检索结果-内蒙古大学图书馆

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Abisha, D. Dhanushree Narmatha, V. Ida Christy, J. Navedha Evanjalin, R. Swetha, S. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

This research investigates the factors influencing income levels using machine learning techniques. Based on a dataset of 48,842 individuals, the study employs LightGBM, Random Forest, and K-Nearest Neighbors algorithms to predict annual income. The results demonstrate that LightGBM achieved the highest accuracy, exceeding 90%. Feature importance analysis revealed age as the most significant predictor of income. This study provides valuable insights into the factors that contribute to income inequality and has potential implications for policy development and social research. By understanding the key determinants of income, policymakers can develop targeted interventions to address income disparities and promote social equity. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Image recognition based attendance system with spoof detection

Image recognition based attendance system with spoof detecti...

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Rakesh, K. Sumer Singh, B. Surya, A. Kaladevi, R. Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

In recent times numerous face recognition algorithms were used for the identification and authentication of a person to a system. The objective of this design is to recognize the human faces and forecast whether the detected faces are real or fake. Our design is mainly useful for institutional purpose to enhance and modernize the present attendance system into more effective and efficient than before. In this design, human face database is dynamically created by new user registration and this data is pumped into the recognizer algorithm. In this design we have added a spoofing discovery technology that’s used to judge whether the face in front of the machine is real or fake. The face presented by other media can be defined as false face, including hardcopy photographs, display screen of electronic products, etc. The dataset which are dynamically created while a new user is registered is stored in the database and whenever a new face is detected on the camera, it compares with the images that are already present in the database to mark the attendance. Also, the result image is generated with the chance of spoofing can be attained from the dataset pushed into the algorithm. Our proposed system has accuracy of above 97.6% and true positive rate is 99.7% in high precision model. © 2024, CRC Press/Balkema. All rights reserved.

关键词： database systems

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Potato Leaf Disease Classification Using CNN and ResNet50 7

Comparative Analysis of Potato Leaf Disease Classification U...

引用

7th International Conference on data science and Its Applications, ICoDSA 2024

作者： Bharti, Riya Srivastava, Vivek Bajpai, Abhishek Sahu, Shalinee Rajkiya Engineering College Department of Computer Science Engineering Kannauj India

ISBN: (纸本)9798350365351

Plant diseases are a significant obstacle to crop quality and quantity, potentially impacting the food supply. Early identification of plant diseases through precise or nearly accurate detection methods can enhance food production quality and minimize economic losses. In recent years, deep learning models have brought tremendous change and improvement in recognizing the accurate results of image classification and object detection systems. In this project, three consecutive stages are employed to identify the type of disease. These stages include preprocessing, feature extraction, and classification using a Convolutional Neural Network (CNN) and ResNet50. These diseases predominantly impact green and living plants. This paper compares two convolutional neural network (CNN) architectures, a custom-designed CNN with six layers and ResNet50 for classifying plant leaf diseases. The dataset consists of 1,706 images, having six classes for training and 567 for validation. Both two architectures were trained and evaluated on the same dataset. Performance metrics such as training and validation accuracy were used to compare the effectiveness of the models in classifying plant leaf diseases. The results indicate that ResNet50 achieved an accuracy of 98.36%, whereas the custom CNN attained an accuracy of 94.29%. This summarises that ResNet50 performs better than the custom CNN, highlighting the strengths and weaknesses of each architecture in this specific application. © 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

An Initial Risk Assessment for Multimodal with LSTM-Based Trust Evaluation Framework for Autonomous Vehicle Security

引用

SN computer science 2025年第2期6卷 1-15页

作者： Renjith, P.N. Balasubramani, S. Ramesh, K. Patnala, Eswar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

This research aims to develop a new approach to increase the safety and reliability of Autonomous Vehicle (AV) through the proposed risk assessment framework, supported by the trust evaluation approach derived from a machine learning algorithm. This work proposes a comprehensive risk assessment model with real-time sensor data, Vehicle-to-Vehicle (V2V), key communication reliability, environmental cognition, and Improved Long Short-Term Memory (I-LSTM). This framework is multidimensional compared to most conventional models, focusing primarily on assessing individual threat factors. Here, the machine learning portion is designed to evaluate and predict trust by quickly processing vast amounts of information from sensors, signals, and prior operation history. Such an approach to trust assessment enables the system to continuously evaluate the dependability of automated vehicle operation and identify threats to security with improved accuracy. The effectiveness of the performance of this method is substantiated by a number of stand-alone and live scenarios, where the model's performance is higher in perceiving possible risks, avoiding threats, and enhancing safety than the conventional approaches. The findings suggest that the safety regulations of the AVs will be improved by using a multi-dimensional risk assessment model and integrating the I-LSTM trust rating with 95.5% accuracy, 96.7% risk identification, and 180 ms response time. This will cause AV and its surrounding environment to be accepted by the public and used on the roads. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous vehicles LSTM model Machine learning Risk assessment Trust evaluation

