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Cognitive Computing for Wheat Leaf Disease Detection System

Cognitive Computing for Wheat Leaf Disease Detection System

引用

2023 international conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Indumathy, P. Abins, Arockia Maheswari, S. Liya, B.S. Justindhas, Y. Computer Science and Engineering Easwari Engineering College Chennai Ramapuram India

ISBN: (纸本)9798350348910

Precision agriculture relies on crop disease detection to boost production and food security. This paper introduces Cognitive Computing-based wheat leaf disease detection. The approach uses machine learning, natural language processing, and interpretability to identify diseases accurately and easily. The results demonstrate the advantage of cognitive computing over traditional methods. In comparison to CNNs and SVMs, our model had superior accuracy, precision, recall, and F1 scores. The model's strong performance metrics across folds were confirmed by cross-validation. The Cognitive Computing model was faster than CNNs and SVMs in inference, making it appropriate for real-time illness identification. Interpretability was examined by showing how the model's decision-making process may be presented in normal language to build user trust. These findings show that Cognitive Computing can revolutionize wheat leaf disease diagnosis, providing farmers and agriculture stakeholders with a realistic and effective solution. The work advances precision agriculture by showing how modern technologies may solve difficult crop health monitoring problems. © 2023 IEEE.

关键词： Decision making

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Advancing Clinical Text Summarization through Extractive Methods using BERT-Based Models on the NBME Dataset 2

Advancing Clinical Text Summarization through Extractive Met...

引用

2nd international conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Sudarshan, R. Sasikala, D. Kalavathi, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Sriperumbudur India

ISBN: (纸本)9798350340235

Medical notes contains a vast amount of healthcare information. NLP techniques are essential for extracting the most relevant insights from abundance data, enabling healthcare professionals to make well-informed *** the study, clinical text feature extraction was performed on the NBME dataset for extractive summarization using PubMedBERT, BioBERT, and DeBERTa. It was found that PubMedBERT achieved 92% accuracy, BioBERT achieved 95% accuracy, and DeBERTa achieved 86% accuracy. PubMedBERT and BioBERT performed well on clinical text summarization task these models have a slight edge over DeBERTa. These results suggest that domain-specific pretrained language models like PubMedBERT and BioBERT are useful for extractive summarization of clinical text. Performing feature extraction for extractive summarization of medical notes can make healthcare information more concise and *** physicians and other healthcare professionals to improve their analysis on medical ***,advances in extractive summarisation of clinical text through NLP can lead to better-informed healthcare professionals and improved patient outcomes. © 2023 IEEE.

关键词： Biobert Deberta Feature Extraction natural language processing PubMed Bert

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29th international Working conference on Requirements engineering: Foundation for Software Quality, REFSQ 2023

29th International Working Conference on Requirements Engine...

引用

29th international Working conference on Requirements engineering: Foundation for Software Quality, REFSQ 2023

ISBN: (纸本)9783031297854

The proceedings contain 25 papers. The special focus in this conference is on Requirements engineering: Foundation for Software Quality. The topics include: Requirements Classification Using FastText and BETO in Spanish Documents;exploring Requirements for Software that Learns: A Research Preview;requirements engineering for Automotive Perception Systems: An Interview Study;An Investigation of Challenges Encountered When Specifying Training Data and Runtime Monitors for Safety Critical ML Applications;A Requirements engineering Perspective to AI-Based Systems Development: A Vision Paper;out-of-Distribution Detection as Support for Autonomous Driving Safety Lifecycle;automatically Classifying Kano Model Factors in App Reviews;data-Driven Persona Creation, Validation, and Evolution;towards a Cross-Country Analysis of Software-Related Tweets;an Empirical Study of the Intuitive Understanding of a Formal Pattern language;integrating Implicit Feedback into Crowd Requirements engineering – A Research Preview;authoring, Analyzing, and Monitoring Requirements for a Lift-Plus-Cruise Aircraft;knowns and Unknowns: An Experience Report on Discovering Tacit knowledge of Maritime Surveyors;feel It, Code It: Emotional Goal Modelling for Gender-Inclusive Design;A Product Owner’s Navigation in Power Imbalance Between Business and IT: An Experience Report;eliciting Security Requirements – An Experience Report;supporting Shared Understanding in Asynchronous Communication Contexts;bringing Stakeholders Along for the Ride: Towards Supporting Intentional Decisions in Software Evolution;understanding the Role of Human-Related Factors in Security Requirements Elicitation;scope Determined (D) and Scope Determining (G) Requirements: A New Categorization of Functional Requirements;using language Models for Enhancing the Completeness of natural-language Requirements;requirement or Not, That is the Question: A Case from the Railway Industry;summarization of Elicitation Conversations to Locate Re

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Pediatric oral health detection using Swin transformer 3

Pediatric oral health detection using Swin transformer

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Bhat, Suvarna Birajdar, Gajanan K. Patil, Mukesh D. Ramrao Adik Institute Of Technology D.Y. Patil Deemed To Be University Department Of Electonics Engineering Navi Mumbai India

