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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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A Heart Disease Prediction Model using Merged XGBoost-SVM Classifier and Particle Swarm Optimization 5

A Heart Disease Prediction Model using Merged XGBoost-SVM Cl...

引用

5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Majumder, Annwesha Banerjee Gupta, Somsubhra Majumder, Sourav Singh, Dharmpal JIS College of Engineering Dept. of Information Technology Kalyani India Swami Vivekananda University Dept. of Comouter Science Kolkata India Capgemini Kolkata India JIS College of Engineering Dept. of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350395235

This study aims to develop an advanced heart disease prediction model using machine learning techniques to address the global health challenge of early identification and accurate diagnosis, especially considering the projected deficits in healthcare by 2030. The proposed model integrates two powerful algorithms, XGBoost and Support Vector Machine Classifier, leveraging their complementary strengths to achieve effective classification. To enhance the model's performance, feature selection has been conducted using the LASSO method, which identifies the most relevant features, while outlier removal based on the Z score ensures data quality. StandardScaler has been utilized to standardize the data, mitigating any biases caused by differing scales. Furthermore, Particle Swarm Optimization has been employed to optimize the model's performance by fine-tuning its parameters. The reported accuracy of 91.8% underscores the model's potential to accurately predict heart disease, offering valuable support in prevention efforts. These findings highlight the significance of technological advancements in healthcare, particularly in addressing the growing demand for accurate and timely diagnoses. © 2024 IEEE.

关键词： Support vector machines

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New Environment Exploration Algorithm combining Bug, Free Segment and Turning Point Algorithm for Multi-Robot System 9

New Environment Exploration Algorithm combining Bug, Free Se...

引用

9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Abhishek, P. Potanpalli, Adarsh Pandey, Karnika Sridevi, V. National Institute of Technology Tiruchirappalli Dept. Instrumentation and Control Engineering Trichy India National Institute of Technology Tiruchirappalli Instrumentation and Control Engineering Trichy India

ISBN: (纸本)9798350334395

Exploration and mapping of an unknown environment is a challenging task and using a single robot is time consuming and inefficient. Multi Robot System(MRS) is used to reduces exploration time as well as to cover larger areas and produces accurate maps. This work focuses on development of new environment exploration algorithm for multi robot system which combines the features of Bug, Free segmentation and turning point algorithms. The Proposed algorithm utilizes capabilities of three mobile robots and tested on simulated outdoor environment with static obstacles. Two sub robots are equipped with LiDAR sensors and are used to explore the region between initial and target location. The point cloud generated by LiDAR sensors of the two robots is shared with the main/mother robot to generate an occupancy map. Mother robot execute A∗ path planning algorithm on the occupancy map to find the shortest path and moves towards the target location. Proposed algorithm has low computational requirements and significantly reduces the time required to explore the given environment. Further, the performance of the proposed algorithm is compared with existing algorithms in terms of the time required to explore the environment and generate occupancy map. © 2023 IEEE.

关键词： Multipurpose robots

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A Novel Deep Ensemble Learning Approach to Predict Eye Diseases from OCT Images 5

A Novel Deep Ensemble Learning Approach to Predict Eye Disea...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Nazim Mia, Md Sajib Uddin, Mohammed Nasir Mahmud, Prince Chandpur Science and Technology University Dept. of Information and Communication Technology Bangladesh Sylhet Engineering College Dept. of Computer Science and Engineering Bangladesh Jagannath University Dept. of Computer Science and Engineering Bangladesh Chandpur Science and Technology University Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

As the world advances in technology, the classification of eye diseases is thought to have clinical applications since it evaluates and analyzes the results. Optometrists research and diagnose age-related eye problems such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen using optical coherence tomography (OCT) of the human eye. Because their symptoms are similar, identifying these diseases is challenging and requires hours of expert analysis. The requirements for eye illness classification should be straightforward in nature, structure, and evaluation. Deep Learning Convolutional Neural Network Classification can create a hierarchical representation of images to differentiate between normal and diseased eye patterns for diagnostic purposes. Therefore, this paper aims to implement and define deep learning to classify and identify eye diseases. Using a four-class classification problem, this work investigates the automatic detection of Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal in Optical Coherence Tomography (OCT) pictures. Multi-transfer learning architecture was used in the suggested deep learning Ensemble classification model. The most incredible accuracy of our proposed Ensemble model for multi- class classification was 83%. © 2023 IEEE.

