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Applying machine learning for American Sign Language recognition: A Brief Survey 4th

Applying Machine Learning for American Sign Language Recogn...

引用

4th international conference on Communication and Intelligent Systems, ICCIS 2022

作者： Singh, Shashank Kumar Chaturvedi, Amrita Varanasi India

ISBN: (纸本)9789819923212

Sign languages play a crucial role in enabling differently-abled people to communicate and express their feelings. the advent of newer technologies in machine learning and sensors has led us to build more sophisticated and complex human-computer interfaces. However, a cost-effective commercial sign language recognition system is still not available. In this paper, we have highlighted recent advancements and limitations related to sign language recognition systems. We have focused our study on American sign language (ASL), which is one of the most widely used sign languages. We have tried to classify most of the surveyed work into two broad areas based on data acquisition techniques: visual and sensor-based approaches. We have also listed publicly available datasets that could be used for further research. this paper aims to provide a clear insight into advancement in the field of sign language recognition systems using American sign language. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Spatiotemporal Visual Tracker Based on Deformable Attention 5

Spatiotemporal Visual Tracker Based on Deformable Attention

引用

5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Wu, Zhuo Hamdulla, Askar Huang, Yucheng School of Computer Science and Technology School of Cyberspace Security Xinjiang University Urumqi China School of Intelligence Science and Technology School of Future Technology Xinjiang University Urumqi China

ISBN: (纸本)9798350355925

At present, the visual tracking method based on Transformer has made significant progress, and has shown excellent performance on various data sets. However, the traditional Transformer tracker pays too much attention to global features, and its intensive attention calculation will increase the space-time complexity of the tracking algorithm. And the features of the relevant region will be affected by the features of the non-relevant region, resulting in a decrease in tracking accuracy. In addition, the Transformer tracker lacks the ability to utilize the spatiotemporal information between frames, which further hinders the improvement of its tracking performance. To solve these problems, this paper proposes a new tracker SVT-DA based on deformable attention. the tracker captures local and global important features through CNN and deformable attention. In addition, it uses a cross-attention module nested with self-attention to achieve deep semantic information interaction between template features and search features. In order to verify the effectiveness of the proposed method, we conduct experiments on LaSOT, GOT-10K, OTB100 and VAU123 datasets. the result indicators are 4%5% higher than the baseline method. the experimental results show that our method improves the tracking performance while reducing the number of model parameters, and is highly competitive with other state-of-the-art methods. © 2024 IEEE.

关键词： Deep learning Deformable attention Feature fusion Transformer Visual object tracking

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Human behaviour analysis and face detection using machine learning 4

Human behaviour analysis and face detection using machine le...

引用

4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Dohare, Anand Kumar Sharma, Megha Pathak, Ravi Shanker Greater Noida Institute of Technology Dept. of Information Technology India Gcet Dept. of Information Technology U.P. Gr. Noida India

ISBN: (纸本)9781665474368

Human behaviour analysis is a difficult aspect to maintain for a normal human being. this human behaviour analysis is unpredictable generally. the alignment of advanced technology such as machine learning is essential in this context. the machine learning technology plays an important role in structuring all human data and analysis their behavioural pattern considering accuracy and frequency of response. this research has evaluated multiple factors of human behaviour analysis considering its major of utilisation. this research has followed secondary research method based few specific and realistic objectives. An ethical consideration section states the ethical norms that this research has maintained. All the collected data of this research have been analysed considering all research objectives. © 2022 IEEE.

关键词： Face recognition

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A Multimodal Framework for Automated Teaching Quality Assessment of One-to-many Online Instruction Videos 26

A Multimodal Framework for Automated Teaching Quality Assess...

引用

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

作者： Pan, Yueran Wu, Jiaxin Ju, Ran Zhou, Ziang Gu, Jiayue Zeng, Songtian Yuan, Lynn Li, Ming Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China Duke Kunshan Univ Ctr Teaching & Learning Kunshan Jiangsu Peoples R China Duke Kunshan Univ Data Sci Res Ctr Kunshan Jiangsu Peoples R China Fumi Hlth & Technol LLC Hunan Fujian Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In the post-pandemic era, online courses have been adopted universally. Manually assessing online course teaching quality requires significant time and professional pedagogy experience. To address this problem, we design an evaluation protocol and propose a multimodal machine learning framework1 for automated teaching quality assessment of one-to-many online instruction videos. Our framework evaluates online teaching quality from five aspects, namely Clarity, Classroom interaction, Technical management of online teaching, Empathy, and Time management. Our method includes mid-level behavior feature extraction, high-level interpretable feature extraction, and supervised learning prediction. Our automated multimodal teaching quality assessment system achieves comparable performance to human annotators on our one-to-many online instruction videos. For binary classification, the best average accuracy of five aspects is 0.898. For regression, the best average means square error is 0.527 on a 0-10 scale.

