, where CNN aims to capture relationship between various sensors and GRU aims to capture time-series information. Furthermore, we also perform multi-step forecasting by applying a rolling mechanism that uses the predicted rainfall and involved features to predict the next 6 steps. The experiment was conducted on hourly rainfall dataset for 6 years (2013-2018) provided from the public government sector in Thailand. We use RMSE as performance metric to evaluate three periods of rainfall: Overall, Rain, and non-rain periods and the results show that our cascading model is the winner with only 4.53% in term of RMSE which is the average percentage of difference from over all regions.

关键词： Deep learning Rain Predictive models data models Sensors Forecasting Task analysis

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Reservoir Water Level Forecasting using Deep Learning

Reservoir Water Level Forecasting using Deep Learning

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Archita Mallick Surajit Ghosh Kounik De Sarkar Sudip Roy Centre of Excellence in Disaster Mitigation and Management Indian Institute of Technology Roorkee Roorkee India Water Futures Data and Analytics International Water Management Institute Colombo Sri Lanka Environmental Science and Engineering Department Indian Institute of Technology Bombay Mumbai India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

Reservoirs play a vital role in water resource management and provide essential ecosystem services. The study focuses on extracting the water extent of Maithon reservoir, Jharkhand, India, from 2017 to 2023 by delineating the boundaries between the water and non-water class extracted from Sentinel-1 data. The scenes were processed using Google Earth Engine to obtain water extent using the Otsu method. This research extends the application of the extracted water extent values, precipitation data from CHIRPS, and weekly historical water level data of the reservoir obtained from the Central Water Commission to monthly forecasts of the water level using the Long Short-Term Memory (LSTM) model. The forecasts were obtained with a mean absolute error of around 80 cm with test data. The study provides initial results and is under further modification, where the improved estimation shall be integrated into flood early warning systems and help to provide actionable insights to stakeholders and decision-makers to mitigate risks and optimize water usage.

关键词： Precipitation Ecosystems Sentinel-1 Alarm systems Reservoirs Floods data mining Stakeholders Long short term memory Streams

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Facial Expression Recognition Using Multi-Block Deep CNN

Facial Expression Recognition Using Multi-Block Deep CNN

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International Conference on Circuits, Communication, Control and Computing

作者： Priyadarshini V Srinivasulu Reddy U Venkata Rami Reddy Chirra Mrudula M Suneetha M Dept. of Computer Science & Engineering National Institute of Technology Andhra Pradesh India Dept. of Computer Applications Machine Learning & Data Analytics lab National Institute of Technology Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Department of CSE Malla Reddy College of Engineering Telangana India Independent Researcher Amaravati India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy.

关键词： Support vector machines Accuracy Computational modeling Face recognition Stability analysis Circuit stability Convolutional neural networks Integrated circuit modeling Thermal stability Random forests

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Multimodal Subspace Independent Vector Analysis Better Captures Hidden Relationships in Multimodal Neuroimaging data

Multimodal Subspace Independent Vector Analysis Better Captu...

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IEEE International Symposium on Biomedical Imaging

作者： Xinhui Li Tulay Adali Rogers F. Silva Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

We introduce multimodal subspace independent vector analysis (MSIVA), a methodology to capture both joint and unique latent sources across data modalities by defining shared and modality-specific subspaces. We compared MSIVA to a unimodal analysis (UA) baseline and tested both methods with four distinct subspace structures on synthetic and multimodal neuroimaging datasets. We demonstrated that both approaches can identify and distinguish the correct subspace structures from incorrect ones on synthetic datasets. We then showed that MSIVA can better capture the subspace structures across two neuroimaging modalities. Results from subsequent per-subspace canonical correlation analysis and brain-phenotype modeling showed that the sources from the optimal subspace structure are significantly associated with phenotype measures including age and sex.

