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2023 International Electrical engineering Congress, iEECON 2023

作者： Tipwong, Wallop Sirikham, Adisorn Konpang, Jessada Kirdpipat, Putthiphong Chongjarearn, Yutana Preutisrunyanont, Oradee Rajamangala University of Technology Krungthep Department of Electrical and Telecommunication Engineering Bangkok Thailand Thammasat School of Engineering Thammasat University Faculty of Engineering Department of Electrical and Computer Engineering Pathum Thani Thailand Dhurakij Pundit University Department of Robotic Engineering and Automation System Bangkok Thailand

ISBN: (纸本)9798350332360

Technologies are currently being used in smart farms to improve productivity. Agricultural innovation is also widespread. Therefore, the development of Internet of Things (IoTs) technologies and devices to collect data is important. Finding essential nutrients for growth such as Nitrogen (N), Phosphorus (P) and Potassium (K) are important factors. Competitiveness is the important key for growing vertically or indoors. However, soil testing can be expensive, complicated and time consuming. The development of devices that can effectively measure nutrient NPK levels could be used over time. This paper presents a suitable clustering using the K-means method and a forecasting method using the Self-Organizing Map (SOM) method to enhance the device's ability to analyze nutrients in real time. A total of 50,688 samples were tested using the K-Means clustering algorithm, and the sample data were also analyzed. A SOM was prepared to predict the accuracy of the analysis. A total of 11,486 samples were tested and analyzed, then the results of the soluble NPK nutrient monitoring system were taken. It has an efficiency of 84.15% compared to using a UV Visible spectrophotometer, allowing data to be predicted during nutrient solubility changes and to operate according to the proposed system. Therefore, the unsupervised machine learning will improve the measurement system of future cultivation systems. © 2023 IEEE.

关键词： Nutrients

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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A Development of 5G Wireless Sensor Network using Man Design 4

A Development of 5G Wireless Sensor Network using Man Design

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Vennila, C. Almusawi, Muntather Kumar, K. Sriram Priya, B. Rawat, Vidhi Amirthayogam, G. Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h TN India The Islamic University of Babylon College of Technical Engineering Department of Computers Techniques Engineering Babylon Iraq Karpagam Institute of Technology Department of Information Technology TN India Nehru Institute of Engineering and Technology Department of Computer Science and Business System Tamil Nadu T.M.Palayam India Ies College of Technology Department of Electrical & Electronics Engg Madhya Pradesh Bhopal India Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

This article investigates the key drivers influencing the design of the forthcoming Metropolitan Area Network (MAN), focusing on the integration of two pivotal elements: Cloud Radio Access Network (C-RAN) for 5G implementation and the deployment of edge computing. As we delve into these aspects, the article systematically uncovers the crucial architectural components essential for effectively addressing the challenges inherent in implementing C-RAN and edge computing. Through this strategic approach, the objective is to ensure a cost-effective solution that aligns with the specific requirements of these applications. The article aims to provide insights into the symbiotic relationship between CRAN and 5G, emphasizing their collaborative role in enhancing network efficiency. Simultaneously, it underscores the significance of edge computing, highlighting its potential to optimize data processing and reduce latency within the evolving landscape of metropolitan networks. This comprehensive exploration contributes to a nuanced understanding of the future MAN design, grounded in innovative solutions and considerations for seamless integration. © 2024 IEEE.

关键词： Radio access networks

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Blue and Ultraviolet Light Beams with Nonlinear TiO2 Metasurfaces

Blue and Ultraviolet Light Beams with Nonlinear TiO2 Metasur...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Sedeh, Hooman Barati Zheng, Yuruo Gao, Jiannan Lai, Fangxing Li, Hao Xiao, Shumin Litchinitser, Natalia M. Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen Shenzhen518055 China

We design and experimentally demonstrate the generation of blue and ultraviolet third-harmonic generation from a TiO2 metasurface supporting quasi-bound state in the continuum with 0.2% conversion efficiency. © O... 详细信息

ISBN: (纸本)9781957171395

关键词： Laser beams

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Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

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computers, Materials & Continua 2022年第5期71卷 3407-3423页

作者： Shakra Mehak M.Usman Ashraf Rabia Zafar Ahmed M.Alghamdi Ahmed S.Alfakeeh Fawaz Alassery Habib Hamam Muhammad Shafiq Knowledge Unit of System and Technology University of Management&TechnologySialkot Campus51310Pakistan Department of Computer Science University of Management and TechnologySialkot Campus51310Pakistan Faculty of Computing and Technology University of Engineering&TechnologyNarowal51610Pakistan College of Computer Science and Engineering University of Jeddah21493Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Breast cancer(BC)is the most widely recognized cancer in women *** 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis of histological *** present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC *** contemplate three elements,*** count,*** formation,and *** atypia,which is a laborious process that witness’s variations in expert’s ***,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much *** atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast *** proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification *** classification results show the value of the multilayer autoencoder model in the evaluation of nuclear *** proposed method has indicated promising results,making them more fit in breast cancer grading.

