检索结果-内蒙古大学图书馆

2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Brar, Simrat Kaur Sharma, Rishabh Vats, Satvik Kukreja, Vinay Chandigarh Group of Colleges Department of Computer Applications Landran India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350311204

Coconut leaf spot (CLS) disease is a major threat to coconut production and can cause severe economic losses. In this study, we propose a deep learning (DL)-based ResNext50 model for automated detection and severity classification of CLS disease. Our model leverages a ResNext50 mode;and is trained and tested on a dataset of coconut leaf images with six severity levels, ranging from healthy leaves to critical severity. The proposed approach achieves high accuracy in detecting and classifying the severity levels of the disease. Our findings suggest that the proposed method is successful in properly identifying and categorizing the illness severity levels, with an accuracy rate of 91.77% overall. The strategy that has been presented has the possibility to significantly improve the efficiency and accuracy of CLS disease detection and monitoring, ultimately leading to better management strategies and increased productivity in the coconut industry. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Graph Encoding-Enhanced Transformer for Drug Recommendation

Graph Encoding-Enhanced Transformer for Drug Recommendation

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Cai, Xunsheng Thamrin, Syauki Aulia Chen, Arbee L.P. Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350324457

Doctors prescribe drugs for the patient with the objective of curing the patient. Some drugs cannot be consumed together since doing so may cause negative effects. This can be avoided by knowing the effects caused by consuming combinations of drugs. However, for complex cases of a patient, it can be difficult to decide the best combination of drugs. Therefore, automatic drug recommendation method was used to recommend drugs with minimal negative effects. It is performed by using a deep learning model which is trained on drug data. A graph called drug-drug interaction (DDI) is used to represent the drugs and effects of consuming one drug with other drugs. Additionally, information about the combination of drugs prescribed in the past for a patient is also important for drug recommendation. It can also be represented as a graph called drug concurrence relation (DCR). The DDI and DCR graphs can be input to the deep learning model through an encoding process. In this paper, we propose a graph encoding-enhanced transformer (GEET) to recommend drugs. The DDI and DCR graphs are encoded by using Graph Attention Network (GAT). The graph encoding model has multi-head attention, which makes the GEET model aware of the most important DDI and DCR from the graphs. Additionally, the encoding outputs are combined, and activation function and normalization methods are used to improve the performance. The model has been evaluated on the publicly available MIMIC-III dataset and has the best results on F1, Jaccard and PRAUC scores compared to the models proposed by the existing related research papers. © 2023 IEEE.

关键词： drug concurrence relation drug graph encoding Drug recommendation drug-drug interaction graph attention network transformer

来源：评论

学校读者我要写书评

暂无评论

Sentiment classification of reviews using combination of Firefly Algorithm and ANN 21

Sentiment classification of reviews using combination of Fir...

引用

21st OITS International Conference on Information Technology, OCIT 2023

作者： Tripathy, Abinash De, Utpal Chandra Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Rao, Ch. Chakradhara Pandey, Trilok Nath KIIT Deemed to Be University School of Computer Applications Odisha Bhubaneswar India Raghu Engineering College Department of Computer Sc. & Engg AP Viskahapatnam India Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350358230

With the availability of many social media sites, the reviews and comments by the customers are freely available for analysis. Most of these reviews are textual as per the content ***, proper processing need to be done for future use of the reviews. As reviews are in text, the amount of words need to be processed for any analysis work is very large in amount. So, an approach needs to be adopted to minimize the amount of word without affecting the result of the analysis. Thus, in this paper the Firefly algorithm is used to select the words / features that can act as a representative of the whole set of words. The output of Firefly algorithm i.e., selected features act as input of ANN. In this paper, a hybridization of Firefly algorithm and ANN is carried out for better accuracy result. The result of the proposed approach is analyzed by using confusion matrix and various evaluation parameters. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Intelligent traffic controller

引用

International Journal of Information Technology (Singapore) 2022年第4期14卷 2141-2153页

作者： Kumar, Sachin Baliyan, Anupam Tiwari, Anurag Tripathi, Aniket Kumar Jaiswal, Balram Department of Computer Science & Engineering Ajay Kumar Garg Engineering College Ghaziabad India Bharati Vidyapeeth’s Institute of Computer Applications and Management New Delhi India

This paper explores the application of dynamic traffic control timings using predefined input parameters. The method is a dynamic traffic algorithm that takes the rate of inflow, rate of outflow and queue length as input parameters to estimate the green-time that must be allocated to each road. The basic idea is to efficiently distribute the green-time based on traffic congestion in contrast to traditional methods of fixed time for traffic lights irrespective of their traffic status. The paper also compares the differences between these two methods based on some efficiency parameters using a simulator. © 2019, Bharati Vidyapeeth's institute of computer applications and Management.

