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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： On, Mehmet Berkay Proietti, Roberto Gül, Gamze Kanter, Gregory S. Singh, Sandeep Kumar Kumar, Prem Yoo, S.J. Ben Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Electronics and Communication Engineering Indian Institute of Technology Roorkee India

We experimentally demonstrate entanglement distribution between a source and two distinct destination nodes in a packet-switched quantum network with >86% fidelity, where the packets consist of classical bits as la... 详细信息

ISBN: (纸本)9781957171395

关键词： Packet switching

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User-Level Emotion Detection Framework for Social Network Data

User-Level Emotion Detection Framework for Social Network Da...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Saranya, M. Saxena, Ravi Shankar Anandan, P. Dineshkumar, S. Dharani, R. Rajasekaran, Arun Sekar Sri Ramakrishna Engineering College Department of Electronics and Instrumentation Engineering Coimbatore India Gmr Institute of Technology Department of Electronics & Communication Engineering AP Rajam India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Government College of Technology Department of Ece Coimbatore India Panimalar Engineering College Department of It Chennai India Sr University Department of Ece Warangal India

ISBN: (纸本)9798350360660

Social media interactions are inherently dynamic, with emotions fluctuating over time based on various events and user engagements. Traditional methods often overlook the temporal aspect of these interactions, limiting the depth of emotional analysis and the insights that can be derived. In this paper, we propose a novel temporal graph framework for detecting and analyzing emotions within social media networks. By leveraging the temporal nature of social media interactions, our framework incorporates both dynamic graph structures and sentiment analysis to track emotional trends over time. Proposed method demonstrate the effectiveness of our approach through empirical experiments on real-world social media data, revealing significant patterns and trends in user emotions. Our results highlight the potential of temporal graph models to enhance emotion detection and provide actionable insights for various applications, including sentiment-driven marketing and crisis management. The framework achieved an accuracy of 89% in detecting user emotions, a significant improvement over traditional static models, which averaged around 76%. Additionally, the temporal model identified emotional trends with a precision of 85% and a recall of 83%, compared to 70% precision and 68% recall achieved by baseline methods. These results indicate that the temporal graph framework not only tracks emotional trends more accurately over time but also provides deeper insights into the evolution of user sentiments within social networks. © 2024 IEEE.

关键词： depression feature extraction machine learning multiclass classification social network analysis

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LQR Based Current Controller for Improved Power Quality of Grid-Tied PV Systems 10

LQR Based Current Controller for Improved Power Quality of G...

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10th IEEE International Conference on Power Systems, ICPS 2023

作者： Hadee, M.A. Akter, Farzana Hossain, Md. Rakib Mahedy Hasan, S.M. Akter, Sheikh Shraboni Datto, Sham Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350318739

This paper proposes a new LQR-based feedforward current control scheme for the three-phase inverter of a grid-tied PV system to achieve better power quality. In order to mitigate the THD in the grid current and variation in the grid voltage, the proposed control mechanism combines an LQR-based feedforward current regulator with a disturbance decoupling approach. The feedback gains of the current regulator are obtained using the LQR method and the fluctuation in the grid voltage is modeled as a disturbance to get rid of its effect. In order to mitigate the computational complexity, the control strategy is developed in the synchronous reference frame (SRF) and to verify its effectiveness, the PV system along with the controller is simulated under various operating situations using the Matlab/Simulink platform. The simulation results show that the proposed LQR-based current control scheme has been more successful in improving the power quality of grid-tied PV systems than some of the conventional controllers, particularly in decreasing the Total Harmonic Distortion (THD) in the grid current. © 2023 IEEE.

关键词： Power quality

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Genetic Neural Network for Diabetes Likelihood Prediction Using Risk Factors 15

Genetic Neural Network for Diabetes Likelihood Prediction Us...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Gamara, Rex Paolo C. Teologo, Antipas T. Recto, King Harold A. Neyra, Romano Q. Bandala, Argel A. Feu Institute of Technology Department of Electronics Engineering Manila Philippines Ateneo de Manila University Computer and Communications Engineering Department of Electronics Quezon City Philippines Feu Institute of Technology Office of the Senior Director for Engineering Manila Philippines De la Salle University Department of Electronics and Computer Engineering Manila Philippines

ISBN: (纸本)9798350381177

Diabetes mellitus is a disease incorporated with carbohydrate metabolism whereas the body becomes unable to generate or react with insulin which leads to abnormal levels of blood sugar (glucose). In a worldwide perspective, Diabetes mellitus is ranked as the 9th leading cause of death based on the records of the World Health Organization and according to the International Diabetes Federation, there are about 463 million diabetic people worldwide in 2019 which is projected to increase to 700 million diabetic people by year 2045. In a regional perspective, about 251 million (45%) diabetic people resides on the Western Pacific and Southeast Asian region, whereas about 140 million people are undiagnosed of the disease. In this study, a genetic algorithm-optimized neural network using MATLAB was developed based on the risk factors. The experimental results show that the best validation performance has a value of 0.014129 and with a regression model coefficient R2 value of 0.95864. © 2023 IEEE.

