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Twitter sentiment analysis using conditional generative adversarial network

International Journal of Cognitive Computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

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The Risks & Realities of Picking an IoT OS

The Risks & Realities of Picking an IoT OS

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computer Applications (ICCA), IEEE Conference on

作者： Pyae Sone Hein Zar Zar Linn Master of Engineering in Computer Science Myanmar Institute of Information Technology Faculty of Computer Science Myanmar Institute of Information Technology

ISBN: (数字)9798331534592

ISBN: (纸本)9798331534608

Internet of Things is no longer a new topic. Long ago, the network of things was a rare thing which could be seen only in offices, industries, and labs. Nowadays it is approaching becoming a ubiquitous appliance as a commonplace in everyday environments. Homes and public places such as traffic and city parks have changed to adapt with things within the ability to connect to a network. As a result, even commoners are becoming familiar with IoT. Researchers lay the new technologies into IoT networks. Developers drive them into industry and practitioners carry the aesthetic essence by carrying on the practice among people. In doing so, choosing the correct operating system for the correct device is a step not to be skipped as the learning curve grows up to advanced. This study aims to aid the practitioners by discussing the risks and realities concerned with the role of OS in IoT devices or systems.

关键词： Industries Job shop scheduling Systematics Operating systems Urban areas Switches Systems engineering and theory Real-time systems Internet of Things Stress

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Sonar Echo Simulation technology Based on Array Phase Weight Estimation

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Journal of Communications and information Networks 2024年第4期9卷 320-329页

作者： Jun Liu Shenghua Gong Tong Zhang Wenxue Guan Zhenxiang Zhao School of Electronic Information Engineering Beihang UniversityBeijing 100083China College of Computer Science and Technology Jilin UniversityChangchun 130012China

Sonar locates underwater targets by receiving reflected sound ***,the complex marine environment makes it difficult to set targets in appropriate locations and *** sonar echo simulator has the function of simulating sonar detection target echo *** a cutting-edge technology,underwater backscatter has led to the emergence of array based acoustic reflection *** research on sonar echo simulators based on backscatter technology has promoted the solution of problems such as target echo modeling,sonar arrival direction estimation,and echo directional *** response to the above problems,this paper designs an end-to-end sonar echo simulator system array phase estimation sonar echo simulation system(APE-SESS),which can independently complete highresolution real-time direction of arrival(DOA)estimation and generate directional simulation echo based on the array *** branch convolutional neural network(DB-CNN)is proposed in the system to estimate the direction of the signal array and directly obtain the phase weights containing azimuth *** DBCNN with conventional methods and classic underwater DOA network models based on classification problems,the results show that DB-CNN exhibits stability,small error,and high real-time performance under different signal-to-noise ratio(SNR).The proposed APE-SESS has end-to-end characteristics,real-time angle estimation,and azimuth simulation functions.

关键词： sonar echo simulation DOA estimation underwater backscatter beamforming

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Collaborative Research on Customer Electricity Demand Forecasting and Power System Planning on Basis of Fuzzy Algorithms 5

Collaborative Research on Customer Electricity Demand Foreca...

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5th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Wu, Fangquan Liu, Yichi Li, Xiong North China Electric Power University School of Computer Science and Technology Baoding071066 China Hunan University Information Science and Engineering Changsha410082 China University of Electronic Science and Technology School of Electronics and Communication Engineering Chengdu611731 China

ISBN: (纸本)9798350355253

Accurate prediction of consumer demand for electricity has always been a goal pursued by the power industry. Rational planning of power system resources so as to achieve power supply reliability and improve energy utilization efficiency is the purpose of this paper. This study is based on fuzzy algorithm to forecast consumer electricity demand through fuzzy reasoning and fuzzy logic methods, and synergize with power system planning. The article collects and organizes historical electricity demand data and related environmental factors data. Then a fuzzy inference model is established and fuzzy sets and fuzzy rules are applied to demand forecasting. The experimental results show that the root-mean-square error of the fuzzy algorithm is 0.01-0.03. The algorithm is able to capture the vagueness and uncertainty of demand with high prediction accuracy and comprehensiveness. The algorithm in this paper can meet the goals pursued by the power industry and bring contribution to the power industry. © 2024 IEEE.

关键词： Demand response

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Integrated Simulation Research of Multi-natural Energy-driven Unmanned Surface Vehicle

Integrated Simulation Research of Multi-natural Energy-drive...

