检索结果-内蒙古大学图书馆

2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Rajendiran, Gowtham Rethnaraj, Jebakumar Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350325799

Aeroponics is a type of soilless agriculture where plants are grown in a misted environment without the use of the soil. The process of yield prediction in aeroponics system for the lettuce crop is one of the key challenges because the crop yield is affected by variety of growth factors such as temperature, humidity, light and nutrient levels. Hence, to predict the lettuce crop yield in aeroponics, the machine learning models were utilized and these models can be trained on the historical data on crop yields and environmental variables. Here, in this work, the Random Forest (RF) regression is chosen as the machine learning model for yield prediction. The data used in this work were collected from the aeroponic lettuce farming tower. The purpose of the research work is to investigate the potential of the random forest model for efficient prediction of the lettuce crop yield in aeroponics system. The RF model can produce accurate prediction results of 92% which is better than the other regression algorithms in terms of forecasting lettuce crop production. Comparing this to the MAE of other regression models, which may be anywhere from 0.098 to 3.76 of the average reported yield, and it is observed that RF has a much more acceptable MAE. Hence, this work provides valuable information for farmers who are interested in utilizing this type of indoor farming to produce the high-yield of lettuce crops. © 2023 IEEE.

关键词： Lettuce

来源：评论

学校读者我要写书评

暂无评论

Comparison of Image Encryption Techniques Using Chaotic Maps and Conventional Cryptographic Techniques 2

Comparison of Image Encryption Techniques Using Chaotic Maps...

引用

2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Varshney, Aanchal Routh, Pratyusha Sujatha, G. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

In past few years, there has been a great deal of research being done on chaos-based cryptosystems. Chaos-based cryptosystems like Arnold cats map can add security when employed with traditional public key cryptosystems like RSA or El-Gamal, despite the fact that these techniques are not standardized like AES, DES, RSA, etc. Although chaotic-map-based encryption techniques are frequently recommended for picture or video encryption because of their computational efficiency, standard encryption algorithms like DES, AES and RSA have traditionally been the preferred option. The encryption methods like chaos-based picture encryption and conventional cryptographic techniques like AES, DES and RSA are surveyed in this work. There is discussion of the great advancements in image encryption. The comparison is done on the basis of quality of image before and after encryption and decryption using various methods like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Mean Squared Error (MSE). The PSNR, MSE, and SSIM values of chaotic map-based cryptography don't perform well when compared to traditional methods like RSA, DES, and AES. Also the time complexities and the key sizes of all the algorithms have been compared. The conventional techniques consistently show nearly perfect values, highlighting their stronger security and reliability in data protection and encryption. In contrast, the chaotic map-based approaches fall short in terms of these metrics, indicating that the traditional methods are more effective in ensuring robust security. © 2024 IEEE.

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Smart GPS Based Vehicle Speed Limit Controller on zone identification using Geo-Fencing Algorithm 3

Smart GPS Based Vehicle Speed Limit Controller on zone ident...

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Upendra, Modem Reddy, Varun Safa, M. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Networking and Communications Kattankulathur Chennai603203 India

ISBN: (纸本)9798350381931

In light of growing concerns about road safety and the surge in traffic accidents, this project aspires to an Internet of Things smart GPS-based vehicle speed limit controller system. The primary objective is to elevate road safety by harnessing real-time data and automated control mechanisms to enforce speed limits, thereby diminishing incidents of speeding-related accidents. This project adopts a comprehensive approach, integrating both hardware and software components. Vehicle-mounted sensors, encompassing speed sensors and GPS receivers, continually gather real-time data on vehicle speed and location. Furthermore, road condition data is sourced from multiple outlets, such as weather stations and traffic cameras. This data is wirelessly transmitted to a central control unit, which processes and analyzes it. The control unit deploys machine learning algorithms to evaluate vehicle speed limits against predefined speed limits for specific road segments. Depending on this analysis, it transmits control signals back to the vehicle when necessary. These signals can encompass driver warnings, power limitations, or autonomous speed control. These methodologies collectively enabled the creation of an intelligent and adaptable GPS-based vehicle speed control system, offering the potential to significantly enhance road safety by dynamically enhancing road safety regulating vehicle speeds in different zones, ensuring the safety of people and drivers alike. The Smart GPS-based vehicle speed limit controller system has undergone successful development and testing, both in controlled environments and on public roads. The outcomes manifest a remarkable reduction in incidents of speeding and an enhanced adherence to speed limits, exhibiting an accuracy level exceeding 96%. In real-world scenarios, the system effectively mitigates speeding-related risks, leading to a substantial decline in traffic accidents. Its ability to adapt to changing road conditions and. Its scalability ren

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Deep ensemble model for skin cancer classification with improved feature set

引用

Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Feature Selection in Learning to Rank for Listwise Approach using Multiobjective Evolutionary Algorithm

Feature Selection in Learning to Rank for Listwise Approach ...

