检索结果-内蒙古大学图书馆

7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Mythry, Sarin Vijay Thalange, Asha Hippargi, Shashikant Martukar, Rakesh Adupa, Chakradhar Venkateswara Rao, J. Kumar, Ganga Santosh Nithin, Gundagoni Department of Electronics and Communication Engineering Vignan Institute of Technology and Science Telangana Hyderabad India Walchand Institute of Technology Maharashtra Solapur India N. B. Navale Sinhgad College of Engineering-Kegaon Maharashtra Solapur India Guru Nanak Dev Engineering College Karnataka Bidar India SR University Telangana Warangal India

ISBN: (纸本)9789819753369

In this article a novel ultra-wide band low noise amplifier (LNA) by cascading common gate and common source technique is designed for diagnosis and monitoring the chronic cases in health care treatments. The cascaded architecture attains noise figure of 4.4 dB in the required frequency band 2.9–8.6 GHz. The common gate stage offers input matching while the common source stage provides good wide band gain characteristics. The design is implemented in CMOS 90 nm and triggered using the provided voltage of 1 V and attained a gain of 20.1 dB ± 1.5 dB in the desired wide band frequency range and obtained input return losses of −10.6 dB, output return losses of −19.4 dB and average power of 33 mw. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： CMOS integrated circuits

来源：评论

学校读者我要写书评

暂无评论

Utilizing Deep Convolutional Neural Network and Local Binary Pattern Fusion for Plant Leaf Disease Identification: An Integrated Approach

Utilizing Deep Convolutional Neural Network and Local Binary...

引用

Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： S. Rahamat Basha Pratibha C. Kaladeep Yalagi Balaram Puli Pandian Sundaramoorthy Rajesh Daruvuri S Vinayagapriya Department of computer science and engineering Malla reddy college of engineering and technology Department of Computer Science and Engineering Walchand Institute of Technology Solapur Pratibhayalagi Engineering and Data Science Everest Computers Inc Application Developer EL CIC-1W-AMI IBM Technical Account Manager Cloud Data and AI Google Dept of ece St.josephs college of engineering

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Precise detection of plant diseases is essential in agriculture to maximize crop management and yield. The present study presents an innovative approach that integrates convolutional neural networks (CNNs) with local binary patterns (LBPs) in order to enhance the categorization of plant leaf diseases. The intricacy of plant disease images complicates the evaluation of their many features by conventional approaches. To solve this, the suggested technique utilizes the strengths of both CNNs and LBPs. The CNN component is highly effective in extracting discriminative features from raw pictures using hierarchical representations, which are crucial for differentiating between different illness categories. Simultaneously, the LBP descriptor enhances its usefulness by acquiring intricate texture information from specific areas of the images, so providing more effectiveness in distinguishing small differences resulting from various diseases. Utilizing a feature fusion approach, the merging of CNN and LBP features merges spatial and textural data, resulting in a more thorough representation of the input images. The fusion described herein significantly improves the precision of plant disease classification. The analysis of benchmark datasets of plant leaf diseases showed that this integrated method outperforms the use of CNNs or LBPs individually in terms of classification accuracy. The research findings indicate that the suggested approach, which combines deep Convolutional Neural Networks (CNNs) with Linear Bayesian Processes (LBPs), provides a more efficient approach for detecting plant leaf illnesses. This, in turn, makes a substantial contribution to crop management and agricultural productivity.

关键词： Productivity Plant diseases Accuracy Merging Crops Benchmark testing Feature extraction Spatial databases Bayes methods Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Implementation and verification of dissemination of tree structure data using signature scheme for XML data

International Journal of Database Theory and Application

引用

International Journal of Database Theory and Application 2015年第4期8卷 215-230页

作者： Waghmare, Vivek N. Thool, Ravindra C. SGGS Institute of Engineering and Technology Nanded India Walchand College of Engineering Sangli India

With wide spread use of digital data communication on networks, security of data has become an important issue which provides integrity and confidentiality of content that ensures the distribution of information appropriately. While dealing with encoded content in the context of XML, its hierarchical tree structure has different level of confidentiality and integrity for various portion of the same content. Thus it imposes the need of dissemination approach specifically tailored for XML data to address the issues of efficiency and scalability. However these characteristics must be achieved without compromising the security and privacy of contents. The goal of efficient proposed Signature scheme for secure & selective distribution of XML content is to provide uniform platform for different data representation for secure and efficient availability of data. Proposed Signature Scheme is used to detect the change of content at information providers to discover and deliver new content to users. This can be achieved using structural properties of hierarchical tree structure data model with multicast topology approach. © 2015 SERSC.

