检索结果-内蒙古大学图书馆

A Novel Predictive Analysis and Classification of Land Subsidence Vulnerability Mapping based on GIS using Hybrid Optimized Machine Learning Techniques and Computer Vision 5

A Novel Predictive Analysis and Classification of Land Subsi...

引用

5th International Conference on Innovative Data communication Technologies and Application, ICIDCA 2024

作者： Senthil, G.A. Prabha, R. Arun, M. Sahaya Anselin Nisha, A. Prabu, S. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Land subsidence is a natural disaster caused by the extraction of the earth's material strength on the surface that causes the earth's surface to settle and sink. Land subsidence can also result in significant financial losses since it causes structural damage and expensive maintenance costs for roads, trains, barriers, pipelines, ground water, oil, natural gas, mines, and buildings. The Geographic Information System (GIS) and Google Earth Platform (GEP) can be used to map this land subsidence. This provides a comprehensive view of land pattern subsidence and its interactions with other geographical elements. It applies to both progressive subsidence caused by fluid extraction (groundwater, oil, or natural gas) and rapid surface subsidence caused by the collapse of an underground mine. Risk mitigation is the most effective way to deal with the experimental model. This research study focuses on the use of Predictive Data Analysis (PDA), a powerful environment in data analytics that provides a set of tools that make it easier to evaluate optimal models. The proposed model makes use of a dataset that includes specific land subsidence circumstances as input. The prediction result is achieved using a hybrid machine learning technique that combines Stochastic Gradient Descent (SGD) with Long Short-Term Memory (LSTM) and is compared to K-Nearest Neighbor (KNN), Generative Adversarial Network (GAN), and Artificial Neural Network (ANN). The proposed system employs LSTM and SGD as optimizers, resulting in a hybrid model that improves accuracy. Among the four algorithms investigated the LSTM-SGD network has the highest accuracy (97%), followed by KNN (96%), GAN (90%), and ANN (89%). Due to their ability to capture complicated nonlinear correlations in data and manage temporal dependencies, optimized hybrid ML models LSTM-SGD and KNN models appear to be particularly well-suited for land subsidence prediction. © 2024 The Authors. Published by Elsevier B.V.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Enhanced and Automatic Defect Detection in Thermography using Stacked Denoising Autoencoder 5

Enhanced and Automatic Defect Detection in Thermography usin...

引用

5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Vesala, G.T. Ghali, V.S. Lakshmi, A. Vijaya Wang, Fei Yerneni, Naga Prasanthi Malla Reddy University School of Engineering Department of Computer Science Engineering Hyderabad India Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Vijayawada India Amrita Sai Institute of Science and Technology Department of Electronics and Communication Engineering Paritala India Harbin Institute of Technology School of Mechatronics Engineering Harbin China

ISBN: (纸本)9798350376685

Active infrared thermography (AIRT) with frequency-modulated stimuli has emerged as a feasible and cost-effective non-destructive testing method for checking a variety of materials, with improved fault identification and depth resolution. However, assessing this non-stationary thermal response while reducing noise and inhomogeneous backgrounds is difficult in FMT. Conventional thermographic data analysis techniques use signal or image processing methods and supervised learning models, which are known for linear representation or feature extraction of thermographic data. This paper describes a stacked denoising autoencoder (SDAE) in frequency modulated thermography (FMT) that extracts non-linear information from temporal thermal profiles to improve flaw detection in a steel structure. The SDAE trains on temporal thermal profiles in order to generate denoised profiles at the other end. Furthermore, the latent space in the middle of the SDAE decreases dimensionality while highlighting flaws. Finally, the latent space of SDAE and local outlier factor (LOF) were coupled to form SDAE-LOF, which provided automatic fault detection in unsupervised learning passion. Experimentation results on a mild steel structure show an improvement in defect signature by reducing noise in temporal thermal profiles, and the proposed SDAE-LOF achieved an AUC of 0.81 in automatic defect detection. © 2024 IEEE.

