The document summarizes a survey on recent developments in human pose estimation. It begins with an introduction to the field and discusses previous surveys. It then covers classifications of pose estimation like 2D vs 3D, single vs multiple person, top-down vs bottom-up approaches. Popular datasets for training models are described. Finally, it discusses performance evaluation metrics and challenges in the field. The main focus is on deep learning approaches optimized for deployment on edge devices to make pose estimation more accessible.
AI Personal Trainer Using Open CV and Media PipeIRJET Journal
This document summarizes previous work on developing an AI personal trainer using computer vision techniques. It discusses early research using the Kinect camera for body posture detection. Later works applied machine learning and deep learning models to activity recognition in gyms and used OpenPose for pose detection in pre-recorded videos. To enable real-time detection, approaches categorized models as kinematic, planar and volumetric. Convolutional neural networks were also used to estimate 2D poses from single images and increase accuracy. More recent works introduced datasets with whole-body annotations and used a single network to address scale variance across body parts. The goal of this project is to build upon these techniques to create an AI trainer that analyzes exercise repetitions in real-time videos
Human Activity Recognition Using SmartphoneIRJET Journal
The document discusses human activity recognition using smartphone sensors. It proposes using a CNN-LSTM model to classify activities like walking, running, and sitting based on accelerometer and gyroscope sensor data from a smartphone. The CNN extracts features from the sensor data, while the LSTM recognizes sequences of activities over time. The model is implemented in an Android application that recognizes activities in real-time and also counts steps, distance, and calories burned. The application uses built-in smartphone sensors like accelerometer, gyroscope, and pedometer to recognize activities affordably and with high availability without external devices. The CNN-LSTM model achieves accurate activity recognition compared to other machine learning techniques.
HUMAN IDENTIFIER WITH MANNERISM USING DEEP LEARNINGIRJET Journal
The document discusses human posture and mannerism identification using deep learning. It describes how a deep learning model can be trained on media data from humans to recognize certain personalities based on their postures. This could then be used for applications like home security systems to prevent unwanted entrance. The document reviews several related works that use techniques like pose estimation, skeleton modeling, and deep neural networks to identify body joints and estimate full-body poses from images and video. Accurately estimating poses of multiple people remains a challenge, but recent methods using techniques like part affinity fields have achieved real-time performance with good accuracy.
AI Personal Trainer Using Open CV and Media PipeIRJET Journal
This document summarizes a research paper that proposes an AI personal trainer system using computer vision techniques. The system uses OpenCV and MediaPipe to detect a user's body pose and angles in real-time video to correct their form during exercises. It aims to help users safely and effectively work out at home without a physical trainer. The system would also connect users with similar fitness goals to encourage motivation. The researchers believe this AI trainer could make exercise more accessible and convenient for users.
1. The document summarizes a student project that aims to create a virtual try-on application using augmented reality. It surveys existing methods for tasks like clothing segmentation, human pose estimation, and virtual try-on that could be used to build the application.
2. It discusses approaches the students investigated like using depth cameras for measurements, non-depth based methods using computer vision, parsing clothes and humans, and existing work on 2D virtual try-on.
3. The students implemented initial modules for their pipeline including a U-Net for clothing segmentation trained on images and masks from the Viton dataset.
Visual Saliency Model Using Sift and Comparison of Learning Approachescsandit
This document discusses a study that aims to develop a visual saliency model to predict where humans look in images. It uses the SIFT feature in addition to low, mid, and high-level image features to train machine learning models on an eye-tracking dataset. Support vector machines (SVM) achieved the best performance, accurately predicting fixations 88% of the time. Including the SIFT feature further improved SVM performance to 91% accuracy. The study evaluates different machine learning methods and determines SVM to be best suited for this binary classification task using high-dimensional image data.
This document discusses using artificial intelligence and deep learning techniques for yoga pose estimation and classification. Specifically, it proposes training a model using the PoseNet and OpenPose frameworks on a dataset of yoga pose videos to identify key points in the human body and classify the pose. The model would use convolutional neural networks and long short-term memory to process video frames in real-time and provide classification scores for the accuracy of pose identification. This type of system could help improve health and provide feedback to users on yoga pose form without an instructor. However, it is currently limited to a small number of poses and requires internet and webcam access.
