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An Improved Detection of Skin Melanoma Using Hybrid CNN-MobileNet V2 Approach with Multi-Scale Attention

An Improved Detection of Skin Melanoma Using Hybrid CNN-Mobi...

引用

Machine Learning and Autonomous Systems (ICMLAS), international conference on

作者： S.P. Maniraj R Thamizhamuthu Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamilnadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Identifying melanoma in dermoscopic images is an immense challenge due to the varied appearances of the lesions. This research introduces a hybrid CNN-LSTM model augmented with a multi-scale attention mechanism to tackle this complexity, utilising CNNs for spatial feature extraction and LSTMs for temporal pattern recognition. Our model surpasses traditional CNN and MobileNet V2-LSTM models, with an accuracy of 96.98%, a recall of 96.65%, a specificity of 97.56%, and a JSI of 95.78 % . These measurements demonstrate enhanced sensitivity and reliability compared to baseline models, signifying strong diagnostic capabilities. This study improves melanoma detection, aiding in more precise diagnoses for clinical environments and remote telemedicine applications. Future initiatives involve enhancing the model for more comprehensive skin lesion categorisation.

关键词： Accuracy Sensitivity Attention mechanisms Telemedicine Melanoma Sensitivity and specificity Skin pattern recognition Lesions Reliability

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Local Mean Directional Intensity pattern: An Efficient Descriptor for Hand Gesture recognition Using SVM Classification 2nd

Local Mean Directional Intensity Pattern: An Efficient Descr...

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2nd international conference on Intelligent computing Systems and Applications, ICICSA 2023

作者： Bahuguna, Arti Bhaumik, Gopa Govil, Mahesh Chandra Department of Computer Science and Engineering National Institute of Technology Sikkim Sikkim India Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India

ISBN: (纸本)9789819754113

Hand Gesture recognition (HGR) promotes the most efficient and natural communication between users and machines. However, the performance of the HGR system is deteriorating due to challenging conditions such as illumination variation, complex backgrounds, inter-class similarities. In this paper, a handcrafted feature descriptor named as Local Mean Directional Intensity patterns (LMDIP) is proposed for effective hand gesture recognition in the aforesaid challenging environment. The proposed LMDIP descriptor aims to maintain the most relevant directional information. It assists in the detection of grey level variations that move in different directions. LMDIP explores differential excitation by calculating the mean intensity of pixels across each angular neighboring pixel. The most significant effect of employing the mutually beneficial connections between adjacent neighbors in different angular directions is that it no longer relies solely on the sign of the intensity differences between the center pixel and one of its neighbors, but also on the sign of the gap across its adjoined neighbors. The performance of the proposed LMDIP is evaluated in terms of accuracy and F1-score on five benchmark datasets: Massey University Dataset (MUGD), ASL Digit Datasets, ASL Static Dataset, NUS Dataset, and OUhand Datasets. The result of the experimental show that the proposed LMDIP achieves an accuracy of 92% (MUGD Set1), 98% (MUGD Set2), 75%(MUGD Set3), 61%(MUGD Set4), 71%(MUGD Set5),99%(ASL Digit), 51%(ASL Static), 83%(NUS-I Dataset), 39% (OUhands Dataset) and respectively which is better compared to the state-of-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gesture recognition

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Text Proofreading Method for Tibetan Sound Similarity Syllables Based on soft-Masked BERT

Text Proofreading Method for Tibetan Sound Similarity Syllab...

引用

pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Rengong Zheng Jie Zhu Ciren Dunzhu Provincial and Ministerial Collaborative Center for Tibetan Informatization Tibet University Lhasa China

Error detection and error correction are two very important elements in the field of text proofreading research. In this paper, we propose an algorithm to construct a Tibetan sound similarity syllables confusion set, which generates a large amount of tagged data by adding noise to the correct Tibetan text, and use the soft-masked technique to construct a model for Tibetan text proofreading, which consists of an error detection network and an error correction network. Experimental results on two different test sets show that the method can effectively detect and correct the wrong syllables in the text.