关键词： Breast cancer nuclear atypia autoencoder

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Blue and Ultraviolet Light Beams with Nonlinear TiO2 Metasurfaces

Blue and Ultraviolet Light Beams with Nonlinear TiO2 Metasur...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Nonlinear optics

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Machine Learning-based Evaluation of Heart Rate Variability Response in Children with Autism Spectrum Disorder 3

Machine Learning-based Evaluation of Heart Rate Variability ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Mohammed, Vazeer Ali Mohammed, Mehmood Ali Mohammed, Murtuza Ali Logeshwaran, J. Jiwani, Nasmin Lewis University Department of Computer and Information System IL United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9781665462167

At present, various electronic devices are used to monitor human heart rates. However, its functions are to avoid predicting the problems caused by heart rate variability in advance and analyzing its implications. It makes it difficult to diagnose problems caused by heart rate variability. A human should have an average heart rate of 72. At the same time, the newborn's heart should beat between 120 and 160 beats per minute. A baby born with autism spectrum disorder may have a lower-than-average heart rate. Complete blockage of the heart at birth is rare. Abnormal heart rate leads to heart block. So, there is a high chance of the child's death due to permanent heart blockage at any time. Most heart diseases in children with Autism Spectrum Disorder (ASD) are present at birth. A significant congenital disability is a hole in the heart. Many people do not realize that having holes in the heart is a common occurrence. Before the baby is born, tiny holes form in the muscular wall that divides the heart into the right and left halves. This paper proposed Machine Learning-Based Evaluation to identify the Heart Rate Variability Response in Children with Autism Spectrum Disorder with Autism Spectrum Disorder. The reasons for this are yet to be identified. However, 70 per cent of perforations resolve spontaneously before or after birth. Exceptionally, Children with Autism Spectrum Disorder with perforations that do not close properly may require surgery or a perforator brace, depending on the location and size of the perforation. © 2023 IEEE.

关键词： Heart

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Hybrid Power Generation Forecasting Using an Intellectual Evolutionary Energy-Preserve Rate Clustering Technique 1

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10th International Conference on Innovations in computer Science and engineering, ICICSE 2022

作者： Webber, Julian L. Jayagopal, Vellingiri Mehbodniya, Abolfazl Sengan, Sudhakar Velayutham, Priya Rangasamy, Rajasekar Stalin David, D. Doha Kuwait Department of Software & System Engineering School of Information Technology & Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal637018 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai600072 India

ISBN: (数字)9789811974557

ISBN: (纸本)9789811974540

The significant growth of Wind Power (WP) has significant challenges due to the inconsistency and uncertainty of Wind Power Generation (WPG) to ensure the power system. Solar energy and WP calculation are great ways to overcome the problem. Mainly voltage and frequency fluctuations and harmonics have a high impact on the power generation and distribution system;in any high case, the power quality of grid systems is reduced. This can be primarily solved with proper design, advanced rapid response control facilities and optimization of power systems. A new type of Multi-term Wind Energy (WE) with an IEEE 30 bus forecasting system based on digital weather forecasting and error modification methods is presented. From the Evolutionary Energy- Preserve Rate Clustering (EEPRC) Technique-based technique for the evaluates the probability ratio in Solar and wind error energy prediction, the error assessment model is proposed, which has the advantage of estimating from real wind farms and statistical Weather Forecast (WF) data and has proven to be a nominal method for improving the predictive accuracy of Multi-term WPG. The primary step of the Energy-Preserve Rate Clustering (EPRC) method is to calculate the uncertainty of real-time data with the predicted value. The computed values are helpful for the increases in Solar WPG production. The proposed wind forecasting system is utilized in the Coastal area-based energy generation. Also, it has been demonstrated that wind speed and WPG with its probability ratio have been evaluated with the EEPRC-based forecasting method, especially for its powerful predictive capability for the complex properties of the controller function. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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Robust Method for Embedding an Image Inside Cover Image Based on Least Significant Bit Steganography

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Informatica (Slovenia) 2022年第9期46卷 53-60页

作者： Almola, Sahera A.S. Qasim, Najat H. Ali Abed Alasadi, Hamid Department of Information System College of Computer Sciences and Information Technology University of Basrah Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Iraq Communications Engineering Department Iraq University College Istqlal Street Basra61004 Iraq

According to the enormous evolution of the internet and communications, the security of private or sensitive information has become the important issue. The private information can be passed between a sender and a receiver with high security using Cryptography and Steganography. By the cryptography, as the information can be secured with the cipher key for encryption. By the Steganography, the secret information can hide within usual data such as images, videos, audios. In this paper we proposed the robust method that includes two phases. The first phase consists of two stages. In the first stage we encrypt a secret image using encryption algorithm in order of increasing security of information. In the second stage, we complete the first stage for embedding the most significant bits (MSB) of the encrypted image into the least significant bits (LSB) of the cover image for instituting the stego image. The second phase is the obverse of the first phase, where, the encrypted image is retrieved from the stego image and then, decrypted process construct the secret image. The proposed method is efficient in terms of security compare with the other methods. Where, both encryption and embedding algorithms have an efficient role for embedding the secret image and preserving the good visual quality of stego images. Furthermore, extraction algorithms obtained better and faster results to a restoration of the secret image. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Steganography

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AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

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