关键词： Genetic algorithms

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A Novel CNN-BiLSTM Fusion Model for Robust Age Estimation from Facial Images 26

A Novel CNN-BiLSTM Fusion Model for Robust Age Estimation fr...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Basak, Anindya Akter, Farzana Hossain, Md. Rakib Srizon, Azmain Yakin Hasan, S. M. Mahedy Faruk, Md. Farukuzzaman Sakib, Shadman Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

This study unveils a novel approach to age estimation from facial images, achieved by amalgamating Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory networks (BiLSTMs) into a cohesive fusion model. A dataset, comprised of facial photographs of 80 individuals aged between 16 and 25 years, was meticulously assembled to facilitate the development of a robust and reliable age estimation model. This specific age bracket is characterized by significant facial transformations attributable to both growth and ageing, thereby posing a considerable challenge for accurate age estimation. The introduced model employs metric regression techniques within a structure that integrates CNN and BiLSTM, capitalizing on their synergistic capabilities to estimate age with precision. CNNs are renowned for their prowess in feature extraction, while BiLSTMs excel in discerning temporal relationships. This amalgamation ensures the comprehensive capture of both static and dynamic facial signals instrumental for precise age estimation. Evaluative metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) are employed to gauge the model's robustness and efficacy within the designated age bracket, offering insights into its competitive standing in the realm of age estimation from facial imagery. © 2023 IEEE.

关键词： Age estimation BiLSTM CNN LSTM

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Performance Evaluation of Enhanced Manhattan Mobility Model Over GM, RWP, Manhattan Grid, SLAW, and TLW Mobility Models in MANETs

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Recent Advances in computer Science and Communications 2022年第7期15卷 992-1000页

作者： Kour, Satveer Singh, Jagpal Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar143005 India Department of Electronics and Communication Engineering Sant Longowal Institute of Engineering and Technology Longowal148106 India

Objective: The mobility model is the basis of simulation experiments in the Mobile Ad-hoc Network. A composite model for mobility for city scenarios which includes a realistic model of obstacle avoidance, and movement in the vertical direction, is proposed. The comparison of its performance with those of other available mobility models is encouraging. We believe that it can upgrade the routing performance. Methods: Here, we discuss the synthetic mobility models (Gauss-Markov, Random Waypoint, Manhattan Grid), and trace-based mobility models (Truncated Levy Walk, Self-Similar Least Action Walk). Then we propose a new mobility model by replacing a speed calculating formula using Bonnmotion-3.0.1 on simulator NS2. The proposed mobility model, named Enhanced Manhattan Mobility Model, is compared with the existing Manhattan Grid mobility model in a tabulated form. AODV, DSR, and DSDV are analysed for above-mentioned mobility models against the proposed one. Furthermore, the accuracy of the best protocol over the best mobility model is investigated through Packet Delivery Ratio (PDR), throughput, average end-to-end delay, packet overhead, and packet drop rate performance metrics. Result: Due to the smooth movements created by the proposed model, it shows an improvement of 1 percent to 7 percent in throughput, 0.8 percent to 1.7 percent in packet overhead, 1 percent to 7 percent in PDR, and 1 percent in dropped packets. Conclusion: It may be attempted in the future to reduce the delay and analyse parameters (load balancing and power consumption). © 2022 Bentham Science Publishers.

关键词： Mobile ad hoc networks

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6G Non-Terrestrial Networks for Intelligent IoT Services

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IEEE Network 2024年第4期38卷 6-8页

作者： Jia, Min Chen, Hsiao-Hwa Chang, Zheng Zhang, Ning Wu, Zhibin Harbin Institute of Technology School of Electronics and Information Engineering National Cheng Kung University Department of Engineering Science University of Jyvaskyla Faculty of Information Technology University of Windsor Department of Electrical and Computer Engineering CETC Potevio Science & Technology Company Ltd.

Recently, the Internet of Things (IoT) has been considered for its applications in diverse settings, such as intelligent urban environments, advanced manufacturing facilities, and environmental surveillance. However, significant obstacles exist in the implementation of these IoT solutions due to various issues, such as heterogeneity, diverse application requirements, restricted resources, and massive connectivity. To tackle these issues, merging satellite-based networks with conventional ground-based networks has been proposed as an effective and affordable option for enhancing IoT device connectivity.

