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An IOT-based Language Recognition System for Indigenous Languages using Integrated CNN and RNN 3

An IOT-based Language Recognition System for Indigenous Lang...

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3rd international conference on Smart Data Intelligence, ICSMDI 2023

作者： Cerna, Patrick D. Ututalum, Charisma S. Evangelista, Ryan S. Darkis, Aldaruhz T. Asiri, Masnona Sabdani Muallam-Darkis, Jehana A. Northern Negros State College of Science and Technology Sagay City Philippines Sulu State College Suc President Ii Sulu Philippines Graduate School / Institute of Technology PolyTechnic University of the Philippines Philippines School of Computer Studies and Engineering Sulu State College Sulu Philippines School of Graduate Studies Sulu State College Sulu Philippines

ISBN: (纸本)9781665464871

Automatic Speech Recognition (ASR) aims to establish communication between humans and computers in a more natural way. The main aim of this study is to build hardware-based automatic speech recognition for Indigenous People (IP)'s ancestral dialects, in particular for Manobo, Mandaya, and B'laan using Raspberry Pi. Jasper is an open source toolkit used for creating voice-activated, always-on applications. The researcher recording audio data from research participants, the study's participants will be located in Davao Occidental and Sarangani for B'laan, Agusan Del Sur for Manobo, and Davao Oriental for Mandaya. A functional microphone and raspberry pi boards serve as the experiment's hardware where audi o input is being fine-tuned from a raspberry pi-powered device that records audio in waveform format, which includes Mandaya, Manobo, and Malita words and phrases. The Tensorflow STFT technique will be used to analyze, generate, transform, and characterize audio signals. JiWER plugins for Similarity measures will also be used The WER output is 98.53%, an acceptable percentage for the number of datasets used © 2023 IEEE.

关键词： Speech recognition

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Design of a multi-mode intelligent drive stroke rehabilitation robot based on limb linkage structure 3

Design of a multi-mode intelligent drive stroke rehabilitati...

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3rd international conference on Robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Cheng, Yusi Zhu, Chen Xu, Jiajun Gao, Yu Ji, Yingying Yang, Longfei Wang, Yumo Nanjing University of Science and Technology School of Intelligent Manufacturing Jiangsu China Nanjing University of Aeronautics and Astronautics College of Mechanical and Electrical Engineering Jiangsu China Nanjing Medical University School of Nursing Jiangsu China The Affiliated Mental Health Center of Jiangnan University Wuxi Central Rehabilitation Hospital Rehabilitation Medicine Department Jiangsu China

ISBN: (纸本)9798350360813

Adequate rehabilitation training after stroke can effectively improve patients' quality of life and reduce the rate of secondary recurrence. Intelligent rehabilitation robots have excellent research value because they can adjust the rehabilitation process through human-computer interaction. In order to solve the problem of patients' lack of exercise initiative in rehabilitation training, this paper proposes a dual-driven control strategy of adaptive heart rate regulation and transcranial electrical stimulation to effectively help patients complete the corresponding training tasks and develop a multi-mode intelligent drive rehabilitation robot based on limb linkage structure. The device collects individual cardiopulmonary function data through sensors and multi-modally senses the individual's physiological condition, according to which quantitative resistance output response is made. It uses transcranial electrical stimulation as a supplementary adjustment mechanism for motion feedback and establishes a heart rate adjustment movement and motion adaptation that adapts to the ontology feeling and can achieve the effect of active movement - a two-way collaborative model of heart rate and actual physiological conditions. The experimental results show that the electromagnetic resistance under different gears and pressure can adjust the output resistance more sensitively. At the same time, the two test results show that the gear adjustment brings a good sense of experience. The movement under the regulation of different output resistances is well adapted to the heart rate, and the corresponding body feeling aligns with the RPE amount table, with a good rehabilitation application effect. © 2023 IEEE.

