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Prediction of Noise-Type and its Elimination from Photoplethysmograph Signal 3

Prediction of Noise-Type and its Elimination from Photopleth...

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3rd IEEE international conference on control, Instrumentation, Energy and Communication, CIEC 2024

作者： Chowdhury, Soujanya Bose, Aytrik Mukherjee, Shatanik Chandra, Jayanta K. Ramkrishna Mahato Govt. Engg. College Dept. of Electrical Engineering West Bengal Purulia India Cognizant West Bengal Kolkata India

ISBN: (纸本)9798350313703

Non-invasive measurement of photoplethysmograph (PPG) signal is an emerging research area. However the presence of noises in such signals may give erroneous interpretations. This paper thus proposes a technique to identify the noise model in the PPG signal, followed by the suitable de-noising techniques. Reservoir computing (RC) model has been used for the classification purpose which reduces numbers of learnable parameters and provided fast training. Cepstrum analysis has been applied for the elimination of convolutive noises. The method has been tested on 700 PPG windows and yielded good results from the point of view of PPG de-noising and estimation of respiration rate (RR) in terms of NRMSE and PCC with a standalone hardware implementation on a low-cost resource-constrained embedded device (Raspberry Pi Zero W) having an end-to-end latency and memory engagement of 165.34 s and 331.10 KB respectively. © 2024 IEEE.

关键词： Additive noise

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FoCLBP+2DGF: Fusion of Complete Local Binary Pattern with 2D Geometry Features for Facial Emotion Recognition in Videos 1

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19th international conference of Intelligent Unmanned Systems, ICIUS 2023

作者： Singh, Lovejit Singh, Sarbjeet Aggarwal, Naveen Singh, Ranjit Department of Computer Science and Engineering Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering Panjab University Chandigarh160014 India

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

People have different facial attributes in terms of their skin color, shape, and size. This diversity makes a challenge in the development of generalized automatic intelligent system for human’s emotions recognition through their facial expressions. Over the decades, researchers have significantly contributed to geometry and texture-based methods to act for facial contents in image/video in the form of feature vector. This paper presents a fusion of texture based complete local binary pattern (CLBP) method with two-dimensional (2D) geometry features (FoCLBP+2DGF) method to represent the facial expressions for emotion recognition in videos. The proposed method better describes the contrast and structural behavior of facial contents in spatial domain with fusion of CLBP and 2D geometry feature computing. It aggregates the facial expression’s behavior in temporal domain by sum normalization method to capture ratio among the features. The constructed final feature vector with proposed method is independent from the duration of video. Further, Bayesian optimization method is used to identify the best hyperparameters of multiclass support vector machines (SVMs) with proposed FoCLBP+2DGF method. Experimental results show that the proposed FoCLBP+2DGF method attained ≈ 88.64 ± 0.38% emotion recognition accuracy with SVMs using Gaussian kernel function in videos of crowd-sourced emotional multimodal actors’ dataset (CREMA-D). The proposed method significantly improved accuracy of intelligent system for human emotion recognition in videos over the human accuracy and existing work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intelligent systems

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An Intelligent automatic Lung Images Classification with an ANFIS based Deep Q-Network Learning Model 14

An Intelligent Automatic Lung Images Classification with an ...

引用

14th international conference on Advances in computing, control, and Telecommunication Technologies, ACT 2023

作者： Jeyanthi, S. Maheswari, N. Uma Government Polytechnic College Karur India PSNA College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

Lung images classifications have attracted extensive concentration from great many researchers in the field of medical image processing. Persistence with that Deep Q-Network learning system give an idea of successful methodology in the field of Machine intelligence investigation which has been applied in various medical applications. With medical image processing turning into a remarkable function in disease screening and finding, numerous machine learning methodologies have been raised as incredible strategies in diagnosing the diseases. An automated feature extraction and classification prompting wonderful achievements in the medical field are found out legitimately and consequently from the images. This research work proposed the significant deep learning methods of fast advancement, its key commitments and best accuracy in classification results. The proposed deep q-network processing system which includes number of phases like classification, segmentation and detection on medical images with respect to various lung images, datasets and benchmarks. In this proposed work, DQN learning model suggested for Multi Detector Computed Tomography lung images which is used to classify the lung images like interstitial pneumonia, pulmonary nodule and pulmonary embolism based on level of infection. In this research work, the utilization of deep learning procedures with machine intelligence to the medical image and an investigation of their future difficulties and potential challenges are examined. © Grenze Scientific Society, 2023.

