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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sara, Umme Das, Utpol Kanti Sikder, Juel Chowdhury, Sudipta Roy Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar Rangamati4500 Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%. © 2025 The Authors. Published by Elsevier B.V.

关键词： Lung cancer

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Framework for Cloud-Based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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2025 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 IEEE.

关键词： Cloud platforms

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Dynamic Network Optimization with Deep Learning and Deployment of Intelligent Reflecting Surfaces 19

Dynamic Network Optimization with Deep Learning and Deployme...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Jang, Nayeon Bae, Jeonghoon Tianyu, Tao Byun, Gyurin Bui, Phuoc-Nguyen Lee, Tae-Jin Choo, Hyunseung Dept. of AI Systems Engineering Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

The proliferation of 5G technologies has presented challenges such as signal attenuation and high energy consumption. This study proposes a novel approach to enhance wireless networks by integrating Intelligent Reflecting Surfaces (IRS) and Artificial Intelligence (AI) to optimize signal propagation and predict the Received Signal Strength Indicator (RSSI). A new framework combines K-means clustering for IRS deployment with a Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) model for signal prediction. Using data from 15,000 Wi-Fi access points in Herefordshire, UK, the results demonstrate significant improvements in signal strength, with the proposed model achieving a Mean Squared Error (MSE) of 37% lower than the existing models, indicating higher prediction accuracy. In addition to enhancing signal quality, the framework achieves throughput increases of 21.26% with single-group IRS and 64.91% with multi-group IRS. These results validate the proposed the potential of the framework to improve performance, making it suitable for next-generation wireless networks. © 2025 IEEE.

关键词： 5G mobile communication systems

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Investigation of VGG 16 Transfer Learning and CNN Integrated Brain Tumour Detection

Investigation of VGG 16 Transfer Learning and CNN Integrated...

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2024 International Conference on computer, Electronics, electrical engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Dept. of Electronics and Engineering Dehradun India

ISBN: (纸本)9798350388534

This study had conducted an intensive exploration of the possibilities of VGG-16 and VGG-19 Convolutional Neural Network models for picture categorisation. Using a thorough method comprising substantial training and severe testing, we may observe both steady loss decrease and significant model accuracy improvements. It is assumed that the models may appropriately keep track of subtle variations in data to which they are adversarial while identifying regular patterns they may generalize. The VGG models' architecture is quite sophisticated, but as seen above, they give strong classification accuracy, with performance in nearly ideal 99 percent in many trials. This integration proves that extremely advanced deep learning instruments are competent to handle and complete even very sophisticated picture recognition assignments. Additionally, our experiments identify a few essential aspects of model adjustment and tuning identifying important techniques considerably enhancing CNN outcomes in application scenarios. This study is pioneering for the automatic picture identification discipline since it provided a strong understanding of the generality and model quicker than many real-world models. Additionally, it exhibits that the CNN structures enhance artificial intelligence's power to recognize visual input and could improve many sectors like the surveillance and health systems. The findings defined here may now open the door to more progress in deep learning and picture process analyses while reiterated the importance of CNN in modern technology. © 2024 IEEE.

关键词： Convolutional neural networks

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A New Fault Diagnosis Approach Based on Extreme-Point Symmetric Modal Decomposition and Energy Operator Demodulation 5

A New Fault Diagnosis Approach Based on Extreme-Point Symmet...

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5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Fu, Sicheng Liu, Baqiao Chen, Zhongzhe Cao, Shuchen Syracuse University Dept. of Electrical Engineering & Computer Science SyracuseNY United States Uiuc Dept. of Computer Science Urbana United States School of Mechanical and Electrical Engineering Uestc Chengdu China Bayer Crop Science Chesterfield Missouri United States

ISBN: (纸本)9798350386943

In this paper, the Extreme-point Symmetric Modal Decomposition (ESMD) was firstly introduced to the fault diagnosis field of rotating machinery, and a new fault diagnosis approach of rotating machinery based on ESMD and Energy Operator Demodulation (EOD) is proposed. Firstly, the gear fault vibration signal is decomposed into the Finite Intrinsic Mode Function (IMF) components though ESMD. Secondly, these IMF components were demodulated by the EOD to form envelope signals. Lastly, the fault feature information is extracted by analyzing IMF components and envelope signals. The ESMD is employed to decompose a simulation signal and made comparison with the Empirical Mode Decomposition (EMD) and the Local Mean Decomposition (LMD). The result shows that the ESMD can overcome the problems of end effect of EMD. Moreover, it avoids low efficiency of LMD. The method is applied to the fault diagnosis of a gear with broken teeth. The results indicated that this gear fault can be effectively identified, which demonstrated that the proposed method is an effective and practical method for fault diagnosis of rotating machinery. © 2024 IEEE.

关键词： EOD ESMD Fault Diagnosis

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SOH Estimation of Batteries using Lithium-Ion Internal Parameters with Convolution Neural Network and Gated Recurrent

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Transactions of the Korean Institute of electrical Engineers 2023年第3期72卷 387-394页

作者： Park, Hyun-Yong Lim, Hee-Sung Lee, Kyo-Beum Dept. of Electrical and Computer Engineering Ajou University Korea Republic of Dept. of IT Convergence Engineering Ajou University Korea Republic of SK Signet Inc Korea Republic of

This paper proposes an estimation method for Lithium-Ion Batteries SOH by learning the batteries’ internal parameters using the Convolution Neural Network and the Gated Recurrent Unit. Various equivalent circuit models exist to represent the batteries’ internal parameters. Among these equivalent circuit models, the most representative model is the Randles model, and the data measured based on the Randles model is used as learning input data. The internal parameters of batteries change non-linearly depending on the operation condition and use time. So, nonlinear features are extracted using the CNN input as the batteries' parameters. The extracted features are used as an input of the GRU to learn the characteristics of change over time, and SOH is predicted through this. The learning dataset utilizes 17IND10 LibForSecUse of EMPIR, which validates the performance of the proposed model. Copyright © The Korean Institute of electrical Engineers.