关键词： Physiological models

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Machine learning integration in Communication system for efficient selection of signals 3

Machine learning integration in Communication system for eff...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Bhardwaj, Ankit Rebelli, Shashank Gehlot, Anita Pant, Kumud Gonzales, Jose Luis Arias Firos, A. University of Connecticut The Math Company United States University of Connecticut Department of Computer Science and Engineering StorrsCT United States SR University Department of ECE Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India Pontifical Catholic University of Peru Departamento of Business Peru Department of Computer Science and Engineering Arunachal Pradesh Doimukh India

ISBN: (纸本)9798350399264

Through combined antenna selecting and power management design, we examine the effectiveness of multi-user multiple-antenna downlinks. A fraction of antennas are chosen to assist the customers in order to fully leverage the geographical diversity and reduce the energy spent by operational radio frequency (RF) modules. First, we suggest the joint antenna selection and power allocation (JASPA) method to increase the network sum rate while taking quality of service (QoS) needs and the total transmission power restriction into consideration. JASPA uses a doubly repetitive technique to overcome the non-convexity of the defined issue. In the internal repetition, the transmission power is incrementally optimized, while in the outside loop, all possible antenna configurations are tested. JASPA has a combinatorial complexity issue while being close to the worldwide optimality, which might restrict its usage in real time system operations. We suggest a learning-oriented approach to antenna selection and power allocation (L-ASPA) to get over this restriction while maintaining JASPA's competitive performance. The fundamental concept behind L-ASPA is to take use of recent developments in ML to create a subordinate relationship between the important system variables and the chosen antennas. Numerical findings that show that JASPA could reach 90% of the ideal performance while lowering more than 93% of computing time serve as evidence of the usefulness of the suggested methods. © 2023 IEEE.

关键词： Machine learning

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Crop weed image recognition of UAV based on improved HRNet-OCRNet 3

Crop weed image recognition of UAV based on improved HRNet-O...

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3rd international conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Yang, Yong Ma, Jing Qu, Fuheng Ding, Tianyu Shan, Haoji College of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China College of Education Changchun Normal University Changchun130032 China

ISBN: (纸本)9781510663473

Aiming at the problem of low model recognition accuracy caused by high similarity and mutual occlusion between crops and weeds in unmanned aerial vehicle (UAV) images, a pixel-level weed recognition method based on improved HRNet-OCRNet is proposed. In this method, a multi-stage and multi-scale feature fusion method is added to HRNet to preserve more details and enhance semantic information at different levels, to solve the problem of high similarity between crops and weeds;The spatial self-attention module of polarized self-attention (PSA) is integrated to HRNet, enhance the network's learning of important features, and reduce the false identification caused by mutual occlusion of crops and weeds;The expansion prediction method is used to generate an accurate distribution map of crop weeds. Compared with Deeplabv3+, GCNet and K-Net, the experimental results show that the proposed method has higher recognition accuracy for crop weeds, and mean intersection over union (mIoU) reaches 85.76%. © 2023 SPIE.

关键词： Crops

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The Smart Handwritten Digits Recognition Using Machine Learning Algorithm 3

The Smart Handwritten Digits Recognition Using Machine Learn...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Mohamed, Nachaat Josphineleela, R. Rakhamaji Madkar, Shankar Vasantha Sena, J. Alfurhood, Badria Sulaiman Pant, Bhasker University Sains Malaysia Rabdan Academy Homeland Security Abu Dhabi United Arab Emirates Panimalar Engineering College CSE Tamil Nadu India College of Engineering Maharashtra Pune India A.P.C.Mahalaxmi College for Women Thoothukudi India Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia CSE Graphic Era Deemed to be University Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

Never before have people relied so much on technology;now, machine learning and based on machine learning can do anything from classifying objects in photos to inserting sounds to old movies. Similar to this, handwritten word extraction is an important field of study and advancement with a wide range of potential outcomes. The capacity of a robot to accept and analyze understandable handwriting inputs via sources like paper files, pictures, smart phones, or other gadgets is called as handwriting, sometimes known at text recognition. Evidently, in this study, they have carried out handwritten digit recognition utilising Support Vector Machines (SVM), Multi-Layer Number of neurons (MLP), and Deep Convolution Network (CNN) models with aid of MNIST dataset. To find the most accurate model for text detection, our major goal is to contrast the following models' execution speed and correctness. The MNIST registry identity, which will be scrawled in numerals, may be recognised by the software. In machine language, the natural handwritten form may be recognised. The deep neural network is a machines educational approach that we use in this case. The nine integers in the MNIST data range from 0 to 9. It could be used to organise number and composition into groups that computers can understand. The main objective of this work is to guarantee reliable methods for identifying written finger numbers and simplifying and eliminating errors in banking processes. The MNIST information will be correctly identified by the system. Comparing the model's precision and system performance is our main objective. © 2023 IEEE.

