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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Ramakrishnan, Kalaimagal Balakrishnan, Vimala Faculty of Science and Engineering University of Nottingham Malaysia Semenyih Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

The rapid dissemination of misinformation (generally known as fake news) has become worrisome, especially during the on-going COVID-19 pandemic both globally, and locally. In fact, the proliferation of health-related misinformation intensified on social media, which many experts believe is contributing to the threats of the pandemic. Sentiment has been shown to improve detection mechanisms in various social media related studies, however this aspect is under-researched in the context of health misinformation. Further, metadata such as location or image that constitute part of real and fake news were not fully explored as well. This study develops a health misinformation detection model using machine learning algorithms, and further assesses the impact of sentiment and image on the model performance. Local data gathered from a fact-checking portal were pre-processed, translated, and used to train the detection model. Evaluation results show Support Vector Machine to yield the best performance with 99.4% for F-measure and accuracy of 99.1%, followed closely by Random Forest when sentiment was included, however, the presence of image was not found to significantly improve health misinformation detection. © 2022 IEEE.

关键词： COVID-19

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Smart Energy Management System Using IoT for Real-Time Monitoring and Theft Detection

Smart Energy Management System Using IoT for Real-Time Monit...

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Information technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Rajermani Thinakaran Somasekar Jalari Vikram Neerugatti Ravindra Raman Cholla K Nagendra Rao Faculty of Data Science and Information Technology INTI International University Malaysia Department of Computer Science and Engineering Faculty of Engineering and technology Jain (Deemed-to-be University) Bangalore India Department of Computer Science and Engineering S V College of Engineering Tirupati Andhra Pradesh India

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

The internet of things technology has developed almost all the sectors including energy management. In traditional energy management system meters are used to recording the number of units and electricity used but they are unable to provide the real time information or updates or any theft of electricity. By focusing this problem, we have developed a system which is able to overcome all the problems in traditional electric management system. It monitors the energy usage and real time theft detection of electricity as it cost lot of money. In our system we have used a current sensor which continuously checks the electricity flow. The microcontroller measures the actual energy usage as well as expected values. It compares both the values. If a big difference found, then it could mean the theft of electricity. So, the GSM module is used to send the alert to the user hence they can take required action. The Wi-Fi module sends the data over the cloud for data analytics. Also, user can see the real time data from the cloud platform. Hence user can track the energy usage and get timely alerts so that user can be aware of energy consumption or energy theft. The system is developed by setting up hardware parts as well as integration with software. The system can be operated or monitored from remote place, as Wi-Fi module connects the system with the internet. System can detect the unusual activities like theft of electricity by analyzing the data. Using the IoT technology for energy management, it can become cost effective as well as smart solution of energy management to avoid any theft. Hence it can overcome the loss.

关键词： Meters GSM Energy consumption Renewable energy sources Costs Electricity Real-time systems Recording Monitoring Wireless fidelity

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Visual recognition of wheel hubs with convolutional neural network 3

Visual recognition of wheel hubs with convolutional neural n...

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3rd International Conference on Consumer Electronics and computer engineering, ICCECE 2023

作者： Dai, Yining Fang, Zaojun Zhong, Caiming Ningbo University Faculty of Electrical Engineering and Computer Science NingBo Zhejiang China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology NingBo Zhejiang China

ISBN: (纸本)9798350331578

The traditional recognition methods of wheel hubs are mainly based on extracted feature matching. In practical production, their accuracy, robustness and processing speed are usually greatly affected. To overcome these problems, this paper proposes a recognition method based on convolutional neural network. The basic steps include two parts: wheel image pre-processing and wheel model classification. The image processing method, mainly using the detection algorithm of hough circles, obtains the center coordinates and radius of the wheel. Then it maps the ring-shaped wheel in right-angle coordinates to polar coordinates by the center coordinates and radius. This stepcan extract the ring-shaped feature information of the wheel image and reduce the influence generated by redundant features. Then a network architecture with an improved Resnet is designed to classify the wheel models. Finally, the wheel model recognition algorithm is evaluated, and the effectiveness of the method is verified through the comparison experiments of SVM, KNN and other models. The experiments show that the recognition accuracy can reach about 99.8% for 10 kinds of wheels. © 2023 IEEE.

关键词： Wheels

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Enhancing Oral Cancer Screening with Deep Learning Algorithms 1

Enhancing Oral Cancer Screening with Deep Learning Algorithm...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Bhaskar, Attarde Viren Soujanya, A. Ramal, P. Janaki Santhana Lakshmi, S. Nithyakalyani, K. Kumar, Bharathi Ramesh Dr. D.Y. Patil Medical College Hospital & Research Centre Pimpri Department of Critical Care Medicine Pune India CVR College of Engineering Ibrahimpatnam Department of Computer Science and Engineering Telangana India Saveetha Engineering College Chennai Department of Computer Science Engineering Tamil Nadu India Mohamed Sathak A.J.college of Engineering Siruseri Department of Computer Science and Engineering Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Department of Biomedical Engineering Tamil Nadu India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Mathematics Chennai India

