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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Rajan, S. Palanivel Premkumar, R. Department of Electronics and Communication Engineering Viraganoor Madurai India Kongunadu College of Engineering and Technology Department of Computer Science and Engineering Thottiam Tiruchirappalli India

ISBN: (纸本)9798350389609

This research work presents an attempt to develop a new optimization algorithm known as Hybrid Glowworm Swarm Optimization with Fish Swarm Optimization and Fuzzy Support Vector Machine which can be used for improving the accuracy rate for detection of ovarian cancer based on computer aided diagnosis. The hybrid approach extracts the strong features of both Glowworm Swarm Optimization (GSO) and Fish Swarm Optimization (FSO) on the feature selection process while the Fuzzy Support Vector Machine (FSVM) is used for the classification process. GSO and FSO were used together in order to enhance the search ability and the rate of convergence, to cope with the feature space of ovarian cancer datasets that might be complicated. The FSVM carries this kind of classifications further and addresses inherent uncertainties and overlapping features within data. This integrated method tries to offer more frequent and accurate tool for early detection and may help improve the condition of patients with ovarian cancer. The proposed HGSO-FSO-FSVM framework is yet to be implemented but expected to devise major steps towards enhancement of medical diagnostics especially in early diagnose of ovarian cancer which is crucial in increasing survival rate. © 2024 IEEE.

关键词： Diseases

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Towards Safer Digital Spaces: Automated Detection of Cyberbullying Through Multi-Modal Learning 5

Towards Safer Digital Spaces: Automated Detection of Cyberbu...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Karthikeyan, M. Saravana Francis Raj, J. Relin Krishnan, R. Santhana Kumari, D. Abitha Murali, S. Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RMK Engineering College Department of Computer Science and Engineering Thiruvallur India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331530013

Cyberbullying and online harassment present significant challenges to digital safety, demanding robust detection systems capable of identifying abusive content across multiple formats. This work proposes a multi-modal framework that leverages CLIP and RoBERTa embeddings to detect and categorize cyberbullying and harassment in both text and image-based posts. By utilizing two curated datasets, HarassDetect-TextImage and CyberGuard-MultiModal, the model is trained on a diverse range of offensive and non-offensive content. Text data is preprocessed through advanced techniques, while CLIP and RoBERTa generate embeddings capturing the context of both textual and visual inputs. For sequential analysis of threaded posts, a Transformer layer further enhances the model's contextual awareness, enabling it to recognize harassment patterns that unfold over multiple messages. A Support Vector Machine (SVM) classifier then categorizes content into Non-Cyberbullying, Minor, Moderate, and Severe classes, each associated with confidence levels guiding automated actions. High-confidence classifications trigger immediate responses, such as content restriction or user warnings, while moderate-confidence cases are flagged for manual review. This adaptive framework not only improves accuracy but also allows for a continuous feedback loop, updating the model to address evolving patterns of cyberbullying. Evaluation results demonstrate the efficacy of this approach in creating safer online environments through automated and contextually aware cyberbullying detection. © 2024 IEEE.

关键词： Support vector machines

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Attention U-Net: A Deep Learning Approach for Breast Cancer Segmentation

Attention U-Net: A Deep Learning Approach for Breast Cancer ...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Mridha, Krishna Sarker, Tasnim Bappon, Suborno Deb Sabuj, Shahriar Mahmud Computer Engineering Marwadi University Gujarat Rajkot India Electrical and Electronics Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Chittagong University of Engineering & Technology Computer Science & Engineering Bangladesh

ISBN: (纸本)9798350314946

One of the main causes of death for women globally is breast cancer. Early discovery can reduce the frequency of early deaths. Breast ultrasound images can help in diagnosis and treatment planning by helping to categories different types of breast cancer. To segment breast cancer pictures in this study, we employed an Attention U-Net. A deep learning model for picture segmentation is called the Attention U-Net. We used a dataset of 780 breast ultrasound pictures divided into three classes-normal, benign, and malignant-to train the Attention U-Net. The mean IoU, precision, recall, and F1 scores for the Attention U-Net were 0.840,0.815,0.848, and 0.831 respectively. These outcomes are comparable to those of other cutting-edge techniques for segmenting breast cancer. The findings of this study indicate that the Attention U-Net is a potential technique for segmenting breast cancer. The great accuracy that the Attention U-Net can attain can assist to increase the precision of diagnosis and treatment planning © 2023 IEEE.