关键词： XML

来源：评论

学校读者我要写书评

暂无评论

***: Detecting bugs and proposing corrective solutions

引用

World Academy of Science, engineering and technology 2010年第62期62卷 213-220页

作者： Chavan, Sheetal Kulkarni, P.J. Shanbhag, Vivek Philips Electronics India Ltd Bangalore-560045 India Walchand College of Engineering and Technology Sangli India

Although achieving zero-defect software release is practically impossible, software industries should take maximum care to detect defects/bugs well ahead in time allowing only bare minimums to creep into released version. This is a clear indicator of time playing an important role in the bug detection. In addition to this, software quality is the major factor in software engineering process. Moreover, early detection can be achieved only through static code analysis as opposed to conventional testing. *** is a static analysis tool, which detects bugs in NET® languages through MSIL (Microsoft Intermediate Language) inspection. The tool utilizes a Parser based on Finite State Automata to carry out bug detection. After being detected, bugs need to be corrected immediately. *** facilitates correction, by proposing a corrective solution for reported warnings/bugs to end users with minimum side effects. Moreover, the tool is also capable of analyzing the bug trend of a program under inspection.

关键词： Defects

来源：评论

学校读者我要写书评

暂无评论

Application of Linguistic Knowledge in Factored Language Modeling for Hindi Language

Application of Linguistic Knowledge in Factored Language Mod...

引用

International Conference on Computing in engineering and technology, ICCET 2019

作者： Babhulgaonkar, Arun R. Sonavane, Shefali P. Department of Computer Science & Engineering Walchand College of Engineering Sangli India Department of Information Technology Walchand College of Engineering Sangli India

ISBN: (纸本)9789813295148

A language model is a technique that shows which words are more or less likely to be generated during some conversation in any natural language. N-gram language modeling is the pioneer technology used to construct language models. N-gram technique considers preceding words only to predict the upcoming word. Factored language modeling is a formalism that provides a facility to undertake other linguistic knowledge of the words like gender, number, part of speech, stem of word along with word itself to predict next word in a sentence. This paper discusses the effect of various combinations of linguistic features of word on predictability of next word in Hindi-language sentence. The paper also discusses how use of linguistic features decreases the perplexity by 31.71% as compared to perplexity of baseline N-gram language model. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

Automatic Feature Extraction for CBIR and Image Annotation Applications

Automatic Feature Extraction for CBIR and Image Annotation A...

引用

International Conference on Computing in engineering and technology, ICCET 2019

作者： Nemade, S.B. Sonavane, S.P. Department of Computer Science & Engineering Walchand College of Engineering Sangli India Department of Information Technology Walchand College of Engineering Sangli India

ISBN: (纸本)9789813295148

In the area of information technology, organizing and indexing of the digital information is a primary concern. In CBIR system, one of the most significant issues is a semantic gap. Semantic gap refers to the difference between the features extracted from image and interpretation of features content in the image or within the regions by human. Hence, automatic image annotation has achieved momentum in the last few years. The objective of the automatic image annotation (AIA) is to allocate textual labels to the image that clearly describes content or objects in the image. Accuracy of the automated image annotation algorithm depends upon the feature extraction process. Therefore, effective feature extraction algorithm is essential. In this paper, feature extraction algorithm using Gabor filter is presented. Gabor filter through its multi-resolution capability successfully extracts effective features from images or regions obtained after segmentation. It is demonstrated that the Gabor filter generates low level, less number of features and accurate description of the image if filter with frequency response in the band of 50–75% of the total frequency is selected. These extracted features further reduce the complexity in the classification algorithms developed using statistical models or soft computing techniques. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Image Segmentation using Convolutional Neural Network for Image Annotation 4

Image Segmentation using Convolutional Neural Network for Im...

引用

4th International Conference on Communication and Electronics Systems, ICCES 2019

作者： Nemade, S.B. Sonavane, S.P. Walchand College of Engineering Department of Computer Science and Engineering Sangli India Walchand College of Engineering Department of Information Technology Sangli India

ISBN: (纸本)9781728112619

Due to rapid advancement in the digital display, communication and storage devices with effective techniques are needed to organize, index, retrieve and annotate a large image database. Image segmentation finds application in various areas of image processing and computer vision. Inferring of low level features from the given image is a challenging task in unstructured regions. Most of the annotation and retrieval algorithms fail to consider region semantics. This paper proposes convolutional neural network (CNN) based image segmentation for image annotation application. The proposed CNN includes pixel based prediction of the regions that are applied to obtain low level image features. The algorithm uses image region information based on the precise color distribution within the image. Experimental results demonstrate better results of image segmentation using CNN. © 2019 IEEE.

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of geometric transformation effects for image annotation using various CNN models 5th

Comparative analysis of geometric transformation effects for...

