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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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EEG emotion recognition framework based on invariant wavelet scattering convolution network

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2181-2199页

作者： Elrefaiy, Ahmed Tawfik, Nahed Zayed, Nourhan Elhenawy, Ibrahim Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computers and Systems Department Electronics Research Institute Cairo Egypt Mechanical Engineering Department The British University in Egypt Cairo Egypt

EEG signals for real-time emotion identification are crucial for affective computing and human-computer interaction. The current emotion recognition models, which rely on a small number of emotion classes and stimuli like music and images in controlled lab conditions, have poor ecological validity. Furthermore, identifying relevant EEG signal features is crucial for efficient emotion identification. According to the complexity, non-stationarity, and variation nature of EEG signals, which make it challenging to identify relevant features to categorize and identify emotions, a novel approach for feature extraction and classification concerning EEG signals is suggested based on invariant wavelet scattering transform (WST) and support vector machine algorithm (SVM). The WST is a new time-frequency domain equivalent to a deep convolutional network. It produces scattering feature matrix representations that are stable against time-warping deformations, noise-resistant, and time-shift invariant existing in EEG signals. So, small, difficult-to-measure variations in the amplitude and duration of EEG signals can be captured. As a result, it addresses the limitations of the previous feature extraction approaches, which are unstable and sensitive to time-shift variations. In this paper, the zero, first, and second order features from DEAP datasets are obtained by performing the WST with two deep layers. Then, the PCA method is used for dimensionality reduction. Finally, the extracted features are fed as inputs for different classifiers. In the classification step, the SVM classifier is utilized with different classification algorithms such as k-nearest neighbours (KNN), random forest (RF), and AdaBoost classifier. This research employs a principal component analysis (PCA) approach to reduce the high dimensionality of scattering characteristics and increase the computational efficiency of our classifiers. The proposed method is performed across four different emotional classific

关键词： Support vector machines

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Deep Learning based Human Activity Recognition in Smart Home 4

Deep Learning based Human Activity Recognition in Smart Home

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4th IEEE International Conference on Data engineering and Communication systems, ICDECS 2024

作者： Anbazhagan, K. Swamy, G. Janani, R. Farakte, Amarsinh Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Vignana Bharathi Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Sant Gajanan Maharaj College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra India

ISBN: (纸本)9798350393354

Human activity recognition (HAR) in the context of smart homes has attracted considerable interest because to its potential to increase residents' quality of life, safety, and energy efficiency. This study dives deeper into the use of deep learning (DL) algorithms for human activity recognition in a smart home scenario. A realistic approach for distinguishing distinct human behaviors like as walking, sitting, cooking, and sleeping is provided by the combination of artificial intelligence and sensor technology. This study offers FCN-LSTM, a revolutionary technique for HAR in smart homes that combines a Fully Convolutional Network (FCN) architecture with a Long Short-Term Memory (LSTM). The proposed FCN-LSTM model capitalises on the strengths of both architectures, combining the FCN's ability to capture specific information spatially with the LSTM's experience modelling temporal correlations. This hybrid method efficiently captures both spatial and temporal variability inherent in human activities, overcoming the HAR issues. The FCN-LSTM model is trained on a huge dataset acquired from sensors deployed in a smart home setting, which includes a wide range of actions encompassing ordinary daily routines. This method is used to the Wireless Sensor Data Mining (WISDM) dataset, which was produced from data collected from a large number of people who engaged in six different activities: walking, sitting, downstairs, running, standing, and upstairs. According on the experimental data, the FCN-LSTM model outperforms established approaches and standalone systems. The results show that the FCN-LSTM algorithm performs well in recognizing human actions. The FCN-LSTM algorithm achieves a testing precision of 95.99%, with recall and f-measure of 97.11% and 99.99%, respectively. © 2024 IEEE.

关键词： Long short-term memory

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Customized IoT based Smart Irrigation for Terrace Plants 6

Customized IoT based Smart Irrigation for Terrace Plants

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Priya, S. Padma Pavalarajan, S. Abarna, P.S. Manjula devi, C. Abinaya, B. Sukaina, S. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India Department of Information Technology Velammal college of Engineering and Technology Madurai India

ISBN: (纸本)9798331522667

An Internet of Things based smart irrigation system for terrace plants is a cutting-edge solution designed to automate and optimize the watering process, ensuring efficient use of water and promoting healthier plant growthSmart irrigation system may vary from field with crops of same variety to terrace plantation with plants of different varieties. As the water requirement may vary based on plant type, environmental condition, growth stage, it is intelligent to provide varying water supply. This Smart irrigation system employs soil moisture to monitor the water content of the terrace garden in real time. Some variety of plants may require minimum quantity of water. The sensors continuously collect data and send it to a central IoT platform or a cloud-based server, where intelligent algorithms analyse the data and determine the precise amount of water needed by the plants. Based on this analysis, the system automatically activates the irrigation mechanism whether it's a drip system, sprinklers, or any other appropriate watering method only when required, thus preventing water wastage and avoiding overwatering or underwatering. © 2025 IEEE.

