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共 35530 条记录，以下是71-80 订阅

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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...

引用

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

Integrated IoT and Machine Learning Solutions for Precision ...

引用

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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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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A comprehensive step of XAI for Bengali and English text detection and recognition from natural scene images 2

A comprehensive step of XAI for Bengali and English text det...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Dutta, Mithun Das, Utpol Kanti Das, Jugal Krishna Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350367461

A critical first step in many applications, including augmented reality, document analysis, and scene comprehension, is text detection from images. Even though text identification for the English language has advanced significantly, there are particular difficulties when applying these methods to languages such as Bengali because of variations in script, morphology, and typographic characteristics. This article offers a wonderful method for leveraging the Faster R-CNN framework to identify and categorize text from images that contain Bengali and English sentences. The study utilizes a dataset named "Bengali and English text image dataset (BETID)"that consists of images with text in both Bengali and English scripts, taken from a variety of sources such as books, documents, natural scenes, internet images, and landscapes. Pre-processing methods are used to improve the resilience and generalization of the model, including image scaling, normalization, and augmentation. The Faster R-CNN architecture is tuned and trained to identify text regions in images and categorize Bengali or English text. The performance of the text Classification model is evaluated using evaluation measures such as accuracy on the BETID dataset. The proposed Faster R-CNN model achieved an average of 97.90% accuracy, which is better than other deep learning models. Empirical findings showcase the capacity of transfer learning to alleviate data scarcity concerns and enhance text detection precision for low-resource languages such as Bengali.) © 2024 IEEE.

关键词： Deep learning

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Time Series Forecasting of Sales Data using Hybrid Analysis 5

Time Series Forecasting of Sales Data using Hybrid Analysis

引用

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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Prediction and Classification of Brain Tumor using Modified Transfer Learning 18

Prediction and Classification of Brain Tumor using Modified ...

引用

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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Evaluating 10Gbps Wireless Optical Communication Using NRZ Modulation: Analyzing BER, Signal Quality, and System Performance 7

Evaluating 10Gbps Wireless Optical Communication Using NRZ M...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Khant, Shailesh Makwana, Mayur Patel, Atul Faculty of Computer Science and Applications Charusat Changa Anand India Dept. Electronics and Communication Engineering Charusat Changa Anand India

ISBN: (纸本)9798350350067

Free space optical (FSO) communication is a fast and wireless data transfer technique that serves as a viable alternative to traditional fiber-optic systems. This study illustrates how FSO can effectively address the growing demand for rapid data transmission and suggests its potential applicability in various scenarios. FSO can be employed to establish connections between buildings within a corporate campus without the need for physical wiring, or it can provide high-speed internet access in locations where installing cables is challenging. We used Optsim software to assess critical parameters such as Bit Error Rate (BER), eye diagrams, power, and signal quality. The results offer valuable insights into enhancing the efficiency and reliability of FSO systems. FSO can find applications in cellular networks to interconnect network equipment, as well as in high-definition video surveillance. These findings highlight the areas in which FSO can advance communication technology and provide valuable insights for future developments. © 2024 IEEE.

关键词： Bit error rate

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Crowd Digital Twins: High-Level Requirements and Architecture 22

Crowd Digital Twins: High-Level Requirements and Architectur...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Casadei, Roberto Delnevo, Giovanni Girau, Roberto Mirri, Silvia University of Bologna Dept. of Computer Science and Engineering Italy

ISBN: (纸本)9798331508050

Crowd management is the problem of dealing with physical crowds of humans, e.g., for safety reasons or to improve services through crowd-awareness. In previous work, the notion of a Crowd Digital Twin (CDT), namely the digital twin associated to physical gatherings of humans, was motivated and proposed as a fundamental component of crowd management systems. In this work, we better characterise the CDT notion, by investigating the functional and non-functional requirements that such a technical solution should satisfy. Then, we consider what are the fundamental components of CDT systems, and accordingly delineate a general architecture aimed at flexibly supporting the corresponding physical-digital thread. © 2025 IEEE.

关键词： Crowdsourcing

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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...

引用

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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Comparison and characterization of some selected models on the basis of their suitability for various applications 7

Comparison and characterization of some selected models on t...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Gupta, Kirti Kumar, Pardeep Upadhyaya, Shuchita Kurukshetra University Dept. of Computer Science & Applications Kurukshetra India

ISBN: (纸本)9798350371406

One of the most important aspects of data forecasting is selecting the most promising algorithm to model and predict a specific phenomenon. As with other data mining jobs, forecasting (or prediction) selects the optimal model for a given problem based on empirical evidence because there is no one best modelling technique. Our careful examination of the last ten years' worth of literature indicates that, nevertheless, very few scholarly works thoroughly outline the advantages and drawbacks of the most widely used prediction algorithms. However, these models have a poor track record of success when used on intricate and highly nonlinear data. In this article, we summarise a number of lessons learnt and offer suggestions regarding the benefits, limitations, and ideal circumstances under which to apply each model. © 2023 IEEE.

关键词： Forecasting

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