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A structural haplotype in the 17q21.31 MAPT region is associated with increased risk for chronic traumatic encephalopathy endophenotypes

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Cell Reports Medicine 2025年第5期6卷 102084-102084页

作者： Han, Xudong Zhang, Yichi Petrosky, Jillian N. Bald, Sarah Sherva, Richard M. Labadorf, Adam Cherry, Jonathan D. Chung, Jaeyoon Farrell, Kurt Abdolmohammadi, Bobak Durape, Shruti Martin, Brett M. Palmisano, Joseph N. Farrell, John J. Alvarez, Victor E. Huber, Bertrand R. Dwyer, Brigid Daneshvar, Daniel H. Jun, Gyungah R. Lunetta, Kathryn L. Goldstein, Lee E. Katz, Douglas I. Cantu, Robert C. Shenton, Martha E. Cummings, Jeffrey L. Reiman, Eric M. Stern, Robert A. Alosco, Michael L. Tripodis, Yorghos Farrer, Lindsay A. Stein, Thor D. Crary, John F. McKee, Ann C. Mez, Jesse Bioinformatics Graduate Program Boston University Boston MA United States Section of Biomedical Genetics Department of Medicine Boston University Chobanian & Avedisian School of Medicine Boston MA United States Department of Neurology Boston University Chobanian & Avedisian School of Medicine Boston MA United States Boston University Alzheimer's Disease Research Center Boston University Chobanian & Avedisian School of Medicine Boston MA United States Boston VA Healthcare System Boston MA United States Boston University Chronic Traumatic Encephalopathy Center Boston University Boston MA United States Department of Pathology and Laboratory Medicine Boston University Chobanian & Avedisian School of Medicine Boston MA United States Departments of Pathology Neuroscience and Artificial Intelligence & Human Health Neuropathology Brain Bank & Research CoRE Friedman Brain Institute Ronald M. Loeb Center for Alzheimer's Disease Icahn School of Medicine at Mount Sinai New York NY United States Biostatistics and Epidemiology Data Analytics Center Boston University School of Public Health Boston MA United States Bedford VA Healthcare System Bedford MA United States Braintree Rehabilitation Hospital Braintree MA United States Department of Physical Medicine and Rehabilitation Harvard Medical School Boston MA United States Department of Rehabilitation and Human Performance Brain Injury Research Center Icahn School of Medicine at Mount Sinai New York NY United States Department of Neurology Icahn School of Medicine at Mount Sinai New York NY United States Department of Biostatistics Boston University School of Public Health Boston MA United States Department of Ophthalmology Boston University Chobanian & Avedisian School of Medicine Boston MA United States Departments of Biomedical Electrical & Computer Engineering Boston University College of Engineering Boston MA United States Departments of Radiology and Psychiatry Boston University Chobanian & Avedisia

Chronic traumatic encephalopathy (CTE) is a neurodegenerative tauopathy associated with repetitive head impact (RHI) exposure. Genetic variation in the 17q21.31 region, containing microtubule-associated protein tau (MAPT), has been implicated in tauopathies but has not been investigated in CTE. The region includes a megabase-long inversion (H1/H2) and copy-number variations, including α, β, and γ segments, which can be characterized as nine segregating structural haplotypes. We leveraged array SNP data and a reference panel across the 17q21.31 region to impute structural haplotypes and test their association with CTE endophenotypes in 447 European ancestry brain donors with RHI exposure. The H1β1γ1 haplotype was significantly associated with dementia and semi-quantitative tau burden in multiple cortical and medial temporal regions commonly affected in CTE. H1β1γ1 differential expression analyses in dorsolateral frontal cortex implicated cis-acting genes and inflammatory pathways. Taken together, the H1β1γ1 haplotype may help explain CTE heterogeneity among those with similar RHI exposure. © 2025 The Authors

关键词： 17q21.31 chronic traumatic encephalopathy dementia differential gene expression immune system MAPT repetitive head impacts structural haplotypes tauopathy traumatic brain injury

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Sketch-and-solve approaches to k-means clustering by semidefinite programming

arXiv

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arXiv 2022年

作者： Clum, Charles Mixon, Dustin G. Villar, Soledad Xie, Kaiying Department of Mathematics The Ohio State University ColumbusOH United States Translational Data Analytics Institute The Ohio State University ColumbusOH United States Department of Applied Mathematics & Statistics Johns Hopkins University BaltimoreMD United States Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering The Ohio State University ColumbusOH United States

We introduce a sketch-and-solve approach to speed up the Peng-Wei semidefinite relaxation of k-means clustering. When the data is appropriately separated we identify the k-means optimal clustering. Otherwise, our approach provides a high-confidence lower bound on the optimal k-means value. This lower bound is data-driven;it does not make any assumption on the data nor how it is generated. We provide code and an extensive set of numerical experiments where we use this approach to certify approximate optimality of clustering solutions obtained by k-means++. © 2022, CC BY.

