检索结果-内蒙古大学图书馆

5th International Conference on Cybernetics and Intelligent systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Low-Rank Tensor Completion for Heart Failure Exacerbation Detection in Multivariate Time Series with Missing Data

Low-Rank Tensor Completion for Heart Failure Exacerbation De...

引用

Annual IEEE Symposium on computer-Based Medical systems

作者： Óscar Escudero-Arnanz Rosa Sicilia Cristina Soguero-Ruiz Inmaculada Mora-Jiménez Diana Lelli Claudio Pedone Antonio G. Marques Dept. Signal Theory & Communications Telematics & Computing Systems Rey Juan Carlos University Fuenlabrada Spain Department of Engineering Unit of Computer Systems & Bioinformatics Università Campus Bio-Medico di Roma Italy Operative Research Unit of Geriatrics Fondazione Policlinico Universitario Campus Bio-Medico Italy

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Heart failure exacerbations (HFE) represent a critical challenge in healthcare due to their significant role in global mortality. The rise of home and wearable devices capable of monitoring cardiac conditions provides valuable opportunities for data-driven analysis and HFE detection. However, these devices frequently generate low-quality measurements with irregular sampling frequencies and high rates of missing data. Our paper presents a methodology that processes these measurements as a three-dimensional tensor, applying a low-rank tensor completion scheme to manage missing data effectively, thus facilitating anomaly detection without necessitating data imputation. We validate our method on a dataset from 4 patients with chronic HF in the compensation phase, collected at the Hospital Fondazione Policlinico Universitario Campus Bio-Medico in Rome, Italy. Our results demonstrate the tensor-based method’s superiority over traditional techniques, highlighting its potential for detecting anomalies within complex multivariate time series data. This research emphasizes the critical role of advanced data analysis in enhancing HFE identification, which could lead to improved patient care and reduced hospitalization rates.

关键词： Tensors Data analysis Hospitals Time series analysis Imputation Data models Frequency measurement

来源：评论

学校读者我要写书评

暂无评论

AI-Based Virtual Screening for Identifying Novel Drug Candidates 5

AI-Based Virtual Screening for Identifying Novel Drug Candid...

引用

5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Radhamahendran, S. Deshmukh, Sheetal Vishal Thiyagasundaram, T. Sivakumar, K. Tilak Babu, S.B.G. Singh, Surya Pratap Vels Institute of Science Technology and Advanced Studies School of Life Sciences Department of Bioinformatics Pallavaram Chennai India Bharati Vidyapeeth Yashwantrao Mohite Institute of Management Department of Computer Applications Maharashtra Karad India Sri Sairam Siddha Medical College Department of NoiNaadal Chennai India Saveetha University Simats School of Engineering Department of Mathematics Chennai Tamilnadu Thandalam India Aditya Engineering College Department of ECE Surampalem India UPUMS Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350351378

VR screening uses computers to find large databases of chemical compounds and predict their potential as medications based on factors such as binding affinity, pharmacokinetics, and toxicity. This accelerates the process of discovering novel medications by concentrating on compounds with a higher likelihood of efficacy. This results in decreased expenses and increased effectiveness over an extended period. The study demonstrates the utilization of many artificial intelligence techniques in virtual screening, including deep learning networks, machine learning models, and other sophisticated computer technologies. The analysis examines the training of AI models on diverse datasets, incorporating data from existing databases, research publications, and experimental studies. This demonstrates the potential of AI in discovering novel chemicals for the treatment of cancer, infectious diseases, neurological disorders, and uncommon ailments. There are limitations associated with utilizing Molecular structure-based CADD methods. This research reviews current tools, applications, and methods for medication production speed and cost reduction. Structure-Based Virtual Screening (SBVS) is essential to medication development, according to research. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Optimal Brain Dissection in Dense Autoencoders: Towards Determining Feature Importance in -Omics Data 5

Optimal Brain Dissection in Dense Autoencoders: Towards Dete...

引用

5th IEEE International Conference on BioInspired Processing, BIP 2023

作者： Amin, Fin Van Den Broeck, Lisa De Smet, Ive Locke, Anna M. Sozzani, Rossangela North Carolina State University Department of Electrical and Computer Engineering Raleigh United States Protealis NV Ghent Belgium Ghent University VIB Center for Plant Systems Biology Department of Plant Biotechnology and Bioinformatics GhentB-9052 Belgium Soybean Nitrogen Fixation Research Unit USDA-ARS Raleigh United States North Carolina State University NC Plant Sciences Initiative Raleigh United States

