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Remote Sensing–Based UAV Imaging in Heat Pattern Analysis Impact on Climate Change Detection Using Fuzzy Stacked Lasso Elastic-Net Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 699-708页

作者： Sailaja, M. Kumar, M. Prema Jyothi, B. Swarna Vanguri, G. L. Narasamba Manjula, S. Priya, D. Divya Department of Computer Science and Engineering Ravindra College of Engineering for Women Andhra Pradesh Kurnool India Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Information Technology Aditya College of Engineering & amp Technology(A) Andhra Pradesh Surampalem 533437 India Department of Computer Science and Business Systems Nehru Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat islands raise surface temperatures, which has an effect on city dwellers’ health and welfare. Urbanisation-related changes to the land surface, which are especially notable right after sunset, have an impact on radiative forcing. Recently, there has been a surge in the usage of unmanned aerial vehicle (UAV) technology, particularly for precision agriculture and plant phenotyping. This is due to the declining cost and increased accessibility of both UAVs and thermal imaging sensors. This interruption affects subsequent operations like target item categorisation and water quality parameter inversion. The goal of this study was to evaluate the potential for combining machine learning (ML) with unmanned aerial vehicle (UAV)–based imaging technology. In this research, the novel technique in UAV image–based temperature pattern detection of various regions of earth and their sea level rise analysis using image classification by machine learning model. Here, the input is collected as UAV images which have been processed for noise removal and normalisation. These image features have been extracted using convolutional active contour–based kernel component analysis and classified using region-based fuzzy stacked lasso elastic-net model. The classified output shows temperature analysis with sea level rise based on image analysis. The experimental analysis has been carried out for various UAV image dataset in terms of detection accuracy, RMSE, F-measure, mean average precision (MAP), and recall. Proposed technique F-measure of 89%, detection accuracy of 96%, MAP of 94%, recall of 92%, and RMSE of 60%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional active contour Heat pattern detection Kernel component analysis Lasso elastic-net model Remote sensing

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Music Generation Algorithms: An In-Depth Review of Future Directions and Applications Explored

Music Generation Algorithms: An In-Depth Review of Future Di...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Duraisamy, Premkumar Naveen, V. Kishore, G.N. Nishal, S. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350374100

Music generation algorithms have made significant progress in recent years, enabling the development of algorithms to generate creative and realistic music. This survey paper provides a comprehensive overview of music generation algorithms organized by algorithm type. We discuss each algorithm type's strengths, weaknesses, and applications. We also cover multi-level representations for music generation, which allow algorithms to learn complex patterns in music data at different levels of granularity. This survey paper is intended to be a valuable resource for researchers and practitioners in music generation. It provides a comprehensive overview of the current state of the art in music generation algorithms and highlights this research area's challenges and future directions. © 2024 IEEE.

关键词： algorithms applications multi-level representations music generation strengths weaknesses

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A Hybrid Learning Model for Tomato Plant Disease Detection using Deep Reinforcement Learning with Transfer Learning 4

A Hybrid Learning Model for Tomato Plant Disease Detection u...

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4th International Conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Raghuram, Kadambari Borah, Malaya Dutta Department of Computer Science and Engineering National Institute of Technology Silchar Assam Silchar India

Plant diseases play a significant role in damaging crop production and food security. Detecting and diagnosing plant diseases in the early stages obtains better management of diseases. Many plants are affected by these diseases, which are very dangerous for crop yield. This paper introduces advanced plant disease detection using an advanced preprocessing technique and a Hybrid Learning Model (HLM). The advanced preprocessing method uses a digital camera to capture high-resolution images of plant leaves from multiple angles. These images are then processed using an enhancement algorithm to improve the visual quality and clarity. The preprocessed images are subjected to a HLM model, which utilizes Deep Reinforcement Learning with Transfer Learning (DRL-TL). The DRL-TL architecture is designed to extract features from the preprocessed images in a three-dimensional manner, considering the spatial information of the leaves. It enables the model to capture precise patterns and variations indicative of disease symptoms. The pre-trained model MobileNetV2 trained on a tomato disease dataset belongs to labeled images;it consists of standard and affected plant leaves to learn the discriminative features associated with different diseases. Results obtained the effectiveness of the hybrid learning model. The preprocessing technique significantly increases the input images' quality, enhancing the subsequent HLM's performance. The model accurately identifies and classifies various plant diseases, outperforming existing methods. Furthermore, the hybrid learning model shows robustness and abstraction, successfully detecting diseases across plant species and environmental conditions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Deep reinforcement learning

