检索结果-内蒙古大学图书馆

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

作者： Sheela, K Santha Deepalakshmi, R. Vijayalakshmi, R. Surya, M. Kumar, M. S. Abishek Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350328202

Food security and population support are fundamentally dependent on agriculture. The agricultural industry, though, must address several issues, such as the need for higher production, effective resource management, and environmental sustainability. In this setting, precision agriculture has evolved as a game-changing strategy that uses technology to improve farming methods while lowering environmental impact. Monitoring the quality of the air and water inside agricultural environments is a crucial component of precision agriculture. The quality of the air and water all directly impacts crop health, yield, and the surrounding ecosystem. Traditional techniques for monitoring these variables require costly and time-consuming procedures, making it difficult for small and medium-sized farmers to get access to vital information. We suggest a mobile-based monitoring system that takes advantage of the capacity of widely used mobile phones to gather and examine real-time data on air and water quality to address this problem. © 2024 IEEE.

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

Tech Meets Agriculture - Precise Plant Disease Detection Using Deep Learning Algorithm

Tech Meets Agriculture - Precise Plant Disease Detection Usi...

引用

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

作者： Arunasakthi, K. Akilandeswari, V. Shalini, K.S. Archana, K. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Plant disease prediction is crucial aspect of agricultural field. Plant diseases are one of the major concerns for agriculture in recent times;most of the crops are prone to damage caused by these diseases, which later hampers global food security. The traditional methods used for disease analysis rely strictly on visual inspection. These methods are time-consuming and prone to errors. Deep learning has therefore emerged as a significant and formidable instrument for the automation of plant disease detection. This project uses the deep learning models, YOLOv5 and YOLOv9. It ranks high on accuracy and ensures efficiency in the system to detect diseases related to plant leaves. It uses annotated datasets for several plant leaf diseases with a convolutional neural network to extractfeatures and enable classification. The system provides real-time automated solutions for the identification of plant diseases through the integration of deep learning models into agricultural practices, with a view to allowing growers to take timely action to avoid further damage to crops. The proposed technique not only heightens the rate of accuracy for disease detection but also contributes toward the growing dimension of precision agriculture, within which technology acts as an assistant in the optimization of crop management and enhancement of yield. © 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

Deep Learning-based Environmental Sound Classification Using Feature Fusion and Data Enhancement

引用

computers, Materials & Continua 2023年第1期74卷 1069-1091页

作者： Rashid Jahangir Muhammad Asif Nauman Roobaea Alroobaea Jasem Almotiri Muhammad Mohsin Malik Sabah M.Alzahrani Department of Computer Science COMSATS University IslamabadVehari CampusPakistan Department of Computer Science University of Engineering and Technology LahorePakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21974Saudi Arabia

Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental *** common aspects such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording make the ESC task much more complicated and *** research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation *** this research,the performance of transformer and convolutional neural networks(CNN)are *** audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted fromtheUrbanSound8K,ESC-50,and ESC-10,***,this research also employed three data enhancement methods,namely,white noise,pitch tuning,and time stretch to reduce the risk of overfitting issue due to the limited audio *** evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on enhanced *** UrbanSound8K,ESC-50,and ESC-10,the highest attained accuracies are 0.98,0.94,and 0.97 *** experimental results reveal that the proposed technique can achieve the best performance for ESC problems.

