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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Zhang, Fanglue Yang, Rui School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China School of Electrical Engineering Electronics and Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9798350361674

This paper presents an approach for bearing fault diagnosis that leverages sensitive feature selection to tackle the challenge of high intra-class distances and low inter-class distances in data pre-processing of conventional deep transfer learning methods under variable working conditions. Initially, bearing fault signals are transformed from time to frequency domain using the Fast Fourier Transform. Then, these frequency-domain features are refined through a selection process optimized by particle swarm optimization, focusing on those with higher sensitivity weights to reduce intra-class distance and enhance inter-class distance. These selected features are input into domain adversarial neural networks for accurate bearing fault diagnosis under different conditions. Experimental results demonstrate the method's effectiveness and increased diagnostic accuracy. © 2024 IEEE.

关键词： Frequency domain analysis

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RPF-ELD: Regional Prior Fusion using Early and Late Distillation for Breast Cancer Recognition in Ultrasound Images

RPF-ELD: Regional Prior Fusion using Early and Late Distilla...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Haosen Zhang, Gengyuan Zhao, Yingnan Lai, Fang Cui, Wenwei Xue, Jiexiao Wang, Qihang Zhang, Hao Lin, Yi Beijing Jiaotong University School of Computer Science and Information Technology Beijing China Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Harbin Engineering University School of Computer Science and Technology Harbin China University of Denver Department of Computer Science Denver United States University of York Wentworth College York United Kingdom Institute of Heilongjiang The Second Surveying and Mapping Harbin China Southeast University School of Information Science and Engineering Nanjing China Harbin Institute of Technology Faculty of Computing Harbin China Harbin Medical University School of Interdisciplinary Medicine and Engineering Harbin China

ISBN: (纸本)9798350386226

Breast cancer is one of the main factors responsible for the deaths of women worldwide. Ultrasound imaging is a key method for early detection of breast cancer, which can help patients gain valuable treatment time and improve their chances of survival. The computer-aided system of breast cancer recognition has started to receive attention due to the lack of experienced sonographers. Presently, most breast cancer recognition methods typically suffer from uncertain locations and proportions of tumor regions in ultrasound images. In this paper, we propose a novel Regional Prior Fusion framework using Early and Late Distillation (RPF-ELD), inspired by the knowledge distillation of the teacher-student framework, for breast cancer recognition in ultrasound images. Firstly, to enhance the concentration of the tumor regions, a high-performing prior-fused model is trained as the teacher model using ultrasound images with the corresponding regional prior information. Next, a diagnostic model is trained as the student model under the prior-fused model distillation using early and late features to implicitly obtain the regional prior knowledge. Finally, the diagnostic model recognizes the categories of breast cancer from only ultrasound images using the experience from distilled prior knowledge. Two publicly released datasets are used to evaluate the proposed RPF-ELD framework. Experimental results demonstrate that the proposed RPF-ELD surpasses current state-of-the-art methods. © 2024 IEEE.

关键词： Ultrasonic imaging

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Explainable Natural Language Inference via Identifying Important Rationales

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 438-449页

作者： Yang, Zongbao Dong, Shoubin Hu, Jinlong South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Zhongshan Institute of Modern Industrial Technology of Scut Zhongshan528437 China

Natural language inference (NLI) is an important task in the field of natural language processing (NLP), which requires certain common sense and logical reasoning abilities. Existing pretrained models applied to NLI have difficulty achieving good performance, and inference models based on deep learning also have the problem of poor interpretability. In this article, we propose an explainable NLI model by identifying important rationales, which can be obtained by the specially designed rationale extractor and selector. The pretrained models can be fine-tuned on the rationale-enhanced dataset. In addition, the extracted rationales can be used to generate natural language explanation (NLE) with a higher quality. Not only can NLE serve as an explanation for the prediction results, but it can also be combined with the original dataset to improve the performance of pretrained models. Experimental results on the Stanford natural language inference corpus (SNLI) show that the proposed model could obtain state-of-the-art results of 94.19% and 94.08%, respectively, on the SNLI development set, as well as on the test set and obtain good performance when transferred to the out-of-domain dataset (multiNLI). Furthermore, the proposed model has a certain improvement in computational efficiency due to the focus on important rationales. © 2020 IEEE.

