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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ravikumar, Rahul Sujith Kumaar, M.V. Krishna, Shirish Jayasurkanth, M. Yasshwanth, V.R. Saravanan, Sanjay Kishore Thangavel, Senthil Kumar Thangeswari, S. Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Center for Computational Engineering & Networking Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Aerospace Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Tamilnadu Agricultural University Department of Plant Pathology Coimbatore India Amrita School of Physical Sciences Department of Mathematics Coimbatore India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350370249

Traditional systems for crop disease detection often rely on manual inspection methods, which are time-consuming, labor-intensive, and prone to human error. In this context, the necessity of using transfer learning becomes apparent as it allows for leveraging pre-existing knowledge from large datasets to improve the performance of models trained on smaller, domain-specific datasets. IoT has also played a significant role in our automation process. Transfer learning leveraging MobilenetV2 is particularly beneficial for this problem of detecting Pokkah Boeng disease in sugarcane crops, as it enables the adaptation of pretrained models, such as MobileNetV2, to accurately identify Pokkah Boeng disease even with limited set of data. LIME is employed to provide transparent explanations for the model's predictions, enhancing its interpretability and facilitating informed decision-making in agricultural management. By combining transfer learning with xAI and aerial drone technology, having add-ons of IoT and facilitating a comprehensive comparative study, PokkahScan offers a comprehensive solution for timely disease detection and crop management in sugarcane farming. © 2024 IEEE.

关键词： Agricultural Analytics Crop Management Disease Detection Early Detection IoT MobilenetV2 Pokkah Boeng Disease Sugarcane Crop Transfer Learning xAI

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Can Automated Metadata Extraction Make Scientific Data More Navigable? 19

Can Automated Metadata Extraction Make Scientific Data More ...

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19th IEEE International Conference on e-Science, e-Science 2023

作者： Skluzacek, Tyler J. Chard, Kyle Foster, Ian National Center for Computational Sciences Oak Ridge National Lab Oak RidgeTN United States University of Chicago Department of Computer Science ChicagoIL United States Data Science and Learning Division Argonne National Lab LemontIL United States

ISBN: (纸本)9798350322231

FAIR principles require that scientific data be findable, discoverable, and reusable by users. To enable FAIRness, practioners of a science repository will often construct a rich, searchable index of metadata derived from the data. Unfortunately, manual metadata annotation methods do not scale to the many data files generated by many projects;and instead automated extraction systems are needed to scalably parse these files - often with nonstandard schema requiring specialized parsing strategies - and deposit representative metadata into a search index. In this work, we evaluate whether, and the extent to which, automatically extracted metadata make research repositories more navigable. We present a two-part user study conducted with scientists at two U.S. national laboratories from projects spanning spectroscopy and battery modeling. We constructed research indexes automatically by using the Xtract metadata extraction system. In the first part of our study, we learned about each user's role and identified key navigation concerns for scientists. We found that participants wished to navigate for purposes of discovery, retrieval, and organization. In the second part, participants completed simulated research data navigation tasks crafted to reflect real-world navigability concerns. We found that regardless of the interface used, participants consistently solved navigation tasks with high degrees of confidence and correctness, and significantly (1.2X-50×) faster than via their alternative methods (e.g., manual directory scans or designing a customized navigational tool). © 2023 IEEE.

关键词： Metadata

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Influence of early through late fusion on pancreas segmentation from imperfectly registered multimodal magnetic resonance imaging

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Journal of Medical Imaging 2025年第2期12卷

作者： Remedios, Lucas W. Liu, Han Remedios, Samuel W. Zuo, Lianrui Saunders, Adam M. Bao, Shunxing Huo, Yuankai Powers, Alvin C. Virostko, John Landman, Bennett A. Vanderbilt University Department of Computer Science NashvilleTN United States Johns Hopkins University Department of Computer Science BaltimoreMD United States National Institutes of Health Department of Radiology and Imaging Sciences BethesdaMD United States Vanderbilt University Department of Electrical and Computer Engineering NashvilleTN United States Vanderbilt University Medical Center Department of Medicine Division of Diabetes Endocrinology and Metabolism NashvilleTN United States VA Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Department of Molecular Physiology and Biophysics NashvilleTN United States University of Texas at Austin Dell Medical School Department of Diagnostic Medicine AustinTX United States University of Texas at Austin Livestrong Cancer Institutes Dell Medical School AustinTX United States University of Texas at Austin Department of Oncology Dell Medical School AustinTX United States University of Texas at Austin Oden Institute for Computational Engineering and Sciences AustinTX United States Vanderbilt University Department of Biomedical Engineering NashvilleTN United States