关键词： Teaching Quality Assessment Multimodal Behavior Coding Interptretable Feature Extraction Speaker Diarization Emotion recognition

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Automatic Disease Classification of Paddy Leaf Images Using Deep learning Algorithms 4

Automatic Disease Classification of Paddy Leaf Images Using ...

引用

4th international conference on Signal Processing and Communication, ICSPC 2023

作者： Velusamy, Durgadevi Mounika, A. Ragulraj, D. Vigneshwaran, K. M.Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9798350300772

Agriculture is considered one of the primary sectors in India. Especially rice is the primary stable food widely cultivated and consumed by 50% of the Indian population. Paddy crop is susceptible to several diseases like other crops that indirectly affect agricultural development and economic loss. To address this limitation, in this study, we have applied deep learning algorithms for the recognition and classification of diseases at an earlier stage. the paddy plant disease diagnosis is made by applying image processing techniques to identify the changes in leaves and its texture. We have used pre-processing techniques for segmentation, feature extraction and classification of disease. First, we have to perform data augmentation to improve the number of images in the dataset. Second, the segmentation of plant leaves are performed to identify the test infected region. then, we applied CNN algorithm to extract features from the data. After feature extraction, we trained different classification algorithms to recognize and classify the images into disease and non-disease images. the application of machine learning and deep learning algorithms helps in the precise location and identification of infectious leaves that help farmers to use automated methods for disease diagnosis. © 2023 IEEE.

关键词： Feature extraction

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Online Document Identification and Verification Using machine learning Model 4th

Online Document Identification and Verification Using Machin...

引用

4th international conference on data, Engineering, and Applications, IDEA 2022

作者： thalange, A.V. Shetgar, P.S. Patnaikuni, Dinkar Chamatagoudar, S.N. Walchand Institute of Technology Solapur India

ISBN: (纸本)9789819700363

In companies or any organizations, the data is collected in various formats along with government-approved identity documents such as Aadhaar, PAN, license, etc. in soft or hard copy format. they contain valuable information but are stored in an unstructured manner once received. the data on the document needs to be extracted and classified and identity documents need to be verified. Nowadays, many document classifier software products are available in the market for extracting text information and classifying documents. However, these products are used in offline mode, once the documents are collected at the receiver end. this paper proposes a novel method for classifying and verifying identity documents in run time while uploading the document online mode itself. For this, support vector machine (SVM) algorithm and optical character recognition (OCR) are used. For invalid documents, the comment is displayed on the screen to upload the correct document, before submitting data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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A 3D-CLDNN Based Multiple data Fusion Framework for Finger Gesture recognition in Human-Robot Interaction 4

A 3D-CLDNN Based Multiple Data Fusion Framework for Finger G...

引用

4th international conference on Control and Robotics (ICCR)

作者： Qi, Wen Fan, Haoyu Xu, Yancai Su, Hang Aliverti, Andrea South China Univ Technol Sch Future Technol Guangzhou Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Politecn Milan Dept Elect Informat & Bioengn Milan Italy

ISBN: (纸本)9781665486415

Finger gesture recognition using surface electromyography (sEMG) became an efficient Human-Robot Interaction (HRI) solution. Although machine learning (ML) techniques are widely applied in this field, the general solutions for labeling and collecting big datasets impose time-consuming implementation and heavy workloads. In this paper, a new deep learning structure, namely three-dimensional convolutional long short-term memory neural networks (3D-CLDNN) for finger gesture identification based on depth vision and sEMG signals, was proposed for human-machine interaction. It automatically labels the depth data by the self-organizing map (SOM) and predicts the hand gesture only adopting sEMG signals. the 3D-CLDNN method is integrated to improve the recognition rate and computational speed. the results showed the highest clustering accuracy (98.60%) and highest accuracy (84.40%) with the lowest computational time compared with different approaches. Finally, real-time human-machine interaction experiments are performed to demonstrate its efficiency.