关键词： Thermography (imaging)

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Stochastic Computing Architectures for Neural Network Performance in FPGA Systems 6

Energy-Efficient Stochastic Computing Architectures for Neur...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Dinesh Babu, A. Gomathy, C. Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9798331522667

This paper presents an energy-efficient stochastic computing (SC) architecture tailored for neural network applications on FPGA platforms. The proposed architecture integrates a Bayesian neural network (BNN) computation unit with a novel hybrid stochastic number generator (SNG) that combines Linear Feedback Shift Registers (LFSRs) and Halton sequences. This hybrid approach enhances the quality of generated bit-streams, significantly reducing correlation and improving the accuracy of SC-based computations. Stochastic computing allows complex arithmetic operations to be performed using simple logical gates, reducing hardware complexity and power consumption. Bayesian neural networks, due to their probabilistic framework, align well with the random nature of SC, allowing for resilient and efficient computations in resource-constrained environments. Experimental evaluations were conducted on an FPGA, measuring power consumption, computational accuracy, and resource utilization across varying bit-stream lengths. Results indicate that the proposed architecture achieves approximately 25% lower power consumption compared to conventional SC-based neural network implementations, with an improvement in mean squared error (MSE) accuracy. This energy efficiency and accuracy make the architecture particularly suitable for embedded systems, IoT devices, and other applications requiring low-power, high-performance neural network solutions. The proposed design offers a scalable solution for future neural network implementations in energy-constrained environments, contributing to advancements in sustainable hardware design. © 2025 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Exploring Spectral Indices for Improved Water Body Classification in Landsat Imagery 3

Exploring Spectral Indices for Improved Water Body Classific...

引用

3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Rajeswari, S. Rathika, P. Psn College of Engineering and Technology Tirunelveli India Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India

ISBN: (纸本)9798350361186

Accurate identification of water bodies in Landsat images is crucial for many applications such as environmental monitoring and water resource management. This paper analytically evaluates various spectral indices such as Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), New Water Index (NWI), Water Ratio Index (WRI) for water classification to enhance the accuracy of water classification in Landsat satellite images. This work systematically investigates the effectiveness of these spectral indices in different landscapes and environmental conditions. Its main objective is to identify optimal spectral indices that ensure a robust and accurate classification of water bodies. The results of this work will play a crucial role in advancing remote sensing applications that will significantly contribute to the accuracy of environmental research and monitoring initiatives. © 2024 IEEE.

关键词： Landsat

来源：评论

学校读者我要写书评

暂无评论

Intelligent Agricultural Drones Utilizing Nano-Fertilizer Dispensation for Precision Farming

Intelligent Agricultural Drones Utilizing Nano-Fertilizer Di...

引用

2024 International Conference on communication, Computing and Internet of Things, IC3IoT 2024

作者： Jayanthi, G. Nancy, W. Umamaheswari, B. Chithrakkannan, R. Sujith, R. Sathya Prasanna, S. Sri Sairam Engineering College Department Of Instrumentation And Control Engineering India Jeppiaar Institute Of Technology Department Of Electronics And Communication Engineering India Sri Sairam Engineering College Department Of Electronics And Communication Engineering India

ISBN: (纸本)9798350352689

An innovative approach to precision agriculture through the integration of artificial intelligence (AI) and nano-fertilizer technology in an agricultural drone system. The proposed system aims to optimize crop yield, reduce environmental impact, and enhance resource efficiency by employing advanced AI algorithms for crop health assessment and nano-fertilizers for targeted nutrient delivery. The drone's real-time decision-making capabilities, coupled with precise dispensation of nano-sized fertilizers, contribute to sustainable farming practices. Field trials demonstrate promising results in terms of improved crop productivity, resource utilization, and minimized environmental footprint. © 2024 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

引用

Computer Systems Science & engineering 2023年第5期45卷 1279-1294页

作者： G.Arunachalam P.SureshKumar Department of Electronics and Communication Engineering Gnanamani College of TechnologyNamakkalTamil NaduIndia Department of Electrical and Electronics Engineering Mahendra Engineering College(Autonomous)NamakkalTamil NaduIndia

