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Electric Vehicle Charging system Using RFID and IoT for Enhanced User Experience 5

Electric Vehicle Charging System Using RFID and IoT for Enha...

引用

5th IEEE Middle East and North Africa communications Conference, MENACOMM 2025

作者： Hassain, Md Mehedi Haque Jamil, Injamul Hoque, Md Mahmudul Hasan, Md. Jahid International Islamic University Chittagong Dept. of Electrical and Electronics Engineering Chittagong Bangladesh University of Greenwich Dept. of Computing and Mathematical Sciences London United Kingdom CCN University of Science and Technology Dept. of Computer Science and Engineering Cumilla Bangladesh Comilla University Dept. of Information and Communication Technology Cumilla Bangladesh

ISBN: (纸本)9798331519957

Electric vehicles (EVs) are surging in popularity globally, offering a greener and more energy-efficient alternative to traditional cars. Our research aims to develop an RFID based system designed to automate EV charging with minimal user input. This system integrates RFID readers, tags, a microcontroller, and a relay module to manage and monitor charging sessions efficiently. An IoT platform supports real time monitoring and remote control, while a secure payment mechanism handles transactions seamlessly. RFID tags identify vehicles for automatic charging initiation, and the relay module prevents overcharging by disconnecting power after a set time. The system uses MQTT for data transmission and offers real-Time insights into energy use and account balances. All information is updated and displayed in real-Time on the server. MySQL ensures scalability, enabling effective handling of large volumes of data. The mobile application dashboard provides a view of the charging and discharging processes for user monitoring, and an email notification is sent after the recharge. EV battery charging status is monitored through visual graphs. Key benefits include a streamlined user experience, improved charging management, and support for EV adoption. This innovation advances sustainable mobility with a user friendly, efficient charging solution. © 2025 IEEE.

关键词： Radio frequency identification (RFID)

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system for Talent Acquisition: Integrating AI, Automation, and Data Analysis in HR 6

System for Talent Acquisition: Integrating AI, Automation, a...

引用

6th International Conference on Artificial Intelligence in Information and communication, ICAIIC 2024

作者： Abitova, Gulnara Serikov, Ayanbek Nikulin, Vladimir Rakhimzhanova, Mira Shuteyeva, Gulnur Kulniyazova, Korlan Astana IT University Dep. Computer Engineeering Astana Kazakhstan Binghamton University Dept. Electical Engineering and Computer Science Binghamton United States Eurasian National University Dept. System Anslysis and Managemnt Astana Kazakhstan

ISBN: (纸本)9798350344349

This article presents a novel system designed for human resources (HR) companies, aiming to predict an individual's personality based on resume analysis through the integration of advanced information technology tools. At the core of this system is Airable, serving as the central database, which is seamlessly I integrated with various other tools such as *** for document handling, GitHub for code repository management, and OpenAI for advanced AI algorithms. The integration and automation of these diverse tools are skillfully orchestrated using Make, a no-code solution that streamlines the workflow and enhances efficiency. This innovative approach allows HR companies to leverage technology for more accurate personality predictions, thereby improving their recruitment processes and decision-making strategies. © 2024 IEEE.

关键词： Machine learning

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Breast Cancer Prediction Using Artificial Intelligence Technology 4

Breast Cancer Prediction Using Artificial Intelligence Techn...

引用

4th International Conference on Advances in Electrical, Computing, communication and Sustainable Technologies, ICAECT 2024

作者： Akila, V. Christaline, J. Anita Srm Institute of Science and Technology Vadapalani Dept. of Electronics and Communication Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

The aim of the project is to compare the performance of four different machine learning algorithms for breast cancer prediction such as decision tree, logistic regression, XG boost, and CAT boost. We used a dataset of patient medical records containing various clinical factors to train and test the algorithms. Accurate diagnosis and early detection are essential for enhancing patient outcomes. Data mining is a prominent tool in the healthcare industry for processing massive amounts of data. To examine massive amounts of complicated medical data, researchers use a variety of data mining and machine learning approaches. The use of these strategies can help medical practitioners forecast the onset of breast cancer. © 2024 IEEE.

关键词： Algorithms Breast cancer Data mining Machine Learning

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Comparative Study of Implementation of Very Deep Super Resolution Neural Network and Bicubic Interpolation for Single Image Super Resolution Quality Enhancement 3

Comparative Study of Implementation of Very Deep Super Resol...

