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检索条件"任意字段=IEEE Computational Systems Bioinformatics Conference"

共 879 条记录，以下是31-40 订阅

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Short-term Weather Forecasting for Wind Energy Generation using A Deep Learning Technique 5

Short-term Weather Forecasting for Wind Energy Generation us...

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5th ieee International conference on Smart systems and Technologies, SST 2022

作者： Zim, Abid Hasan Zeyad, Mohammad Ahmed, S.M. Masum Hossain, Eftakhar Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh ABCD Laboratory Bangladesh Chattogram Bangladesh Heriot-Watt University Edinburgh United Kingdom University of Antwerp Faculty of Applied Engineering - Construction Antwerp Belgium

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

Inadequate or erroneous weather predictions have a great impact on wind turbine energy output. Certain weather fluctuations and interruptions affect the efficient operation of wind turbines. Short-term weather predictions that are calibrated and verified make it simple to reduce the threat presented by meteorological uncertainty. Conventional physics-based techniques for forecasting are relatively weaker than data-driven approaches since wind turbine components usually have a multipart nonlinear relationship. Compared to physical methods, non-parametric alternatives like deep learning are thought to be more stable and lead to fewer mistakes in analyst predictions. This research presents the state-of-the-art Temporal Convolutional Network (TCN), one of the deep learning methods, which is introduced as a means of enhancing the precision of short-term forecasting of weather variables. TCN is an architecture that makes use of casual convolutions and dilations in order to be adaptable for sequential data with its temporality and huge receptive fields. Seven significant meteorological parameters from the NASA POWER dataset of Chouchala Sea Beach in Chittagong, Bangladesh, over a period of four months were used in this research. The accuracy was measured using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results obtained from the suggested TCN model demonstrate that the predicted outcome is pretty accurate and consistent. © 2022 ieee.

关键词： Wind turbines

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Assessing the Impact of Vulnerabilities on Confidentiality, Integrity, and Availability in Smart systems 2

Assessing the Impact of Vulnerabilities on Confidentiality, ...

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2nd International conference on Informatics, ICI 2023

作者： Minhass, Shubham Chauhan, Ritu Kaur, Harleen Amity University Amity Institute of Information Technology UP India Amity University IoT and Artificial Intelligence Lab Center for Computational Biology and Bioinformatics UP India Jamia Hamdard Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350343830

As Internet of Things (IoT) technologies proliferate and smart devices become more and more integrated into our daily lives and workplaces, it is becoming more and more important to address the complex issues of data security and privacy within smart homes and buildings. Even though the Internet of Things (IoT) has many benefits, such as remote control, real-time monitoring, and sophisticated security features, it also raises serious concerns about how sensitive data is protected. By examining the particular problems associated with protecting sensitive data in the IoT era and guaranteeing the continuous provision of dependable services within smart environments, this research paper seeks to close a significant knowledge gap. In order to do this, the research focuses on the complex data-centric features of smart ecosystems, including data processing, generation, and storage. This paper thoroughly examines the range of current IoT technologies, data management practices, and privacy challenges by using a qualitative research approach. It also offers a thorough examination of the privacy protections and security measures that are now in place in smart buildings and homes. This research focuses primarily on the interface between technology, security, and privacy of data. This work effectively fills in the significant research vacuum by clarifying the specific issues it seeks to solve. It provides useful advice and insights that can help individuals and organizations navigate the complex world of smart technologies, securely store their data, and realize the full potential of these game-changing advancements. © 2023 ieee.

关键词： Sensitive data

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Anomaly Detection in Individual Specific Networks through Explainable Generative Adversarial Attributed Networks

Anomaly Detection in Individual Specific Networks through Ex...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Pietro Hiram Guzzi Ugo Lomoio Tommaso Mazza Pierangelo Veltri Department of Surgical and Medical Sciences Magna Graecia University Catanzaro Italy Relatech SPA IRCCS Casa Sollievo Della Sofferenza Rome Italy Department of Informatics Modelling Electronics and Systems DIMES University of Calabria Rende Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Recently, the availability of many omics data source has given the rise of modelling biological networks for each individual or patient. Such networks are able to represent individual-specific characteristics, providing insights into the condition of each person. Given a set of networks of individuals, a network representing a particular condition (e.g., an individual with a specific disease) may be seen as an anomaly network. Consequently, the use of Graph Anomaly Detection techniques may support such analysis. Among the others, Generative Adversarial Networks present optimal performances in anomaly detection. This paper presents ADIN (Anomaly Detection in Individual Networks), a framework based on Generative Adversarial Attributed Networks (GAANs) for anomaly detection in convergence/divergence patients attributed networks. Preliminary results on networks generated from computational biology gene expression data demonstrate the effectiveness of our approach in detecting and explaining bladder cancer patients.