IRJET - A Review on: Face Recognition using LaplacianfaceIRJET Journal
This document reviews face recognition using LaplacianFace, an appearance-based method that maps face images into a subspace using Locality Preserving Projections (LPP) to analyze local information and detect essential face manifold structure. The Laplacianfaces are optimal linear approximations of the eigenfunctions of the Laplace Beltrami operator on the face manifold, which can eliminate unwanted variations from lighting, expression, and pose. The paper compares LaplacianFace to Eigenface and Fisherface methods on three datasets, finding Laplacianface provides better representation and lower error rates. It also surveys related work applying PCA, LDA, LPP and other techniques to challenges like single image training and discusses the LaplacianFace method's modules for loading images, res
AI Personal Trainer Using Open CV and Media PipeIRJET Journal
This document summarizes previous work on developing an AI personal trainer using computer vision techniques. It discusses early research using the Kinect camera for body posture detection. Later works applied machine learning and deep learning models to activity recognition in gyms and used OpenPose for pose detection in pre-recorded videos. To enable real-time detection, approaches categorized models as kinematic, planar and volumetric. Convolutional neural networks were also used to estimate 2D poses from single images and increase accuracy. More recent works introduced datasets with whole-body annotations and used a single network to address scale variance across body parts. The goal of this project is to build upon these techniques to create an AI trainer that analyzes exercise repetitions in real-time videos
Human Activity Recognition Using SmartphoneIRJET Journal
The document discusses human activity recognition using smartphone sensors. It proposes using a CNN-LSTM model to classify activities like walking, running, and sitting based on accelerometer and gyroscope sensor data from a smartphone. The CNN extracts features from the sensor data, while the LSTM recognizes sequences of activities over time. The model is implemented in an Android application that recognizes activities in real-time and also counts steps, distance, and calories burned. The application uses built-in smartphone sensors like accelerometer, gyroscope, and pedometer to recognize activities affordably and with high availability without external devices. The CNN-LSTM model achieves accurate activity recognition compared to other machine learning techniques.
HUMAN IDENTIFIER WITH MANNERISM USING DEEP LEARNINGIRJET Journal
The document discusses human posture and mannerism identification using deep learning. It describes how a deep learning model can be trained on media data from humans to recognize certain personalities based on their postures. This could then be used for applications like home security systems to prevent unwanted entrance. The document reviews several related works that use techniques like pose estimation, skeleton modeling, and deep neural networks to identify body joints and estimate full-body poses from images and video. Accurately estimating poses of multiple people remains a challenge, but recent methods using techniques like part affinity fields have achieved real-time performance with good accuracy.
AI Personal Trainer Using Open CV and Media PipeIRJET Journal
This document summarizes a research paper that proposes an AI personal trainer system using computer vision techniques. The system uses OpenCV and MediaPipe to detect a user's body pose and angles in real-time video to correct their form during exercises. It aims to help users safely and effectively work out at home without a physical trainer. The system would also connect users with similar fitness goals to encourage motivation. The researchers believe this AI trainer could make exercise more accessible and convenient for users.
1. The document summarizes a student project that aims to create a virtual try-on application using augmented reality. It surveys existing methods for tasks like clothing segmentation, human pose estimation, and virtual try-on that could be used to build the application.
2. It discusses approaches the students investigated like using depth cameras for measurements, non-depth based methods using computer vision, parsing clothes and humans, and existing work on 2D virtual try-on.
3. The students implemented initial modules for their pipeline including a U-Net for clothing segmentation trained on images and masks from the Viton dataset.
Visual Saliency Model Using Sift and Comparison of Learning Approachescsandit
This document discusses a study that aims to develop a visual saliency model to predict where humans look in images. It uses the SIFT feature in addition to low, mid, and high-level image features to train machine learning models on an eye-tracking dataset. Support vector machines (SVM) achieved the best performance, accurately predicting fixations 88% of the time. Including the SIFT feature further improved SVM performance to 91% accuracy. The study evaluates different machine learning methods and determines SVM to be best suited for this binary classification task using high-dimensional image data.