关键词： Deep learning

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Scalable Modular Battery Management System Using Model-Based Approach for EV Applications 2

Scalable Modular Battery Management System Using Model-Based...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Kumar, Srinath Jawaharram, Vishnu Ram Balashivakumaran, R. Chandrakala, K.R.M. Vijaya Kumar, S. Sampath Nadar, Kannan Paulish Amrita School of Engineering Department of Electrical and Electronics Engineering Coimbatore India Institute of Technology Jijiga University Department of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798350328202

The global automotive industry is in the phase where Internal Combustion vehicles are in decline and witnessing a shift towards sustainable development. The major parameter of a successful EV is an efficient battery pack and the protection for it which is the Battery management system (BMS). In this work model-based approach in the design of a Modular Battery Management System (BMS) takes into account various protection schemes such as over and under-voltage scenarios adopted in Li-ion batteries and monitoring the State of Charge (SoC) using the Coulomb Counting technique. The design and the simulation-based approach are adopted to study the behaviour of Li-ion Battery and BMS in a wide operational condition where the testing is difficult using MATLAB/Simulink. The proposed technique is a Modular Battery Management System that works on the Master-Slave Architecture. © 2024 IEEE.

关键词： Battery management systems

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An Initial Solution Based Selective Harmonic Elimination Method for Multilevel Inverter

CPSS Transactions on Power Electronics and Applications

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CPSS Transactions on Power electronics and Applications 2024年第1期9卷 63-78页

作者： Kala, Peeyush Sharma, Abhinav Jately, Vibhu Joshi, Jyoti Yang, Yongheng Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Ghaziabad201204 India Department of Electrical and Electronics Engineering Dehradun248007 India Dehradun248007 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun248002 India College of Electrical Engineering Zhejiang University Hangzhou310027 China

This article presents an initial solution based selective harmonic elimination (SHE) method for multilevel inverter (MLI) that aims to solve SHE problem with high accuracy while significantly reducing the number of iterations. Initial SHE solution is defined as a set of initial switching angles that has less accuracy as compared to the final SHE solution. To find initial SHE solution, tunicate swarm algorithm (TSA), grey wolf optimization (GWO) and whale optimization (WO) are used to solve five-dimensional SHE problem. A comparative analysis demonstrates that TSA achieved the desired initial SHE solution within lesser iterations as compared to GWO and WO for a wide range of modulation indices. The initial switching angles found using TSA are further optimized using Newton-Raphson (NR) method being the proposed TSANR method, which exploits the SHE search space with quadratic rate of convergence to find final SHE solution. The proposed TSANR method significantly minimizes the computational time and further improves the accuracy of the SHE solution as compared to the state-of-the-art SHE methods. Detailed simulations, experimental and harmonic analysis under dynamic change in modulation index are presented to show the efficacy of the proposed SHE method in terms of control over fundamental and detrimental harmonics. © 2017 CPSS.

关键词： Harmonic analysis

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Intelligent IoT Based Temperature and Humidity Monitoring System for Tobacco Curing Barn 5

Intelligent IoT Based Temperature and Humidity Monitoring Sy...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Chundawat, Dipendra Pal Singh Mary, Gerardine Immaculate Julian, Anitha School of Electronics Engineering Vellore Institute of Technology Vellore632014 India Saveetha Engineering College Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798331518097

A Cloud-based real-time temperature and humidity monitoring device that monitors the tobacco curing process, automates the alarm, notifies in case of any deviation from prescribed levels of temperature and humidity, transmits temperature and humidity data to the cloud, and compares the data against the specified ranges. This system predicts the future values of temperature and humidity so one can calculate the efficiency of the system. The system tackles the issue of the Barn Monitoring System using a low-cost controller. The main idea behind the Intelligent IoT-based temperature and humidity monitoring system is to maintain the temperature and humidity inside the barn in each stage of curing. The device has a sensor for temperature and humidity. If the humidity crosses the threshold value, it can alert the barn driver and farmer via a Buzzer, SMS, or e-mail. © 2024 IEEE.

