检索结果-内蒙古大学图书馆

Recent Trends in computer science and technology (ICRTCST), International Conference on

作者： Surjeet Kumar Md Shamsher Alam Zeeshan Khursheed Shadan Bashar Neha Kalam Computer Science & Engineering Assistant Professor in Government Engineering College Sheikhpura Bihar India Computer Science and Engineering Associate Professor in R.V.S College of Engineering & Technology Jamshedpur India DGM Jio Platform Limited Mumbai India Master of Computer Application Assistant Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science and Engineering Student R.V.S College of Engineering & Technology Jamshedpur India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

In the contemporary digital era, data has evolved into the cornerstone of myriad sectors, encompassing academia, commerce, and medical research. Yet, the acquisition of insights from this expansive reservoir of information has historically demanded a level of proficiency in formal languages such as SQL, thereby imposing a substantial barrier for individuals lacking technical expertise. In response to this pervasive challenge, the advent of Natural Language Interface to Databases (NLIDB) has emerged as a promising and transformative solution. NLIDB offers users the capability to engage with databases through the utilization of their natural language, whether expressed in textual or verbal form. This paradigm shift empowers both seasoned experts and neophytes to effortlessly extract valuable information from databases without the prerequisite of specialized technical skills. In this research paper, we propose an innovative system that capitalizes on an intermediary SQL generation process, facilitating the seamless bridge between natural language input and subsequent database retrieval. This novel approach eliminates the need for users to possess awareness of the intricate underlying complexities of the database management process. By simplifying the interaction between users and databases, our system endeavours to democratize data access and promote the universal utilization of this invaluable resource across diverse domains.

关键词： Structured Query Language Databases Natural languages Semantics Relational databases Manuals Reservoirs

来源：评论

学校读者我要写书评

暂无评论

Filtering Decision Rules Driven by Sequential Forward and Backward Selection of Attributes: An Illustrative Example in Stylometric Domain

Filtering Decision Rules Driven by Sequential Forward and Ba...

引用

Federated Conference on computer science and Information Systems (FedCSIS)

作者： Beata Zielosko Urszula Stańczyk Kamil Jabloński University of Silesia in Katowice Institute of Computer Science Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Gliwice Poland

The paper presents investigations concerning the decision rule filtering process controlled by the estimated relevance of available attributes. In the conducted study, two search directions were used, sequential forward selection and sequential backward elimination. The steps of sequential search were governed by three rankings obtained for variables, all related to characteristics of data and rules that can be induced, as follows, (i) a ranking based on the weighting factor referring to the occurrence of attributes in generated decision reducts, (ii) the OneR ranking exploiting short rule properties, and (iii) the proposed ranking defined through the operation of greedy algorithm for rule induction. The three rankings were confronted and compared from the perspective of their usefulness for the selection of rules performed in the two directions and with two strategies for rule selection. The resulting sets of rules were analysed with respect to the properties of the constituent decision rules and from the point of performance for all constructed rule-based classifiers. Substantial experiments were carried out in the stylometric domain, treating the task of authorship attribution as classification. The results obtained indicate that for all three rankings and search paths it was possible to obtain a noticeable reduction of attributes while at least maintaining the power of inducers, at the same time improving characteristics of rule sets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS

引用

Procedia computer science 2025年 252卷 365-380页

作者： Senthil G. A S.U. Suganthi L. Prinslin R. Selvi R. Prabha Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Assistant Professor Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Associate Professor Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially Artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene

关键词： Generative AI Deep Learning Agriculture Smart Farming Crop Health Yield Prediction Global Positioning System Geographic Information System Artificial Neural Networks Generative Adversarial Networks Sustainability You Only Look Once (YOLO)

来源：评论

学校读者我要写书评

暂无评论

Proficient Detection of Flash Attacks Using a Predictive Strategy 6th

Proficient Detection of Flash Attacks Using a Predictive Str...

引用

6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Om Kumar, C.U. Sathia Bhama, Ponsy R. K. Department of Computer Science & Engineering SRM Easwari Engineering College Chennai600 044 India Associate Professor Department of Computer Technology MIT Campus-Anna University Chennai600 044 India