ISBN: (纸本)9798350330052

Recently, there has been increased interest in ma-chine learning explainability. Understanding the complex relationship between input features of a model and their respective outputs is of increased relevance, especially in biological science. In this paper, we introduce Optimal Brain Dissection (OBD), an innovative methodology designed to examine the importance of first-layer connections in a biology-inspired autoencoder. We incorporated regulator-target interactions within the first autoencoder layer, representing biological regulatory networks, and identified their importance to the reconstruction error, a critical aspect in navigating the complexity of high-dimensional omics data. Through a combination of pruning techniques and counterfactual reasoning, OBD offers a method to quantify feature importance, factoring in both weight magnitude and time-to-laziness. To implement this method, we propose a Dense Autoencoder (DAE) architecture, aiming for increased efficiency and reduced computation. Tailored for omics data, the DAE employs skip concatenations and circumvents non-existent target-target interactions. Our approach aims to understand the relative importance of connections for autoencoder performance, a critical step towards better counter-factual reasoning for neural networks. © 2023 IEEE.

关键词： au-toencoder bioinformatics counter-factual reasoning feature importance ML explainability omics pruning

来源：评论

学校读者我要写书评

暂无评论

Pothole Detection in Asphalt Roads: A Comprehensive Approach for Enhanced Road Maintenance and Safety with AlexNet Model

Pothole Detection in Asphalt Roads: A Comprehensive Approach...

引用

2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Abdelmalak, Mark Emad Sobhi Khodadadi, Nima Zaki, Ahmed Mohamed Eid, Marwa M. Rizk, Faris H. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Abualigah, Laith El-Kenawy, El-Sayed M. Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt University of Miami Department of Civil and Architectural Engineering Coral GablesFL United States Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Bahrain Polytechnic Faculty of Engineering Isa Town Bahrain Ain Shams University Faculty of Computer and Information Sciences Department of Computer Science Cairo11566 Egypt Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Jadara University Jadara Research Center Irbid Jordan

ISBN: (纸本)9798350351408

The research article described in this paper puts forward a novel method of using an integrated software approach and high-end hardware devices for adaptive and intelligent detection of potholes on asphalt roads. The Pothole Detection Dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15% accuracy, 91.38 % sensitivity (TPR), and a surprisingly high F-score, which reached 96.52%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies. © 2024 IEEE.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Oil Spill Detection: A Machine Learning Approach for Satellite Image Classification

Revolutionizing Oil Spill Detection: A Machine Learning Appr...

引用

2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Sherif, Khaled Rizk, Faris H. Zaki, Ahmed Mohamed Eid, Marwa M. Khodadadi, Nima Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Abualigah, Laith El-Kenawy, El-Sayed M. Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt University of Miami Department of Civil and Architectural Engineering Coral GablesFL United States Isa Town Bahrain Ain Shams University Faculty of Computer and Information Sciences Department of Computer Science Cairo11566 Egypt Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Jadara Research Center Jadara University Irbid Jordan

ISBN: (纸本)9798350351408

Identifying and labeling oil spills in satellite imagery is an essential activity of both environmental monitoring and disaster response actions. This work is dedicated to applying an Artificial Neural Network (ANN) model for gathering oil spill data by using a dataset that is specially curated for this reason. Our dataset was developed from satellite pictures of the ocean, some of which depict oil spills and some that do not. The features were extracted from each picture using computer vision algorithms. Our ANN model is trained to distinguish between two classes: The metrics that are looked at consist of accuracy, sensitivity, specificity, PPV, NPV, and statistical significance, and they illustrate how the model performs. As a result, the ANN model gets an accuracy of 96.88% and a sensitivity of 92.86% at the same time, while the specificity is 99.88%. The sensitivity of this diagnostic test is 96.30%, and the specificity is 94.74%. A p-value of 0.985997 means that the reported finding reaches a statistical significance, which is enough to support our hypothesis. This can be concluded from the results of ANN, showing the potential of this model to successfully classify the image patches into two sets, namely the ones covered by oil spills and the oil spill-free ones. The research work is a great contribution to the development of the area of environmental monitoring through the machine learning methods used for quick and appropriate detection of environmental hazards. © 2024 IEEE.

关键词： Environmental monitoring

来源：评论

学校读者我要写书评

暂无评论

Efficient Detection of Microplastics on Edge Devices with Tailored Compiler for TinyML Applications

引用

IEEE Access 2025年 13卷 90970-90982页

作者： Cerioli, Alessandro Petrosino, Lorenzo Sasso, Daniele Laroche, Clement Piechowiak, Tobias Pezzarossa, Luca Merone, Mario Vollero, Luca Sabatini, Anna DTU Compute Technical University of Denmark Kongens Lyngby2800 Denmark GN Audio Ballerup2750 Denmark Department of Engineering Università Campus Bio-Medico di Roma Research Unit of Computer Systems and Bioinformatics Via Alvaro del Portillo 21 Rome00128 Italy University of Naples Federico II Department of Biology Naples80138 Italy