关键词： Environmental sound classification convolutional neural network deep learning transformer data augmentation

来源：评论

学校读者我要写书评

暂无评论

Adversarial multi-image steganography via texture evaluation and multi-scale image enhancement

引用

Multimedia Tools and Applications 2025年第9期84卷 5793-5823页

作者： Li, Fengyong Li, Longwei Zeng, Yishu Yu, Jiang Qin, Chuan College of Computer Science and Technology Shanghai University of Electric Power Shanghai201306 China School of Information and Computer Shanghai Business School Shanghai201400 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200092 China

Multi-image steganography refers to a data-hiding scheme where a user tries to hide confidential messages within multiple images. Different from the traditional steganography which only requires the security of an individual image, multi-image steganography considers an overall security for a batch of images. However, existing multi-image steganography all faces a nontrivial problem: how to optimally allocate payload into multiple images to guarantee the security of batch images. To address this problem, this paper proposes an adversarial multi-image steganographic scheme. A multi-scale texture evaluation mechanism is firstly calculated to determine the embeddable cover images. Subsequently, a series of multi-scale filters are introduced to enhance the image content, which can be used to guide the optimal payload assignment of each image. Furthermore, an adversarial embedding mechanism is designed by dynamically adjusting the random gradient mapping of batch images, and finally achieving secure multi-image steganography. Our proposed scheme can optimize the overall steganographic security performance of multiple images, while ensuring the anti-steganalysis capability of a single image. Extensive experiments demonstrate that our scheme can achieve superior performance for multi-image steganography over different large-scale image sets, and outperforms state-of-the art schemes in terms of both single-image and multi-image anti-steganalysis capability. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Improved Harris Hawks Algorithm and Its Application in Feature Selection

引用

computers, Materials & Continua 2024年第10期81卷 1251-1273页

作者： Qianqian Zhang Yingmei Li Jianjun Zhan Shan Chen School of Computer Science and Information Engineering Harbin Normal UniversityHarbin150025China College of Systems Engineering National University of Defense TechnologyChangsha410073China

This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration ***,and a lack of thorough exploitation *** tackle these shortcomings,it proposes enhancements from three distinct perspectives:an initialization technique for populations grounded in opposition-based learning,a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration,and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation *** effectiveness of the Improved Harris Hawks Optimization algorithm(IHHO)is assessed by comparing it to five leading algorithms across 23 benchmark test *** findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving ***,this paper introduces a feature selection method leveraging the IHHO algorithm(IHHO-FS)to address challenges such as low efficiency in feature selection and high computational costs(time to find the optimal feature combination and model response time)associated with high-dimensional *** analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight *** results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality,while also enhancing the efficiency of feature ***,IHHO-FS shows strong competitiveness relative to numerous algorithms.

关键词： HHO IHHO population diversity energy factor update strategy deep exploitation strategy feature selection

来源：评论

学校读者我要写书评

暂无评论

Enhancing Artwork Resilience Through Variable Embedding Technique 2

Enhancing Artwork Resilience Through Variable Embedding Tech...

引用

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

作者： Deepalakshmi, R. Senthilmurugan, K.R. Preetha, S. Aishwarya, S.Sakthi Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350328202

In the rapidly evolving digital world, safeguarding creative works against unapproved modifications has emerged as a critical issue. Because of the shortcomings of traditional procedures, stronger solutions are being studied. The study uses a novel application of variable embedding technology to address the important problem of maintaining copyright integrity for digital artwork. This paper provides a systematic analysis of the method's effectiveness and a detailed understanding of how appropriate it is to certain image characteristics. And, the paper has illustrated the method's resistance to possible attacks by in-depth experimental investigations, offering a viable path for protecting copyrighted digital art. This study intends to add to the discussion about enhancing authenticity and data protection in the field of artistic expression as the world of digital media expands further. © 2024 IEEE.

关键词： Copyrights

来源：评论

学校读者我要写书评

暂无评论

Language Bridge - Bridging Voices Through NLP and Spoken Input Transcription 10

Language Bridge - Bridging Voices Through NLP and Spoken Inp...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Anu Lisba Raj, M. Nixan, Nimble Sankar Krishna, K. Sunny, Sweety Sara Sreetha, E.S. Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

This paper concentrates on enhancing customer care and booking services with the introduction of a system named Language Bridge. Utilizing advanced technologies, in-cluding speech recognition and natural language processing, the Language Bridge system aims to elevate customer satisfaction by effectively addressing language barriers. Through precise speech recognition capabilities, the system seamlessly converts spoken input into text, offering a user-centric experience that accommodates various dialects. The overarching goal is to redefine global standards for customer service, providing users with a seamless and inclusive platform that transcends linguistic limitations. Through the integration of cutting-edge technologies, Language Bridge endeavors to set a new benchmark in customer care and booking services, fostering improved communication and accessibility for diverse user bases. © 2024 IEEE.

