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Audiovisual speech recognition based on a deep convolutional neural network

Data science and Management 2024年第1期7卷 25-34页

作者： Shashidhar Rudregowda Sudarshan Patilkulkarni Vinayakumar Ravi Gururaj H.L. Moez Krichen Department of Electronics and Communication Engineering JSS Science and Technology UniversityMysuru570006India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobar34754Saudi Arabia Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal560064India Department of Information Technology Faculty of Computer Science and Information Technology(FCSIT)Al-Baha UniversityAlaqiq65779-7738Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia

Audiovisual speech recognition is an emerging research *** is the recognition of what someone is saying using visual information,primarily lip *** this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual *** features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural *** feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural ***,integration was performed using a deep convolutional *** audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 *** training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 *** integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively.

关键词： Audiovisual speech recognition,Custom dataset 1D Convolution neural network(CNN) Deep CNN(DCNN) Long short-term memory(LSTM) Lipreading Dlib Mel-frequency cepstral coefficient(MFCC)

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Performance Analysis of Higher Order Tensor Storages for Highly Sparse Multidimensional Data 5

Performance Analysis of Higher Order Tensor Storages for Hig...

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5th International Conference on Electrical Information and communication Technology, EICT 2021

作者： Safayet Hossain, Md. Azharul Hasan, K.M. Tsuji, Tatsuo Department of Computer Science and Engineering Khulna Bangladesh University of Fukui Faculty of System Design Engineering Fukui Japan

ISBN: (纸本)9781665409063

To compute multidimensional data, tensor algebra is a powerful tool and it has many applications in machine learning and data analytics. This multidimensional data which is represented with higher-order tensors for multi-Aspect data analysis is very sparse for an actual dataset. The degree of data sparsity increases with the increase of dimensionality. This is because the volume of the tensor increases very fast with the increase of dimensionality but the actual data to the tensor does not increase at such a rate for high-performance scientific computing. Storing and applying operations on this highly sparse multidimensional data is a challenge for data scientists. Therefore, an efficient storage scheme for such sparse tensor is very important. There are many storage schemes in the literature for storing sparse tensors. In this paper, we analyze and compare different tensor storage formats using synthetic and actual datasets. We found that no single storage scheme is efficient for all the operations or applications of tensors. We calculated the memory required for per-nonzero elements among these tensor formats and compared them for different parameters. © 2021 IEEE.

关键词： Tensors

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XU-NetI: Simple U-shaped encoder-decoder network for accurate imputation of multivariate missing data

Franklin Open

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Franklin Open 2024年 8卷

作者： Firdaus, Firdaus Nurmaini, Siti Tutuko, Bambang Rachmatullah, Muhammad Naufal Islami, Anggun Darmawahyuni, Annisa Sapitri, Ade Iriani Karim, Muhammad Irfan Fachrurrozi, Muhammad Zarkasi, Ahmad Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya South Sumatera 30139 Indonesia Department of Informatic Engineering Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia Department of Computer Engineering Faculty of Computer Science Universitas Sriwijaya Indonesia

Patients in intensive care unit (ICU) often have multiple vital signs monitored continuously. However, missing data is common in ICU settings, negatively impacting clinical decision-making and patient outcomes. In this study propose a multivariate data imputation method based on simple U-Shaped encoder-decoder network imputation (XU-NetI) method to learn the underlying patterns in the data and generate imputations for missing values of vital signs data with ICU patients. To evaluate the performance of this study's imputation methods, this study employed a publicly available database such the medical information mart for intensive care III (MIMIC III) v1.4. In this study proposed model has been developed to analyze 219.281 vital sign worth of data, focusing on eight essential vital sign features: body temperature, heart rate, respiration rate, systolic blood pressure, diastolic blood pressure, mean blood pressure, oxygen saturation, and glucose. The evaluation results demonstrates the effectiveness of the imputation techniques in improving the accuracy of predictive models. This study compared the XU-NetI approach to other state-of-the-art imputation methods including Autoencoder and Convolutional Neural Networks. As a result found, the technique using XU-NetI architecture outperformed them, in terms of root mean square error (RSME) by approximately 0.01, mean absolute error (MAE) by approximately 0.009, and R square (R2) by approximately 0.99. The XU-NetI method has the potential to enhance clinical decision-making and improve patient outcomes. © 2024 The Author(s)

关键词： Convolutional neural network Deep Learning Missing data imputation U-shaped encoder-decoder network

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Recommending Public Transit Route: Ant-Colony Optimization or Dijkstra?