关键词： Biological system modeling Soft sensors Scalability Precision medicine Machine learning Data models Gene expression bioinformatics Anomaly detection Medical diagnostic imaging

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An Optimal Feature Selection with Neural Network-Based Classification Model for Dengue Fever Prediction 6

An Optimal Feature Selection with Neural Network-Based Class...

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6th International conference on Information systems and Computer Networks, ISCON 2023

作者： Ramasamy, Vidhyavathi Vadivel, Sameswari Kothandapani, Sudha Mahilraj, Jenifer Sivaram, P. Sharma, Bhisham Alagappa University Department of Bioinformatics Tamilnadu Karaikudi India Alagappa University Department of Computational Logistics Tamilnadu Karaikudi India Dr M.G.R. Educational and Ri University Department of Cse Madhuravoyal TN Chennai India Npr College of Engineering Department of Cse Tamilnadu Natham India G H Raisoni College of Engineering Department of Artificial Intelligence Maharashtra Nagpur440016 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350346961

A vast range of neural network applications has been reported in recent research. This model can make various predictions using this model, and this research has taken dengue disease prediction. Uncertain Neural Network (UNN) is introduced to efficiently classify the dengue disease dataset. Research can be conducted in a wide range of fields to resolve the issue of missing and uncertain data. Also, real-time data sets contain a lot of missing attributes or values. Researchers use real-time data because it is approximately measured. Researchers thus pay more attention to disease prediction. The problem can be solved by putting into action the dengue prediction system that has been developed. The primary goal of this study is to create a reliable classification model based on feature selection for predicting dengue fever. This research work is applied for feature model creation and relative analysis to improve dengue virus prediction correctness in three stages: Collecting dengue virus datasets from the UCI Machine Learning library was the first step;secondly, a Genetic Algorithm (GA) is used for feature selection, the UNN model is used to create a classification model;As part of the final stage, Accuracy (A), Specificity (Sp), and Sensitivity (Se) can be calculated for justifying the result. A simulation of the proposed method is conducted using the WEKA tool. Therefore, the proposed classification procedure was the most effective model for predicting and detecting dengue fever.. © 2023 ieee.

关键词： Forecasting

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Neuro-Degenerative Disorder Prediction Using Curated Short Mutated Genome Sequences Through Hybrid Deep Neural Networks

Neuro-Degenerative Disorder Prediction Using Curated Short M...

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ieee International conference on bioinformatics and computational Biology (ICBCB)

作者： Naman Bhatia L V S K B Kasyap Varanasi Parth Raghuwanshi Avinash Saroj Chandra Mohan Dasari Computer Science and Engineering Indian Institute Of Information Technology Sri City India Computer Science and Engineering Group Indian Institute of Information Technology Sri City India

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

Psychological disorders are a range of conditions characterized by significant disruptions in a person's thoughts, emotions, and behavior. The WHO reports that over a billion people worldwide have neurological illnesses, and ineffective diagnostics hinder treatment. Mental health disorders continue to be a pervasive global health concern, necessitating innovative early detection and intervention approaches. The classical methods often rely on expensive clinical diagnoses and computational techniques in genome-wide association studies (GWAS) that can be time-consuming. We curate the short genome sequence data for five disorders of fixed and variable lengths through two novel approaches to address these limitations. To our knowledge, this is the first work that suggests extracting short genetic sequences for psychiatric disorders. We investigate different deeplearning (DL) techniques to predict mental health disorders based on the prepared datasets. Our study aims to enhance the accuracy and timeliness of mental health assessments, potentially revolutionizing mental health care delivery. The classification accuracies for the proposed graph neural network with HMM representation on fixed length genome sequence were as follows: SCZ=80%; BP=71% which is the highest when compared to the other DL models. The outcomes of our research reveal promising results, with the curated short genome sequences significantly contributing to identifying mental health disorders. The proposed data curation approaches lay a solid basis for the adoption of DL based mental health prediction tools in healthcare systems. The model encourages early intervention and ultimately improves patient outcomes.