This document discusses using artificial intelligence and deep learning techniques for yoga pose estimation and classification. Specifically, it proposes training a model using the PoseNet and OpenPose frameworks on a dataset of yoga pose videos to identify key points in the human body and classify the pose. The model would use convolutional neural networks and long short-term memory to process video frames in real-time and provide classification scores for the accuracy of pose identification. This type of system could help improve health and provide feedback to users on yoga pose form without an instructor. However, it is currently limited to a small number of poses and requires internet and webcam access.
IRJET - A Review on: Face Recognition using LaplacianfaceIRJET Journal
This document reviews face recognition using LaplacianFace, an appearance-based method that maps face images into a subspace using Locality Preserving Projections (LPP) to analyze local information and detect essential face manifold structure. The Laplacianfaces are optimal linear approximations of the eigenfunctions of the Laplace Beltrami operator on the face manifold, which can eliminate unwanted variations from lighting, expression, and pose. The paper compares LaplacianFace to Eigenface and Fisherface methods on three datasets, finding Laplacianface provides better representation and lower error rates. It also surveys related work applying PCA, LDA, LPP and other techniques to challenges like single image training and discusses the LaplacianFace method's modules for loading images, res
A SURVEY ON HUMAN POSE ESTIMATION AND CLASSIFICATIONIRJET Journal
This document summarizes recent techniques for human pose estimation and classification from images and videos. It discusses the following:
1. The main approaches for human pose estimation are generative, discriminative, top-down, and bottom-up. Generative models use a representation of the human body, while discriminative methods directly map inputs to poses.
2. Popular human body models include skeleton-based using joint positions, contour-based using silhouettes, and volume-based using 3D meshes.
3. Keypoint detection methods discussed are OpenPose, DeepCut, RMPE, and Mask RCNN. They detect body parts and connect them to estimate poses.
4. Benchmark datasets for
Human Action Recognition Using Deep LearningIRJET Journal
This document discusses human action recognition using deep learning models. It proposes using two deep learning models - Convolutional Neural Networks (CNN) and Long-term Recurrent Convolutional Networks (LRCN) - to recognize human actions in videos. The Kinetics dataset is used to train and evaluate the models. Results show that both CNN and LRCN are able to accurately recognize human actions like playing piano or archery in test videos. The LRCN model achieves slightly higher accuracy compared to the traditional two-stream CNN method.
Paper Gloria Cea - Goal-Oriented Design Methodology Applied to User Interface...WTHS
This document describes a user interface designed for a mobile application called the Functional Assessment System (FAS). The FAS allows users to assess their aerobic fitness on their own without specialized equipment. The design of the mobile application interface was guided by the Goal-Oriented Design methodology. This methodology focuses on representing users as characters with specific goals and designing scenarios to help users achieve those goals. The document also discusses evaluating the usability of the interface using the AttrakDiff questionnaire to assess pragmatic and hedonic qualities. The results showed satisfactory user interaction with the FAS mobile application interface.
IRJET- Design an Approach for Prediction of Human Activity Recognition us...IRJET Journal
The document proposes a framework for human activity recognition using smartphones. It involves collecting data from a smartphone's accelerometer and gyroscope sensors worn on the waist during various activities of daily living. The data is preprocessed and classified using machine learning algorithms like Naive Bayes, logistic regression, and SVM. The proposed framework first loads and preprocesses the sensor data, then generates features before splitting the data into training and test sets. Various classifiers are applied and evaluated to select the best performing one for activity recognition. The authors conclude that implementing tri-axial acceleration from sensors provides different accuracy for different algorithms, with SVM achieving maximum accuracy in previous work.
Region wise processing of an image using multithreading in multi core environIAEME Publication
This document presents a method for region-wise parallel processing of images using multithreading in a multi-core environment. The method divides large images into distinct regions of interest and assigns each region to a separate processing core. Each core then calculates statistical features for its assigned region in parallel. Experimental results on cell images show speedups of up to 600% when using 8 threads on an Intel Xeon processor compared to sequential processing on a single core. The document concludes that region-wise parallel processing provides significantly more efficient results than existing parallel image processing methods. This approach has applications in medical imaging where fast analysis of large images is important.