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2023 International Conference on Applied Mathematics and Digital Simulation, AMDS 2023

作者： Chen, Ao Chen, Tianding He, Hong Wang, Hui Fan, Yimei Yu, Xin School of Physics and Information Engineering Minnan Normal University Fujian Province China School of Computing and Information Science Fuzhou Institute of Technology Fujian Province China School of Data and Computer Science Xiamen Institute of Technology Fujian Province China

In response to the limitations of existing unmanned surface vehicles in complex marine environments, where they often fail to meet the requirements for extended endurance, broad operational range, offshore missions, and prolonged operational durations, an autonomous Unmanned Surface Vehicle (NUSV) has been designed and developed. The NUSV is propelled by wind, solar, and wave energy sources. Concerning wind energy, a micro wind energy storage mechanism based on a spiral spring has been designed to enable power generation in low-wind conditions. A wave energy transmission device, complete with a dynamic model, has been devised for wave energy. This device converts torque generated from wave action in different directions into a unified torque, providing continuous power to drive the generator. Solar energy is harnessed using Maximum Power Point Tracking (MPPT) algorithms to ensure optimal operation of the solar panels. The electrical energy generated from these three natural sources is stored using Pulse Width Modulation (PWM) and DC-DC Boost converters. A programmable logic controller manages and allocates this stored electrical energy for the NUSV's equipment, including underwater propulsion systems, surface cameras, laser radar, GPS, and other electronic devices. This integration of wind, solar, and wave energy sources enables the NUSV to meet power demands, allowing for long-duration operations at sea, extended offshore missions, wide operational ranges, and prolonged mission durations. © Published under licence by IOP Publishing Ltd.

关键词： DC-DC converters

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An Effective Approach Using Machine Learning for Fall Prediction 9

An Effective Approach Using Machine Learning for Fall Predic...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Vinora, A. Maheswari, P. Uma Deborah, R.Nancy Ajitha, E. Srinivasan, A. Sivakarthi, G. Velammal College of Engineering and Technology Department of Information Technology Madurai India St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350314090

The ability to fall has now increased due to the usage of phones as well as a variety of distractions on the streets that has led to a major public concern. Fall is an activity that can be caused to carelessness or incoordination. It is also attributed to monitoring senior citizens who tend to have a higher fall rate due to age-related illness, diseases, or poor visibility. It can be predicted for also soldiers for an unknown battle field based on their momentum. A dataset of 40000 rows have been obtained from Kaggle which was procured using a three-axis gyroscope that was deployed on a smartphone to collect data on falls during activities such as walking, jogging, standing, and sitting. A set of Machine Learning (ML) algorithms such as Scaled Conjugate Gradient, Bayesian Regularization, Levenberg-Marquardt, BFGS Quasi-Newton Backpropagation, Resilient Backpropagation, Conjugate Gradient Backpropagation, Conjugate Gradient Backpropagation With Polak-Ribiére Updates, One-Step Secant Backpropagation, Gradient Descent With Momentum And Adaptive Learning Rate Backpropagation and Gradient Descent With Momentum Backpropagation were deployed on the built model to check for fall prediction. Among them, Bayesian Regularization provided high accuracy rates considering cross entropy as a performance metric. This technology helps to prevent the fall of a person that can lead to an uneventful situation. © 2023 IEEE.

关键词： Gyroscopes

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AI - Driven CBR and Ontological Integration Framework for Enhancing Breast Cancer Care: Case Study 4

AI - Driven CBR and Ontological Integration Framework for En...

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4th International Conference on Robotics, Automation, and Artificial Intelligence, RAAI 2024

作者： Baroud, Shadia Yahya El- Halees, Alaa M. Baraka, Rebhi S. Yahaya, Nor Adnan University Malaysia of Computer Science and Engineering Department of Computing Petaling Jaya Malaysia Islamic University of Gaza Faculty of Information Technology Gaza Palestine

ISBN: (纸本)9798331520038

Breast cancer remains a significant health challenge, particularly in resource-limited settings such as the Gaza Strip. This study introduces a Case-Based Reasoning (CBR) framework, an Artificial Intelligence (AI) approach with broad applicability to building intelligent systems in health sciences domains, designed to enhance BC's diagnostic and treatment capabilities by leveraging historical patient data and clinical expertise. CBR is a reasoning paradigm that relies on specific knowledge from previously solved instances of problems rather than on general rules or models. The proposed framework integrates ontological modeling and semantic similarity measures to improve case retrieval and adaptation processes. This approach supports the formalization of reasoning procedures for collaborative medical acts, particularly in determining the best treatment for diseases and identifying best practices. It not only aids in medical decision-making but also fosters significant collaborations and ensures traceability, which are crucial for the proper deployment of medical applications. Effective information management mechanisms with traceable reasoning are also vital in the fields of epidemiology and public health. The study utilized datasets from 205 medical records collected from A1-Shifa Hospital in Gaza City to evaluate the system's performance. The proposed system comprises an Ontology and several modules for querying, reasoning, diagnosing and recommending treatments. The system achieved an 86% accuracy rate in diagnosing breast cancer and a 75% accuracy rate in recommending treatments. The treatment recommendations were validated by comparing the system's suggestions with those provided by expert oncologists and actual treatments administered to patients. © 2024 IEEE.