关键词： Accuracy computational modeling Genomics Hidden Markov models Mental health Medical services Graph neural networks Encoding bioinformatics computational biology

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NeDRex-Web: An Interactive Web Tool for Drug Repurposing by Exploring Heterogeneous Molecular Networks

NeDRex-Web: An Interactive Web Tool for Drug Repurposing by ...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Andreas Maier Mahdie Rafiei Elisa Anastasi Olga Zolotareva James Skelton Maria L. Elkjaer Ana I. Casas Cristian Nogales Harald H. H. W. Schmidt Tim Kacprowski David B. Blumenthal Anil Wipat Sepideh Sadegh Jan Baumbach Institute for Computational Systems Biology University of Hamburg Hamburg Germany School of Computing Newcastle University Newcastle Upon Tyne UK Department of Neurology Odense University Hospital Odense Denmark Department of Pharmacology and Personalised Medicine Maastricht University Maastricht The Netherlands Braunschweig Integrated Centre of Systems Biology Technical University of Brunswick Brunswick Germany Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Finding new indications for approved drugs is a promising alternative to the often very lengthy and expensive process of de novo drug development. systems medicine has brought forth several different approaches to tackle this important task. We recently published NeDRex, a network medicine tool for the identification of disease modules and drug repurposing. NeDRex-Web (https://***) brings existing and new features of the NeDRex platform to a user-friendly and research-oriented web application, enabling online exploration of large heterogeneous molecular networks. Focusing mainly on drug repurposing, NeDRex-Web implements customizable disease module identification and drug prioritization workflows to support users of diverse backgrounds in their research. Users are assisted during every step of their analysis, including the definition of relevant input sets, the selection from various algorithms for module identification or drug prioritization, and the prioritization of the results by their statistical significance. A guided connectivity search provides an easy way to identify links between node sets of interest and can be used to create user-specific induced networks.

关键词： Drugs Focusing Knowledge graphs bioinformatics Diseases

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Cross-chain Sharing of Personal Health Records: Heterogeneous and Interoperable Blockchains

Cross-chain Sharing of Personal Health Records: Heterogeneou...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yongyang Lv Xiaohong Li Yingwenbo Wang Kui Chen Zhe Hou Ruitao Feng College of Intelligence and Computing Tianjin University Tianjin China School of Information and Communication Technology Griffith University Brisbane Australia Faculty of Science and Engineering Southern Cross University Gold Coast Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the widespread adoption of medical informatics, a wealth of valuable personal health records (PHR) has been generated. Concurrently, blockchain technology has enhanced the security of medical institutions. However, these institutions often function as isolated data silos, limiting the potential value of PHRs. As the demand for data sharing between hospitals on different blockchains grows, addressing the challenge of cross-chain data sharing becomes crucial. When sharing PHRs across blockchains, the limited storage and computational capabilities of medical Internet of Things (IoT) devices complicate the storage of large volumes of PHRs and the handling of complex calculations. Additionally, varying blockchain cryptosystems and the risk of internal attacks further complicate the cross-chain sharing of PHRs. This paper proposes a scheme for sharing PHRs across heterogeneous and interoperable blockchains. Medical IoT devices can encrypt and store real-time PHRs in an InterPlanetary File System, requiring only simple operations for data sharing. An enhanced proxy re-encryption(PRE) algorithm addresses the differences in blockchain cryptosystems. Multi-dimensional analysis demonstrates that this scheme offers robust security and excellent performance.

关键词： Performance evaluation Limiting Hospitals Internet of Medical Things Biomedical measurement Real-time systems Blockchains InterPlanetary File System bioinformatics

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A Type-2 Fuzzy Logic-Based Explainable Artificial Intelligence for the Prediction of Enhancers

A Type-2 Fuzzy Logic-Based Explainable Artificial Intelligen...

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ieee International conference on Fuzzy systems (FUZZ-ieee)

作者： Khizra Maqsood Hani Hagras Nicolae Radu Zabet The Computational Intelligence Centre School of Computer Science and Electronic Engineering University of Essex Colchester UK Blizard Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

Enhancers are the non-coding regions of the genome responsible for controlling the activity of the genes. Approximately 80-90% of mutations causing human diseases (including cancers) are located in the non-coding part of the human genome, often in enhancers. To provide a better understanding of how these mutations lead to disease states, machine and deep learning approaches have been developed to annotate enhancers. However, these opaque box models lack transparency and fail to provide genomics and clinical researchers with an accurate explanation of how these models predict specific regions as enhancers over other regions. Hence, there is a need for eXplainable Artificial Intelligence (XAI) systems that can be easily understood, analysed, and augmented by domain experts. In this paper, we will present a type-2 fuzzy logic-based XAI system to predict the enhancer regions which was trained and applied in the Drosophila genome. The model provides explainable rules that are easily understandable. The model has significantly recovered the experimentally validated enhancers, along with the large set of novel enhancers that display similar characteristics to the experimentally validated enhancers. This research is the first step to provide a mechanistic understanding at a molecular level of how the genome is regulated.