ISBN: (纸本)9798350353648

The majority of the conventional approaches used in current oral cancer screening methods, such as biopsy and visual inspection, result in late-stage detection, high false-positive/negative rates, and higher healthcare expenses. These techniques need specialized staff and are labor-intensive and subjective. To improve diagnostic speed and accuracy, the research offers a unique approach that makes use of deep learning methods, particularly Transformer models and Hybrid Neural Networks (HNNs). To enhance input quality, the system incorporates sophisticated image preparation methods such as data augmentation and normalization. While HNNs integrate Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for robust feature extraction and temporal analysis, Transformers use attention techniques to extract contextual information. Its potential for early and accurate oral cancer detection is highlighted by the results, which show a significant improvement in detection accuracy (94% vs. 80-85% in existing systems), sensitivity (90% vs. 70-78%), and specificity (92% vs. 75-82%), along with a reduction in processing time to just 2 minutes. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine learning based fraudulent detection system for financial transactions

Machine learning based fraudulent detection system for finan...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Alam, Wahaj Ali, Raja Hashim Ali, Nisar Imad, Muhammad ul Abideen, Zain Shah, Muhammad Huzaifa Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14476 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain

ISBN: (纸本)9798331504465

Maintaining the integrity of financial systems and preventing people and organizations from suffering financial losses depend heavily on the ability to spot fraudulent financial transactions. Traditional rule-based fraud detection methods have trouble identifying intricate patterns and developing fraud tactics. Machine learning approaches have become powerful fraud detection tools in recent years, utilizing the strength of data-driven models to spot fraudulent actions. The Existing rule-based fraud detection has difficulty in identifying complex patterns and evolving fraud tactics. This study thoroughly investigates the use of machine learning techniques, including decision trees and random forests, for financial transaction fraud detection, while also exploring feature engineering approaches to extract essential data from transaction records such as temporal, spatial, and relational features. In this study, real-world financial transaction datasets are used to conduct experimental evaluations, comparing the performance of various machine learning models based on accuracy, precision, recall, and F1-score. The results indicate that certain algorithms outperform others, demonstrating promising and favorable outcomes for CatBoost algorithm. This significance of our study lies in showcasing the effectiveness of machine learning techniques, compared to traditional rule-based methods, for detecting fraud in financial transactions, leading to more promising outcomes and contributing to the integrity and security of financial systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

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computers, Materials & Continua 2023年第8期76卷 2261-2279页

作者： Ayman Khallel Al-Ani Shams Ul Arfeen Laghari Hariprasath Manoharan Shitharth Selvarajan Mueen Uddin Cybersecurity Research Group Faculty of Computing and InformaticsUniversity Malaysia SabahJalan UMSKota KinabaluSabah88400Malaysia National Advanced IPv6 Centre(NAv6) Universiti Sains MalaysiaGelugorPenang11800Malaysia Department of Electronics and Communication Engineering Panimalar Engineering CollegePoonamalleeChennai600123TamilnaduIndia Department of Computer Science&Engineering Kebri Dehar UniversityKebri Dehar250Ethiopia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar

In this paper,the application of transportation systems in realtime traffic conditions is evaluated with data handling *** proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the *** to the existing approach,the design model in the proposed method is made by dividing the computing areas into several cluster regions,thereby reducing the complex monitoring system where control errors are ***,a route management technique is combined with Artificial Intelligence(AI)algorithm to transmit the data to appropriate central ***,the combined objective case studies are examined as minimization and maximization criteria,thus increasing the efficiency of the proposed ***,four scenarios are chosen to investigate the projected design’s *** all simulated metrics,the proposed approach provides better operational outcomes for an average percentage of 97,thereby reducing the amount of traffic in real-time conditions.

关键词： Transportation Artificial Intelligence(AI) data-driven Internet of Things(IoT)

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Effects of rare-earth Pr^(3+)-Dy^(3+)doping on structural,magnetic,optical and dielectric properties of Zn_(2)Y hexaferrite

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Journal of Rare Earths 2025年第2期43卷 329-336,I0004页

作者： Ibrahim Mohammed Mohamed E.El Sayed Ali Shawabkeh Mohammad N.Murshed Ibtehaj F.Alshdoukhi Zeinhom M.El-Bahy J.Mohammed A.K.Srivastava Department of Physics Lovely Professional UniversityPhagwara14411PunjabIndia Department of Science Technology Waziri Umaru Federal Polytechnic Birnin KebbiPM.B 1034Birnin KebbiKebbiNigeria Physics Department Faculty of Science and ArtsKing Khalid UniversityMuhayl AsserSaudi Arabia College of Engineering and Technology American University of the Middle East.Kuwait Department of Basic Sciences College of Science and Health ProfessionsKing Saud Bin Abdulaziz University for Health ScienceKing Abdullah International Medical Research CenterRiyadhSaudiArabia Department of Chemistry Faculty of ScienceAl-Azhar UniversityNasr City11884CairoEgypt Department of Physics Faculty of ScienceFederal University DutseP.M.B.7156DutseJigawaNigeria

