检索结果-内蒙古大学图书馆

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

作者： Inbavalli, A. Lakshmipriya, S. Krishnakumar, M. Manimegalai, C. Karunagaran, S. Lakshaa, R.S. IFET College of Engineering Department of Computer Science and Engineering Tamil Nadu Villupuram India

ISBN: (纸本)9798331530013

Alzheimer's disease is a very serious neurological condition. Memory loss and a progressive decrease in mental capabilities are what ensue. This AD impacts millions of people across the world. Deep emotional disturbances and social complications arise from this for patients as well as their families and friends. Prompt therapies that reduce disease progression and enhance quality of life depend on early and correct diagnosis. This work presents Neuro Vision AI, which uses MRI data to diagnose Alzheimer's disease stages. The system categorises instances into four categories: Non-Demented, Very Mildly Demented, Mildly Demented, and Moderately Demented. To enhance performance as well as reliability, Neuro Vision AI uses the EfficientNetB3 convolutional neural network, which is famous for its feature extraction skills. We use Bayesian optimisation to fine-tune hyperparameters, ensuring optimum accuracy and reducing the risk of overfitting. Advanced preprocessing approaches. For example, data augmentation and normalisation, enhance dataset diversity while boosting generalisation. Regularisation strategies like dropout layers and batch normalisation stabilise the model during training. The proposed system works well as it is able to achieve more than its initial target of 95% accuracy with a test accuracy of 98.67%. Neuro Vision AI demonstrates the potential of artificial intelligence in medical diagnostics that presents an efficient and scalable tool for early diagnosis and personalised therapy in the management of Alzheimer's disease. © 2024 IEEE.

关键词： Alzheimer's Disease Bayesian Optimization Cognitive Decline Early Detection EfficientNetB3 MRI Analysis

来源：评论

学校读者我要写书评

暂无评论

Multi Attribute Case Based Privacy-preserving for Healthcare Transactional Data Using Cryptography

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 2029-2042页

作者： K.Saranya K.Premalatha Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401India

Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy *** this background,several authentication and accessibility issues emerge with an inten-tion to protect the sensitive details of the patients over getting published in open *** solve this problem,Multi Attribute Case based Privacy Preservation(MACPP)technique is proposed in this study to enhance the security of privacy-preserving *** information can be any attribute information which is categorized as sensitive logs in a patient’s *** semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive *** addition to this,crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log veriﬁcation that protects the personalized sensitive ***,linear integrity veriﬁcation provides authentication rights to verify the data,improves the performance of privacy preserving techni-que against intruders and assures high security in healthcare setting.

关键词： Privacy-preserving crypto policy medical data mining integrity and veriﬁcation personalized records cryptography

来源：评论

学校读者我要写书评

暂无评论

Chronic Kidney Disease Prediction Using Machine Learning Algorithms

Chronic Kidney Disease Prediction Using Machine Learning Alg...

引用

2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Nagamani, T. Kavya, R. Lavanya, D. Miruthula, S. Kongu Engineering College Department Of Computer Science And Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350371567

Early detection and characterisation play a crucial role in effectively managing and treating chronic illness. Given the increasing prevalence of chronic kidney disease (CKD), the healthcare system faces a significant burden, as CKD raises the risk of developing end-stage renal disease and related morbidity and mortality. The timely identification of CKD is vital to saving numerous lives. This study uses a feature selection strategy alongside various Machine Learning (ML) algorithms to develop a diagnostic system for detecting CKD. The study utilised approximately 400 clinical records of CKD patients sourced from the UCI Machine Learning repository. To refine the datasets for the predictive model, various data preparation techniques were applied, including feature encoding, label encoding, data cleaning, ambiguity removal, handling missing values, and feature selection. Ultimately, the Extra Trees classifier outperformed the other two machine learning models, exhibiting the capability to diagnose chronic kidney disease (CKD) with an impressive 98% accuracy rate and a low 2% true negative rate, all without any data leakage. © 2024 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Deep learning model for plant-leaf disease detection in precision agriculture

引用

International Journal of Intelligent Systems Technologies and Applications 2023年第1期21卷 72-91页

作者： Bajpai, Chandrabhanu Sahu, Ramnarayan Naik, K. Jairam Department of Computer Science and Engineering National Institute of Technology Raipur Chhattisgarh Raipur India