关键词： Lithium-ion batteries

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Exploring the Impact of Robotics in STEM Education Activities and Competitive Challenges 2

Exploring the Impact of Robotics in STEM Education Activitie...

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2nd International Conference on Cybernetics and Innovations, ICCI 2023

作者： Farooq, Adil Zukhraf, Syeda Zillay Nain Maryam, Hafsa Attard, Conrad Kamal, Mohsin University of Cyprus Dept. of Electrical and Computer Engineering Cyprus National University of Computer and Emerging Sciences Dept. of Electrical Engineering Pakistan University of Malta Dept. of Computer Information Systems Msida Malta

ISBN: (纸本)9798350336573

As robotics and technology become increasingly integrated into education and daily life, it is important to recognize their essential role. Even young children have access to electronic toys and gadgets, which can facilitate their learning and interaction. This study highlights the significance of STEM (science, technology, engineering, and mathematics) education at the elementary school level, emphasizing how university students from a variety of disciplines can design and construct robots capable of carrying out challenging tasks for humanitarian purposes. The study also emphasizes the need to encourage women's participation in robotics to showcase their skills and innovative ideas. Furthermore, the study demonstrates how robotics can effectively be used in both STEM education and competitive challenges, showcasing its effectiveness in teaching and promoting technological advancement. By encouraging early engagement in STEM education and promoting diversity in the field of robotics, we can empower future generations to lead the way in creating innovative solutions to global challenges. © 2023 IEEE.

关键词： Intelligent systems

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Design of Alcohol Detection and Alert System for Vehicles 1

Design of Alcohol Detection and Alert System for Vehicles

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Bhardwaj, Indu Saravanan, D. Bhardwaj, Ajay Yadav, Daman Kumari, Vijeta Kumar, R Ramesh Galgotias University Dept. of Electrical Electronics and Communication Engineering Greater Noida India Madanapalle Institute of Technology and Science Dept. of Electrical and Electronics Engineering India Delhi Technological University Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350356816

Recent days accidents are increasing on an exponential basis due to drinking and drive. Driving after drinking alcohol is a major cause of the majority of the accidents due to which sometimes innocent people lose their lives. As the number of vehicles is increasing day by day, accidents are also occurring due to many reasons but alcohol consumption is a major factor in many of the accidents. To avoid these accidents, an alcohol detection and alert system for vehicles is proposed in this paper that is based on MQ3 sensor. The proposed system gives a coherent solution to control injuries because of consumption of alcohol while driving. The alcohol detected by the MQ-3 sensor is processed via an ATmega328P Microcontroller that collate it with a fixed threshold for compliance. The device will nonstop monitor concentration of alcohol by the alcohol recognition sensor and consequently flip off the working of automobile's engine if the alcohol concentration is exceeding threshold value. The replica can even send the alert of locality of the car via SIM900A (GSM module). The model presents an adequate answer to regulate injuries by the influence of alcohol driving. The LCD module is used to show that alcohol has been identified. The proposed system is cost effective, less bulky and more reliable. © 2024 IEEE.

关键词： Microcontrollers

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A Mobile Application for the Crowdsensing of Road Pavements Conditions 22

A Mobile Application for the Crowdsensing of Road Pavements ...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Delnevo, Giovanni Bartolucci, Nicola Mirri, Silvia Salomoni, Paola Prandi, Catia University of Bologna Dept. of Computer Science and Engineering Italy University of Bologna Bachelor Degree in Computer Science and Engineering Italy

ISBN: (纸本)9798331508050

Urban environments face significant challenges due to deteriorating road pavements, affecting all transportation modes' safety and comfort. Traditional methods of road assessment are costly and infrequent, but advancements in mobile technology and crowdsensing offer real-time, large-scale data collection solutions. This paper introduces 'ShakeSensing', a mobile application that uses Android device sensors to measure vibrations experienced by cyclists and scooter users, providing insights into road quality. The collected data, analyzed to assess road conditions, can benefit both users and municipal authorities, promoting safer and more comfortable travel. © 2025 IEEE.

关键词： Urban transportation

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Systematic Common Information Model (CIM) Profile Management for Advanced Distribution Management System

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Transactions of the Korean Institute of electrical Engineers 2024年第1期73卷 1-9页

作者： Kim, Hyun Joong Oh, Jae Young Yoon, Yong Tae Jung, Wonwook Shim, Jaeseong Shon, Jin-Man Dept. of Electrical Engineering Hoseo University Korea Republic of Dept. of Energy Engineering Korea Institute of Energy Technology Korea Republic of Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Korea Electric Power Corporation Research Institute Rep. of Korea Korea Republic of

Common Information Model (CIM) is an essential technique for ensuring interoperability in Energy Management Systems (EMS) by defining data profiles for information exchange. Given the changing system environment, CIM undergoes periodic revisions, necessitating updates to the profiles used in EMS. However, comparing and interpreting CIM as defined in the Unified Modeling Language (UML) can be time-consuming and may lead to human errors. In this context, this study proposes systematic approaches for managing data profiles using an automatic tracking function for CIM and a structured process for profile management. The effectiveness of this approach is demonstrated by applying it to profiles defining the key functions of Korea's advanced distribution management system (ADMS), such as operational limits, renewable energy sources, and power transformers. This research can serve as a guideline for addressing practical issues in securing interoperability in ADMS. © 2024 Korean Institute of electrical Engineers. All rights reserved.

关键词： Interoperability

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