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

A Survey on Fish Detection and Species Recognition 2

A Survey on Fish Detection and Species Recognition

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Sheela, A Jeba Madhan Raj, R. Manoj, E. Roshan, G.K. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

Automated fish identification is essential to overcome the challenges and time constraints associated with manual identification processes. Various techniques are explored, evaluating their performance based on factors like pre-processing methods, significant characteristics, and accuracy. Fish classification is a well-studied problem with applications in diverse fields, including target marketing and government initiatives to manage fish supply and maintain ecological balance. These automated methods play a pivotal role in sectors like commercial fisheries, agriculture, marine science, and the broader industrial fish market, supporting industries such as nutrition and canning factories. One approach involves enhancing fish recognition algorithms based on models like AlexNet, IDNet, SAFNet. Methods such as item-based soft attention mechanisms, reduced structural complexity, and transfer learning have been employed to improve accuracy and reduce training time. Similarly, researchers have applied deep learning algorithms like Mask R-CNN, MobileNet, and Fast RCNN to tackle species identification, length estimation, and object detection in marine environments. These techniques have shown promising results, especially when dealing with overlapping fish or elongated ***, lightweight models have been developed for underwater object detection, considering issues like low visibility, colour distortion, and small target size. Techniques like attentional feature fusion, modified feature pyramid generation, and adaptive anchor generators have been proposed to optimize performance, achieving a balance between accuracy and processing speed. © 2024 IEEE.

关键词： Fish

来源：评论

学校读者我要写书评

暂无评论

Autonomous Parking Space Detection for Electric Vehicles Based on Advanced Custom YOLOv5 3

Autonomous Parking Space Detection for Electric Vehicles Bas...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Balusamy, Deepa Shanmugam, Geetha Rameshkumar, Baazilkumar Palanisamy, Agathiyan Kingston, Giftson Ravi, Logeshraj Nandha Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350372120

This research contributes to the development and deployment of smart parking system utilizing cutting-edge computer vision techniques. The study utilizes a subset of the Google Open Images dataset focused on parking spaces to train a Custom Object Detection model tailored for low-computation devices. Employing YOLOv5 architecture and the Feature Pyramid Network (FPN), the system effectively detects parking spaces and objects within them. Utilizing ResNetv250 as the backbone architecture ensures precise feature extraction, resulting in high accuracy in identifying parking spots. Experimental results reveal the efficacy of the envisioned methodology, with Efficient YOLOv5 model achieving a mean Average Precision (mAP) of 0.970 for object detection in aerial dimensional frames. Graphical representations of F1-Confidence Curve and Precision-Confidence Curve demonstrate the model's robustness in balancing precision and recall, even with an imbalanced dataset. This research underscores the potential of the proposed smart parking system to revolutionize urban parking management, providing an efficient and accurate solution for parking space identification. © 2024 IEEE.

关键词： Object detection

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

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

Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

引用

computers, Materials & Continua 2023年第1期74卷 2195-2209页

作者： S.Rama Sree Inderjeet Kaur Alexey Tikhonov E.Laxmi Lydia Ahmed A.Thabit Zahraa H.Kareem Yousif Kerrar Yousif Ahmed Alkhayyat Department of Computer Science&Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Computer Science&Engineering Ajay Kumar Garg Engineering CollegeGhaziabadUttar PradeshIndia Department of Human Resource Management Moscow Aviation InstituteMoscowRussia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Computer Communication Engineering Al-Rafidain University CollegeBaghdadIraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Technical Engineering Al-Hadba University CollegeMosulIraq College of Technical Engineering The Islamic UniversityNajafIraq

Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and *** ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with *** of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were ***,healthcare and machine learning(ML)industries are combined for determining the existence of various *** article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)*** goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical *** proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform *** addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature ***,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ***,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU *** experimental assessment of the JSODL-ASDDC model is investigated against distinct *** experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.

关键词： Autism spectral disorder biomedical data deep learning feature selection hyperparameter optimization data classification 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：