检索结果-内蒙古大学图书馆

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Shinde, Swapnil K. Buchade, Amar Mahalle, Parikshit Vengurlekar, Mayuri Yedge, Satish Arsalwad, Gajanan Vishwakarma Institute of Technology Maharashtra Pune India Kj College of Engineering and Management Research Maharashtra Pune India Trinity College of Engineering and Management Research Maharashtra Pune India

ISBN: (纸本)9798350354348

At present, eye conditions or eye diseases are the primary reason for blindness and vision loss around the globe. Identifying and forecasting eye illnesses with conventional techniques are difficult, lengthy, and susceptible to human mistakes. Any delay in identifying and foreseeing future issues will lead to the patient losing their eyesight or experiencing vision problems. In order to prevent this, we require a system that is capable of detecting and predicting eye diseases with effectiveness, efficiency, and automation. In this article, we developed a computerized system using a pre-existing EfficientNetB3 CNN family structure to detect eye diseases through deep learning technology. The dataset utilized in this research was obtained at no cost from the web and comprises 4,217 pictures. The model was trained with transfer learning and enhanced with an improved dataset through augmentation methods. Precision score, Recall score, and F1-score and accuracy are utilized for assessing the model's performance. Our findings indicate that the EfficientNetB3 model performs better than conventional CNN structures, achieving high precision in identifying various eye diseases. The accuracy of model 97% on the training dataset and 95% on the test dataset by incorporating image augmentation. We utilized characteristics from the EfficientNetB3 structure, tailored for identification and categorization in image datasets. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Novel add-drop filter based on serial and parallel photonic crystal ring resonators (PCRR)

引用

Journal of Optical Communications 2024年第2期45卷 235-247页

作者： Fakhruldeen, Hassan Falah Al-Asady, Heba Abdul-Jaleel Mahinroosta, Tayebeh Sohrabi, Foozieh Hamidi, Seyedeh Mehri Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-sadiq University Baghdad Iraq Electrical Engineering Department College of Engineering University of Kufa Kufa Iraq Computer Technical Engineering Department College of Technical Engineering The Islamic University Najaf Iraq Magneto-Plasmonic Lab Laser and Plasma Research Institute Shahid Beheshti University Tehran*** Iran

Photonic crystal ring resonators (PCRR) as momentous candidates for future photonic crystal integrated circuits (PCICs) draw worldwide attention. In this paper, different configurations are proposed based on single, parallel, and serial PCRRs. To be precise, the different coupling lengths and alignments have been discussed in double and triple PCRRs in parallel and serial configurations to achieve the highest efficiency concerning the desired applications such as an add-drop filter (ADF) and a power splitter. Moreover, in the achieved optimum double and triple PCRRs, the effect of coupling radius change has been discussed. © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Photonic crystals

来源：评论

学校读者我要写书评

暂无评论

Visualizing the Full Spectrum Optimization of K-Nearest Neighbors From Data Preprocessing to Hyperparameter Tuning and K-Fold Validation for Cardiovascular Disease Prediction

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第2期49卷 355-374页

作者： Joseph, Jeena Kartheeban, K. Department of Computer Applications Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Computer Applications Marian College Kerala Kuttikkanam India Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India

Cardiovascular disease (CVD) is a prominent cause of death worldwide. This alarming need requires an accurate prediction model using machine learning that can detect and help prevent or mitigate the risk. This study focuses on this issue and has come up with new dimensional capabilities to enhance the K-Nearest Neighbors (KNN) algorithm to predict cardiovascular diseases at an early stage by incorporating various techniques for data preprocessing and feature selection thereby improving the efficiency of the model. The proposed model identifies the most relevant features using Principal Component Analysis. The main innovation revolves around fine tuning the hyperparameter of K-Nearest Neighbors, specifically the choice of neighbors (K), using a data driven approach to ensure accuracy across different datasets. The performance of the optimized K-Nearest Neighbors algorithm is evaluated using the Framingham heart disease dataset. This model achieved an impressive prediction accuracy of 92.46% and outperformed methods that solely rely on traditional K-Nearest Neighbors. As machine learning techniques plays an important role in the development of prediction models for early detection and prevention of cardiovascular disease, this model can be considered as a valuable tool for healthcare professionals and researchers. The core contribution of this study lies in offering a comprehensive optimization of the traditional K-Nearest Neighbors (KNN) algorithm. This includes robust data preprocessing using the Hampel filter for outlier removal, feature selection through Principal Component Analysis (PCA), and performance enhancement using grid search for hyperparameter tuning combined with 10-fold cross-validation. Unlike prior studies that apply KNN with minimal adjustments, this research emphasizes the importance of an end-to-end machine learning pipeline. This holistic refinement significantly improves the predictive performance and reliability of KNN for cardiovascular diseas

