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Proceedings - 2022 4th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

Proceedings - 2022 4th International Conference on Artificia...

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4th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

ISBN: (纸本)9781665463997

the proceedings contain 180 papers. the topics discussed include: a computer vision technology applied in the task of mechanical parts inspection;development of gold bracelets automatic calendar equipment;a multi-objective optimization algorithm and process for Modelica model;research and implementation of oil trading data analysis and prediction based on random forest regression algorithm;modeling and prediction of vehicle trajectory in the internet of vehicles;application of online bypass intelligent device in discovering process error of new energy batter;a test scheme design of Chinese speech recognition system;a study of reversible data hiding technology with an authentication function;research on night scene image enhancement algorithm based on half-pixel constraint;a novel fault inspection method of steel plate surface;design and realization of electric power data remote acquisition system;and research on optimization of human-skilled matching of SMEs based on ant colony optimization algorithm.

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Fusing image and Text Features for Scene Sentiment Analysis Using Whale-Honey Badger Optimization Algorithm (WHBOA) 27th

Fusing Image and Text Features for Scene Sentiment Analysi...

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27th international conference on pattern recognition, ICPR 2024

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

ISBN: (纸本)9783031781650

Developing a real-time sentiment analysis application that relies solely on features extracted from images or textual content falls short of capturing human emotions’ nuanced and multifaceted nature. the unlabeled dataset, though useful, has limitations for sentiment analysis due to its general image descriptions, which lack emotional depth and do not include direct sentiment labels. Finding scene sentiment is a challenging task. To address this, combining textual descriptions with visual features is crucial. Important parameters include entropy, bag of words, and parts of speech (nouns, adjectives, and verbs) for textual analysis, alongside visual features like SIFT, SURF, and color histograms. these features are integrated to capture a comprehensive range of sentiment cues, enhancing the accuracy and depth of sentiment insights. this paper proposes an optimized adaptive neuro-fuzzy inference system for a compelling feature enhancement using the Whale-Honey Badger Optimization Algorithm (WHBOA). the proposed method identifies the most relevant and effective features from both textual and visual data. It captures visual-specific attributes to provide a richer and more detailed representation of visual content, addressing the limitations of general image descriptions and paving the way for the development of predictive models. Additionally, text pre-processing cleans and normalizes the textual data. We conducted an extensive comparative performance evaluation to assess the effectiveness and accuracy of the proposed model. the model is compared with the Nearest Neighbor, Support Vector Machine (SVM), and Decision Tree classification algorithms for the performance *** results demonstrate that the optimized model performs better, achieving an accuracy of approximately 91.2%, compared to the other models. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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Exploring ASR Models in Low-Resource Languages: Use-Case the Macedonian Language 1

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4th international conference on Deep Learning theory and Applications (DeLTA)

作者： Bogdanoski, Konstantin Mishev, Kostadin Simjanoska, Monika Trajanov, Dimitar Ss Cyril & Methodius Univ Fac Comp Sci & Engn Rugjer Boshkovikj 16 Skopje North Macedonia iReason LLC 3rd Macedonian Brigade 37 Skopje North Macedonia

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

We explore the use of Wav2Vec 2.0, NeMo, and ESPNet models trained on a dataset in Macedonian language for the development of Automatic Speech recognition (ASR) models for low-resource languages. the study aims to evaluate the performance of recent state-of-the-art models for speech recognition in low-resource languages, such as Macedonian, where there are limited resources available for training or fine-tuning. the paper presents a methodology used for data collection and preprocessing, as well as the details of the three architectures used in the study. the study evaluates the performance of each model using WER and CER metrics and provides a comparative analysis of the results. the findings of the research showed that Wav2Vec 2.0 outperformed the other models for the Macedonian language with a WER of 0.21, and CER of 0.09, however, NeMo and ESPNet models are still good candidates for creating ASR tools for low-resource languages such as Macedonian. the research presented provides insights into the effectiveness of different models for ASR in low-resource languages and highlights the potentials for using these models to develop ASR tools for other languages in the future. these findings have significant implications for the development of ASR tools for other low-resource languages in the future, and can potentially improve accessibility to speech recognition technology for individuals and communities who speak these languages.

关键词： Machine learning Deep learning Automatic speech recognition Inference metric ASR pipeline Speech-to-text Wav2Vec2 ESPNet NeMo Natural language processing Macedonian language Low-resource languages Word error rate WER Character error rate CER

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3D Printing Design Simulation of Ceramic Products Based on 3D image Reproduction Technology 4th

3D Printing Design Simulation of Ceramic Products Based on 3...

