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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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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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Sign Language recognition of Autistic Children using Wearable Sensors Signals Features Selection

Sign Language Recognition of Autistic Children using Wearabl...

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international conference on Advanced Communication Technologies and Networking (CommNet)

作者： Farman Ullah Mohamed Isa Ahmed Abdulraheim Alhammadi Abdalla Ahmed Ali Alyassi Shumayla Yaqoob Ihtesham Jadoon College of Information Technology United Arab Emirates University Al-Ain Abu Dhabi

ISBN: (数字)9798350367027

ISBN: (纸本)9798350367034

Children with autism spectrum disorder (ASD) often face difficulties and challenges in social interactions, communication, and eye contact due to neurological disorders. this neurological disorder causes children to have a lack of social interaction, sensory processing issues, repetitive behaviours, trouble adapting to change, and executive functioning problems. this article proposes the autistic children’s gesture recognition framework using wearable sensors feature selection-based machine learning techniques. the system records and analyzes individuals’ gestures, providing valuable data for understanding their behaviour. A dataset of $\mathbf{2 4}$ gestures from 10 children with ASD was collected, and various machine learning models, such as KNN-Manhattan, KNN-Euclidean, neural networks, and random forests, were trained using time- and frequency-domain features extracted from the sensor data. Cross-validation techniques were applied to evaluate the models’ accuracy, with most classifiers achieving around $\mathbf{8 8 \%}$ and the highest accuracy of about $\mathbf{9 1 . 5 \%}$. this approach demonstrates the potential of wearable technology and machine learning in improving the understanding and support of individuals with ASD.

关键词： Neurological diseases Sign language Accuracy Frequency-domain analysis Face recognition Feature extraction data models data mining Wearable sensors Random forests

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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 on mining Potential Adverse Drug Reactions for Pharmacovigilance 2021

Machine Learning on Mining Potential Adverse Drug Reactions ...

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4th international conference on data Science and Information Technology, DSIT 2021

作者： Jia, Ke Wuhan Donghu University China

ISBN: (纸本)9781450390248

Drugs are related to the lives and health of patients. mining and discovering potential adverse drug reactions from a large number of unstructured texts play an important role in drug research and pharmacovigilance. It is a hot research issue in the field of biomedicine in recent years. A large number of scholars have discovered a large amount of information on adverse drug reactions from different data sources through effective machine learning methods. this paper reviews the research progress in recent years to provide a reference for future research. © 2021 ACM.

关键词： Deep learning

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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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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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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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Research on Hemorrhagic Stroke Based on K-medoids Cluster Analysis and data mining

Research on Hemorrhagic Stroke Based on K-medoids Cluster An...

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

作者： Chongjing Sun Zhongji Yang Mengqi Gao Shen’Ao Ma China University of Geosciences Beijing Beijing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

data mining is an effective computer-aided analysis technology, which can uncover hidden information from large data sets and provide basis for decision-making. At the same time, clustering analysis, as an important branch of data mining, is also widely used in various fields, among which the k-medoids clustering algorithm has better robustness and anti-noise ability than the traditional k-means algorithm. In order to expand the value of these two computer analysis techniques in medical applications, this paper attempts to combine k-medoids cluster analysis and data mining methods to apply them to the analysis and research of clinical hematoma. Specifically, this research first used feedforward neural network to construct a simulation of edema volume changes, which laid a foundation for subsequent cluster analysis. then, the k-medoids algorithm was used to divide the different edema patients into three clusters. On this basis, the therapeutic effect of multiple treatment modalities on link edema was analyzed by controlling for a single variable. Finally, Pearson correlation analysis was used to explore the treatment options most related to the volume of hematoma. In general, this research successfully applied the data mining technique and k-medoids clustering algorithm to the field of clinical medicine, which provides a useful reference for the application of more computer analysis methods in the field of medical and health care. In the future, we can further explore how to integrate more machine learning algorithms into medical data analysis, to provide more intelligent support for clinical diagnosis and treatment.

关键词： Analytical models Technological innovation Correlation machine learning algorithms data analysis Computational modeling Clustering algorithms data models data mining Medical diagnostic imaging

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Sentiment Analysis of Hotel Customers' Feedback in Bangla Using machine learning

Sentiment Analysis of Hotel Customers' Feedback in Bangla Us...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Jairul Islam Md. Shohidul Islam Husnul Ajra Department of CSE Khulna Khan Bahadur Ahsanullah University Khulna Bangladesh Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Kuantan Malaysia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

In the rapidly expanding internet era, customers or tourists can easily find information about hotels. the growth of the hospitality industry in Bangladesh has led to a rise in the number of online hotel reviews. In this respect, it becomes a concern to understand the customer experience and review the quality level of hotels relying on their services. Sentiment analysis is one of the intimate extents of text mining, where digital text-based consumer responses or emotions are reviewed using machine learning methods. the purpose of this study is to construct a benchmark dataset of hotel customers' feedback in Bangla and preprocess the data for sentiment analysis using natural language processing techniques. In accordance with this, it has proposed a machine learning method to accomplish sentiment analysis on Bangla text-based hotel reviews. It has been employed a set of machine learning algorithms like Decision Tree (DT), Random Forest (RF), Support Vector machines (SVM), and Naive Bayes (NB) to explore the accuracy of Bangla text data regarding customer feedback. these algorithms classify the reviews as positive, negative, or neutral. this study performs these algorithms and finds the consequences of machine learning models in terms of accuracy. However, the proposed analysis from these algorithms shows that RF provides the highest accuracy of 83% for sentiment analysis tasks. these findings suggest that combining machine learning techniques can be an effective way to analyze Bangla text-based hotel customers' feedback and provide valuable insights for the hospitality industry in Bangladesh.

关键词： Radio frequency Support vector machines Industries Sentiment analysis Analytical models machine learning algorithms Accuracy Reviews machine learning Classification algorithms

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