关键词： Computational efficiency

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Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

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computers, Materials & Continua 2023年第3期74卷 6703-6717页

作者： K.Priyadarsini Arun Kumar Sivaraman Abdul Quadir Md Areej Malibari Department of Data Science and Business Systems School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChennai603203India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of *** major concerns in this domain are security and ***,attaining a reliable level of confidentiality in the fog computing environment is a pivotal *** different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing ***,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog *** Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,*** boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat *** sequence generated by this map is used to transform the coordinates of the fog *** improved range of the proposed map is depicted using bifurcation *** quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate *** also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical *** experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

关键词： Chaotic cat map fog computing encryption 3D point fog 3D mesh

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Multifactorial Ant Lion Optimisation Algorithm 6

Multifactorial Ant Lion Optimisation Algorithm

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6th Asian Conference on Artificial Intelligence technology, ACAIT 2022

作者： Wu, Yue Wang, Ruobin Ding, Hangqi Gong, Maoguo Ma, Wenping Miao, Qiguang Xidian University School of Computer Science and Technology Xi'an China Xidian University School of Electronic Engineering Xi'an China Xidian University School of Artificial Intelligence Xi'an China

ISBN: (纸本)9781665453110

Evolutionary multitasking is a new research direction proposed in 2016. It is designed to simultaneously improve the convergence properties of multiple optimisation problems by seamlessly transferring knowledge between them. As an effective realization paradigm of evolutionary multitasking, multifactorial optimisation (MFO) has received extensive attention since it was proposed. Here, to further explore the generality of MFO, we employ an emerging optimisation algorithm to implement the MFO paradigm. In this work, for the first time, we use the ant lion optimisation algorithm as a search mechanism to achieve multifactorial optimisation and propose a multifactorial ant lion optimisation (MFALO) algorithm. The main purpose is to further improve its optimisation performance by realizing the multi-tasking processing of ant lion optimisation so as to obtain better convergence speed and accuracy. We compare the proposed algorithm with the base algorithms on various benchmark problems. Experimental results show that multifactorial ant lion optimisation can effectively improve the accuracy of benchmark problems through an effective assortative mating design. © 2022 IEEE.

关键词： Multitasking

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Grapevine Leaf Disease Classification with Deep Learning and Feature Extraction Using IoT 4th

Grapevine Leaf Disease Classification with Deep Learning and...

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4th International Conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Kansal, Isha Bhardwaj, Vivek Verma, Jyoti Khullar, Vikas Popli, Renu Kumar, Rajeev Institute of Engineering and Technology Chitkara University Chitkara University Punjab Rajpura India School of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering Punjabi University Punjab Patiala India

ISBN: (纸本)9789819706990

Grapevine diseases may possess a substantial effect on crop yield and quality, but early identification and mitigation can help prevent losses. The manual detection and diagnosis of vineyard diseases can be time-consuming, subjective, and difficult for producers who lack the requisite knowledge. Therefore, automated systems may offer a more effective and accurate method for classifying grapevine diseases. This paper recommends an investigation on Grapevine Leaf Diseases Classification with Deep Learning Techniques and Feature Extraction Using the Internet of Things. The purpose of this research is to develop an IoT-based system for identifying and categorising different kinds of grapevine leaf diseases in real time. The system will capture photos of grapevine leaves using a network of IoT devices outfitted with high-resolution cameras. The images are going to be transmitted to a cloud-based infrastructure for analysis using techniques for deep learning and the extraction of features. The system that is suggested will enable cultivators to detect and categorise grapevine leaf illnesses in real time, enabling them to take swift action to prevent the disease’s spread and increase crop yield. The research will utilise the Grapevine foliage image dataset accessible via Kaggle and other datasets that are freely accessible to train and evaluate deep learning models. The models will be fine-tuned and optimised for the IoT platform in order to process and analyse images in real-time. The efficacy of the system will be evaluated using the metrics of precision, recall, precision, accuracy, and F1-score. The proposed research has the potential to revolutionise the grapevine field by providing producers with an affordable and effective instrument for detecting and classifying grapevine leaf diseases. In addition to being extensible to other crop diseases, the IoT-based system can contribute to developing sustainable agricultural practices. © The Author(s), under exclusive licens

关键词： Feature extraction

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Brain Tumor Detection Using Xception Model

Brain Tumor Detection Using Xception Model

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Philip, Joshwa Joy Rani, Ruchi Kumar, Sumit School of Computer Engineering and Technology Dr Vishwanath Karad Mit World Peace University Department of Computer Science and Engineering Pune India School of Computer Engineering and Technology Dr Vishwanath Karad Mit World Peace University Department of Computer Engineering and Technology Pune India Department of Electronics and Telecommunication Maharashtra Pune India