Recommending Public Transit Route: Ant-Colony Optimization o...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Abdul Jabbar Aryo Pinandito Informatics Engineering Department Faculty of Computer Science Universitas Brawijaya Malang Indonesia Information System Department Faculty of Computer Science Universitas Brawijaya Malang Indonesia

Advances in algorithmic studies have been made recently, especially in the area of finding the shortest path. Despite taking a greedy approach, the Dijkstra algorithm is well recognized for its efficacy and has established itself as the industry standard for identifying the shortest path in a graph. Conversely, scientists have embraced the Ant-Colony Optimization (ACO) approach, which draws inspiration from the behavior of ants. This study examines the two methods and determines if, given equivalent resources, ACO can generate Dijkstra-like recommendations. Research data is subjected to both algorithms, and the outcomes are presented visually. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. Memory consumption, computation time, initial memory allocation, and final memory allocation are evaluation parameters. ACO is more efficient than Dijkstra in terms of execution time and number of pathways traversed, according to the study's repeated evaluation methodology. This iterative assessment procedure is carried out to address the possibility of single measurements resulting from the JavaScript Garbage Collector. The study's findings imply that a larger memory may be necessary for ACO to deliver the desired outcomes. These results could also be impacted by each algorithm's distinct qualities.

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IoT Based Health Monitoring system with AI-Powered Disease Prediction 9

IoT Based Health Monitoring System with AI-Powered Disease P...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Radha Mahendran, S. Khan, Mohammad Shahbaz Hari Priya, M.P. Obaid, Ahmed J. Mohsen, Karrar Shareef Shareef, Ashraf Mohammed Department of Bioinformatics Chennai Pallavaram117 India Operations And Regulatory Affairs Children's National Hospital WashingtonDC United States Department of Computer Science Kothanur K.Narayanapura Karnataka Bangalore560077 India University of Kufa Technical Engineering College Al-Ayen University Faculty of Computer Science And Mathematics Najaf Iraq Department of Computer Technical Engineering Thi-Qar Iraq Scientific Research Center Al-Ayen University Information And Communication Technology Research Group Thi-Qar Iraq National University of Science And Technology DhiQar Iraq

ISBN: (纸本)9798350365092

Healthcare monitoring systems have advanced significantly in emergency rooms and many other health settings. Today, many nations are deeply concerned about the rise of small healthcare monitoring systems. In-person advising and telemedicine are two different ways that the Internet of Things (IoT) is further developing healthcare. The Internet of Things makes things and individuals reliably available, and its relationship with the cloud improves our personal satisfaction. Proactive clinical exploration can help change a responsive healthcare approach into a proactive one, as man-made reasoning (AI) and high-level human insight become increasingly more coordinated into the healthcare area. Within the field of machine learning, deep learning holds significant potential in precisely and swiftly analysing vast quantities of data, producing perceptive experiences, and resolving intricate issues. Preventive treatment and early intervention for persons who are at risk depend on accurate and useful illness prediction. The constant inflow of electronic medical information necessitates the need for more accurate prediction models in recurrent neural network versions of deep learning that handle successive time-series data. Devices connected to the Internet of Things provide data to the proposed system, and future analysis will be conducted on cloud-stored electronic clinical records pertaining to patient histories. © 2024 IEEE.

关键词： Forecasting

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Smart Design and Implementation of Self Adjusting Robot using Arduino

Smart Design and Implementation of Self Adjusting Robot usin...

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2022 International Conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

作者： Vidhya, R.G. Singh, Kamlesh Paul, P John Sai Srinivas, T. Aditya Prasad Patra, Jyoti Sagar, K.V.Daya HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Graphic EraHill University Department of Computer Science Engineering Dehradun Campus Dehradun India Ellenki College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India G Pulla Reddy College of Engineering Department of Computer Science and Engineering Andhra Pradesh Kurnool India Odisha University of Technology and Research Faculty of Electrical OUTR Mahalaxmi ViharGhatikia Techno campus Odisha Bhubaneswar India Koneru Lakshmaiah Education Foundation Department of ECE Guntur Andhrapradesh Vaddeswaram India

