检索结果-内蒙古大学图书馆

A Developer-Focused Genetic Algorithm for IoT Application Placement in the Computing Continuum

IEEE Transactions on Services Computing 2025年第03期18卷 1185-1198页

作者： Herrera, Juan Luis Moya, Alejandro Berrocal, Javier Murillo, Juan Manuel Navarro, Elena University of Bologna Department of Computer Science Engineering Italy University of Castilla-La Mancha LoUISE Research Group Spain University of Extremadura Department of Computer Science and Communications Engineering Spain

The rise of the Internet of Things (IoT) paradigm has led to an interest in applying it not only in tasks for the general public but also to stringent domains such as healthcare. However, the developers of these next-generation IoT applications must consider additional non-functional requirements related to the criticality of the processes they automate, such as low response times or low deployment costs, as well as technical constraints, which include organizational, legal and policy-related constraints on where data can be processed or stored. While the Computing Continuum paradigm emerges as a valuable alternative for placing such applications, identifying the deployments that satisfy all these requirements becomes a tough challenge. The NP-hard nature of the problem makes it impractical to manually find such a deployment, and traditional approaches fail to consider the technical constraints. In this paper, we present the Genetic Algorithm for Application Placement (GAAP), an evolutionary computing-based meta-heuristic designed to help IoT application developers find deployments that satisfy their Quality of Service, business and technical constraints. Our evaluation of an Internet of Medical Things use case shows that GAAP supports larger scenarios than traditional approaches and gives IoT application developers more options while providing better scalability. © 2008-2012 IEEE.

关键词： Internet Of Things Microservice Architectures Quality Of Service Costs Time Factors Electrocardiography Biomedical Monitoring Blood Pressure Genetic Algorithms Business Internet Of Things Computing Continuum Quality Of Service Service Placement Deployment Genetic Algorithm Internet Of Things Internet Of Things applications Response Time Service Quality Technical Constraints Deployment Cost Low Response Time Internet Of Medical Things Heuristic Objective Function Replica Decision Variables Solution Space Quality Of Experience Internet Of Things Devices Pareto Front Multi Objective Algorithm Capital Expenditure Crossover Operator Quality Of Service Requirements Operational Expenditure Set Of Hosts Workflow Dijkstras Algorithm Non Dominated Solutions Average Response Time Pareto Optimal Solutions Response Cost Multi Objective Genetic Algorithm

来源：评论

学校读者我要写书评

暂无评论

Maximizing Data Privacy: Empirical Analysis of Multi-Keyword Data Search and Privacy Protection over Cloud Computing Paradigm 1

Maximizing Data Privacy: Empirical Analysis of Multi-Keyword...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Prakash, V.S. Karthikeyan, N. Kanagaraj, A. Shriidhar, P.J. Viiayakumar, P. Kristu Jayanti College Department of Computer Science Bengaluru India VMKV Engineering College Department of Computer Science Engineering Tamil Nadu Salem India Kongu Engineering College Department of Computer Applications Tamilnadu Erode India

ISBN: (纸本)9798350361780

In the realm of cloud computing, querying on secured information has emerged as a technology of critical importance. These kinds of searches make it possible for the owner of the data to search through encrypted data that is kept in the cloud without revealing any pertinent information. Numerous strategies, the most of which concentrate on conjunctive and disjunctive keyword searches, have been presented by researchers in an effort to improve the quality of the search experience. On the other hand, a disjunction will provide an excessive number of results, but a conjunction of all the keywords could only produce a small number of results. It is tough to customize the relevance of the keywords given the present schemes, which makes it difficult to acquire the results that are required. For the purpose of resolving these issues, we offer a unique technique that we name the Secured Multiple Keyword Searching Scheme (SMKSS). This scheme allows for the search to be conducted with the user specifying the amount of keywords that are included in the search result. A cross-validation is performed between the proposed scheme and the traditional Ranked Keyword Searching Model (RKSM) in order to determine how effective the novel scheme is. This particular quantity, n, can be utilized to personalize the relevance of the term. Furthermore, as a consequence of this, the owner of the data might acquire the desired search results that include any n terms from a keyword collection. Not only does the proposed system allow the conventional disjunctive and conjunctive keyword searches, but it also supports these searches when n equals one or the size of the keyword collection, respectively. There is a possibility that the keyword will be critical. Initially, we provide a formal definition of its security, and then we demonstrate that the proposed scheme is safe against the adaptive selected keyword attack in the standard model, and that it is also capable of providing some degree of def