Purpose: Combining different types of medical imaging data, through multimodal fusion, promises better segmentation of anatomical structures, such as the pancreas. Strategic implementation of multimodal fusion could improve our ability to study diseases such as diabetes. However, where to perform fusion in deep learning models is still an open question. It is unclear if there is a single best location to fuse information when analyzing pairs of imperfectly aligned images or if the optimal fusion location depends on the specific model being used. Two main challenges when using multiple imaging modalities to study the pancreas are (1) the pancreas and surrounding abdominal anatomy have a deformable structure, making it difficult to consistently align the images and (2) breathing by the individual during image collection further complicates the alignment between multimodal images. Even after using state-of-the-art deformable image registration techniques, specifically designed to align abdominal images, multimodal images of the abdomen are often not perfectly aligned. We examine how the choice of different fusion points, ranging from early in the image processing pipeline to later stages, impacts the segmentation of the pancreas on imperfectly registered multimodal magnetic resonance (MR) images. Approach: Our dataset consists of 353 pairs of T2-weighted (T2w) and T1-weighted (T1w) abdominal MR images from 163 subjects with accompanying pancreas segmentation labels drawn mainly based on the T2w images. Because the T2w images were acquired in an interleaved manner across two breath holds and the T1w images on one breath hold, there were three different breath holds impacting the alignment of each pair of images. We used deeds, a state-of-the-art deformable abdominal image registration method to align the image pairs. Then, we trained a collection of basic UNets with different fusion points, spanning from early to late layers in the model, to assess how early through lat

关键词： Image segmentation

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Integer Programming Framework for Pangenome-Based Genome Inference 29th

Integer Programming Framework for Pangenome-Based Genome In...

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29th International Conference on Research in computational Molecular Biology, RECOMB 2025

作者： Chandra, Ghanshyam Helal Hossen, Md Scholz, Stephan Dilthey, Alexander T. Gibney, Daniel Jain, Chirag Department of Computational and Data Sciences Indian Institute of Science KA Bangalore560012 India Department of Computer Science The University of Texas at Dallas RichardsonTX75080 United States Institute of Medical Microbiology and Hospital Hygiene Heinrich Heine University Düsseldorf Düsseldorf Germany Center for Digital Medicine Heinrich Heine University Düsseldorf Düsseldorf Germany

ISBN: (纸本)9783031902512

Affordable genotyping methods are essential in genomics. Commonly used genotyping methods primarily support single nucleotide variants and short indels but neglect structural variants. Additionally, accuracy of read alignments to a reference genome is unreliable in highly polymorphic and repetitive regions, further impacting genotyping performance. Recent works highlight the advantage of haplotype-resolved pangenome graphs in addressing these challenges. Building on these developments, we propose a rigorous alignment-free genotyping framework. Our formulation seeks a path through the pangenome graph that maximizes the matches between the path and substrings of sequencing reads (e.g., k-mers) while minimizing recombination events (haplotype switches) along the path. We prove that this problem is NP-hard and develop efficient integer-programming solutions. We benchmarked the algorithm using downsampled short-read datasets from homozygous human cell lines with coverage ranging from 0.1× to 10×. Our algorithm accurately estimates complete major histocompatibility complex (MHC) haplotype sequences with small edit distances from the ground-truth sequences, providing a significant advantage over existing methods on low-coverage inputs. While the current design of the algorithm is applicable to haploid samples, we outline potential directions for extending it to diploid samples. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Combinatorial optimization

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Decoding Speech Categorization using Microstate Cortical EEG Signals and Machine Learning

Decoding Speech Categorization using Microstate Cortical EEG...