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A Deep Learning Approach With Sparse Autoencoder for Alzheimers Disease Classification 23th

A Deep Learning Approach With Sparse Autoencoder for Alzheim...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Oommen, Deepthi K. Arunnehru, J. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Tamilnadu Chennai India

ISBN: (纸本)9783031648120

Alzheimer's Disease is a progressive neurodegenerative condition distinguished by a steady deterioration in cognitive abilities. Given the empirical inquiries conducted, it is imperative to take age into account as a fundamental criterion when selecting participants. Younger individuals are more vulnerable to the transient aspect compared to those who are older. The current work focused on selecting young-onset subjects, as early diagnosis can significantly impact patients' lives. The identification of this disease at an early stage using traditional means poses significant difficulties. Deep Learning (DL) has emerged as a highly effective approach for enhancing the performance of diagnostic processes and boosting forecast accuracy. The research utilized deep learning techniques and neuroimaging approaches to autonomously identify and categorize the condition based on its phases of mild cognitive impairment. The suggested study is conducted through a series of three sequential phases. Prior to further analysis, the 3D input visuals need to undergo image preprocessing with Weiner filtering in order to remove noise and smooth the image. Following, Transfer learning models are utilized to extract features, which are then compressed through the use of cascaded Auto Encoders. The ultimate stage involves the utilization of a fine tuned Deep Neural Network (DNN) for categorizing of the phases of AD into five classes. The combination of the ResNet-18 and sparse autoencoder with the deep neural network model yielded a remarkable accuracy rate of 95.62%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep neural networks

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Parameter-Dependent Competitive Analysis for Online Capacitated Coverage Maximization through Boostings and Attenuations 41

Parameter-Dependent Competitive Analysis for Online Capacita...

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41st International Conference on Machine Learning, ICML 2024

作者： Xu, Pan Department of Computer Science New Jersey Institute of Technology Newark United States

In this paper, we consider a model called Online Capacitated Coverage Maximization, characterized by two features: (1) the dynamic arrival of online agents following a known identical and independent distribution, and (2) each offline agent is associated with a specific coverage valuation over the groundset of online agents. Additionally, both offline and online agents are assigned integer capacities, reflecting finite budgets and operational constraints. We introduce and analyze two matching policies. The first, a non-adaptive policy, utilizes offline statistics derived from solving a benchmark linear program. The second is an enhanced version equipped with real-time boostings and attenuations. We conduct a comprehensive competitive analysis and characterize the competitive ratio for both policies as functions of two crucial parameters: a lower bound on the matching capacity among offline agents and an upper bound on the number of online agents covering any specific feature for offline agents. Copyright 2024 by the author(s)

关键词： Budget control

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Twitter Scraping and Sentiment Analysis on Stock Price

Twitter Scraping and Sentiment Analysis on Stock Price

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2024 International Conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Iniyan, S. Thangakumar, J. Rajendran, P. Selvi Hindustan Institute Of Science And Technology Dept. Of Computer Science And Engineering Chennai603103 India

ISBN: (纸本)9798350364828

social media is useful in the community for sharing and discussing various events with others. The study's main objective is to predict the sign of the stock by sentimental analysis regarding the Hindenburg report on Adani group stocks by obtaining tweets from Twitter. The study's findings will aid in understanding the effect of the Hindenburg report on the stocks of the Adani group. Excel files are downloaded from Twitter containing tweets from social media users. Tweets are cleaned up by eliminating noise data after extraction. Machine learning algorithms, such as Logistic Regression (LR), Linear support classification (SVC), Random Forest (RF), and voting, were used to classify the data into positive, Neutral, and negative groups. © 2024 IEEE.

关键词： Logistic regression

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Detection of Malicious URLs using Logistic Regression 5

Detection of Malicious URLs using Logistic Regression

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Usha Sri, B. Bamla, Varun Singh Bandari, Venu Bheemagani, Manikanta Uppuganti, Chaitanya Akkenapally, Ruthvik B v Raju Institute of Technology Computer Science Engineering Narsapur India

ISBN: (纸本)9798350379945

The rise of mobile devices has driven real- world activities online, exposing users to the growing threat of malicious URLs that can compromise network security by distributing malware, launching phishing attacks, and causing data breaches. This paper proposes using logistic regression, a powerful binary classification tool, to differentiate between malicious and benign URLs. By extracting and analyzing features such as character sequences, suspicious keywords, and specific subdomains, the logistic regression model can identify patterns indicative of malicious intent. Although effective, logistic regression's assumption of linearity between features and maliciousness poses a limitation, as cybercriminals employ complex strategies that may avoid detection. Therefore, while logistic regression offers interpretability and initial success, enhancing this approach with advanced machine learning techniques could provide deeper insights and more robust protection against cyber threats. © 2024 IEEE.