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4th international conference on Machine Learning, image processing, Network Security and Data Sciences (MIND)

作者： Yao, Lan Shanghai Art & Design Acad Shanghai Peoples R China

ISBN: (纸本)9783031243660;9783031243677

With the development of communication technology, people will be exposed to more and more graphics and images in the process of life andwork. Like using digital devices such as camera, scanner and camera to obtain images, but these instruments and equipment can only obtain two-dimensional image information of objects, which is completely insufficient. Inmany fields, three-dimensional information of objects is necessary. In this paper, the 3D printing design of ceramic products is simulated based on 3D image reproduction technology. the satisfaction of users with the ceramic visual effect and hand-held comfort produced by 3D image reproduction simulation technology is investigated by means of questionnaire, and the computer vision technology and stereo matching technology are compared. the results show that more than 85% of users are very satisfied with the ceramic visual effect and hand-held comfort of three-dimensional image reproduction simulation technology, and less than 5% of users are not satisfied;the satisfaction of ceramic visual effect produced by computer vision technology and stereo matching technology is less than 60%, and the hand-held comfort is less than 70%.

关键词： 3D image reproduction technology Ceramic products 3D printing design Simulation research

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recognition of Impediments for Visually Impaired Using Open CV 4

Recognition of Impediments for Visually Impaired Using Open ...

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Ganesh, B.A. Sreekumar, N.R. Amrita School of Arts and Sciences Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Eye sight is one among the most vital senses for survival. Millions of individuals throughout the world suffer from vision impairment. People who are blind or visually handicapped have a more difficult time doing their regular duties. Even if they don't want to, the majority of them are dependents for assistance. A handful of technologies for visually impaired people's aid have been created. Because of its cost and accessibility, machine eyesight-based remedies are appearing as one among many effective technologies being used to help the blind. the suggested system attempts to provide a wearable visual aid that would allow visually impaired persons to move autonomously without the need for external support by utilising a live object detection arrangement. To detect hurdles uses a image processing (machine learning) mechanism and via the camera and notify visually impaired person about the obstacle and its position via voice output. On the Open CV platform, python programming language with Raspberry Pi 3 model-B is used to develop artificial eyesight. Proposed Classification accuracy of 80% in reading text and 90% in detecting the object is observed. © 2022 IEEE.

关键词： Python

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Comparison of EfficientNet and VGGNet Using RGB Photos for Human Action recognition

Comparison of EfficientNet and VGGNet Using RGB Photos for H...

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Signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Ran An Beijing - Dublin International College Beijing University of Technology Beijing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

In the field of computer vision, human action recognition, is a key technology, underpinning numerous application scenarios such as video surveillance, human-computer interaction, sports analysis, and more. Convolutional Neural Network (CNN)-based models have demonstrated impressive performance in human action detection difficulties due to the swift advancement of deep learning techniques. the subject matter focuses on the research of human action identification using two advanced CNN models, VGGNet and EfficientNet, on RGB image datasets. It also compares the differences in feature extraction and classification capabilities between the VGGNet and EfficientNet models. this paper selects an RGB image dataset containing information on 15 categories of human actions as the experimental basis, providing strong support for model training. During the evaluation phase, accuracy is adopted as the metric, and each model's capabilities undergo evaluation. the results indicate that the EfficientNet model exhibits higher accuracy and better generalization capabilities in human action recognition tasks, primarily due to its efficient network structure and optimized model design. the categorization of various postures aids in the advancement of technology for recognizing human behavior and helps to highlight the diversity and complexity of human behavior.

关键词： Training Human computer interaction Technological innovation Accuracy Video surveillance Behavioral sciences Human activity recognition Convolutional neural networks Testing Sports

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Novel approach of Using Periocular and Iris Biometric recognition in the Authentication of ITS 4

Novel approach of Using Periocular and Iris Biometric Recogn...

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4th international conference on Cognitive Computing and Information processing, CCIP 2022

作者： Rao, Manjula Gururaj Priyanka, H. Pawar, Sumathi Reddy, K Hemant Kumar Divakarla, Usha NMAM Institute of Technology Nitte Karkala India PESU Computer Science and Engineering Bangaluru India VIT AP University Computer Science and Engineering India

ISBN: (纸本)9781665456487

In a fast technology growing world with respect to the data, information security is the major criteria. To have the robust system the security is the foremost task. the personal identification system and biometrics are playing the important part in security of the system. To secure the system, the biometrics, OTP generation, irisdetection etc., are some of the securities concerned aspects are taken. Biometrics security system is one of the old security systems. During the biometric security systems, the main challenge is acquiring images with Visual Wavelength or Near-Infrared lighting in limited and unconstrained situations. this paper proposes a unique approach for personal authentication based on the merging of periocular and iris sensors. this approach may be used in the ITS (Intelligent Transport System). In the ITS, depending on the identification of the passengers, only privileged or the authorized passengers can be transported in the vehicle. the suggested system includes components for feature learning for categorization after image pre-processing. During image pre-processing, a slicedannular iris from an ocular image is turned into a remediedimage region. the local periocular region was extracted using iris localization parameters. images suffer from varied noise abnormalities. Depending on the abnormality of the image, various data inadequacy and complicating situation arises. To deal with this problem, a novel data augmentation technique has been developed. © 2022 IEEE.

