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NLP based Text Summarization Techniques for News Articles

NLP based Text Summarization Techniques for News Articles

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： B. Logesh Kumaran N. V. Ravindran Dept. of Computer Science and Engineering Saveetha Engineering College (Autonomous) Chennai India

This defines a news summarization web application using Streamlit, allowing users to access and read news articles from various sources. Here's an abstract of what the project does the necessary Python libraries are imported, including Streamlit for the web application, BeautifulSoup for web scraping, PIL for image processing, and Newspaper for article extraction. Three functions are defined for fetching news based on different criteria: fetch_news_search_topic for searching news based on a topic, fetch_top_news for fetching the top news, and fetch_category_news for fetching news based on a specific category. A function, fetch_news_poster, is defined to fetch and display the image associated with each news article. The display_news function is defined to present the news articles on the Streamlit web application, along with their associated images, summaries, and publication dates. The run function configures the Streamlit application layout and functionality, including options for selecting news categories, displaying trending news, choosing favorite topics, and searching for specific topics. The application displays a title, logo, and various news categories, allowing users to select their preferred category and view the corresponding news articles. Overall, this application provides a convenient way for users to access and read summarized news articles based on their preferences.

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An Efficient Explainable AI Method Combining CNN and SVM for Corn Leaf Disease Detection and Visualization

An Efficient Explainable AI Method Combining CNN and SVM for...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Hasibul Islam Peyal Md. Nazrul Islam Mondal Shafiun Miraz Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Agriculture plays a crucial role in Bangladesh's economy, with corn serving as a key crop. Plant diseases pose significant threats to agricultural productivity and economic stability worldwide. Effective monitoring and prediction are essential to mitigate these risks, as the prevalence of infectious diseases severely disrupts agricultural production. This study focuses on diagnosing agricultural diseases and pests affecting corn stalks using convolutional neural networks (CNN) combined with an SVM classifier. The proposed model achieves a classification accuracy of 98.34%, comparable to transfer learning models such as VGG-16 (97.42%) and VGG-19 (97.13%), while utilizing approximately 0.95 million parameters—about 145 times and 151 times fewer than VGG-16 and VGG-19, respectively. The model exhibits outstanding performance, with accuracy, recall, and F1 scores nearing 97% and an exceptional Area Under Curve (AUC) score of 99%. Furthermore, the model's compact design requires minimal disk space (approximately 4 MB) and features a significantly reduced parameter count. To enhance interpretability, the study integrates explainable AI techniques, including Gradient Weighted Class Activation Mapping (Grad-CAM), Shapley Additive Explanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME). These methods provide visual explanations through heatmaps, highlighting regions critical for classification, thereby facilitating a clearer understanding of the disease diagnosis process.

关键词： Support vector machines Visualization Plant diseases Accuracy Explainable AI Computational modeling Transfer learning Agriculture Stability analysis Convolutional neural networks

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Time Series Forecasting of Sales Data using Hybrid Analysis

Time Series Forecasting of Sales Data using Hybrid Analysis

引用

Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： T Rajasree R. Ramyadevi dept. of Computer Science and Engineering Saveetha Engineering College (Autonomous) Chennai India

Forecasting of sales data based on time for businesses and organizations is absolutely an efficient thing to be done for the successful market trend analysis, and it will be an added advantage for the manufacturing sector to meet the business needs for an organization. Knowing about the future trends will be a crucial thing for sales and production, hence sales forecasting plays a vital role in achieving higher sales rate and thereby improving the efficiency of the businesses. Using machine learning methods like random forest, linear regression, XG-boosting, K Neighbors regression model, and Keras deep neural network regressor to predict sales for future demands with precision. This Forecasting approach is based on the product attributes, customer information, and various necessary parameters such as data related to product sales, past sales performance, customer demands according to time considering all these factors the analysis were done in the trained data.

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A Study on Applying Decentralized Constraint Optimization to Mobile Sensor Teams with Range Sensors 21

A Study on Applying Decentralized Constraint Optimization to...

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Proceedings of the 5th International Conference on Advances in Artificial Intelligence

作者： Toshihiro Matsui Dept.of Computer Science Nagoya institute of Technology Japan

ISBN: (纸本)9781450390699

Observation systems using autonomous mobile sensor agents have been studied for application in emergency response and surveillance of hazardous areas. Several major types of sensor agents have range sensors and one of their tasks is to observe an environment toward building a map. Such a task requires finding solution to several cooperation problems of resource allocation to mobile sensor agents. We focus on an observation system where multiple agents cooperatively perform an observation task and then build a map of a floor or premises. For the observation task, we address the problem of setting sub-goal locations to be observed by the agents. To solve the problem, we apply a decentralized constraint optimization approach, which is a fundamental framework of multiagent cooperation based on constraint optimization problems that are distributed among agents. As a first case study for range sensors, we mainly address the situation where agents should avoid visible areas of other agents to mitigate masking range data of other agents. The proposed framework is experimentally evaluated within a simulated environment.

