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Smart Agritech : An Enhanced Solution for Advanced Plant Disease Detection, Remediation, and Data-Driven Agriculture 2

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

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Rekha, K.S. Haridas, Aakash Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360523

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. © 2024 IEEE.

关键词： Convolutional neural networks

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Fire and Smoke Detection Using Deep Learning 2

Fire and Smoke Detection Using Deep Learning

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Divya, B. Kavitha, S. Muthu Pandeeswari, R. Sreenidhi, R. Ponvaishnavi, M.R. Loshine, S. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India India Rajalakshmi Engineering College Dept of Computer Science and Engineering Thandalam India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

Of late, wildfires and commercial fires like fires in a shopping complex, firework factories, and industries, continue to cause extensive destruction throughout the world, frequently causing human fatalities. The solution is divided into two systems, where the first system focuses on the wildfire and the second system focuses on commercial fires. Identifying the fire and smoke correctly plays an important role. Gradient-weighted Class Activation Mapping (Grad-CAM) is used to identify the smoke region in the image. This algorithm is used to assure the smoke or fire in the image. The algorithms like ResNet, CNN (Convolutional neural network), and VGG16 (Visual Geometry Group) algorithms are used in transfer learning. This aims to increase the accuracy of the detection of fire and smoke in forests to avoid mishaps. The GradCAM is an algorithm that finds out the positive score in the image. This confirms the smoke in the image using the heat map generation. The ReLU activation function is used to show the positive pixels. The saliency map is also used in finding out the difference between smoke and fog in case of wildfire. In the case of the building fire shopping complex, firework shops, factories, and industries would also require a better solution because the detection is easier but the rescue process is more difficult in such places. The same procedure is followed for detecting and finding the origin of the fire as the wildfire model. The YOLOv8 algorithm is used for real-time analysis of the building. The model constantly looks for the unusual behavior of the environment and this in turns induces the notification. The additional feature is added to the model for rescue purposes. Fire control and evacuation have become more complex and hence the solution to such a problem is not yet brought into consideration. Even after detecting the fire, most of the people die due to the poor rescue process. This is because the rescue team cannot find the people inside the buildi

关键词： Smoke

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Integrated IoT and Machine Learning Solutions for Precision Farming: Crop Recommendation and Leaf Disease Diagnosis 8

Integrated IoT and Machine Learning Solutions for Precision ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Harish, M. Devi, J. Vimala Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376425

The digital convergence of the Internet of Things and machine learning has therefore redefined precision agriculture with respect to productivity and sustainability. This work therefore proposes a smart agriculture system via the following IoT sensors: Soil Moisture, DHT, and NPK sensors, along with data storage in ThingSpeak cloud *** Similarly, farmers learn which plants are best to grow based on their projected data from machine learning algorithms. A convolutional neural network named ResNet was used for its control to instantly and correctly identify plant diseases in live mode. For the system's front end, HTML, CSS, and JavaScript were used while Python facilitated creation of the back end that provides users with an easy-to-use interface. It has achieved high accuracy levels in crop recommendation through models like Decision Tree, Naive Bayes, SVM, Logistic Regression, Random Forest and XGBoost among others have attained an accuracy of 90%, 99.09%, 97.95%, 95.27%, 99.09% and 99.31% respectively in crop recommendation. By ResNet model in the identification of leaf disease, a density of 99.2% was achieved. © 2024 IEEE.

关键词： Smart agriculture

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Constrained Quadratic Model Formulations for MDCVRPTW: Quantum Vs Classical 16

Constrained Quadratic Model Formulations for MDCVRPTW: Quant...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Kadambi, Soumitri Panduranga Rao, M.V. Iit Hyderabad Dept of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The Vehicle Routing Problem is a combinatorial optimization problem of immense practical importance, and has been studied extensively in the past. In this paper, we discuss classical and quantum approaches to the Multi-Depot Capacitated Vehicle Routing Problem with Time Windows (MDCVRPTW) using node based and time expanded formulations. The MD-CVRPTW that we study has additional features like multiple pairs of depots for different vehicles and non-overlapping routes. We pose this problem as Constrained Quadratic Model problem and use CPLEX for the classical approach and DWave's hybrid quantum-classical solver for the quantum approach. Initial empirical results suggest that the quantum approach outperforms the classical approach in terms of quality of solution for comparable running times. © 2024 IEEE.

关键词： Constrained Quadratic Model DWave Hybrid Classical-Quantum Solver Vehicle Routing

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Prediction and Classification of Brain Tumor using Modified Transfer Learning 18

Prediction and Classification of Brain Tumor using Modified ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Roy, Sukanya Mandal, Bijoy Kumar Khamaru, Koustov Bhattacharya, Arghya University of Engineering and Management Dept. of Computer Science Engineering Kolkata India Nshm Knowledge Campus Dept. of Computer Science Engineering Durgapur India