关键词： Biometrics

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Detection Method of Casing Joint based on computer vision 4

Detection Method of Casing Joint based on Computer Vision

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4th international conference on Intelligent Control, Measurement and Signal processing, ICMSP 2022

作者： Zhao, Yao Zhang, Jiatian Guo, Liang Zhang, Zhiwei Xi'an Shiyou University Laboratory of Shaanxi Province for Gas & Oil Logging and Control Technology Xi'an China

ISBN: (数字)9781665486583

ISBN: (纸本)9781665486583

Aiming at the current problem of low detection efficiency and high detection error when manually inspecting the casing joint for calibration depth in the visual inspection of oil and gas wells, a casing joint intelligent recognition based on YOLOv5 algorithm is proposed, which can realize the intelligent detection of casing joint. Firstly, a large number of pictures of oil and gas well casing joints were collected and the dataset was made by data enhancement method. then, the enhanced dataset which was annotated with Labelimg tool was sent to YOLOv5 network for training. Finally, use the best trained weights for testing result. the test results show that the method has a high detection accuracy, short detection time, wide applicability, and great advancement and practicality in casing joint detection. © 2022 IEEE.

关键词： Large dataset

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Plant Species recognition Using Custom-Developed Neural Network with Optimized Hyperparameters 4th

Plant Species Recognition Using Custom-Developed Neural Netw...

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4th international conference on Machine Intelligence and Signal processing, MISP 2022

作者： Anubha Pearline, S. Sathiesh Kumar, V. Department of Computer Science Engineering SRM Institute of Science and Technology Vadapalani Campus Tamilnadu Chennai600026 India Department of Electronics Engineering MIT Campus Anna University Tamilnadu Chennai600044 India

ISBN: (纸本)9789819900848

computer vision-based plant species recognition in unconstrained environment is challenging due to the enormous diversity in plant species, illumination changes, variation in background and orientation or viewpoint changes. In this paper, a Parametric Rectified Linear Unit (PReLU)-based Neural Network (PNN) with back-propagation algorithm is proposed to perform an effective recognition of plant species. the optimization of hyperparameters (Optimizers, Learning Rate, Activation Function, Epoch) is carried out. three datasets (Flavia, Swedish Leaf and Custom-developed Leaf 12) are considered for evaluating the proposed architecture. From the experimental analysis, it is observed that the accuracy in plant species recognition is greatly improved by utilizing the optimized hyperparameters (Adam optimizer, Learning rate of 0.001, PReLU-based Activation function with α value of 0.1). Higher accuracies for the Flavia (Top-1 (85.83%) and Top-5 (98.75%)) and Swedish Leaf datasets (Top-1 (94.97%) and Top-5 (100%)) are obtained at 200 Epoch, while custom-developed Leaf 12 (Top-1 (87.50%) and Top-5 (99.41%)) dataset exhibited better accuracy at 50 Epochs. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chemical activation

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Ayurvedic Medicinal Plant Identification System Using Embedded image processing Techniques 1

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4th international conference on Communication, Computing and Electronics Systems, ICCCES 2022

作者： Das, Arnab Siva Sai Kumar, B. Shiva Shankar Reddy, S. Naveen Reddy, S. Peeyush, K.P. Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9789811977534

ISBN: (纸本)9789811977527

Plants can be classified based on various classification methods such as cell, genetic and serum etc. It's difficult for an individual to explore the various classification methods and it's practically not feasible as it demands good knowledge in plant taxonomy and long-term time investment. Due to the shortage of experienced and qualified taxonomists in identification and classification of medicinal plants, with the help of different image processing algorithms and computer vision, the above difference can be bridged. the main objective is to develop a Deep Learning and Machine Learning based model to identify and classify plants based on various features, which is done with the help of Gabor filter and Gray Level Co-occurrence Matrices (GLCM) and using classifiers such as Random Forest (RF), and Light Gradient Boosting Machine (LGBM), and made a comparative analysis which resulted an accuracy of 95.5% with LGBM and GLCM filter and used to develop a standalone device that clicks a picture and identifies the medicinal plant. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Gabor filters

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