检索结果-内蒙古大学图书馆

FAMMNPB: Design of an Enhanced Forensic Analysis Model with Multidomain Feature Extraction and Advanced Neural Architecture for Permissioned Blockchains 9

FAMMNPB: Design of an Enhanced Forensic Analysis Model with ...

引用

9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Mate, Ashwini Y. Khandare, Kiran S. Hassan, Mohammad Bode, Kiran Nikesh Bode, Nikesh S. Missal, Karishma K. Nagpur India Nagpur India J D College of Engineering And Technology Department of Electronics And Telecommunication Nagpur India J D College of Engineering And Technology Department of Computer Science And Engineering Nagpur India Suryodaya College of Engineering And Technology Department of Computer Science And Engineering Nagpur India J D College of Engineering And Technology Department of Information Technology Nagpur India

ISBN: (纸本)9798350394474

This work introduces a novel approach that leverages multidomain feature extraction coupled with a 1D Convolutional Neural Network (CNN) for robust anomaly detection. To address the challenge of transaction delay predictions, we employ a Bi-directional Gated Recurrent Unit (BiGRU) RNN, for its capability to capture temporal sequences with high precision. For the task of chain splitting, the Teaching-Learning-based Optimization (TLbO) technique is used to ensure the efficient creation of sidechains. Further, we integrated DeepSHAP, in tandem with 1D CNN and BiGRU, to enhance the performance of our models, fostering better trust in the results. Our proposed methodology yields notable improvements over existing techniques. As we observed enhancements in the precision of anomaly detection by 4.9%, accuracy in detecting anomalous events by 3.5%, Recall by 4.5% and AUC by 3.9%. These advancements fortify the security posture of permissioned blockchains and optimize their operational efficacy, thus marking a significant improvement in blockchain forensics. © 2024 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Under water motion tracking and monitoring using wireless sensor network and Machine learning

引用

Materials Today: Proceedings 2023年 80卷 3511-3516页

作者： Gite, Pratik Shrivastava, Anurag Murali Krishna, K. Kusumadevi, G.H. Dilip, R. Manohar Potdar, Ravindra Department of Computer Science and Engineering IPSA-IES Indore India Lakshmi Narain College of Technology and Science Indore Madhya Pradesh India Department of Electronics and Communication Engineering Vignan's Institute of Information Technology Duvvada Andhra Pradesh Visakhapatnam India Department of Electrical and Electronics Engineering Acharya Institute of Technology Bangalore560107 India Department of Mechatronics Engineering Acharya Institute of Technology Karnataka Bengaluru560107 India Department Of Electronics & Telecommunication Engieering Bhilai Institute Of Technology Chhattisgarh Durg India

Wireless Sensor Networks (WSNs) plays an indispensable role in different application and development in new generation technology. In 5G technology, one of the essential features is the connectivity with registered and unregistered networks to promote remote automation. By which the technology can be harnessed at its best and scaled in multi-dimensional applications. These applications are also enabled for diverse motion detection and object tracking applications. In this presented work, an underwater object tracking technique has been proposed, which enable the prediction of the mobile sensor node. The method involves a simulation which demonstrates a 2D-UASN architecture of underwater scenario and in this network using a different number of nodes using random positioning static sensor nodes are deployed. Finally, a mobile sensor node with random mobility is introduced into the network. The approach follows statistical analysis to compute the next possible position of the node. Based on experiments, the accuracy of the predictive methodology is measured in two different scenarios. In the first scenario, the number of nodes is variable, and the simulation area kept fixed size, i.e., 1000X1000. Additionally, in the second scenario, the number of nodes remains fixed, and the simulation area is variable. The results demonstrate that when we deploy sensor nodes randomly in a large area and network node density is less, the detection accuracy significantly affects and demonstrates low accuracy compared to dense area networks. Secondly, the prediction trend from dense to less dense area demonstrates the reducing accuracy. © 2021

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Advancing Liver Cancer Diagnosis: Innovative Deep Learning Algorithms for Segmentation and Classification

Advancing Liver Cancer Diagnosis: Innovative Deep Learning A...