引用

2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Amala, K.J. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Information retrieval systems commonly depend on ranking models to prioritize search results, which requires efficient feature selection to improve accuracy. Feature selection improves the model's query relevance ranking by selecting the most relevant features. This unique method avoids diluting the ranking model with unnecessary variables, resulting in more accurate ranks. The proposed approach consists of three separate stages: multi-objective instance selection, feature selection, and ensemble method. By utilizing a listwise methodology, the algorithm directly enhances the optimization of ranking criteria resulting in enhanced performance. The effectiveness of FS-listwise was evaluated using standardized datasets. After conducting experiments, results shown that the proposed method have peak performance than MOFSRank and classical ranking algorithms, such as ListNet and LambdaMART, in terms of Normalized Discounted Cumulative Gain and consistently selecting a minimal yet highly effective feature set. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

引用

Intelligent Automation & Soft computing 2023年第3期35卷 3829-3844页

作者： Siva Sankari Subbiah Senthil Kumar Paramasivan Karmel Arockiasamy Saminathan Senthivel Muthamilselvan Thangavel Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management StudiesChittoor517127India School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computing College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India

Wind speed forecasting is important for wind energy *** the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed *** main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overﬁtting and non-linearity *** curse of dimensionality and overﬁtting issues are handled by using Boruta feature *** uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory(Bi-LSTM).In this paper,Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed *** model identiﬁes relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time *** proposed BFS-Bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),Long Short Term Memory(LSTM),LSTM with Boruta(BFS-LSTM)and Bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and *** BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of *** experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

关键词： Bi-directional long short term memory boruta feature selection deep learning machine learning wind speed forecasting

来源：评论

学校读者我要写书评

暂无评论

An Efficient Technique for Feature Selection in Learning to Rank Using NSGA-II

An Efficient Technique for Feature Selection in Learning to ...

引用

2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Feature Selection (FS) is essential for optimizing Learning to Rank (LTR) models by determining the vital choice of attributes from a complex dataset. This study examines feature selection strategies within the framework of learning to rank, specifically emphasizing a multi-objective evolutionary algorithm utilizing the listwise approach. Conventional ranking algorithms frequently experience issues with superfluous or unnecessary features, undermining their efficacy. The suggested method employs a combination of instance selection, evolutionary optimization, and ensemble techniques to choose feature subsets. The methodology seeks to reduce the feature count while enhancing ranking precision, assessed via the Normalized Discounted Cumulative Gain (NDCG) metric. Experiments were performed using LETOR 4.0 dataset (MQ2007-list and MQ2008-list), evaluating proposed method against established listwise ranking algorithms like ListMLE, ListNet, RankCosine and p-ListMLE. The findings indicate that, although there are performance discrepancies relative to current algorithms, the suggested method attains substantial feature reduction up to 60 percent while maintaining competitive ranking accuracy. © 2024 IEEE.

关键词： Optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

引用

Frontiers of Computer Science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

来源：评论

学校读者我要写书评

暂无评论

The Establishment of a Hypoxia Cellular Morphology Model Based on Deep Convolutional Neural Networks and Intelligent Screening of Anti-hypoxia Drugs 41st

The Establishment of a Hypoxia Cellular Morphology Model Bas...

引用

41st Computer Graphics International Conference, CGI 2024

作者： Zhang, Xinyi Wang, Zheng School of Chemical Engineering and Technology Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (纸本)9783031820236

The rapid development of artificial intelligence has brought many innovative achievements to fields such as drug design and drug discovery [1]. Combining traditional graphics methods with deep learning methods can significantly improve the accuracy of drug screening. Hypoxia refers to the process in which tissues or cells in the body undergo abnormal changes in morphology, physiological functions, and metabolism due to insufficient oxygen supply or oxygen utilization obstacles [2]. In the previous study, Enlargement of the cell nucleus is a recognizable morphological feature of hypoxia cells [3]. Based on a deep learning multi-cell image classification model, this study constructed a high-throughput compound screening system for discriminating the anti-hypoxia activity of thousands of compounds. By simultaneously performing prediction scoring using AC16 and H9C2 models, the anti-hypoxia activity of thousands of compounds was predicted, and some compound molecules with anti-hypoxia effects were successfully screened. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

The Emergent Role of Artificial Intelligence as Tool in Conducting Academic Research 16

The Emergent Role of Artificial Intelligence as Tool in Cond...

引用

16th International Conference on Society and Information Technologies, ICSIT 2025

作者： Ferdousi, Bilquis School of Information Security and Applied Computing College of Engineering and Technology Eastern Michigan University YpsilantiMI United States

ISBN: (纸本)9781950492831

Academic and non-academic research institutes are the two key global sectors that are accomplishing the research agenda. This paper focuses on the implications of using AI tools in academic research. Academic institutions conduct rigorous research to find the impact of different issues on society. Artificial Intelligence (AI) impacts society, organizations, and governance. AI has significantly transformed various domains, including higher education and academic research. Because of its effect on research and education in multidisciplinary fields, there is increasingly significant interest in finding AI's effect on research. In this context, there is a growing concern to find the impact of AI on research in academic institutions. This article aims to engage in a rigorous dialogue on AI’s role in academic research. Conducting a systematic literature review of AI's impact on academic research, this study aims to investigate 1) how AI tools are affecting academic research and 2) ethical dilemmas in using AI in academic research. By exploring the advantages and challenges of using innovative applications of AI tools in academic research, this study will give stakeholders, especially researchers, educators, and students, a comprehensive view of how AI could shape educational and interdisciplinary research practices in the future. © 2025 International Conference on Society and Information Technologies. All Rights Reserved.

关键词： Academic Research Artificial Intelligence Ethical Concern

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：