关键词： Tobacco

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A Comb-shaped Corrugated Antipodal Vivaldi Antenna with a Parasitic Patch for UWB Application 3

A Comb-shaped Corrugated Antipodal Vivaldi Antenna with a Pa...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Razib, Mohammad Rahman Das, Debprosad Murshed, Abu Hena Istiaque, Arif Hossain, Md. Azad Chittagong University of Engineering & Technology Department of Electronics and Telecommunication Engineering Bangladesh The University of Texas at Dallas Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350388282

This paper presents a performance comparison of a comb-shaped Vivaldi antenna for ultrawideband (UWB) applications, which is antipodal in nature. The antenna utilizes a 50 Ω microstrip feed line for energizing the antenna. To achieve ultrawide-band behavior, a parasitic elliptical-shaped patch and corrugated slots are employed. The patch controls frequency shifting along with high-gain behavior via electromagnetic coupling. To validate the proposed antenna, Rogers RT5880 substrate is used ( =2.2., tan .δ=0.0009). The proposed antenna operates in a frequency range from 5 to 28 G H z , with the highest gain of 12.5dBi at 27.5GHz. It has a total radiation efficiency of 97\% at 21.25GHz. The proposed Vivaldi antenna is a suitable candidate for high-gain wireless applications. The CST Studio Suite is employed to extract the simulation results of the antenna. © 2024 IEEE.

关键词： Microstrip antennas

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Joint trust-based detection and signature-based authentication technique for secure localization in underwater wireless sensor network

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Multimedia Tools and Applications 2025年 1-20页

作者： Kumar, Manni Goyal, Nitin Qaisi, Ramy Mohammed Aiesh Najim, Mohd Gupta, Sachin Kumar Department of Computer Science & Engineering Chandigarh University Gharuan Punjab India Department of Computer Science Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Electronics and Communication Engineering Central University of Jammu UT of J&K Jammu181143 India

Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interference. This research addresses this issue by proposing a Joint Trust-based Detection and Signature-based Authentication (JTDSA) technique, aimed at improving both the security and accuracy of node localization in UWSNs. The technique utilizes Intrusion Detection System (IDS) methods to calculate the trust values of secondary-anchor nodes (SNs), ensuring only the most trusted SNs are selected. Furthermore, a digital signature algorithm secures the Received Signal Strength (RSS) data from tampering. The method incorporates a hybrid approach combining Angle of Arrival (AoA) and RSS ranging techniques to achieve robust localization. Simulation results demonstrate that JTDSA improves localization accuracy by 4% compared to existing methods, achieving an overall accuracy of 99.2%. The technique also reduces packet drop by 35% and enhances attack detection accuracy by 3%. These results indicate that the proposed JTDSA technique offers an effective solution for secure and reliable localization in UWSNs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sensor nodes

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Vituperative Content Detection: A Multidomain Architecture Using OpenCV

Vituperative Content Detection: A Multidomain Architecture U...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Cheruvu, Sai Manoj Sesank, Domakonda Sandilya, K. Sriram Gupta, Manu Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819959730

Vituperation refers to abuse or something bitter that is against the ethics. Vituperation is disturbing to victims and might lead to consequences like depression, anxiety, fear etc. Due to the COVID-19 outbreak many businesses have chosen online platforms to operate smoothly. The involvement of networks has increased transactions of various multi-media like images, videos, live videos etc. The cyber criminals or people with faulty mindsets might utilize these platforms to transmit multi-media containing different types of abuse to disturb individuals or business. Therefore, a need for preventing, filtering the impairing content has increased exponentially. The proposed work is capable of detecting multiple vituperative activities: Abusive content in image detection, Alcohol detection, Guns detection, Cuss gesture detection, and Religious flags in images and videos. This multi-domain architecture is built upon an Open-Source computer Vision technique using YOLOv5 and optical character recognition methodologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Network architecture

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