引用

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

作者： Suganthi, B. Vidhya, U. Dhanalakshmi Srinivasan University School of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Dhanalakshmi Srinivasan University School of Engineering and Technology Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798331541170

Liver cancer is a significant worldwide health issue that requires precise diagnosis techniques and efficient treatment options. This paper presents sophisticated deep learning (DL) algorithms for the segmentation and classification of liver cancers, providing a thorough comparative comparison. The advancements in DL have led to significant progress in other domains. As a result, there is a rising excitement for using DL techniques in liver research to aid clinicians in diagnosing and treating medical conditions. The Improved Fully Convolutional Neural Network (IFCNN) has been introduced in this study as a solution to the problem of liver tumor segmentation in cases of liver cancer. The IFCNN has been mathematically designed to address this issue. Transfer learning improves the ability to apply knowledge gained from one patient group to other patient groups with different characteristics. Comparative studies performed against ResNet and VGG16 demonstrate higher sensitivity, specificity, and overall classification metrics. The algorithms' efficacy and real-time processing capabilities have been extensively validated on multiple datasets, including different kinds and stages of liver cancer. This paves the way for eventual incorporation into clinical processes. These advanced algorithms exhibit superior precision and efficiency compared to current models, offering significant potential for improving clinical practice. They provide more dependable tools for precise diagnosis and informed treatment planning in the fight against liver cancer. © 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

Development of an Ontology-Based Technique for Labeling Land Cover Classes with Minimum Utilization of SAR Features

引用

SN computer Science 2023年第6期4卷 731页

作者： Gupta, Shruti Singh, Dharmendra Kumar, Sandeep Computer Science and Engineering and Information Technology Department Jaypee Institute of Information Technology Noida India Electronics and Communication Engineering Department Indian Institute of Technology Roorkee Roorkee India Computer Science and Engineering Department Indian Institute of Technology Roorkee Roorkee India

The availability of satellite imagery to "domain" experts, along with advancements in image processing and analysis techniques, has revolutionized numerous fields, enabling better understanding, planning, and management of our planet's resources. However, formalizing the knowledge gained from domain experts is essential for preserving, sharing, and leveraging their expertise. In this context, knowledge representation methods like ontology play a significant role in the development of applications based on satellite image analysis. Land cover labeling is one of the significant applications of satellite image analysis and plays a vital role in various domains by providing valuable information about the Earth's surface. Although several works have been reported to focus on land cover land use classification and labeling, very few are properly documented or formalized. Therefore, in this paper, an LCL (land cover labeling) ontology has been proposed for labeling the land cover classes using PALSAR-I satellite data. The ontology is based on an adaptive algorithm, as the labeling criterion is independent of specific range values and depends on feature image statistics. For algorithm development, four types of features, namely, polarimetric features, texture features, color features, and wavelet features were examined. For selecting optimal feature set, random forest was utilized and, consequently, for further labeling the classes a set of rules has been formed by applying Otsu thresholding on the selected class-wise feature sets. The derived rules were finally formalized to develop the ontology for labeling the land cover classes. The proposed ontology was applied to distinct study sites using PALSAR-I data which resulted in a satisfactory classification accuracy of around 85%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Color features Ontology PALSAR Polarimetric features Random forests Texture features Wavelet features

来源：评论

学校读者我要写书评

暂无评论

Optimizing Sheep Breed Classification with Bat Algorithm-Tuned CNN Hyperparameters

引用

SN computer Science 2024年第2期5卷 219页

作者： Ravikiran, H.K. Jayanth, J. Sathisha, M.S. Bindu, K. Department of Electronics and Communication Engineering Navkis College of Engineering Karnataka Hassan 573217 India Department of Electronics and Communication Engineering GSSSIETW Karnataka Mysuru 570016 India Department of Computer Science and Engineering Navkis College of Engineering Karnataka Hassan 573217 India Department of Electronics and Communication Engineering Global Academy of Technology Karnataka Bengaluru 560098 India