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

IOT-Enabled Smart Crop Recommendation System Using Machine Learning 5

IOT-Enabled Smart Crop Recommendation System Using Machine L...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Saranya, S.S. Dharani, A.S. Kavitha, M.N. Selya Dharsnee, M. Pragatheeswari, E. Selya Varsnee, M. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Technology - Ug Perundurai India Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Technology Perundurai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

Agriculture, a foundation of India's economy, faces challenges in maximizing yield and resource efficiency. This research presents a machine learning-based system to recommend optimal crops and fertilizers based on soil quality and geographic location. By providing data-driven insights, the proposed system aims to enhance crop productivity, promote sustainable farming practices, and empower farmers to make informed decisions. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Fake and propaganda images detection using automated adaptive gaining sharing knowledge algorithm with DenseNet121

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3519-3531页

作者： Muthukumar, A. Raj, M. Thanga Ramalakshmi, R. Meena, A. Kaleeswari, P. Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India Department of Electronics and Communication Engineering KLN College of Engineering Tamil Nadu Pottapalayam India

An additional tool for swaying public opinion on social media is to present recent developments in the creation of natural language. The term "Deep fake" originates from deep learning technology, which effortlessly and seamlessly steers someone toward digital media. Artificial Intelligence (AI) techniques are a crucial component of deep fakes. The generative powers of generative capabilities greatly reinforce the advancements in language modeling for content generation. Due to low-cost computing infrastructure, sophisticated tools, and readily available content on social media, deep fakes propagate misinformation and hoaxes. These technologies make it simple to produce misinformation that spreads fear and confusion to everyone. As such, distinguishing between authentic and fraudulent content can be challenging. This study presents a novel automated approach for the identification of deep fakes, based on Adaptive Gaining Sharing Knowledge (AGSK) and using DenseNet121 architecture. During pre-processing, the image’s sensitive data variance or noise is eliminated. Following that, CapsuleNet is used to extract the feature vectors. The deep fake is identified from the resulting feature vectors by an AGSK with DenseNet121 architecture, together with the hyper-parameter that has been optimized using the AGSK model. Propaganda and defamation pose less of a concern, and the results of the suggested deepfake image recognition model show how reliable and successful the model is. The accuracy of detection is 98% higher than other cutting-edge models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

A smart recommender model based on learning method for sentiment classification

引用

Multimedia Tools and Applications 2024年 1-21页

作者： Chiranjeevi, Phaneendra Rajaram, A. Research Scholar Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam611002 India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam611002 India

The problem of classifying the text is not only the topic since it contains the sentiment. Large amount of information are availed but determining the positive and negative thoughts is a major task. The existing method uses various classification technique and machine learning techniques for data processing. Naïve Bayes, entropy classification, linear regression, etc., are the existing algorithm. These algorithms are not performing well with time-consuming analysis. The proposed method uses the Structured Support Vector Machine Learning Algorithm for classification. Here the multifaceted text classification dataset is taken for the analysis. This proposed research work recognizes the favorable and unfavorable sentiments towards specific subjects. The techniques for giving the sentences and terms in a document their semantic significance so they can be used more effectively than existing possibilities. Based on the sentiment keyword from the user review, we can rank the positive and negative weights. By this application, the user will get to know which is best and suitable related to their requirement since they can access their requirement in efficient way. Entire survey should be model in MATLAB 2018a suite. The accuracy of this proposed analysis been increased when compared to the existing analysis. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Performance evaluation of heat transfer characteristics of heat exchanger in longitudinal fluid flow with serpentine tubes of constant pitch

引用

Materials Today: Proceedings 2023年 72卷 864-869页

作者： Shrirao, Pankaj N. Chaudhary, Manoj K. Patil, Amol N. Department of Mechanical Engineering Zeal College of Engineering and Research Pune 411041 India Department of Mechanical Engineering Trinity Academy of Engineering Pune 411048 India Department of Mechanical Engineering Dr. D. Y. Patil School of Engineering Pune 412105 India

