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Estimation of Soil Moisture with Meteorological Variables in Supervised Machine Learning Models 3

Estimation of Soil Moisture with Meteorological Variables in...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Hussain, Mariam Sharmin, Nusrat Shafiul, Sumayea Binte Ewha Womans University Dept. of Climate and Energy Systems Engineering Seoul Korea Republic of University of Science and Technology Dept. of Computer Science and Engineering Chittagong Bangladesh Bangladesh University of Health Sciences Dept. of Epidemiology Dhaka Bangladesh

ISBN: (纸本)9798350345360

Water cycles, climate-related hazards, and agroirrigation are strongly controlled by soil moisture (SM) content. For water resource management, prediction is a key to mitigate and regulate expected economic losses and property damages. This paper compares two supervised machine learning (ML) techniques: support vector regression (SVR) and random forest (RF) to predict SM. In RStudio, various meteorological variables: temperature, relative humidity, wind speed, and rainfall are trained to estimate SM. For eight divisions, SM and weather variables are obtained from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER). The experiments include daily observations for 39 (1982 to 2020) to develop SVR and RF models. To estimate SM from the predictive model, datasets from diverse regions: Rajshahi, Mymensingh, Chittagong, and Sylhet are utilized in training (60%) and Rangpur, Barisal, Khulna, and Dhaka are segregated for validation (40%) resulting in accuracy of 88 to 95.8%. This model further is applied to forecast daily SM for each city including two districts (Bogra and Jessore) and found slightly higher model performance for SVR (90.7%) than RF (90.1%) on average (Year: 2021). For agricultural, industrial and urban water supplies as well as drought, landslides, and river erosions can be mitigated by an accurate estimation of soil moisture. The investigations encourage for providing SM budget to public with supervised ML techniques mostly among data-sparse regions. © 2023 IEEE.

关键词： Soil moisture

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Power and Rate Allocation for Energy-Efficient Rate-Splitting Multiple Access via Deep Q-Learning

Power and Rate Allocation for Energy-Efficient Rate-Splittin...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Diamanti, Maria Kapsalis, Georgios Tsiropoulou, Eirini Eleni Papavassiliou, Symeon Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350370218

Rate-Splitting Multiple Access (RSMA) has recently emerged as an effective technique for increasing network capacity by smartly controlling the tradeoff between decoding and treating interference as noise. In this paper, aligned with the need for sustainable wireless networks, we study the energy-efficient power and rate allocation of the common and private messages in the downlink of a network where rate-splitting is adopted. The corresponding energy efficiency maximization problem is transformed into a multi-agent Deep Reinforcement Learning (DRL) problem, based on which each private stream transmitted in the downlink constitutes a different DRL agent. The formulated DRL problem is solved by using Deep Q-Learning (DQL) algorithm and training a single Deep Q-Network (DQN) from the cumulative experiences gained from the DRL agents and their exploration of the environment, i.e., the exploration of different private-message power allocations. Numerical results obtained via modeling and simulation verify the effectiveness of the proposed DQL algorithm, demonstrating that it concludes solutions that outperform existing approaches from the literature in the achieved energy efficiency. © 2023 IEEE.

关键词： Deep Reinforcement Learning (DRL) Energy Efficiency Maximization Rate-Splitting Multiple Access (RSMA)

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Leveraging Artificial Intelligence for Enhanced Data Generation in Addressing Imbalance in Binary Classification System 2

Leveraging Artificial Intelligence for Enhanced Data Generat...

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

作者： Bandopadhyay, Srijita Dutta, Srimonti Haider, Imran Anuraag, Bhavaraju Zhu, Jerry Bazaz, Saad Ahmed University of Engg. & Management Dept. of Electronics and Communication Engineering Kolkata India Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India Vellore Institute of Technology Dept. of Electronics and Communication Engineering Vellore India University of Waterloo Faculty of Mathematics ON Canada National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350360523

This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority Over-sampling Technique (SMOTE), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) to enhance classification outcomes. Traditional classification models tend to favor the majority class, while the impact of imbalanced misclassification costs is often overlooked. The integration of SMOTE, GANs, and VAEs in binary classification, or SMOTE-GAN-VAE, addresses these challenges by generating synthetic instances, refining data representations, and capturing latent features. To evaluate the effectiveness of various data generation methods, a credit card fraud dataset is used. The performance metrics considered include F0.5-score, F1-score, and F2-score, which account for both precision and recall. The results indicate that SMOTE-GAN-VAE outperforms individual methods, such as SMOTE, GANs, and VAEs, demonstrating its potential to enhance data representation and classification accuracy, and outperformed the ß- VAE filtered approach employed in previous literature. © 2024 IEEE.

关键词： Generative adversarial networks

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Comparison of Mel Frequency Cepstral Coefficient (MFCC) and Mel Spectrogram Techniques to Classify Industrial Machine Sound 15

Comparison of Mel Frequency Cepstral Coefficient (MFCC) and ...

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15th International Conference on software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Hafiz, Nur Fatinah Mashohor, Syamsiah Shazril, M.H.S.E.M.A. Rasid, M. Fadlee A. Ali, Azizi Universiti Putra Malaysia Faculty of Engineering Dept. of Computer and Communication Systems Engineering Selangor Serdang43400 Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350316551

The utilization of sound recognition to detect the faulty machines are getting the interest to be explored with the technology advancement in IR 4.0. In this paper, we investigated the performance of two acoustic features in representing the sound of a fan in industrial machines, that is acquired from the MIMII dataset. The Mel Frequency Cepstral Coefficient (MFCC) and Mel Spectrogram techniques are explored and compared in this paper. The classification is performed using Convolutional Neural Network (CNN) with the input of images from both techniques. The dataset of the input images are the audio files of 6dB fans sound in industrial machine. The results suggest that Mel Spectrogram is an effective feature for faulty machine sound recognition when used with CNN model. © 2023 IEEE.