关键词： Multimodal data fusion Human-robot interaction Deep learning Signal processing

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Comparative Analysis on Medical Image Prediction of Breast Cancer Disease using Various machine learning Algorithms 4

Comparative Analysis on Medical Image Prediction of Breast C...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Gurusamy, Ravikumar Rajmohan, V. Sengottaiyan, N. Kalyanasundaram, P. Ramesh, S.M. M.P.Nachimuthu M.Jaganathan Engineering College Department of Ece Erode India Simats School of Engineering Chennai India Jain Global Campus Computer Science Faculty of Engineering and Technology Jakkasandra Kanakapura India Ksr Institute for Engineering and Technology Department of It Tiruchengode637215 India Kpr Institute of Engineering and Technology Department of Ece Coimbatore641407 India

ISBN: (纸本)9798350300093

Breast Cancer disease is the utmost characterized heterogeneous illnesses consisting of various types. Apart from lung cancer, Breast cancer is spreading widely everywhere. this research work confines to accurately analyzing the benign cells and the defective malignant cells by data mining technique like Support Vector machine (SVM). To have the comparative study, a total number of 659 sample are drawn from the UCI machine learning laboratory. the G power calculation with a confidence interval of 0.8 using maximum level of acceptable error rate of 0.5 is used for this analysis. Support Vector machine offer better prediction in terms of F1 score, precision and recall as 100%, 92%, 97% for benign cells 94%, 100%, 97% for malignant cells respectively. the significance value is arrived as 0.36 for this proposed system. the SVM appears to have better results in finding the benign and malignant cells diagnosis using Wisconsin dataset. © 2023 IEEE.

关键词： Support vector machines

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Autoencoder Attractors for Uncertainty Estimation 26

Autoencoder Attractors for Uncertainty Estimation

引用

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

作者： Da Cruz, Steve Dias Taetz, Bertram Stifter, thomas Stricker, Didier IEE SA Bissen Luxembourg Univ Kaiserslautern Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI Darmstadt Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the reliability assessment of a machine learning model's prediction is an important quantity for the deployment in safety critical applications. Not only can it be used to detect novel sceneries, either as out-of-distribution or anomaly sample, but it also helps to determine deficiencies in the training data distribution. A lot of promising research directions have either proposed traditional methods like Gaussian processes or extended deep learning based approaches, for example, by interpreting them from a Bayesian point of view. In this work we propose a novel approach for uncertainty estimation based on autoencoder models: the recursive application of a previously trained autoencoder model can be interpreted as a dynamical system storing training examples as attractors. While input images close to known samples will converge to the same or similar attractor, input samples containing unknown features are unstable and converge to different training samples by potentially removing or changing characteristic features. the use of dropout during training and inference leads to a family of similar dynamical systems, each one being robust on samples close to the training distribution but unstable on new features. Either the model reliably removes these features or the resulting instability can be exploited to detect problematic input samples. We evaluate our approach on several dataset combinations as well as on an industrial application for occupant classification in the vehicle interior for which we additionally release a new synthetic dataset.

关键词： Training Visualization Uncertainty Computational modeling Estimation Training data Safety

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Optimizing Feature Selection for Industrial Casting Defect Detection Using QLESCA Optimizer 1

引用

12th international conference on Robotics Vision Signal Processing and Power Applications

作者： Hamad, Qusay Shihab Salehe, Sami Abdulla Mohsen Suandi, Shahrel Amin Samma, Hussein Hamad, Yasameen Shihab Al Amoudi, Ibrahim Univ Sains Malaysia Sch Elect & Elect Engn Intelligent Biometr Grp Engn Campus Nibong Tebal 14300 Penang Malaysia Univ Informat Technol & Commun UOITC Baghdad Iraq King Fand Univ Petr & Minerals SDAIA KFUPM Joint Res Ctr Artificial Intelligence Dhahran Saudi Arabia Minist Educ Baghdad Iraq

ISBN: (数字)9789819990054

ISBN: (纸本)9789819990078;9789819990054;9789819990047

Feature selection is critical in fields like data mining and pattern classification, as it eliminates irrelevant data and enhances the quality of highly dimensional datasets. this study explores the effectiveness of the Q-learning embedded sine cosine algorithm (QLESCA) for feature selection in industrial casting defect detection using the VGG19 model. QLESCA's performance is compared to other optimization algorithms, with experimental results showing that QLESCA outperforms the other algorithms in terms of classification metrics. the best accuracy achieved by QLESCA is 97.0359%, with an average fitness value of - 0.99124. the proposed method provides a promising approach to improve the accuracy and reliability of industrial casting defect detection systems, which is essential for product quality and safety. Our findings suggest that using powerful optimization algorithms like QLESCA is crucial for obtaining the best subsets of information in feature selection and achieving optimal performance in classification tasks.

关键词： machine learning Support vector machine (SVM) classifier Convolutional neural networks Swarm intelligence Feature engineering

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