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Detection of Brain Tumour using CNN in Federated machine learning 5

Detection of Brain Tumour using CNN in Federated Machine Lea...

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5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Bhadauriya, Siddharth Merothiya, Tanishk Yadav, Shiv Chandra Chandraprabha, M. Galgotias College Of Engineering And Technology Computer Science And Engineering Greater Noida India Galgotias College Of Engineering And Technology Greater Noida India

ISBN: (纸本)9798350330861

Brain tumours are a deadly illness that kills many people. the earlier a brain tumour is detected, the better the treatment, and the longer the patient lives. Artificial intelligence (AI), which has recently emerged as a promising strategy for the early identification of cancers, is a crucial subject of research in the diagnosis of brain tumours. the implementation of brain tumour makes advantage of federated machine learning. It's a method for building AI models without allowing anybody else to view or access your data, acting as a conduit for making data accessible to new AI applications. Federated learning, which is comparable to a group presentation or report, allows for remote data sharing by several persons in order to cooperatively and iteratively train a single deep learning model. Each participant downloads the model, which is typically a foundation model that has already been trained, from a cloud datacenter. CNN is the algorithm in use. Convolutional neural networks are a specific class of multi-layered neural networks. It evaluates data that is laid out in a grid-like pattern and then draws out important traits. the significant advantage of CNN use is that it requires less photo preprocessing. the suggested technique yields rapid and exact tumour site identification in addition to accurate tumour detection in the brain. this process has allowed us to attain 96% accuracy. © 2023 IEEE.

关键词： Diseases

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An Enhanced Deep learning Model for Handwritten Tamil Character Identification 5

An Enhanced Deep Learning Model for Handwritten Tamil Charac...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Lincy, R. Babitha Rubia, J. Jency Shibi, C. Sherin Kavitha, M. Sri Eshwar College of Engineering Cce Department Tamil Nadu Coimbatore India K.Ramakrishnan College of Engineering Department Ece Tamil Nadu Trichy India Srm Institute of Science and Technology School of Computing Department of Ci Tamil Nadu Kattankulathur India K.Ramakrishnan College of Technology Department Ece Tamil Nadu Trichy India

ISBN: (纸本)9781665474672

In the data processing industry, data entry is regarded as a great bottleneck. An eminent Character recognition (OCR) system acts as a key solution for this concern. the OCR is being familiaized in universal level for different languages. Till now, only a limited explores were made for the Tamil Handwritten character recognition (HCR). Existing research works provides best performances for many languages, but for the work towards Tamil language is not satisfactory in the recognition area. To resolve this demerit this paper proposed an enhanced CNN for Tamil HCR utilizing Adam optimizer. Initially, encode the target variable using 'one-hot4ncode' method. then, the encoded image is preprocessed using RGB to grey conversion, image resizing, slant removal and array conversion. Subsequently, word and characters are segmented as of the preprocessed image. Next the segmented images are provided as input to the CNN classifier. the CNN is enhanoed by utilizing Adam optimizer algorithm (AOA) which optimizes the weight value for minimizing its loss function. finally, the experiential outcomes of the proposed CNN are contrasted to the prevailing SVM and some other machine learning algorithms. the proposed recognition system shows preeminent performance when comparing with the prevailing methods. © 2023 IEEE.

关键词： Support vector machines

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Vietnamese Text Summarization Using Transformer Approach 6th

Vietnamese Text Summarization Using Transformer Approach

引用

6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Hung, Bui thanh Tan, Phan Nguyen Trong Long, Tran Ho Phi Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh Viet Nam

ISBN: (纸本)9789819780891

Text summarization is a crucial aspect of natural language processing, finding widespread applications in various aspects of our lives. Numerous studies have delved into this task, employing diverse approaches ranging from linguistic analysis to machine learning and deep learning methodologies. However, there has been limited exploration of transformer-based models for Vietnamese text summarization. In our research, we leverage transformer models, namely, PhoBERT, BARTpho, and ViT5, to develop and refine effective summarization models. through experimentation on the Vietnews dataset and evaluation using ROUGE metrics, our proposed method demonstrates noteworthy achievements in the realm of Vietnamese text summarization, showcasing its potential for advancing the quality of summarization tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BARTpho BERT Deep learning PhoBERT Text summarization Transformer ViT5

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An Improved Causal Disentanglement Single-Source Domain Generalization Method for Bearing Fault Diagnosis 5

An Improved Causal Disentanglement Single-Source Domain Gene...

