检索结果-内蒙古大学图书馆

Bioinformatics and Biomedical Computing

Fundamental Research 2024年第4期4卷 713-714页

作者： Hongmin Cai Jiazhou Chen Fa Zhang Jianxin Wang The School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China The School of Medical Technology Bejing Institute of TechnologyBejing 100081China The School of Computer Science and Engineering Central South UniversityChangsha 410083China

The rapid advancement of high-throughput sequencing technologies and the explosive growth of biological data have revolutionized the field of bioinformatics and biomedical computing[1-4].The generation of vast amounts of genomic,transcriptomic,proteomic,and metabolomic data has created unprecedented opportunities for understanding the complexities of biological systems and their implications for human health[5-6].Moreover,the emergence of spatial omics technologies,such as spatial transcriptomics and spatial proteomics,has added a new dimension to our understanding of the spatial organization and heterogeneity of biological *** cutting-edge technologies enable the mapping of molecular information at a high spatial resolution,providing valuable insights into the tissue microenvironment and the interplay between cells in various physiological and pathological conditions[7-10].

关键词： enable dimension insight

来源：评论

学校读者我要写书评

暂无评论

Particle segregation mechanism of S–D system in a rotating drum

引用

Particuology 2024年第10期93卷 300-308页

作者： Erliang Xiao Wei Zhang Zhipeng Chi Ran Li Hui Yang School of Optical-Electricaland Computer Engineering University of Shanghai for Science and Technology Shanghai 200093China Collegeof Medical Instrumentation Shanghai University of Medicine&Health SciencesShanghaiChina

Complex segregation occurs in a binary particle system with differing particle sizes and densities,particularly when the larger particles are heavier(S–D system,i.e.,size minus density system).Predicting the segregation pattern driven by multiple mechanisms simultaneously is often *** study explores the segregation mechanisms in a quasi-2D circular drum containing a S–D system,realizing a transition between the S-core and Core-and-band patterns by adjusting the drum rotation *** the transition of the segregation pattern,only the S-core pattern chiefly driven by the percolation mechanism is initially *** the rotation speed increases,the buoyancy mechanism and particle diffusion gradually strengthen,jointly driving the formation of the Core-and-band pattern.A dimensionless strength ratio,λ=H/h,where H and h respectively represent the diffusion and buoyancy strengths at length scales,is introduced to elucidate this *** Core-and-band pattern emerges whenλreached 1.4.

关键词： Particle segregation S-Dsystem Segregationmechanisms competition Segregation pattern Rotating drum

来源：评论

学校读者我要写书评

暂无评论

Effect of Graphite Addition on Microstructure, Mechanical Properties and Thermal Properties of Injection Molded AZ91D Alloy+1

引用

Materials Transactions 2024年第4期65卷 374-380页

作者： Hideshima, Yasutoshi Maeda, Fumiya Fukuta, Tadao Ozaki, Koichi Course of Advanced Systems Engineering Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Soja719-1197 Japan Process Technology Development Department Technology Development Division Seiko Epson Corp. Suwa399-0211 Japan Department of Systems Engineering Faculty of Computer Science and Systems Engineering Okayama Prefectural University Soja719-1197 Japan

Magnesium chips were coated with a high concentration of graphite using a binder and were used as the raw material for injection molding. The microstructure of the magnesium injection-molded product with added graphite exhibited a dispersion of needle-like graphite particles. No significant voids were observed at the interfaces between the graphite and the matrix. The addition of more than 0.5 mass% graphite decreased the proof stress and tensile strength of the injection-molded products. The Young’s modulus of the graphite-added products tended to decrease with an increase in the graphite content, which is consistent with the lower limit of the rule of mixtures. The thermal conductivity of the 6.9 mass% graphite-added product increased compared with that of the AZ91D magnesium alloy and the coefficient of linear thermal expansion decreased. Both values are within a range that satisfied the rules of mixtures. © 2024 The Japan Institute of Metals and Materials.

关键词： Thermal expansion

来源：评论

学校读者我要写书评

暂无评论

COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

引用

UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

来源：评论

学校读者我要写书评

暂无评论

Advancing Underwater Communication with NBO-MFO Hybrid Routing in UWSNs 10

Advancing Underwater Communication with NBO-MFO Hybrid Routi...

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kalaiselvan, S.A. Kumar, M.Lenin Amanullah, M. Mani Chigurupati, T.R. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vignan Institute of Technology and Science Department of Electronics and Communications Engineering Telangana India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350353068

In this research provides a solution for optimizing routing path selection in Underwater Wireless Sensor Networks (UWSNs). The algorithm combines Namib Beetle Optimization (NBO) and Moth-Flame Optimization (MFO), integrating them into the ILEACH methodology for efficient routing path selection. The algorithm operates in an iterative manner, commencing with NBO's feature selection process, evaluating fitness through average classification error and feature selection ratio. Simultaneously, MFO mimics moth behavior, updating positions via a logarithmic spiral function based on flame locations. The mutual learning phase facilitates information exchange between NBO and MFO, enhancing their collective intelligence and refining the routing path selection process. ILEACH's contribution lies in its fitness aggregation and selection methods. It amalgamates various criteria, including energy levels, distances, and node conditions, into a comprehensive fitness metric. Subsequently, ILEACH selectively identifies optimal routing paths aligned with energy-efficient criteria and node statuses, ensuring efficient data transmission and energy conservation within UWSNs. This integrated approach fosters a comprehensive strategy for routing path selection in UWSNs, ensuring sustainability and efficiency in data transmission within challenging underwater environments. © 2024 IEEE.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Investigation of DNA discontinuity for detecting tuberculosis

