检索结果-内蒙古大学图书馆

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Divya, S. Saranya, S. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

The massive progression of the multi-tier edge cloud computing systems integration has created the possibility of data orchestration and resource control in edge and cloud layers. However, there are many shortcomings, including inappropriate methods of scheduling tasks and security breaches that prevent scalability. To solve these problems, we designed an energy-efficient and security-aware task offloading framework for multi-tier edge cloud computing. Firstly, we estimate the energy ratio of tasks using a Fuzzy Logic Energy Management (FL-EM) algorithm and distribute resources according to different layers. Subsequently, the task-offloading parallel squirrel (TO-PS) method is used to effectively extend the task with better energy consumption and computational efficiency. Finally, it is decided to protect the work data using the Provable Energy-Aware Cryptographic Security (PE-ACS) algorithm. The proposed algorithm achieves a 96.7% success rate in protecting high-level data from cybercrime. Additionally, it enhances security and task performance while reducing energy consumption in multi-tier edge cloud computing systems. © 2025 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

Skin Cancer Detection Using Deep Learning 10

Skin Cancer Detection Using Deep Learning

引用

10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Mahalakshmi, A. Padmavathy, C. Priya, N. Priyanka, G. Babu, Lidiya Archana, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353778

Skin cancer is a significant global health concern that requires early detection and accurate diagnosis for effective treatment. Traditionally, dermatologists with specialized training have been responsible for diagnosing skin cancer. However, the emergence of deep learning models, particularly Convolutional Neural Networks (CNNs), offers a promising approach for utilizing dermatoscopic images in the early identification and categorization of skin cancer. The HAM10000 dataset, comprising a vast library of high-quality dermatoscopic images displaying a variety of skin lesions, significantly contributes to advancing skin cancer diagnosis. This research leverages the HAM10000 dataset to develop and evaluate a CNN model tailored for accurate skin cancer classification. The suggested CNN model is an advanced deep learning architecture adept at image classification tasks, particularly in recognizing various forms of skin cancer. It consists of multiple layers of dense neural networks, pooling, and convolution designed to extract detailed characteristics from skin lesion images. To ensure comprehensive representation of various skin lesions and maximize performance, the training dataset is extensively oversampled. This oversampling technique enhances the model's ability to generalize across different lesion types, thereby improving classification accuracy. Furthermore, the Adam optimizer refines the model's learning process by effectively adjusting its parameters during training, leading to increased accuracy. By training the model for more than one hundred epochs with a batch size of 323, it learns intricate patterns and distinguishing features within skin lesion photos, which enhances its ability to classify skin cancer accurately. These advancements in deep learning-based skin cancer categorization represent a significant step towards leveraging artificial intelligence to improve early diagnosis and detection. Such innovations have the potential to support medical profe

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Digital twinning of vertical centrifugal casting

引用

Concurrent engineering Research and Applications 2024年第1-4期32卷 78-89页

作者： Anadkat, Dhaval Dave, Anjali Sata, Amit Shukla, Minal Jarboui, Slaheddine Department of Mechanical Engineering Marwadi University Rajkot India Department of Mechanical Engineering Lukhdhirji Engineering College Morbi India MGL Group Rajkot India Deparment of Computer Engineering College of Computer Science King Khalid University KSA Abha Saudi Arabia

This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation. © The Author(s) 2024.

关键词： Centrifugal casting

来源：评论

学校读者我要写书评

暂无评论

An Interactive Augmented Reality Application for Agriculture using Unity 17

An Interactive Augmented Reality Application for Agriculture...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Maasthi, Manasvi J. Vidyavardhaka College of Engineering Department of Computer Science and Engineering Mysuru India

ISBN: (纸本)9798331531195

Augmented reality is defined as a 3D computer generated imagery that is embedded into the real world. There are numerous areas in which augmented reality can be applied, including: education, medical care, entertainment, & so on. Unlike VR, AR does not require the use of a HMD (Head Mounted Device);a projection can be performed on a mobile phone making it affordable. Most farmers are facing problems today, for instance, loss of crops. In this Paper an application has been created aimed at enabling farmers to know important things like when to grow certain crops and what to use to assist in soil fertility, through the development of augmented reality applications using Unity and Vuforia. That way, the farmers will have an idea of what other crops they should be able to grow during the other season. So that they will not be undergoing any kind of loss. © 2025 IEEE.

