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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Gupta, Manu Shirisha, Gangadi Chandana, Aela Mani Ankitha, Belide Sreenidhi Institute of Science and Technology Department of Electronics and Computer Engineering Hyderabad India

ISBN: (纸本)9798350347579

A brain tumor is the irregular development of a mass tissue within the brain or close to it, which has the capacity to spread and duplicate the wildly affecting organs within the body. Brain tumor classification is a vital assignment to assess the tumors and make a treatment choice concurring with their classes. In this study, a model for the classification of brain tumors from MRI images utilizing transfer learning is developed. The convolution neural network (CNN), VGG16 and InceptionV3 techniques are applied to extract features and perform classification with brain MRI image dataset as input. The precision, accuracy, recall, and f1-score metrics are computed to evaluate the performance of the developed model. The results obtained show that the proposed pre-trained model VGG16 achieved better results than other models with an accuracy of 98%. © 2023 IEEE.

关键词： Magnetic resonance

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Analyzing Students’ Emotion and Activities in the Classroom: A Rural Education Perspective 4th

Analyzing Students’ Emotion and Activities in the Classr...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Konar, Koushik Suman, Shailabh Chaurasia, Diraj Pal, Aniruddha Saha, Sujoy Electronics and Communication Engineering National Institute of Technology Durgapur India Computer Science and Engineering National Institute of Technology Durgapur India Viterbi School of Engineering University of Southern California Los Angeles United States Computer Science and Engineering Bengal College of Engineering and Technology Durgapur India

ISBN: (纸本)9789819726103

Analyzing the concentration levels of students in a classroom and locating their positions pose significant challenges due to varying environmental conditions and diverse features. Facial expressions can reflect an individual’s mood, with different muscle positions beneath the skin indicating various emotions. Head orientation serves as an additional parameter to generalize student concentration levels. Moreover, different body postures can also provide insights into a person’s mood and concentration. This paper presents a lightweight application designed to analyze student concentration levels in a classroom, particularly to support rural education systems. Our work aims to address the challenges of cost-effectiveness and lightweight implementation suitable for running on affordable smartphones commonly used by rural faculty members. We employ the popular Dlib library for face detection, the FaceNet model for feature extraction, and the Siamese network for face recognition. We achieve 95% accuracy in face recognition using publicly available celebrity datasets, and our in-house dataset collected from NIT, Durgapur yields an 88% accuracy. Additionally, we implement MobileNet transfer learning for emotion recognition, focusing on relevant emotions such as Anger, Disgust, Fear, Happy, Neutral, Sad, and Surprise, achieving an accuracy of 73.76%. For Human Activity Recognition, we consider activities such as Clapping, Reading, Raising Hand, Looking Around, and Standing. We employ R-CNN and Random Forest techniques, achieving an accuracy of 64.92% on the in-house NIT, Durgapur dataset. Finally, we develop a mobile app that encompasses all functionalities, including class recording, data synchronization with Internet storage, and registration features for students and volunteers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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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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Magnetics Design Optimization for LLC Converter employing Machine Learning 11

Magnetics Design Optimization for LLC Converter employing Ma...

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11th International Conference on Power electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Tung, Nguyen Tan Pong, Man-Hay Chiu, Huang-Jen National Taiwan University of Science and Technology Department of Electronics and Computer Engineering Taiwan

ISBN: (纸本)9788957083505

This paper proposes an intelligent and machine-learning based optimization method that targets to optimal windings layer setup for LLC converter transformer with small number of optimizing iterations. The research utilizes Bayesian optimization (BO) to guide designers toward winding layers setup that achieves the maximum efficiency at nominal operating point of LLC converters. Furthermore, a new multiphysics simulation platform is proposed for feeding efficiencies to optimizing iterations. This simulation platform is able to provide entire losses estimation, high simulation accuracy is verified by measurement. An LLC converter prototype with 33V input and 400V/250W output is implemented to demonstrate effectiveness of proposed optimization method. © 2023 The Korean Institute of Power electronics.

关键词： Simulation platform

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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An Artificial Intelligent System for Driving Content Engagement in Video Sharing Platform 5

An Artificial Intelligent System for Driving Content Engagem...

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

作者： Karim, Muntaha Akter, Farzana Hossain, Md. Rakib Hasan, S.M. Mahedy Datto, Sham Rajshahi University of Engineering & Technology Dept of Electronics & Telecommunication Engineering Bangladesh Rajshahi University of Engineering & Technology Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

YouTube is constantly serving as a hub and has significantly become the leading online video-sharing platform for thousands of video creators all over the world. By uploading a video on YouTube, Facebook, or any other hosting website people are not only making fun but also creating an income stream for the website or app. As a result, it has become crucial for the content creator to reach the largest possible audience. The number of views indicates the popularity of the video and provides an estimate of how much money it will make. However, content creators encounter some challenges in engaging more audiences, one of which is selecting a catchy title for the content. A prediction of views of the YouTube videos according to the content title can be extremely helpful for the content creators to get an idea of how much views they can get in that video. In this paper, a model has been proposed to predict the views of the YouTube video based on the content title, likes and dislikes to help the content creators in choosing the most engaging title for their content. From all the proposed algorithms, Random Forest (RF) shows the minimum error (2.765%) and hence a prediction model using Random Forest algorithm has been deployed. © 2023 IEEE.

