检索结果-内蒙古大学图书馆

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Srivastava, Akhilesh Kumar Chauhan, Saransh Mishra, Tribhuvan Mishra, Vaishnavi Abes Engineering College Department of Computer Science Engineering Ghaziabad India

ISBN: (纸本)9798350383522

India is a country of agriculture where 60 percent of the population, according to the census, 2011 is involved in the agriculture industry. A survey depicts that a huge amount of the worth of the crop gets wasted due to plant disease every year in India. The paper deals with a solution for the problem of plant disease by creating a system that can predict the disease in a plant by referring to the image of the infected plant. The paper aims not only for disease detection but also recommends a treatment for dealing with the disease in the infected plant. The author's team had worked on the PlantVillage dataset for creating the model, which contains a large collection of images of various plant species and their associated diseases. The proposed model uses deep learning technique based on Convolutional Neural Network (CNN) and utilizes the *** library of Python. The ResNet34 model has been used for the analysis. ResNet34 use Convolutional Neural Network (CNN), with residual learning. Research for the treatment of disease is also conducted, by the author's team. This paper aims to bring our research to the knowledge of every farmer, researcher, and inquisitive who wants to put their efforts into the field of agriculture. © 2024 IEEE.

关键词： Agriculture Batches Deep Learning Fast.ai Normalization Plant Disease Python

来源：评论

学校读者我要写书评

暂无评论

Tokenization of Energy Assets: A Multichain Blockchain Approach 5

Tokenization of Energy Assets: A Multichain Blockchain Appro...

引用

5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Akiladevi, R. Sardha, S. Shruthi, R. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350395235

The biggest barrier is the financial strain caused by rising electricity costs from using too much power. The security of businesses in the energy sector is threatened by this financial burden, therefore finding creative ways to get through these rough waters is imperative. Moreover, the intricacies involved in the issuance and exchange of energy certificates have intensified the need for enhanced openness and economy. Industry stakeholders are now seriously concerned about these nuances. Our project offers a methodical strategy based on multichain blockchain technology to address these enormous obstacles. invested in the future of the industry. This innovative approach focuses on tokenizing energy assets, optimizing energy management procedures, and providing industry stakeholders with a safe environment. The work on the project in stages, starting with collecting a lot of data. The carefully collect standardized data on power use, expressed in terawatt-hours (TWh), and then improve data quality to make sure the information is reliable and credible. The principal aim is to bring in an era characterized by security, efficiency, and transparency in the administration of energy resources, hence fostering growth and resilience within the energy industry. The ultimate objective is to clear the path for a future in energy asset management marked by openness, security, and efficient operations, all of which will support the growth and sustainability of the sector. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Deep Learning - Driven RPA Bot for Automated Evaluation of Handwritten Examination Papers Using Gen AI

Deep Learning - Driven RPA Bot for Automated Evaluation of H...

引用

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

作者： Duraimurugan, N. Vaishnavi Sri, S.M. Subhikshaa, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331543617

The incorporation of Robotic Process Automation (RPA) and deep learning in the educational evaluation system brings a strong, automated solution for assessing handwritten exam assessments. Educational institutions have exponentially started using RPA systems for monotonous activities like data storage and management, but the capacity extends far beyond these activities. This paper gives an automated solution for evaluating handwritten examination papers in educational institutions by combining advanced deep learning models and RPA (Robotic Process Automation). Using CNNs (Convolutional Neural Networks) and BiLSTM (Bidirectional Long Short-Term Memory) networks, the system converts handwritten responses into machine-readable text, trained to various handwriting styles. For grading, a Gen-AI model is used to assess the answers recognized by CNN and BiLSTM. This provides precise grading that aligns closely with human correction. RPA does end-to-end automation, from scanning and processing papers through deep learning models for text recognition to grading and data management thus reducing human effort, enhancing consistency, saving time and improving efficiency in the evaluation process. This technique streamlines examination, allowing teachers and professors to focus more on personalized instruction and academic development. © 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

Integrating Bi-LSTM with Deep Learning for Emotion Analysis from Brain Wave Patterns

Integrating Bi-LSTM with Deep Learning for Emotion Analysis ...