Crop disease in the agricultural is the main factor limiting yield and food quality. It requires timely diagnosis of crop illnesses for the better economy developing country. Manual crop illness assessment is limited due to lesser accuracy and restricted accessibility. It is very difficult to accurately identify and classify plant diseases due to corrupt in the data samples, lesser intensity of foreground and background, and the extreme similarity between unhealthy and healthy leaves in terms of colouring and size of crop leaves. Hence the employments of automated and computerised optimisations are needed. To identify plant leaf diseases, a DNSVM classification strategy fusing DenseNet-201 with support vector machine (SVM) is proposed in this work. Plant-Village dataset that provides good-variations, colour-differences, differences in orientation and size-of-leaves. Sugarcane plants-leaves were used for performance analysis of proposed model and obtained 97.78% of classification accuracy over the existing DenseNet-121-based classifier model (94%). © 2023 Inderscience Enterprises Ltd.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

A Novel High Step-Up DC-DC Converter with Zero Current Switching and Coupled Inductors for Enhanced Efficiency and Voltage Gain 16

A Novel High Step-Up DC-DC Converter with Zero Current Switc...

引用

16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Esfahani, Seyed Mohammad Naji Zahiri, Seyed Hamid Delshad, Majid University of Birjand Department of Electrical and Computer Engineering Birjand Iran Branch Department of Electrical Engineering Isfahan Iran

ISBN: (纸本)9798331533946

This study introduces a high step-up DC-DC converter integrated with a novel, lossless snubber circuit. To achieve an elevated voltage gain, the design combines a voltage-lifting mechanism with a voltage-doubling circuit. An auxiliary circuit, comprising two resonant inductors and a resonant capacitor, facilitates soft-switching conditions across all semiconductor components, thereby enhancing efficiency and reducing switching losses. This configuration not only enables high voltage gain but also minimizes voltage stress on the switching element, contributing to the converter's overall high efficiency. Furthermore, the leakage inductance of the transformer is absorbed by the resonant inductor in the auxiliary circuit, effectively mitigating voltage spikes across the switch. A prototype of the proposed converter has been constructed to validate the theoretical analysis, confirming its performance improvements. © 2025 IEEE.

关键词： Capacitor bank

来源：评论

学校读者我要写书评

暂无评论

A Review of 3D Indoor Positioning and Navigation in Geographic Information Systems 8th

A Review of 3D Indoor Positioning and Navigation in Geogr...

引用

8th International Conference on Smart City Applications, SCA 2023

作者： Kazangirler, Buse Yaren Karas, Ismail Rakip Ozcan, Caner Department of Computer Technologies Kastamonu University Kastamonu Turkey Department of Computer Engineering Karabuk University Karabuk Turkey Department of Software Engineering Karabuk University Karabuk Turkey

ISBN: (纸本)9783031543753

Geographic information system (GIS) has become widespread with the management of geographically-based information, constituting a large part of the new data obtained and produced today in electronic platform. Three-dimensional (3D) models provide an overview of the more rapid establishment of new ideas that a two-dimensional (2D) visualization cannot present. Thus, 3D GIS demands data to be comprehensive and continious. 3D indoor positioning and 3D navigation are technologies that provide the ability to position and navigate indoors using these systems. The 3D navigation system is a field that can show covering objects in 3D along the route to the goal when precision is involved. In this study, navigation and indoor positioning studies in 3D GIS applications are mentioned, and particularly cutoff edge studies are included. It contains research on the use and benefits of 3D indoor modeling and navigation techniques. The literature examined for the study were evaluated, and it was concluded that 3D indoor positioning and navigation operations were generally performed with algorithms such as map-matching, location-based service, or ML/DL models. We comprehensively review positioning and navigation techniques in the 3D GIS field by combining existing studies, findings, and different approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Geographic information systems

来源：评论

学校读者我要写书评

暂无评论

Enhancing E-Learning: EEG Signal Classification to Evaluate Students' Understanding of Online Lectures 6

Enhancing E-Learning: EEG Signal Classification to Evaluate ...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Bhuiyan, Md Khairul Bayesh, Md Rishadul Das, Sunanda Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna9203 Bangladesh

ISBN: (纸本)9798350385779

Online learning is increasingly popular, and the COVID-19 pandemic has only accelerated its adoption. Neverthe-less, a significant challenge in online education is accurately assessing students' comprehension and engagement during lectures. Conventional evaluation methods like quizzes and assignments often fall short in providing a precise representation of students' understanding and lack real-time feedback. In our proposed approach, we tackle this challenge by developing a deep learning-based classifier capable of identifying students who have grasped the online lecture content and those who haven't. We sourced a 14-electrode EEG dataset from an online repository. During EEG signal recording, multiple frequencies, including noise, artifacts, and biological signals, were present. We addressed this issue by applying the Fast Fourier Transform to denoise the raw EEG sig-nals. To classify the EEG dataset using machine learning models, we employed two techniques: the Short-time Fourier Transform and Continuous Wavelet Transform for feature extraction. For each feature, we created two classes of images-one for students who comprehended the lecture and another for those who did not. In pursuit of this task, we utilized four pre-trained models, namely VGG-16, VGG-19, ResNet-50, and ResNet-101. Among these models, ResNet-50 emerged as the top performer, achieving an accuracy rate of 85.31% for Continuous Wavelet Transform, alongside an F1 score of 87%. This research presents a promising approach to assessing online learning comprehension in real-time, leveraging advanced deep learning techniques and EEG data analysis. © 2024 IEEE.

