检索结果-内蒙古大学图书馆

1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Geetha, B. Sushmitha, Boggarapu Ilanchezhian, P. Alabdeli, Haider Ahila, R. Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Chennai India Avn Institute of Engineering and Technology Hyderabad Department of Computer Science and Engineering India Sona College of Technology Salem Department of Information Technology India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, fake profiles are programmatically created online individualities that mislead and influence others, which are done by frequently using stolen photos, fraudulent information, or fictional particulars. Hence, these fake profiles are used for malicious activities such as phishing, social engineering, catfishing, and scattering misinformation. The expansion of fake profiles is increased with triggering more damage than any other cyber-attacks. The solution has been recommended in various ways to detect fake profiles, but the existing models have more overfitting problems. To overcome these issues, a combined model of Bi-directional Gated Recurrent Unit (BiGRU) and Long Short-Term Memory (LSTM) algorithms is proposed. Initially, data preprocessing is done on TwiBot-20 dataset with Natural Language Processing (NLP) and tokenization to remove unnecessary characters. Next, a word embedding technique name called GloVe is used to extract the optimal features, Finally, from the extracted features, the proposed BiGRU-LSTM model gives a precise rate in detecting and classifying fake profiles as human and bot generated. The proposed BiGRU-LSTM gives better results than the other compared existing methods Convolutional Neural Network (CNN) in terms of accuracy, precision, recall, and f1 score of 99.73 %, 98.23 %, 99.56 %, and 99.63 %respectively. © 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

Faster R-CNN for Detection and Classification of Banana Leaf Diseases 1

Faster R-CNN for Detection and Classification of Banana Leaf...

引用

1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Bharathi Raja, N. Selvi Rajendran, P. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350342796

Plant diseases are important factors in reducing the appearance of agricultural commodities. Consequently, it is essential to recognize and diagnose these disorders as early as possible. To this end, the researchers suggest a Deep Learning (DL) technique to categorize diseases of the banana leaf that have been fully automated. In this paper, a transfer learning strategy reorientation of a Faster Region-based Convolutional Neural Network (Faster R-CNN) is proposed to develop an uncertainty detection. Using images acquired from different parts of the Banana plant, six distinct designs were built from 18 different classes. Early results show that the proposed method works even in challenging situations such as poor lighting, a complex environment, and variable resolution, shape, position, and orientation of real-world images. The effectiveness of multiple automated uncertainty disease detection systems is also measured through improvement and verification measures. The proposed FR-CNN model uses a pre-formed disease detection algorithm to create a network that could produce correct estimates through Deep Transfer Learning (DTL). The developed model performed better in terms of accuracy and needed less training time when compared to other deep-learning models. © 2023 IEEE.

关键词： Accuracy Agriculture Deep Transfer Learning (DTL) Faster Region-based Convolutional Neural Network (Faster R-CNN) Plant Disease Detection

来源：评论

学校读者我要写书评

暂无评论

Damaged Image Repair using Masks with computer Vision Inpaint Method 2

Damaged Image Repair using Masks with Computer Vision Inpain...

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Ramakrishna, Doddi Purna Gana Emmanuel, G. S. Michael Selvan, Mercy Paul Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665456302

Image inpainting is the technique used to automatically fix damaged areas using data from sections that have been saved. With the development of deep learning in recent years, image drawing performance has substantially increased. This research study reviews the main methods used for automating image inpainting. This research study provides a brief overview of traditional techniques while concentrating on deep learning-based inpainting techniques, covering model categorization, strengths and drawbacks, scope of application, and performance comparison. Finally, the challenges and trends surrounding automated image inpainting are examined and foreseen. A tool called image inpainting uses the data from the remaining components to repair damaged areas. With the advancement of society, image inpainting has become a vital research area in the field of computer vision. It is extensively used in culture, daily life, and security, including object removal and the preservation of digital cultural assets. Conventional methods build geometric models based on geometric consistency and image content similarity, or they use texture generation to patch up small sections of damaged images. It partially solves the problem of loose coupling between high-level semantics and low-level image properties, enabling deep learning to gradually overtake traditional methods in computer vision. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Enhanced capsule generative adversarial network with Blockchain fostered Intrusion Detection System for Enhancing Cyber security in Cloud 2

Enhanced capsule generative adversarial network with Blockch...