关键词： Average waiting time Congestion Control IOT Queue length

来源：评论

学校读者我要写书评

暂无评论

Recognition and Classification of Apple and Sugarcane Plant Leaf Diseases using SVM with DAE Models 2

Recognition and Classification of Apple and Sugarcane Plant ...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sunil Kumar, V. Jyothi, A.P. Sumanth, S. Nagamani, H.S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Peenya Bengaluru India Department of Computer Science and Computer Applications Kolar India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350382952

For this leaf disease study, apple and sugarcane plants are identified and classified using a mix of character features and in-depth automated coding. Farm sugar cane leaves were copied for the online apple Plant Village Dataset. Pre-process images to reduce size and improve HLG filters. Image segmentation using DWT and GLCM feature extraction follows. Like the Low-Low (LL) band, DWT images produce 4-orthogonal subbands. Deep Auto Encoder and SVM algorithms retrieved subgroup functions and identified diseases from training data. If classification findings are ignored, the division stage receives data with HL, LH, and HH bands to explain approach consistency. Deep Auto encoder classifies illness using training data after extracting sub-band features. The Auto Encoder and SVM classifier demonstrated that the proposed model outperformed alternatives in LL band. Other subbands performed worse than the LL band. This study found that LL sub band data provided fine image data for better sickness categorization than HH, HL, and LH sub band data. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Sentiment analysis of reviews using Nature Inspired PSO and ANN

Sentiment analysis of reviews using Nature Inspired PSO and ...

引用

2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Tripathy, Abinash De, Utpal Chandra Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Rao, Ch. Chakradhara Pandey, Trilok Nath Kiit Deemed to Be University School of Computer Applications Odisha Bhubaneswar India Raghu Engineering College Department of Computer Sc. & Engg AP Viskahapatnam India Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350308167

Social media now plays an important role is sharing the views or comments about a topic. These comments are available to the users for their processing and future decision making process. As the reviews contains mostly text or emojis, thus, processing is required to obtained the required information. Nature is always a source for inspiration and *** activities of the nature can act as a source for optimization and classification. In this paper, two nature inspired algorithms are considered for analysis. Firstly, Particle Swarm Optimization (PSO) act as an optimization technique for feature selection i.e., best features are selected and these best features act as input to Artificial Neural Network (ANN) for further classification purpose. The reviews are processed using the proposed algorithms and finally, the confusion matrix is generated, which helps to generate the evaluation parameters to evaluate the results. © 2023 IEEE.

关键词： Artificial Neural Network (ANN) Confusion matrix Particle Swarm Optimization(PSO) Performance evaluation parameter Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

CDCAT: A Multi-Language Cross-Document Entity and Event Coreference Annotation Tool

引用

Tsinghua science and Technology 2022年第3期27卷 589-598页

作者： Yang Xu Boming Xia Yueliang Wan Fan Zhang Jiabo Xu Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing 100083China Research Institute with Run Technologies Company Ltd.Beijing 100192China School of Information Engineering Xinjiang Institute of EngineeringUrumqi 830091China

A tool for the manual annotation of cross-document entity and event coreferences that helps annotators to label mention coreference relations in text is essential for the annotation of coreference corpora. To the best of our knowledge, CROss-document Main Events and entities Recognition(CROMER) is the only open-source manual annotation tool available for cross-document entity and event coreferences. However, CROMER lacks multi-language support and extensibility. Moreover, to label cross-document mention coreference relations, CROMER requires the support of another intra-document coreference annotation tool known as Content Annotation Tool, which is now unavailable. To address these problems, we introduce Cross-Document Coreference Annotation Tool(CDCAT), a new multi-language open-source manual annotation tool for cross-document entity and event coreference, which can handle different input/output formats, preprocessing functions, languages, and annotation systems. Using this new tool, annotators can label a reference relation with only two mouse clicks. Best practice analyses reveal that annotators can reach an annotation speed of 0.025 coreference relations per second on a corpus with a coreference density of 0.076 coreference relations per word. As the first multi-language open-source cross-document entity and event coreference annotation tool, CDCAT can theoretically achieve higher annotation efficiency than CROMER.

