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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Vishnupriyan, S. Benitta, D. Angeline Atab, Marzouq Yasser Shareef, Ashraf Mohammed Ali, Aqeel Al-Tahee, Mustafa Hindustan Institute of Science and Technology Department of Computer Application Chennai India National University of Science and Technology Collage of Engineering Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad Iraq

ISBN: (纸本)9798350399264

Today people's live in the "Era of Data". The data has been tortured and indulged in confession in many ways. The term big data deals with large volumes of data that streams in a fraction of a second and of-course in the format of unstructured data. As the data are big the challenges are high and the process of handling data is becoming so daunting. And the security issues of big data are becoming a great threat to the data which is very critical for the development of many organizations. As the security solution cannot be given as a one-stop solution a layered methodology has been proposed in this work for securing confidential data. It is a combination of heavy encryption traditional algorithm RSA and data masking technique which will lead to better security for the data. The reason why the RSA algorithm is proposed is that by using this algorithm it is very safe to exchange data over the internet, it maintains the confidentiality of the data to a high extent and this algorithm will maintain high toughness because breaking the keys through interceptors is difficult. The results and discussions of our proposed methodology will lead to an enhanced security system with fuzzy procedures which efficiently strengthen the system. © 2023 IEEE.

关键词： Big data

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New Speed Sensorless ANN-IFOC Flux Hybrid Estimator Controller for Induction Motor 3

New Speed Sensorless ANN-IFOC Flux Hybrid Estimator Controll...

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3rd IEEE Industrial Electronics and Applications Conference, IEACon 2022

作者： Sepeeh, Muhamad Syazmie Zulkifli, Shamsul Aizam Sim, Sy Yi Chiu, Huang-Jen Jumaat, Siti Amely Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronics Engineering Department of Electrical Power Engineering Johor Batu Pahat Malaysia Faculty of Engineering Technology Department of Electrical Engineering Technology Johor Pagoh Malaysia National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipe Taiwan

ISBN: (数字)9781665480062

ISBN: (纸本)9781665480062

This paper is to suggest a new version of sensorless control for induction motor using flux estimator generation to represent the speed input for speed control. The induction motor that been used is been presented as small EV motor in order to replicate a motor EV. This flux-speed hybrid controller is used with ANN-IFOC to replace the actual speed sensor in order to increase the tracking speed and trajectory effect while reduce the cost operation based on flux estimation speed equation. The flux parameter is been selected because any changes on the load the flux will automatically change simultaneously that is good to be applied to the EV motor. The speed controller is still needed in after the speed signal has been generate at ANN-IFOC for flux parameter but without speed sensor. At the end, all the results show, this new sensorless improved version has better time response to track the speed reference even though the load is changes. © 2022 IEEE.

关键词： Speed control

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A Design of Planar Reflective Linear-to-Circular Polarization Conversion Pattern Manipulation Surfaces with Enhanced Axial Ratio Bandwidth

A Design of Planar Reflective Linear-to-Circular Polarizatio...

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IEEE International Wireless Symposium (IWS)

作者： De Yin Lei Zhu Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Taipa China

ISBN: (数字)9798350389999

ISBN: (纸本)9798350390001

In this article, a planar linear-to-circular polarization conversion pattern manipulation surfaces (LCPC-PMS) is designed. Firstly, the application and the structure is studied. Secondly, the patch element with variable microstrip length is analyzed, and the working condition is obtained based on equivalent circuit model. Then, the performance of the element is evaluated by calculation. To broaden the axial ratio (AR) bandwidth, fil- tenna design method is introduced to bring in extra minima. The 3dB AR bandwidth is thus broadened to larger than 3% in measurement. Finally, to verify our design concept, based on phased array theory, three prototypes are designed, fabricated, and measured. The three prototypes successfully steer the incident LP wave to the desired direction with circular polarization. The proposed linear-to-circular polarization conversion pattern manipulation surfaces is a promising candidate for 6G networks.

关键词： Wireless communication Phased arrays Polarization Phase measurement Surface waves Prototypes Bandwidth Microstrip Integrated circuit modeling Equivalent circuits

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Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

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computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

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The Colony Predation Algorithm

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Journal of Bionic engineering 2021年第3期18卷 674-710页

作者： Jiaze Tu Huiling Chen Mingjing Wang Amir H.Gandomi College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Faculty of Engineering and Information Technology University of Technology SydneyUltimoNSW 2007Australia

This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in *** utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another ***,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment *** paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation *** proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering *** results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other ***,the source code of the CPA will be publicly available after publication.

关键词： Colony Predation Algorithm optimization nature-inspired computing meta-heuristic engineering problems

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Arabic Sentiment Analysis of Mobile Banking Services Reviews 10

Arabic Sentiment Analysis of Mobile Banking Services Reviews

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10th International Conference on Social Networks Analysis, Management and Security, SNAMS 2023

作者： Khabour, Safaa M. Mustafa, Dheya Althebyan, Qutaibah Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Al Ain University Dept. of Cybersecurity Al Ain United Arab Emirates Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9798350318906

Online customer reviews have developed into a significant source of information about a business's performance. Due to shifting consumer expectations and growing internet penetration, the Middle East, especially Jordan, is seeing an increase in the usage of online banking. This propensity toward digital transition has been accelerated by the COVID-19 pandemic. Mobile banking service companies aim to effectively use customers' feedback to identify areas for improvement in customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on mobile banking reviews in Arabic, exploiting machine learning, natural language processing, and resampling methods to obtain subjective feedback, determine polarity, and identify customers' feelings in the banking domain. This work walks the reader through detailed steps of cleaning and preprocessing a manually annotated dataset of online banking reviews in Arabic, followed by features extraction and modeling, dataset splitting, and balancing training data. Finally, we examined classification algorithms including Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Random Forest (RF). Ensemble hard and soft voting ML methods were also evaluated. We achieved a maximum accuracy of about 94% using the LR classifier and a maximum precision of 94% using the LR and hard voting methods. Our methodology will enable banks to acquire insights on how to develop their online presence and meet the demands of customers and stakeholders. © 2023 IEEE.