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1149-1163页

作者： Nimmy, Sonia Farhana Sarowar, Md. Golam Dey, Nilanjan Ashour, Amira S. Santosh, K.C. Department of Computer Science and Engineering Notre Dame University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information Technology Techno India College of Technology Kolkata India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Tanta Egypt Vermillion United States

Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughing, fatigue, unintentional weight loss and fever on aged people due to the disorder in the DNA. Breaks or mutations over long DNA sequences are the pivotal reasons for this fatal disease. This study developed an automated machine learning technique to assess the total number of such breaks in the long DNA sequences. Data cleansing and deep neural network techniques are applied to handle this big data. The National Center for Biotechnology Information (NCBI) database has been used to extract the amino acid sequences for Tuberculosis disease from the big DNA datasets. Results reveal that the proposed automated approach is significantly effective for the determination of DNA sequence breaks for the tuberculosis diseases due to the high sensitivity of Markov chain as well as the effective normalization techniques. This approach fixed the size of the training datasets and recursively divide the whole dataset into certain length. The study also adopts multiple predictions approaches, such as the hidden Markov chain, Box-Cox transformation and linear transformation to forecast about the breaks for any long positions of the training and testing datasets. The results demonstrated that hidden the Markov chain model provided faster analysis with more accurate and reliable results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： DNA

来源：评论

学校读者我要写书评

暂无评论

Attention-Guided CNN with LSTM for Temporal Gastrointestinal Disease Progression Prediction in Endoscopic Video Sequences 4

Attention-Guided CNN with LSTM for Temporal Gastrointestinal...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohamad, Fayazoddin Dhumpati, Raghu Reenadevi, R. Rao, P.S.V. Srinivasa Sutaria, Kamal Kulkarni, Shirish NY United States Bahrain Polytechnic Department of Computer Science & Engineering Bahrain City Bahrain Salem India Joginpally B R Engineering College Department of Computer Science and Engineering Hyderabad India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The detection and tracking of changes in the progression of GI disease using endoscopic video analysis remains difficult due to temporal changes and image complexity. Proper models of prediction are critical in diagnosing individuals at an early stage and offering them treatment. In the work presented in this paper, we introduce novel Attention-Guided Convolutional Neural Network (AG-CNN) combined with LSTM networks to analyze temporal endoscopic video sequences. It uses spatial attention to catch necessary areas and temporal attention to capture assertive illness transformation in video frames. For disease progression prediction the proposed model yield 94.8% accuracy with 93.2% sensitivity and 95.6% specificity. The attention mechanism increased the feature extraction speed by 22 percent when compared to traditional CNN-LSTM classes. Independent validation checks confirmed that the two models produced a prediction concordance of 92.7% based on different patient samples. The overall performance of temporal GI disease progression prediction by the AG-CNN-LSTM framework shows potential usage in clinical diagnosis by providing automated disease progression monitoring and treatment planning. © 2024 IEEE.

关键词： Video analysis

来源：评论

学校读者我要写书评

暂无评论

Comparison of CNN-based deep learning architectures for rice diseases classification

引用

Artificial Intelligence in Agriculture 2023年第3期9卷 22-35页

作者： Md Taimur Ahad Yan Li Bo Song Touhid Bhuiyan Department of Computer Science and Engineering Daffodil International UniversitySavarBangladesh School of Mathematics Physics and Computing University of Southern QueenslandToowoombaAustralia School of Engineering University of Southern Queensland ToowoombaAustralia Department of Computer Science and Engineering Faculty of Science and Information TechnologyDaffodil International UniversityBangladesh

Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice ***,most CNN-based rice disease detection studies only considered a small number of diseases in their *** these shortcomings were addressed in this *** this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in *** addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble *** results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf *** high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural *** research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

关键词： Deep learning Convolutional neural networks(CNNs) Transfer learning Plant leaf disease detection

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Blob Detection Techniques Using Image Processing 4

Performance Evaluation of Blob Detection Techniques Using Im...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sathva, S. Madhumathi, R. Manjula, D. MeenaKowshalya, A. Sri Ramakrishna Engineering College Department of Computer Science & Engineering Coimbatore India Department of Community Medicine Karpagam Faculty of Medical Sciences and Research Coimbatore India Government College of Technology Department of Computer Science & Engineering Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Blob detection is a primary requirement in computer vision and image processing tasks. Unique visual traits are obtained by identifying blobs in an image. Variations in colour, texture, intensity, or shape are just examples of these attributes, which make blob detection a flexible tool for a variety of image analysis applications. The technique of separating the objects in the binary image is called Binary Large Object (blob) extraction. Every representation technique provides distinct insights into the properties of the blob, which facilitates further processing and interpretation. Techniques for classifying blobs according to their size, shape, texture, and colour are presented, highlighting their importance involving object recognition and classification. The categorization of blob detection techniques presented in this work includes, encompassing Laplacian of Gaussian (LoG), Difference of Gaussian (DoG), Difference of Hessian (DOH), and Connected Components (Labelling). The results demonstrate that LoG and DoG detect a higher number of blobs, with LoG detecting the most (1,366 blobs), while DoH focuses on larger blobs but with a smaller count (777 blobs). Moreover, DoH had the largest mean blob size and higher variability in blob size, while DoG exhibited the fastest detection time (4.27 seconds). © 2024 IEEE.

关键词： Binary images

来源：评论

学校读者我要写书评

暂无评论

Innovative Approach for Early Heart Disease Prediction: Gaussian Mixture Model and K-Nearest Neighbour 3

Innovative Approach for Early Heart Disease Prediction: Gaus...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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：