关键词： event coreference entity coreference manual annotation tool natural language processing

来源：评论

学校读者我要写书评

暂无评论

Hybrid Classifier for Software Defect Prediction by Using Filter-Based Feature Selection Techniques 3rd

Hybrid Classifier for Software Defect Prediction by Using Fi...

引用

3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Kumar, Raghvendra Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology SRM Nagar Tamil Nadu Kattankulathur603203 India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering GIET University Gunupur India

ISBN: (纸本)9789819705726

Software’s capacity for error-free operation determines how reliable it is. Unfortunately, mistakes can happen at any stage of the software development process. Predicting software flaws early on in the development process is now of utmost importance in the field of software engineering. Using classification techniques, which are a popular strategy for predicting software faults, modules that are illustrated by a group of metrics or code properties are divided into defective or non-defective categories. However, there are low-quality, unreliable, redundant, and noisy data sources that have an adverse impact on the process of observing information and helpful patterns. Researchers must therefore use feature selection techniques to extract pertinent info from massive databases. In feature selection, the redundant and unnecessary features are eliminated in order to focus on the most significant ones. In this paper, we looked in to the impact of filter-based feature selection on classification techniques in software defect prediction. NASA and the Metric Data Program software repository both include publicly available datasets that were used in this study. Principal component analysis (PCA) and CFS was used to assess the dataset’s top discriminator features. The datasets were classified by using hybrid ensemble learning, for hybrid ensemble learning classifiers like DT, SVM, K-NN, NB, and LR were used. The experimental findings showed that it is preferable and should be encouraged to apply feature selection to datasets prior to classification in order to anticipate software defects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Multi Task Learning for Plant Leaf Segmentation and Counting

Multi Task Learning for Plant Leaf Segmentation and Counting

引用

2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Chaudhury, Bharathi Joshi, Vasudha Mitra, Pabitra Sahadevan, Anand S. Indian Institute of Technolog Kharagpur Department of Computer Science and Engineering Kharagpur India Indian Space Research Organization Space Applications Centre Ahmedabad India

ISBN: (纸本)9781665461634

In this paper, we propose an efficient method to segment the leaves and count the number of leaves in digital plant images, important tasks in plant phenotyping. However, for large cropping lands, automated leaf counting and segmentation are challenging and time-consuming due to the plant's rapid growth, varying size of leaves and shape, and presence of occlusions, which pose the task challenging to even human experts. Deep learning methods show considerable improvement when employed in image-based plant phenotyping tasks like plant leaf counting and leaf segmentation. However, most of the research related to plant phenotyping was designed to perform a single-task at a time. Therefore, a multi-task deep learning (MTL) framework is proposed to infer the two tasks simultaneously (i)leaf segmentation and (ii)leaf counting. Compared to single-task learning, MTL achieved the segmentation mean intersection over union score of 0.7478 (improved by 2%), and the difference in the count is decreased by two times when employed in agricultural field images. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Increasing Productivity in Software Development Through the Use of Docker Technology 1

引用

3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Information Technology Guru Ghashi Das University Bilaspur Bilaspur India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

The Docker technology makes the process of developing software substantially more effective by resolving issues related to deployment and hosting. Through the use of virtualization, it is able to function independently of the underlying hardware infrastructure, and it relies on its own code to carry out the execution of programs. The encapsulation of an application's whole environment together with its source code is at the heart of Docker's methodology. This enables Docker to efficiently eliminate the problems that are associated with having a variety of working environments, but it may also result in the images having a greater file size. "Images" is the term that Docker uses to refer to these bundled environments. Docker was used in this investigation to carry out a variety of tests, which allowed identical code to be executed on a variety of platforms, so permitting extensive comparisons. Software developers made use of Docker's ability to analyze the outcomes, which enabled them to make decisions based on accurate information. This study simplifies the software development and deployment procedures for the development community by providing significant insights into the benefits and considerations of Docker across numerous platforms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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：