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

An Anomaly Detection Enhancement Method Using Interpretable Unsupervised Machine Learning in Industrial Information Systems at Multivariate Time Series Environment

引用

Journal of Institute of Control, Robotics and Systems 2024年第3期30卷 245-252页

作者： Jeon, Janggun Kim, Namgi Department of Computer Science Kyonggi University Korea Republic of Department of AI Computer Engineering Kyonggi University Korea Republic of

Time series anomaly detection is a technology that finds outliers in observed data over time, and is a significant research field associated with many applications or platforms. In this paper, we propose a method called Encoded-IF, a new and improved version of the isolation forest (IF) machine learning algorithm using Auto-Encoder to detect time series anomalies. Encoded-IF is a non-parametric and unsupervised method for detecting anomalies in time series data. It efficiently detects anomalies in each dimension of high-dimensional data and quickly learns nonlinear correlations between dimensions. Experiments were conducted on four publicly available time series data sets to evaluate the performance of the proposed method. The results improvement in performance of up to 4%. © ICROS 2024.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Natural Language Processing (NLP) based Phonetic Insights for Improving Voice Recognition and Synthesis 4

Natural Language Processing (NLP) based Phonetic Insights fo...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Priyadharshini, N. Subbulakshmi, N. Kalasalingam Academy of Research and Education Department of Computer Applications Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Computer Science and Engineering Krishnankoil India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Voice analysis in Natural Language Processing (NLP) involves the processing of spoken language to develop applications such as speech recognition, synthesis, and speaker identification. This survey explores the phonetic intricacies of Tamil and other Dravidian languages, examining their impact on NLP. The extensive phonetic inventory of the Tamil Language is characterized by its complex vowel system, retroflex consonants, and the distinction between aspirated and unaspirated stops, which presents unique challenges and opportunities for computational linguistics. Phonetics helps to process the human language both accurately and naturally and it works as a bridge between the spoken and written communication forms. This paper emphasizes the necessity of developing specialized phonetic modeling techniques and robust algorithms. Additionally, it highlights the importance of creating comprehensive phonetic datasets and leveraging advancements in machine learning and deep learning to improve NLP tasks such as speech recognition and synthesis. A brief comparison on algorithms and applications associated with voice-assisted technologies. By advocating for interdisciplinary collaboration among linguists, computer scientists, and language technologists, this survey aims to inspire further research and innovation in processing Tamil and other Dravidian languages, ultimately contributing to more inclusive and efficient NLP systems for these linguistically rich languages. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

SAFED: secure and adaptive framework for edge-based data aggregation in IoT applications

引用

Discover Internet of Things 2025年第1期5卷 1-28页

作者： Naaz, Zaineb Joshi, Gamini Sharma, Vidushi Department of Computer Science and Engineering School of ICT Gautam Buddha University Greater Noida India Department of Computer Science and Engineering PES University Electronic City Campus Bangalore India

The paper proposed a secured and efficient data aggregation mechanism leveraging the edge computing paradigm and homomorphic data encryption technique. The paper used a unique combination of Paillier cryptosystem and RSA digital signature mechanism to preserve data privacy, confidentiality, and integrity while maintaining the efficiency of the IoT network. The efficiency and implementation of the proposed scheme have been demonstrated through a case study, showcasing how the scheme can be effectively applied in real-world scenarios, such as in a smart grid network. Additionally, the future scope has been outlined, with potential techniques to address challenges that the scheme may face. © The Author(s) 2025.