关键词： Virtual environments

来源：评论

学校读者我要写书评

暂无评论

Food Hazines and Tracking System for Farmers using Blockchain Technologies 1

Food Hazines and Tracking System for Farmers using Blockchai...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jananipriya, R. Ramya, P. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

It is quite challenging to monitor goods quality or security issues due to the intricacy of a tracking system, particularly for the fundamental farming food supply chains that contribute to making up everyday feeds of individuals. There are a number of significant issues with the current agricultural food tracking systems, including a large number of subjects, challenging interactions brought on by lengthy logistics phases, and user and centrally managed distrust of data. The traceability issue in agricultural food tracking systems is successfully resolved by the development of blockchain technology. In order to reduce the requirement for central organizations and departments and enhance the dependability, safety, and authenticity of records of transactions, this work suggests a structure based on smart contracts and consortiums to monitor and document the process of agricultural food tracking system, implement transparency and accessibility, and dismantle data centers between businesses. Farmers simultaneously store file hashes in smart contracts and record environmental and agricultural development information in the InterPlanetary File System (IPFS), which improves the safety of information and mitigates the issue of blockchain storage overflow. The proposed system demonstrated superior performance with reduced delay in response (0.035) and increased transmission speed (265), reception speed (305) and processing power (17.65) compared to Ethereum and Hyperledger Fabric. © 2025 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Integrated Hydroponics Automation System Utilizing IoT for Enhanced Plant Growth 3

Integrated Hydroponics Automation System Utilizing IoT for E...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nilahni, N. Sivasakthi, P. Kavitha, P. Madhumathi, S.M. Sakthi Lakshmi Priya, C. Saranyavaishalini, V.G. Department of Computer Science and Engineering PSR Engineering College Sivakasi India

ISBN: (纸本)9798331527549

Growing plants in nutrition solution with any growing medium or roots dipped in distilled water is Hydroponics. Hydroponics, the practice of growing plants in a nutrient-rich solution without soil, offers significant advantages such as higher yield, reduced water usage, and efficient resource management compared to traditional farming. However, existing hydroponic systems often require separate monitoring and control platforms for different setups, such as Wick and Drip systems, increasing complexity and cost. This paper proposes a Combined Hydroponics Automation System utilizing IoT, integrating both Wick and Drip systems into a single, unified platform. The system employs Nodemcu ESP8266 to monitor key parameters such as pH, humidity, moisture, and water levels, with data transmitted to a cloud platform for real-time access and control. The unified approach reduces development and operational costs while simplifying the monitoring process. The outcome demonstrates improved efficiency in plant management, with streamlined data visualization, remote control, and optimized resource allocation, making it a cost-effective solution for urban and resource-limited farming environments. © 2025 IEEE.

关键词： Hydroponics

来源：评论

学校读者我要写书评

暂无评论

Performance of pre-trained deep learning models for land use land cover classification using remote sensing imaging datasets

引用

Environmental Earth sciences 2025年第11期84卷 1-36页

作者： Haider, Irfan Khan, Muhammad Attique Masood, Saleha Algamdi, Shabbab Ali Alasiry, Areej Marzougui, Mehrez Nam, Yunyoung Department of Computer Science HITEC University Taxila Pakistan Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia IRC for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Software Engineering College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia College of Computer Science King Khalid University Abha61413 Saudi Arabia Department of Computer Science and Engineering Soonchunhyang University Asan31538 Korea Republic of

This study presents a comparative analysis of ten pre-trained convolutional neural network (CNN) models, evaluated across three remote sensing datasets: EuroSat, NWPU, and Earth Hazards (Land Sliding). We investigate the interplay between model architecture and classifier selection by incorporating five different neural network (NN) classifiers, emphasizing their impact on predictive accuracy and computational efficiency. Due to its densely connected architecture, DenseNet201 achieved the highest accuracy—97% on EuroSat, 99.40% on NWPU, and 97.80% on Earth Hazards. In contrast, MobileNetV2, while slightly less accurate, demonstrated superior computational efficiency, recording the shortest prediction times of 39.943 s on EuroSat, 27.482 s on NWPU, and 2.8986 s on Earth Hazards. Additionally, classifier choice significantly influenced performance, with the Wide NN classifier excelling in diverse datasets and the Medium NN classifier optimizing speed. Our findings underscore the importance of balancing accuracy and efficiency in selecting CNN models for remote sensing applications, suggesting future research should explore ensembling techniques and lightweight models to enhance performance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Software design

