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A Smart System for Sign Language recognition using machine learning Models 4

A Smart System for Sign Language Recognition using Machine L...

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

作者： Rajalingam, B. Kumar, R. Santhosh Deepan, P. Santosh Kumar Patra, P. Bavankumar, S. St. Martins Engineering College Cse Secunderabad India St. Martins Engineering College AIDS Secunderabad India St. Martins Engineering College AIML Secunderabad India

ISBN: (纸本)9781665474368

When people exchange information, thoughts, or sentiments via the medium of communication, it is called communication. Both parties must be conversant in the same language in order for communication to occur. On the other hand, those who are deaf or dumb must use various communication methods. If you can't hear, you have deafness;if you can't speak, you have dumbness. As a group, they use sign language to communicate, but most people don't see it as a valuable skill. To communicate with someone who is deaf or mute is difficult since not everyone is familiar with or understands sign language. You may develop a model using machine learning to get around this roadblock. It is possible to teach a model to recognize different gestures used in sign language and to translate those gestures into English. Many people will get the ability to converse with the deaf and dumb as a result of this. By using a camera to capture Sign Language data, we may then use machine learning techniques like Convolutional Neural Networks to the datasets. Some other well-known models have been compared to a new strategy that has been presented for the same problem. Principal Component Analysis (PCA), Histogram of Gradients (HOG), and Local Binary patterns (LBP) are some of the pre-processing approaches employed. ORB, Canny edge detection, and the state of word approach are all used to create the new model. this preprocessed data is then sent through a variety of classifiers in order to provide useful findings (including Random Forests and Support Vector machines, as well as Nave Bayes and Logistic Regression). the new models are substantially more accurate than the previous ones. the method achieves good accuracy even with a little dataset. © 2022 IEEE.

关键词： Principal component analysis

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the Efficiency of learning Methodology for Privacy Protection in Context-aware Environment during the COVID-19 Pandemic

The Efficiency of Learning Methodology for Privacy Protectio...

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12th international conference on Ambient Systems, Networks and Technologies (ANT) / 4th international conference on Emerging data and Industry 4.0 (EDI40)

作者： Alawadhi, Ranya Hussain, Tahani Kuwait Univ POB 5969 Safat 13060 Kuwait Kuwait Inst Sci Res POB 24885 Safat 13109 Kuwait

When the COVID-19 coronavirus hit, the context-aware application users were willing to relax their context privacy preferences during the lockdown to cope their lives while staying home. Such disturbance in the privacy behavior affected the performance of machine learning (ML) algorithm that is trained on normal behavior. In this paper, we present the impact of the pandemic on the efficiency of the learning algorithm implementation of a privacy protection system. the system is composed of three modules, in this work we focus on Privacy Preferences Manager (PPM) module which is implemented using hybrid methodology based on a Statistical Model (SM) and Logistic Regression (LR) learning algorithm. the efficiency of the hybrid methodology is assessed using two real-world datasets collected prior and during the COVID-19 pandemic. the results show that the pandemic significantly impacted the efficiency of the hybrid methodology by 13.05% and 15.22% for the accuracy and F1 score respectively. (C) 2021 the Authors. Published by Elsevier B.V.

关键词： Privacy Behavior recognition Context-aware machine learning Logistic Regression COVID-19 Protection Intelligent System

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A Residual Neural Network-Based Incremental learning Model for Driver State Perception

A Residual Neural Network-Based Incremental Learning Model f...

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Electronic Communication and Artificial Intelligence (IWECAI), international Workshop on

作者： Jie Cai Jingming Zhang Erxin Sun Jianjun Cai Research and Development Department Automotive Data of China (Tianjin) Company Ltd Tianjin China

this paper presents a model for implementing active machine sensing of fatigue states in an intelligent cockpit. To make the model more robust and achieve higher accuracy, the model introduces residual modules in the convolutional neural network and incorporates input primitive attributes and contour information in the learning. In addition, new learning branches are added to the network. By learning the user’s personalized performance data in the new branch, the model is more adaptable to different drivers. With reference to the user state by the model perceived, the cockpit switches between different cloud scenario modes. Finally, in comparative experiments, our model shows advantages over other models in driver state recognition.

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IoT-Enabled WBAN and machine learning for Emotion recognition

IoT-Enabled WBAN and Machine Learning for Emotion Recognitio...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Hritesh Mathur Harsh Panwar Rahul Karki Vivek Jangra Ramnaresh Yadav Anjali Kapoor Department of Information Technology Amity University Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

All industries, including computer science and health care and customer service are in dire need of efficient recognition of human feelings. this work is a script for a novel approach to affect recognition which involves both vocal emotion analysis and face emotion detection. Voice sentiment analysis project used machine learning technique to identify the prospects of emotional content in voice data. the model also assists in the classification of emotions with reference to the speech emotional states based on added features of MFCC and chroma. On the other hand, face emotion recognition project by applying deep learning algorithms identifies emotions by face expressions. With the help of convolutional, neural networks (CNNs) determining the happiness, sadness, and anger it analyses the facial features correctly. Combining all of these results in a coherent system that allows for the recognition of feelings through voice and or facial signals. Consequently, as demonstrated in the experimental results provided hereinafter, the accuracy of the presented system in correctly identifying human emotions is significantly higher than when using single models on their own. therefore this combination creates space for other uses such as healthcare marketing among other fields hence improving user experience while at the same time providing unforeseen analysis of human conduct during an interaction with intelligent devices.