关键词： Phishing

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Telemedicine Platform for Patient-Doctor Connectivity, Real-Time Prescription, and Report Management

Telemedicine Platform for Patient-Doctor Connectivity, Real-...

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2024 IEEE International Conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Mustafa Nawaz, S.M. Arun Kumar, T. Balaji, M. Aadhithyan, S. Rhahul, R. Ashok, P. Sri Sai Ram Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798331522988

The Integrated Telemedicine Platform connects patients and doctors seamlessly, enabling video consultations, report uploads, real-time prescriptions, and nearby pharmacylocators. Enhancedbymachinelearning for early disease prediction, the platform offers secure patient data handling, intuitive multi-platform access, and compliance with data privacy standards, providing a reliable, user-friendly telemedicine solution for remote healthcare. © 2024 IEEE.

关键词： Telemedicine

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SARIMA Model: An Efficient Machine Learning Technique for Weather Forecasting 2

SARIMA Model: An Efficient Machine Learning Technique for We...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Kumari, Shabnam Muthulakshmi, P. Department of Computer Science SRM Institute of Science and Technology Tamilnadu Kattankulathur Chennai603203 India

Weather forecasting is a critical tool for many different applications, from agriculture and transportation to disaster preparedness and response. While weather forecasts are not always perfect, they provide valuable information that can help people make informed decisions and take appropriate actions to protect themselves and their property from the impacts of extreme weather events. In this paper, to forecast the weather we are using the SARIMA model as RMSE is 1.24 and analyse how it forecast the weather with high accuracy. Note that the SARIMA model is a machine learning (ML) technique used to forecast time series data that has both trend and seasonal components. It is an extension of the ARIMA model, which stands for Autoregressive Integrated Moving Average. To account for periodic changes in the data, the SARIMA model augments the ARIMA model with a seasonal component. The SARIMA model is trained on historical weather data and can be used to predict future weather patterns. It is a powerful tool for weather forecasting as it can accurately predict both short-term and long-term weather trends. The quality of the input data determines how accurate the SARIMA model will be, and the model can be adjusted to perform better. In conclusion, the model known as SARIMA is a ML based approach that has been successfully used to predict weather. It is a powerful tool that can handle non-stationary data and seasonal components and can accurately predict short-term and long-term weather trends. © 2024 Elsevier B.V.. All rights reserved.

关键词： Weather forecasting

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Empowering Diagnosis Techniques: Deep Learning in Oral Cancer Detection 2

Empowering Diagnosis Techniques: Deep Learning in Oral Cance...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Raghavendra, S. Prasanna Kumar, M.J. Computer Science Engineering Department BGS Institute of Technology Adichunchanagiri University India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

A complex problem, oral cancer affects people all over the world. In India, for example, eighth most common type of cancer and takes 130,000 lives yearly. Diagnoses can be challenging because it can appear anywhere in the mouth, including the tonsils, face, neck, and salivary glands. Conventional diagnostic techniques such as biopsies provide information, but they are not very good at identifying cancer cells and estimating their spread. This work uses digital processing technologies to identify and categorize oral cancer cells in order to address this. The paper uses wavelet features, bag histogram of directed gradients, feature extraction, and other sophisticated techniques to discover ideal characteristics before applying the fuzzy optimization algorithm. Next, a Convolutional Neural Network (CNN) classifier based on regions is utilized for classification. The effectiveness of the suggested strategy is demonstrated by evaluation criteria such as Recall Rate, Classification Accuracy, Precision Rate, and Error Rate, with RCNN appearing as the best oral cancer detection technique. The suggested model has been put to the test for FUZZY and RCNN separately over a number of epochs. We determine that, with an accuracy of 98.8%, precision rate of 1, recall rate of 63.4, and error rate of 20.1, the RCNN model is more efficient than the FUZZY model. © 2024 IEEE.

关键词： Convolutional neural networks

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