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2022 International Young Engineers Forum in Electrical and computer engineering, YEF-ECE 2022

作者： Lisnic, Victor Ferrada, Filipa Correia, Patricia FCT-NOVA Nova School of Science and Technology Department of Electrical and Computer Engineering Caparica Portugal Department of Electrical and Computer Engineering Caparica Portugal Faculty of Sciences University of Lisbon Fcul Centre of Ecology Evolution and Environmental Changes Lisbon Portugal

ISBN: (数字)9781665467315

ISBN: (纸本)9781665467315

Agriculture is an industry that is essential for the food supply for the world population. Although this industry is so important, there are many challenges associated with it, such as the pollution/waste present in the agricultural practices and the aging of the farmers. The application of technological solutions can provide a way to solve these challenges, by retrieving data from the physical environment, based on which future decisions can be taken, using actuators. The goal of this work is to present an architecture through which the different layers can communicate with each other's, and then based on this architecture develop a system that can help monitor the agricultural fields. The project is applied in a controlled environment (a greenhouse), and the objective is to verify both the waste of water within the cultures, and if there are benefits in changing the plastics used on top of the soil in terms of production, from the traditional one (black), to a plastic that is white and thicker. © 2022 IEEE.

关键词： Cyber Physical System

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Leveraging Deep Learning for Improved Sentiment Analysis in Natural Language Processing 3

Leveraging Deep Learning for Improved Sentiment Analysis in ...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Kulkarni, Aniket Gollavilli, Venkata Surya Bhavana Harish Alsalami, Zaid Bhatia, Manpreet Kaur Jovanovska, Sashka Absur, Md Nurul Drata Inc San DiegoCA United States Under Armour MD United States College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Asian School of Media Studies Department of Data Science Noida India Goce Delcev University Faculty of Philology Department of English Language and Literature Stip Macedonia Cuny Graduate Center Computer Science New York United States

ISBN: (纸本)9798350354379

Sentiment analysis is viewed as quite possibly of the main works in mental science and normal language handling. To work on the productivity of sentiment analysis techniques, it is crucial for separate the useful words that add to the grouping choice as well as characterize the sentences as indicated by their profound names. Profound brain networks that depend on the consideration component have taken huge steps toward this path as of late. Reads up on consideration processes for message arrangement, and especially sentiment analysis, are as yet not many, in any case. This research fills this gap by presenting a Convolution Neural Network (CNN) combined with an attention layer that can extract relevant words and give them greater weights according to the context. The suggested model uses a context vector at the attention layer and attempts to gauge a word's relevance based on how similar the word vector and context vector are to one another. New vectors from the consideration layer are incorporated into sentence vectors and utilised for organization once they have been supplied. The suggested model was validated using a small number of tests on the Stanford datasets. The trial discoveries show that the recommended model works far superior to past research studies and can separate significant expressions from setting that have an incentive for analysis and application. © 2024 IEEE.

关键词： Convolutional neural networks

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Low-Complexity Interference Reduction for GFDM Systems Using Corrective Coefficients

Low-Complexity Interference Reduction for GFDM Systems Using...

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2022 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2022

作者： Jirdehi, Azadeh Abedi Abdolee, Reza Tazehkand, Behzad Mozaffari Aghdam, Mohammad Reza Ghavidel University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran California State University Channel Islands Department of Computer Science CamarilloCA93012 United States Özyeǧin University Department of Electrical and Electronics Engineering Istanbul34794 Turkey

ISBN: (纸本)9781665473408

Generalized Frequency Division Multiplexing (GFDM) is a promising multi-carrier modulation scheme for next-generation wireless communication systems. GFDM uses a non-orthogonal pulse shape, which leads to self-interference among sub-carriers. In this paper, we propose a new coded GFDM method to reduce the self-interference in each block by using a space-frequency block coding (SFBC) scheme. To this end, we introduce corrective coefficients based on channel state information and the demodulation matrix for each modulated data. We evaluate the bit error rate (BER) performance of the system by comparing SFBC-GFDM transmission in Rayleigh channels with a matched filter (MF) receiver. Our computer simulations show that the proposed scheme substantially improves the system performance in BER. Per our results presented in this paper, we achieve 5 dB performance improvement at BER of 10-2 compared to previous counterparts. © 2022 IEEE.