关键词： Deep learning Wireless communication Emotion recognition Sentiment analysis Accuracy Face recognition Speech recognition Medical services User experience Stability analysis

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Detection of Skin Cancer using Artificial Intelligence & machine learning Concepts 4

Detection of Skin Cancer using Artificial Intelligence & Mac...

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4th international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2022

作者： Suhasini, V.K. Patil, Prerana B. Vijaykumar, K.N. Manjunatha, S.C. Sudha, T. Kumar, P. Ramachandraiah, Gopalaiah Pavithra, G. Manjunath, T.C. Maharashtra Navi Mumbai India Dayananda Sagar College of Engg. Karnataka Bangalore India Mechanical Engineering Dj Sanghvi College of Engg. Vile Parle Maharashtra Mumbai India Dept. of Electrical and Electronics Engg. Karnataka Chitradurga India Dayananda Sagar College of Engg. Dept. of Ece Karnataka Bangalore India Dayananda Sagar College of Engg. Dept. of Electronics and Instrumentation Engg. Karnataka Bangalore India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

In this paper, the detection of Skin Cancer using Artificial Intelligence & machine learning Concepts is presented. Convolutional Neural Networks (CNN) in the area of image recognition are one of the many complex challenges that Deep learning has gained attention for in recent years. Transfer learning (TL) approaches are also becoming more and more common as a way to retrain some of the layers of Neural Networks that learnt from a generic dataset, putting it to use in another situation. this is because Neural Networks can occasionally attain more than 50 layers deep. In this work, we try to use CNNs and TL methods on pre-trained Neural Networks to separate seborrheic keratosis and nevus benign tumours from melanoma skin cancer. Skin cancer can be detected early thanks to AI. Deep convolutional neural networks, for instance, can be used to construct a system that assesses skin image data to detect skin cancer. For skin cancer to be effectively treated and to have better outcomes, early detection is crucial. the use of artificial intelligence (AI) technologies, such as shallow and deep machine learning-based techniques that are trained to detect and categorise skin cancer using computer algorithms and deep neural networks, is therefore being made to assist in the diagnosis of skin cancer. the purpose of this study was to categorise and list the many AIbased technologies that are used to find and classify skin cancer. By examining the relationship between the size of the data set, the number of diagnostic classes, and the performance measures employed to assess the models, the study also looked at the validity of the papers that were chosen. the results shows the effectiveness of the method put in this paper © 2022 IEEE.

关键词： Convolutional neural networks

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Moving Object recognition and Tracking Based on Image-fusion Frame Differencing and Differential Translational Transform

Moving Object Recognition and Tracking Based on Image-fusion...

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IEEE international conference on Human-machine Systems

作者： Minghui Chen Yingjun Ji Aoyuan Wang Jiacun Wang College of Information Liaoning University Shenyang China Department of Computer Science and Software Engineering Monmouth University West Long Branch NJ USA

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

To address the issue of sensitivity to external interference and noise in traditional moving object recognition and tracking algorithms, this paper proposes a novel algorithm that combines Image Fusion-based Frame Difference (IFD) with Differential Translation Transformation (DTT). Firstly, image enhancement and noise reduction are applied to adjust contrast and brightness in the images of moving objects. Subsequently, the IFD algorithm is employed to adjust the blending of new frame with the background, smoothly fusing them. Pixel differences between the fusion frame and the background are calculated to identify moving objects in the image. Matching is performed between the target areas obtained by the IFD algorithm based on pixel displacement differences between consecutive frames and the target displacements obtained by the DTT algorithm. this allows for recognition and tracking of the targets. To validate the effectiveness of the algorithm, experiments are conducted on two sets of moving-object image data, resulting in accuracies of 90.12% and 90.87%.

关键词： Training Visualization Accuracy Target tracking machine learning algorithms Sensitivity Target recognition

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machine learning for Real-Time Language Translation: A Server-Side Approach to Overcome Cross-Cultural Communication Challenges

Machine Learning for Real-Time Language Translation: A Serve...

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Soft Computing for Security Applications (ICSCSA), international conference on

作者： S. Elango Mohanaprakash T A D. Siva Gowtham A Sasikumar A N Sunitha T Department of Computer Science and Engineering SOET CMR University Bengaluru Department of Computer Science and Applications SRM Institute of Science and Technology Chennai Department of CSE (Cyber Security) Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

the evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-the-art results in language translation, a universal solution remains elusive. this research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. the process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). this approach aims to enhance real-time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers.