ISBN: (纸本)9781665489621

Self-adjusting robots are an excellent approach for keeping up with breakthroughs and developments in advanced mechanics. This idea is based on the basic pendulum theory. A framework goes through a cycle of self-adjustment to reinforce itself from inside. This study intends to overcome the difficulties of stabilizing an unstable structure by supplying the robot with the essential components for self-adjustment and balance. Data from the accelerometer and gyrator are used to compute the robot's accurate three-dimensional location, which is then relayed to a controller. The regulator then uses its own code to legally direct the wheel rotation of the drive module, boosting the robot's stability. This robot has an advantage over others with four wheels since it can spin faster and around obstacles more readily. As a result, it is a necessary tool in many current scenarios. © 2022 IEEE.

关键词： Accelerometers

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Machine Learning Techniques with LowDimensional Feature Extraction for Improving the Generalizability of Cardiac Arrhythmia

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-10页

作者： Nurmaini, Siti Tutuko, Bambang Rachmatullah, Muhammad Naufal Darmawahyuni, Annisa Firdaus, Firdaus A Professor of Computer Engineering Intelligent System Research Group Universitas Sriwijaya Palembang30139 Indonesia A lecturer of Computer Engineering Department Faculty of Computer Science Universitas Sriwijaya Palembang30139 Indonesia A research assistant of Intelligent System Research Group Universitas Sriwijaya Palembang30139 Indonesia

Automatic heartbeat classification is an important stage in identifying cardiac arrhythmia. Several machine learning (ML) techniques have been proposed to perform this, but they produce an accuracy result of below 99%. In this study, a deep neural network (DNN) structure is applied to improve ML performance. The feature selection method is based on the combination of discrete wavelet transform (DWT) and principal component analysis (PCA). To avoid computational complexity, the components of PCA are derived by low-dimensional DWT coefficients. The results show that the proposed ML model achieves good performance, producing 99.76% accuracy, 91.80% sensitivity, 99.78% specificity, 93.02% precision, and 92.31% F1-score. To benchmark the proposed model, the support vector machine (SVM) and random forest (RF) techniques are used as the baseline models. The DNNs are 2.3% more sensitive than SVM, while the RF fails to classify the ECG heartbeat. Four datasets are used to analyze the robustness and generalization performance of the proposed model: MIT-BIH, SVDB, MITDB, and IncartDB. All testing results produce satisfying performance. The proposed ML model offers a potential solution to improve the generalizability of a DNN-based model in different cardiac datasets for classifying tasks. © 2021. All Rights Reserved.

关键词： Principal component analysis

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Svm-pca based handwritten devanagari digit character recognition

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Recent Advances in computer science and communications 2021年第1期14卷 48-53页

作者： Khamparia, Aditya Singh, Sanjay K. Luhach, Ashish K. Department of Computer Science and Engineering Faculty of Technology and Sciences Lovely Professional Universi-ty Phagwara Punjab India Department of Electrical and Communication Engineering Faculty of Technology and Sciences The PNG University of Technology MorobePapua New Guinea Australia

Objective: The blended fusion of Support Vector Machine (SVM) and Principal Component Analysis (PCA) have been widely used in recognizing handwritten digit characters of Devanagari script. The feature information from the character is extracted using its skeleton structure which optimally reduce data dimensionality using PCA. There is ample information available on handwritten charac-ter recognition on Indian and Non-Indian scripts but very few article emphasized on recognition of Devanagari scripts. Therefore, this paper presents an efficient handwritten Devanagari character recognition system based on block based feature extraction and PCA-SVM classifier. Methods: We have collected samples of handwritten Devanagari characters from different handwritten experts for classification. Results: For experimental work, total of 100 images having Devanagari digit characters been used for the purpose of training and testing. The proposed system achieves a maximum recognition accuracy of 96.6 % and 96.5% for 5 & 10 fold validations with 70% training and 30% testing data using block based feature and SVM classifier having different kernels. Conclusion: The obtained results achieve maximum accuracy using SVM classifier for digit character recognition. In future deep learning networks will be considered for accuracy enhancement and precision. © 2021 Bentham science Publishers.