关键词： Customer satisfaction

来源：评论

学校读者我要写书评

暂无评论

Bridge the Generalization Gap: A Comprehensive Framework for Enhance Reliability and Robustness in Deep Learning Models

Bridge the Generalization Gap: A Comprehensive Framework for...

引用

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

作者： Vasanth, S. Madhu Karthick, A. Vijay Akash, S. Jovita, B. Kanishka, P. Sruthi, R. Rmk College of Engineering and Technology Department of Computer Science and Engineering Thiruvallur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Deep reinforcement learning (DRL) has been successful in a wide range of domains but its broad acceptance is limited by the generalization gap, which measures the performance difference between an environment a model was trained with versus new, unseen environments. This gap implies a challenge that needs to be addressed for DRL models used in real-world adaptation. This paper considers a strategy whose methodology outlines combining domain randomization, multitask learning, and regularization techniques. The study trains the models on multiple tasks and diverse environments, thus generalizing the ability of models. Our regularization strategy keeps the model from over-specialization to the training data and ensures improved performance. Benchmark environments are evaluated based on our approach, reducing the generalization gap with improvement in average returns in unseen environments and increasing the resilience of DRL models. In this regard, our study opens up the avenue for a significant contribution to the generalization capacity of more robust DRL models for real-world applicability. © 2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Classroom Accessibility for Deaf Students through Sign Language Recognition 10

Enhancing Classroom Accessibility for Deaf Students through ...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Monisha, K.B. Sushmitha, G. Shanthalakshmi Revathy, J. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

This paper addresses the communication barriers faced by the deaf and voiceless community by introducing a system that translates sign language into spoken or written language. The primary aim is to foster better communication between these communities and the hearing population, ultimately supporting academic success for individuals with hearing impairments. By utilizing the deep learning model VGG19, the system is able to recognize sign language with an astounding 99.97% accuracy rate, which is important for guaranteeing fair access to education. Because VGG19 can efficiently extract complicated features from images, a property that is essential for precisely interpreting the intricate hand gestures and motions that are a part of sign language communication, it is used in sign language recognition. It highlights the importance of real-time captions and interpretations as transformative tools in educational environments. Furthermore, the paper explores the implementation of this technology, outlining its benefits for educators and deaf students alike. Through examination of challenges, and future directions, it underscores the growing significance of sign language recognition in education, emphasizing its role in promoting inclusivity and enhancing learning experiences. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

A Predictive and Adaptive Mobile/Web Application for Alzheimers Disease Using Machine Learning 9

A Predictive and Adaptive Mobile/Web Application for Alzheim...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Balamuralikrishnan, G. Boomika, S. Sri Nandhini, R. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

A neurodegenerative condition, Alzheimer's disease (AD) affects millions of individuals globally, that causes memory loss and cognitive impairment. Improving patient outcomes and delaying the course of disease need early detection and continuous treatment. The paper proposes a predictive and adaptive mobile/web application for Alzheimer's disease management, leveraging transfer learning. In this research, we utilize transfer learning techniques, specifically with pre-trained deep learning architectures like ResNet and VGG. These models, originally trained on large datasets, are fine-tuned with Alzheimer's disease-specific data to enhance diagnostic accuracy and efficiency within our mobile/web application. By offering cognitive activities, remote health tracking, and personalized care, the platform improves patient involvement and aids medical professionals in managing patient conditions. Transfer learning increases diagnostic precision while requiring less data. © 2024 IEEE.

关键词： Transfer 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：