ISBN: (纸本)9789811613371

The availability of service through cloud computing upon the emergence of IoT and wireless networking has exposed the security of information to an even greater risk of abuse. These security threats are caused by cyber criminals through Malware, Cracker, Insider and Zombie. Among the strategies attacking the network DoS or DDoS is disastrous as its objective is to paralyse the complete network system. The bursty or volatile nature of traffic is indicative of a surge in traffic flow. Such an event also implies the incidence of a DDoS attack. If left unchecked this attack will cause a disruption in service. The study focuses on deriving a statistical model for discriminating malign traffic by devising a predictive model using error optimization technique and Hurst Correlogram analysis for effectively detecting and controlling the spread of bot-induced DDoS attack in a cloud environment. This work proposes ARIMA model that detects DDoS with an accuracy of 92% which is further assessed through MLR with an accuracy of 84%. The Hurst estimation of the univariate lies very close to the Hurst of raw data needs mention and proves the maturity of the prediction strategy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Malware

来源：评论

学校读者我要写书评

暂无评论

FusionINN: Decomposable Image Fusion for Brain Tumor Monitoring

arXiv

引用

arXiv 2024年

作者： Kumar, Nishant Tao, Ziyan Singh, Jaikirat Li, Yang Sun, Peiwen Zhao, Binghui Gumhold, Stefan Chair of Computer Graphics and Visualization Faculty of Computer Science Technische Universität Dresden Dresden Germany School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Radiology Shanghai Tenth People’s Hospital Tongji University Medical School Shanghai China

Image fusion typically employs non-invertible neural networks to merge multiple source images into a single fused image. However, for clinical experts, solely relying on fused images may be insufficient for making diagnostic decisions, as the fusion mechanism blends features from source images, thereby making it difficult to interpret the underlying tumor pathology. We introduce FusionINN, a novel decomposable image fusion framework, capable of efficiently generating fused images and also decomposing them back to the source images. FusionINN is designed to be bijective by including a latent image alongside the fused image, while ensuring minimal transfer of information from the source images to the latent representation. To the best of our knowledge, we are the first to investigate the decomposability of fused images, which is particularly crucial for life-sensitive applications such as medical image fusion compared to other tasks like multi-focus or multiexposure image fusion. Our extensive experimentation validates FusionINN over existing discriminative and generative fusion methods, both subjectively and objectively. Moreover, compared to a recent denoising diffusion-based fusion model, our approach offers faster and qualitatively better fusion results. The source code of the FusionINN framework is available at: https://***/nish03/FusionINN. © 2024, CC BY.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

An efficacy analysis of data encryption architecture for cloud platform

引用

Procedia computer science 2023年 218卷 989-1002页

作者： Sheenam Malhotra Williamjeet Singh Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Punjabi University Patiala Punjab India Assistant Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Punjabi University Patiala Punjab India

In recent times, cloud computing is being utilized largely for storage and information sharing purposes in several established commercial segments, particularly those where online businesses are prevalent, such as Google, Amazon, etc. Cloud system presents several benefits to users in terms of easy operations, low implementation, and maintenance expenses. However, significant risks are encountered in the data security procedures of cloud systems. Although the area is frequently being analyzed and reformed, the concern of cloud data protection and user reliability remains under uncertainty due to growing cyber-attack schemes as well as cloud storage system errors. To deal with this risk and contribute to the endeavor of providing optimal data security solutions in cloud data storage and retrieval system, this paper proposes a Symmetric Searchable Encryption influenced Machine Learning based cloud data encryption and retrieval model. The proposed model enhances data security and employs an effective keyword ranking approach by using an Artificial Neural Network. The comparative assessment of the proposed model against multiclass SVM and Naïve Bayes has established the better operational potentiality of the model. The effectiveness of the proposed work is justified by the association between high TPR and low FPR. Further, a low CCR of 0.6973 adds up to the success of the proposed work.

关键词： Cloud Computing Symmetric Searchable Encryption Machine Learning

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

引用

IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

来源：评论

学校读者我要写书评

暂无评论

Optimized Prognostic Models for Oral Cancer Survival using Feature Selection Methods

引用

Procedia computer science 2024年 235卷 1832-1840页

作者： Deepali Neelam Goel Padmavati University Institute of Engineering and Technology Panjab University Chandigarh 160014 India Assistant Professor Department of Computer Science Guru Nanak College Budhlada 151502 India Department of Computer Science Punjab Engineering College (deemed to be university) Chandigarh 160012 India

Accurately predicting survival time holds significant importance in various medical and scientific domains. This study proposes a novel survival prediction model for oral cancer using multi-omics data including clinical, DNA methylation, copy number alteration, and mRNA data. The proposed model incorporates four feature selection methods, namely LASSO, Elastic Net, Random Forest Regressor, and Recursive Feature Elimination (RFE), to improve the accuracy. The findings reveal that RFE outperforms the other methods, achieving the highest C-Index of 0.944. This is a significant improvement over previous models, which have a c-index of 0.694 using only clinical data and 0.916 using multi-omics data. In terms of precision, the Random Forest Regressor is the most precise with an MSE of 18.37, followed by RFE of 21.61. Notably, both RFE and Random Forest Regressor outperformed LASSO and Elastic Net in predictive accuracy. Additionally, the results highlight the consistency of RFE, as indicated by its low Median Absolute Error (MedAE) of 2.86. Random Forest Regressor also performed well in this regard, with a MedAE of 4.05. In contrast, Elastic Net exhibited a higher MedAE of 4.55, while LASSO showed more variability, with a MedAE of 8.58. These findings offer valuable insights into survival analysis and feature selection, helping researchers and practitioners select appropriate approaches for survival time prediction, and improving accuracy and reliability in critical applications.

