检索结果-内蒙古大学图书馆

Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR)in WSNs

computer Systems science & Engineering 2023年第1期44卷 99-112页

作者： D.Loganathan M.Balasubramani R.Sabitha S.Karthik Department of Electronics and Communication Engineering Info Institute of EngineeringCoimbatore641107India Department of Computer Science and Engineering SNS College of TechnologyCoimbatore641035India

Sensors are considered as important elements of electronic *** many applications and service,Wireless Sensor Networks(WSNs)are involved in significant data sharing that are delivered to the sink node in energy efficient man-ner using multi-hop ***,the major challenge in WSN is the nodes are having limited battery resources,it is important to monitor the consumption rate of energy is very much ***,reducing energy con-sumption can increase the network lifetime in effective *** that,clustering methods are widely used for optimizing the rate of energy consumption among the sensor *** that concern,this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR),which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member *** providing equal rate of energy consumption among nodes,the dimensions of framed clusters are ***,the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base *** evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head ***,the model also provides higher throughput and minimal delay in delivering data packets.

关键词： Wireless sensor networks energy consumption load balanced clustering finest routing

来源：评论

学校读者我要写书评

暂无评论

Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images

Prompting DirectSAM for Semantic Contour Extraction in Remot...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Miao, Shiyu Chen, Delong Liu, Fan Zhang, Chuanyi Gu, Yanhui Guo, Shengjie Zhou, Jun College of Computer Science and Software Engineering Hohai University Nanjing China Hong Kong University of Science and Technology Hong Kong School of Computer and Electronic Information Nanjing Normal University Nanjing China School of Information and Communication Technology Griffith University QLD Australia

ISBN: (纸本)9798350368741

The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where semantic contour extraction-such as identifying buildings, road networks, and coastlines-holds significant practical value. Those applications are currently handled via training specialized small models separately on small datasets in each domain. We introduce a foundation model derived from DirectSAM, termed DirectSAM-RS, which not only inherits the strong segmentation capability acquired from natural images, but also benefits from a large-scale dataset we created for remote sensing semantic contour extraction. This dataset comprises over 34k image-text-contour triplets, making it at least 30 times larger than individual dataset. DirectSAM-RS integrates a prompter module: a text encoder and cross-attention layers attached to the DirectSAM architecture, which allows flexible conditioning on target class labels or referring expressions. We evaluate the DirectSAM-RS in both zero-shot and fine-tuning setting, and demonstrate that it achieves state-of-the-art performance across several downstream benchmarks. © 2025 IEEE.

关键词： Remote sensing Semantic contour extraction Vision-language learning

来源：评论

学校读者我要写书评

暂无评论

Comparative Evaluation of Deep Learning Models in Alzheimer's Disease Diagnosis 3

Comparative Evaluation of Deep Learning Models in Alzheimer'...

引用

3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Arya, Leena Sharma, Yogesh Kumar Nayak, Smitha Sivakumar, Janaki Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur522302 India Computing Department Muscat College Oman Programme Leader Computer Security and Forensics Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

Alzheimer's Disease (AD) is an ever-evolving neurodegenerative confusion that influences the neurons of the mind in a manner that hinders the patients' ability to solve problems and remember issues and that requires timely and proper identification to enable appropriate management and treatment. Based on MRI medical images, deep learning models have become one of the most effective approaches for analyzing the signs of the early stages of AD. This research, therefore, carries out a comparative analysis of five deep learning models such as Convolutional Neural Networks (CNNs) like AlexNet, VGGNet, and ResNet, as well as Long Short-Term Memory (LSTM) networks and 3D Convolutional Neural Networks (3D-CNNs) in the finding of Alzheimer's Sickness from medical imaging data. Each model's arrangement parameters, for example, layer configurations, learning rate, batch size, optimizer, number of epochs, loss function, applying techniques for regularization, activation functions, and the input dimensions, using pre-trained weights, were carefully initialized to yield a proper comparison between the models. Diagnostic performances were evaluated utilizing precision, responsiveness(recall), specificity, and region under the receiver operating characteristic curve (AUC-ROC). ResNet outperformed other profound learning models in diagnosing Alzheimer's Disease from MRI scans with a precision of 0.91 and AUC-ROC of 0.95. At the same time, VGGNet, AlexNet, LSTM, and 3D-CNNs demonstrated varying strengths and limitations in balancing accuracy, computational efficiency, and feature extraction. © 2025 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Post-Quantum Cryptography Driven Key Exchange Scheme for Cloud Computing Environments 6th

A Hybrid Post-Quantum Cryptography Driven Key Exchange Schem...

