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Brinjal Leaf Pathology: An Improved Disease Classification in a Federated Learning-CNN Approach 2

Brinjal Leaf Pathology: An Improved Disease Classification i...

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

作者： Gaur, Poonam Aggarwal, Priyanshi Joshi, Kireet Mehta, Shiva Kukreja, Vinay Cgc Landran Department of Computer Application Mohali India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350360523

A study about brinjal leaf disease diagnosis using federated learning using CNN is presented here. Based on the data from four clients, we can identify four levels of severity for brinjal leaf disease. As a result of the use of federated learning, it is possible to train a CNN model collaboratively while maintaining data privacy and locality, which are essential when dealing with agricultural data. A federated average algorithm was used in our approach to combine local updates of models into a global model. Using this method, the model gains experience from different data sources, thus becoming more generalized and reliable. As evidenced by enhanced model performance among clients, this approach produces tangible outcomes. There were several significant results from the analysis. The macro average of disease diagnosis accuracy through clients is 97.55% in NA-6 and 89.71% in NA-1. As a result of accounting for disease severity classes, the weighted average (Weighted-average) also displays a rising trend. Also, the Micro Average (Micro-ave) shows this trend, running from 89.55% (na-1) to 97.55% (na-6), which represents a general classification success rate. Federation learning can manage brinjal disease severity strata's intricacies and disparities successfully. For a reliable classification of medical conditions, the model must accommodate various inputs, especially among later clients. The study improves agricultural technology by addressing field infections with a practical, scalable, and privacy-preserving method. This study suggests that federated learning and CNN can be used to diagnose crop diseases, revolutionizing the practice. © 2024 IEEE.

关键词： Statistical methods

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Analytical Evaluation of Web Performance Testing Tools: Apache JMeter and SoapUI 12

Analytical Evaluation of Web Performance Testing Tools: Apac...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Tiwari, Vatsya Upadhyay, Sachin Goswami, Jayati Krishna Agrawal, Sanjiv Bsa College of Engineering & Technology Department of Computer Science & Applications Mathura India GLA Univ. Deparmtnent of Computer Engineering & Application Mathura India G L Bajaj Group of Institutions Computer Sciecnce & Engineering Mathura India

ISBN: (纸本)9781665462617

Computing on the Web is based on distributed system applications that use the client-server architecture. Since web-based applications have been beta-tested, the quality criteria cannot be fixed. Whenever a website is launched, the question is always raised as to how many HTTP requests of users can the website handle at once. As part of this paper, various tools for performance testing will be used, including Apache SoapUI, JMeter, etc., and selection tools for load testing will be discussed. © 2023 IEEE.

关键词： Load testing

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Face Recognition using Opencv with LBPH Calcuation System for More Safety 3

Face Recognition using Opencv with LBPH Calcuation System fo...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Nivesh Goswami, Laxmi Chandigarh School of Business Department of Master of Computer Application Jhanjeri India Sanskriti University Department of Electrical Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350399264

A continuing chin added layer for residences and places of business has been presented in this article. It was produced using a computation known as the Binarization Profile (LBPH). Face recognition has been effectively employed to support schemes that aim to increase home and minimize employee as well as criminal surveillance via ID through such espionage frameworks. The LBPH computation takes into consideration both of these variables. Differences in stance and features of human characteristics, like lighting, have been critical constraint among most face recognition algorithms. This article discusses the advantages of this analysis over competing ones and explains why VLC should be used to carry out the computation rather than FORTRAN. © 2023 IEEE.

关键词： Face recognition

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Transfer Learning: A Paradigm for Machine Assisted Knowledge Transfer 1

Transfer Learning: A Paradigm for Machine Assisted Knowledge...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Gurjar, Aparna Voditel, Preeti Department of Computer Application Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9781607685395

This paper surveys transfer learning as a sustainable knowledge transfer mechanism. Conventional machine learning algorithms require a huge amount of labeled data for supervised learning. In absence of such data, the models suffer from performance degradation. Transfer learning enables the prior knowledge gained in doing a particular task to be reused or transferred to another new task of similar nature. This can speed up and improve the learning curve of the tasks in the new domain. The paper gives an overview of the transfer learning process and highlights how this innovative artificial intelligence (AI) technique can help achieve the goals of sustainable development. The literature survey highlights widely used mechanisms of Transfer Learning like homogeneous, heterogeneous, as well as instance-based, parameter-based, and relational-based implementation of transfer learning. It discusses how these mechanisms are utilized to create efficient AI-based applications which aid sustainability in the long run. © The Electrochemical Society

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Extractive Text Summarization Using LSTM Based Encoder-Decoder Classification 1

Extractive Text Summarization Using LSTM Based Encoder-Decod...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Thakare, Abhijeet Voditel, Preeti Department of Computer Application Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9781607685395