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2024 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2024

作者： Mahmud, M. Hasan, M. Yeasin, M. Bidelman, G. Univ. Of Tennessee Health Science Center Div. Of General Internal Medicine MemphisTN United States Middle Tennessee State University Computational And Data Science MurfreesboroTN United States University Of Memphis Department Of Electrical And Computer Engineering TN United States Indiana University Language And Hearing Sciences Department Of Speech BloomingtonIN United States

ISBN: (纸本)9798350388572

Categorical perception (CP) is a perceptual phenomenon that refers to the tendency of humans to group speech sounds into discrete units. In this work, we used cortical event-related potential (ERP) signals, recorded during a speech identification task, as input to a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and machine learning (ML) classifiers to examine how neural signatures of the brain distinguish prototypical vs. ambiguous speech token categories. Particularly, we used extreme gradient boosting (XGBoost), support vector machine, and random forest classifiers. Our analysis shows that using source reconstructed whole-brain data, the XGBoost classifier yielded the best classification accuracy of 94.12% with (area under the curve (AUC) 94.13%, Precision 94.00%, F1-score 94.00%) in the 197-258 ms time window after the stimulus onset. We also identified 15 important brain regions that distinguished vowel classes with accuracy of 90.28% (AUC 90.17%, F1-score 90.00%, Precision 90.00%), 4% less than whole-brain data. Remarkably, out of these 15 critical brain regions, nine were from the left hemisphere, consistent with the left-brain dominance for language processing. The best speech decoding was obtained from early portions of the ERP (~180-320 ms), which corroborates notions that sensory encoding is critical for successful phonetic processing. © 2024 IEEE.

关键词： Hidden Markov models

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DeeReCT-APA:Prediction of Alternative Polyadenylation Site Usage Through Deep Learning

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Genomics, Proteomics & Bioinformatics 2022年第3期20卷 483-495页

作者： Zhongxiao Li Yisheng Li Bin Zhang Yu Li Yongkang Long Juexiao Zhou Xudong Zou Min Zhang Yuhui Hu Wei Chen Xin Gao King Abdullah University of Science and Technology(KAUST) Computational Bioscience Research Center(CBRC)ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionThuwal 23955-6900Saudi Arabia Department of Biology Southern University of Science and Technology(SUSTech)Shenzhen 518055China Cancer Science Institute of Singapore Singapore 117599Singapore

Alternative polyadenylation(APA)is a crucial step in post-transcriptional *** bioinformatic studies have mainly focused on the recognition of polyadenylation sites(PASs)in a given genomic sequence,which is a binary classification ***,computational methods for predicting the usage level of alternative PASs in the same gene have been ***,all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into *** address this,here we propose a deep learning architecture,Deep Regulatory Code and Tools for Alternative Polyadenylation(DeeReCT-APA),to quantitatively predict the usage of all alternative PASs of a given *** accommodate different genes with potentially different numbers of PASs,DeeReCT-APA treats the problem as a regression task with a variable-length *** on a convolutional neural network-long short-term memory(CNN-LSTM)architecture,DeeReCT-APA extracts sequence features with CNN layers,uses bidirectional LSTM to explicitly model the interactions among competing PASs,and outputs percentage scores representing the usage levels of all PASs of a *** addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene,we show that our method consistently outperforms other existing methods on three different tasks for which they are trained:pairwise comparison task,highest usage prediction task,and ranking ***,we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA *** code and data are available at https://***/lzx325/DeeReCT-APA-repo.

关键词： Polyadenylation Gene regulation Sequence analysis Deep learning Bioinformatics

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Towards the Question of Packet Recovery at the Transport Layer

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Moscow University computational Mathematics and Cybernetics 2025年第1期49卷 16-29页

作者： Borisov, A. V. Gurov, S. I. Semenikhin, K. V. Smelyanskiy, R. L. Stepanov, E. P. Federal Research Center ‘‘Computer Science and Control’’ of the Russian Academy of Sciences Moscow Russia Department of Computational Mathematics and Cybernetics Moscow State University Moscow Russia Moscow Aviation Institute (National Research University) Moscow Russia

A problem of using Forward Error Correction (FEC) codes at the transport layer for packet recovery is considered. This problem aims at avoiding multiple transmission of the same packet, reducing data transmission delay, and preventing a waste of network resources. The main idea of FEC coding methods is the introduction of redundancy into the transmitted data, which makes possible to recover lost data at the receiver end. The paper studies various FEC methods applied at the transport layer to select the most promising, in terms of computational complexity in encoding transmitted data and decoding it at the receiver end, as well as the effect of redundancy on data transmission delay and loss level in the transport connection. The redundancy level in the selected FEC schemes is evaluated taking into account the constraints on losses in the transport connection and quality-of-service characteristics.