关键词： Special issues and sections 6G mobile communication Internet of Things Security Sensors Semantics Resource management computer architecture Collaboration Intelligent systems

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IoT Data and Random Forest Algorithm for Optimized Crop Rotation Planning for Sustainable Agriculture 10

IoT Data and Random Forest Algorithm for Optimized Crop Rota...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Gupta, Nukala Sujata Jehan, C. Suguna, M. Raja Priya, S. Arunachalam, G. Kadumbadi, Vijayakumar Chaitanya Bharathi Institute of Technology Department of Computer Engineering and Technology Financial District Hyderabad Telangana Gandipet India Vel Tech Multitech Dr.Rangarajan Dr.Sagunthala Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Deemed To Be University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Adhiparasakthi Engineering College Department of Technology Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Modern farming has prioritized developing sustainable agricultural practices to combat food insecurity and environmental damage. A reliable strategy for maintaining healthy soil and increasing yields is crop rotation. This study proposes a novel method that integrates Internet of Things (IoT) data with the Random Forest algorithm to optimize crop rotation plans. IoT sensors are real-time monitoring of the soil's moisture, temperature, and nutrient content using IoT, and a comprehensive overview is presented by combining this data with historical records and predictions. This dataset is analyzed using the Random Forest technique, which considers various characteristics, including crop compatibility, soil conditions, and climatic trends. The program uses machine learning to create optimal crop rotation plans for productivity, pest and disease control, and the promotion of sustainable agriculture. Our findings show that this strategy enhances crop rotation planning, increasing agricultural output while decreasing its ecological footprint. It helps in the development of sustainable farming methods and the expansion of precision agriculture via the use of Io T and machine learning. © 2024 IEEE.

关键词： Internet of things

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Prediction of Medical Insurance Costs: A SHAP-Enhanced Predictive Analysis for Transparency and Interpretability 26

Prediction of Medical Insurance Costs: A SHAP-Enhanced Predi...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Emon, Shahriar Hossain, Md. Rakib Hasan, S. M. Mahedy Srizon, Azmain Yakin Akter, Farzana Faruk, Md. Farukuzzaman Hossain, Md. Shakib Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

The escalation of healthcare expenses has been markedly pronounced in the aftermath of the COVID-19 pandemic's onset. Consequently, health insurance has ascended to prominence as a critical domain of investigation, with a series of ongoing endeavors underway to navigate this complex issue. The task of forecasting medical insurance expenditures is laden with challenges, attributed to the dynamic nature of healthcare, the intricacies and occasional disarray of data, unpredictable human behaviors, economic oscillations, unforeseen events, and prevailing privacy apprehensions. In the quest to surmount these obstacles, a predictive model has been unveiled, engineered to impart insights into the nuances of healthcare pricing structures. The examination of SHAP values facilitates the discernment of pivotal elements that steer cost prognostications, a process instrumental in augmenting transparency and enabling judicious choices for both insurance entities and the insured. This inquiry underscores the cardinal role of amalgamating predictive precision with an element of transparency within the realm of healthcare insurance. © 2023 IEEE.

关键词： Gradient Boosting Interpretability Machine Learning Multiple Linear Regression Random Forest Regression Ridge Regression Support Vector Regression

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CNN supported automated recognition of Covid-19 infection in chest X-ray images

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Materials Today: Proceedings 2022年 66卷 1201-1210页

作者： Padmakala, S. Revathy, S. Vijayalakshmi, K. Mathankumar, M. Department of Computer Science and Engineering St.Joseph's Insitute of Technology Chennai India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

Automatic recognition of lung system is use to identify normal and covid infected lungs from chest X-ray images of the people. In the year 2020, the coronavirus forcefully pushed the entire world into a freakish situation, the foremost challenge is to diagnosis the coronavirus. We have got standard diagnosis test called PCR test which is complex and costlier to check the patient's sample at initial stage. Keeping this in mind, we developed a work to recognize the chest X-ray image automatically and label it as Covid or normal lungs. For this work, we collected the dataset from open-source data repository and then pre-process each X-ray images from each category such as covid X-ray images and non-covid X-ray images using various techniques such as filtering, edge detection, segmentation, etc., and then the pre-processed X-ray images are trained using CNN-Resnet18 network. Using PyTorch python package, the resnet-18 network layer is created which gives more accuracy than any other algorithm. From the acquired knowledge the model is correctly classifies the testing X-ray images. Then the performance of the model is calculated and analyzed with various algorithms and hence gives that the resnet-18 network improves our model performance in terms of specificity and sensitivity with more than 90%. © 2022

关键词： Coronavirus

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