引用

2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Ravi Kanth, R. Prem Jacob, T.P. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348002

Cloud computing (CC) is vulnerable to existing information technology attacks, since it extends and utilizes information technology infrastructure, applications and typical operating systems. In this manuscript, an Enhanced capsule generative adversarial network (ECGAN) with blockchain based Proof of authority consensus procedure fostered Intrusion detection (ID) system is proposed for enhancing cyber security in CC. The data are collected via NSL-KDD benchmark dataset. The input data is fed to proposed Z-Score Normalization process to eliminate the redundancy including missing values. The pre-processing output is fed to feature selection. During feature selection, extracting the optimum features on the basis of univariate ensemble feature selection (UEFS). Optimum features basis, the data are classified as normal and anomalous utilizing Enhanced capsule generative adversarial networks. Subsequently, blockchain based Proof of authority (POA) consensus process is proposed for improving the cyber security of the data in cloud computing environment. The proposed ECGAN-BC-POA-IDS method is executed in Python and the performance metrics are calculated. The proposed approach has attained 33.7%, 25.7%, 21.4% improved accuracy, 24.6%, 35.6%, 38.9% lower attack detection time, and 23.8%, 18.9%, 15.78% lower delay than the existing methods, like Artificial Neural Network (ANN) with blockchain framework, Integrated Architecture with Byzantine Fault Tolerance consensus, and Blockchain Random Neural Network (RNN-BC) respectively. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Multi-Criteria-based Graph Neural Networks for a Medical Emergency Response System 7

Multi-Criteria-based Graph Neural Networks for a Medical Eme...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prakash, J. Princess, P. Joyce Beryl Karunya Institute of Technology And Science Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Science Division of Artificial Intelligence And Machine Learning Coimbatore India

ISBN: (纸本)9798350359299

The ability to get an immediate response from medical emergencies quickly and more effectively is essential for saving both lives and ensuring people's well-being. In the Traditional medical emergency response systems often struggle to correctly assess the seriousness and urgency of emergency response, leading to ineffective resource allocation and prolonged response times. This research work suggests a unique method for medical emergency response systems based on a multi-criteria-based graph neural network to resolve such problems in recent times. The process of choosing the best hospital based on a variety of conditions is optimized by the graph neural network (GNN), which makes use of the graph structure with hospital nodes and accident location nodes. The GNN will examine several conditions to choose the best hospital after identifying the nearby hospitals. It evaluates whether each nearby hospital has the medical resources required to meet the needs of the patient, including available beds, emergency gear, and doctor availability to get a quick medical treatment. To promote quick transportation, the GNN also confirms the availability of ambulances connected to each nearby hospital and alerts the hospital management to arrange needs for patients. The above approach improves the effectiveness and efficiency of medical emergency response systems, guaranteeing that injured persons receive immediate and appropriate medical treatment. © 2024 IEEE.

关键词： Hospitals

来源：评论

学校读者我要写书评

暂无评论

An Algorithmic and Machine Learning Approach for Yoga Asanas Feedback 3

An Algorithmic and Machine Learning Approach for Yoga Asanas...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Saxena, Prakhar Sharma, Vishnu Yadav, Anuj Singh, Rohit Pratap Singh, Laiphrakpam Dolendro National Institute of Technology Department of Computer Science and Engineering Assam Silchar India

ISBN: (纸本)9798331523893

Yoga, rooted in the ancient traditions of India, epitomizes the pursuit of holistic harmony, seeking to integrate life's physical, mental, and spiritual aspects. Innovative solutions are emerging to enhance yoga practices in response to the growing interest in integrating artificial intelligence and machine learning into physical well-being. The proposed work addresses the limitations of human-based feedback systems in yoga sessions by proposing an algorithmic approach aided by machine learning models to provide unbiased feedback on Yoga Asanas. Moreover, the proposed work mitigates injuries resulting from incorrect yoga practices and the inherent subjectivity in human assessments and offers feedback on yoga performances based on predefined criteria. © 2025 IEEE.

关键词： feedback machine learning Yoga Pose Recognition

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Urban Waste Management: Cutting-Edge Image Processing for Precise Solid Waste Classification 5

Revolutionizing Urban Waste Management: Cutting-Edge Image P...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rama Krishna, S. Patan, Muzeeb Khan Sudarsan, J.S. Padmapriya, R. Bhagavathiperumal, S. Balaram, Allam Department of Computer Science & Engineering Hyderabad502329 India College of Engineering and Technology University of Technology and Applied Sciences Department of EEE Muscat Oman India Sathyabama Institute of Science and Technology Department of Civil Engineering Chennai India Sri Krishna College of Engineering and Technology Coimbatore India MLR Institute of Technology Department of Computer Science and Engineering Hyderabad500043 India

ISBN: (纸本)9798331530013

Due to rapid urbanization and population growth, urban waste management has become one of the major challenges faced by cities around the world. Solid waste classification and sorting are necessary for intensifying recycling, safeguarding environment and manage additional landfilling of materials that are reusable or recyclable, and are getting in landfill of solid waste in certain extent. In this study, an original ensemble model using the VGG19 and EfficientNet architectures is proposed to classify solid waste in urban area. This proposed model was applied on a dataset consists of 2,527 number of images in to six classes to increase the accuracy of classification as well as to maximize the efficiency of waste. It preprocesses the incoming data using various techniques such as image resizing, normalization and augmentation to make the dataset more diverse and robust. The ensemble mechanism performed better combining the detailed feature extraction skills of VGG19 and versatile and scalable architecture of EfficientNet. On test set, it outperformed individual models and the accuracy for the proposed model was 98.75% which closely followed, previously obtain the best result. © 2024 IEEE.