关键词： Principal component analysis

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A Novel Dual-Band Printed SIW Antenna Design Based on Fishnet & CCRR DGS Using Machine Learning for Ku-Band Applications

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Progress In Electromagnetics Research C 2021年 116卷 207-219页

作者： Nakmouche, Mohammed F. Magray, Muhammad I. Allam, Abdemegeed M. Fawzy, Diaa E. Lin, Ding Bing Tarng, Jenn-Hwen Faculty of Engineering Izmir University of Economics Izmir Turkey Hsinchu City Taiwan Department of Communication Engineering German University in Cairo Cairo Egypt Department of Electronics and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

—This paper analyzes and solves the complexity to determine the optimum positions of the Fishnet & Complementary Circular Ring Resonator (CCRR) based Defected Ground Structures (DGS) for Substrate Integrated Waveguide (SIW) based antennas. A new state-of-art technique based on Artificial Neural Network (ANN)-Machine Learning (ML) is proposed for overcoming the lack of solid and standard formulations for the computation of this parameter related to a targeted frequency. As a proof of concept and to test the performance of our approach, the algorithm is applied for the determination of the CCRR and Fishnet-DGS’s optimal positions for a SIW based antenna. The SIW technique provides the advantages of low cost, small size, and convenient integration with planar circuits. The ANN-ML based technique is optimized to attain dual-band resonances with optimal gain and radiation efficiency. The simulation results of the first Fishnet-DGS based antenna show good minimum return losses at two center frequencies, namely, 16.6 GHz (with gain of 6 dB and radiation efficiency of 95%) and 17.7 GHz (with gain and radiation efficiency of 9 dB and 96%, respectively). The second CCRR-DGS based antenna shows about 8 dB gain and a radiation efficiency of 87% at 17.3 GHz, and gain and efficiency of about 8.5 dB and 85% are observed at 17.8 GHz. The proposed CCRR and Fishnet-DGS based antenna are low profiles, low costs, with good gains and radiation efficiencies, making both designs very suitable for Ku-band applications. There is a fair agreement between the measured and simulated results. The achieved dual-band resonances act as a proof of concept that the proposed ANN-ML techniques can be employed for the determination of the optimal positions for CCRR and Fishnet thereby attaining any target dual-bands in the Ku-band with good accuracy of about 98% and a save of 99% in the overall the computational time. © 2021, Electromagnetics Academy. All rights reserved.

关键词： Radiation efficiency

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A Dynamic Resource-Aware Routing Protocol in Resource-Constrained Opportunistic Networks

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computers, Materials & Continua 2022年第2期70卷 4147-4167页

作者： Aref Hassan Kurd Ali Halikul Lenando Slim Chaoui Mohamad Alrfaay Medhat A.Tawfeek Department of Computer Systems and Communication Technologies Faculty of Computer Science and Information TechnologyUniversiti Malaysia Sarawak(UNIMAS)Kota Samarahan94300Malaysia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka2014KSA Unit-Lab of Sciences of Electronics Technologies of Information and TelecommunicationsSfax UniversitySfaxTunisia Department of Basic Sciences First Common Year DeanshipJouf UniversitySakaka2014KSA Department of Computer Sciences College of Computer and Information SciencesJouf UniversitySakaka2014KSA Department of Computer Sciences Faculty of Computers and InformationMenoufia UniversityEgypt

Recently,Opportunistic Networks(OppNets)are considered to be one of the most attractive developments of Mobile Ad Hoc Networks that have arisen thanks to the development of intelligent *** are characterized by a rough and dynamic topology as well as unpredictable contacts and contact *** is forwarded and stored in intermediate nodes until the next opportunity ***,achieving a high delivery ratio in OppNets is a challenging *** is imperative that any routing protocol use network resources,as far as they are available,in order to achieve higher network *** this article,we introduce the Resource-Aware Routing(ReAR)protocol which dynamically controls the buffer usage with the aim of balancing the load in resource-constrained,stateless and non-social *** ReAR protocol invokes our recently introduced mutual informationbased weighting approach to estimate the impact of the buffer size on the network performance and ultimately to regulate the buffer consumption in real *** proposed routing protocol is proofed conceptually and simulated using the Opportunistic Network Environment *** show that the ReAR protocol outperforms a set of well-known routing protocols such as EBR,Epidemic MaxProp,energy-aware Spray and Wait and energy-aware PRoPHETin terms of message delivery ratio and overhead ratio.

关键词： Opportunistic networks mobile ad hoc networks routing protocols resource-constrained networks load balancing buffer management

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