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Multimodal Biometrics recognition Using soft computing 9th

Multimodal Biometrics Recognition Using Soft Computing

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9th international conference on Frontiers in Intelligent computing: Theory and Applications, FICTA 2021

作者： Ramani, U. Thilagaraj, M. Mueeswaran, V. K.Ramakrishnan College of Engineering Trichy India Karpagam College of Engineering Coimbatore India Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (纸本)9789811666155

The acknowledgment of human from the Gait, Footprint, and Voice was useful in security purposes. The ID of the people by the framework needs some extraordinary instruments and will be helpful in PC vision measure. Perceiving the people utilizing the component esteems extricated from Footprint and Gait is more solid since the impressions of similar people have the same examples. The voice of the people was likewise more solid in the ID of the individual people. This technique is tried by different preparing tests with the yield shows that the right characterization rate is high for the mix of Gait and impression contrasted with ID utilizing walk and impression alone. The new system produces high accuracy when compared to the previous system algorithms which indicates that the misclassifications are cheap to a better extent. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： soft computing

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pattern recognition in Apple Orchards During Dormancy and Bud Development 4th

Pattern Recognition in Apple Orchards During Dormancy and ...

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4th international conference on Smart Multimedia, ICSM 2024

作者： Sehgal, Bhavya Gandhi, Ritik Cheng, Irene Department of Computing Science University of Alberta EdmontonABT6G 2R3 Canada

ISBN: (纸本)9783031824746

Effective management of apple orchards during dormancy and bud development stages is crucial for optimizing fruit production and tree health. Automation using computer vision and deep learning techniques offers a promising solution for tree segmentation and labeling in complex outdoor environments. However, existing methods face challenges in accurately segmenting trees with bare branches and adapting to changing perspectives. This paper presents innovative solutions for bare tree segmentation and labeling, combining depth sensing and advanced image processing with deep learning approaches. The proposed methods address the limitations of current techniques, offering improved accuracy and robustness. We also introduce a novel tree labeling algorithm that utilizes spatial information and YOLOv4 for accurate tree identification and labeling across video frames. Experimental results demonstrate the effectiveness of our approach in handling complex outdoor conditions and bare tree segmentation. Our research contributes to advancing automated orchard management and precise monitoring during dormancy and bud development stages, enhancing fruit quality and overall orchard productivity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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pattern recognition of sEMG Recordings on Arm Muscles among Different Training Motions

Pattern Recognition of sEMG Recordings on Arm Muscles among ...

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2022 international conference on computing Innovation and Applied Physics, CONF-CIAP 2022

作者： Lyu, Zhijin Pan, Liyang Huang, Qiuzi Qu, Wenyu Electrical Engineering Technical University of Denmark Lyngby2800 Denmark EECS University of Michigan Ann Arbor48109 United States College of Textile and Apparel Engineering Suzhou University Suzhou215000 China Ealing International School Dalian116000 China

The paper develops a prediction model based on the surface electromyography (sEMG) signals to recognize the five different arm-related motions. We collect 100 groups of the three-channels signals on Biceps, Triceps, Brachioradialis as the raw data set. Then we extract four features from the data to describe the characteristics of different motions in the data set. In the pre-processing stage, we compare three types of EMD-based filtration methods to denoise signals and choose the most efficient one, then use principle component analysis to reduce the dimension of the data. We use three different methods to classify and predict the processed data for a higher accuracy. The contributions are following: This project successfully automated the pattern recognition of arm-related motions. Moreover, the project generated a valuable data set;Compared performances of EMD, EEMD and CEEMDAN in sEMG signals;Found that FCNN is the most accurate algorithm in this project. Future works include obtain more data from more volunteers to expand the data set. © Published under licence by IOP Publishing Ltd.

关键词： pattern recognition

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Clipping-based Neural Network Post Training Quantization for Object Detection

Clipping-based Neural Network Post Training Quantization for...

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2023 IEEE international conference on Control, Electronics and Computer Technology, ICCECT 2023

作者： Liqun, Cui Lei, Hu Ware Liaoning Technical University School of Soft Huludao China

ISBN: (纸本)9798350399028

Convolutional Neural Networks (CNN) have achieved amazing breakthroughs in computer vision, speech recognition, and other fields. However, with the development of deep learning, the number of weights in the neural network is increasing, consuming a lot of storage and computing resources. The high storage cost and computational complexity severely restrict the deployment of deep learning on embedded mobile devices. Therefore, the compression and acceleration of convolutional neural networks becomes particularly important. Therefore, in order to compress the size of the target detection model and speed up the calculation, this paper proposes a post-training quantization method for clip limit operation, limits are performed on the input data stream and weight parameters during training, then uses the parameter quantization method to quantize the model parameters from 32-bit floating point to 8-bit Integer. Under the premise of no impact on the performance of the algorithm, the model can be effectively compressed and the calculation speed can be improved. On the public data set PASCAL VOC2012, the performance of this algorithm is evaluated by two classic target detection algorithms, Faster R-CNN and YOLO V3. The experimental results show that compared with the original model, it saves 75% of the storage space and improves the calculation speed 2 times faster;FPS is also improved compared to standard quantization. © 2023 IEEE.