Efficiently classifying sheep breeds through image analysis is pivotal in modern animal husbandry, influencing critical management and breeding decisions. This study delves into automating this process by harnessing Convolutional Neural Networks (CNNs), with a particular focus on optimizing key hyperparameters—the learning rate and dropout rate—essential for refining model performance. Manual hyperparameter tuning is often time-consuming and demands expertise. To overcome this challenge, we introduce an innovative approach that utilises the Bat algorithm, a bio-inspired optimization technique. This algorithm mimics bat echolocation behaviors, skillfully navigating the complex hyperparameter search space to determine optimal values. By dynamically adjusting CNN hyperparameters, our research aims to boost classification accuracy while simplifying the tuning process. Empirical results highlight significant gains in classification accuracy and emphasizes the Bat algorithm's efficacy. The optimized CNN model, empowered by fine-tuned hyperparameters, demonstrates superior performance, promising practical applicability in real-world sheep breed classification scenarios. This study meticulously adjusts pulse rate and loudness, revealing an optimal combination of [0.001, 0.24404868], which substantially improves model performance. The findings emphasize the Bat algorithm's role in streamlining hyperparameter tuning and its potential impact on automated sheep breed classification. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： BAT algorithm Classification CNN Dropout Hyperparameter tuning Learning rate

来源：评论

学校读者我要写书评

暂无评论

Flexible Ring Slot Antenna for Optimized 5G Performance in N77 and N78 Frequency Bands for Wearable Applications

引用

Progress in Electromagnetics Research C 2024年 150卷 47-55页

作者： Soni, Gaurav Kumar Yadav, Dinesh Kumar, Ashok Sharma, Chanchal Yadav, Manish Varun Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Electronics and Communication Engineering Government Mahila Engineering College Rajasthan Ajmer India Department of Computer Science Engineering in Artificial Intelligence and Machine Learning Saraswati College of Engineering Maharashtra Navi Mumbai India Department of Aeronautical and Automobile Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

The growth of 5G communications has created a demand for advanced wearable and flexible antennas due to supporting the high speeds, low latency, and capability of mechanical deformation conditions such as bending and conformability. In this paper, the design and analysis of a defected ground structure (DGS)-based ring slot antenna for N77 (3.3–4.2 GHz) and N78 (3.3–3.8 GHz) frequency bands is demonstrated. The antenna is made of an RT/Duroid 5880 substrate and has a loss tangent (tan δ) of 0.0009 and dielectric constant (Εr) of 2.2. A DGS-based ring slot microstrip antenna is simulated, tested, and experimentally characterized on different body locations (leg, chest, and hand) along with bending, and their results are presented accordingly. The magnitude of S11 (|S11|) of the proposed antenna is −26.81 dB at resonant frequency of 3.45 GHz, with the impedance bandwidth of 22 MHz (3.486 GHz to 3.508 GHz), peak gain of 6.27 dBi, and radiation efficiency of 85.02%. The simulated specific absorption rate (SAR) for 1 g and 10 g human body tissues is 0.263 W/Kg and 0.076 W/Kg, respectively. The total volume of the antenna is 0.58λ0 × 0.58λ0 × 0.00595λ0 (at 3.5 GHz). The proposed antenna is suitable for 5G wearable devices. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Microstrip antennas

来源：评论

学校读者我要写书评

暂无评论

Identification of Medicinal Plants and Related Fungal Diseases based on Deep Learning Model 6

Identification of Medicinal Plants and Related Fungal Diseas...

引用

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

作者： Kani, Mathina M. Mahalakshmi, R. Shunmugavel, V. Kareem, Abdul M. Department of Computer Science and Engineering Sethu Institute of Technology Virudhunagar India Department of Electrical and Electronics Engineering Quantanics TechServ pvt Ltd Madurai India

ISBN: (纸本)9798331522667

In today’s technological landscape, manual methods are increasingly being replaced by automated computer systems, streamlining tasks and enhancing efficiency. Within the realm of agricultural research, two critical areas plant identification and disease classification have shifted toward computerized solutions. With millions of plant species globally, medicinal plants hold particular significance due to their healing properties in traditional medicine. These readily accessible and culturally important remedies not only contribute to affordable healthcare but also sustain livelihoods in indigenous and rural communities. To address these challenges, our study evaluates the performance of two widely used pre-trained deep learning models U-Net and F-Net. By comparing their accuracy levels, we aim to enhance disease detection. Additionally, we present performance metrics such as accuracy and error rates, visualized through graphs. Our user interface (UI) leverages Kivy, an open-source multiplatform GUI development library for Python. Kivy enables cross-device app development, emphasizing code reusability and rapid prototyping, making it a valuable tool for interaction design across iOS, Android, Windows, OS X, and GNU/Linux platforms. © 2025 IEEE.