The current study examines the experimental evaluation of Q of fluids in shell and tube type of HE with oval shaped serpentine tubes of constant pitch in longitudinal flow. The results obtained with present research are further compared with the conventional of heat exchanger with oval shaped straight tubes. The comparison is done for same inlet fluid parameters as kept with the serpentine tubes. To evaluate precise results, a highly specified test rig is designed and fabricated. Experiments were performed using oval shaped serpentine tubes and straight coils. In a turbulent regime (4000 Re 16000) water was employed as the test fluid. The findings of the experiment reveal that for given Re, Nu, h and performance of HE systems is consistently higher for fluid flowing through serpentine tubes as compared to straight tubes. It was observed that pressure drop for fluid flowing through serpentine oval shaped tubes is slightly greater than that of conventional straight tubes. However, serpentine tube bundle improves the overall thermo- hydraulic performance of modified HE with its reduction in fouling factor. © 2022

关键词： Heat exchangers

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Image Classification Method Based on Global Space-Spectral Attention Mechanism

引用

Journal of Shanghai Jiaotong University (Science) 2025年 1-11页

作者： Qin, Rui Wu, Benze Liu, Xinfu Wu, Yirui College of Computer Science and Software Engineering Hohai University Nanjing211100 China Nangjing Research Institute of Electronic Engineering Nanjing210007 China College of Artificial Intelligence and Automation Hohai University Jiangsu Changzhou213200 China

In hyperspectral remote sensing imagery, pixel interactions within defined spatial extents result in the mixing of adjacent pixels. Additionally, the high similarity of adjacent spectra leads to information redundancy, which hinders the extraction of global spatial and spectral correlations. In order to solve the problems of mixed adjacent pixels and redundant adjacent spectra, this work offers a hyperspectral image classification approach that uses a global space-spectral attention mechanism. First, the proposed method’s global spatial attention module uses multi-scale dilated convolution to produce a bigger receptive field to be capable of capturing global spatial correlation and obtain unmixed pixel information. Then, the global spectral attention module designs a spectral domain partition algorithm, using the combination of regional density as well as information entropy as the threshold to divide spectrum into dispersed subsets and eliminate redundant information. The global context information for entire spectral band is fully exploited, and correlation of the global spectral information is extracted. Finally, the two modules combine to provide a global correlation of space and spectrum. Experiments demonstrate that the suggested method obtains overall accuracies of 97.28%, 94.73%, and 95.76% on the three WHU-Hi hyperspectral datasets, surpassing comparison methods. © Shanghai Jiao Tong University 2024.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

引用

Journal of Rock Mechanics and Geotechnical engineering 2024年第9期16卷 3812-3840页

作者： M.E.Al-Atroush A.E.Aboelela Ezz El-Din Hemdan Civil and Environmental Engineering Program Engineering Management DepartmentCollege of EngineeringPrince Sultan UniversityRiyadhSaudi Arabia Department of Structural Engineering Faculty of EngineeringAin Shams UniversityCairoEgypt Structure and Materials Research Lab Prince Sultan UniversityRiyadhSaudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt

In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled *** study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these ***,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter *** extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this *** paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

关键词： Laterally loaded drilled shaft load transfer and failure mechanisms Physics-informed neural networks(PINNs) P-y curves Artificial intelligence(AI) Dataset

来源：评论

学校读者我要写书评

暂无评论

Detection of Manipulated Multimedia In Digital Forensics Using Machine Learning 2

Detection of Manipulated Multimedia In Digital Forensics Usi...

引用

2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Anvekar, Preetam Gudnavar, Anand Naregal, Keerti Nagarmunoli, Sreedevi Jain College of Engineering and Research Department of Computer Science and Engineering Belagavi India Graphic Era University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372847

The surge in digital media usage has spurred an uptick in multimedia manipulation., spanning images., videos., and audio. This manipulation., with its potential to spread misinformation and manipulate public opinion., poses serious threats. Detecting genuine from fake media is challenging due to the diverse tools employed. Consequently., cybercrime involving manipulated media is on the rise. researchers are countering this issue with machine learning techniques., particularly Convolutional Neural Networks (CNNs). This paper presents an application leveraging CNN s to identify genuine and fake media., bolstered by results from experiments on real and manipulated datasets., yielding high accuracy and robustness. Deep learning models excel in detecting various manipulation types., positioning them as potent weapons against manipulated content proliferation. To ascertain the models' effectiveness., the study includes comprehensive validation., testing procedures., and robustness analyses against sophisticated manipulations., including adversarial attacks and deepfake variations. This research advances multimedia forensics., offering a holistic approach to detect manipulated media with deep learning models., underscoring CNN s' effectiveness in curbing manipulated content dissemination., and emphasizing the necessity of ongoing advancements to tackle the evolving multimedia manipulation landscape. © 2024 IEEE.

关键词： Digital forensics

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：