关键词： Forecasting

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Intelligent Decision-Making in Cybersecurity Using Machine Learning 12th

Intelligent Decision-Making in Cybersecurity Using Machine L...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Sakhare, Nitin N. Maruthi Shankar, B. Kalnawat, Aarti Sharma, Abha Rege, P.R. Wagh, Kapil Adhar Department of Computer Engineering BRACT’s Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India Pune India Electronics and Communication Engineering Arya College of Engineering Rajasthan Jaipur India Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601462

In the constantly changing field of cybersecurity, the need for advanced decision-making tools is more crucial than ever. This study explores machine learning in order to introduce an innovative method for strengthening cybersecurity protocols. The methodology involves combining Random Forests (RF) and Support Vector Machines (SVM) using the AdaBoost algorithm in an ensemble framework. The ensemble model is intricately crafted to address the complexities of intrusion detection, utilizing the combined capabilities of Random Forest’s data handling and Support Vector Machine’s pattern discrimination. AdaBoost enhances the ensemble’s predictive abilities by iteratively adjusting weights to focus more on misclassified instances. This adaptive learning process enables the model to constantly develop and adjust to new cyber threats. Validation against benchmark datasets demonstrates an impressive accuracy rate of 97.78%, highlighting the strength of the ensemble methodology. The True Positive Rate (TPR) of 98.33% demonstrates the ensemble’s effectiveness in accurately detecting and responding to potential cyber threats. The results demonstrate the superior performance of the proposed ensemble method and highlight its ability to achieve a balance between accuracy and adaptability. This research significantly advances intelligent decision-making in cybersecurity by introducing a potent ensemble approach that smoothly integrates various machine learning algorithms. The method’s proven accuracy and sensitivity make it a powerful tool in combating the increasing complexity of cyber threats. The combination of RF, SVM, and AdaBoost sets a standard for future research in strengthening digital defenses and ensuring the resilience of critical information systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Deep Learning Approach to Analyze the Relationship Between Gender, Height, Weight, and Basal Metabolic Rate from Face Images 2

A Deep Learning Approach to Analyze the Relationship Between...

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2nd International Conference on Power, Electrical, electronics and Industrial Applications, PEEIACON 2024

作者： Siam, Shykul Islam Chowdhury, S.A.H. Ahmed, Nafi Biswas, Swapneel Rajshahi University of Engineering & Technology Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Ahsanullah University of Science & Technology Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798331517984

In response to existing limitations, a novel convolutional neural network (CNN) model tailored for facial attribute estimation is introduced in this study. The methodology encompasses meticulous data preprocessing techniques, including dataset splitting, resizing, cleaning, augmentation, and cropping, to ensure the integrity of the dataset. Through rigorous real-world testing, remarkable accuracy in predicting gender, height, weight, and basal metabolic rate (BMR) from facial photos is demonstrated by our CNN architecture. Notably, unlike conventional approaches where BMR is directly estimated from facial images, three output neurons are employed by our model to simultaneously predict gender, height, and weight, which are sub-sequently utilized for BMR calculation. A primary contribution is made through the development of a comprehensive methodology that advances the reliability of facial attribute estimation by achieving an outstanding accuracy of 98.50% on our dataset named Face-ete and a commendable accuracy of 88.29% on another dataset. Our model outperforms the second-best model by percentage relative improvement factors of 2.19 and 4.28 in terms of BMR prediction accuracy for the 'Faceere' and the other dataset, respectively. This work represents a significant step forward in the field of facial attribute estimation. © 2024 IEEE.

关键词： Convolutional neural networks

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A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

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computers, Materials & Continua 2024年第4期79卷 201-241页

作者： Naglaa F.Soliman Fatma E.Fadl-Allah Walid El-Shafai Mahmoud I.Aly Maali Alabdulhafith Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia ***-quality images require well-tuned communication *** Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and *** data need a space away fromunauthorized access,modification,or *** requirements are to be fulfilled by digital image watermarking and *** paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication *** introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver *** proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD *** addition,image encryption is implemented based on chaos and DNA *** encrypted watermarked images are then transmitted through the wireless SC-FDMA *** linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted *** subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this *** study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked *** addition,different variants of SC-FDMA bas

关键词： Cybersecurity applications image transmission channel models modulation techniques watermarking and encryption

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Query-attentive video summarization: a comprehensive review

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Multimedia Tools and Applications 2024年 1-40页

作者： Kadam, Bhakti D. Deshpande, Ashwini M. Department of Electronics & amp Telecommunication Engg. MKSSS’s Cummins College of Engineering for Women Pune India Department of Electronics & amp Telecommunication Engg. SCTR’s Pune Institute of Computer Technology Pune India

Since the last decade, the diverse applications of video summarization have gained increased attention, motivating researchers in the domain of computer vision to generate optimal and comprehensible video summaries. The main challenge in the research of video summarization is user perception and preference as humans are the ultimate consumers of generated summary. A single video summary cannot satisfy all users unless the summarization algorithm interacts with end users and adapts to their requirements. Conventional video summarization can not tackle the user requirements. This study explores various state-of-the-art techniques developed for generating user-intended video summaries, focusing on query-attentive video summarization. Query-attentive video summarization is a multi-modal summarization method that generates a video summary that satisfies the viewer’s requirements by taking input queries from the viewers. This paper discusses the fundamental aspects of query-attentive video summarization, tracing its progress and evolution over time. Contemporary approaches are explored in detail, highlighting developed techniques with advantages and limitations. Additionally, the article also studies publicly available datasets, including extensively utilized Query-Focused Video Summarization dataset, since these datasets ensure the validity and applicability of developed techniques. Evaluation metrics, which are essential tools for measuring performance and assessing user satisfaction are also studied and performance comparisons are presented. After investigating the domain of query-attentive video summarization, this article addresses the current research challenges and identifies potential future research objectives. This comprehensive review offers a complete guide for new researchers in the field of query-attentive video summarization, covering both existing and future real-time applications. © The Author(s), under exclusive licence to Springer Science+Business Media

关键词： Deep neural networks

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