Region wise processing of an image using multithreading in multi core environIAEME Publication
This document discusses region-wise parallel processing of images using multithreading in a multi-core environment and its applications in medical imaging. It proposes dividing large images into regions of interest and assigning each region to a separate processor core for parallel processing. This approach could provide significantly faster results than existing parallel image processing methods. The document describes calculating statistical features like mean, standard deviation, and variance for each individual region. It presents experimental results showing speedups of around 200% for a core i3 processor and 600% for an Intel Xeon processor compared to sequential processing. The approach and its speed benefits are proposed to have applications in processing large medical images commonly used in areas like CT, PET, and MRI scans.
IRJET - Real Time Facial Analysis using Tensorflowand OpenCVIRJET Journal
This document presents a real-time facial analysis system using TensorFlow and OpenCV. The system can detect facial expressions, age, and gender from images and video in real-time. It uses deep learning models trained on facial datasets to analyze faces. The system is designed for applications like security, attendance tracking, and finding lost children. It works by extracting facial features from images, applying preprocessing techniques, classifying faces, and making predictions about attributes. The document discusses the methodology, existing techniques like PCA and HMM, the proposed system architecture, sample code, and conclusions.
There has been over the past few years, a very increased popularity for yoga. A lot of literatures have been published that claim yoga to be beneficial in improving the overall lifestyle and health especially in rehabilitation, mental health and more. Considering the fast-paced lives that individuals live, people usually prefer to exercise or work-out from the comfort of their homes and with that a need for an instructor arises. Hence why, we have developed a self-assisted system which can be used to detect and classify yoga asanas, which is discussed in-depth in this paper. Especially now when the pandemic has taken over the world, it is not feasible to attend physical classes or have an instructor over. Using the technology of Computer Vision, a computer-assisted system such as the one discussed, comes in very handy. The technologies such as ml5.js, PoseNet and Neural Networks are made use for the human pose estimation and classification. The proposed system uses the above-mentioned technologies to take in a real-time video input and analyze the pose of an individual, and classifies the poses into yoga asanas. It also displays the name of the yoga asana that is detected along with the confidence score.
This document presents a facial expression recognition system that identifies and classifies seven basic expressions: happy, surprise, fear, disgust, sad, anger, and a neutral state. The system consists of four main parts: image acquisition, pre-processing, feature extraction, and classification. It was developed using both OpenCV and a web-based JavaScript approach. The system was tested on both real-time and pre-recorded video streams and can identify emotions in images and video input from a webcam in real-time. Evaluation showed the JavaScript implementation using a generalized dataset provided more accurate real-time predictions compared to the OpenCV approach.
Image super resolution using Generative Adversarial Network.IRJET Journal
This document discusses using a generative adversarial network (GAN) for image super resolution. It begins with an abstract that explains super resolution aims to increase image resolution by adding sub-pixel detail. Convolutional neural networks are well-suited for this task. Recent years have seen interest in reconstructing super resolution video sequences from low resolution images. The document then reviews literature on image super resolution techniques including deep learning methods. It describes the methodology which uses a CNN to compare input images to a trained dataset to predict if high-resolution images can be generated from low-resolution images.
IRJET- Hand Gesture Recognition and Voice Conversion for Deaf and DumbIRJET Journal
This document describes a research project that aims to help deaf and dumb people communicate more easily. It presents a system using hand gesture recognition and voice conversion. The system uses a webcam to detect hand gestures, then converts the gestures to text via image processing and matching to a database of gestures and texts. It also aims to convert the text to voice so deaf people can understand via voice. It reviews previous related work on sign language recognition systems and discusses the proposed system's image processing and matching techniques, including feature extraction using principal component analysis and classification using k-nearest neighbors. The goal is to help reduce communication barriers for deaf and dumb people.
IRJET- A Survey on Image Retrieval using Machine LearningIRJET Journal
This document summarizes a research paper on using machine learning for image retrieval. It discusses using optical character recognition (OCR) to extract text from images and then translate that text into machine-readable formats. The document reviews related work on combining visual and text features for image retrieval. It also provides an overview of the proposed system architecture, which combines color, texture, and edge features for image retrieval and evaluates performance using metrics like sensitivity, specificity, retrieval score, and accuracy.