The current study aims to train and benchmark AI models tailored for the detection of microplastic in water from scattered signals. We trained two different models, the first based on a Multi-Layer Perceptron (MLP) and the second on a Gated Recurrent unit (GRU). A Neural Architecture Search algorithm was used to determine the optimal configuration for each of the two models. Moreover, for deployment on edge devices, a specific custom-made compiler was designed and used. The compiler is specifically designed for TinyML applications and, therefore, for resource-constrained devices. It bypasses traditional inference engines, compiling the NNs to native C code using only standard C libraries. Our approach demonstrated better performance compared to state-of-the-art frameworks such as ONNX Runtime, achieving better latency, memory usage, energy consumption, and a higher portability. This highlights the potential of our method for efficient and effective microplastic detection in environmental monitoring. © 2013 IEEE.

关键词： Program compilers

来源：评论

学校读者我要写书评

暂无评论

Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera

Automated Food Intake Monitoring System to Prevent Malnutrit...

引用

2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Konstantakopoulos, Fotios S. Plati, Daphni K. Pliakou, Labrina A. Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy Forth Institute of Computer Science Crete HeraklionGR 70013 Greece

ISBN: (纸本)9798350383386

The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation *** Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients. © 2023 IEEE.

关键词： Nutrition

来源：评论

学校读者我要写书评

暂无评论

Image Processing Pipeline to Compute Homologous Recombination Score 22

Image Processing Pipeline to Compute Homologous Recombinatio...

引用

12th International Conference on Biomedical engineering and Technology, ICBET 2022

作者： Vundavilli, Haswanth Tumiati, Manuela Hautaniemi, Sampsa Datta, Aniruddha Kauppi, Liisa Texas A&M University United States The University of Texas MD Anderson Cancer Center United States Research Program in Systems Oncology Faculty of Medicine University of Helsinki Finland Department of Electrical and Computer Engineering The Center for Bioinformatics and Genomic Systems Engineering Texas A&M University United States

ISBN: (纸本)9781450395779

DNA double-strand breaks (DSBs) occur frequently in eukaryotic cells, and the homologous recombination pathway (HR) is one of the major pathways required to repair these breaks. However, tumor cells that are able to repair DSBs are unlikely to die due to damage incurred by DNA damaging chemotherapies, such as platinum compounds. While platinum-based therapies have been effective in treating various cancers, they also carry harsh side effects, and thus ideally platinum should be used when the probability of treatment resistance is low. HR scores provide a measure for patients' tumor's HR capacity and have been shown to predict their chemotherapy response and long-term survival. Calculating this score manually from immunofluorescence microscopy images for each patient is error-prone and time-consuming. Herein, we propose an image processing pipeline that takes as input imaging data from three emission channels (representing nuclei, S-phase cells, and HR-mediated repair in a tumor slice) from an epifluorescence microscope and computes the HR score. Our open-source methodology forms a rationale to develop similar approaches in predicting chemotherapeutic responses and facilitating to make treatment decisions. © 2022 ACM.

关键词： Chemotherapy

来源：评论

学校读者我要写书评

暂无评论

Learn2Evade: Learning-based generative model for evading PDF malware classifiers

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2021年第4期2卷 299-313页

作者： Bae, Ho Lee, Younghan Kim, Yohan Hwang, Uiwon Yoon, Sungroh Paek, Yunheung Department of Cyber Security EwhaWomans University Seoul03760 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Interdisciplinary Program in Bioinformatics Interdisciplinary Program in Artificial Intelligence Automation and Systems Research Institute Institute of NewMedia and Communications Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of

Recent research has shown that a small perturbation to an input may forcibly change the prediction of a machine learning (ML) model. Such variants are commonly referred to as adversarial examples. Early studies have focused mostly on ML models for image processing and expanded to other applications, including those for malware classification. In this article, we focus on the problem of finding adversarial examples against ML-based portable document format (PDF) malware *** deem that our problem is more challenging than those againstMLmodels for image processing because of the highly complex data structure of PDF and of an additional constraint that the generated PDF should exhibit malicious behavior. To resolve our problem, we propose a variant of generative adversarial networks that generate evasive variant PDF malware (without any crash), which can be classified as benign by various existing classifiers yetmaintaining the original malicious behavior. Our model exploits the target classifier as the second discriminator to rapidly generate an evasive variant PDF with our new feature selection process that includes unique features extracted from malicious PDF files. We evaluate our technique against three representative PDF malware classifiers (Hidost 13, Hidost 16, and PDFrate-v2) and further examine its effectiveness with AntiVirus engines from VirusTotal. To the best of our knowledge, our work is the first to analyze the performance against the commercial AntiVirus engines. Our model finds, with great speed, evasive variants for all selected seeds against state-of-The-Art PDF malware classifiers and raises a serious security concern in the presence of adversaries. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：