关键词： Medical applications

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A Smart Security System Using IOT

A Smart Security System Using IOT

引用

2024 IEEE international conference on Intelligent computing and Emerging Communication Technologies, ICEC 2024

作者： Joshika, S. Kumar, A. Senthil Santhi, L. Sakthivel, R. CSE- Cybersecurity SRM Institute of Science and Technology Tamilnadu Trichy India Skyline University School of Science and Information Technology Nigeria Psgr Krishnammal College for Women Department of Library Tamilnadu Coimbatore India M.Kumarasamy College of Engineering Department of Library Tamiladu Karur India

ISBN: (纸本)9798331508432

The need for advanced security systems that combine maximum protection with user- friendliness has increased in the quickly changing world of today, especially for residential applications. Driven by the desire to safeguard their homes and loved ones from possible dangers, homeowners look for cutting-edge solutions that offer ease of use and safety. The project involves designing an intelligent security system. This system includes an automatic door lock mechanism. It also features an intruder alarm system. The system uses facial recognition technology for security is the main goal of this project. By using face recognition technology, the suggested system makes sure that only users who have registered are able to access it and keeps out illegal users. The intruder alarm system is set off in the event that security is attempted to be breached;it instantly notifies the relevant authorities, guaranteeing a timely reaction. The system incorporates Internet of Things (IoT) technology to improve user experience by allowing remote control and monitoring via Telegram and other platforms. The system's dependability and efficiency are guaranteed by the employment of a Wi-Fi module in conjunction with an Arduino-based microcontroller. This system greatly lowers the danger of theft and burglary in homes and buildings by integrating state-of-the-art face recognition technology with automatic alarms. It also offers consumers more convenience and peace of mind in addition to improving security. © 2024 IEEE.

关键词： Wi-Fi

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Tuberculosis Diagnosis from Chest X-Ray Image Using Deep Learning Techniques 5

Tuberculosis Diagnosis from Chest X-Ray Image Using Deep Lea...

引用

5th IEEE international conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Shams, Karib Saha, Ratul Rodela, Mahmuda Islam Simran, Sanjana Rashid Khan, Musharrat East West University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798350356236

Tuberculosis (TB) is a significant global health challenge, necessitating precise and efficient diagnostic tools. This work investigates the effectiveness of various deep learning models such as EfficientNetB3, DenseNet121, VGG19, DenseNet201, and ResNet50 in detecting TB from chest X-ray images. Utilizing the TB Chest X-ray Database, we evaluated pre-trained models, developed custom architectures, and employed the k-fold validation method. The EfficientNetB3, with a customized design, demonstrated superior performance, while the DenseNet201 excelled in the k-fold validation setup. Our findings highlight the potential of the EfficientNetB3 and DenseNet201 in advancing TB diagnostics, offering significant improvements in speed and accuracy. We got 100% accuracy in EfficientnetB3. This study not only contributes to the growing field of AI in medical imaging but also underscores the ethical considerations required in deploying AI for healthcare, ensuring patient privacy and equitable algorithmic decisions. The outcomes of this study hold promise for improving real-time healthcare through more effective TB detection and patient care. © 2025 IEEE.

关键词： Disease control

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Research on strength reliability improvement of a high-speed cam based on stress-strength interference theory 10

Research on strength reliability improvement of a high-speed...

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2024 10th international Forum on Manufacturing Technology and engineering Materials, ICMTEM 2024

作者： Wang, Shuman Gu, Bin Zhang, Fanfan Zhang, Pengfei Nanjing University of Science and Technology No. 200 Xiaolingwei Jiangsu Nanjing China Northwest Institute of Mechanical and Electrical Engineering No. 5 Biyuan East Road Shaanxi Xianyang China

To realize the control of high speed, heavy load, and intermittent motion law, the rotary bore automatic machine often adopts the "herringbone"cylindrical roller cam mechanism. Under the high-speed transient impact, the stress in the curved groove and commutator of the cam mechanism is on the high side, and it is easy to cause structural damage. In the past, deterministic models are often established without considering the influence of randomness such as load and materials on structures. Based on the stress-strength interference theory, considering the random characteristics of structural clearance material and load, the strength reliability of the cam mechanism is evaluated. The results show that the strength reliability of curved grooves and commutator structures is low. By optimizing the curvature of the curved groove and commutator transition part, the stress level of the curved groove and commutator is effective, and the reliability of the cam mechanism is improved. This has important reference significance for carrying out the strength reliability improvement of similar problems. © Published under licence by IOP Publishing Ltd.

关键词： Cams

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Safe Transportation System using IoT based Alcohol Detection 7

Safe Transportation System using IoT based Alcohol Detection

引用

7th international conference on Intelligent computing and control Systems, ICICCS 2023

作者： Pradeepkumar, G. Vijayakumar, P. Chandrasekaran, N. Rohith Bhat, C. Senthilkumar, C. Kumar, Neelam Sanjeev KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Saveetha School of Engineering Department of Computer Science and Engineering SIM ATS Chennai India SIMATS University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Department of Biomedical Engineering SIMATS Chennai India