引用

6th International Conference on Intelligent Sustainable Systems, ICISS 2023

作者： Ilias, Shaik Mohammad Sharmila, V. Ceronmani Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819917259

In connection with Post-Quantum Cryptography (PQC), it is indispens-able to upgrade cloud data security schemes and also key exchange schemes. The former is for data security while the latter is for secure key exchange in untrusted channels. This paper focuses on proposing a hybrid key exchange scheme for secure key exchange to withstand PQC threats. The hybrid key exchange scheme is known as Elliptic Curve Supersingular Isogeny Diffie-Hellman (ECSIDH) which is made up of two existing key agreement schemes known as Elliptic-Curve Diffie-Hellman (ECDH) and Supersingular Isogeny Diffie-Hellman (SIDH). By combining these two schemes, the proposed scheme becomes highly secure as it strengthens PQC candi-date SIDH with classical primitive ECDH. SIDH is a key exchange scheme that could establish secret key, over untrusted channel, between two parties. It was designed to be one of the PQC candidates but failed to withstand PQC attack scenarios. Elliptic Curve Supersingular Isogeny Diffie-Hellman (ECSIDH) is designed to overcome this problem by combining SIDH and ECDH. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Geometry

来源：评论

学校读者我要写书评

暂无评论

Abnormality Detection of Retinal Images Using Dual Autoencoder 21

Abnormality Detection of Retinal Images Using Dual Autoencod...

引用

21st IEEE India Council International Conference, INDICON 2024

作者： Karthikayan Nagarajan, P. Bairwa, Sachin Kumar Lakshmi Priya, A. Jayaraman, Umarani Indian Institute of Information Technology Design and Manufacturing Department of Computer Science and Engineering Kancheepuram Chennai India Psg College of Technology Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350391282

This paper presents a dual autoencoder model for abnormality detection from retinal fundus images. In this paper, the dual autoencoder along with (Kernal Density Estimation) KDE is used to improve the result as compared to a single autoencoder with MSE for disease classification. The proposed model makes use of an autoencoder because it can be trained on unlabeled data. It is a good fit for abnormality detection scenarios in which access to labeled data may be limited or costly. It is useful to have a model that does not require labeled examples of abnormalities because abnormalities are usually rare and diverse. Dual autoencoders combined with Kernel Density Estimation (KDE) offer a powerful framework for abnormality detection by leveraging the complementary strengths of both techniques. The dataset used in this research is the Ocular Disease Intelligent Recognition (ODIR) which is a structured ophthalmic database with seven classes of abnormal covering most of the common retinal eye diseases. The evaluation criteria used in this work include MSE for reconstruction error and accuracy for abnormality detection. © 2024 IEEE.

关键词： Abnormality Detection Disease Classification Dual Autoencoder Eye Disease ODIR Dataset Retinal Images

来源：评论

学校读者我要写书评

暂无评论

A Critical Analysis and Classification of Breast Cancer Using Histopathology Images

A Critical Analysis and Classification of Breast Cancer Usin...

引用

2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Singh, Pritpal Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Department of Cse Punjab 140413 India National University of Science and Technology Faculty of Computer Science and Mathematics College of Technical Engineering Dept of Computer Science Dhi Qar Iraq

ISBN: (纸本)9798350359688

In Breast Cancer ML is instrumental in early detection through the analysis of mammographic and histopathological images, assessing individual risk factors, personalizing treatment plans based on genomic data, and providing decision support for healthcare professionals. ML also contributes to post-treatment monitoring, integrates diverse datasets for comprehensive insights, and accelerates research and drug discovery efforts. Overall, it enhances the accuracy, efficiency, and personalized nature of breast cancer care across the entire healthcare continuum. The paper presents a comparative review of recent techniques that use histopathological images for breast cancer classification. The study synthesizes findings from a range of research efforts employing diverse methodologies, including deep learning, ensemble techniques, and innovative model architectures. Researchers have explored the application of artificial neural networks, convolutional neural networks, and transfer learning in increasing the accuracy and objectivity of breast cancer classification. Additionally, novel approaches such as ViT-AMCNet, Ensembled Transfer Learning (ETL), and Curriculum Feature Alignment Network (CFAN) have been introduced, each contributing to the evolving landscape of histopathology image analysis. This review also includes a case study based on the EfficientNetB3 architecture that achieved a 98% accuracy in the diagnosis of breast cancer, with remarkable recall values of 0.95 and 0.97 for positive and negative cases, respectively, along with precision rates of 0.96 for positive cases and 0.95 for negative cases. The review highlights the significance of these approaches in addressing challenges related to feature extraction, nuclei segmentation, and classification accuracy, ultimately advancing our understanding of breast cancer pathology through the lens of computational methodologies. © 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

Multisensor information Fusion for Condition Based Environment Monitoring

引用

Intelligent Automation & Soft Computing 2023年第4期36卷 1013-1025页

作者： A.Reyana P.Vijayalakshmi Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641050India Department of Electronics and Communication Engineering Hindusthan College of Engineering and TechnologyCoimbatore641050India

Destructive wildﬁres are becoming an annual event,similar to climate change,resulting in catastrophes that wreak havoc on both humans and the *** result,however,is disastrous,causing irreversible damage to the *** location of the incident and the hotspot can sometimes have an impact on earlyﬁre detection *** the advancement of intelligent sen-sor-based control technologies,the multi-sensor data fusion technique integrates data from multiple sensor *** primary objective to avoid wildﬁre is to identify the exact location of wildﬁre occurrence,allowingﬁre units to respond as soon as *** to predict the occurrence ofﬁre in forests,a fast and effective intelligent control system is *** proposed algorithm with decision tree classiﬁcation determines whetherﬁre detection parameters are in the acceptable range and further utilizes a fuzzy-based optimization to optimize the complex *** experimental results of the proposed model have a detection rate of ***,providing real-time monitoring of certain environ-mental variables for continuous situational awareness and instant responsiveness.