关键词： Bit error rate

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Early Detection of Black Fungus Using Deep Learning Models for Efficient Medical Diagnosis 4

Early Detection of Black Fungus Using Deep Learning Models f...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Hassan, Esraa Saber, Abeer El-Kenawy, El-Sayed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial intelligence Information technology Dept. Damietta34517 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350387063

Mucormycosis, also known as black fungus, is a rare infection caused by mould that can affect the lungs, brain, skin, and sinuses. People with weakened immune systems due to underlying health conditions (e.g., organ transplant, uncontrolled diabetes, cancer, neutropenia, AIDS, and COVID-19) or drug use are more likely to contract the infection. Recent reports have indicated that a small number of COVID-19 survivors with co-morbidities are particularly vulnerable to black fungus. Therefore, recovered COVID-19 patients should seek medical attention immediately if they experience any mucormycosis symptoms. Patients with COVID-19 have numerous concerns regarding this life-threatening infection, particularly those with immunocompromised states such as solid organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) or tumors, who are more likely to be infected. Early diagnosis and treatment are essential to prevent patient mortality. In this paper, we present the Multi-Class Black Fungus (MCBF) dataset, which consists of three compressed folders containing images. The first folder includes 66 images of eye black fungus after data preprocessing, while the second folder contains 63 images of mouth black fungus after data preprocessing. The third contains 62 images of the mouth after applying data preprocessing, and the last one contains 79 images of black skin fungus after applying data preprocessing. The MCBF dataset contains images that aid in training and validation when using deep learning algorithms to recognise and classify black fungus diseases, then we apply a resnet(50) pretrained model by using MCBF accuracy reaches 96.12%. © 2024 IEEE.

关键词： COVID-19

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Review of Different Current Control Strategies for Thyristor-Controlled LC-Coupling Hybrid Active Power Filter 32

Review of Different Current Control Strategies for Thyristor...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Sou, Wai-Kit Chan, Pak-Ian Gong, Cheng Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China University of Macau Institute of Microelectronics China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350399714

With the characteristics of low DC-link voltage and wide operating range, thyristor-controlled LC-coupling hybrid active power filter (TCLC-HAPF) is a promising power quality compensator in the medium-voltage-level power system. This paper reviews the design and implementation of different current control strategies for the TCLC-HAPF, which are hysteresis current control (HCC), multi-quasi-proportional-resonant control (MQPRC), H∞ optimal control (HOC), finite-set model predictive control (FS-MPC), second-order sliding mode control (SOSMC) and deadbeat current control (DBCC). Also, their pros and cons are fully discussed. In order to provide a deeper understanding about the steady-state and transient performances of these control strategies, the simulation study is presented under both inductive and capacitive loads. © 2023 IEEE.

关键词： Thyristors

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FT-FEDTL: A fine-tuned feature-extracted deep transfer learning model for multi-class microwave-based brain tumor classification

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computers in Biology and Medicine 2024年 183卷 109316-109316页

作者： Hossain, Amran Islam, Rafiqul Islam, Mohammad Tariqul Kirawanich, Phumin Soliman, Mohamed S. Department of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Gazipur1707 Bangladesh Faculty of Engineering and Built Environment Department of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Department of Electrical Engineering Faculty of Engineering Mahidol University Salaya Phuttamonthon Nakhon Pathom73170 Thailand Department of Electrical Engineering College of Engineering Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electrical Engineering Faculty of Energy Engineering Aswan University Aswan81528 Egypt

The microwave brain imaging (MBI) system is an emerging technology used to detect brain tumors in their early stages. Multi-class microwave-based brain tumor (MBT) identification and classification are crucial due to the tumor's patterns and shape. Manual identification and categorization of the tumors from the images by physicians is a challenging task and consumes more time. Recently, to overcome these issues, the deep transfer learning (DTL) technique has been used to classify brain tumors efficiently. This paper proposes a Fine-tuned Feature Extracted Deep Transfer Learning Model called FT-FEDTL for multi-class MBT classification purposes. The main objective of this work is to suggest a better pathway for brain tumor diagnosis by designing an efficient DTL model that automatically identifies and categorizes the MBT images. The InceptionV3 architecture is utilized as a base for feature extraction in the proposed FT-FEDTL model. Thereafter, a fine-tuning method is applied to the additional five layers with hyperparameters. The fine-tuned layers are attached to the base model to enhance classification performance. The MBT data are collected from two sources and balanced by augmentation techniques to create a total of 4200 balanced datasets. Later, 80 % images are used for training, 20 % images are utilized for validation, and 80 samples of each class are used for testing the FT-FEDTL model for classifying tumors into six classes. We evaluated and compared the FT-FEDTL model with the three traditional non-CNN and seven pretrained models by applying an imbalanced and balanced dataset. The proposed model showed superior classification performance compared to other models for the balanced dataset. It attained an overall accuracy, recall, precision, specificity, and Fscore of 99.65 %, 99.16 %, 99.48 %, 99.10 %, and 99.23 %, respectively. The experimental outcomes ensure that the proposed model can be employed in biomedical applications to assist radiologists for multi-c

关键词： Deep neural networks

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A Neutrosophic Study for the Transmission of Infection with Pathogenic Fungi from Males of Olive Fly Insects to Their Females

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Neutrosophic Sets and Systems 2024年 64卷 38-45页

作者： Alhabib, Amani Alhabib, Rafif Khalid, Huda E. Salama, A.A. Faculty of Agricultural Engineering Albaath University Homs Syria Department of Mathematics Faculty of Science Hama University Hama Syria Telafer University Telafer Iraq Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt

This paper presents the study of the effectiveness of horizontal transfer of local isolates of the pathogenic fungus Beauveria bassiana (Balsamo) on adults of olive fruit fly Bactrocera oleae (Rossi) at a concentration of 106 spores/ml in laboratory conditions (this work was carried out in specialized scientific laboratories). In addition, it is not possible to reach the desired results in such experiments effectively when the data and observations of the study are not clear and accurate. For this reason, in this paper, experimental data will be presented with inaccurate or uncertain observations using neutrosophic statistics. The purpose is to know the success of males contaminated with pathogenic isolates in the transmission of infection to females. In laboratory conditions through a neutrosophic reading of the study data. This proposed presentation provides greater accuracy, flexibility, and applicability than the classic experimental design in the case of uncertainty. © (2023), (University of New Mexico). All Rights Reserved.