ISBN: (纸本)9789380544519

This study uses a novel method of segmenting and computing the tissue area in brain MRI images to predict the presence of brain tumors and classify the type of tumor into common types (pituitary, meningioma, and glioma). The tissues that make up the brain have intricate boundaries. In order to more accurately classify the different types of tumors, we have thus divided the brain tissues into three segments and examined for enlarged segments using Convolutional Neural Networks (CNNs) and a Modified CNN that uses transfer learning and VGGNet. The paper's methodology involves using CNNs with Transfer Learning for efficient image segmentation and clustering, followed by VGGNet for noise reduction and image segmentation refining. We are optimizing the internal parameters of VGGNet in order to outperform the competition. The volume and density of malignant clusters are computed through pixel counting. This kind of method works pretty well for figuring out tissue volume in a wide range of illnesses that affect people of all ages. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Brain Tumor Glioma Image-segementation Meningioma Neural Networks. Image classification Pituitary ResNet Transfer Learning VGGNet

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Early estimation of the number of hidden HIV infected subjects: An extended Kalman filter approach

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Infectious Disease Modelling 2023年第2期8卷 341-355页

作者： Paolo Di Giamberardino Daniela Iacoviello Dept.of Computer Control and Management EngineeringSapienzaUniversity of RomeVia Ariosto 2500185RomeItaly

In the last decades several epidemic emergencies have been affecting the world,influencing the social relationships,the economics and the *** particular,starting in the early 080,the Acquired Immunodeficiency Syndrome,AIDS,is representing one of the most worrying sanitary emergency,that has caused up to now more than 25 million of dead *** infection is caused by the Human Immunodeficiency Virus,HIV,that may be transmitted by body fluids;therefore with wise behaviours the epidemic spread could rapidly be *** sanitary emergency is peculiar for the long incubation time:it can reach even 10 years,a long period in which the individual can unconsciously infect other *** identification of the number of infected unaware people,mandatory to define suitable containment measures,is here obtained by using the extended Kalman filter applied to a noisy model in which,reasonably,only the number of infected diagnosed patients is *** simulations and real data analysis support the effectiveness of the approach.

关键词： Epidemic modeling Infection spread HIV-AIDS Extended Kalman filter

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Multiple Cancer Prediction with Image Analysis and Machine Learning 5

Multiple Cancer Prediction with Image Analysis and Machine L...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Nithya Sree, N. Ramyadevi, R. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Cancer, the second biggest cause of death globally, was recently discovered to pose a risk sickness for humans, despite early discovery not doing much to keep people from dying. Thus, efforts to give an ecologically friendly design with proven cancer-prevention estimations and means during the preliminary stages identification of cancer are critically needed. The growth of learning machine procedures have gotten better the cancer diagnosis sector by beating people in terms of both effectiveness and mistake rate. During the previous period, there certainly has seen a massive shift in the learning from machines supported tactics for separating and identifying separate cancerous tumors. This paper gives an outline of numerous forms of cancer detection processes that utilize learning algorithms alongside deep neural-based computers, in addition to a multitude of strategies, to process diverse data types. for finding illustrates and typical datasets have been employed in recent study. The objective of this endeavor is to put into practice machines instruction and algorithms that use deep learning to assess, rate, and fix the current improvements in cancer diagnosis and recognition for four types of cancer: brain, lung, kidney, and oral cancer. Various new methods were combined, and the results were examined using important performance measures such as accuracy, area under the curve, precision, sensitivity, and Dice score against standard datasets. These results will be discussed in future works. © 2024 IEEE.

关键词： cancer deep learning image processing oncology

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NLP based Text Summarization Techniques for News Articles 2

NLP based Text Summarization Techniques for News Articles

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Kumaran, B. Logesh Ravindran, N.V. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360523

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. © 2024 IEEE.

关键词： Image processing

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Time Series Forecasting of Sales Data using Hybrid Analysis 5

Time Series Forecasting of Sales Data using Hybrid Analysis

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Rajasree, T. Ramyadevi, R. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

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. © 2024 IEEE.

关键词： Accuracy prediction Comparison Customer Needs Data analysis Machine learning methods Market Trend Analysis Model analysis Sales forecasting

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A Semi-Supervised Approach For Detecting Agricultural Land Reduction In Bangladesh Using Satellite Images

A Semi-Supervised Approach For Detecting Agricultural Land R...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Shafin, Wahid Mahmood Ahsan, Sk. Md. Masudul Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798331529765

Recently, remote sensing change detection has significantly improved because of greater access to global satellite images and developments in satellite technology. Several CNN-based techniques have demonstrated exceptional precision. Detecting the amount of agricultural area that has been reduced is a type of change detection problem that can easily be solved using deep learning methods on satellite data. But the majority of deep learning methods have been specifically created for high-resolution images. This data lacks global coverage, consists of at most 3 to 4 bands, and is costly. The Sentinel-2 satellite, which was launched in 2015, offers medium-resolution multi-spectral image data consisting of 14 bands and provides free global coverage. Having more bands enhances the capabilities for model analysis. But there is a lack of labeled satellite data. This research proposes a semi-supervised method for generating ground truth by employing a multi-step procedure. This technique began with semi-supervised classification to label the images, followed by a sequence of refinement processes to enhance the accuracy of the ground truth mask. The mask was created using the XGBoost machine learning technique. The dataset was assessed using the output images generated by the trained model. Detecting changes in low-resolution image may be difficult, but proposed method will greatly streamline the procedure. Applying this approach revealed an approximately 8.35% decrease in agricultural land area inside the test image area between 2018 and 2022. In the test area of 237.77 square kilometers, the reduced area measures approximately 19.861 square kilometers. © 2024 IEEE.

关键词： Self-supervised learning

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