关键词： Sentiment analysis

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Sensor-Based Cattle Behavior Classification Using Deep Learning Approaches

Sensor-Based Cattle Behavior Classification Using Deep Learn...

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

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

The usage of precision livestock has grown due to the need for higher efficiency and productivity in response to the high demand for food. To ensure sustainable development and quality control of the inputs required by the industry, it is essential to monitor and classify the behavior of cattle. Sensor-based monitoring systems provide accurate information by capturing raw data and identifying behavior through machine learning and deep learning algorithms. This approach has allowed farmers to better understand the individual needs of their animals. This study presents a deep residual neural network for the classification of cattle behavior. The performance of the ResNeXt model was evaluated using a public real-world dataset collected from sensors attached to the neck of six different Japanese black beef cows. The experimental results showed that the presented ResNeXt model achieved the highest average accuracy of 94.96% and the highest average F1-score of 93.66%. Compared to other baseline deep learning models and the current state-of-the-art model for cattle behavior classification, the presented model outperformed them and achieved better performance. © 2023 IEEE.

关键词： Quality control

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A New Classification Method for Drone-Based Crops in Smart Farming

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第9期16卷 164-174页

作者： Al-Rami, Bandar Alheeti, Khattab M. Ali Aldosari, Waleed M. Alshahrani, Saeed Matar Al-Abrez, Shahad Mahdi Dean of Faculty of Computer Studies Arab open University Riyadh Saudi Arabia College of Computer Sciences and Information Technology Computer Networking Systems Department University of Anbar Ramadi Iraq Department of Computer Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia Faculty of Computer Science and Information Technology Saudi Electronic University Riyadh Saudi Arabia

During the past decades, smart farming became one of the most important revolutions in the agriculture industry. Smart farming makes use of different communication technologies and modern information sciences for in-creasing the quality and quantity of the product. On the other hand, drones showed a major potential for enhancing imagery systems and re-mote sensing usage for many different applications such as crop classification, crop health monitoring and weed management. In this paper, an intelligent method for classifying crops is proposed to use a transfer learning approach based on a number of drone images. Moreover, the Convolution-al Neural Network (CNN) method is used as a classifier to improve efficiency for obtaining more accurate results in the training and testing phases. Various metrics are measured to evaluate the efficiency of the proposed model such as accuracy rate of detection, error rate and confusing matrix. It is found to be proven from the experimental results that the proposed method presents more efficient results with an accuracy detection rate of 92.93% © 2022. International Journal of Interactive Mobile *** Rights Reserved.

关键词： Drones

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Creation of a Smart Energy Management System in the RTU Student Campus in Kipsala 65

Creation of a Smart Energy Management System in the RTU Stud...

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65th IEEE Annual International Scientific Conference on Power and Electrical engineering of Riga Technical University, RTUCON 2024

作者： Dzelzitis, Egils Apse-Apsitis, Peteris Lungis, Edijs Institute of Heat Gas and Water Technology RTU Riga Latvia Institute of Industrial Electronics Electrical Engineering and Energy RTU Riga Latvia RTU Faculty Of Computer Science Information Tehnology And Energy Riga Latvia

The task of the energy management system is to create conditions for maximizing the efficiency of electricity and heat consumption in RTU buildings, while ensuring a comfortable indoor climate and enabling continuous ...

ISBN: (纸本)9798350365771

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Speed and Torque Optimization of Motor Drive Through Intelligent Control Approaches

Speed and Torque Optimization of Motor Drive Through Intelli...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Kantari, Hanumaji Vadivel, M. Jagadeesan, Pavithra Nagalakshmi, T.J. Meenakshi, B. Velmurugan, S. Malla Reddy Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350382723

The need for better control approaches in Permanent Magnet Synchronous Motors (PMSMs) has increased due to the increased usage of electric motors in many different areas. The unique characteristics of PMSM motors make it difficult to use conventional control techniques to dynamically adjust speed and torque. This research proposes a novel approach to real-time speed and torque optimization of PMSM motor drives by integrating Sliding Mode Control (SMC) in Reinforcement Learning (RL). This could alleviate these issues. This study introduces a dual-mode control approach for permanent magnet synchronous motors (PMSM) that leverages the robustness of SMC and the flexibility of RL. In order to improve the PMSM motor drive's overall efficiency, a synergistic effect is achieved by creating an RL-based Maximum Torque and Speed Tracking (MTST) algorithm. Incorporating SMC ensures precise and stable control of speed and torque. Results from both theoretical and practical investigations using a PMSM motor operating system model in Matlab/Simulink validate the proposed method. The results reveal improved control over speed and torque, proving the efficacy and adaptability of the proposed method for PMSM actuators to function in different environments. © 2024 IEEE.

关键词： Sliding mode control

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