ISBN: (纸本)9798350397253

Every year, drunk driving injures and sometimes kills a number of people. It is critical to investigate an IoT-based alcohol detection system for safe mobility. Because it uses sensors and a microprocessor to monitor the driver's BAC and cuts off the car's ignition if it suspects that the driver has been drinking while driving, this system can detect and prevent drunk driving. The proposed system will use IoT to remotely monitor and analyze the driver's Blood Alcohol Level (BAC). A communication module, a microprocessor, and an alcohol sensor comprise the system. The communication component sends real-time data to a BAC server for monitoring and analysis. The proposed strategy reduces the likelihood of accidents, improves driver and passenger safety, and lowers the likelihood of transportation companies having to pay for injuries or damages. IoT-based technologies that detect alcohol can aid in the prevention of drunk driving. By monitoring drivers' BACs in real time and discouraging drunk driving, this device has the potential to significantly reduce traffic-related injuries and fatalities. © 2023 IEEE.

关键词： Internet of things

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Invention of Line-ChatBot: An Innovative Application of ChatGPT API and LINE Bot for Enhanced Student Learning 6

Invention of Line-ChatBot: An Innovative Application of Chat...

引用

6th IEEE international conference on Knowledge Innovation and Invention, ICKII 2023

作者： Chen, Bing-Hong Chen, Chih-Cheng Feng Chia University Electrical and Communications Engineering Taiwan Feng Chia University Department of Automatic Control Engineering Taiwan

ISBN: (纸本)9798350323535

The intelligent teaching assistant system, Line-ChatBot based on the ChatGPT API and LINE Bot is used to for homework recommendations and learning guidance. The system uses a generative language model as an accessible and cost-effective learning support tool. It has been designed to effectively comprehend and decipher difficulties in doing homework and provides tailored suggestions and guidance. Overall, Line-ChatBot is a tool for aiding students in their learning by offering an efficient, flexible, and natural learning support system. We proposed the application of this system in educational contexts for the innovation and advancement of education. © 2023 IEEE.

关键词： Students

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AI Based Security for Internet of Transportation Systems 4

AI Based Security for Internet of Transportation Systems

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Kousar, Heena Fatima, Shaista Ahmed, Syed Imran Sajithra, S. Kushwaha, Sumit Balaji, N. Alangudi East Point College of Engineering and Technology Department of Computer Science and Engineering Karnataka Bengaluru560049 India Mj College Department of Electronics and Communication Engineering Telangana Hyderabad500053 India R.M.K. Engineering College Department of Computer Science Engineering Tamilnadu Kavaraipettai601206 India University Institute of Computing Chandigarh University Department of Computer Applications Punjab 140413 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350300888

The intelligent transportation systems (ITS) use a combination of sensors, cameras, and communication technologies to provide real-time data on traffic conditions. This data can be used to adjust traffic lights, but it can also be used to provide information to drivers, such as real-time traffic updates and recommended routes. Overall, control of traffic lights is an effective solution to address traffic congestion problems in urban areas. By optimizing traffic flow, cities can reduce travel times, improve safety, and reduce emissions from idling vehicles. However, the success of these systems depend on the effective planning, implementation, and ongoing maintenance to ensure that they continue to meet the changing needs of the city. In this work, the interest is to control of traffic lights in order to solve problems related to congestion;in this sense, it is proposed five mechanisms to make traffic lights intelligent and not automatic based on machine learning. The mechanisms implemented are based on four algorithms of machine learning: decision tree (DT), support vector machine (SVM), k nearest neighbor (KNN) and random forest (RF). The solutions obtained make it possible to predict the decision in order to act in traffic depending on several parameters such as the road, cars, passengers etc. © 2023 IEEE.

关键词： Traffic congestion

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Traffic Operation Status Research Based on Multi-source Data Fusion 6th

Traffic Operation Status Research Based on Multi-source Data...

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6th international conference on electrical engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Cao, Ling Wang, Pangwei Dong, Honghui School of Traffic and Transportation Beijing Research Center of Urban Traffic Information Intelligent Sensing and Service Technologies Beijing Jiaotong University Beijing100044 China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China School of Electrical and Control Engineering North China University of Technology Beijing100144 China

ISBN: (纸本)9789819993185

At present, modern technologies such as big data, cloud computing and vehicle-road collaboration are developing rapidly, and the existing traffic detection data based on a single detector cannot meet the demand for high-precision data in the vehicle-road cloud environment. Facing the massive, heterogeneous and complex traffic data, this paper selects the data collected by LiDAR, HD camera and V2X communication unit as the data sources for this research, establishing the multi-source traffic data fusion model with wavelet neural network and genetic algorithm optimized BP neural network respectively, and builds the traffic status classification model based on fuzzy C-mean clustering algorithm. The fusion model and the traffic status classification model are verified for the single intersection road section in the vehicle-road cloud cooperative environment. The results show that the fusion accuracy of genetic algorithm optimized BP neural network reaches 93% and the status classification model has high feasibility, which can provide data support for intersection signal control and traffic guidance optimization. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Data fusion

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