关键词： Population statistics

来源：评论

学校读者我要写书评

暂无评论

CO_(2) mineralization by typical industrial solid wastes for preparing ultraﬁne CaCO_(3): A review

引用

Green Energy & Environment 2024年第11期9卷 1679-1697页

作者： Run Xu Fuxia Zhu Liang Zou Shuqing Wang Yanfang Liu Jili Hou Chenghao Li Kuntong Song Lingzhao Kong Longpeng Cui Zhiqiang Wang Department of Coal and Syngas Conversion Sinopec Research Institute of Petroleum Processing CO.Ltd.Beijing100083China School of Environmental Science and Engineering Suzhou University of Science and TechnologySuzhou215000China

Mineral carbonation is a promising CO_(2) sequestration strategy that can utilize industrial wastes to convert CO_(2) into high-value CaCO_(3).This review summarizes the advancements in CO_(2) mineralization using typical industrial wastes to prepare ultraﬁne CaCO_(3).This work surveys the mechanisms of CO_(2) mineralization using these wastes and its capacities to synthesize CaCO_(3),evaluates the effects of carbonation pathways and operating parameters on the preparation of CaCO_(3),analyzes the current industrial application status and economics of this *** to the large amount of impurities in solid wastes,the purity of CaCO_(3) prepared by indirect methods is greater than that prepared by direct *** CaCO_(3) includes three *** polymorph of CaCO_(3) synthesized by carbonation process is determined the combined effects of various *** parameters essentially impact the nucleation and growth of CaCO_(3) by altering the CO_(2) supersaturation in the reaction system and the surface energy of CaCO_(3) *** the initial pH of the solution and the CO_(2)ﬂow rate favors the formation of vaterite,but calcite is formed under excessively high *** formation is favored at lower temperatures and residence *** increased temperature and prolonged residence time,it passes through aragonite metastable phase and eventually transforms into ***,polymorph modiﬁers can decrease the surface energy of CaCO_(3) grains,facilitating the synthesis of ***,the large-scale application of this technology still faces many problems,including high costs,high energy consumption,low calcium leaching rate,low carbonation efﬁciency,and low product ***,it is necessary to investigate ways to accelerate carbonation,optimize operating parameters,develop cost-effective agents,and understand the kinetics of CaCO_(3) nucleation and crystallization to obtain products with speciﬁc crystal ***

关键词： Industrial solid wastes Resource utilization Mineral carbonation Ultraﬁne CaCO_(3) Carbon emission reduction

来源：评论

学校读者我要写书评

暂无评论

A secured deep learning based smart home automation system

引用

International Journal of Information technology (Singapore) 2024年第8期16卷 5239-5245页

作者： Sanjay, Chitukula Jahnavi, Konda Karanth, Shyam Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

With the expansion of modern technologies and the Internet of Things (IoT), the concept of smart homes has gained tremendous popularity with a view to making people’s lives easier by ensuring a secured environment. Several home automation systems have been developed to report suspicious activities by capturing the movements of residents. However, these systems are associated with challenges such as weak security, lack of interoperability and integration with IoT devices, timely reporting of suspicious movements, etc. Therefore, the given paper proposes a novel smart home automation framework for controlling home appliances by integrating with sensors, IoT devices, and microcontrollers, which would in turn monitor the movements and send notifications about suspicious movements on the resident’s smartphone. The proposed framework makes use of convolutional neural networks (CNNs) for motion detection and classification based on pre-processing of images. The images related to the movements of residents are captured by a spy camera installed in the system. It helps in identification of outsiders based on differentiation of motion patterns. The performance of the framework is compared with existing deep learning models used in recent studies based on evaluation metrics such as accuracy (%), precision (%), recall (%), and f-1 measure (%). The results show that the proposed framework attains the highest accuracy (98.67%), thereby surpassing the existing deep learning models used in smart home automation systems. © The Author(s) 2024.

关键词： CNN IoT Motion recognition and security Sensors Smart homes

来源：评论

学校读者我要写书评

暂无评论

Hybrid Vision Transformer and CNN for Detection of Overripe Coffee Berry Disease (OCBD) in Coffee Plantation 2

Hybrid Vision Transformer and CNN for Detection of Overripe ...

引用

2nd IEEE International Conference on Emerging research in Computational science, ICERCS 2024

作者： Selvanarayanan, Raveena Surendran, R. Gomathi, T. Kartheesan, L. Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

The article proposes an innovative Hybrid Vision Transformer and CNN architecture for the precise identification of Overripe Coffee Berry Disease (OCBD) in coffee farms. The suggested model adeptly integrates the advantages of Vision Transformers and CNNs, allowing it to capture both the global and local characteristics of coffee berries. Experimental findings indicate that the Hybrid model outperforms conventional CNN-based methods, with an impressive accuracy of 96.7% on a varied dataset. The model's resilience to fluctuating illumination conditions and obstructions makes it a viable alternative for practical applications in coffee plantation management. © 2024 IEEE.

关键词： Image segmentation

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：