A SOFTWARE REQUIREMENT ENGINEERING TECHNIQUE USING OOADA-RE AND CSC FOR IOT B...ijseajournal
This Internet of things is one of the most trending technology with wide range of applications. Here we are going to focus on Medical and Healthcare applications of IOT. Generally such IOT applications are very complex comprising of many different modules. Thus a lot of care has to be taken during the requirement engineering of IOT applications. Requirement Engineering is a process of structuring all the requirements of the users. This is the base phase of software development which greatly affects the rest of the phases. Thus our best should be given in the engineering of requirements because if the effort goes down here, it will greatly affect the quality of the end product. In this study we have presented an approach to improve the requirements engineering phase of IOT applications development by using Object Oriented Analysis and Design Approach(OOADA) along with Constraints Story Card(CSC) templates.
IRJET - Human Pose Detection using Deep LearningIRJET Journal
This document discusses using deep learning for human pose detection. It begins with an introduction to human pose detection and challenges in the field. It then describes how deep learning can be used for this task by training neural networks on large datasets of images annotated with body joint locations. Specifically, it trained models like COCO and MPII to identify and locate body parts. OpenCV and Flask were used to process video frames and build a graphical interface. The trained models were able to detect poses and provide feedback on proper form for exercises. Graphs and skeletal representations visualized the poses and joint angles. The system was able to perform human pose detection in real-time with low hardware requirements. In conclusion, it achieved an effective low-cost software model for motion
Human pose detection using machine learning by GrandelGrandelDsouza
This document discusses using deep learning for human pose detection. It begins with an introduction to human pose detection and challenges in the field. It then describes how deep learning can be used for this task by training neural networks on large datasets of images annotated with body joint locations. Specifically, it trained models like COCO and MPII to identify and locate body parts. OpenCV and Flask were used to process video frames and build a graphical interface. The trained models were able to detect poses and provide feedback on proper form for exercises. Graphs and skeletal representations visualized the poses and joint angles. The system was able to perform human pose detection in real-time with low hardware requirements. In conclusion, it achieved an effective low-cost software model for motion
BitLocker is drive encryption software included with Windows that encrypts the entire contents of the drive to protect against unauthorized access to data even if the drive is removed from the device. It stores the encryption key in the computer's Trusted Platform Module (TPM) chip or on an external USB drive for added security. BitLocker requires a Trusted Platform Module version 1.2 or higher, or the ability to store the recovery key on an external drive in order to encrypt the system drive.
Augmented Reality (AR) for Education SystemIRJET Journal
This document discusses using augmented reality (AR) technology to improve education, specifically for teaching human anatomy. It provides background on AR and how it is being applied in medical education. The document then reviews several existing studies on using AR for anatomy education. Most used portable devices and the Unity/Vuforia platforms to display 3D anatomy models over real-world images. The studies found that AR improved learning outcomes for anatomy compared to traditional methods. This document proposes developing an AR application with 3D models of all human organs to provide more detailed information during anatomy studies.
IRJET- Recurrent Neural Network for Human Action Recognition using Star S...IRJET Journal
This document discusses a Recurrent Neural Network (RNN) methodology for human action recognition using star skeletonization. Star skeletonization is a technique that connects the geometric center of an object to its contour extremes, representing the human body as a five-dimensional vector connecting the head and limbs. A series of star skeleton vectors over time can represent a human action. The RNN model is suitable because the extracted features are time-dependent. Previous research on human action recognition is discussed, including approaches using depth motion maps, augmented constraints between joints, and bag-of-visual-words models. The challenges of human action recognition from video sequences are also summarized.
Person Acquisition and Identification ToolIRJET Journal
The document proposes a facial recognition system using CCTV video to identify individuals and generate timestamp data on their presence. It involves three steps: 1) face detection on video frames, 2) super resolution to standardize face sizes, and 3) face recognition using a Siamese network to identify known and new identities with one-shot learning. The system aims to reduce time spent reviewing surveillance footage for law enforcement. It analyzes existing research on low-resolution face recognition, pedestrian detection, and proposes its pipeline as a solution to semi-automate target individual tracking from video data through facial matching and timestamps.