This study was conducted to investigate the properties of Pr^(3+)-Dy^(3+)doped Zn_(2)Y-type hexaferrite for potential technological applications.X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FTIR)and field-emission scanning electron microscopy(FESEM)were used to analyze structural *** crystallite size in the synthesized samples varies between 57.54 and 68.57 *** vibrational bands at 400 and 600 cm^(-1),common to all hexaferrites,were confirmed through *** FESEM analysis reveals an agglomeration of magnetic grains and a decrease in the average grain size from 1.24 to1.06μ*** M-H loops show that,with x values of 0.0,0.1,and 0.2,the saturation magnetization is determined to be 34.76,34.23,and 32.52 emu/g,*** corresponding coercivity values are21.24,30.39,and 33.99 ***-visible spectroscopy using Tauc theory reveals an increase in the optical band gap from 2.32 to 2.50 eV,indicating a tunable energy band structure by incorporating Pr^(3+)-Dy^(3+)*** dielectric constant increases,whereas AC conductivity decreases with increased Pr^(3+)-Dy^(3+)*** obtained results suggest the potential suitability of these materials for various technological applications.

关键词： Zn_(2)Y-type hexaferrite Pr^(3+)-Dy^(3+) Magnetic hysteresis Optical band gap Dielectric constant Rare earths

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Advanced BERT and CNN-Based Computational Model for Phishing Detection in Enterprise Systems

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computer Modeling in engineering & sciences 2024年第12期141卷 2165-2183页

作者： Brij B.Gupta Akshat Gaurav Varsha Arya Razaz Waheeb Attar Shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon College of Business Administration Management DepartmentPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong Kong518031China

Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive *** study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information ***’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing *** an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing *** approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.

关键词： Phishing BERT convolutional neural networks email security deep learning

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Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends

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IET COMMUNICATIONS 2022年第5期16卷 400-420页

作者： Elfatih, Nada M. Hasan, Mohammad Kamrul Kamal, Zeinab Gupta, Deepa Saeed, Rashid A. Ali, Elmustafa Sayed Hosain, Md. Sarwar Department of Electrical and Electronics Engineering Red Sea University Port Sudan Sudan Faculty of Information Science and Technology The National University of Malaysia (UKM) Bangi Selangor Malaysia Amity Institute of Information Technology Amity University Uttar Pradesh Delhi NCR India Department of computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Department of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Department of Information and Communication Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh

The Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) concept have emerged from IoT technology, which refers to connecting many vehicles with various applications to the internet. The 5G new radio is based on a cloud-radio access network (CRAN), considered as the communication infrastructure for IoV. However, due to the significant challenges and issues, researchers have been working on IoV and V2X. One of the main challenges for V2X is resource allocation and management for a high-speed vehicular environment. This paper discusses and provides complete detail for resource allocation and management for IoV over 5G RAN networks focusing on artificial intelligence techniques. The paper also presented reviews on integrating the multi-layers of vehicular network architecture with AI strategy to identify advancement and future directions for resource allocation and management issues.

关键词： Internet of Things Vehicle to Everything artificial intelligence techniques resource allocation IoT technology IoV radio access networks vehicle resource management V2X 5G mobile communication Internet of Vehicles communication infrastructure artificial intelligence vehicular ad hoc networks 5G RAN networks 5G new radio high-speed vehicular environment 5G networks resource management telecommunication computing

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Classification Method of Surface Defects of Aluminum Profile Based on Transfer Learning 2

Classification Method of Surface Defects of Aluminum Profile...

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2nd International Conference on Machine Learning and Intelligent Systems engineering, MLISE 2022

作者： Li, Bin Ren, Fuji Ni, Hongjun Kang, Xin Lv, Shuaishuai Hao, Zhuangzhuang Tokushima University Faculty Of Engineering Tokushima Japan Nantong University School Of Mechanical Engineering Nantong China University Of Electronic Science And Technology School Of Computer Science And Engineering Chengdu China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

In the field of industrial defect detection based on machine learning, small and unbalanced number of sample data has been a challenge to the defect classification task. In this paper, the number of aluminum profile defect pictures is increased by digital image processing methods such as rotation and flipping. Then, a VGG16 network is pre-trained on ImageNet dataset, in which the weights of feature extraction modules are transferred to the classification task of surface defects of aluminum profile. Furthermore, the classification modules of VGG16 are structurally fine-tuned, the number of neurons in the second and third fully connected layers of which is adjusted to 256 and 10 respectively. The experimental results show that the classification accuracy of 10 types of aluminum profile surface defects reaches 98.47%, and the training process is stable without any obvious overfitting phenomenon. Our results suggest that the proposed method for aluminum profile surface defect classification is smooth in feature transferring and structural fine-tuning and renders reliable results in defect classification. © 2022 IEEE.

关键词： Surface defects

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