Nowadays, Text Summarization is one of the important areas to be focused on. As the World Wide Web is growing, a huge amount of text articles (especially Blogs, scientific articles) are also generated on the internet. Automatic Text Summarization is one of the important techniques to shorten the original text in such a way that, shorten or summarized text covers incisive and meaningful sentences of original huge text. Extractive Summarization extracts important sentences from original documents and then aggregates all these sentences to generate the summary. We have proposed a novel LSTM based encoder-decoder, which plays a vital role in the extractive Text summarization process. CNN news article dataset is utilized for training our model. Our model is evaluated on standard metrics like Gold Standard, Recall Oriented Understudy for Gisting Evaluation (ROUGHE)-1, and ROUGHE-2. After evaluation, our model achieved an average F1-Score of 0.8353. Our model also outperformed other models available in the literature. © The Electrochemical Society

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Transformative Insights: Harnessing Machine Learning for Big Data Analytics 3

Transformative Insights: Harnessing Machine Learning for Big...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Bhatt, Chandradeep Sagta, Divas Chauhan, Rahul Dhondiyal, Shiv Ashish Sharma, Sanjay Baloni, Dev Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Dehradun India Sgrr Univeristy Computer Application Department Dehradun India Quantum University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350342338

Like oil in the past, data has become a very important resource in the modern world. But just as oil must be refined in order to be valued, data must also be handled and evaluated in order to yield worthwhile insights. Big Data analytics is a discipline that has developed in response to the expanding demand for efficient data analysis and interpretation. In order to find hidden patterns, connections, and insights, it involves examining huge, complicated datasets utilizing cutting-edge tools and methods. Big Data analytics' importance stems from its capacity to decipher the secrets contained in any dataset and glean insightful information. When appropriately analyzed and addressed, these insights can produce beneficial results. Big Data Analytics is extremely important in the context of consumer behavior. This technology is being used by marketers more frequently to analyze customer behaviors more accurately. Marketers can better understand their target market's preferences, demands, and buying habits by analyzing customer behavior. With the use of this information, marketers can make more informed choices and modify their marketing plans to specifically address customer needs. In the end, this may help to improve firm performance outcomes including boosted sales, enhanced client contentment, and general expansion. The aforementioned study seeks to offer a thorough overview of Big Data technology, including its goal, importance, and potential applications. It also looks into how Big Data technologies affect customer behavior and overall business effectiveness. The goal of this study is to shed light on these elements and the potential advantages and consequences of applying big data analytics across different fields. © 2023 IEEE.

关键词： Big Data Data Analytics Machine Learning RDBMS

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Interpreting Deep Convolutional Neural Networks for Medical Diagnosis Using Perceptual Encoding and Retrieval 15

Interpreting Deep Convolutional Neural Networks for Medical ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kasture, Shreyas Maurya, Sudhanshu Verma, Rohan Yadav, Ajay Kumar Shukla, Amit Nagpur Campus Pune India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India United Institute of Management Department of Computer Application Prayagraj India Centre of Artificial Intelligence & Robotics Mandi India

ISBN: (纸本)9798350370249

The robust performance of deep neural networks (DNNs) in many areas, including medical diagnosis, is not accessible from the problem of interpretability. Even though DNNs have high accuracy, they tend to operate as black boxes, usually without any explanation of which basis their predictions are made. This interpretability issue has become important, especially for high-stakes applications, such as medical image analysis and disease diagnosis, in which people's lives are at risk. The study was structured to raise novel approaches for DNNs, internal representation translation and provide visualizations to assist medical diagnosis tasks and ensure reliable and transparent decision-making procedures. Such techniques have been developed to improve the interpretability of DNN without sacrificing the performance. The models for proposed models succeeded in the clinical tasks of medical diagnosis. They reached competitive accuracy with the best model, obtaining an accuracy of 95.7% and an F1-score of 0.914 on lung abnormality detection from chest X-rays. AUC-ROC high sensitivity and specificity of 0.963 were observed when diagnosing brain tumors based on MRI scans. These models also provided improved faithfulness perceived through saliency maps, which had an average score of 0.82, and concept activation vectors, with a consistency of 0.77 from the evaluation done by medical experts. The suggested methods offer a chance for a better patient outcome via high-precision and safe diagnostics and treatment options with an expected change in misdiagnosis rate of 10-15%. © 2024 IEEE.

关键词： Active Learning Deep Neural Networks Interpretation Synthesis Medical Diagnosis Network Traffic Forecasting Saliency Map Aggregation and Saliency Maps

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CLAACS-IOD: Certificate-embedded lightweight authentication and access control scheme for Internet of Drones

CLAACS-IOD: Certificate-embedded lightweight authentication ...

