检索结果-内蒙古大学图书馆

International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Khan, Mohammed Abdul Hafeez Rudraraju, Rohan V. Swarnalatha, R. Department of Computer Science Engineering Birla Institute of Technology and Science Pilani Dubai Campus Dubai International Academic City Dubai United Arab Emirates Department of Electronics and Communication Engineering Birla Institute of Technology and Science Pilani Dubai Campus Dubai International Academic City Dubai United Arab Emirates Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Dubai Campus Dubai International Academic City Dubai United Arab Emirates

ISBN: (纸本)9789819932498

Many people have been exercising at home since the beginning of the COVID-19 period. Due to this, they haven’t had the supervision of a professional trainer who could correct them, rectify their mistakes, and prevent from harmful injuries. One very common workout that people frequently do is the bicep curl exercise, yet they fail to maintain the right posture without realizing it and end up straining their back muscles and pulling their shoulders too far forward. This is very dangerous as it could result in long-term back pain and tendonitis. Considering this issue, a methodology has been proposed for the people at home to continue their regular exercises without any professional gym trainer and gym environment. This research is oriented toward the correction of bicep form during the bicep curl exercises and preventing injuries. The acquisition fragment is designed with Myoware, an open-source electromyography sensor along with a three-axis accelerometer for capturing the essential segments required for the analysis. Naïve Bayes, logistic regression, K-nearest neighbor, decision tree, and random forest classification models have been used to perform the classification of the data acquired. The analysis of the novel dataset attained has been effective for determining the model with the best results to detect the bicep form. The random forest classifier has yielded the highest individual accuracy of 90.90%. Ultimately, an app prototype has been developed using the MIT app inventor platform. It has been integrated with a Bluetooth module and google firebase for showcasing and collecting data in real time from the sensory modules to the person working out and simultaneously storing them in the database for the deployment of this test set in the machine learning model. With this, the results of the accuracy and performance of a person are then acquired on the app for expressing the nature of their workout session. © 2023, The Author(s), under exclusive license to Springe

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach for Sign Language Video Generation Using Deep Networks

A Novel Approach for Sign Language Video Generation Using De...

引用

Data Science and Network Security (ICDSNS), International Conference on

作者： Sachin Kumar Deepa B Kavitha T Tamilselvi M Sathiyapriya V Natarajan B Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electroncis and Communication Engineering SRMIST Ramapuram Campus Ramapuram India Department of Computer Science and Engineering MA.M College of Engineering and Technology Trichy India Department of Computer Science and Engineering Roever Engineering College Perambalur India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India School of Computer Science and Engineering Vellore Institue of Technology Chennai Campus Chennai India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Sign language enhances the communication capabilities of the deaf-mute community, allowing for a deeper understanding of their needs and emotions. These languages are highly structured and visual, using gestures and various upper body movements such as those of the hands, face, eyes, and gaze. Researchers face numerous challenges in recognizing and translating the diverse variations in sign movements, which requires specialized expertise in computer vision and artificial intelligence. Sign language recognition and translation research has garnered global attention. This research work introduces a novel methodology for generating sign gesture videos from text inputs by integrating various intelligent techniques. The proposed model employs an enhanced generative adversarial network (GAN) to create sign videos from input sentences. Experiments with the proposed VideoGAN model using diverse sign language datasets from multiple countries have demonstrated its effectiveness. The research outcomes highlight its contribution to high-quality video production, with improved evaluation metrics underscoring the model's superior performance.

关键词： Measurement Training Sign language Visualization Face recognition Refining Production Assistive technologies Generative adversarial networks Videos

来源：评论

学校读者我要写书评

暂无评论

Assisting the Visually Impaired with Situation Awareness: A New Perspective

Assisting the Visually Impaired with Situation Awareness: A ...