关键词： Oral Cancer Survival Time Feature Selection Accuracy

来源：评论

学校读者我要写书评

暂无评论

Design of a Multiple Phased Convolution Model for Pre-emption of Breast Cancer via Extensive Learning Operations (MPC2EL)

引用

Procedia computer science 2024年 233卷 638-650页

作者： Pravin Malve Dr. Vijay Gulhane Research Scholar Department of Computer Science & Engineering Sipna College of Engineering & Technology Amravati 444607 India Professor Department of Information Technology Sipna College of Engineering & Technology Amravati 444607 India

Breast cancer diagnosis from mammographic scans is a complex challenge, demanding accurate and efficient methodologies. Existing approaches often rely on time-consuming manual feature extraction or lack scalability when employed with diverse clinical image sets. To address these limitations, this paper presents an innovative solution: the MPC2EL (Multiple Phased Convolution model for Pre-emption of Breast Cancer via Extensive Learning Operations). The core problem tackled by this research is the need for improved breast cancer detection, especially concerning scalability, accuracy, and practicality. To achieve this, MPC2EL leverages extensive learning operations and multiple phased convolutions to process extensive clinical datasets containing both cancerous and benign images. Our novel approach begins with the conversion of these images into multimodal feature sets, encompassing frequency, cosine, wavelet, and Gabor domains. These feature sets enable enhanced classification of images into different cancer stages. The proposed methodology lies in a Dual 1D Convolutional Neural Network (D1D CNN) architecture, capable of categorizing images initially into malignant and benign types and subsequently refining classification for malignant images. Experimental results reveal a substantial 5.9% increase in classification accuracy and a 4.5% improvement in recall compared to conventional deep learning methods. Moreover, the model demonstrates lower processing delay and higher precision, making it a promising candidate for real-world clinical deployment.

关键词： Cancer Mammogram Multiple Phased CNN Gabor Cosine

来源：评论

学校读者我要写书评

暂无评论

Collaborative Defense Strategies: AI, ML, and Cybersecurity: A Comprehensive Study 12th

Collaborative Defense Strategies: AI, ML, and Cybersecurity:...

引用

12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Borah, Samarjeet Dhabliya, Dharmesh Dhage, Prashant Shukla, Jyoti Rai, Sumit Kumar Bhattacharya, Saurabh Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Maharashtra Pimpri Pune India Assistant Professor SCSE Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819601462

In the fast-changing field of cybersecurity, the rise of advanced threats requires new and creative defense strategies. This study thoroughly examines collaborative defense strategies, focusing on the utilization of artificial intelligence (AI) and machine learning (ML) techniques. The study aims to evaluate the effectiveness of five prominent machine learning algorithms (random forest, support vector machine (SVM), gradient boosting, isolation forest, and one-class SVM) in the field of cybersecurity. The assessment is carried out utilizing the NSL-KDD dataset, which is a standard in the industry. The study commences by investigating the origins of collaborative defense, analyzing its historical development and fundamental principles. The text explains the importance of AI in collaborative defense, highlighting machine learning algorithms and natural language processing as essential elements. The study explores the uses of artificial intelligence, specifically in identifying, averting, and reacting to threats. A substantial part of the research focuses on evaluating the effectiveness of the five chosen machine learning algorithms. The evaluation includes predictive analysis to forecast cyber threats and proactive defense measures. The study examines how machine learning improves the sharing of threat intelligence by facilitating real-time information exchange between collaborating entities. The challenges and considerations related to incorporating AI and ML into cybersecurity are examined, emphasizing ethical implications, bias, fairness, transparency, and accountability. Case studies and success stories from real-world scenarios showcase examples of successful collaborative defense strategies applied in various industries and sectors. The study evaluates and contrasts the random forest, SVM, gradient boosting, isolation forest, and one-class SVM algorithms based on performance metrics like accuracy, precision, recall, and F1 score, utilizing the NSL-KDD dataset. T

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：