关键词： Convolution

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Polarity Classification of Twitter Data Using Machine Learning Approach

Polarity Classification of Twitter Data Using Machine Learni...

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2023 Human-Centered Cognitive systems, HCCS 2023

作者： Bangyal, Waqas Haider Iqbal, Muddesar Bashir, Ahsan Ubakanma, George Kohsar Univeristy Murree Dept. of Computer Science Muree Pakistan London South Bank University School of Engineering School of Engineering Cognitive Systems Research Centre United Kingdom Abasyn University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350359183

Sentiment analysis has become very important nowadays because there are lots of social media platforms peoples are using to express their opinion. Twitter is one of the most popular social media platforms which is used for microblogs. People use to express their opinion on current affairs, and there is a challenge for researchers to classify the sentiment accurately. In this research study, we proposed a greatly efficient technique for the detection of fake news on covid-19. The data set of fake news is taken from the corpus and executes the NLP cycle. In this research, we applied five machine learning to predict the sentiment of fake or real news. Support Vector Machine, Logistic Regression, KNN, Decision Trees, and Random Forest are machine learning classifiers used in this research, and results are compared. © 2023 IEEE.

关键词： Sentiment analysis

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Enhanced Driver Stress Prediction from Multiple Biosignals via CNN Encoder-Decoder Model 46

Enhanced Driver Stress Prediction from Multiple Biosignals v...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Singh Roha, Vishal Gaber Gendy, Maggie Ezzat Pavan, Kaveti Deserno, Thomas M. Ganapathy, Nagarajan Yuce, Mehmet R. Monash University Dept. of Electrical & Computer Systems Melbourne Australia Indian Institute of Technology Hyderabad Dept. of Biomedical Engineering Hyderabad India Technical University of Braunschweig Plri Lab for Medical Informatics Brunswick Germany

ISBN: (纸本)9798350371499

In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions. © 2024 IEEE.

关键词： Photoplethysmography

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Asymptotic Stability Preservation of Input Delayed Nonlinear systems under Sampled-Data Feedback 63

Asymptotic Stability Preservation of Input Delayed Nonlinear...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Yu, Xin Lin, Wei Jiangsu Normal University School of Electrical Engineering and Automation Xuzhou221116 China Case Western Reserve University Dept. of Electrical Systems and Computer Engineering ClevelandOH United States

ISBN: (纸本)9798350316339

We study the problem of global asymptotic stability preservation (GASP) for C0 non-smoothly stabilizable systems with input delay under sampled-data feedback. With the aid of Halanay inequality and the notion of homogeneity, the following sampled-data control results are established under a fast sampling: 1) GAS is preservable if the nonlinear system is homogeneous of degree zero and globally asymptotically stabilizable by homogeneous feedback;2) As a consequence, GAS via sampled-data feedback is achieved for a class of non-smoothly stabilizable systems with input delay in a lower-triangular or upper-triangular form. © 2024 IEEE.

关键词： Delay control systems

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, engineering, Science, and Technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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EPA ECC: Error-Pattern-Aligned ECC for HBM2E

EPA ECC: Error-Pattern-Aligned ECC for HBM2E

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2023 International Technical Conference on Circuits/systems, computers, and Communications, ITC-CSCC 2023

作者： Kwon, Kiheon Kim, Dongwhee Park, Soyoung Kim, Jungrae Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Semiconductor and Display Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Artificial Intelligence Suwon Korea Republic of Sungkyunkwan University Dept. of Semiconductor Systems Engineering Suwon Korea Republic of

ISBN: (纸本)9798350326413

DRAM vendors introduced On-Die Error Correction Codes (OD-ECC) to correct errors internally. Most OD-ECCs are based on Single Error Correction to correct individual bit errors. However, recent soft error experiments on HBM2 reveal that DRAM frequently experiences multi-bit errors, necessitating a stronger OD-ECC solution. This paper introduces a novel OD-ECC, EPA ECC, specifically designed to correct frequently-observed multi-bit error patterns. The key innovation of EPA ECC is the construction of multi-bit symbols aligned with common error patterns, and the application of Reed-Solomon codes to correct severe errors without increasing the redundancy ratio. Our evaluation demonstrates that EPA ECC provides higher memory reliability than the current SEC-DED OD-ECC without incurring significant system performance degradation. © 2023 IEEE.

关键词： Dynamic random access storage

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Content based Image Retrieval using K-Nearest Neighbours and Convolutional Neural Networks 15

Content based Image Retrieval using K-Nearest Neighbours and...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kumar, C. Siva Bhaskaru, O. Komatlapalli, Bhargavi Areddula, Phani Sai Kumar Maddali, Manikanta Reddy, Busa Rajasekhar Tirupati India Dept. of CSE Gates Institute of Technology Gooty A.P Ananthapur India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Today, there is a lot of digital image material on the Internet. As the need for better image search tools grows, new waysmust be found to solve the problem of telling the difference betweenquestion pictures and received images. This study presents a new technique to enhance the searching of images in retrieval based on image content. The technique involves using several deep neural network methods, such as ResNet50, InceptionResNetV2,VGG19, InceptionV3, DenseNet121, Xception, and Improved CNN. These models make it easier to find interesting photos. The study tested thetechnique on the Dataset and a dataset from a Kaggle competition, using metrics like the confusion matrices, F1 scores, and mAP to assess the model performance. The tests show that the proposed system works very well, taking away some of the problems that come with picture searches being unclear and showing how well it can find appropriate images. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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