引用

2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Jyothi, G.N. Nagesh, H.R. Canara Engineering College Department of Computer Science and Engineering Mangaluru India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517892

Emotion detection and classification are becoming new frontiers in the capability of brain machine interface in the present times. Several studies have demonstrated that audio, video, as well as EEG data are helpful in the automated detection of affective states. EEG based affect detection is an important aspect of psychiatric evaluation for persons. In case sensor EEG data is gathered from various experimental sessions or participants, the signals obtained tend to be non-stationary. Building an intelligent emotion recognition system, which performs fair in terms of accuracy remains a problem due to the presence of numerous noisy, non-stationary, and nonlinear EEG signals. Some researchers have provided evidence that specific EEG brain waves reflect certain thoughts or feelings. This research proposes a completely new system responsible for automatically recognizing the emotions of users using deep learning techniques of computer network EEG signals acquired during playing computer games. EEG data from 28 participants were acquired on 14 channels Emotive Epoc + portable and wearable devices. Each participant played four distinct emotional games for five minutes each, yielding a total of 20 minutes of EEG data per volunteer. It is also shown that this framework is capable of identifying four fundamental emotion types in real time during games. The findings indicate that the suggested model-based emotion identification framework is a useful way for detecting emotions from EEG data. The network achieves 99.32% accuracy while using less computation time. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Crop Disease Detection using YOLOv8 15

Deep Learning for Crop Disease Detection using YOLOv8

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rakesh Kumar, M. Rengalakshmi, N. Saghana Shree, R.P. Akiladevi, R. Kumar, P. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Agriculture, deeply rooted in India's history, remains as the root of the country's economy and a primary source of employment. Despite the ageold practice, smallholder farmers in India sometimes face problems, especially in getting critical information that would allow them to make knowledgeable farming decisions. In this era of technology, knowledge is the foundation of advancement, farmers productivity and profitability could be impacted due to a lack of widely accessible agricultural knowledge. Crop health is an important aspect in productivity in agriculture. Crop diseases can have major implications for quality and quantity, as well as farmer's livelihoods. This technology aims to assist farmers, increase productivity, and contribute to agriculture's sustainability by providing up-to-date knowledge regarding crop health, disease identification, and optimal farming practices. Crop disease identification used to rely on digital image processing, but new advances in deep learning have exceptionally outperformed previous techniques. In this paper, we focus on the effectiveness of employing deep learning for crop disease identification. Our research is based on a database of images of diseased crop leaf and healthy leaf, with a focus on prevalent crop diseases in India. An application has been created with this ideology in mind to aid farming practices by detecting plant disease. There are two types of input format can be given to the application such as image and video of the diseased plant leaves. YOLO v8 is the deep learning model that is used in this application to effectively identify the disease. © 2024 IEEE.

关键词： Agriculture productivity Crop disease Crop health Deep Learning YOLOv8

来源：评论

学校读者我要写书评

暂无评论

Unified Payment Interface Imposture and Scam Detection Using Deep Learning And ANN 3

Unified Payment Interface Imposture and Scam Detection Using...

引用

3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Karthick, N. Leema Roselin, G. Tamilarasan, M. Kalaiselvi, K. Sudha, S. Jayanthi, J. IFET College of Engineering Department of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350391565

The Unified Payment Interface (UPI) has revolutionized digital payments in India, providing a convenient and secure way for individuals to transfer funds. However, the increasing popularity of UPI has also attracted malicious actors engaging in imposture and scams to defraud users. A novel approach is proposed to detect UPI imposture and scams using Deep Learning and Artificial Neural Networks (ANN). This system leverages the power of deep learning techniques, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to analyze transaction data and user behavior patterns. A comprehensive dataset is collected and preprocessed, containing legitimate UPI transactions as well as historical data associated with known scams and imposture cases. By training the model on this dataset, it learns to identify suspicious patterns and anomalies in real-time transactions. © 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

WeatherSphere: Redefining Weather Interaction through Augmented Reality

WeatherSphere: Redefining Weather Interaction through Augmen...