关键词： Object detection

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Research on Speaker recognition Based on LPCMCC and PSRCC Fusion Features

Research on Speaker Recognition Based on LPCMCC and PSRCC Fu...

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Intelligent computing and Human-Computer Interaction (ICHCI), international conference on

作者： Wensheng Gu Zhoulin Chang Rong Huang Zhanhuang Lu Department of Mechanical and Engineering Guangdong University of science and technology Dongguan China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Mel frequency cepstral coefficient (MFCC) is an important feature in speech speaker recognition, and many people like to use MFCC and neural network algorithms to form speaker recognition systems. However, the MFCC coefficient loses the phase information of the speech signal during the extraction process. Therefore, this article proposes a speech recognition method that combines LPC Mel frequency cepstral coefficients (LPCMCC) and phase based spectral root cepstral coefficients (PSRCC). After combining the two, the amplitude and phase information of the speech signal can be compatible and used as input data for the neural network. This article introduces an attention mechanism based on ResNeXT to adjust the weights of feature parameters, highlight key areas, and improve recognition performance. Finally, the AISHELL dataset was used for training and validation, and the experimental results showed that the proposed combined features and improved ResNeXT can effectively improve the accuracy of speaker recognition.

关键词： Training Human computer interaction Accuracy Computational modeling soft sensors Neural networks Speech recognition Speaker recognition Mel frequency cepstral coefficient Speech processing

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Retrieval-based Annotation of Multi-channel Time-Series Data for HAR

Retrieval-based Annotation of Multi-channel Time-Series Data...

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20th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Altermann, Erik Rueda, Fernando Moya Rusakov, Eugen Fink, Gernot A. TU Dortmund Univ Dept Comp Sci Pattern Recognit Grp Dortmund Germany

ISBN: (纸本)9781665416474

This contribution proposes a semi-automated annotation procedure for multi-channel time-series data for human activities. This procedure leverages the human annotators' decision by employing ranked hypotheses from a deep architecture, decreasing annotation time and maintaining consistency. In general, our approach performs retrieval by querying activity classes, Query-by-Activity. First, retrieved segments are ranked w.r.t a query using a similarity metric. Then, a human annotator can accept the ranked segments instead of selecting from a set of classes and attributes for each segment. We evaluate the approach in two parts, a retrieval and an annotation task. The retrieval performance is examined via the average precision of retrieved activities on a testing set. The effort is measured in terms of annotation time and consistency when annotating a small dataset for human activities.

关键词： Activity recognition Activity Retrieval Annotation Reliability Deep Learning Attribute Representation

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Prediction and Optimization of Mechanical Manufacturing Quality Based on Deep Learning 7th

Prediction and Optimization of Mechanical Manufacturing Qual...

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7th international conference on Innovative computing, IC 2024

作者： Han, Dong Dong, Tao Zhu, Songyu Industrial Center Harbin Vocational and Technical College Harbin150000 China Research Office Harbin Vocational and Technical College Harbin150000 China School of Electronics and Information Engineering Harbin Vocational and Technical College Harbin150000 China

ISBN: (纸本)9789819741922

The application of deep learning technology in mechanical design parameter optimization has opened up new research directions. The limitations of traditional design methods, especially in the search for global optimal solutions, have prompted researchers to explore more efficient solutions. Deep learning, with its outstanding performance in pattern recognition and predictive analysis, is considered a powerful tool for mechanical design optimization. Through comparative analysis and case studies, the accuracy of deep learning models in predicting mechanical manufacturing quality and their potential application in design parameter optimization were demonstrated. Through experiments, it was found that the deep learning technology quality prediction system predicted 187 perfect products, 145 medium products, and 147 defective products for mechanical quality, with a prediction error of 21, and a high overall judgment accuracy rate. It can be seen that deep learning methods can significantly improve design efficiency, reduce costs, and improve the overall performance of mechanical systems. This study aimed to provide an efficient optimization strategy for the field of mechanical design to promote the improvement of performance and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： pattern recognition

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