引用

Information and communication Technology (ICICT), International Conference on

作者： Sheezanul Hassan Md. Liakot Ali Muhammad Sheikh Sadi Institute of Information and Communication Technology (IICT) Bangladesh University of Engineering and Technology (BUET) Dhaka Bangladesh Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Dhaka Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

Safe and independent mobility presents a significant challenge for visually impaired people, who must detect obstacles and navigate unfamiliar areas. Accurate and efficient solutions for mobility and pathfinding, particularly in recognizing currency, complicated by similar surfaces and sizes, are essential for the visually impaired. This paper proposes a system that assists the visually impaired by recognizing objects and their lateral positions and identifying currency notes. Serving as a comprehensive walking guide for both indoor and outdoor navigation, this system enhances spatial awareness and provides valuable information about surroundings. Our model can recognize fifteen classes of objects and has been evaluated with real-world items. Experimental results demonstrate its superior performance compared to existing approaches, highlighting its effectiveness in improving mobility and independence for visually impaired individuals.

关键词： Legged locomotion Accuracy Navigation Visual impairment Transfer learning Neural networks Real-time systems Information and communication technology Object recognition Currencies

来源：评论

学校读者我要写书评

暂无评论

IoT based Smart U-Turn Vehicle Accident Prevention System

IoT based Smart U-Turn Vehicle Accident Prevention System

引用

Sustainable Computing and Data communication Systems (ICSCDS), International Conference on

作者： G Pradeepkumar G Praveen Santhoshkumar C Rohith Bhat M Jeyalakshmi T Muthukumar Neelam Sanjeev Kumar Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Nandha Engineering College Erode Tamilnadu India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Electronics and Communication Engineering SSM Institute of Engineering and Technology Dindigul Department of Electrical and Electronics Engineering Kongunadu College of Engineering and Technology Department of Biomedical Engineering Saveetha School of Engineering SIMATS Chennai India

Unintentional deaths occur at a very high rate in developing countries. Curved roads have significantly more fatalities than straight roads. This occurs mainly on U-turns, hairnin turns, and narrow mountain roads. Drivers in this position cannot see the vehicle approaching from the opposite direction. As a result, thousands of people are killed in car accidents every year. The best way to avoid further accidents is to alert the car driver approaching from the side. Place the ultrasonic range detection sensor on one side of the road before the bend and the light indicator system on the opposite side after the bend. When a vehicle approaches from afar, an ultrasonic sensor on one side of the road sends a signal to the other side of the road via a light system. In response to a warning, the driver may stop the car until the other vehicle has passed. A buzzer will also be used to warn the driver of the car that is approaching.

关键词： Earth Roads Developing countries Acoustics Automobiles Data communication Monitoring

来源：评论

学校读者我要写书评

暂无评论

Power-Efficient Deceptive Wireless Beamforming Against Eavesdroppers

arXiv

引用

arXiv 2025年

作者： Chrysanidis, Georgios Argyriou, Antonios Tran, Le-Nam Zhang, Yanming Liu, Yanwei Department of Electrical and Computer Engineering University of Thessaly Greece School of Electrical and Electronic Engineering University College Dublin Ireland Department of Electronic Engineering The Chinese University of Hong Kong NT Shatin Hong Kong Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

Eavesdroppers of wireless signals want to infer as much as possible regarding the transmitter (Tx). Popular methods to minimize information leakage to the eavesdropper include covert communication, directional modulation, and beamforming with nulling. In this paper we do not attempt to prevent information leakage to the eavesdropper like the previous methods. Instead we propose to beamform the wireless signal at the Tx in such a way that it incorporates deceptive information. The beamformed orthogonal frequency division multiplexing (OFDM) signal includes a deceptive value for the Doppler (velocity) and range of the Tx. To design the optimal baseband waveform with these characteristics, we define and solve an optimization problem for power-efficient deceptive wireless beamforming (DWB). The relaxed convex Quadratic Program (QP) is solved using a heuristic algorithm. Our simulation results indicate that our DWB scheme can successfully inject deceptive information with low power consumption, while preserving the shape of the created beam. Copyright © 2025, The Authors. All rights reserved.

关键词： Heuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification 1

Equilibrium Optimizer with Deep Learning Model for Autism Sp...