关键词： E-learning

来源：评论

学校读者我要写书评

暂无评论

Visual Sort: A User-Friendly GUI for Tree Canopy Segmentation

Visual Sort: A User-Friendly GUI for Tree Canopy Segmentatio...

引用

2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Vasavi, S. Likhitha, Atluri Lakshmi Premchand, Veeranki Sai Yasaswini, Jampa Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350309140

Graphical User Interface (GUI), is a visual way for users to interact with software, utilizing graphical elements like icons, buttons, and windows instead of text commands. It enhances user experience by providing an intuitive, user-friendly interface for navigation and control. A tree canopy refers to the upper layer of branches, leaves, and stems formed by the crowns of trees when viewed collectively, which shows difficulties in VHRS (Very High-Resolution Satellite) images due to its complex and dense structure, which makes it difficult to accurately detect and classify objects beneath it. The proposed model employs a interactive GUI system that leverages a novel semantic segmentation approach for detecting and classifying objects covered by tree canopies within the VHRS images. The input image is preprocessed using pre-processing techniques and uses U-Net with the backbone of ResNet34 for improving tree canopy mapping by detecting and classifying objects into buildings (white), roads (red), and water bodies (blue) and got accuracy of 84 © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Selection of Type 2 Diabetes Specific Genes and Microarray Datasets 1

引用

Congress on Smart Computing Technologies, CSCT 2023

作者： Saxena, Aditya Department of Computer Engineering and Applications Institute of Engineering & Technology GLA University Mathura India

ISBN: (数字)9789819750818

ISBN: (纸本)9789819750801

Type 2 diabetes is a chronic complex disease whose mechanism is still elusive. Gene expression profiling using microarray is routinely used to identify etiologically important genes between diabetics and normal individuals, but this method suffers with some of the limitations such as background noise, low sensitivity, and high dimensionality.. To rectify them, statistical meta-analysis is extensively used by combining multiple datasets. In this study, we highlighted the application of some machine learning algorithms: SVM, LDA, and LASSO for selection of relevant genes and expression datasets after meta-analysis of microarray datasets and also provided biological significance of identified genes in light of published GWAS studies. Association of these selected genes with GWAS could likely point toward the probable link between genome and transcriptome. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

RAKTAMITRA- An Alert Centric Blood Donation Initiative 4

RAKTAMITRA- An Alert Centric Blood Donation Initiative

引用

4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Alekhya, Inti Chinta, Mukesh Maddela, Likitha Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350387063

Blood is a critical component for human health, yet the process of finding blood donors can be time-consuming and inefficient, especially during emergencies. Current methods often involve multiple rounds of communication, which delays the timely procurement of life-saving blood. To address these challenges, we have developed 'RAKTAMITRA,' a mobile application designed to streamline the blood donation process. This app connects blood donors with recipients by integrating blood type compatibility and geographical proximity. The key technical components of the application include an intuitive and user-friendly interface developed using Android Studio for cross-platform compatibility on both Android and iOS devices. A robust backend powered by Firebase handles real-time data management and synchronization, storing user information, blood type, and location details securely. The app utilizes Google Maps API for geolocation services, enabling accurate tracking of donor and recipient locations to facilitate proximity-based matching. Additionally, Firebase Cloud Messaging (FCM) is used to implement the push notification system, ensuring that potential donors are promptly notified of nearby donation requests. Users in urgent need of blood can post their requirements on the app, triggering an instant search for potential donors with matching blood types in their vicinity. Once a match is identified, the app notifies donors based on their location to ensure rapid assistance. The platform employs stringent security measures to protect users' personal information, and admin oversight to monitor and address any inappropriate content or any other activities. 'RAKTAMITRA' significantly expedites the blood donation process by leveraging technology to reduce the time and effort needed to locate compatible donors. The integration of blood type and location ensures that help is available swiftly, potentially saving lives in critical moments. The app's secure framework maintains user co

关键词： Information management

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：