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Deep Learning Approach for Automatic Melanoma Diagnosis: Integrating Convolutional Neural Networks with Vision Transformers 4

A Hybrid Deep Learning Approach for Automatic Melanoma Diagn...

引用

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

作者： Priyanka, G. Mahalakshmi, A. Priya, N. Sivaranjani, N. Archana, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Hindusthan college of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

The most lethal type of skin lesion is melanoma. The likelihood of survival for melanoma is significantly increased by early detection. Nevertheless, a number of characteristics, such as diminished contrast between the skin and abrasion and visual similarities between non-melanoma and melanoma, make it challenging to identify melanoma with accuracy. The novel melanoma recognition method described in this study consists of three primary phases: preprocessing, segmentation and classification. To maximize image quality and get the data ready for segmentation, the preprocessing step included denoising techniques, grayscale conversion, Gaussian thresholding, and Contrast Limited Adaptive Histogram Equalization (CLAHE). Three widely used edge detection algorithms-Canny, Sobel, and Prewitt-were applied to the segmentation process in order to identify lesion boundaries and extract features unique to melanoma. Performance criteria such as Root Mean Square Error (RMSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) were used to assess the accuracy of the segmentation. Based on the comparison analysis, Sobel was found to be the best algorithm for correctly identifying melanoma lesions. The outcomes showed how well the preprocessing methods worked to enhance image quality and how well the segmentation algorithms identified melanoma lesions. This research's subsequent phase will concentrate on classifying images using deep learning models. The segmented images are used to train deep learning model namely Convolutional Neural Networks with Vision Transformer. Superior performance is obtained in terms of accuracy, precision, recall, and F1 score when denoising, segmentation, and classification techniques are combined. This highlights the potential of deep learning and image processing fusion in improving skin disease recognition and performance of classification systems. © 2024 IEEE.

关键词： Dermatology

来源：评论

学校读者我要写书评

暂无评论

Enhancing Interest Message Forwarding in NDN-based VANETs using Black Hole Optimization 2

Enhancing Interest Message Forwarding in NDN-based VANETs us...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Devi, S. Padma Dhanalakshmi, K. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350373110

In Intelligent Transportation Systems (ITS) and smart cities, Vehicular Ad hoc Networks (VANETs) are vital but face challenges due to their dynamic topology, making traditional IP-based content retrieval impractical. Named Data Networking (NDN) offers an alternative by using content names for retrieval, though locating content-holding nodes efficiently in NDN-based VANETs remains difficult. This paper introduces an optimized method using Black Hole optimization to enhance interest message forwarding in NDN-based VANETs, aiming to improve packet delivery ratios and reduce latency. Simulation studies comparing proposed method with classic NDN and Particle Swarm Optimization NDN (PSO-NDN) show that proposed approach significantly outperforms these methods, achieving higher packet delivery ratios and lower latency. This research advances efficient communication strategies in NDN-based VANETs, crucial for robust ITS in smart cities, demonstrating the potential of Black Hole optimization to enhance VANET performance and support smart city infrastructure. © 2024 IEEE.

关键词： Vehicular ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Diagnosis of Autism Spectrum Disorder by Imperialistic Competitive Algorithm and Logistic Regression Classifier

引用

computers, Materials & Continua 2023年第11期77卷 1515-1534页

作者： Shabana R.Ziyad Liyakathunisa Eman Aljohani I.A.Saeed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah41411Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia

Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in *** can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five *** research study aims to develop an automated tool for diagnosing autism in *** computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification *** most deterministic features are selected from the self-acquired dataset by novel feature selection methods before *** Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this *** performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research *** experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired *** ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different *** Exploratory Data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.

关键词： Autism spectrum disorder feature selection imperialist competitive algorithm LASSO logistic regression random forest

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：