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5th international conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Wang, Hongqi Wang, Yujing Kang, Shouqiang Wang, Qingyan Harbin University of Science and Technology Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception Harbin China

ISBN: (纸本)9798331529192

In the field of domain generalization for fault diagnosis, the majority of approaches concentrate on extracting domain-invariant features from multi-source domains. However, collecting samples from multi-source domains is extremely difficult, and the data typically originate from a single-source domain. To tackle the issue of inadequate generalization capability in unknown target domains when trained on only a single source domain, an improved prototypeguided causal disentanglement domain generalization network (ICDDG) is proposed for mechanical fault diagnosis. this network combines feature mean, similarity, and triplet loss to construct an improved prototype-based triplet loss function, which reduces the influence of outlier samples and achieves more effective prototype learning. the improved triplet loss function effectively guides the causal disentanglement network to separate causal features from non-causal features better, enhancing the model's adaptability and robustness when encountering unseen domains. Diagnostic experiments performed using two bearing datasets substantiate the efficacy of the ICDDG method. © 2024 IEEE.

关键词： bearing causal disentanglement fault diagnosis single-source domain generalization triplet loss

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Traffic Sign Classification using Deep learning Comparative Study 5

Traffic Sign Classification using Deep Learning Comparative ...

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5th international conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Assemlali, Hamza Sael, Nawal Laboratory of Information Technology and Modeling Faculty of Sciences Ben M'Sick Hassan II University Casablanca Morocco

In recent years, the advancement of deep learning technologies has significantly impacted various domains and the field of transportation is no exception. As the demand for robust and accurate traffic sign classification systems continues to rise, this study presents an in-depth exploration and comparison of various techniques employed in the field. Focusing on state-of-the-art methodologies, we assess the effectiveness and performance of multiple classification approaches for traffic sign recognition. the review presents an extensive survey of the literature, encompassing traditional computer vision methods, machine learning algorithms, and the latest advancements in deep learning. the comparative analysis aims to identify the strengths and limitations of each technique, considering factors such as computational efficiency and accuracy. Additionally, the paper implements four models—CNN, ResNet50, VGG19, and EfficientNetB7—for traffic sign classification on the GTSRB dataset, the accuracy results are reported as 99.25%, 99.28%, 98.95%, and 98.24% respectively. © 2024 the Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Multi-speaker Emotional Speech Synthesis Based on Self-Supervised Speech Representations

Multi-speaker Emotional Speech Synthesis Based on Self-Super...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Mingming Yin Zhihua Huang Kexin Lu Haiyang Zhou School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In recent years, emotional speech synthesis has shown considerable progress. However, some existing emotional speech synthesis methods only model emotion from a single scale, resulting in only global or average emotional expression, unable to synthesize significant emotional speech. In this paper, we propose a multi-scale multi-speaker emotional speech synthesis method based on self-supervised speech model HuBERT. Firstly, the emotion information is extracted from the feature extractor HuBERT. Secondly, the extracted emotion information is modeled by coarse-grained emotion modeling and fine-grained emotion modeling respectively to obtain utterance-level emotion embedding and phoneme-level emotion embedding. through subjective and objective evaluation, we verify that the method is significantly better than the comparison model in terms of speech quality and emotional expression.

关键词： Speech recognition machine learning Feature extraction Speech synthesis data mining

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Deep Saliency Map Generators for Multispectral Video Classification 26

Deep Saliency Map Generators for Multispectral Video Classif...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Bayer, Jens Muench, David Arens, Michael Fraunhofer Ctr Machine Learning & Fraunhofer IOSB Ettlingen Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Despite their black box nature, deep neural networks have been successfully used in practical applications lately. In areas where the results of these applications can lead to safety hazards or decisions of ethical relevance, the application provider is accountable for the resulting decisions and should therefore be able to explain, how, and why a specific decision was made. For image processing networks, saliency map generators are a possible solution. A saliency map gives a visual hint on what is of special importance for the network's decision, can reveal possible dataset biases and give a more profound insight in the decision process of the black box. this paper investigates how 2D saliency map generators need to be adapted for 3D input data, and additionally, how the methods behave when applied not only to ordinary video input but rather multispectral 3D input data. this is exemplarily shown on 3D video input data in human action recognition in the infrared and visual spectrum and evaluated by using the insertion and deletion metrics. the dataset used in this work is the Multispectral Action dataset, where each scene is available in the long-wave infrared as well as the visual spectrum. To be able to draw a more general conclusion, the two investigated networks, 3D-ResNet 18 and Persistent Appearance Network (PAN), follow a different mindset. It could be shown, that the saliency methods can also be applied to 3D input data with remarkable results. the results show that a combined training with both, infrared and RGB 3D input data, lead to more focused saliency maps and outperform a training with only RGB or infrared data.