ISBN: (纸本)9798350318609

Oral health exams in children are important for a variety of reasons, including their impact on a child's overall well-being and development. Traditional dental health detection methods still need to be improved, particularly in actual diag-nostic environments. Artificial intelligence (AI) has the potential to have a substantial impact on pediatric dentistry by providing innovative solutions to numerous difficulties in children's oral healthcare. By analyzing X-rays or intra-oral scans, AI can aid in the early detection of disorders like tooth decay, and developmental anomalies, when correction is most successful. AI can provide additional insight by analyzing X-rays and data, allowing dentists to make more accurate treatment plan decisions. Transformer is an instance of a deep neural network that was first employed for natural language processing. Its main foundation is the self-attention mechanism. In this study, we assess the swin transformer model's effectiveness in pediatric oral health detection. The data classes were enhanced as a pre-processing step using the fuzzy color approach, and the images that were structured with original X-rays were stacked. The proposed system achieved an accuracy of 85.34 percent. The result obtained implies that swin transformer has prospects in medical imaging and pre-training may be a useful strategy for enhancing its model's accuracy on oral health detection tasks. © 2024 IEEE.

关键词： Pediatrics

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A knowledge-Driven Graph Convolutional Network for Spectrum Prediction 24

A Knowledge-Driven Graph Convolutional Network for Spectrum ...

引用

24th IEEE international conference on Communication Technology, ICCT 2024

作者： Guo, Yibo Huang, Yang Zhu, Qiuming Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing China

ISBN: (纸本)9798350363760

In the 6G mobile communication systems, the integration of sensing, communication, and array signal processing technologies heralds a new era of advanced situational awareness. However, this advancement raises critical concerns about privacy and security, especially with the widespread deployment of devices equipped with radar-like sensing capability, including malicious ones. Moreover, suffering from rapidly changing spectrum environments and the restricted capability of spectrum prediction, jamming effectiveness evaluation values may not be fully obtained. Therefore, in order to effectively jam the malicious sensing device and improve the spectrum utilization, spectrum prediction is expected to be a key enabler, which features predicting the jamming effectiveness evaluation values and recommending frequency-domain subbands for utilization. In order to characterize the relation between jamming devices and malicious sensing devices when predicting spectrum, we construct a spectrum knowledge graph (SKG). In order to encode the KGE vectors from the triplets composed of entities and relations, we develop the CNN-based KGE model, where the CNN can be exploited to derive embedding encoded with knowledge of relations. In order to address the problem of predicting spectrum, we convert this issue into a node regression problem based graph model and graph convolution network (GCN). In order to address the problem of sparse feature vectors related to entities and relationships within the SKG, we exploit KGE vectors generated by a CNN-based KGE algorithm to replace the one-hot encoding method, which can be used to formulate the feature matrix in the graph model. Simulation results indicate that the spectrum prediction algorithm based on a knowledge-driven graph convolutional network (KGCN) can significantly improve the accuracy of spectrum prediction. © 2024 IEEE.

关键词： knowledge graph

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The Improved Inception Networks based on Attention Mechanism 23

The Improved Inception Networks based on Attention Mechanism

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8th international conference on Intelligent Information processing, ICIIP 2023

作者： Yu, Yiwang Sun, Yongke Cao, Yong Big data and intelligence engineering school Southwest Forestry University China

ISBN: (纸本)9798400708091

Deep learning is a machine learning method and it have enabled major progresses in image, natural language process and other more various and wide fields. Inception is a classic deep learning module included in Google net and it has good performance in image recognition, classification and segmentation. Attention is essentially a distribution mechanism, with the core idea of highlighting certain important characteristics of the object. In this paper, we introduced attention mechanism into Inception net model. We applied our model on MNIST, CIFAR-10 and CIFAR-100 public dataset to train and test. The performance of the model based on Inception and Attention Mechanism is improved greatly compared to the other models. At the same time, we also apply SENet model with attention mechanism to the same datasets and the experiment results show our improved Inception model is superior to SENet. These demonstrated that our proposed attention mechanism has better effectiveness. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Image recognition

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Comment Compass: An Approach to Analyze YouTube Comments through Web Scraping, Sentiment Analysis, and Generative AI for Actionable Insights 1

Comment Compass: An Approach to Analyze YouTube Comments thr...

引用

1st international conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Rathod, Manav Yadav, Ankita Phutane, Soham Bhise, Suvarna Vasantdada Patil Pratishthan's College of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798350365016

In this paper, we introduce 'Comment Compass,' a novel system designed to revolutionize the analysis of YouTube video comments by content creators. The hypothesis driving this research is that integrating web scraping, sentiment analysis, and generative AI can provide content creators with deeper insights into their audience's sentiments and perspectives, thereby enhancing content strategy and engagement. Methodologically, 'Comment Compass' employs Python-based web scraping to extract comments from YouTube videos. Sentiment analysis is conducted using the SentimentIntensityAnalyzer from the natural language Toolkit (NLTK) library, which produces a pie chart depicting the distribution of positive, negative, and neutral comments. Additionally, the system leverages Generative models to generate further insights from the comments, such as future content ideas, criticisms, and inquiries. Our results indicate that 'Comment Compass' offers content creators actionable insights that can significantly optimize content strategies for better audience engagement and impact. This work demonstrates the substantial potential of combining web scraping, sentiment analysis, and generative AI, contributing valuable knowledge to these fields. The findings are contextualized within existing literature, underscoring the innovative contributions of this research. © 2024 IEEE.