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VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GROUNDING IN VIDEO-LANGUAGE MODELS 12

VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GRO...

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12th International Conference on Learning Representations, ICLR 2024

作者： Kesen, Ilker Pedrotti, Andrea Dogan, Mustafa Cafagna, Michele Acikgoz, Emre Can Parcalabescu, Letitia Calixto, Iacer Frank, Anette Gatt, Albert Erdem, Aykut Erdem, Erkut Koç University KUIS AI Center Turkey Koç University Department of Computer Engineering Turkey University of Pisa Department of Computer Science Italy Institute of Information Science and Technologies Italian National Council of Research Italy Hacettepe University Department of Computer Engineering Turkey Aselsan Research Turkey University of Malta Institute of Linguistics and Language Technology Malta Heidelberg University Department of Computational Linguistics Germany Amsterdam UMC University of Amsterdam Department of Medical Informatics Netherlands Amsterdam Public Health Methodology & Mental Health Amsterdam Netherlands Utrecht University Department of Information and Computing Sciences Netherlands

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present VILMA), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, VILMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. VILMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs' grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： computational linguistics

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A Machine Learning Approaches in Sustainable Healthcare for Predicting Lumpy Skin Disease 2nd

A Machine Learning Approaches in Sustainable Healthcare for ...

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2nd International Conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Alankar, Bhavya Chauhan, Ritu Mishra, Aarushi Jena, Mehak Kaur, Harleen Center for Computational Biology and Bioinformatics Amity University UP Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031717284

The integration of machine learning in healthcare has transformed disease prognosis and diagnosis. Healthy livestock contributes to a better environment, sustainable resource use, and the prosperity and expansion of rural communities. Various machine-learning techniques have been used in tasks such as disease prediction, medical image analysis, and drug discovery. These techniques have the potential to improve diagnostics, treatment strategies, and patient outcomes. Machine learning algorithms, using diverse data sources, enable early detection and tailored interventions. Ongoing research aims to use machine learning algorithms in healthcare databases to identify effective patterns for future predictive modeling. This study investigates machine learning algorithms and descriptive analysis methodologies to determine an individual's diabetic status. Four machine learning classifiers, including K Nearest Neighbour, Support Vector Machine, Decision Tree, and Random Forest, are examined, and compared. These classifiers show potential in enhancing diabetes prediction and improving sustainable healthcare decision-making with respective accuracies of 81.16%, 84.84%, 77.16%, and 89.83%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Imitating and exploring the human brain's resting and task-performing states via brain computing: scaling and architecture

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National Science Review 2024年第5期11卷 17-28页

作者： Wenlian Lu Longbin Zeng Jiexiang Wang Shitong Xiang Yang Qi Qibao Zheng Ningsheng Xu Jianfeng Feng DTB Consortium Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of Education Fudan University Shanghai Center for Mathematical Sciences Fudan University Department of Computer Science University of Warwick Zhangjiang Fudan International Innovation Center Fudan University

A computational human brain model with the voxel-wise assimilation method was established based on individual structural and functional imaging data. We found that the more similar the brain model is to the biological counterpart in both scale and architecture, the more similarity was found between the assimilated model and the biological brain both in resting states and during tasks by quantitative metrics. The hypothesis that resting state activity reflects internal body states was validated by the interoceptive circuit's capability to enhance the similarity between the simulation model and the biological brain. We identified that the removal of connections from the primary visual cortex(V1) to downstream visual pathways significantly decreased the similarity at the hippocampus between the model and its biological counterpart,despite a slight influence on the whole brain. In conclusion, the model and methodology present a solid quantitative framework for a digital twin brain for discovering the relationship between brain architecture and functions, and for digitally trying and testing diverse cognitive, medical and lesioning approaches that would otherwise be unfeasible in real subjects.

关键词： digital twin brain computational cortico-subcortical model fMRI data assimilation interoceptive circuit

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