This paper deals with an adaptive guidance system for software processes. The aim is to define
an approach of process modeling with a recursive adaptation of the guidance. This one is
specifically adapted to the developer model through its role and its qualification, and in relation
to the activity model associated with the context of the activity in progress. This guidance system
is provided on the basis on the choice criteria like the suitable access mode, the assistance
function to be ensured and the object of assistance to be considered. The description of the suggested approach is done using the modeling formalism SPEM extended by new concepts and principles dedicated to the modeling of the adaptive guidance.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A SURVEY ON HUMAN POSE ESTIMATION AND CLASSIFICATIONIRJET Journal
This document summarizes recent techniques for human pose estimation and classification from images and videos. It discusses the following:
1. The main approaches for human pose estimation are generative, discriminative, top-down, and bottom-up. Generative models use a representation of the human body, while discriminative methods directly map inputs to poses.
2. Popular human body models include skeleton-based using joint positions, contour-based using silhouettes, and volume-based using 3D meshes.
3. Keypoint detection methods discussed are OpenPose, DeepCut, RMPE, and Mask RCNN. They detect body parts and connect them to estimate poses.
4. Benchmark datasets for
Human Action Recognition Using Deep LearningIRJET Journal
This document discusses human action recognition using deep learning models. It proposes using two deep learning models - Convolutional Neural Networks (CNN) and Long-term Recurrent Convolutional Networks (LRCN) - to recognize human actions in videos. The Kinetics dataset is used to train and evaluate the models. Results show that both CNN and LRCN are able to accurately recognize human actions like playing piano or archery in test videos. The LRCN model achieves slightly higher accuracy compared to the traditional two-stream CNN method.
Paper Gloria Cea - Goal-Oriented Design Methodology Applied to User Interface...WTHS
This document describes a user interface designed for a mobile application called the Functional Assessment System (FAS). The FAS allows users to assess their aerobic fitness on their own without specialized equipment. The design of the mobile application interface was guided by the Goal-Oriented Design methodology. This methodology focuses on representing users as characters with specific goals and designing scenarios to help users achieve those goals. The document also discusses evaluating the usability of the interface using the AttrakDiff questionnaire to assess pragmatic and hedonic qualities. The results showed satisfactory user interaction with the FAS mobile application interface.
IRJET- Design an Approach for Prediction of Human Activity Recognition us...IRJET Journal
The document proposes a framework for human activity recognition using smartphones. It involves collecting data from a smartphone's accelerometer and gyroscope sensors worn on the waist during various activities of daily living. The data is preprocessed and classified using machine learning algorithms like Naive Bayes, logistic regression, and SVM. The proposed framework first loads and preprocesses the sensor data, then generates features before splitting the data into training and test sets. Various classifiers are applied and evaluated to select the best performing one for activity recognition. The authors conclude that implementing tri-axial acceleration from sensors provides different accuracy for different algorithms, with SVM achieving maximum accuracy in previous work.
Region wise processing of an image using multithreading in multi core environIAEME Publication
This document presents a method for region-wise parallel processing of images using multithreading in a multi-core environment. The method divides large images into distinct regions of interest and assigns each region to a separate processing core. Each core then calculates statistical features for its assigned region in parallel. Experimental results on cell images show speedups of up to 600% when using 8 threads on an Intel Xeon processor compared to sequential processing on a single core. The document concludes that region-wise parallel processing provides significantly more efficient results than existing parallel image processing methods. This approach has applications in medical imaging where fast analysis of large images is important.
Region wise processing of an image using multithreading in multi core environIAEME Publication
This document discusses region-wise parallel processing of images using multithreading in a multi-core environment and its applications in medical imaging. It proposes dividing large images into regions of interest and assigning each region to a separate processor core for parallel processing. This approach could provide significantly faster results than existing parallel image processing methods. The document describes calculating statistical features like mean, standard deviation, and variance for each individual region. It presents experimental results showing speedups of around 200% for a core i3 processor and 600% for an Intel Xeon processor compared to sequential processing. The approach and its speed benefits are proposed to have applications in processing large medical images commonly used in areas like CT, PET, and MRI scans.