关键词： Decision tree communication wildﬁre data fusion wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

COVID-19 Improved Diagnoses Based on the Open-morphology Filter and Deep-learning

引用

Recent Patents on Engineering 2022年第3期16卷 122-130页

作者： Younus, Majid D. Zedan, Mohammad J. M. Malallah, Fahad L. Saeed, Mustafa G. Computer and Information Engineering Department College of Electronics Engineering Ninevah University Mosul Iraq Computer Science Dept. College of Science Cihan-Slemani University Slimani Iraq

Background: Coronavirus (COVID-19) has appeared first time in Wuhan, China, as an acute respiratory syndrome and spread rapidly. It has been declared a pandemic by the WHO. Thus, there is an urgent need to develop an accurate computer-aided method to assist clinicians in identifying COVID-19-infected patients by computed tomography CT images. The contribution of this paper is that it proposes a pre-processing technique that increases the recognition rate compared to the techniques existing in the literature. Methods: The proposed pre-processing technique, which consists of both contrast enhancement and open-morphology filter, is highly effective in decreasing the diagnosis error rate. After carrying out pre-processing, CT images are fed to a 15-layer convolution neural network (CNN) as deep-learning for the training and testing operations. The dataset used in this research has been publically published, in which CT images were collected from hospitals in Sao Paulo, Brazil. This dataset is composed of 2482 CT scans images, which include 1252 CT scans of SARS-CoV-2 infected patients and 1230 CT scans of non-infected SARS-CoV-2 patients. Results: The proposed detection method achieves up to 97.8% accuracy, which outperforms the reported accuracy of the dataset by 97.3%. Conclusion: The performance in terms of accuracy has been improved up to 0.5% by the proposed methodology over the published dataset and its method. © 2022 Bentham science Publishers.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Predictive Factors Influencing Discharge Destination Following Spinal Cord Injury 1

Predictive Factors Influencing Discharge Destination Followi...

引用

1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Kaviarasu, K. Dinesh, K. Karthi, P. K S Rangasamy College of technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371802

SCI is a crippling illness that can significantly lower a person's quality of life. The severity and location of the injury determine the extent of functional disability experienced by the patient. Consequently, SCI patients often require an extended hospital stay for both acute care and rehabilitation. In our research, we have proposed the use of Gradient boosting, a machine learning algorithm, to accurately predict the likelihood of specific events. This algorithm constructs a series of models, each aiming to correct the errors of the previous one. Gradient boosting has shown promise in forecasting a number of outcomes, such as the location of discharge following a spinal cord injury. To predict the likelihood of SCI patients being released to non-routine settings, like long-term acute care hospitals or skilled nursing institutions, we used a gradient boosting technique in our study. Our model incorporated several predictive factors, including injury severity, level of injury, completeness of injury, age, social support, and insurance coverage. Impressively, our model achieved a commendable level of accuracy in predicting the discharge destination. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

MetaCo-Distillation: A Robust Collaborative Distillation Framework for Multivariate Knowledge 22

MetaCo-Distillation: A Robust Collaborative Distillation Fra...

引用

22nd IEEE International Conference on Smart City, SmartCity 2024

作者： Wang, Jianing Liu, Jiqiang Zhang, Tao Wang, Jian Tang, Xiangyun Sun, Geng Yang, Zhaohui Han, Zhu Niyato, Dusit School of Cyberspace Science and Technology Beijing Jiaotong University Beijing China School of Information Engineering Minzu University of China Beijing China College of Computer Science and Technology Jilin University Changchun China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering University of Houston HoustonTX United States School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798331540487

Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, has shown great potential in the emerging Metaverse areas. Although numerous studies have theoretically demonstrated the feasibility of FL, practical applications in the Metaverse face challenges such as learning forgetting due to non-independent and identically distributed (non-i.i.d.) data, high communication overhead from model parameter transmission, and the inability to accommodate heterogeneous models. To address these challenges, this paper proposes a collaborative distillation framework called Co-distillation. In this distributed environment, each device independently trains its local model using private data, communicates predictions on an unlabeled public dataset, and generates surrogate labels for the public dataset based on received predictions. These surrogate labels, along with the received predictions, serve as teacher logits for local model distillation. This approach is more adaptable to heterogeneous streaming data continuously generated by industrial devices, offers higher communication efficiency than traditional methods, allow for heterogeneous models. Experiments have been conducted, demonstrating the superior performance of the proposed framework. © 2024 IEEE.

关键词： Federated 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：