关键词： Support vector machines

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Hybrid deep learning in remote passive fall detection 3

Hybrid deep learning in remote passive fall detection

引用

3rd international conference on Digital Signal and computer Communications, DSCC 2023

作者： An, Xiaofeng Raga, Rodolfo C. College of Computer and Information Technologies National University Manila Philippines Education Quality Monitoring Center Jilin Engineering Normal University Jilin Changchun China Computer Studies and Engineering Department Jose Rizal University Mandaluyong Philippines

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

The camera image recognition and active wearable sensor scheme are classic methods in fall detection, but the camera scheme has the problem of privacy leakage, while the active wearable sensor scheme has the problem of wearable comfort. This paper proposes a method of human motion recognition using passive sensors and fall detection using hybrid depth learning model, and designs an intelligent human motion sensing clothing based on passive RFID tag positioning technology. The experimental results show that using passive RFID tag as a sensor scheme can achieve efficient and accurate fall detection, and achieve a recognition rate of 99.5%.The proposed method does not require additional equipment, realizes non-invasive monitoring, and is comfortable to wear and inexpensive. It can be widely used in other fields such as medical health, family safety, old-age care and so on. It is of great significance to improve the practicability of fall detection. © 2023 SPIE.

关键词： Fall detection

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Particle swarm optimization for optimal design of experiments in quantile regression 3

Particle swarm optimization for optimal design of experiment...

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3rd international conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2023

作者： Chen, Xing School of Computer Qilu University of Technology Shandong Academy of Sciences 3501 Daxue Road Shandong Jinan China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

The nonlinear quantile regression model has been widely applied in various fields such as drug development, medical research, and industrial production. However, due to the non-convexity of its objective function, a universally applicable optimization design for the quantile regression model has not been developed so far. Although a few existing studies have made some progress, these studies can only be realized under some special mathematical assumptions. In practice, experimental designs often cannot meet these assumptions, making it difficult to apply these research results in actual experiments. Therefore, we need to find a new method to solve this problem. In this paper, based on the idea of particle swarm optimization algorithm, we have successfully solved the optimal experimental design problem of nonlinear quantile regression model. Unlike traditional experimental design methods, we can find the optimal solution without any mathematical assumptions, which greatly enhances the universality of the quantile regression model. This research achievement has important guiding significance for designing effective quantile regression experiments in practical experimental design and can provide effective reference for related fields' practical work. © 2023 SPIE.

关键词： Design of experiments

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Stellar Classification vis-À-vis Convolutional Neural Network 3

Stellar Classification vis-À-vis Convolutional Neural Netwo...

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3rd IEEE international conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

作者： Dutta, Anurag Antony Raj, Albert Stephan Ramamoorthy, A. Harshith, John Soni, Yash Sadh, Unnati Government College of Engineering and Textile Technology Serampore Department of Computer Science Calcutta India Bharathiar University Coimbatore The Tipsglobal Institute Department of Mathematics Tamil Nadu Coimbatore India Anna University Velammal Engineering College Department of Mathematics Tamil Nadu Chennai India Vellore Institute of Technology Department of Computer Science Tamil Nadu Vellore India Shri G. S. Institute of Technology and Science Department of Information Technology Madhya Pradesh Indore India SRM Institute of Science & Technology Delhi-NCR Campus Department of Computer Science Uttar Pradesh India