关键词： Transmissions

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Classification of Leukemia and Lymphoma using Faster R-CNN

Classification of Leukemia and Lymphoma using Faster R-CNN

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2022 International Conference on Microelectronics, ICM 2022

作者： Al Zaouk, Salimar Matar, Tala Ali, Mohamad Abou Kassem, Abdallah Hamawy, Lara Lebanese International University Department of Biomedical Engineering Lebanon University of the Basque Country Department of Computer Science and Artificial Intelligence Spain Notre Dame University-Louaize Faculty of Engineering DECCE Department Lebanon United States

ISBN: (纸本)9781665493246

Blood disorders are disorders that impair the ability of the blood to function normally. There are various types such as leukemia and lymphoma, and the symptoms differ accordingly. Many death cases are related to these malignant tumors as a result of the difficult therapy and its negative effects. Furthermore, patients require a variety of diagnostic tests, which are costly, time-consuming, and can be stressful and hazardous to the patients. Furthermore, only a qualified pathologist can distinguish and identify various forms of lymphomas and leukemias. In this paper, the "Faster Region-Based Convolutional Neural Network;Faster R-CNN"algorithm is implemented to classify normal patients and ill patients into four categories: Acute Lymphoblastic Leukemia (ALL), Chronic Lymphocytic Leukemia (CLL), Follicular Lymphoma (FL), and Mantle Cell Lymphoma (MCL). This is done by dealing with Region of Interest (ROI) convolutional layer, then applying Vgg-16 model & Region Proposal Network (RPN) layer and adding a classifier layer. The dataset is based on microscopic and biopsies images. The proposed model achieved an accuracy of 87,575% after training in an acceptable computational time. © 2022 IEEE.

关键词： Diseases

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A High-Performance Three-Coil Wireless Charging System for Empowering Unmanned Surface Vehicle 49

A High-Performance Three-Coil Wireless Charging System for E...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Ieong, Chi-Fong Wong, Hou-Wa Iam, Io-Wa Lam, Chi-Seng University of Macau State Key Laboratory of Analog and Mixed-Signal Vlsi China Institute of Microelectronics University of Macau China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350331820

The use of unmanned surface vehicles (USVs) in oceanography research is widespread due to their ability to provide real-time data. Due to the limited battery size, recharging operations including plugging and unplugging decrease the overall utilization of the USV. To address this issue, wireless power transfer (WPT) can be implemented by installing coils at the dock and inside the USV. In this paper, a three-coil WPT system is designed for offshore USV wireless charging applications. The proposed three-coil structure is designed to ensure adequate coupling and achieve high transfer efficiency. Aiming to meet practical battery charging specifications, LCC-S-S compensation topology is analyzed with detailed parameter design to provide constant current output. Finally, simulations are conducted to verify the coupling and output characteristics of the system. © 2023 IEEE.

关键词： Energy transfer

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CAPAST: Content Affinity Preserved Arbitrary Style Transfer

CAPAST: Content Affinity Preserved Arbitrary Style Transfer

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinyuan Zheng Xiaojie Li Canghong Shi Jia He Zhan Ao Huang Xian Zhang Imran Mumtaz School of Computer Science Chengdu University of Information Technology China School of Computer and Software Engineering Xihua University China Faculty of Engineering and Mathematical Sciences The University of Western Australia Australia Department of Computer Science University of Agriculture Faisalabad Pakistan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Balancing the consistency of style and the integrity of content is the main challenge in arbitrary style transfer domain. Currently, local style details can be effectively captured by attention mechanism but easily produce distorted style patterns and inconsistent content structure. In this paper, we propose a Content Affinity Preserving Arbitrary Style Transfer (CAPAST) framework to ensure style features can be stably integrated into the content structure. Considering the local feature learning ability of CNN and the global feature representation advantage of transformer, a dual encoder is proposed to capture local and global features of images with the combination between transformer and CNN. In addition, a channel and spatially aligned attention (CSAA) is introduced to generate high-quality results by stably fusing style features and content features. In experiments, we demonstrated the superior performance of our method in preventing content structure distortion and maintaining consistency between style and content. Codes are available at https://***/miaopashi-zxy/CAPAST.

关键词： Representation learning Acoustic distortion Codes Attention mechanisms Fuses Transformers Acoustics Speech processing

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