关键词： Sprinkler systems (irrigation)

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Blockchain-based Patient Recommendation System for Smart Healthcare

Blockchain-based Patient Recommendation System for Smart Hea...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Mehta, Raj Mehta, Mahek Kakkar, Riya Oza, Parita Agrawal, Smita Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat 382481 India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia

ISBN: (纸本)9798350350548

In recent times data breaches in various sectors of industry have become a common threat. It has become very crucial to secure patient data in the health industry. The upcoming Healthcare 4.0 techniques can play an important role in this. We have implemented these techniques in our proposed model to protect personalised health information of the individual health profiles of the patient using blockchain. The proposed model is also a recommending system with the aim to offer relevant advice to the patients to keep a check on the various health parameters like blood pressure, body temperature, blood sugar etc. © 2024 IEEE.

关键词： Electronic health record

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Posture Prediction in Response to Seat Angle Change with Smart Chair

Posture Prediction in Response to Seat Angle Change with Sma...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Esumi, Tsubasa Takemura, Noriko Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Department of Artificial Intelligence Fukuoka Japan

ISBN: (纸本)9781665410205

Long-term desk work with lousy posture causes physical disabilities. To solve this problem, we have developed a smart chair with a controllable seat angle in our previous research. In order to control the seat angle and guide the user to the correct posture using this smart chair, it is necessary to investigate how the posture changes in response to the seat angle change. In this study, we first constructed a dataset by capturing posture data from 30 subjects while changing the seat angle of the smart chair. This dataset trained a model based on a graph neural network to predict posture in response to seat angle changes. We compared the model's prediction performance in the evaluation experiment depending on the training data and the body part. © 2024 IEEE.

关键词： Graph neural networks

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Enhanced Security through Distributed Fragmentation and Encryption for Cloud based Data Storage 2

Enhanced Security through Distributed Fragmentation and Encr...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Palani, S. Ajay, B. Bhavadharani, B. Varshini, A. Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Integrating cloud computing into organizational data management demands paramount protection of sensitive information. This paper presents a framework to enhance data security through distributed fragmentation and advanced encryption techniques. By segmenting sensitive data into multiple fragments distributed across various cloud storage locations, the approach mitigates risks associated with centralized data repositories. Each fragment is encrypted with robust cryptographic algorithms, ensuring that unauthorized access renders intercepted data unintelligible. This fragmentation not only strengthens security but also improves performance through parallel data processing and retrieval, optimizing resource utilization. Additionally, decentralized storage reduces the impact of single points of failure, enhancing resilience against data loss or breaches. Simulation results demonstrate the effectiveness of this model in countering attack vectors, including data tampering and unauthorized access. Overall, this research contributes to secure cloud storage solutions, offering a robust framework for organizations to protect sensitive information, ultimately fostering trust in cloud technologies and enabling safer data management practices. © 2024 IEEE.

关键词： Cloud security

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Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM 3

Hybrid Approach for Electricity Price Forecasting using Alex...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sambana, Bosubabu Devi, Kotamsetty Geethika Reddy, Bandi Rajeswara Hussain, Galeti Mohammad Siddartha, Gownivalla School of Computing Mohan Babu University Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331527549

The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively. © 2025 IEEE.

关键词： Prediction models

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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Improved Multi-Path Routing for QoS on MANET

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computer systems science & engineering 2023年第6期45卷 2521-2536页

作者： M.Vargheese Surbhi Bhatia Shakila Basheer Pankaj Dadheech Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl HasaSaudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia

A Mobile Ad hoc NETwork(MANET)is a self-configuring network that is not reliant on *** paper introduces a new multipath routing method based on the Multi-Hop Routing(MHR)*** is the consecutive selection of suitable relay nodes to send information across nodes that are not within direct range of each *** to ensure good MHR leads to several negative consequences,ultimately causing unsuccessful data transmission in a *** research work consists of three *** first to attempt to propose an efficient MHR protocol is the design of Priority Based Dynamic Routing(PBDR)to adapt to the dynamic MANET environment by reducing Node Link Failures(NLF)in the *** is achieved by dynamically considering a node’s mobility parameters like relative velocity and link duration,which enable the next-hop *** method works more efficiently than the traditional *** the second stage is the Improved Multi-Path Dynamic Routing(IMPDR).The enhancement is mainly focused on further improving the Quality of Service(QoS)in MANETs by introducing a QoS timer at every node to help in the QoS routing of *** QoS is the most vital metric that assesses a protocol,its dynamic estimation has improved network performance *** method uses distance,linkability,trust,and QoS as the four parameters for the next-hop *** is compared against traditional routing *** Network Simulator-2(NS2)is used to conduct a simulation analysis of the protocols under *** proposed tests are assessed for the Packet Delivery Ratio(PDR),Packet Loss Rate(PLR),End-to-End Delay(EED),and Network Throughput(NT).

关键词： Multi-path routing quality of service node-link failure packet delivery ratio

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