IRJET - Real Time Facial Analysis using Tensorflowand OpenCVIRJET Journal
This document presents a real-time facial analysis system using TensorFlow and OpenCV. The system can detect facial expressions, age, and gender from images and video in real-time. It uses deep learning models trained on facial datasets to analyze faces. The system is designed for applications like security, attendance tracking, and finding lost children. It works by extracting facial features from images, applying preprocessing techniques, classifying faces, and making predictions about attributes. The document discusses the methodology, existing techniques like PCA and HMM, the proposed system architecture, sample code, and conclusions.
There has been over the past few years, a very increased popularity for yoga. A lot of literatures have been published that claim yoga to be beneficial in improving the overall lifestyle and health especially in rehabilitation, mental health and more. Considering the fast-paced lives that individuals live, people usually prefer to exercise or work-out from the comfort of their homes and with that a need for an instructor arises. Hence why, we have developed a self-assisted system which can be used to detect and classify yoga asanas, which is discussed in-depth in this paper. Especially now when the pandemic has taken over the world, it is not feasible to attend physical classes or have an instructor over. Using the technology of Computer Vision, a computer-assisted system such as the one discussed, comes in very handy. The technologies such as ml5.js, PoseNet and Neural Networks are made use for the human pose estimation and classification. The proposed system uses the above-mentioned technologies to take in a real-time video input and analyze the pose of an individual, and classifies the poses into yoga asanas. It also displays the name of the yoga asana that is detected along with the confidence score.
This document presents a facial expression recognition system that identifies and classifies seven basic expressions: happy, surprise, fear, disgust, sad, anger, and a neutral state. The system consists of four main parts: image acquisition, pre-processing, feature extraction, and classification. It was developed using both OpenCV and a web-based JavaScript approach. The system was tested on both real-time and pre-recorded video streams and can identify emotions in images and video input from a webcam in real-time. Evaluation showed the JavaScript implementation using a generalized dataset provided more accurate real-time predictions compared to the OpenCV approach.
Image super resolution using Generative Adversarial Network.IRJET Journal
This document discusses using a generative adversarial network (GAN) for image super resolution. It begins with an abstract that explains super resolution aims to increase image resolution by adding sub-pixel detail. Convolutional neural networks are well-suited for this task. Recent years have seen interest in reconstructing super resolution video sequences from low resolution images. The document then reviews literature on image super resolution techniques including deep learning methods. It describes the methodology which uses a CNN to compare input images to a trained dataset to predict if high-resolution images can be generated from low-resolution images.
IRJET- Hand Gesture Recognition and Voice Conversion for Deaf and DumbIRJET Journal
This document describes a research project that aims to help deaf and dumb people communicate more easily. It presents a system using hand gesture recognition and voice conversion. The system uses a webcam to detect hand gestures, then converts the gestures to text via image processing and matching to a database of gestures and texts. It also aims to convert the text to voice so deaf people can understand via voice. It reviews previous related work on sign language recognition systems and discusses the proposed system's image processing and matching techniques, including feature extraction using principal component analysis and classification using k-nearest neighbors. The goal is to help reduce communication barriers for deaf and dumb people.
IRJET- A Survey on Image Retrieval using Machine LearningIRJET Journal
This document summarizes a research paper on using machine learning for image retrieval. It discusses using optical character recognition (OCR) to extract text from images and then translate that text into machine-readable formats. The document reviews related work on combining visual and text features for image retrieval. It also provides an overview of the proposed system architecture, which combines color, texture, and edge features for image retrieval and evaluates performance using metrics like sensitivity, specificity, retrieval score, and accuracy.
A SOFTWARE REQUIREMENT ENGINEERING TECHNIQUE USING OOADA-RE AND CSC FOR IOT B...ijseajournal
This Internet of things is one of the most trending technology with wide range of applications. Here we are going to focus on Medical and Healthcare applications of IOT. Generally such IOT applications are very complex comprising of many different modules. Thus a lot of care has to be taken during the requirement engineering of IOT applications. Requirement Engineering is a process of structuring all the requirements of the users. This is the base phase of software development which greatly affects the rest of the phases. Thus our best should be given in the engineering of requirements because if the effort goes down here, it will greatly affect the quality of the end product. In this study we have presented an approach to improve the requirements engineering phase of IOT applications development by using Object Oriented Analysis and Design Approach(OOADA) along with Constraints Story Card(CSC) templates.