关键词： Optical tomography

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Enhancing Feature Prediction in Image Generation Models via Task Arithmetic 13

Enhancing Feature Prediction in Image Generation Models via ...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Unehara, Muneyuki Ogata, Kento Yukawa, Takashi Hokkaido Musashi Women's University Dept. of Business Administration Sapporo Japan Graduate School of Information and Management Systems Engineering Nagaoka University of Technology Nagaoka Japan Nagaoka University of Technology Dept. of Information and Management Systems Engineering Nagaoka Japan

ISBN: (纸本)9798350373332

In the task of generating images, various image generation models have been developed. 'Stable Diffusion' has garnered significant attention for its ability to generate high-quality derivative images with minimal artifacts. However, developing various derivative models requires extensive trial and error and considerable experience. In this study, we propose three novel methods for the effective development of desired derivative models used in Stable Diffusion. The proposed methods facilitate the creation of new image generation models through 'merging' process, making it easier to predict the features of the merged model. The proposed methods extend task arithmetic, which enables parameter operations between models, into the merging process. To achieve this, we propose and utilize the 'feature score' using CLIP. In this paper, we conducted experiments on the overall parameter operations using feature scores with five models and specific labels. The experimental results showed that the reflection of features based on the task vector's multiply value acquired results with relatively small prediction errors. The potential of the proposed methods was confirmed. © 2024 IEEE.

关键词： Prediction models

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Improving Liver Disease Diagnosis Through Generative Adversarial Network Driven Synthetic Tabular Data

Improving Liver Disease Diagnosis Through Generative Adversa...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Mahendran, S. Venkatasekhar, D. Shanmugasundaram, G. Annamalai University Department of Information Technology Chidambaram India Chennai Institute of Technology Dept. of Computer Science & Engineering Chennai India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540821

Liver ailments pose a significant worldwide health concern, with their extensive effects and severe repercussions highlighting the pressing necessity for precise diagnosis. Diagnosing hepatic disorders is frequently intricate and expensive. This work presents a Generative Adversarial Network (GAN) based methodology for producing synthetic tabular data to enhance early prediction of liver ailments, employing a dataset of Indian liver patients. The contribution of the work is the introduction of innovative architecture that integrates Stacked Ensemble Model with advanced preprocessing techniques and the use of Generative Adversarial Networks for data augmentation not extensively explored in current data-driven machine learning approaches for liver ailments prediction. The Stacked Ensemble Model has two tiers: the base-level and the meta-level. The base-level learning component trains various base learners, including Gradient Boosting, Ride classifier, Multi-layer Perceptron, and LightGBM on the input dataset. A strong prediction model is then constructed by combining and feeding the outputs of these several classifiers into a Random Forest classifier, which is the meta-learner. The results show that the model performs much better when synthetic data generated by GANs is included, obtaining an overall accuracy of 95%. This approach aims to greatly improve the prediction of liver ailments, hence enabling quick intervention and more effective treatment. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing Hyperspectral Image Compression Through Stacked Autoencoder Approach 6

Enhancing Hyperspectral Image Compression Through Stacked Au...

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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Afrin, Afsana Haque, Md. Rakibul Mamun, Md. Al Inst. of Information and Communication Technology Rajshahi University of Engineering Technology Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

Hyperspectral Image (HSI) consisting of numerous high-resolution spectral bands creates challenges of the high dimensionality problem in HSI classification which hinder real-life applications despite their abundance of information. We present a lossy compression approach using stacked autoencoders to reduce high dimensionality problem in this paper. The pro-posed method utilizes stacked autoencoders to extract features from HSIs, allowing compression and subsequent reconstruction. The study demonstrates improved Peak Signal-to-Noise Ratio (PSNR) of 70.43, 60.87 and 61.36 on three distinct HSI datasets-Salinas, Botswana, and KSC respectively compared to previous autoencoder-based compression method. Additionally, the impact of compression on classification accuracy is assessed using a 3D-2D CNN model, achieving an average accuracy of 99 % across the datasets. Improved compression along with high classification accuracy shows our proposed method's usefulness. © 2024 IEEE.