引用

2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Ananthajothi, K. Anand, K. Duraimurugan, N. Vinodh Kumar, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331541170

This research introduces a novel mobile application that employs augmented reality (AR) to enhance user engagement with weather information. By integrating Unreal Engine for advanced 3D visualization and React Native for cross-platform accessibility, the app creates immersive and interactive weather experiences. The developed application successfully transforms static weather data into dynamic, three-dimensional representations that users can directly interact with in their real-world environment. This AR-driven UI significantly improves user engagement and understanding of weather patterns. The findings of this study demonstrate the potential of AR to revolutionize weather app design. By prioritizing accessibility and user experience, the developed application offers a new paradigm for how individuals consume and interact with weather information. Future research could explore expanding AR features, integrating additional weather data, and conducting user studies to further refine the app's design. © 2024 IEEE.

关键词： 3D modeling

来源：评论

学校读者我要写书评

暂无评论

Enhancing Glaucoma Diagnosis with a Novel Optic Disk Localization and Classification Framework Based on U-Net and ResNet-50

Enhancing Glaucoma Diagnosis with a Novel Optic Disk Localiz...

引用

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

作者： Anandhi, S. Anand, K. Rajalakshmi Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798331543617

Glaucoma is one of the chronic eye ailments that result in irreversible visual deterioration and is widely recognized as a significant contributor to the phenomenon of blindness on global terms. Early detection will help in drastically reducing the course of the disease. The paper proposes a three-phase novel framework for the localization of optic discs and glaucoma detection. It concentrated on early-stage identification to improve treatment outcomes. First, the median filtering and histogram equalization constitute the techniques for that process. With these techniques, different kinds of noise, such as Rayleigh, salt and pepper, Gaussian, and gamma noise, can efficiently be reduced in fundus images. At the result of this technique, the clarity of the images gets improved to a large extent and also makes the further analysis more accurate. In the next stage, location and segmentation of the optic cup and optic disc will be done accurately to obtain the desired output. To get the desired outcome, the optic disc and cup will be precisely located and segmented in the stage that comes next. For this purpose, use is made of the U-Net architecture in delineating these critical structures that lie within the eye. In the last stage, fundus images are classified to be either glaucomatous or non-glaucomatous using transfer learning-based models, prominently ResNet-50. With ResNet-50, the overall capability of the model has been highly enhanced to successfully analyze and identify complex patterns inside retinal images. Experimental results of this study, conducted using the ORIGA dataset, further testify to the effectiveness of the suggested framework. An accuracy rate of 96.78% is attained by the proposed framework, which turned out superior to the state-of-the-art approaches. © 2024 IEEE.

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

An effective object detection and tracking using automated image annotation with inception based faster R-CNN model

引用

International Journal of Cognitive Computing in engineering 2024年第1期5卷 343-356页

作者： Vijiyakumar, K. Govindasamy, V. Akila, V. Department of Computing Technologies School of computing SRM institute of Science and Technology Kattankulathur campus Tamilnadu Chengalpattu district India Department of Information Technology Pondicherry Engineering College Pondicherry India Department of Computer Science and Engineering Pondicherry Engineering College Pondicherry India

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking. © 2024

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

A Framework for Suggesting a New Methodology for Early Detection of Epilepsy Seizure 7

A Framework for Suggesting a New Methodology for Early Detec...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Subash, K. Saravanan, A. Padmavathi, B. Easwari Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

Epilepsy, a severe and chronic neurological condition, is detectable through the analysis of brain signals, typically monitored using Electroencephalogram (EEG) and Electrocardiography (ECG). Despite the complexity and noise associated with these signals, they are essential for diagnosing epilepsy, which accounts for approximately 1% of the global disease burden. The recurrence of seizures is a primary indicator of epilepsy, posing significant challenges for seizure identification and understanding brain-related phenomena. This study proposes a predictive analysis system for epilepsy seizure detection, empowering medical practitioners with timely insights for patient care. Leveraging an ensemble machine learning model combining SVM, Random Forest, and Logistic Regression, the system achieves early identification of seizure activity. Utilizing PhysioNet datasets for training, the model accurately predicts epilepsy seizures, enhancing diagnostic accuracy by integrating brain and heart signals, thus surpassing existing models. This research significantly contributes to advancing seizure diagnosis and treatment efficacy. © 2024 IEEE.

关键词： Electrocardiography

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：