引用

3rd IEEE International Conference on Applied Electromagnetics, Signal Processing, and communication, AESPC 2023

作者： Ganesan, P. Ravichandran, M. Sathish, B.S. Joseph, L.M.I.Leo Sajiv, G. Murugesan, R. Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Sri Venkateshwara College of Engineering Dept. of Computer Science Engineering Ai Bengaluru India Sr University Dept. of Electronics and Communication Engineering Telangana Warangal India Saveetha School of Engineering Simats Chennai India Narshima Reddy Engineering College Dept. of Electronics and Communication Engineering Secunderabad India

ISBN: (纸本)9798350358742

To improve the resolution and quality of a low level image, single image super resolution (SISR) is a challenging endeavor in the turf of computer vision. It is essential to many applications, including image editing, security systems, medical imaging, and others. In order to increase the spatial quality of a low level image, SISR attempts to approximate the high frequency details that are missing from the image. The field was formerly based on manually created features and interpolation techniques, but current developments in deep learning have completely changed it. The suggested work compares the effectiveness of bicubic interpolation and neural network based very deep super resolution for single image super resolution image quality improvement. Utilizing blind and complete reference picture quality measures, the effectiveness of the two approaches is evaluated. The effectiveness of the very deep super resolution for single image is clearly demonstrated by the experimental results. © 2023 IEEE.

关键词： Medical imaging

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Optimizing Urban Mobility: A Comprehensive Framework for Smart Parking systems Integration Using IoT and Sensor Technologies 7

Optimizing Urban Mobility: A Comprehensive Framework for Sma...

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7th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2023

作者： Sen, Sayak Mukherjee, Unmesh Banerjee, Subhabrata Biswas, Aparna Ray, Sutapa Chowdhuri, Sayantani Institute of Engineering & Management Dept. of Electronics and Communication Engineering Kolkata India Institute of Engineering & Management Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350305517

Smart Parking systems have gained prominence as a pivotal component of urban infrastructure, addressing the persistent challenge of limited parking spaces in burgeoning cities. This abstract delves into the evolution and functionality of Smart Parking systems. Leveraging technology such as microcontroller, sensors, IoT, and data analytics, these systems optimize parking space utilization and enhance the overall parking experience. By providing real-time information to drivers, reducing traffic congestion, and minimizing environmental impact, they contribute to more sustainable urban development. This paper explores the various components and benefits of smart parking systems, showcasing their potential to revolutionize urban mobility and improve quality of life. As cities continue to grow, smart parking systems offer a promising solution to the parking conundrum, fostering a smarter, more efficient, and eco-friendly urban environment. © 2023 IEEE.

关键词： Microcontrollers

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The Effect of Balanced and Imbalanced Dataset on Voice Pathology Detection Using Online Sequential Extreme Learning Machine (OSELM)

The Effect of Balanced and Imbalanced Dataset on Voice Patho...

引用

2024 IEEE International Conference on Advanced Telecommunication and Networking Technologies, ATNT 2024

作者： Sazihan, N. F. S. Md Latiff, N. M. Abdul Malik, N. N. N. Abd Mashohor, S. Alaghbari, K. Al-Dhief, F.T. Universiti Teknolgi Malaysia Utm Faculty of Electrical Engineering Johor Bahru Malaysia Universiti Putra Malaysia Faculty of Engineering Dept. of Computer and Communication Systems Engineering Serdang Malaysia

ISBN: (纸本)9798350353501

Voice pathology detection is crucial for early diagnosis and treatment of vocal disorders. This research studies the effect of balanced and imbalanced datasets on the accuracy of voice pathology detection using Online Sequential Extreme Learning Machine (OSELM) on the Saarbruecken Voice Database (SVD). The dataset's balance has been identified as having a significant influence on the performance of the machine learning models. In this work, the voice signals from vowel /a/ of pathology and non-pathology classes are taken from SVD. The dataset is divided into two category which are balanced and imbalanced dataset. Then, the features of the voice signals are then extracted using Mel-Frequency Cepstral Coefficient (MFCC) and fed into classifier called OSELM. The result of balanced and imbalanced dataset is assessed for its accuracy. The results showed that the model achieved an accuracy of 66.25% with the balanced dataset compared to 48% for the imbalanced dataset. These findings highlight the importance of addressing dataset balance to enhance the reliability and effectiveness of voice pathology detection systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Health Care Data Mining using Intelligent Classification Techniques 5

Health Care Data Mining using Intelligent Classification Tec...

引用

5th IEEE International Conference on Advances in Computing, communication Control and Networking, ICAC3N 2023

作者： Antil, Jyoti Rishi, Rahul Sharma, Sonia Yadav, Rajkumar University Institute Of Engineering And Technology Mdu Dept. Of Computer Science Engineering Hrayana Rohtak India University Institute Of Engineering And Technology Mdu Dept. Of Electronic And Communication Engineering Hrayana Rohtak India

ISBN: (纸本)9798350330861

Classification models can be learned from complicated multivariate temporal data and this is a topic we investigate in electronic health record systems. The difficulty is to come up with a collection of attributes that accurately reflect the data's temporal nature. Using temporal abstractions and pattern mining, we extract the classification features from the data. There are frequently a lot of temporal patterns that aren't related to the classification job that are returned by temporal pattern mining. © 2023 IEEE.