关键词： Deep learning Accuracy Explainable AI Genomics Predictive models bioinformatics Fuzzy systems

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Extracting Drug-Drug Interactions from Biomedical Texts Using BioBERT with Improved Focal Loss

Extracting Drug-Drug Interactions from Biomedical Texts Usin...

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ieee International conference on bioinformatics and computational Biology (ICBCB)

作者： Weixin Xie Huirui Han Jiayu Xu Chengkui Zhao Tianyi Li Limei Wang Jin Li Weixing Feng College of Intelligent Systems and Engineering Harbin Engineering University Harbin China Key Laboratory of Tropical Translational Medicine of Ministry of Education Bioinformatics for Major Diseases Science Innovation Group College of Biomedical Informatics and Engineering Hainan Medical University Haikou China

Drug-drug interaction (DDIs) extraction has become a vital task for biomedical and clinical research and public health safety. There are many excellent and effective methods based on neural network models have been proposed on DDIs extraction task and achieved good performance. However, most of the previous models suffer from the serious problem of imbalance data, which can affect the relation extraction results. In this study, a bidirectional encoder representation from transformer pretrained on biomedical corpora (BioBERT) is applied to DDIs extraction from biomedical literature. After the semantic vectors are obtained to represent the entities in a sentence by BioBERT, the embeddings of two entities in a sentence are averaged into two vectors, concatenate the first token, and go through the fully connected layer with an activation operation. Then, we explore an improved focal loss function to dealing with the imbalance data. The performance of our proposed approach gets an F-score of 80.45% on the DDIExtraction 2013 and achieves a significant improvement over the methods of BioBERT with traditional cross-entropy loss function.

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Automatic Segmentation of the Substantia Nigra in Susceptibility-Weighted Imaging of Non-Parkinson and Parkinson's Disease Patients

Automatic Segmentation of the Substantia Nigra in Susceptibi...

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ieee International conference on bioinformatics and computational Biology (ICBCB)

作者： Helena Rico Pereira Hugo Alexandre Ferreira José Manuel Fonseca Faculdade de Ciências Instituto de Biofísica e Engenharia Biomédica Universidade de Lisboa Lisboa Portugal Centre of Technology and Systems UNINOVA Caparica Portugal Physics Department NOVA School of Science and Technology NOVA University of Lisbon Caparica Portugal Electrical and Computer Engineering Department NOVA School of Science and Technology NOVA University of Lisbon Caparica Portugal

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

The loss of neurons in the substantia nigra (SN) is a pathological feature of Parkinson's disease (PD), but its segmentation in medical imaging is challenging. This study proposes an automatic method to segment Non-PD and PD patients' left and right SN in susceptibility-weighted images (SWI) and Magnitude SWI-derived images. The images used in this study were obtained from the NTUA Parkinson's disease database: 11 SWI image volumes (4 Non-PD and 7 PD) and 8 Magnitude image volumes (3 Non-PD and 5 PD). One Non-PD template was created per each image type to obtain a mask of the left and right SN. All images were registered to the respective template. The segmentation method consisted of setting a seed point based on the masks of 2D image slices created to perform a flood fill algorithm. The method's performance was evaluated using the dice similarity coefficient (DSC) between automatic and manual segmentation. The segmentation method achieved higher performance using the SWI images (DSC: SN $=0.684 \pm 0.182$ ; left SN $=0.722 \pm 0.137$ ; right SN: $0.664 \pm 0.226$ ) than using the Magnitude images (DSC: SN $=0.589 \pm 0.249$ ; left SN $=0.604 \pm 0.223$ ; right SN $=0.569 \pm$ 0.284), suggesting that the SWI images are more suitable for SN segmentation. Although not yet as effective as manual segmentation, the proposed method may aid in SN segmentation.

关键词： Image segmentation Pathology Parkinson's disease Databases Neurons Manuals Floods bioinformatics Biomedical imaging computational biology

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