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作者： Sadhukhan, Dipanwita Ray, Sangram Dasgupta, Mou Rodrigues, Joel J. P. C. Department of Computer Science and Engineering National Institute of Technology Sikkim Sikkim Ravangla India Department of Computer Application National Institute of Technology Raipur Raipur India Qingdao China Instituto de Telecomunicações Covilhã Portugal

In recent era, the unmanned aerial vehicles (UAVs) commonly known as drones has emerged as a one of the most significant and promising tools which has demonstrated its wide range of implementations variating from commercial domain to the field of defense due to its distinct capabilities such as inspection, surveillance, precision and so forth. Internet connected drones provides a propitious trend that boosts the flying safety, and service qualities of the UAVs where numerous low-altitude drones winged in different flying regions for executing a precise task such as gathering the real-time information from the unuttered environment to be interpretated by users. Nevertheless, the open-access insecure communications in hostile environment, the issues like safety and confidentiality threats, various security concerns such as the leakage of flying courses, identities, position, and gathered data by the drones are upstretched. To address these security concerns, access control mechanism provides a potential service in terms of authentication and key agreement for securing the communication between the individual drones within their respective flying regions. This article introduces a robust, efficient, lightweight, and privacy preserving ECC integrated access control approach by employing digital certificate with considering the high dynamicity and mobility of the drones. The designing of this proposed approach, that is, CLAASC-IoD is influenced by the aim of inter-drone and drone-to-ground station communication in the IoD paradigm. The detailed analysis of security using probabilistic random oracle model as well as simulation using well-accepted security verification tool AVISPA and comparative performance evaluation supports the claim of robustness, effectiveness, and proficiency. © 2023 John Wiley & Sons Ltd.

关键词： Internet of things

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Comparison of Machine Learning Techniques Based on Feature Reduction to Detect Diabetic Retinopathy 9

Comparison of Machine Learning Techniques Based on Feature R...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Singh, Devendra Joshi, Deepika Dobhal, Dinesh C. Pant, Janmejay Pant, Himanshu Graphic Era Hill University Computer Science and Engineering Department Nainital India Amrapali Group of Institutes Faculty of Technology and Computer Application Haldwani India Graphic Era Deemed to Be University Computer Application Department Uttarakhad Dehradun India Graphic Era Hill University School of Computing Department Nainital India

ISBN: (纸本)9798350397376

Diabetic retinopathy is considered as an eye disorder condition that can affect persons who are diagnosed positive against diabetes. This condition causes damage to the retina's blood vessels. Diabetic retinopathy detection at an early stage may help patients protect their vision. This disease is often asymptomatic in the early stages but has a significant impact in the latter stages. The timely identification and intervention of diabetic retinopathy (DR) can greatly mitigate the potential for visual impairment. It takes time, effort, and money for ophthalmologists to manually diagnose DR retina fundus images. So there is a need for a screening system that can detect the disease in its early stages. There are various machine learning-based approaches available to detect diabetic retinopathy. In our proposed work first, we have to select the optimal features then we perform the comparative analysis with the help of various machine learning algorithms. We have used Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machine (SVM), Naïve Bayes, and AdaBoost. In our experiment, we observed that Logistic Regression achieved the highest accuracy of 74.2 with optimal features. © 2023 IEEE.

关键词： Adaptive boosting

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Clustering-based Image-Text Graph Matching for Domain Generalization 27th

Clustering-based Image-Text Graph Matching for Domain Genera...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Park, Nokyung Chae, Daewon Shim, Jeongyong Kim, Sangpil Kim, Eun-Sol Kim, Jinkyu Department of Computer Science and Engineering Korea University Seoul Korea Republic of Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Artificial Intelligence Application Hanyang University Seoul Korea Republic of Department of Computer Science Hanyang University Seoul Korea Republic of

ISBN: (纸本)9783031781919

Learning domain-invariant visual representations is important to train a model that can generalize well to unseen target task domains. Recent works demonstrate that text descriptions contain high-level class-discriminative information and such auxiliary semantic cues can be used as effective pivot embedding for domain generalization problems. However, they use pivot embedding in a global manner (i.e., aligning an image embedding with sentence-level text embedding), which does not fully utilize the semantic cues of given text description. In this work, we advocate for the use of local alignment between image regions and corresponding textual descriptions to get domain-invariant features. To this end, we first represent image and text inputs as graphs. We then cluster nodes within these graphs and match the graph-based image node features to the nodes of textual graphs. This matching process is conducted both globally and locally, tightly aligning visual and textual semantic sub-structures. We experiment with large-scale public datasets, such as CUB-DG and DomainBed, and our model achieves matched or better state-of-the-art performance on these datasets. The code is available at: https://***/noparkee/Graph-Clustering-based-DG. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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