关键词： Translation machine learning algorithms Speech recognition machine learning Speech enhancement Cross-cultural communication Real-time systems Speech synthesis machine translation Security

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A Novel Deep-learning Pipeline for Light Field Image Based Material recognition 25

A Novel Deep-learning Pipeline for Light Field Image Based M...

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25th international conference on pattern recognition (ICPR)

作者： Wang, Yunlong Zhang, Kunbo Sun, Zhenan CRIPAC NLPR CASIA 95 ZhongGuanCun East St Beijing 100190 Peoples R China

ISBN: (纸本)9781728188089

the primitive basis of image based material recognition builds upon the fact that discrepancies in the reflectances of distinct materials lead to imaging differences under multiple viewpoints. LF cameras possess coherent abilities to capture multiple sub-aperture views (SAIs) within one exposure, which can provide appropriate multi-view sources for material recognition. In this paper, a unified "Factorize-Connect-Merge" (FCM) deep-learning pipeline is proposed to solve problems of light field image based material recognition. 4D light-field data as input is initially decomposed into consecutive 3D light-field slices. Shallow CNN is leveraged to extract low-level visual features of each view inside these slices. As to establish correspondences between these SAIs, Bidirectional Long-Short Term Memory (Bi-LSTM) network is built upon these low-level features to model the imaging differences. After feature selection including concatenation and dimension reduction, effective and robust feature representations for material recognition can be extracted from 4D light-field data. Experimental results indicate that the proposed pipeline can obtain remarkable performances on both tasks of single-pixel material classification and full-image material segmentation. In addition, the proposed pipeline can potentially benefit and inspire other researchers who may also take LF images as input and need to extract 4D light-field representations for computer vision tasks such as object classification, semantic segmentation and edge detection.

关键词： Pipelines

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Research on Bioengineering Algorithm Based on Deep learning Neural Network 4

Research on Bioengineering Algorithm Based on Deep Learning ...

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4th IEEE international conference on Information Systems and Computer Aided Education, ICISCAE 2021

作者： Wang, Hanyu Boston University Boston United States

ISBN: (纸本)9781665441247

Deep learning (DL) is a fresh study orientation in the field of machine learning in computer science. It is recommend into machine learning to make it nearer to the customary artificial target intelligence. DL is the inherent law and express level of learning sample data, and the information get in the learning procedure is of mighty help to the explain of data such as words, images and sounds. CNN (Convolutional Neural Network) combines feature extraction with itemize process to train neural network, which has acquire mighty successful in the field of image classification. this paper focuses on the automatic classification of fetal facial ultrasound images. A 19-layer convolution network is proposed and improved. By using data enhancement, adding global mean pooling layer, reducing the number of channels in the full connection layer of the model, and optimizing learning based on parameter transfer learning of fine-tuning training, the automatic classification of fetal facial ultrasound images with limited data volume can be realized. Match with the present solutions, the depth network proposed in this paper can effectively avoid ultrasonic noise interference and learn deep features more effectively. A heavy quantity of specific analysis test have proved its effectiveness. © 2021 IEEE.

关键词： Image recognition

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A machine-learning Pipeline for Semantic-Aware and Contexts-Rich Video Description Method 22

A Machine-Learning Pipeline for Semantic-Aware and Contexts-...

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4th international conference on pattern recognition and Intelligent Systems, PRIS 2022

作者： Aun, Yichiet Khaw, Jasmina Yen Min Gan, Ming-Lee Tin, Ley-Ter Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Malaysia

ISBN: (纸本)9781450396080

Video description (VD) methods use machine learning to automatically generate sentences to describe video contents. Global-description based VD (gVD) methods generates global description to provide the big picture of video scenes but they lack finer grain entities information. Meanwhile, modern entity-based VD (eVD) use deep learning to train ML models like object model (YOLOv3), human activity model (CNN), location tracking (DeepSORT) to resolve individual entity that made up the complete sentences. However, existing eVD are limited in the types of supported entities;thus, resulting in eVD generating sentences that contexts-deprived and incomplete to clearly describe video scenes. In addition, the entities resolved by eVD are isolated since they are inferred from different ML models;resulting in sentences that are not semantically cohesive;contextually and grammatically. In this paper, a two-stages ML pipeline (teVD) is proposed for a holistic and semantic-aware VD sentence generation. Firstly, a ML pipeline is designed to aggregate several high performing ML models for resolving fine grain entities to improve the accuracy of resolved entities. Second, the components in the entities set are g-stitched' together using an entity trimming method to (1) remove shadow entities and (2) to re-arrange entities based on linguistic rules to generate video descriptions that are context-aware and less ambiguous. the experimental results showed that teVD successfully improved the quality of generated sentences in short videos;achieving BLEU score of 48.01 and METEOR score of 32.80 on MSVD dataset. © 2022 ACM.

关键词： Semantics

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