关键词： Medicinal chemistry

来源：评论

学校读者我要写书评

暂无评论

Resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication 3rd

Resource Allocation in 6G Network for High-Speed Train Using...

引用

3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Garg, Aakansha Arya, Rajeev Singh, Maheshwari Prasad Wireless Sensor Network Lab Department of Electronics and Communication Engineering Patna India Department of Computer Science and Engineering National Institute of Technology Patna India

ISBN: (纸本)9789819743582

The growth of 6G results in rising data transmission rate in static scenario. To achieve fast communication and computation with quality service in high-speed train is a challenge. This is due to fast handover and Doppler shift. To effectively communicate in high-speed train, the placement of a mobile relay (MR) with small server at the rooftop of train coach is proposed. The base station sends channel state information to mobile relay which stores it in the stack. The resource assignment has been conducted by an improved grey wolf optimization algorithm. The focus is at improving throughput with reduction in latency and energy consumption. The simulation outcomes manifests that the presented algorithm carry-out finer in comparison of the Hungarian algorithm. The accomplished results are refined almost three times in respect of energy consumption, latency, and throughput. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Marine Robot for Underwater Surveillance

Marine Robot for Underwater Surveillance

引用

2025 IEEE International Students' Conference on Electrical, electronics and computer Science, SCEECS 2025

作者： Vishnu Gowda, H.P. Reddy, Deepa N Gowda, Tejas B Shrikala, R. Yogitha, H.N. Bms Institute of Technology and Management Student of Department of Electronics and Communication Engineering Bengaluru560064 India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Bengaluru560064 India Bms Institute of Technology and Management Student of Department of Computer Science Engineering Bengaluru560064 India

ISBN: (纸本)9798331529833

Underwater surveillance has become a crucial area of research, particularly for marine exploration and environmental monitoring, with marine robots playing a vital role. These robots, equipped with sensors and cameras, capture real-time data and images from underwater environments, reducing the need for human intervention in hazardous areas. However, a major challenge in this field is developing reliable communication networks, as underwater environments face limitations in bandwidth, range, and signal attenuation, hindering real-time data transmission. This paper presents a simple marine robot prototype based on the Raspberry Pi Model 4B, utilizing Electronic Speed Controllers (ESCs) and brushless motors for efficient movement. The robot's body is manually designed using 3D printing technology, with PETG (polyethylene terephthalate glycol) material chosen for its water resistance, durability, and buoyancy. The design ensures the robot's stability and functionality underwater, offering a cost-effective solution for marine robotics while underscoring the need for improved underwater communication technologies. © 2025 IEEE.

关键词： Marine communication

来源：评论

学校读者我要写书评

暂无评论

Improving Tomato Disease Diagnosis Through ResNet50: An Innovative Approach 4

Improving Tomato Disease Diagnosis Through ResNet50: An Inno...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Dhamotharan, R. Raj, J. Relin Francis Krishnan, R. Santhana Kavitha, S. Soundiraraj, N. Kumar, V. Vinoth Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331540364

Tomato crops are vulnerable to numerous diseases that can severely affect both yield and quality. Early and precise identification of these diseases is indispensable for implementing active management and intervention measures. This research introduces an advanced approach to tomato disease detection by employing the ResNet50 architecture, pretrained on the ImageNet dataset. Through transfer learning, the ResNet50 model leverages its pre-learned features to classify tomato plant images affected by diseases such as Tomato Mosaic Virus (ToMV), Bacterial Spot, Blossom End Rot, Early Blight, and Late Blight. The ResNet50 model is fine-tuned with custom classification layers tailored to the specific diseases, allowing it to achieve high accuracy in disease detection. Preliminary results indicate that the ResNet50 model significantly outperforms traditional machine learning approaches, demonstrating its efficacy in accurately diagnosing tomato diseases. This study provides an effective solution for improving disease detection in tomatoes and highlights the transformative role of deep learning in advancing agricultural practices, fostering sustainability, and enhancing productivity. © 2024 IEEE.

关键词： Fruits

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：