The main components of Cognitive Radio networks are Primary Users(PU)and Secondary Users(SU).The most essential method used in Cognitive networks is Spectrum Sensing,which detects the spectrum band and opportunistically accesses the free white areas for different *** the free spaces helps to increase the spectrum *** the existing spectrum sensing techniques such as energy detectors,cyclo-stationary detectors suffer from various problems such as complexity,non-responsive behaviors under low Signal to Noise Ratio(SNR)and computational overhead,which affects the performance of the sensing *** algorithms such as Long-Short Term Memory(LSTM),Convolutional Neural Networks(CNN),and Recurrent Neural Networks(RNN)play an important role in designing intelligent spectrum sensing techniques due to the excellent learning ability of deep learning frameworks,but still require improvisation in terms of sensing accuracy under dynamic environmental *** paper,we propose the novel and hybrid CNN-Cuttle-Fish Optimized Long Short Term Memory(COLSTM),an improved version of LSTM that is well suited for the dynamic changes of environmental SNR with less computational overhead and *** proposed COLSTM based spectrum sensing technique exploits the various statistical features from spectrum data of PU to improve the sensing ***,the addition of shuttle-fish optimization in LSTM has reduced the computational overhead and complexity which in turn enhanced the sensing *** proposed methodology is validated on spectrum data acquired using RaspberryPi-RTLSDR experimental *** proposed spectrum sensing technique and the existing classical spectrum sensing techniques are *** results show that the proposed scheme has shown the brighter enhancement of performance under different SNR ***,the improvised performance has been achieved at low complexity and low computational over

关键词： Spectrum sensing cuttle-fish long short term memory raspberry pilow SNR convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Investigation of Cylindrical Gate All Around Field Effect Transistors Exploring Variation in Low Voltage Using Visual TCAD

Investigation of Cylindrical Gate All Around Field Effect Tr...

引用

2023 IEEE International Conference on Next Generation electronics, NEleX 2023

作者： Krishna, Raji Srujana, B. Jayanthi, D. Kavitha, K. Maurya, Naveen School of Electrical Engineering Vellore Institute of Technology Chennai Campus Chennai India Electronics and Communication Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Electronics and Communication Engineering V.S.B. Engineering College Karur India Electronics and Communication Engineering Vishu Institute of Technology Bhimavaram India

ISBN: (纸本)9798350319088

This paper conducts a comprehensive simulation study on the gate all around field effect transistor (GAAFET), specifically examining the impact of temperature variations at 300K and a doping concentration of 1 × 10-18cm-3. The device architecture has three fin structures, and gate material surrounds the channel. This configuration is strategically designed to mitigate the short-channel effect (SCE) and minimize electrostatic capacitance. The study meticulously assesses a range of critical parameters, including threshold voltage (Vth), Ion/Ioff ratio, drain-induced barrier lowering (DIBL), and sub-threshold swing (SS). The threshold voltage (Vth), or gate voltage necessary to start the transistor's conduction, of the GAAFET device under research is 0.37 volts (V). This device mainly shows that SS, a critical parameter that measures how fast the drain current changes about the subthreshold region's gate voltage, impacts a big part, about 78%, of the device's performance. Furthermore, 28% of the device's behaviour is explained by DIBL, which is the term used to describe the phenomenon of reduced threshold voltage brought on by short-channel effects. Additionally, it has been discovered that the device's Ion/Ioff current ratio is 2.35 × 10-7 μA, which represents the contrast between the device's "on"and "off"states. The device compares the performance of SS and DIBL in FinFET. Therefore, GAAFET is a suitable device with less leakage current. © 2023 IEEE.

关键词： Threshold voltage

来源：评论

学校读者我要写书评

暂无评论

VitiNet: Precision Grape Disease Identification with Deep Learning 3

VitiNet: Precision Grape Disease Identification with Deep Le...