引用

1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Praveena, A. Senthamilarasi, N. Karthik, T.S. Abirami, S.K. Vijayakrishna Rapaka, E. Das, Shyamali Department of Computer Science and Engineering Jansons Institute of Technology Karumathampatti Tamilnadu Coimbatore India Department of Information Technology Panimalar Engineering College Tamilnadu Chennai India Department of Electronics and Communication Engineering Aditya College of Engineering and Technology Andhrapradesh Surampalem India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Tamilnadu Coimbatore India Data Science Indian Institute of Technology Madras Tamilnadu Chennai India Karnataka Bangalore India

ISBN: (纸本)9781665460842

Autism Spectrum Disorder (ASD) is a developing disorder if the symptoms develop obvious in the initial years of age but it could be present in some age groups. ASD is mental health problem that affects communicational, social, and non-verbal performances. It could not be cured entirely but is decreased when identified initially. The primary analysis was hampered by the difference and severity of ASD symptoms and containing symptoms usually realized in other mental health problems as well. With the application of machine learning (ML) for the predictive and recognition of several diseases with optimum accuracy, a ray of hope to initial recognition of ASD dependent upon many physiological and physical parameters is projected. This article designs an Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification (EODL-ASDC) technique. The presented EODL-ASDC technique mainly focuses on the identification and classification of ASD. To attain this, the presented EODL-ASDC technique exploits the deep belief network (DBN) system to perform the classification procedure. In addition, the EO algorithm is employed for the optimal hyperparameter tuning of the DBN approach. To demonstrate the enhanced ASD classification result of the EODL-ASDC approach, an extensive range of experimental evaluates was executed. The experimental results demonstrate the improvements of the EODL-ASDC technique over other approaches. © 2022 IEEE

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

A Review on Protein Subcellular Localization Prediction using Microscopic Images 6

A Review on Protein Subcellular Localization Prediction usin...

引用

6th IEEE International Conference on Signal Processing, Computing and Control, ISPCC 2021

作者： Aggarwal, Sonam Gupta, Sheifali Ahuja, Rakesh Chitkara University Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Punjab India Institute Of Engineering And Technology Chitkara University Department Of Computer Science Engineering Punjab India

ISBN: (纸本)9781665425520

Subcellular localization of proteins can provide essential information about their functions and structures in cells. With the rapid advancement in modern molecular imaging techniques, bioimages have received considerable attention for automatically assessing the location of proteins in subcellular compartments. Fluorescence microscopy is the most commonly used technique to obtain images of protein patterns in the cell. Knowledge of protein subcellular localization has also proven helpful in early diagnosis of the disease and drug targeting treatment. In this paper, the recent progress in automated Protein Subcellular Localization (PSL) prediction using microscopic images obtained from confocal microscopy has been systematically reviewed. First, an overview of different datasets available for protein subcellular localization prediction has been given. Then, an overview of various machine learning methodologies has been presented, followed by various deep learning techniques applied for detecting protein subcellular localization. Finally, this review summarizes the future prospects and challenges faced in this field. © 2021 IEEE.

关键词： Molecular imaging

来源：评论

学校读者我要写书评

暂无评论

A Study on Depth Estimation from Single Image Using Neural Networks 13

A Study on Depth Estimation from Single Image Using Neural N...

引用

13th International Conference on Computing communication and Networking Technologies, ICCCNT 2022

作者： Shree, Ramya Madagaonkar, Suraj B Singh, Mansi Chandra, M.T. Aakash Rathnamma, M.V. Venkataramana, V. Chandrasekaran, K. Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Computer Science and Engineering National Institute of Technology Surathkal Karnataka India Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Proddatur Andhra Pradesh India YSR Kadapa Andhra Pradesh India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Depth estimation is fundamental in upcoming technology advancements like scene understanding, robot vision, intelligent driver assistance systems, and many new technologies. Estimating the depth of objects from a viewport can be achieved using various mathematical, geometrical, and stereo concepts, but the process is unaffordable and erroneous. Depth estimation from a single can be accurately done using neural networks. Although this is a challenging task, researchers around the globe have published various works. The works include different neural network standards like CNN, GANs, Encoder-Decoder. The paper analyses and examines famous works in this field of study. Later in the paper, a comparative survey of depth estimation approaches using neural networks is done. © 2022 IEEE.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Multiplierless In-filter Computing for tinyML Platforms