关键词： Case based reasoning

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FastGCN Framework for IoT-Powered Accident Detection and Emergency Response 4

FastGCN Framework for IoT-Powered Accident Detection and Eme...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Reddy, T. Chinna Venkata Kosalairaman, T. Tiwari, Shashank Shekhar Kumar, A.Sasi Somasundaram, K. Bandi, Sanjay Srinivasa Ramanujan Institute of Technology Anantapur India V. S. B. Engineering College Department of Information Technology Karur India Department of Information Technology UP India School of Science and Computer Studies Cmr University Bangalore India Bannari Amman Institute of Technology Department of Information Science and Engineering Erode India Srkr Engineering College Department of Ece Bhimavaram India

ISBN: (纸本)9798350352931

More cars on the road means more daily accidents. Medical and rescue delays are maj or causes of death in such incidents. A cognitive agent-based smart accident alert and rescue system could save lives by reducing delays. This concept fits inside intelligent transport systems (ITS), which are known to improve traffic safety, especially in smart cities. The proposed system includes preprocessing, model training, and feature selection. To ensure data quality, preprocessing cleans the dataset of missing values, noisy data, outliers, and normalisation. Structure-From-Function-Movement (SOFM) was used to find and remove duplicates, optimising the dataset. The FG-Convolutional Recurrent Neural Network (FG-CRNN) was used to train the model because it can capture complex temporal patterns in accident data. Our method beat CNN and RNN models. This model's average accuracy of 95.86% shows its reliability and efficacy in real-time accident prediction and alert systems. This cognitive agent-based system could enhance accident rescue reaction times, making ITS in smart cities viable. The FG-CRNN model's accuracy shows its usefulness and stresses the importance of sophisticated feature selection and data preparation for optimal results. © 2024 IEEE.

关键词： Recurrent neural networks

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Cutting-Edge Climate Analysis: Combining MLP-GRU and Remote Sensing Technologies

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 348-363页

作者： Shaik, Reddi Khasim Priya, S. Shanmuga Saranya, N. R, Kotteeswaran Ramya, S. Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India SRM Institute of Science and Technology Tiruchirappalli India Department of EIE St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai Erode India Department of Information Technology Prathyusha Engineering College Chennai India

Climate change poses significant challenges to societies worldwide, necessitating accurate and reliable climate prediction models to inform mitigation and adaptation strategies. The ability to forecast climate variables such as temperature, precipitation, and soil moisture is crucial for a wide range of applications, including agriculture, water resource management, and disaster preparedness. Traditional models have often struggled to capture the complex, nonlinear relationships and temporal dependencies inherent in climate data. In this study, we present a novel Hybrid MLP-GRU Network for climate prediction, leveraging the combined strengths of Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) models. This hybrid approach aims to capture both complex, nonlinear relationships and temporal dependencies in climate data, enhancing the accuracy and reliability of predictions. We utilized comprehensive datasets from the Copernicus Climate Change Service (C3S), focusing on essential variables such as temperature, precipitation, soil moisture, and vegetation indices. The data underwent rigorous preprocessing, including normalization, temporal alignment, and feature extraction, to ensure high-quality inputs for the model. The model was trained and validated on extensive climate datasets, demonstrating superior performance metrics with an accuracy of 97.77%, RMSE of 0.140, and MAE of 0.120 on validation data. Comparative analysis with other models, including Linear Regression, Random Forest, LSTM, and CNN, highlights the efficacy of our hybrid approach. The Hybrid MLP-GRU Network consistently outperformed these models in terms of RMSE, MAE, and accuracy. Additionally, hyperparameter tuning, cross-validation, and deployment assessments were conducted to optimize the model’s performance and ensure robust predictions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate data Climate prediction Data preprocessing Feature extraction Gated Recurrent Unit Hybrid MLP-GRU Network Machine learning Multi-Layer Perceptron Nonlinear relationships Temporal dependencies

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Saline Infusion Monitoring and Control System Using IoT 2

Saline Infusion Monitoring and Control System Using IoT

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Ranjani, J. Hariharan, Shanmugasundaram Jeyashree, Anupama Aishwarya, E. Sri Sairam Engineering College Department of Information Technology Chennai India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350347456

The use of technology in healthcare is becoming increasingly common with the growth of population and advancements in technology. The healthcare industry is integrating engineering principles to create automated systems instead of manual labor. So, we have proposed an IoT-based saline infusion monitoring system which can greatly improve patient safety by continuously monitoring the saline level using a level sensor and alerting healthcare providers if it falls below 10%. The system includes sensors to monitor the patient's blood pressure and heart rate, and a connection module for remote monitoring to send the alert notifications. The system stops the flow of saline if the level is low, ensuring that the patient's vein is not damaged. This system can significantly improve patient care and minimize the need for monitoring and management. © 2023 IEEE.

关键词： Blood pressure

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