ISBN: (纸本)9798350388800

Numerous morphological and histological characteristics of brain tumors make diagnosis and treatment very challenging. Traditional diagnosis methods often depend on the analysis of medical images, which can lead to unclear conclusions and errors. In recent years, machine learning (ML) and deep learning techniques (DL) have shown great scope in improving the detection and classification of brain tumors. These methods extract the features and classify medical imaging data, such as MRI, CT, and PET scans. Convolutional and recurrent neural networks give better performance even though there are challenges in the dataset. In the proposed work, Xception architecture is used which is a type of convolution neural network. This model is pretrained on the ImageNet dataset. The proposed model achieves 99.90% accuracy in brain tumor detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Profanity Detection from Audio Recordings Using Natural Language Processing Techniques

Profanity Detection from Audio Recordings Using Natural Lang...

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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Patil, Swapnil Chandre, Pankaj R. Vanarote, Viresh Pathan, Shafi Nimbalkar, Madhukar Shinde, Gitanjali Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Loni Kalbhor India Department of Computer Science and Engineering Vishwakarma Institute of Information Technology Kondhwa India

ISBN: (纸本)9789819998104

Profanity detection has become increasingly important in various industries, including media and online content moderation. In this paper, we propose a novel approach to identifying profane words from audio using natural language processing (NLP) techniques. We first convert the audio files into textual transcripts using automatic speech recognition (ASR) tools. We then apply pre-processing techniques such as tokenization, stemming, and stop-word removal to the transcripts. Finally, we train various machine learning models, such as Naive Bayes, Support Vector Machines (SVMs), and Random Forests, to classify the transcripts as either containing profane language or not. The proposed approach can be integrated into various applications, such as content moderation tools, online chat systems, and automatic transcription services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audio recordings

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Non-Contact Vital Prediction Using rPPG Signals 1

Non-Contact Vital Prediction Using rPPG Signals

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Madhu, Meghna Gurudas, V.R. Manjunath, Chinthakunta Naik, Praveen Kulkarni, Praveen School of Engineering and Technology Department of Computer Science and Engineering Bangalore India Dayananda Sagar University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

In this paper, we present the clinical significance of various cardiac symptoms with the use of heart rate detection, ongoing monitoring and present emotions. The development of algorithms for remote photoplethysmography has drawn a lot of interest during the past decade (rPPG). As a result, using data gathered from the video feed, we can now precisely follow the heart rate of individuals who are still seated. rPPG algorithms have also been developed, in addition to technique based on hand-crafted characteristics. Deep learning techniques often need a lot of data to train on, but biomedical data frequently lacks real-world examples. The experiment described in this work, we looked at how illumination affected the rPPG signals' SNR. The findings show that the SNR in each RGB channel varies depending on the colour of the light source. Paper describes development in video filtering for recognising the comprehending human face emotions. In our method, emotions are deduced by identifying facial landmarks and analysing their placement. © 2023 IEEE.

关键词： Photoplethysmography

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EEG emotion recognition via Identity based Multi-gate Mixture-of-Experts network

EEG emotion recognition via Identity based Multi-gate Mixtur...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Yang, Liying Liu, Dunhui Zhang, Qingyang Chao, Si Ni, Pei Wang, Qiang Sun, Haoxuan Xidian University School of Computer Science and Technology Xi'an China

ISBN: (纸本)9781665468190

Empowering computer systems to automatically recognize human emotions has become an urgent need in the field of human-computer interaction (HCI). Two-dimensional emotion (Valence-Arousal) models are commonly used to represent emotions. Up to now, the correlation between emotion dimensions has rarely been investigated, and subject-independent EEG emotion recognition is still a challenging task. For this purpose, we introduce multi-task learning (MTL) into EEG emotion recognition. MTL learns different emotion dimensions simultaneously and extracts correlation information between dimensions in task-sharing space to coordinate the optimization of multiple emotion dimensions. We further propose Identity based Multi-gate Mixture-of-Experts (IDMMOE), which allocates part of model subspace for each subject in a customized manner according to the subject's identity. Extensive experiments were conducted on DEAP dataset. Three MTL models were implemented: Shared-Bottom, Multi-gate Mixture-of-Experts, and Customized Gate Control respectively. They were compared with a single-task learning model trained separately on valence and arousal. Experimental results demonstrate that two emotion dimensions are intrinsically related, and MTL acquires such correlation information and improves prediction accuracy in both emotion dimensions. In addition, IDMMOE achieves average accuracies of 89.5% and 89.7% for valence and arousal respectively and it is effective for subject-independent experiment. © 2022 IEEE.

关键词： Emotion Recognition

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