引用

3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Rajan, K. Palanivel Raj, J. Relin Francis Krishnan, R. Santhana Shankar, S. Vijay Kumar, V. Vinoth Kavitha, S. Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331532420

Grape diseases pose a critical threat to vineyards worldwide, affecting both yield and crop quality. To address this, VitiNet, a deep learning-based algorithm utilizing EfficientNetB7, is designed for precise and scalable grape disease identification. The model focuses on five major grapevine diseases: Powdery Mildew, Downy Mildew, Anthracnose, Black Rot, and Esca. Two custom datasets, GrapeVine Dataset Dataset and VineHealthDataset, were curated to provide a diverse set of images for model training and evaluation. EfficientNetB7, known for its scalability and high performance in image classification, was fine-tuned by unfreezing the last five layers to allow for the extraction of disease-specific features. This fine-tuning enables the model to adapt to the complexities of grape disease identification while maintaining computational efficiency. The model’s implementation includes data augmentation techniques to enhance the diversity of the training set, while Stochastic Gradient Descent (SGD) and Categorical Cross-Entropy optimize the model’s learning process. While accuracy, precision, recall, and F1 score were evaluated on both datasets, the core strength of VitiNet lies in its ability to generalize well across varying environmental conditions and disease manifestations. VitiNet presents a significant advancement in precision viticulture, offering a reliable and automated solution for real-time disease monitoring. By enabling early and accurate disease identification, this model supports proactive disease management, ultimately improving vineyard productivity and sustainability. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Comparative Study of Breast Cancer Detection Using Histopathology Images 10

Comparative Study of Breast Cancer Detection Using Histopath...

引用

10th International Conference on Advanced Computing and communication Systems, ICACCS 2024

作者： Shiva Reshma, R. Pradeep, R. Prabhavathy, S. Tharanisrisakthi, B.T. Sri Shanmugha College of Engineering and Technology Department of Electronics and Communication Engineering Salem India Christ the King Engineering College Department of Electronics and Communication Engineering Tamilnadu India Christ the King Engineering College Department of Electrical and Electronics Engineering Tamilnadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Breast cancer continues to be a major health issue, necessitating efficient and accurate diagnostic methods for early detection. Deep learning approaches have recently showed significant potential in improving the accuracy and efficiency of breast cancer detection, notably in histopathology image processing. This review provides a thorough analysis of existing research on deep and machine-learning applications in breast cancer detection, highlighting the diverse diagnostic setups utilized in various studies. Deep learning is particularly promising in the detection of breast cancer because of its ability to provide accurate, automated, and early detection of potential malignancies, ultimately contributing to better patient outcomes and more effective healthcare practices as a result. The paper explores the range of techniques employed, with a specific focus on different classifiers that works on supervised learning. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Design and Energy Estimation of QCA Based Simple Data Path Selector Cum Router Unit for Nano communication

引用

China communications 2022年第11期19卷 231-240页

作者： Angshuman Khan Rajeev Arya Department of Electronics and Communication Engineering National Institute of Technology PatnaBihar 800005India Department of Electronics and Communication Engineering University of Engineering and ManagementJaipurRajasthan 303807India

Quantum dot cellular automata(QCA)is promising nanotechnology due to the three main advantages:faster speed,nanoscale size,and ultrasmall power *** paper proposed a simple data path selector cum router as the‘multiplexerchannel-demultiplexer’unit using QCA,an unavoidable building block of nano communication.A Simple 2×2 block and the extended 4×4 block of data path selectors have been proposed in this *** layouts of the proposed designs have been verified in QCADesigner,and the energy dissipation has been evaluated using two tools,QCAPro and QCQDesigner-E(QDE).The suggested designs reached a significant improvement in cell complexity(cell count)and covered area over the existing *** precise,the proposed 2×2(4×4)block shows 86%(63%)lower cell complexity and 87%(37%)smaller area than the prior reported similar *** addition,the currently reported 2×2(4×4)unit has 86%(60%)lower QDE based energy dissipation compared with prior reported designs.

关键词： nanocommunication quantum dot cellular automata router switch

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：