ISBN: (纸本)9798350338263

As a result of recent advancements in technology, a variety of new computational fields have emerged. Some examples of these fields are machine learning and intelligence, information science, the internet of things, and others. The advancement of humanity will be greatly aided by these fields. The development of Artificial Intelligence led to the creation of a great deal of Neural Networks. Convolutional Neural Networks are one variation of Neural Networks that we are utilizing in this work. These networks are known to perform quite admirably for Image Categorization, which is one of the purposes for which we are utilizing them. The work encompasses Stellar Classification. There are many stellar entities occupying the region known as universal space. Astrophysicists are familiar with a good number of them, but there are still a great many of these types of entities that have not been discovered yet. Because of the great distance that separates our planet from other stellar entities, any attempt to communicate with them through any channel is highly unlikely to be successful. The most information we could possibly acquire is just a guess as to what kind of entity they are. So, if any scientific observatory comes with a nascent search of any distant entity, we could potentially predict which stellar group they belong to. For the purposes of this work, we are only going to focus on two different types of Stella: Stars and Galaxies. For the purpose of training the Convolutional Neural Network, we have used a dataset on Stellar Types with Image Categorization created by the Aryabhata Research Institute of Observational Sciences (ARIES), which is located in Nainital, India. This dataset was made publicly available. © 2023 IEEE.

关键词： Stars

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Analysis and Forecasting of Saudi Stock Market Using Time Series Algorithms 3

Analysis and Forecasting of Saudi Stock Market Using Time Se...

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3rd international conference on Computing and Information technology, ICCIT 2023

作者： Alasiri, Razan Ahmed Qahmash, Ayman King Khalid University College of Computer Science Depaertment of Information Systems Saudi Arabia

ISBN: (纸本)9798350321487

Forecasting the stock market is essential for investors to help assess the potential financial movement of the market value. Therefore, accurate estimation of the movement and price of the stock contributes more to the profit of investors through making informed investment judgments. Thus, the popularity of forecasting the stock market increased because of the difficulty of this field and that it is unforecastable and cannot be controlled because of its volatile nature and is affected by several factors. Nevertheless, the field of machine learning succeeded in forecasting the future stock market based on the analysis of time series data collected over time. The main objective of this paper is to develop day-to-day forecast models in ways that have been popular among researchers through time series data collected from the Saudi stock market, Tadawul, containing daily observations of the Tadawul All Share Index (TASI), and a different sample of some companies listed under the TASI. In our analysis, we relied on the technical approach, so it was important to choose technical indicators most closely related to the closing price of the next day using feature engineering and feature selection. Then, this study conducted two different models of Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) time series techniques. Best expected results Performance was evaluated using the Root Mean Square Error (RMSE) scale. The results show that LSTM algorithms based on deep learning are better than the ARIMA statistical model in stock market forecasting. © 2023 IEEE.

关键词： Long short-term memory

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Detection of Myocardial Infarction using Spectral Textures through ECG 3

Detection of Myocardial Infarction using Spectral Textures t...

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3rd international conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Aziz, Sumair Khan, Muhammad Umar Iqtidar, Khushbakht Sharma, Siddhant Montes, Gabriel Axel Gareeballah, Awadia University of Engineering & Technology Taxila Taxila Pakistan University of Canberra Canberra Australia National University of Sciences and Technology Dept. of Computer Software Engg. Islamabad Pakistan Wright State University FairbornOH United States Neural Axis MiamiFL United States University of Newcastle CallaghanNSW Australia Taibah University College of Applied Medical Sciences Saudi Arabia

ISBN: (纸本)9798350332391

Myocardial infarction (MI), commonly known as a heart attack, can cause nonrecoverable impairment to the cardiac muscles, which may even result in fatality. Therefore, it is crucial to diagnose MI accurately and reliably. This article proposes a novel framework for detecting MI using a 12-channel Electrocardiogram (ECG) signal. The method filters the signal to eliminate motion artifacts and high-frequency noise, followed by the extraction of novel spectral texture patterns and Mel frequency cepstral coefficients (MFCCs) from all ECG signal channels. These features are combined to form a comprehensive feature representation. ANOVA is then applied to reduce feature dimensions, and various state-of-the-art classification methods such as support vector machines (SVM), Artificial Neural Networks (ANN), AdaBoost (AB), and K-nearest neighbors (KNN) are utilized. The AB classifier achieved the highest performance with 98.7% accuracy and 98.4% recall using 10-fold cross-validation. The proposed method is reliable and holds the potential for clinical adoption in hospitals. © 2023 IEEE.

关键词： Support vector machines

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