关键词： Image enhancement

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Novel All-Metal Dual-Band Phase-Shifting Cells: A Generic Design and Their Applications

Novel All-Metal Dual-Band Phase-Shifting Cells: A Generic De...

引用

2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Ahmed, Foez Singh, Khushboo Esselle, Karu P. Dept. of Information and Communication Engineering University of Rajshahi Bangladesh School of Electrical and Data Engineering University of Technology SydneyNSW Australia

ISBN: (纸本)9798350379693

Phase-shifting cells are the building blocks of radio frequency (RF) components that allow for the flexible manipulation of electromagnetic waves. They are typically constructed based on a 360◦ phase-range database, which expands the search space and necessitates significant computational simulations. This further complicates the design challenges of cells employed for dual-band applications. A general design approach for an all-metal dual-band cell is presented in this article. First, databases for phase vs. magnitude operating at two selected frequencies are built. From there, every phase pair combination functioning at dual bands can be generated without simulating and creating a full 360◦ phase database for each phase pair combination. These cells can be widely employed in creating various RF components, including phase-correction and phase-gradient metasurfaces, transmitarrays, and planar lenses. © 2024 IEEE.

关键词： Scanning antennas

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5G the Missing Piece in Digital Transformation of the Construction Sector 28

5G the Missing Piece in Digital Transformation of the Constr...

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28th International Computer Science and engineering Conference, ICSEC 2024

作者： Chnaidini, Shaik Khan, M. Jebran Ullah, Fasee Khan, Arafat Ahmad Prashar, Deepak Gulf American University Dept. of Civil Engineering Fujairah United Arab Emirates Sarhad University of Science and Information Technology Dept of Computing and Information Technology Peshawar Pakistan Uinversiti Teknology Petronas Dept. of Computer and Information Sciences Seri Iskandar Malaysia Khonkaen University Dept. of Computer Science Khon Kaen Thailand Lovely Professional University Dept. of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350366860

The 5th generation of wireless technology with a continuum leap rushing towards the future in the world of communication has a puzzle that enables the autonomous machine to work in site making a louder voice that shows the era where even if we cannot be fully robotic, we can make the machines do the work by sitting on a faraway control room. Making ways clear to see the work done miles away on construction sites the 5th generation has not only brought the people close through communication but also made them work collegiately with a clearer view front. The paper discusses 5G and the other related technologies that are emerging all over the world and how it is needed specifically in the construction industry. In the present paper, we tried to shed light on the acceptance and implementation of 5G technology in the construction sector. It is seen that the construction sector in India is lagging behind when it comes to implementation and availability of required support and resources for the 5G implementation. © 2024 IEEE.

关键词： Construction industry

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Fuzzy Logic-Based Automatic Water Quality control System in Smart Aquaponics 10

Fuzzy Logic-Based Automatic Water Quality Control System in ...

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10th International Conference on information technology, Computer, and Electrical engineering, ICITACEE 2023

作者： Mustika, I Wayan Najib, Faisal Yusriadi, Yusriadi Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350322729

Aquaponics is an advanced farming technique that relies on continuous water flow between aquaculture and hydroponic components. The success of this system is determined by the maintenance of the specific water chemistry to ensure sustainability and proper operation. The automated water quality control can greatly assist farmers in efficiently managing water conditions. To achieve this, an aquaponics water quality control system is required with a focus on monitoring and regulating parameters such as dissolved oxygen (DO), total dissolved solids (TDS), and pH levels. The DO control involves the use of pumps to increase DO levels in water;TDS control uses pumps to deliver nutrient mixtures to increase TDS and pH control utilizes pumps to flow and adjust pH levels. Fuzzy logic methods are employed to process data in the development of this control system. The results indicated that the fuzzy method can regulate the operation time of various pumps based on the input values of DO, TDS, and pH. Even under extreme conditions, the control system can effectively regulate the actuators. © 2023 IEEE.

关键词： Fuzzy logic

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