引用

5th International Conference on Computing in engineering and technology,ICCET 2020

作者： Nemade, Sangita Sonavane, Shefali Department of Computer Science & Engineering Walchand College of Engineering Sangli India Department of Information Technology Walchand College of Engineering Sangli India

ISBN: (纸本)9789811540288

For image classification and image annotation applications, a convolutional neural network (CNN) has revealed superior accuracies and computationally efficient methods. Researchers are putting significant efforts across the globe to come up with enhanced deep network architectures. However, the latest CNN models are unable to cope with the geometric transformation of images. In the training stage, the state-of-the-art algorithms mostly make use of data augmentation techniques, that expand training samples but it leads to data overfitting. This paper focuses on how geometric transformation affects the annotation performance of various CNNs such as Alexnet, GoogleNet, ResNet50, and DenseNet201 on the Corel dataset. Comparative analysis of these deep learning models for image annotation is evaluated using the F1 score and is presented with the help of a box plot. Based on the obtained results, it is observed that the DenseNet201 model performs better than other models. It is further observed that the ResNet50 model works better for a specific class of images. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Fault diagnosis of a CNC hobbing cutter through machine learning using three axis vibration data

引用

Heliyon 2025年第2期11卷 e41637页

作者： Tambake, Nagesh Deshmukh, Bhagyesh Pardeshi, Sujit Salunkhe, Sachin Cep, Robert Nasr, Emad Abouel Department of Mechanical Engineering Walchand Institute of Technology Maharashtra Solapur India Department of Mechanical Engineering COEP Technological University Maharashtra Pune India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Gazi University Faculty of Engineering Department of Mechanical Engineering Maltepe ANKARA Turkey Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB-Technical University of Ostrava 17. Listopadu 2172/15 Ostrava 708 00 Czech Republic Department of Industrial Engineering College of Engineering King Saud University PO Box 800 Riyadh 11421 Saudi Arabia

This research presents a novel approach to fault diagnosis for CNC hobbing cutters using machine learning techniques, leveraging three-axis vibration data to ensure machining precision and tool reliability. Traditional methods of tool monitoring are insufficient for real-time and complex machining environments, prompting the integration of automated machine learning models. A robust dataset was collected from a CNC hobbing machine, capturing vibration signals under healthy and faulty tool conditions. Statistical features, including Root Mean Square (RMS), Crest Factor, and Kurtosis, were extracted from the vibration data for model training. Various machine learning algorithms, including Decision Trees, Efficient Linear models, Neural Networks, and Ensemble methods, were evaluated for their classification accuracy. Among these, the Ensemble model achieved perfect classification accuracy (100 %) with minimal computational cost, making it optimal for real-time applications. Explainable AI techniques, such as LIME and Shapley values, were employed to interpret model predictions, enhancing the system's transparency and reliability. The proposed framework demonstrated superior performance compared to existing methodologies in the literature, addressing key gaps such as overfitting, data quality, and model explainability. Real-world deployment challenges, including diverse operating conditions and generalizability across machines, were also discussed, with recommendations for incorporating multi-sensor data and transfer learning approaches in future research. This study establishes a foundation for predictive maintenance in CNC machining, significantly reducing downtime and improving operational efficiency through precise fault diagnosis in hobbing cutters. © 2025 The Authors

关键词： CNC hobbing cutter Ensemble Model Explainable AI Fault diagnosis Feature engineering Machine learning Vibration data

来源：评论

学校读者我要写书评

暂无评论

Graph partitioning using heuristic kernighan-lin algorithm for parallel computing 5th

Graph partitioning using heuristic kernighan-lin algorithm f...

引用

5th International Conference on Computing in engineering and technology, ICCET 2020

作者： Patil, Siddheshwar V. Kulkarni, Dinesh B. Department of Computer Science and Engineering Walchand College of Engineering SangliMH India Department of Information Technology Walchand College of Engineering SangliMH India

ISBN: (纸本)9789811548505

The goal of parallel computing is to distribute the load on available processors such that all processors should be utilized in a fair manner. This minimizes the overall execution time required to execute a complex task. So, the load balancing issue becomes an important aspect of parallel computing. It is abstracted as a graph partitioning problem in which the nodes represent computation cost, edges represent communication cost, and number of partitions should be equal to number of available processing units. So, the objective is to cut the graph into k-partitions such that—(i) total node weight should be equal for each partition and—(ii) minimize total edge weight across the partitions. A heuristic Kernighan-Lin graph partitioning algorithm for two-way partitioning is evaluated in this paper. It starts with an initial random graph partition and consecutively exchanges the nodes between partitions, determines cut size at each stage and saves the minimum cut found so far. After the desired number of swaps has been performed, the saved minimum cut will give optimal partitions. The graph data for experimental work is obtained from DIMACS tenth implementation challenge Web site. The experimental results show minimum cut-cost with respect to balance constraint. The results are compared with ground truth results for validation. © Springer Nature Singapore Pte Ltd 2021.

关键词： Parallel processing systems

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：