IRJET - Human Pose Detection using Deep LearningIRJET Journal
This document discusses using deep learning for human pose detection. It begins with an introduction to human pose detection and challenges in the field. It then describes how deep learning can be used for this task by training neural networks on large datasets of images annotated with body joint locations. Specifically, it trained models like COCO and MPII to identify and locate body parts. OpenCV and Flask were used to process video frames and build a graphical interface. The trained models were able to detect poses and provide feedback on proper form for exercises. Graphs and skeletal representations visualized the poses and joint angles. The system was able to perform human pose detection in real-time with low hardware requirements. In conclusion, it achieved an effective low-cost software model for motion
Human pose detection using machine learning by GrandelGrandelDsouza
This document discusses using deep learning for human pose detection. It begins with an introduction to human pose detection and challenges in the field. It then describes how deep learning can be used for this task by training neural networks on large datasets of images annotated with body joint locations. Specifically, it trained models like COCO and MPII to identify and locate body parts. OpenCV and Flask were used to process video frames and build a graphical interface. The trained models were able to detect poses and provide feedback on proper form for exercises. Graphs and skeletal representations visualized the poses and joint angles. The system was able to perform human pose detection in real-time with low hardware requirements. In conclusion, it achieved an effective low-cost software model for motion
BitLocker is drive encryption software included with Windows that encrypts the entire contents of the drive to protect against unauthorized access to data even if the drive is removed from the device. It stores the encryption key in the computer's Trusted Platform Module (TPM) chip or on an external USB drive for added security. BitLocker requires a Trusted Platform Module version 1.2 or higher, or the ability to store the recovery key on an external drive in order to encrypt the system drive.
Augmented Reality (AR) for Education SystemIRJET Journal
This document discusses using augmented reality (AR) technology to improve education, specifically for teaching human anatomy. It provides background on AR and how it is being applied in medical education. The document then reviews several existing studies on using AR for anatomy education. Most used portable devices and the Unity/Vuforia platforms to display 3D anatomy models over real-world images. The studies found that AR improved learning outcomes for anatomy compared to traditional methods. This document proposes developing an AR application with 3D models of all human organs to provide more detailed information during anatomy studies.
IRJET- Recurrent Neural Network for Human Action Recognition using Star S...IRJET Journal
This document discusses a Recurrent Neural Network (RNN) methodology for human action recognition using star skeletonization. Star skeletonization is a technique that connects the geometric center of an object to its contour extremes, representing the human body as a five-dimensional vector connecting the head and limbs. A series of star skeleton vectors over time can represent a human action. The RNN model is suitable because the extracted features are time-dependent. Previous research on human action recognition is discussed, including approaches using depth motion maps, augmented constraints between joints, and bag-of-visual-words models. The challenges of human action recognition from video sequences are also summarized.
Person Acquisition and Identification ToolIRJET Journal
The document proposes a facial recognition system using CCTV video to identify individuals and generate timestamp data on their presence. It involves three steps: 1) face detection on video frames, 2) super resolution to standardize face sizes, and 3) face recognition using a Siamese network to identify known and new identities with one-shot learning. The system aims to reduce time spent reviewing surveillance footage for law enforcement. It analyzes existing research on low-resolution face recognition, pedestrian detection, and proposes its pipeline as a solution to semi-automate target individual tracking from video data through facial matching and timestamps.
This paper deals with an adaptive guidance system for software processes. The aim is to define
an approach of process modeling with a recursive adaptation of the guidance. This one is
specifically adapted to the developer model through its role and its qualification, and in relation
to the activity model associated with the context of the activity in progress. This guidance system
is provided on the basis on the choice criteria like the suitable access mode, the assistance
function to be ensured and the object of assistance to be considered. The description of the suggested approach is done using the modeling formalism SPEM extended by new concepts and principles dedicated to the modeling of the adaptive guidance.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024