关键词： Video analysis

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A Study on NLP Based Approach in AI and Text Data Mining for Automated Highlighting of New Information in Clinical Notes 6

A Study on NLP Based Approach in AI and Text Data Mining for...

引用

6th international conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Dhar, Swapnaneel Das, Sushanta Majumder, Swanirbhar The Icfai University Tripura Agartala Dept. Computer Science & Engineering Agartala India Dept. Information Technology Agartala India

ISBN: (纸本)9781665410359

Artificial Intelligence(AI) has been a vast area in modern science. From our day to day lives it has contributed with so many devices that actually make sense as intelligent. Human beings can operate any gadgets operating by speech, text only. One of the techniques of AI is text mining or text data mining. Text mining uses natural language processing (NLP). for processing text to make it structured. So Text mining helps by using AI technology to produce new information from the structured data. natural language processing (NLP) is the technique of artificial intelligence (AI). This allows machines to understand human language. NLP combines the powers of linguistics and computer science to study the rules and structure of language. It also creates intelligent systems (powered by machine learning and NLP algorithms) that can understand, analyze, and extract meaning from text and speech. natural language processing (NLP) in healthcare can help physicians to make better decisions. This study describes the potential of NLP to maximize the value of Electronic Medical records(EMR's) and healthcare data, making a critical and trusted component in improving health outcomes and also focus on process, the EMRs processing and emphatically analyzes the key text data mining processing techniques. EMRs have been doing an absolute help to automated highlighting of new information in clinical notes. This study tries to give an overview of how AI techniques plays an important role in healthcare. © 2022 IEEE.

关键词： Health care

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Safety Stock Forecasting based on Materials Correlation 15

Safety Stock Forecasting based on Materials Correlation

引用

15th IEEE international conference on Wireless Communications and Signal processing, WCSP 2023

作者： Zhang, Yaqiang Tao, Xiaoming Cui, Zhoujuan Duan, Yiping Lu, Jianhua Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798350324662

Safety stock is an inventory to cope with the randomness and risk in all aspects of supply, production, and sales. In order to solve the problems of low inventory level leading to production interruption and high inventory level leading to high inventory cost and overstocking, safety inventory forecasting is needed. With the consideration, we propose a safety stock forecasting method based on spatial-temporal sequence model. Firstly, we adopt a clustering method based on time series shape to classify the materials, and the results of the classification are used as classification prior knowledge to enhance the prediction performance. Then, the dosage forecasting model is established for different classes, respectively. By proposing the correlation matrix, the co-occurrence frequency of different materials in the product dosage pattern is used as the adjacency matrix, which is correlation prior knowledge. With the combined effect of classification and correlation prior knowledge, the accuracy of predicting the dosage of a specific material can be greatly improved in a period of time in the future. Through experiments, it is proved that our method greatly improves the prediction accuracy and successfully explores the correlation between materials, compared with the traditional method. © 2023 IEEE.

关键词： clustering graph convolutional networks machine learning Safety stock time series forecasting

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An Intelligent Search Engine Based on knowledge Graph for Power Equipment Management 5

An Intelligent Search Engine Based on Knowledge Graph for Po...

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5th international conference on Energy, Electrical and Power engineering, CEEPE 2022

作者： Wu, Xingping Tang, Yachen Zhou, Chunlei Zhu, Guangxin Song, Jinwei Liu, Guangyi State Grid Corporation of China Big Data Center Beijing China Envision Digital Redwood CityCA United States

ISBN: (纸本)9781665479059

The intelligent search engine (ISE) can accurately identify users' search intention and interests automatically by parsing the natural language questions, can provide fast retrieval and relevance ranking of direct and associate search results, and can filter and push information to the user intelligently. Since ISE can save inestimable time and workforce in data search and correlation analysis, it has been widely used in the Internet field. This article proposed an ISE based on a constructed electrical power knowledge graph to extract the target and conditions of natural interrogation based on semantic analysis, to generate index network via vertex-to-edge mapping and ontology network path analysis, and to perform the direct and associate knowledge searches to provide users with an intuitive visualization. This method thoroughly combined knowledge graph and graph computing characteristics, realized high-speed multi-hop data mining and correlation analysis in a domain knowledge graph. The proposed work can also provide a basis for automatic identification of user interests, intelligent information filtering and push. © 2022 IEEE.

关键词： knowledge engineering Power engineering Correlation Semantic search natural languages Process control Search engines

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