关键词： Optimal systems

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Glacier: Glass-Box Transformer for Interpretable Dynamic Neuroimaging

Glacier: Glass-Box Transformer for Interpretable Dynamic Neu...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Usman Mahmood Zening Fu Vince Calhoun Sergey Plis Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA Department of Computer Science Georgia State University Atlanta GA USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Deep learning models can perform as well or better than humans in many tasks, especially vision related. Almost exclusively, these models are used to perform classification or prediction. However, deep learning models are usually of black-box nature, and it is often difficult to interpret the model or the features. The lack of interpretability causes a restrain from applying deep learning to fields such as neuroimaging, where the results must be transparent, and interpretable. Therefore, we present a ’glass-box’ deep learning model and apply it to the field of neuroimaging. Our model mixes spatial and temporal dimensions in succession to estimate dynamic connectivity between the brain’s intrinsic networks. The interpretable connectivity matrices produced by our model result in beating state-of-the-art models on many tasks using multiple functional MRI datasets. More importantly, our model estimates task-based flexible connectivity matrices, unlike static methods such as Pearson’s correlation coefficients.

关键词： Neuroimaging Deep learning Magnetic resonance imaging Signal processing Predictive models Brain modeling Transformers

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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Advanced machine learning in neuroimaging studies via federated learning

Advanced machine learning in neuroimaging studies via federa...

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IEEE International Conference on Big data

作者： Sunitha Basodi Javier Romero Sandeep Panta Dylan Martin Sergey Plis Anand D. Sarwate Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA Department of Electrical & Computer Engineering Rutgers University NJ USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Federated analysis can help perform large-scale analyses using neuroimaging datasets across various research groups overcoming the limitations of institutional data-sharing policies, privacy or regulatory concerns as it requires no data sharing. In this work, we employ a federated neuromark algorithm to generate independent component analysis (ICA) time courses data from functional magnetic resonance imaging (fMRI) data and feed it to a federated deep neural network (DNN) model to perform classification of schizophrenia patients versus controls.

关键词： Neuroimaging data privacy Machine learning algorithms Federated learning Artificial neural networks Independent component analysis Functional magnetic resonance imaging Schizophrenia data models Feeds

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Protecting Deep Neural Networks in Cloud-Based Environments Towards Backdoor Assaults

Protecting Deep Neural Networks in Cloud-Based Environments ...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Manjunatha R Naresh Kaushik Yaduvir Singh Department of Data analytics and Mathematical Sciences School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of uGDX ATLAS SkillTech University Mumbai Maharashtra India Department of Computer Science and Engineering (AI) Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

When it comes to training deep neural networks, especially those with complicated architectures, the accompanying financial and temporal costs often force resource-constrained customers to outsource their learning process via cloud service providers. But there's a risk that dishonest cloud providers may include backdoors and jeopardise the security of the overall model. Users may examine the model closely and utilise state-of-the-art defence techniques to reduce this danger. The goal of this research is to create a robust backdoor attack technique called RobNet that can circumvent current defences from the perspective of malevolent cloud providers. Furthermore, multi-trigger backdoor assaults are introduced, which enlarge the attack area by misclassifying inputs with different triggers into target labels that are either identical or dissimilar. RobNet is tested against four popular defence techniques (Pruning, NeuralCleanse, Strip, and ABS) using datasets from MNIST, GTSRB, and CIFAR-10. RobNet has a higher attack success rate and more robustness against possible defensive measures when compared to two state-of-the-art baselines: Hidden Backdoors and BadNets.

关键词： Training Technological innovation Strips Neurons Artificial neural networks Robustness Service-oriented architecture Security Biological neural networks Resilience

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