关键词： Classification (of information)

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Highly Efficient Switched Capacitor Converters for Rechargeable Hearing Aids 3

Highly Efficient Switched Capacitor Converters for Rechargea...

引用

3rd International Conference on communication, Control, and Intelligent systems, CCIS 2024

作者： Anandi, V. Muthuswamy, Ramesh Pranav, A. Ramaiah Institute of Technology Dept. of Electronics and Communication Bengaluru India School of Management Presidency University Bengaluru India Vit Dept. of Computer Science Engineering Chennai Campus Chennai India

ISBN: (纸本)9798331528201

The paper deals with switched capacitor-based designs for power-efficient rechargeable hearing aids. Switched-capacitor DC-DC power converters are a subset of DC-DC power converters that use only switches and capacitors to convert one voltage to another efficiently. By using switched-capacitor circuits, the aim is to increase the efficiency of hearing aids. To achieve long battery life, a high conversion efficiency is required. At the same time, the physical size should be low as it is desirable for the end-user to have small hearing aids. The voltage levels thus have to be stepped down from the voltage provided by a Li-ion (rechargeable) to levels closer to that of Zn-Air (non-rechargeable). © 2024 IEEE.

关键词： Capacitor bank

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Explainable AI for Enhanced Intrusion Detection systems 3

Explainable AI for Enhanced Intrusion Detection Systems

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ananya, G. Siri, N. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Electronic and Communication Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350354171

As everything is turning digital there is a hike in the invasion of digital space, because of which advanced tools are needed. One such tool is the Intrusion Detection system (IDS). In this work, we have tried to detect and classify suspicious connections accurately. This is done by incorporating advanced computing into Intrusion Detection systems. To achieve this, we have used the Coburg Intrusion Detection Dataset. To achieve this, we used the Coburg intrusion detection dataset. We investigate the effectiveness of Single Classifiers and Bagging and Boosting in this context. We proposed a four-stage Ensemble model to classify the connections using its attributes. The proposed model uses four Single Classification models: Logistic Regression, KNN, Decision Tree, and SVM. Before applying the algorithms, the data was Preprocessed. Later, the models were hyper-tuned to increase the accuracy. The results showed that Random Forest outperforms other single classifiers and machine learning algorithms, with a mean accuracy of 99.99%, a precision of 99.99%, and an F1-score of 99.99%. Furthermore, as models are usually hard to understand by using Explainable AI we can increase the transparency of the model. Explainable AI techniques are used to make the advanced computing models reliable and more understandable. This study aims to develop advanced and dependable intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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ULung:A Novel Approach for Lung Image Segmentation 6

ULung:A Novel Approach for Lung Image Segmentation

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Pal, Osim Kumar Roy, Shuvasish Modok, Anup Kumar Teethi, Tajmihir Islam Sarker, Siddhartha Kumer Dept. of Biomedical Engineering University of Kragujevac Kragujevac34000 Serbia Dept. of Electrical and Electronic Engineering American International University-Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Daffodil International University Ashulia Savar Bangladesh Dept. of Space Communication and Navigation Technology BSMRAAU Old Airport Dhaka Bangladesh

ISBN: (纸本)9798350373875

ULung is an innovative medical technology used for segmenting medical images in pulmonary diagnostics. It introduces a modified approach to analyzing and understanding pulmonary conditions. ULung employs cutting-edge deep learning and image processing techniques to isolate intricate characteristics in medical lung images properly. The model uses advanced convolutional neural network architecture as UNet layer stricture to detect abnormalities and delineate anatomical regions reliably. ULung exhibits versatility in handling various lung ailments after extensive training using diverse datasets. The innovative methodology ensures enhanced segmentation performance, enhancing diagnostic precision and expediting medical evaluations. The advent of ULung has greatly enhanced medical imaging, providing clinicians with a potent tool for precise and expeditious lung evaluation. ULung is an impressive advancement in medical image segmentation that can revolutionize standards in respiratory healthcare due to its robustness and adaptability. The model's accuracy achieved using EfficientNetV2 is 99 %, while its precision and recall rates are 98% and 96 %, respectively. The model achieves a Mean Intersection over Union (MeanloU) of 91.48% after the 14th epoch. © 2024 IEEE.

关键词： Image segmentation

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