关键词： Measurement Training Visualization three-dimensional displays TV Neural networks Generators

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5th international conference on Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

5th International Conference on Emerging Technologies in Com...

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5th international conference on Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

ISBN: (纸本)9783031070112

the proceedings contain 60 papers presendted at a virtual meeting. the special focus in this conference is on Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent. the topics include: machine learning Based Rumor Detection on Twitter data;performance Analysis of machine learning Algorithms in Intrusion Detection and Classification;fruit Classification Using Deep Convolutional Neural Network and Transfer learning;assessment of Network Intrusion Detection System Based on Shallow and Deep learning Approaches;an Efficient Classifier Model for Opinion mining to Analyze Drugs Satisfaction Among Patients;a Case Study on machine learning Techniques for Plant Disease Identification;detection of Network Intrusion Using machine learning Technique;scrutinization of Urdu Handwritten Text recognition with machine learning Approach;a machine learning Approach for Multiclass Sentiment Analysis of Twitter data: A Review;efficacy of Online Event Detection with Contextual and Structural Biases;analysis of Text Classification Using machine learning and Deep learning;evaluation of Fairness in Recommender Systems: A Review;detection of Liver Disease Using machine learning Techniques: A Systematic Survey;Association Rule Chains (ARC): A Novel data mining Technique for Profiling and Analysis of Terrorist Attacks;classification of Homogeneous and Non Homogeneous Single Image Dehazing Techniques;comparative Study to Analyse the Effect of Speaker’s Voice on the Compressive Sensing Based Speech Compression;Employing an Improved Loss Sensitivity Factor Approach for Optimal DG Allocation at Different Penetration Level Using ETAP;a Method for data Compression and Personal Information Suppressing in Columnar databases;the Impact and Challenges of Covid-19 Pandemic on E-learning;identification of Dysgraphia: A Comparative Review;blockchain Framework for Agro Financing of Farmers in South India.

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Food recognition and Calorie Measurement Using machine learning 1

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5th international conference on Computer and Communication Technologies, IC3T 2023

作者： Vasudha, Maganti Rashmi, D. Mahalakshmi Jain, B.A. Department of Information Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (数字)9789819997046

ISBN: (纸本)9789819997039

this study is one of many that investigate the relationship between determining the nutritional ingredients in food and calculating the calories using data analysis utilizing machine learning techniques. Due to the availability of multifood photos, which must be cropped before processing, the Indian food recipes database is used for the research. the study uses a large dataset of various food photos to train state-of-the-art deep convolutional neural networks (CNNs) to recognize and categorize distinct food items with an amazing 99.89% accuracy. this study’s applicability spans several sectors in addition to food recognition, including calorie measurement, meal planning services, and nutritional monitoring systems. the solution is widely available to a wide range of users thanks to a user-friendly web interface. the system’s 99.89% accuracy in food detection and calorie measurement demonstrates its dependability and distinguishes it from competing options. Its ability to improve individual health, fight obesity, and encourage healthy eating habits makes it a vital tool in today’s health-conscious culture. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Large datasets

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Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface 26

Factorization Approach for Sparse Spatio-Temporal Brain-Comp...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Lee, Byeong-Hoo Cho, Jeong-Hyun Kwon, Byoung-Hee Lee, Seong-Whan Korea Univ Dept Brain & Cognit Engn Seoul South Korea Korea Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performance in brain-computer interfaces (BCIs) which deal with brain signals. Basically, brain signals are difficult to collect in large quantities, in particular, the amount of information would be sparse in spontaneous BCIs. In addition, we conjecture that high spatial and temporal similarities between tasks increase the prediction difficulty. We define this problem as sparse condition. To solve this, a factorization approach is introduced to allow the model to obtain distinct representations from latent space. To this end, we propose two feature extractors: A class-common module is trained through adversarial learning acting as a generator;Class-specific module utilizes loss function generated from classification so that features are extracted with traditional methods. To minimize the latent space shared by the class-common and class-specific features, the model is trained under orthogonal constraint. As a result, EEG signals are factorized into two separate latent spaces. Evaluations were conducted on a single-arm motor imagery dataset. From the results, we demonstrated that factorizing the EEG signal allows the model to extract rich and decisive features under sparse condition.

关键词： Training Face recognition Feature extraction Brain modeling Electroencephalography Brain-computer interfaces Adversarial machine learning

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