Multiplierless In-filter Computing for tinyML Platforms

引用

International Conference on VLSI Design

作者： A.R. Nair P.K. Nath S. Chakrabartty C.S. Thakur Department of Electronic Systems Engineering Indian Institute of Science Bangalore India Department of Electrical and Systems Engineering Washington University St. Louis USA Department of Electronics Communication Engineering Pandit Deendayal Energy University Gandhinagar

Wildlife conservation using continuous monitoring of environmental factors and biomedical classification, which generate a vast amount of sensor data, is a challenge due to limited bandwidth in the case of remote monitoring. It becomes critical to have classification where data is generated. We present a novel multiplierless framework for in-filter acoustic classification using Margin Propagation (MP) approximation used in low-power edge devices deployable in remote areas with limited connectivity. The entire design of this classification framework is based on template-based kernel machine, which uses basic primitives like addition/subtraction, shift, and comparator operations, for hardware implementation. Unlike full precision training methods for traditional classification, we use MP-based approximation for training, including backpropagation mitigating approximation errors. The proposed framework is general enough for acoustic classification. However, we demonstrate the hardware friendliness of this framework by implementing a parallel Finite Impulse Response (FIR) filter bank in a kernel machine classifier optimized for a Field Programmable Gate Array (FPGA). The FIR filter acts as the feature extractor and non-linear kernel for the kernel machine implemented using MP approximation. The FPGA implementation on Spartan 7 shows that the MP-approximated in-filter kernel machine is more efficient than traditional classification frameworks with just less than 1K slices.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi Objective Evaluator Model Development for Analyze the Customer Behavior

Multi Objective Evaluator Model Development for Analyze the ...

引用

Advancement in electronics & communication engineering (AECE), International Conference on

作者： R. Krishnamoorthy K. P. Kaliyamurthie BSH Shayeez Ahamed Nimmala Harathi R.Senthamil Selvan Department of computer science and engineering Bharath Institute of Higher Education and Research Chennai India Department of computer science and engineering Madanapalle Institute of Technology and Science Madanapalli India Department of Electronics and communication engineering School of Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Department of Electronics and communication engineering Annamacharya Institute of Technology and Sciences Tirupati India

Predicting consumer behaviour elucidates demographics, tastes, and underlying patterns. By keeping tabs on consumer activity, businesses may learn more about their wants and requirements, allowing them to provide better suggestions and increase conversion rates. Numerous factors affect consumer choices, including customer economics, buyer segmentation, and product quality. The most pressing problem that needs fixing is extracting actionable insights from these massive data sets to predict customers' actions. Author presented the multi objective evolutionary method to considerably improve the precision of consumption-related predictions as part of a cutting-edge quantitative research approach for predicting and analyzing customers' consumption habits. As the foundation for the whole prediction model, the data is first compiled based on customer preference and behaviours. As part of the data preparation, min-max normalization eliminates extraneous or irrelevant information. The author uses the Word2vec model for extraction of features, and we adopt boosting ant colony optimization (BACO) for feature selection. Multi-objective evolutionary algorithms (MOEAs) are used to make the predictions. The performance of the proposed system is evaluated, and its metrics are compared with those of well-established methods. The results indicate that the proposed multi objective evolutionary algorithm (MOEA) technique outperforms traditional Machine Learning (ML), excessive gradient boosting (XGB), Artificial intelligence (AI), and naive bayes (HNB) algorithms in various performance metrics. These metrics include accuracy (96 per cent), prediction quality (97per cent), accuracy (98 %), Recall (94 %), F1 score (99 %), and forecast time fifty seconds). Therefore, the results indicate that the regression model is viable and can be maintained over time. The proposed system for predicting consumption behaviour has shown its effectiveness in enhancing profitability.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：