关键词： cooperation multiagent system mobile sensor agents range sensors distributed constraint satisfaction

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Security Coding Scheme for Satellite Communications

Security Coding Scheme for Satellite Communications

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Chen, Jong-Shin Sheu, Yung-Hoh Yang, Cheng-Ying Yao, Jenq-Foung J.F. Hwang, Min-Shiang Dept. of Info. and Comm. Eng. Chaoyang Uni. of Tech Taichung Taiwan Department of Computer Science and Information Engineering National Formosa University Yunlin Taiwan Department of Computer Science University of Taipei Taipei Taiwan Dept of Computer Science Georgia College and State University Milledgeville United States Dept. of C.S. & Info. Eng. Fintech and Blockchain Research Center Asia University Taichung Taiwan Dept. of Medical Research University Hospital China Medical University Taichung Taiwan

ISBN: (纸本)9798350389210

The major goal of a secure coding is to enable the authentic receiver could successfully recover the original message from the source. However. the satellite communications provide an open wireless environment. It hardly protects the transmitted information from the eavesdroppers. Usually, the secure system employs the encryption, authorization and authentication schemes to avoid from the attacks. However, the undesired attacks have been continuously developed. In order to reduce the computing resource exhausted in the encryptions. Physical layer security coding based on Shannon's perfect secrecy, error-free cryptogram, might be applied to the satellite communications. Moreover, the coding scheme could reach the required critical security capacity. Also, it could approach the low error probability. However, the information rate and the coding rate will suffer the system performance. To tradeoff the security level, information rate and coding rate could be the important issue in the satellite communications. © 2024 IEEE.

关键词： Coding rate Information rate Physical-layer security Coding Secrecy capacity Shannon theorem

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A Deep Learning Approach to Ringworm and Vitiligo Identification in Skin Imagery

A Deep Learning Approach to Ringworm and Vitiligo Identifica...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Kavya Kamasani Deepa Venna Harshitha Badavathula Namratha Ranga dept of Computer Science & Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijaywada India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Diagnosing skin illnesses such as vitiligo and ringworm can be difficult because of their similarity to other dermatological conditions and their different looks. The suggested approach combines Local Binary Patterns and Grey Level Co-occurrence Matrix, two potent feature extraction techniques. While Local Binary Patterns depicts the local texture patterns inside an image, Grey Level Co-occurrence Matrix analyses the spatial relationships between pixels to collect textural information. These traits are successfully used to depict the feature extraction with the unique qualities of vitiligo and ringworm lesions with accuracy $98 \%$, having been retrieved from dermatological photographs. However, previous machine learning research produced inaccurate results. Then, for classification we tried different deep learning models like Google Net, Mobile Net, ResNet50, ResNet101, Xception, CNN, and VGG19. Among these, we found that VGG19 provided good results for us with an accuracy of about $99.19\%$. The pre-trained VGG19 model is improved by utilising deep learning and the extracted characteristics to reliably identify and categorise vitiligo and ringworm lesions. The model’s discriminative power is increased by integrating Grey Level Co-occurrence Matrix and Local Binary Patterns features, and it maintains strong classification performance with less training data by utilising VGG19. All things considered, the suggested method presents a User Interface for the automated diagnosis of vitiligo and ringworm, giving medical practitioners a useful instrument for early identification and treatment, eventually enhancing patient outcomes and care quality.

关键词： Deep learning Accuracy Training data User interfaces Feature extraction Skin Lesions Reliability Medical diagnostic imaging Residual neural networks

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Deep Learning Model for Weather Prediction

Deep Learning Model for Weather Prediction

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Computing, sciences and Communications (ICCSC), International Conference on

作者： Shubhanshi Singh Neha Tyagi dept. Computer Science and Engineering Amity School of Engineering and Technology Noida India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Weather forecasting predicts atmospheric conditions at a particular time and place. It is an important task with a wide range of applications, including agriculture, transportation, and disaster prevention. Traditional weather forecasting methods rely on physical models of the atmosphere, which can be complex and computationally expensive. In recent years, deep learning techniques are effective for weather forecasting. Deep learning models can learn complex patterns from data, which can be used to predict future weather conditions accurately. This project aims to create a deep-learning model for forecasting weather. A huge dataset of historical meteorological data, including radar, satellite, and surface measurements, will be used to train the model. Numerous meteorological factors, such as temperature, precipitation, and wind speed, will be predicted by the model. We will compare the deep learning model's performance with conventional weather forecasting techniques. Creating a deep learning model that is more accurate and effective than current approaches is the project's main objective. The initiative may result in improved weather forecasts that are prompt and accurate, better agricultural, transportation, and disaster prevention decision-making, and less money lost because of weather-related incidents. Possible obstacles for the project are gathering and purifying a sizable dataset of meteorological data, using this data to train a deep learning model, and then comparing the model's output with more conventional techniques.