关键词： Data aggregation Edge computing Internet of things Paillier cryptosystem Privacy-preserving Security

来源：评论

学校读者我要写书评

暂无评论

Advanced UAV - based leaf disease detection: Deep Radial Basis Function Networks with multidimensional mixed attention

引用

Multimedia Tools and applications 2024年 1-29页

作者： Das, Bappaditya Raghuvanshi, C.S. Research Scholar Department of Computer Science and Engineering FET Rama University Kanpur India Professor Department of Computer Science and Engineering FET Rama University Kanpur India

Plant disease causes a highly significant impact on production due to its destructive characteristics. A variety of chemical techniques should be applied to agriculture at different stages of disease infestation to enable precise plant protection. The integration of unmanned aerial vehicles (UAVs) for obtaining high-resolution images has become necessary due to the growing demand for precision agriculture, making crop health monitoring more efficient. In this study, we developed an innovative approach that combines the Deep Radial Basis Function Network (DRBFN) with the Multidimensional Mixed Attention (MMA) method. Since DRBFNs use radial basis functions as effective feature extractors, the model can detect even the smallest changes in the composition, color, and texture of leaves. This capability is particularly helpful when dealing with a diversity of plant species and pathogens exhibiting different symptoms. By deliberately considering pertinent spatial and spectral attributes, MMA is applied to enhance the discriminatory abilities of the network. By integrating spatial and spectral attention, the network can efficiently acquire information from both local and global sources, thereby enhancing the overall resilience and precision of the detection system. Our proposed model leverages UAV-captured images to achieve a classification accuracy of 96.95% in identifying leaf diseases across two different plant species. The integration of mixed attention mechanisms improved feature representation, leading to a 47.63% reduction in Root Mean Square Error (RMSE) compared to traditional methods. Furthermore, our model demonstrates significant efficiency by processing 2000 images, making it suitable for real-time applications. These results underscore the potential of UAV-based systems combined with advanced deep learning techniques for precision agriculture, offering scalable solutions for early disease detection and management. © The Author(s), under exclusive licence to S

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Classification of Ultrasound Breast Cancer Image Using Tuning Up the Hyper-Parameter of Convolutional Neural Network

SSRN

引用

SSRN 2023年第7期17卷 264-269页

作者： Bansal, Satish Jadon, Rakesh S. Gupta, Sanjay Kumar Prestige Institute of Management & Research Gwalior India Department of Computer Engineering Madhav Institute of Technology and Science Gwalior India SOS in Computer Science & Applications Jiwaji University Gwalior India

Breast cancer in women is a significant public health concern worldwide, with many cases going undiagnosed until the advanced stages. Early detection is crucial for proper treatment and improved outcomes. There are some pre-trained models used by authors for the detection of breast tumor, but these models require extensive computation power due to their many layers and parameters. To address this issue, it is required to proposed convolutional neural network (CNN) model with fewer training parameters for classification of ultrasound images dataset to determine that a particular image is either benign or malignant. In this paper, CNN model is proposed with changes in some hyperparameters like the number of filters, filter size, batch normalization, learning rate, epoch, and batch size, to achieve better accuracy with less computational power. The proposed model was compared to other pre-trained models, including ResNet50, Efficient Net, and VGG16, using two databases (database A for training and validation data, and database B for testing data). Our proposed classifier outperformed than pre-trained classifiers in terms of accuracy. © 2023, The Authors. All rights reserved.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Scene text visual question answering by using YOLO and STN

引用

International Journal of Speech Technology 2024年第1期27卷 69-76页

作者： Nourali, Kimiya Dolkhani, Elham Computer Science Department Semnan University Semnan Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran

Extracting text from an image using a Visual Question Answering (VQA) system is an application at the intersection of computer vision and Natural Language Processing (NLP) to help blind people better view and comprehend textual information within the image. A VQA system takes a natural language question and an image as its input and then targets different areas of the image to extract an answer from the text within the image. To detect and recognize text in the image, the Look-Read-Reason-Answer (LoRRA), DCD_ZJU and Multimodal Multi-Copy Mesh methods, classify image features by using an Optical-Character-Recognition (OCR) module. Due to the curvature, distortion, background noise, and different fonts, understanding the text in natural scenes is a challenge as OCR techniques are mainly designed to detect textual features from within clean image files. In this paper, we introduce a novel architecture that rectifies perspective and curved text in an image using Thin-Plate-Spline (TPS) transformations and detects objects through the utilization of You-Only-Look-Once networks to extract answers. The results of our experiments show that our proposed VQA method reaches higher accuracy levels on the OpenImage dataset as compared with the existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Application and comparison of K-means and PCA based segmentation models for Alzheimer disease detection using MRI

引用

Discover Artificial Intelligence 2024年第1期4卷 11页

作者： Olle Olle, Daniel Georges Zoobo Bisse, Julien Abessolo Alo’o, Ghislain Department of Computer Science University of Yaoundé 1 Yaoundé Cameroon Department of Computer Engineering University of Ebolowa Ebolowa Cameroon

Machine learning algorithms can be used to detect Alzheimer disease with RMI-images. One of the challenges of these algorithms is to clearly extract image features that show small variants of brain cells changes, which reveal the condition of dementia at the intermediate stages of mild cognitive impairment. In this article, we explore the abilities of two approaches to diagnose Alzheimer’s disease with MRI. In the first approach, after noise reduction and correction of alterations was conducted by a non-linear filter size 3*3, a kmeans algorithm is used for segmentation of cells showing white and grey matters of brain images. A Convolutional neural network (CNN) model is trained to indicate variations on these cells and the presence of Alzheimer Disease. The second approach performs image feature reduction using principal component analysis (PCA) to extract white and grey matters, and the cerebrospinal fluid as the three relevant features for Alzheimer diagnosis. A multilayer perceptron algorithm is trained to classify Alzheimer cases. Experiments are conducted on both approaches to compare accuracy and processing time using a real dataset of 602 images from the Alzheimer’s disease Neuroimaging Initiative (ADNI) of cognitively normal and Alzheimer’s disease patients. Results show that the accuracy can be enhanced when PCA is used to extract relevant features on RMI images;and with relatively low processing time. © The Author(s) 2024.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

A moment-based pooling approach in convolutional neural networks for breast cancer histopathology image classification

引用

Neural Computing and applications 2025年第2期37卷 1127-1156页

作者： Kumar, Arvind Singh, Chandan Sachan, Manoj Kumar Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur148106 India Department of Computer Science Punjabi University Punjab Patiala147002 India

Automated analysis of breast cancer (BC) histopathology images is a challenging task due to the high resolution, multiple magnifications, color variations, the presence of image artifacts, and morphological variability in the tissue images, often resulting in misdiagnosis. We propose four CNN models using a novel Zernike moments (ZMs)-based pooling approach, which replaces the existing global average pooling for the effective classification, referred to as CZP-CNN, MZP-CNN, SPMZP-CNN, and SPCZP-CNN. We utilize the ZMs-based pooling at three intermediate and last convolution layers. Of the four models, the two models, SPMZP-CNN and SPCZP-CNN, employ spatial pyramidal structures of feature maps to represent global and local characteristics of the feature maps. They effectively integrate the ZMs of various orders derived from the pyramidal representation of a feature map, representing the global and local aspects of shape and fine morphological details. The ability of ZMs is fully utilized to capture shape and morphological information available in the feature map using only a few low-order moments, which adds to the robustness of the proposed approach. Detailed experimental results on the publicly available BreaKHis dataset with four magnification levels and on the high-resolution BACH dataset demonstrate the proposed models’ effectiveness, robustness, and generalization ability. Among these proposed models, the complex ZMs-based SPCZP-CNN model achieves an impressive mean classification accuracy of 94.16% on the BreaKHis dataset and overall best binary and multiclass classification accuracy of 96.25% and 93.75% on the BACH dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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：