关键词： Deep learning Accuracy Computational modeling Atmospheric modeling Weather forecasting Transportation Predictive models Data models Agriculture Meteorology

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Smart Agritech : An Enhanced Solution for Advanced Plant Disease Detection, Remediation, and Data-Driven Agriculture

Smart Agritech : An Enhanced Solution for Advanced Plant Dis...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： K.S Rekha Aakash Haridas Dept. of Computer Science and Engineering Saveetha Engineering College(Autonomous) Chennai India

Plant diseases can have a detrimental effect on crop yield and quality, posing significant challenges to global food security. Early and accurate disease detection is essential for timely intervention and mitigation. In this study, we propose a novel approach for plant disease detection using a deep learning method based on a state-of-the-art convolutional neural network, ResNet9 architecture. The ResNet9 model is trained on a diverse dataset of plant images, including both healthy and diseased plants, representing different plant species and diseases. Our method leverages the power of deep learning to automatically learn discriminative features from plant images, enabling it to distinguish between healthy and diseased plants with a high degree of accuracy. We evaluate the performance of our model on a real-world dataset of plant images, and the results demonstrate its effectiveness in disease detection. The model achieves a high accuracy rate, indicating its potential for field deployment to assist farmers in monitoring and managing plant health. In conclusion, our study presents a promising solution for automated plant disease detection, providing farmers and agronomists with a practical tool to identify and combat plant diseases early, ultimately contributing to improving crop productivity and food security.

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MeQANet: Deep Learning-based Framework for Classifying Medical Query Answering in Bengali

MeQANet: Deep Learning-based Framework for Classifying Medic...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Fairooz Tasnia Mohammed Moshiul Hoque Dept. of Computer Science and Engineering Chittagong University of Engineering & Technology Chattagram Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

The automatic classification of medical QA pairs is crucial for improving healthcare efficiency, allocating specialized care, optimizing resource distribution, and facilitating robust data analysis. Bengali, the 7 th most widely spoken language globally, has a growing volume of health-related queries. However, unlike English, there is no benchmark large-scale medical QA dataset, pre-trained language model, or human baseline score for medical QA in Bengali. This paper introduces a deep learning-based framework called MeQANet that leverages CNN and BiLSTM techniques for classifying medical QA texts in Bengali. It also presents a novel medical QA corpus in Bengali (BnMedQA), consisting of approximately 4500 medical QA pairs across eight domains: Orthopedic, ENT, Gastroenterology and Hepatology, Gynecology, Pediatrics, General Medicine, Dermatology, and Cardiology. Various techniques (including LR, SVM, RF, MNB, AdaBoost (AB), CNN, BiL-STM, and CNN+BiLSTM) are employed to perform the classification tasks. Experimental results indicate that the CNN+BiLSTM approach achieved the highest F1-score of 0.85 among all employed techniques.

关键词： Support vector machines Radio frequency Data analysis Natural languages Dermatology Gynecology Medical services Gastroenterology Data models Resource management

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A Virtual Reality 3D Game: A Comparison Between an Immersive Virtual Reality Application and A Desktop Experience

A Virtual Reality 3D Game: A Comparison Between an Immersive...

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2023 IEEE International Conference on Image Processing Challenges and Workshops, ICIPCW 2023

作者： Vrigkas, Michalis Nikou, Christophoros University of Western Macedonia Dept. Communication and Digital Media Kastoria52100 Greece University of Ioannina Dept. Computer Science and Engineering Ioannina45110 Greece

ISBN: (纸本)9798350302585

The work aims to design and implement a 3D interactive and addictive object avoidance game using the Unity platform. The implementation of the immersive virtual reality application uses any smart mobile device as an input and output device, utilizing its accelerometer and compass to record the orientation and rotation data of the device in 3D space and capture the digital environment stereoscopically on the device screen. A comparative study between a virtual reality and a desktop real-time 3D game is performed to analyze the various attributes of the game and determine which medium is most effective. © 2023 IEEE.

关键词： Virtual reality

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